GEOTECHNICAL EFFECTS ON FIBER OPTIC DISTRIBUTED …

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GEOTECHNICAL EFFECTS ON FIBER OPTIC DISTRIBUTED GEOTECHNICAL EFFECTS ON FIBER OPTIC DISTRIBUTED

ACOUSTIC SENSING PERFORMANCE ACOUSTIC SENSING PERFORMANCE

Meghan C. L. Quinn University of Rhode Island, [email protected]

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GEOTECHNICAL EFFECTS ON FIBER OPTIC

DISTRIBUTED ACOUSTIC SENSING PERFORMANCE

BY

MEGHAN C. L. QUINN

A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE

REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

IN

CIVIL AND ENVIRONMENTAL ENGINEERING

UNIVERSITY OF RHODE ISLAND

2021

DOCTORATE OF CIVIL AND ENVIRONMENTAL ENGINEERING

OF

MEGHAN C. L. QUINN

APPROVED:

Dissertation Committee:

Major Professor Christopher D. P. Baxter Gopu R. Potty Aaron Bradshaw James Miller

Brenton DeBoef

DEAN OF THE GRADUATE SCHOOL

UNIVERSITY OF RHODE ISLAND 2021

ABSTRACT

Distributed Acoustic Sensing (DAS) is a fiber optic sensing system that is used

for vibration monitoring. At a minimum, DAS is composed of a fiber optic cable and

an optic analyzer called an interrogator. The oil and gas industry has used DAS for

over a decade to monitor infrastructure such as pipelines for leaks, and in recent years

changes in DAS performance over time have been observed for DAS arrays that are

buried in the ground. This dissertation investigates the effect that soil type, soil

temperature, soil moisture, time in-situ, and vehicle loading have on DAS

performance for fiber optic cables buried in soil. This was accomplished through a

field testing program involving two newly installed DAS arrays. For the first

installation, a new portion of DAS array was added to an existing DAS array installed

a decade prior. The new portion of the DAS array was installed in four different soil

types: native fill, sand, gravel, and an excavatable flowable fill. Soil moisture and

temperature sensors were buried adjacent to the fiber optic cable to monitor seasonal

environmental changes over time. Periodic impact testing was performed at set

locations along the DAS array for over one year. A second, temporary DAS array was

installed to test the effect of vehicle loading on DAS performance. Signal to Noise

Ratio (SNR) of the DAS response was used for all the tests to evaluate the system

performance. The results of the impact testing program indicated that the portions of

the array in gravel performed more consistently over time. Changes in soil moisture or

soil temperature did not appear to affect DAS performance. The results also indicated

that time DAS performance does change somewhat over time. Performance variance

increased in new portions of array in all material types through time. The SNR in

portions of the DAS array in native silty sand material dropped slightly, while the

SNR in portions of the array in sand fill and flowable fill material decreased

significantly over time. This significant change in performance occurred while testing

halted from March 2020 to August 2020 due to the Covid-19 pandemic. These

significant changes in performance were observed in the new portion of test bed, while

the performance of the prior installation remained consistent. It may be that, after

some time in-situ, SNR in a DAS array will reach a steady state. Though it is

unfortunate that testing was on pause while changes in DAS performance developed,

the observed changes emphasize the potential of DAS to be used for infrastructure

change-detection monitoring. In the temporary test bed, increasing vehicle loads were

observed to increase DAS performance, although there was considerable variability in

the measured SNR. The significant variation in DAS response is likely due to various

industrial activities on-site and some disturbance to the array while on-boarding and

off-boarding vehicles. The results of this experiment indicated that the presence of

load on less than 10% of an array channel length may improve DAS performance.

Overall, this dissertation provides guidance that can help inform the civil engineering

community with respect to installation design recommendations related to DAS used

for infrastructure monitoring.

iv

ACKNOWLEDGMENTS

Throughout this Ph.D. dissertation process, I have received support, guidance,

and direction from several individuals and programs, to whom I owe a great deal of

gratitude.

I would like to express my deepest appreciation to my advisor, Dr. Christopher

Baxter for his guidance, support, advice, mentoring, friendship, and enthusiasm. Your

feedback and insight are invaluable. I am grateful for your trust in this new area of

research. To Dr. Gopu Potty, your generosity of knowledge, kindness, signal

processing guidance, and overall support were critical to my growth as a researcher.

Thank you to my committee members Dr. James Miller and Dr. Aaron Bradshaw f or

your support and attending several meetings along the way. I cannot express my

thanks enough to Dr. Baxter, Dr. Potty, Dr. Bradshaw, and Dr. Miller and your ability

to question and critique aspects of this research while simultaneously reassuring and

guiding my research goal. Your guidance has made this dissertation and the research

here-in better.

I owe significant gratitude to the Engineering Research and Development

Center’s Long Term Training Program (LTT) for sponsoring the pursuit of this Ph.D.

including my year studying at the University of Rhode Island. LLT is an extraordinary

opportunity and I am honored to have been accepted as a participant. A huge “thank

you” to Mr. Bryan Baker and Mr. David Finnegan of the Remote Sensing GIS Center

of Expertise for supporting my application to LLT and your continued encouragement

through Ph.D. completion.

v

To my URI graduate school office-mate and fellow PhD candidate, Mr. Paul

Sauco, thank you for friendship, encouragement, commiseration, and empathy

(sympathy?) through this process. It was extremely helpful to have a peer in a similar

phase of life working through a Ph.D. process.

To my research partners, Mr. Darren Flynn, Mr. Ryan Carlson, and Ms. Jen

McGunigal, much of this research would not have been possible without you. Thank

you for performing field data collects on dates that I could not be onsite. Your

precision, accuracy, and detailed notes were critical to the research presented herein. I

look forward to our continued research partnership.

Mostly, I would like to thank my family. To my parents, Kathy and Paul, thank

you for your never-ending support and encouragement. I could not have accomplished

the nighttime coursework, nor written and oral comprehensive exams without your

loving support for my children and thereby me. To my children, Genevieve and

Benjamin, thank you for snuggles, love, and support. I hope you see that you are never

too old to go back to school and life is about learning and growing. Lastly, to my

husband, Brian, you have followed and supported me through my educational journey

from the start. We have spent the past 18 years in a sprint, pursuing career and

education opportunities. I will do my best to slow down. Thank you for “going with

the flow” and your unconditional love and support.

vi

PREFACE

This dissertation is comprised of traditional dissertation. The first chapter

introduces the readers to the overall research, problem statement, and existing research

knowledge gaps. The second chapter is a review of the literature on Distributed

Acoustic Sensing (DAS) technology, current DAS research and applications, and

knowledge gaps as identified in publications. The third chapter describes the DAS test

beds and testing methodology. The fourth chapter presents the results of DAS

performance in different soil types, under vehicle loading, through soil moisture and

soil temperature changes, and over time. The results are discussed in this chapter and

include an examination on soil stiffness and impedance ratio which likely contribute to

the observed results. Finally, the fifth chapter summarizes the results and implications

of this research program, presents on-going work, and discusses future research.

vii

TABLE OF CONTENTS

ABSTRACT…………………………………………………………………………...ii

ACKNOWLEDGMENTS…………………………………………………………...iv

PREFACE…………………………………………………………………………….vi

TABLE OF CONTENTS……………………………………………………………vii

TABLE OF TABLES…………………………………………………………………xi

TABLE OF FIGURES………………………………………………………………xii

CHAPTER 1: INTRODUCTION……………………………………………………1

1.1 Statement of the Problem………………………………………………...1

1.2 Knowledge Gaps…………………………………………………………..4

1.3 Scope of Research…………………………………………………………6

1.4 Organization of this Dissertation………………………………………...8

CHAPTER 2: REVIEW OF LITERATURE………………………………………..9

2.1 DAS using Fiber Optics…………………………………………………..9

2.1.1 Basics of Light Transmission…………………………………10

2.1.2 Rayleigh Scattering……………………………………………13

2.1.3 Sensors used to Measure Rayleigh Scattering……………….15

2.1.4 A DAS System………………………………………………….18

2.2 Seismic Waves and DAS ………………………………………………...20

2.3 DAS versus Geophones and Seismometers…………………………….23

2.4 Civil Engineering Applications of DAS………………………………..27

2.4.1 Soil, Slope, and Seismic Monitoring with AE……………….28

viii

2.4.2 Fluid Flow Monitoring…………………………………….…..29

2.4.3 Geotechnical Subsurface Investigations………….………..…30

2.5 DAS, Geotechnical Engineering, and the Future ……………………..32

CHAPTER 3: METHODOLOGY………………………………………………….35

3.1 Permanent DAS Test Bed……………………………………………….35

3.1.1 Design Considerations and Layout…………………………...36

3.1.2 Test Bed Installation…………………………………………..38

3.1.3 Moisture and temperature sensors…………………………...41

3.1.4 Impact testing and data recording…………………………...43

3.1.5 Channel mapping……………………………………………...45

3.2 Temporary DAS test bed………………………………………………..46

3.3 Temporary DAS test bed………………………………………………..51

CHAPTER 4: RESULTS AND DISCUSSION……………………………..……..55

4.1 Soil Type and Time In-situ…......……………………………………….55

4.2 Vehicle Loading...………………………………………………………..59

4.3 Soil Moisture and Temperature……………………………….………..63

4.4 Discussion of Results: Impedance Ratio, Soil Stiffness, and DAS

response…..……………………………………………………….……...69

CHAPTER 5: CONCLUSIONS……………………………………………………81

5.1 Impact of research on civil engineering ……………………………….82

5.2 Engineering recommendations ………………………………………...85

5.3 Future work ……………………………………………………………..86

APPENDIX A: MANUSCRIPT …………………………………………………....87

ix

APPENDIX B: CONFERENCE MANUSCRIPT ……………………………….104

APPENDIX C: MATLAB CODE ………………………………………………...127

BIBLIOGRAPHY …………………………………………………………………147

x

TABLE OF TABLES

Table 3.1: Soil water content and dry density………………………………………...41

Table 3.1: CRREL test bed experiment loading sequence……………………………49

Table 4.1: CRREL test bed experiment loading sequence……………………………60

Table 4.2: In-situ average soil density and water content.……………………………69

Table 4.3: Impedance ratio calculations………………………………………………75

Table 4.4: Impedance ratio fluctuations with water content……….…………………79

11

TABLE OF FIGURES

FIGURE 1.1: Commercially available DAS interrogators (Silixa on the left and

Optasense on the right). Images courtesy of silixa.com and optasense.com.….2

FIGURE 1.2: DAS interrogator connected to a fiber optic cable with vibrations

exciting the fiber optic cable………………………………………………..….3

FIGURE 2.1: Illustration of Snell’s Law (based off of Krohn et al. 2014)…………..11

FIGURE 2.2: Reflection of light in the core of a single mode fiber optic cable……..12

FIGURE 2.3: Rayleigh backscatter due to laser impulse propagating down a single

mode fiber optic cable………………………………………………………...15

FIGURE 2.4: Basics OTDR set-up and example of OTDR trace…………………….16

FIGURE 2.5: Typical DAS system…………………………………………………...18

FIGURE 2.6: Strain Transfer from soil to fiber core (based on Soga et al. 2018)…...20

FIGURE 2.8: Seismic waves in near surface soils: compression waves (A), shear

waves (B), Rayleigh waves (C), and Love waves (D)…...…………………...22

FIGURE 2.10: DAS versus geophones (via Erogov et al. 2018)……………………..25

FIGURE 2.11: DAS versus seismometers (via Lindsey et al. 2020)…………………26

FIGURE 2.12: AE with stress and displacement (Smith and Dixon, 2018)………….29

FIGURE 2.13: Effect burial and compaction has on DAS power spectral density

(Shukla et al. 2020)…………………………………………………………...30

FIGURE 2.14: Example of DAS responding to digging (Parker et al. 2014)……..…31

FIGURE 3.1: Permanent Test Bed Layout ………………………………………..37

FIGURE 3.2: Trench Dimensions……………………………………………………37

FIGURE 3.3: Complete excavated trench for flowable fill…………………………..39

12

FIGURE 3.4: Transition between gravel and sand in-trench ………………………..39

FIGURE 3.5: Fiber optic cable along the center line of the sand trench……………..40

FIGURE 3.6: Placement of CS650 sensors relative to the fiber optic cable…………42

FIGURE 3.7: Location of the CS650 sensors in the test bed …………………….….42

FIGURE 3.8: Impact source: standard proctor hammer and aluminum plate………..43

FIGURE 3.9: Location of hammer testing sequence relative to the test bed…………44

FIGURE 3.10: DAS response to entire testing sequence where channel 95 is on the

bottom and channel 138 is at the top………………………….………………45

FIGURE 3.11: CRREL DAS array layout……………………………………………46

FIGURE 3.12: Impact testing at CRREL test bed……………………………………47

FIGURE 3.13: Load cells used for vehicle load testing……………………………...48

FIGURE 3.14: Experimental layout………………………………………………….48

FIGURE 3.15: Vehicle for loading C on load cells…………………………………..50

FIGURE 3.16: Vehicles associated with surcharge…………………………………..50

FIGURE 3.17: Typical DAS response to impact testing………………………...…...52

FIGURE 3.18: Signal-to-Noise visual example from January 2, 2020. Response from

the nineth impact at source location E.……………………………………….53

FIGURE 4.1: Source locations used to evaluate DAS performance in different soil

types……………………………………………………..……………………56

FIGURE 4.2: SNR of prior installation vs. new installation in native material..…….57

FIGURE 4.3. SNR with distance: sand, gravel, and flowable fill……………………59

FIGURE 4.4: Approximate location of channels analyzed during DAS vehicle load

testing.………………………………………………………………………...59

13

FIGURE 4.5: Photos of loading C (A), loading E (B), and impact source location (C)

with respect to the temporary DAS array.……...............................................60

FIGURE 4.6: SNR for loading (closed circles) and unloading (open circles) during

vehicle loading experiment. The error bars for loading (continuous line) and

unloading (dashed line) are also shown………………………………………61

FIGURE 4.7: Noise in the CRREL test bed (red dashed lines) overlaying noise and

signal from the permanent test bed to illustrate the magnitude of the ambient

noise at CRREL.……………………………………………………………...62

FIGURE 4.8: Permanent test bed layout with Impact and CS650 locations…………63

FIGURE 4.9: Permanent test bed layout with Impact and CS650 locations…………64

FIGURE 4.10: Variation of SNR with moisture or prior install, new native material

and sand……………………………………………………………………….65

FIGURE 4.11: Variation of SNR with temperature…………………………………..66

FIGURE 4.12: SNR in one channel for each material from August 2019 to September,

2020……………………………………....……...............................................67

FIGURE 4.13: Impact source wave rays to DAS channels and resulting SNR

differences…………………………………………………………………….71

FIGURE 4.14: DAS response in different material types due to in-line above trench

material source location………………………………………………………76

FIGURE 4.15: Observed volumetric water content changes in trench materials over

time…...……………………………………………………………………….77

FIGURE 4.16: DAS performance over time. …….…………………………………..80

1

CHAPTER 1

INTRODUCTION

1.1 Statement of the Problem

In the early 2000s, the oil and gas industry began using fiber optic Distributed

Acoustic Sensing (DAS) to monitor long, remote lengths of pipeline for leaks (Jousset

et al. 2018). Within the past the past decade interest in other geophysical and

engineering applications of DAS has peaked. Researchers are demonstrating that DAS

is a vibration monitoring instrument that can be used to evaluating subsurface

stratigraphy with methods such as vertical seismic profiling (Mateeva et al. 2014 and

Egorov et al. 2018) or multichannel analysis of surface waves (Dou et al. 2017 and

Costley et al. 2018). Because of its discrete and distributed nature, DAS has the

potential to be a powerful infrastructure-monitoring tool of the future.

A DAS system, or array, is comprised of a fiber optic cable and an interrogator.

The fiber optic cable can be as simple as telecommunication fiber optic cable (i.e.

discrete and inexpensive) or as complex as a specially fabricated cable with unique

materials and orientation. The fiber optic cable connects to the interrogator, which is

an optical time-domain reflectometer (OTDR). Figure 1.1 provides examples of two

commercially available DAS interrogators (please note there are other commercial

venders). The interrogator houses at least one laser that pulses light into the fiber optic

cable core. Light propagates down the fiber optic cable core and the light scatters due

to anomalies in the core material (Krohn et al. 2014). Some of the scattered light

returns towards the interrogator (termed backscatter) as light continues to propagate

down the length of the fiber optic cable. Figure 1.2 shows the general concept of a

2

DAS interrogator with fiber optic cable. The interrogator measures the power of the

backscattered light and sorts the backscatter by return time (Sang 2011, Owen et al.

2012, Schenato 2017, and Wang et al. 2019). This return time is associated with a

distance down the fiber optic cable.

DAS measures vibrational strains over channel lengths; 10-meter channel lengths

are most commonly in cited publications. Channel length is the segment length over

which feedback is distributed along the length of fiber optic cable connected to the

DAS interrogator. For example, a 20-kilometer-long fiber optic cable connected to a

DAS interrogator set at 10-meter channel lengths would yield 2,000 evenly spaced

data feedback channels from one system.

FIGURE 1.1: Commercially available DAS interrogators (Silixa on the left and

Optasense on the right). Images courtesy of silixa.com and optasense.com.

DAS is sensitive to the vibrational strain field acting on the fiber optic cable at the

resolution of the channel length set in the interrogator (Lindsey et al. 2020). Figure 1.2

shows the general concept of a DAS interrogator with fiber optic cable. The DAS user

can select channel length (typically 10 meters), power level, and sampling rate

(typically greater than 2,000 Hz) of the system. These criteria are set to balance

tradeoffs between spatial resolution and dynamic range (Eyal et al. 2017).

3

FIGURE 1.2: DAS interrogator connected to a fiber optic cable with vibrations

exciting the fiber optic cable.

The fiber optic cable lengths achievable in DAS systems make this instrument

ideal to monitor kilometers of infrastructure along roadways and railways. Alsabhan et

al. 2019 used the DAS response to train-induced seismic waves seismic and infer

changes in ballast and subsurface material below railway rails. With DAS, engineers

can localize where changes occur and perform further engineering investigation

regarding whether the changes will affect the infrastructure safety and/or performance.

Recent research (such as Wang et al. 2018 and Lindsey et al. 2020) indicates that DAS

arrays yield results comparable to that of seismometers and geophones, suggesting that

DAS arrays might replace several point sensors in the future and/or supplement

existing point sensor monitoring systems.

The effect of external influences on DAS performance remain unknown. For the

civil engineering community to embrace DAS as the next generation infrastructure

vibration monitoring tool, the effects of soil type, in-situ conditions (i.e. seasonal

fluctuations), and overburden pressure on DAS performance must be understood.

Additionally, civil engineers must have confidence in the long-term viability of the

technology to recommend its use.

4

This dissertation explores the effect of variables external to the fiber optic cable

of the DAS system on performance, keeping infrastructure monitoring applications in

mind. DAS performance is defined herein as the repeatability and comparability of a

fiber optic cable array in different soil types to sense a calibrated impact source on the

ground surface through seasonal environmental changes. The hypotheses tested herein

were designed to better understand geotechnical installation considerations on long-

term DAS monitoring systems.

1.2 Knowledge Gaps

Although DAS is commercially available, fundamental aspects of DAS

performance in soil are not yet fully understood. The fiber optic cable portion of a

DAS system cannot be coupled to soil the same way it can be rigidly coupled to a

pipeline, a metal borehole casing, or the interior of a rock mine. The DAS research

community acknowledges that the coupling between the fiber optic cable and the host

medium affects the performance of the DAS system (Lindsey et al. 2020). Most of

these studies compared grouting a fiber optic cable to a well casing to tying a fiber

optic well casing, or hanging a fiber optic cable in a well casing. Lindsey et al. (2020)

hypothesized that horizontally installed fiber optic cable for DAS may have more

coupling issues than vertical fiber optic installations due to the variability in cable-to-

soil contact, including age of installation, installation depth, and changing drainage

soil conditions. Zhang et al. (2016) agrees that a major barrier in the accepted use of

distributed fiber optic sensing is the lack of understanding about the interaction

between fiber optic cable and soil during vibration and strain events. Additionally,

5

Zhang et al. (2016) recognizes that changes in the medium surrounding the cable

would affect the system response, noting that external fluctuations such as rainfall and

ground water elevation would likely affect the measured data.

Studying cable-to-soil coupling and the variables that affect this coupling remains

a singificant knowledge gap in DAS performance. Researchers (e.g. Iten 2011, Zhang

et al. 2016, and Winters et al. 2019) have attempted to study cable-to-soil coupling

with cable pull-out tests (varying soil moisture and density). Zhang et al. (2014),

Zhang et al. (2015), and Zhu et al. (2015) studied soil-cable interaction with

overburden pressure in a laboratory setting. The results of these studies indicate that

the cable to soil interface is sensitive to overburden pressure, density of soil, and water

content of soil. Further, these studies discuss that environmental changes affect the

physical and mechanical properties of soils. These studies indicate that an increase in

overburden pressure is proportional to the frictional pull-out resistance on the fiber

optic cable which could infer better cable-to-soil coupling and thus better

performance. Zhang et al. (2014), Zhang et al. (2015), and Zhu et al. (2015) allude that

a fiber optic cable imbedded in a soil with high effective stress (and a low water

content) will out-perform a DAS array with a fiber optic cable in a soil with a high

water content. However, these tests are showing changes in frictional resistance along

the fiber optic cable due to overburden pressure, and not directly showing how these

changes relate to the DAS response.

While significant gains in DAS performance have been achieved through

technological advancements in the DAS interrogation units and fiber optic cable

composition, little research has been documented on best soil installation practices, i.e.

6

improving performance with external influences. Much of the existing body of

published research on DAS arrays in soil regard the following:

1. Short-term data collects (e.g. Miller et al. 2018 and Parker et al. 2018)

2. Significant seismic events on previously installed arrays (e.g. Lindsey et

al. 2017 and Wang et al. 2018).

3. Advancements in data processing methods (e.g. Martin et al. 2018).

From a civil engineering perspective, knowledge gaps preventing DAS from wide

acceptance seem clear. Civil engineers need to be able to provide installation

recommendations for DAS that will promote consistent performance over time. Part of

providing engineering recommendations for DAS installation include knowing how

site conditions, available material, and seasonal changes will affect DAS performance.

This dissertation aims to provide information related to these areas of concern.

1.3 Scope of Research

The objective of this research is to investigate the relationship between the

material type surrounding the fiber optic cable and DAS performance. It is

hypothesized that DAS response is affected by soil type, moisture content,

temperature, surface loading, and time in situ.

To test this hypothesis, a permanent, new portion of DAS test bed was

constructed alongside of an existing DAS test bed installed ten years prior. The new

portion of DAS array includes channels of the fiber optic cable in sand, gravel,

excavatable flowable fill, and the native silty sand material. The layout of the array

was designed to provide maximum performance comparison between array segments

7

in differing soil types. This field test bed is exposed to seasonal changes and weather

events, allowing for observation of changes in soil moisture and soil temperature that

may affect DAS response. Soil volumetric water content and temperature sensors were

installed in each soil type as the depth of the fiber optic cable. To evaluate DAS

performance consistently throughout this research, impact tests were performed

periodically at set locations along the DAS array. The DAS response to impact testing

was processed for Signal-to-Noise-Ratio (SNR) as the response performance criteria,

where higher SNR equates to better performance. Previous laboratory studies have

looked at a single soil type and pull-out resistance of a fiber optic cable, inferring that

higher pull-our resistance means better soil-to-cable coupling which infers better

performance. Previous field studies looked at DAS in a single soil type under short-

term conditions (moisture or loading) and inferred long-term performance. This

dissertation presents novel research as it simultaneously investigates DAS

performance in four soil types through fluctuating soil moisture and temperature

conditions over the course of one year and provides performance of the portion of

DAS array installed a decade prior.

To test surface loading effects on DAS performance, a separate temporary test

bed was constructed at the U.S. Army Corps of Engineers Cold Regions Research and

Engineering Laboratory (CRREL) to test DAS performance with loading and

unloading cycles, where the loading is increased to cable failure. The temporary test

bed contained ten channels of DAS array surrounded by a dry, silty soil. Load cell

pads were placed on the top of the silt blanket containing the DAS array. These load

cell pads measured the exact force applied to the top of the silt blanket while impact

8

testing was performed. Increased load was applied to the test bed through loading and

unloading cycles until the fiber optic cable failed.

1.4 Organization of this Dissertation

This chapter, Chapter 1, introduces DAS, existing knowledge gaps, and the

objectives and scope of this research.

Chapter 2 provides a literature review on DAS beginning with fundamental

concepts, progressing towards how DAS works. This chapter explores published

research comparing processed DAS data to that of geophones and seismometers. This

chapter concludes with DAS applications in civil engineering and clear knowledge

gaps as identified by other researchers.

Chapter 3 presents a detailed description of the construction of the permanent and

temporary DAS test beds.

Chapter 4 presents the results of this study and discusses the results.

Chapter 5 provides a discussion of the research presented in this dissertation with

conclusions, and a discussion of on-going and future research.

Appendix A provides a manuscript discussing the effect of soil type on DAS

performance over seven months.

Appendix B provides a manuscript discussing the use of DAS for Acoustic

Emission monitoring in different soil types over time.

Appendix C provides MATLAB code used to process the DAS data collected as

part of this dissertation effort.

9

CHAPTER 2

REVIEW OF LITERATURE

The research presented in this dissertation focuses of the response of fiber optic

distributed acoustic sensing (DAS) systems embedded in near surface soil. Before

discussing civil engineering uses of DAS, this chapter provides a basic explanation of

how DAS works and how it responds to vibrations in the material surrounding the

fiber optic cable. This review highlights studies comparing DAS to other in-situ

vibration monitoring systems. Lastly, this review summarizes civil engineering

applications of DAS and knowledge gaps with DAS as defined by the research

community that this research aims to address.

2.1 DAS using Fiber Optics

Nearly 60 years ago, research into fiber optics began with a focus on

telecommunications (Schenato 2017). In the 1990s, distributed fiber optic sensing

(DFOS) was used for distributed temperature sensing (often abbreviated to DTS), see

Johansson (1997), and distributed strain sensing (commonly known as DSS), see Peck

(1994). For DTS and DSS, the DFOS system exploits changes in light scattering

within the fiber optic cable to infer information about the medium surrounding the

fiber optic cable, such as temperature and strain. DTS uses Raman scattering to

observe temperature and DSS uses Brillouin scattering to monitor both strain and

temperature. DAS, however, uses Rayleigh backscattering which is sensitive to

longitudinal strain (i.e., down the axis of the fiber). This section discusses

fundamentals of DAS, beginning with light transmission, Rayleigh scattering, and

10

how DAS uses Rayleigh scattering to infer vibrational strains acting along the length

of the fiber optic cable.

2.1.1 Basics of Light Transmission

Before discussing Rayleigh scattering of light, here is a review the basics of how

photons move inside of a fiber optic cable.

When a laser pulses light (i.e. photons) into one end of the fiber, the composition

of the fiber optic cable and Snell’s Law (Equation 1 and Figure 2.1) governs how light

propagates down the length of the fiber optic core. In Equation 1 and in Figure 2.1, n0

is the index of refraction of the medium in which the light is initially travelling, n1 is

the index of refraction of the second medium, ɸ0 is the angle between the incident ray

and the normal to the interface, and ɸ1 is the angle between the refracted ray and the

normal to the interface. Some of the ray is refracted and some of the incident ray is

reflected. If ɸ0 = ɸc, no refraction occurs, where the critical angle is ɸc. For ɸ0 > ɸc , all

of the ray is reflected at the interface (i.e. no refraction), which is called total internal

reflection (Krohn et al. 2014).

EQ. 1

11

FIGURE 2.1: Illustration of Snell’s Law (based off of Krohn et al. 2014)

When considering Snell’s Law for a fiber optic cable, Numerical Aperture (NA)

is a measure of light acceptance and is affected by the difference between the fiber

core and its cladding’s refractive index, see Equation 2, (Krohn et al. 2014). In

Equation 2, the fiber core has a refractive index n0, a cladding refractive index n1, and

a surrounding refractive index n. For context, a higher NA relates to a higher amount

of light remaining in the fiber, allowing for more severe grazing angles and increasing

the acceptance angle of light entering the fiber. Maximum light-collection efficiency

occurs for large-diameter-core fibers with larger NA fiber optic cables (Krohn et al.

2014).

EQ 2

12

FIGURE 2.2: Reflection of light in the core of a single mode fiber optic cable

The research in this dissertation uses a single mode fiber optic cable. Single-

mode transmission requires small fiber core size and low values of NA. Figure 2.2

illustrates how light would propagate down a single mode fiber. A single-mode fiber

allows for only one propagation mode and one polarization state due to their small

core diameter (Nikles et al. 1997 and Soga and Luo 2018).

The effect of the critical angle and the amount of light that can be injected into

the fiber is reduced in a bent fiber. Most bending loss occurs at the transition from the

straight to the bent section (Krohn et al. 2014). Fundamentally cable bending increases

photo power loss because the photons must slightly alter its direction. Subtle bends do

not provide and observable effect on DAS response, but tight bends (i.e. wrapped

around a pencil) will cause a noticeable loss. Thus, it is common to mark the end of a

test section in the field by wrapping the fiber optic cable around a pencil and thus

dropping the power at that location.

Attenuation, measured in decibels per unit length (dB/km), is loss and it is

defined by power input (Pi) and power output (Po), see Equation 3.

13

EQ 3

Attenuation in a typical telecommunications fiber optic cable is about 0.15 dB/km

(Miah and Potter 2017 and Lindsey et al. 2020). For perspective, attenuation less

than 1dB/km is “ultralow loss” fiber (Krohn et al. 2014).

Aside from bending, other causes of attenuation include absorption, scattering,

and microbending. Power losses at the end of the fiber optic cable are due to

reflection. Losses also occur in connections between the fiber and optical devices or

new fiber spliced onto existing fiber and are part of overall system losses.

2.1.2 Rayleigh Scattering

Changes in Rayleigh scattered light within the fiber optic cable are used in DAS

to infer vibrational strains acting along the fiber optic cable length. When a laser

pulses light into the core of a fiber optic cable, scattering occurs at sub-microscopic

anomalies in the composition and density of the glass (Krohn et al. 2014). Some of the

scattered light returns down the fiber towards the laser; the return of the scattered light

is called backscatter. Anomalies in the fiber causing these scattering centers could be

voids, density variations, impurities, composition fluctuations, and structural

variations.

Rayleigh scattering is an elastic process in which no energy is transferred as

photos reflect, meaning that returning scattered photons from the laser pulse travel at

the same velocity as original outbound photons and have the same wavelength. Other

14

types of DFOS use nonlinear, inelastic scattering processes such as Raman scattering

(used for DTS) and Brillouin scattering (used for DSS).

Combined loss effects cause transmission attenuation in the fiber. The

relationship of the exponential decay of optical power (light intensity), termed P(z),

down the length of the fiber optic cable (z) is show in Equation 4, where Po is the input

power and αT is the attenuation coefficient (Krohn et al. 2014). The power of the

backscattered light per length is Pbackscatter, Equation 5, is a function Numerical

Aperture, NA (related to the angle of the incident photo ray), the fiber core’s refractive

index nc, and δl is the backscatter power per unit length. Figure 2.3 illustrates

backscattering occurring at scattering centers, while the light from the laser pulse

continues to propagate down the length of the fiber. The purpose of displaying

Equations 4 and 5 is to show that the power of the photons making their way down the

fiber is a function of the input power and the total attenuation. The total attenuation is

a function of attenuation due to absorption, scattering, bending, and wave guide losses.

For example, a fiber with many significant bends in it will have higher attenuation due

to bending. And lastly, as shown in Equation 5, the power of the backscatter (which is

the response signal we evaluate with the DAS interrogator) is a function of the input

power, attenuation due to scattering, and properties of the fiber optic cable (i.e.

numerical aperture and refractive index).

EQ 4

EQ 5

15

FIGURE 2.3: Rayleigh backscatter due to laser impulse propagating down a

single mode fiber optic cable

Sang (2011) and Krohn et al. (2014) provide a detailed discussion on

numerical analysis of Rayleigh scattering.

2.1.3 Sensors used to Measure Rayleigh Scattering

In the 1970s, the optical time-domain reflectometer (OTDR) instrument was

developed to evaluate attenuation over telecommunication fiber optic cable lengths

(Personick 1977). The OTDR was used to troubleshoot bends, breaks and poor

connection in telecommunication cables (Sang 2011). The OTDR interrogator houses

a laser that pulses light in the fiber core. A photodetector, also in the interrogator,

measures the amount of light backscattered from the incident laser pulse. The detected

backscatter signal is termed the Rayleigh signature. As light propagates down the

fiber, it is sensitive to vibrations acting on the fiber optic cable; and the vibrations are

then observed in the Rayleigh signals (Soga and Luo 2018). OTDR profiles help to

find fiber breaks/faults, to evaluate splices and connectors, and to assess the overall

quality of a fiber optic cable connection. The trace slope represents the attenuation

16

factor within the fiber such that faults produce a peak intensity of back-reflected signal

in the trace profile followed by a drop, as shown in Figure 2.4.

OTDR systems rely on high-sensitivity photodetectors that can capture the low-

level reflected Rayleigh signals. Settings in the OTDR interrogator compromise

between dynamic range (i.e. the longer the pulse duration, the greater the signal

strength) and spatial resolution (i.e. the smaller the pulse width) over the known length

of fiber optic cable under test (Eyal et al. 2017, Krohn et al. 2014, Schenato 2017, and

Miah and Potter 2017).

FIGURE 2.4: Basic OTDR set-up and example of OTDR trace.

Rayleigh Backscatter collected by the OTDR interrogator is summed and

“binned” by time of return (Owen et al. 2012). The elastic nature of Rayleigh

scattering (velocity outbound equals velocity inbound) means that the return time

17

provides information about how far down the cable the backscatter occurred (Sang

2011, Owen et al. 2012, Schenato 2017, Wang et al. 2019). Soga and Luo 2018 show

that to calculate the location of each backscattered light center (z is the distance from

the interrogator), one only needs to know the time delay between launch and receive

(Δt), the speed of light in a vacuum (c), the refractive index of the core (n), and divide

by two because the light is elastically travelling down the fiber from the interrogator

and back to the interrogator (Equation 7).

EQ 7

The OTDR collects the Rayleigh signature signals induced by vibrational strain

along the fiber optic cable along with sources of noise. Sources of noise observed by

an OTDR include optical fluctuations, amplified spontaneous emission, and thermal

noise (Uyar et al. 2019). Another source of noise can occur when a signal is observed

in all DAS channels at once, called a common-mode noise, which is usually caused by

a seismic event occurring where the OTDR interrogator in located (Ajo-Franklin et al.

2019, Dou et al. 2017, and Lindsey et al. 2020). Poor cable-to-soil coupling, termed

“reduced amplitude channel noise,” is another possible source of noise in the signal

(Ajo-Franklin et al. 2019, Becker et al. 2017, Reinsch et al. 2017, and Lindsey et al.

2020), which is due to poor energy transferred to the fiber optic cable and within the

fiber optic cable. A seismic wave travels through the soil to a poorly coupled fiber

optic cable will inefficiently and inconsistently excite the fiber optic cable and thus the

backscatter inside of the fiber.

The two most common types of Rayleigh scattering sensors are conventional

OTDR and phase-sensitive OTDR (Soga and Luo 2018). The research herein this

18

dissertation uses a conventional OTDR interrogation system. For more information on

phase-sensitive OTDR interrogators (please see Sifta et al. 2015, Muanenda et al.

2016, Miah and Potter 2017, and Wang et al. 2017).

2.1.4 A DAS System

DAS systems must have two major components: 1) an optical fiber cable and 2)

an OTDR optical fiber analyzer for data acquisition, processing, transmission, and

storage (Soga et al. 2015). This combination allows for sensing of the vibration strain

field acting on the fiber optic cable (Lindsey et al. 2020). Figure 2.5 provides a

generalized schematic of the interrogator-fiber optic cable system.

FIGURE 2.5: Typical DAS system.

DAS provides average vibrational strains measured over a channel length,

commonly 10-meters-long. The interrogator can adjust laser power levels, dynamic

range, and channel length to refine the Signal-to-Noise Ratio, SNR. To improve SNR,

typically either power must be increased (pulse width or peak power) or the spatial

resolution must be reduced (i.e. longer channel lengths). When adjusting the

interrogator settings, the user should be aware that DAS arrays are most sensitive to

longitudinal waves propagating along the cable length and at 45° to the cable

19

centerline and the least sensitive to waves propagating perpendicular to the cable axis

(Martin et al. 2018 and Zhan 2019).

Vibrational strains in the soil transfer into strains on the cable jacket, which

transfers through the internal fiber optic cable geometry until reaching the fiber core.

As described in Soga and Luo (2018), strain transfer to the fiber core is caused by

shearing along the tightly bonding interfaces between series of materials from the

cable jacket to the cladding to the core, see Figure 2.6. The cable-to-material coupling

and the mechanical properties of the fiber optic cable affect the way the vibrational

strain is transferred from external material to fiber optic cable core (Ansari 2007,

Culshaw et al. 1996, and Soga and Luo 2018). The fiber optic cable cannot be

coupled to soil the same way it is coupled rigidly to metal or rock (Mateeva et al. 2014

and Lindsey et al. 2020). Differences in strain transfer due to difference in cable

coupling was first observed while performing vertical seismic profiling surveys where

the fiber optic cable was fixed to oil and gas wells using different methods (Mateeva et

al. 2014 and Lindsey et al. 2020), showing that DAS performance is highly dependent

on the coupling. As shown in Figure 2.6 a vibration strain in the soil transfers to the

cable jacket and eventually to the fiber core. The vibrational strain compresses the

cable in the direction of strain transfer which cause the fiber to slightly elongate

accordingly. Currently DAS systems cannot infer which way the strain is occurring,

but the vibration will be reflected in the DAS signal response.

20

FIGURE 2.6: Strain Transfer from soil to fiber core (based on Soga et al. 2018).

2.2 Seismic Waves and DAS

This section investigates the seismic waves that DAS systems sense. Near surface

installed DAS arrays are sensitive to compression waves (p-waves), shear waves (s-

waves), Rayleigh waves and, Love waves (Yang 2001, Kouretzis et al. 2007, and

Martin et al. 2018). The sensitivity DAS has to seismic waves depends on the DAS

gauge length, the wavelength of the source, and the orientation of the wave to the fiber

optic cable core (Martin et al. 2018).

The particle movement generated by the seismic waves transfers to the fiber optic

cable portion of the DAS system. Figure 2.8 illustrates the different particle motion

caused by p-waves (A), s-waves (B), Rayleigh waves (C), and Love waves (D). P-

waves, or compression waves, cause particle compression and expansion through the

soil medium. S-waves cause a shearing motion (and deformation) between particles.

21

Near the ground surface, shear s-waves are considered to be in the vertical direction,

while Love waves similar to shear waves but in the horizontal direction; both causing

deformations (Kramer 1996 and Martin et al. 2018). Rayleigh waves involve an

elliptical particle motion, see Figure 2.8 C.

For this dissertation, the impact source used (alluded up in Chapter 1 with a

detailed discussion in Chapter 3) is on the ground surface and likely generates p-

waves, s-waves, and Rayleigh waves. Therefore, this section will not discuss Love

waves. It has been documented that Rayleigh waves and shear waves from a given

source typically have similar velocities (Addo and Robertson 1992, Ananasopoulos et

al. 2000, and Yang 2001). Therefore, when searching for seismic response in DAS,

often the Rayleigh wave and shear wave signals overlap.

The study of Rayleigh wave motion in soil began with Biot (1956), who

considered saturated soil a fully saturated porous medium and treated it as a

poroelastic continuum. Building upon Biot’s work, Tajuddin (1984),

Philippacopoulos (1987), and Yang (2001) studied Rayleigh waves in fully saturated

poroelastic space. Yang (2002) performed field studies that indicated soil saturation

significantly affected p-wave propagation. Yang (2005) followed on this work to study

the effect of ground water fluctuation on Rayleigh wave propagation. Yang (2005)

found that, with increasing saturation, the Rayleigh wave velocity approaches the S-

wave velocity, but that the Rayleigh wave appeared to have greater soil particle

elliptical movement with a higher degree of saturation. Zhou and Xia (2006) also

studied Rayleigh wave propagation in partially saturated soils, specifically with a

22

thorough numerical analysis of how soil saturation on Rayleigh waves; and found that

saturation has greater affect at low frequencies.

FIGURE 2.8: Seismic waves in near surface soils: compression waves (A), shear

waves (B), Rayleigh waves (C), and Love waves (D)

23

If soil saturation affects Rayleigh wave and p-wave propagation, then soil

saturation may also affect the transfer of vibrations to the fiber optic cable core. Some

researchers have considered that the fiber optic cable itself affects wave propagation

through soil. Kouretzis et al. (2007) assumed that the flexible pipeline (or fiber optic

cable) is fully coupled with the ground motion. The authors indicate that this is a

conservative assumption because in real life, the pipeline (or a fiber optic cable) is not

fully coupled and thus the predicted/modeled strains are greater/stronger than what

they would be in reality. Kouretzis et al. (2007) validated this assumption with blast-

induced displacement measurements on flexible pipelines in soil (Siskind et al. 1994).

Several researchers have studied how seismic events are recorded by DAS (Moran et

al. 1999, Dean et al. 2017, and Parker et al. 2014, and Lindsey et al. 2020). Lindsey et

al. (2020) acknowledges that there is likely a small effect between the fiber and the

soil and the propagating wave, but that this effect can be ignored due its insignificance

at most propagating wavelengths.

2.3 DAS versus Geophones and Seismometers

In the past decade, the geophysics community has embraced DAS technology

(Daley et al. 2013, Karrenbach et al. 2018, and Zhan 2019). Geophysicists have

compared DAS performance to other seismic activity instrumentation, such as the

geophones, accelerometers, and seismometers. All these instruments are used to

evaluate the motion of the ground in response to mechanical ground vibrations.

Researchers such as Martin et al. (2018) detail the intensive data processing required

to extract the response of a DAS array to various wave types. Martin et al. (2018)

24

indicates that the DAS array must be configured to account for directionality of the

fiber optic cable components. Her research indicates that the DAS array responds

consistently well to propagating Rayleigh waves.

As DAS technology and data processing have improved, DAS response has

become more comparable to that of geophones. Several studies have evaluated the use

of DAS for vertical seismic profiling, VSP (e.g., Mateeva et al. 2014, Olofsson and

Martine, 2017, Egorov et al. 2018, and Miller et al. 2018). Figure 2.10, from Erogov et

al. (2018), shows the comparison of processed DAS response to processed geophone

response. Also, DAS has been used by several researchers (e.g., Daley et al. 2013,

Bakulin et al. 2017, Castongia et al. 2017, Dou et al., 2017, Hornman, 2017, Jreij et al.

2017, Costley et al. 2018, Spikes et al. 2019, and Miller et al. 2018) in ground surface

deployments to perform multichannel analysis of surface waves, commonly referred to

as MASW. Both VSP and MASW are used to estimate the shear wave velocity of soil

profiles.

25

FIGURE 2.10: DAS versus geophones (via Erogov et al. 2018).

Studies such as Daley et al. (2016) and Egorov et al. (2018) compare DAS

generated VSP to the VSP generated from the vertical component of the geophone (the

direction vertically installed DAS fiber most sensitive) at CO2 injection sites. These

studies found that the DAS and geophones have comparable SNRs, where DAS is

slightly less sensitive to some frequencies, as shown in Figure 2.10. Spikes at al.

(2019) demonstrates that DAS using different fiber optic cables laid on the ground

surface compares well to surface geophones.

Researchers have also compared DAS response to seismometer response. Martins

et al. (2019) and Zhu et al. (2019) describe DAS as a dense array of seismometers

where a several kilometer-long fiber optic cable, yields seismometer response every

26

few meters (determined by channel length). Parker et al. (2018) used DAS and nodal

seismometers to perform P-wave velocity tomography. Parker et al. (2018) found that

seismometers outperformed the DAS system by providing a higher SNR and wider

range of frequencies, but that DAS provided more point data. Lindsey et al. (2020)

found that DAS response is comparable to a high-quality broadband seismometer,

sensing the same broadband frequencies as seismometer. Lindsey et al. (2020)

compares DAS response to micro-seismic events to seismometer response, as shown

in Figure 2.11.

FIGURE 2.11: DAS versus seismometers (via Lindsey et al. 2020).

27

With confidence in DAS’s use to detect seismic events, there is developing

research using existing fiber optic cable infrastructure (i.e. dark fiber) to look at

ambient noise and potentially pick up earthquake induced seismic activity (Jousset et

al. 2018, Martin et al. 2018, Ajo-Franklin et al. 2019 and Yu et al. 2019). Note that the

configuration of DAS channel lengths must be selected based on the seismic

wavelength of interest (Dean et al., 2017, Lindsey et al. 2017, Martin et al., 2018,

Lindsey et al. 2020).

2.4 Civil Engineering Applications of DAS

Due to its discrete, malleable, and distributed nature, DAS has clear applications

as a structural health monitoring tool. Common instrumentation, such as strain gauges

and piezometers, provides monitoring data for one point in space, whereas DFOS

provides monitoring capabilities along the entire length of the fiber optic cable.

Researchers (Soga et al. 2008, Bao and Chen 2011, Luo et al. 2016, Luo et al. 2019)

are using DFOS to monitor strain in various types of infrastructure. Civil engineering

applications of DFOS currently include continuous monitoring of bridges, dams,

pipelines, piles, mines, and security (Luo et al. 2016, Soga and Luo 2018, Jousset et al.

2018, Li et al. 2018, Luo et al. 2019). This dissertation focuses on DAS, but if the

reader wishes to learn more about other DFOS such as DTS and DSS (Brillouin

optical time domain reflectometry or BOTDR), please see Soga and Luo (2018) and

Soga et al. (2015) for excellent overviews.

28

2.4.1 Soil, Slope, and Seismic Monitoring with AE

DAS is one of the technologies under consideration for Acoustic emission (AE)

monitoring. The idea behind AE is a form of change detection, such that changes in

the AE of a structure relate to changes in its condition. There is ongoing research to

link measured AE with strength and deformation behavior of soil (Smith and Dixon

2018). Researchers Heather-Smith et al. (2018), Smith et al. (2017a), and Smith and

Dixon (2018) used AE to study wave propagation and attenuation of Rayleigh waves

in laboratory samples of soil. Figure 2.12 (from Smith and Dixon 2018) shows that AE

is suspected to (a) increase with loading and unloading and (b) increase with

increasing displacement. The variables affecting AE and attenuation in soil include the

soil density/Young’s modulus, Poisson’s ratio, subsurface environment, and the

above-ground environment. A conference manuscript regarding AE is provided in

Appendix B of this dissertation

While there are currently ongoing studies using DFOS to evaluate slope stability

(Wang et al. 2019, Zhu et al. 2014, Picarelli et al. 2015), AE monitoring has been

shown to be a potential metric used to monitor slope stability (Tanimoto and Tanaka

1986, Smith et al. 2014, Dixon et al. 2015, Smith et al. 2017b, Dixon et al. 2018).

DAS could be used to monitor AE within geotechnical infrastructure, as it could be

placed within foundation materials or embankments.

29

FIGURE 2.12: AE with stress and displacement (Smith and Dixon, 2018)

2.4.2 Fluid Flow Monitoring

The oil and gas industry has been using DAS to continuously monitor for pipeline

leaks (Shukla et al. 2020). Researchers have applied DAS monitoring of pipeline leaks

to monitor fluid flow through conduits. Johannessen et al. (2012) used DAS in wells to

evaluate AE along a well and correlated changes in monitored AE with changes in

fluid flow. Paleja et al. (2015) used DAS identified velocity changes that could

indicate fluid leaks in pipelines. Shukla et al. (2020) explores the effect soil backfill

has on pipeline AE monitoring used for leak detection. Figure 2.13 shows the effect

that burying and compacting soil around the pipe has on the measured power spectra

density. It appears that soil (whether it is compacted) around suppresses the pipeline

AE response.

30

FIGURE 2.13: Effect burial and compaction has on DAS power spectral density

(Shukla et al. 2020)

Researchers are beginning to look at using DAS to monitor seepage in dams and

levees. Shukla et al. (2020) indicates that DAS through an earth embankment would

be sensitive to changes in fluid flow velocities. Miller et al. (2018) discusses co-

locating DAS and DTS cables in to evaluate environment processes and fluid flow.

2.4.3 Geotechnical Subsurface Investigations

As discussed in Section 2.3, DAS can be used to estimate soil velocities and

thereby supplementing geotechnical subsurface investigations. DAS has been used by

researchers to estimate in-situ wave velocities to estimate stiffness. Researcher are

using DAS to perform Spectral Analysis of Surface Waves (often referred to as

SASW) and MASW (Stokoe et al. 1994, Athanasopoulos et al. 2000, Yang 2005) to

interpret subsurface stratigraphy. Duo et al. (2017) uses traffic as the active source for

MASW collected by dark fiber buried along roadways to estimate the shear wave

velocity in the upper 30 m of soil (termed VS30). Alsabhan et al. (2019) is using a

31

similar technique with trains as the active source to monitor railway ballast and

subgrade.

The discrete nature of a fiber optic cable that makes the sensor simple to deploy

on pipelines and in wells also makes DAS relatively easy to install just below the

ground surface as a security monitoring system. Vibrations induced by activity near

the installed DAS induce submicroscopic changes in the fiber length, core refractive

index, and core diameters (Liu et al. 2016). DAS can be trained to identify an activity

of interest (Madsen et al. 2008, Wu et al. 2015, and Friedli et al. 2019). Figure 2.14

provides an example of a digging signature recorded by DAS as presented in Parker et

al. 2014.

FIGURE 2.14: Example of DAS responding to digging (Parker et al. 2014)

32

2.5 DAS, Geotechnical Engineering, and the Future

DAS can be efficiently installed for both short term and long-term monitoring

programs. The spatial resolution of DAS can help civil engineers understand

infrastructure in a way previously not possible. DAS installed during infrastructure

construction could help us truly understand infrastructure performance and aging, as

well as inform design and maintenance (Soga et al. 2015). Soga and Luo (2018) urge

engineering field demonstrations to build confidence in DFOS within the civil

engineering community.

While there are many “pros” to using DAS technology for monitoring, one “con”

is that DAS response is presently unquantified (Soga and Luo 2018, Lindsey et al.

2020), meaning that the amplitude of the response signal does not precisely correlate

to a unit of measurement. However, DAS response is proportional to the actual

vibrational strain. While it is possible to evaluate the quantifiable response for one

particular DAS channel, the strain measurement will be site specific, channel specific,

cable specific, and source specific (Lindsey et al. 2020). Co-locating DAS with

another instrument such as a seismometer or geophone could ease engineering

concerns about accuracy and precision, allowing the vibration signature to be

calibrated by the local point sensor.

While DAS predominantly measures the dynamic strain field acting on a fiber

optic cable, changes in temperature could cause the index of refraction in the fiber

optic cable core or a slight change fiber optic length, which could noticeably affect the

DAS response. Most studies do not consider the effect of temperature changes, as

many of the studies herein observe a short-term event on which the timescale cause

33

seasonal change to be irrelevant (Lindsey et al 2020). The effect of temperature

change is one of the variables possibly affecting DAS response considered in this

dissertation.

While DAS is sometimes fixed to a well or a pipeline, many of the DAS systems

used for research or proposed for future research are shallowly installed parallel to the

ground surface. Lindsey et al. (2020) discussed how horizontally buried fiber optic

cables will have the same coupling issues (if not more) than vertical installs. The strain

transfer from the soil to the cable cladding to the fiber core, the amount of contact

between the cable and the surrounding soil, age of installation, trench depth, and

drained versus undrained soil conditions will affect DAS performance. Lindsey et al.

(2020) states, “more work is required to understand these potential impacts, which…

(will likely) vary within each DAS array.”

When DAS is used down-hole, in mines, or on rails, it is fixed to a rigid structure.

Fiber optic cables cannot be fixed to soil rigidly making cable to soil coupling an open

area of research. Zhang et al. (2016) stated that joining “cables to soil remains one of

the major barriers to successful use of distributed fiber optic sensing with regards to

strain and vibrations.” Zhang et al. (2016) recognized that everything that affects the

medium surrounding the fiber optic cable effects the fiber optic sensor response too.

External changes such as seasonal water fluctuations and rainfall infiltration will likely

affect the cable to soil interaction, which could affect the overall performance of a

DAS array.

Several researchers attempt to study cable to soil interaction with pull-out tests, if

changes in pull-out resistance correlate to the bond between soil and cable (Iten 2011,

34

Zhang et al. 2016, and Winters et al. 2020). Zhang et al. (2014), Zhang et al. (2015),

and Zhu et al. (2015) studied the relationship between overburden pressure and cable

to soil interaction. These studies indicate that the cable to soil coupling varies with

overburden pressure, soil density, and soil water content. They observed that a high

effective stress (high overburden and low water content) and high-density soil samples

yield an increased pull-out resistance inferring a tighter cable to soil extending

sensitivity range. Zhang et al. (2014), Zhang et al. (2015), and Zhu et al. (2015)

discuss that changes in water content and density would affect physical and

mechanical properties of soils and thus effect DAS response.

This dissertation investigates DAS performance in different soil materials through

seasonal water content and temperature, under different overburden pressure, and over

time. As discussed in Chapter 3, a new DAS test bed was installed adjacent to an

existing DAS array to observe the effects of soil moisture and temperature changes on

a new DAS installation and an aged DAS installation. A temporary DAS array

installed to observe DAS performance through loading and unloading above the array.

These experiments were designed to test the dissertation objectives mentioned in

Section 1.3.

35

CHAPTER 3

METHODOLOGY

DAS is used monitor vibration response along fiber optic cable lengths. For this

dissertation, DAS response is observed over time in different soil types with varying

soil moisture and temperature and with additional overburden pressure. Two test beds

were constructed to study the effects of these variables on DAS performance. The first

was a permanent test bed designed to consider the effect of soil type, soil moisture,

soil temperature, and time in-situ on DAS response. A second temporary test bed was

constructed to test effects of loading on DAS response. The permanent test bed will

be discussed first in this chapter.

3.1 Permanent DAS Test Bed

A permanent DAS test bed was constructed by splicing a new portion of fiber

optic cable into an existing portion of DAS array installed a decade prior (referred to

as the “prior installation” or the “legacy” fiber). The test bed is in a relatively urban

area and adjacent to a wetland. The upper meter of soil is a random urban fill

consisting of brown silty sand with gravel and some cobbles and some debris. Below

the fill is one to two meters of glacial till underlain by bedrock. Groundwater is

observed at a depth between two and three feet below the ground surface. The ground

surface of the test site is relatively level.

36

3.1.1 Design Considerations and Layout

The area available for the new portion of DAS test bed limited the fiber optic

cable layout options. The 10-meter channel spacing also limited the design options as

it was desired to have at least three DAS channels in each soil type for redundancy.

The new portion of fiber optic cable used was the same fiber optic cable as that of the

existing DAS array (i.e. came from the same spool of original cable). The fiber optic

cable in this DAS array is a single mode fiber with a water-proof buffer tube, armor,

and a polyethylene jacket. The test bed layout (Figure 3.1) maximizes our ability to

compare DAS response in different soil types, while allowing the comparison between

new and existing portions of array in native material.

The materials to be tested include the native fill material, a sand fill (well graded

sand), an angular gravel fill, and excavatable flowable fill. A long portion of new

native fill trench parallel the existing DAS array allowed for the comparison of

multiple DAS channel responses. The native fill trench was excavated to a depth of

two feet (about 0.5 meters) below the ground surface and the fiber optic cable was laid

at the bottom of the trench two-feet-deep (0.5-meters-deep). Portions of the array in

non-native material types (excavatable flowable fill, sand, and gravel) were excavated

to a depth of three feet (about one meter) such that one foot of non-native fill material

would be placed below the fiber optic cable and two feet of non-native fill placed

above the fiber optic cable. All trenches are two-feet-wide (about 0.5-meters-wide).

The fiber optic cable was placed down the trench centerline and surveyed. Figure 3.2

provides the trench dimensions in plan and profile views.

37

FIGURE 3.1: Rhode Island Test Bed Layout

FIGURE 3.2: Trench Dimensions

38

3.1.2 Test Bed Installation

The new portion of test bed was constructed in August 2019. Native fill was

excavated to create one long trench (the length of the entire new portion of DAS

array), and the native material was stockpiled onsite to be used as backfill for native

fill trenches. Cobbles and debris were removed from the native fill stockpile before

being placed and compacted above the placed fiber optic cable in native fill portions

of the test bed. Figure 3.3 shows the excavated trench prior to flowable fill placement.

Note ground water seepage up from the bottom of the trench. The flowable fill

provided for this effort was defined in Rhode Island Department of Transportation

(RIDOT) Bluebook Section 603 as a Class I excavatable flowable fill. Exacavatable

flowable fill is classified as a controlled low strength material that self-consolidates,

levels, and stiffens. The sand fill is an ASTM C33 sand, which is a well graded sand.

The gravel fill is defined as “keystone” in the RIDOT Bluebook Section M.01.09

Table 1 Column III where most of the stone is between ½ inch and one inch in size.

The sand fill was compacted with a vibratory plate compactor. The gravel was

tamped with the excavator bucket, and the flowable fill was poured from one concrete

truck. Once the flowable fill cured overnight (and was firm enough to stand on), the

new portion of fiber optic cable was placed along the centerline of the flowable fill

trench, and a few days later, a concrete truck delivered the second lift of flowable fill.

Figure 3.4 shows the transition between sand and gravel soil types in the trench.

Figure 3.5 shows the new portion of fiber optic cable placed along the centerline of the

sand trench.

39

FIGURE 3.3: Complete excavated trench for flowable fill

FIGURE 3.4: Transition between gravel and sand in-trench

40

FIGURE 3.5: Fiber optic cable along the centerline of the sand trench.

A nuclear density gauge (NDG) with a 10-inch probe depth was used to measure

the in-situ density and water content at the bottom of each excavated trench, and in

each sand and native material layer once compacted per ASTM D6938. The nuclear

density gauge values were compared to the optimum density calculated via the

Modified Proctor, Method B (ASTM D1557). Table 3.1 provides values of optimum

water content and maximum dry density, the in-situ NDG measured water content and

dry density, the calculated total unit weight, and the percent compaction. For the

flowable fill, four 4-inch by 8-inch cylinders were collected and the seven-day

compressive strength of the cylinders ranged from 50 to 60psi.

41

Table 3.1: Soil water content and dry density

Soil

Avg measured dry unit weight (pcf)

Avg. measured

water content

(%)

Cal. total unit weight (pcf)

Max dry

density (pcf)

Opt. water

content (%)

Percent Compac

tion (%)

Native material along trench bottom

99.4 25 124 132.5 7.5 75

Native material placed and compacted above the cable

106 15 122 132.5 7.5 80

Sand placed and compacted below the cable

107 2 110 114.5 13.6 94

Sand placed and compacted above the cable

108 3 110 114.5 13.6 95

3.1.3 Moisture and temperature sensors

Moisture and Temperature sensors were installed adjacent to the new portion of

fiber optic cable for continuous monitoring of the in-situ soil temperature and moisture

throughout this research. Campbell Scientific (CS) 650 volumetric moisture sensors

were installed (as shown in Figure 3.6). A CS 650 was installed in each soil type, as

shown in Figure 3.7, to continuously monitor soil moisture and temperature over time.

Note that no CS 650 sensor was installed in the flowable fill; this choice was based on

a previous test install where the CS 650 did not function correctly in flowable fill

material. The CS 650 sensors were placed parallel-to, but not touching the fiber optic

cable with the power and data transmitting cable placed along the edge of the trench.

42

The CS 650 sensors were set to take recordings every 15 minutes and save datalogger

SD. The recorded data is manually retrieved form the data logger as needed.

FIGURE 3.6: Placement of CS650 sensors relative to the fiber optic cable

FIGURE 3.7: Location of the CS650 sensors in the test bed

43

3.1.4 Impact testing and data recording

To evaluate DAS performance, a calibrated, repeatable impact source was used.

A standard proctor hammer, used for geotechnical compaction control, was selected as

the impact source. A standard proctor hammer is a 2.5kg weight with a set drop-height

of 305mm. This source was selected as it is calibrated, does not require power, and is

easy to travel, making it a uniform way to compare DAS response at any other DAS

array. The impact was delivered to the ground surface by placing the standard proctor

hammer on an aluminum plate, lifting the hammer head to the top of the confined

hammer and releasing the hammer handle such that the hammer strikes the plate,

delivering a repeatable amount of energy to ground surface (see Figure 3.8).

FIGURE 3.8: Impact source: standard proctor hammer and aluminum plate

44

Impact hammer source locations were established during the construction of the

DAS test bed. The source locations (shown in Figure 3.9) were selected to generate

DAS responses in comparable section of new and existing portions of the array,

portions of the array in different soil types, and in line with portions of the array. The

standard operating procedure for the test was to begin at Location A and work towards

Location H and to deliver ten impacts at each location. The source locations were

marked physically and measured for repeatability with each data collect.

FIGURE 3.9: Location of hammer testing sequence relative to the test bed

The new portion of the DAS array was spliced into the existing array while the

fiber optic cable along the trench centerline remained uncovered, and open to the

atmosphere. This allowed our first data collect of the entire testing sequence

(Locations A through H) to occur on an uncovered array. Impact testing was

performed continued throughout construction and then on a bi-weekly to monthly

basis. Additionally, impact testing was performed to capture significant changes in

temperature, moisture, or weather (e.g. before and after storms). The DAS interrogator

45

used for this field effort is a conventional OTDR settings of 10-meters-long channels

and a sampling rate of 2500Hz.

3.1.5 Channel mapping

A channel map was developed by repeatedly overlaying the known test bed

layout, with the known source locations, and the DAS array response. Data sets were

processed using MATLAB; see Appendix B for more detail. Figure 3.10 shows how

the DAS array responds to the testing sequence shown in Figure 3.9.

FIGURE 3.10: DAS response to entire testing sequence where channel 95 is on

the bottom and channel 138 is at the top

Once several data sets were reviewed, a DAS array channel map was generated

and validated. Figure 3.9 presents the validated channel map for this DAS array, i.e.

where the channels are located along the array length. Further discussion on the

46

analysis of the data and results are presented in Chapters 4 and Chapter 5 of this

dissertation.

3.2 Temporary DAS test bed

To test the effects of loading on DAS performance, a temporary DAS test bed

was constructed at the U.S. Army Corps of Engineers Cold Regions Research and

Engineering Laboratory (CRREL) in Hanover, NH on March 5, 2020. A bed of dry

silty material was placed on top of dense, frozen earthen roadway material. The silt

bed dimensions were approximately 2.5 meters-wide by 16 meters-long. Fiber optic

cable was laid on top of the silt bedding in switch backs such that at least ten DAS

channels would be well within the test bed. Impact testing was performed in line with

the array channels prior to additional silt material being placed on top of the fiber optic

cable. Figure 3.11 illustrates the CRREL test bed layout and location of the impact

testing. Figure 3.12 shows a photograph of the impact testing. The impact tests were

performed with a modified proctor hammer.

FIGURE 3.11: CRREL DAS array layout

47

FIGURE 3.12: Impact testing at CRREL test bed

Once the fiber optic cable was embedded within the silt material, another set of

impact testing was performed to establish the performance upon initial cable to soil

coupling. With data collection for initial coupling complete, load testing was begun.

To efficiently test the effect of loading and unloading on the DAS array performance

with available time and materials, it was decided to use vehicles of increasing weight

as the means of applying load to the array. To measure the load imparted to the array,

calibrated vehicle load cell pads (shown in Figure 3.13) were placed on the silt

material above where the DAS array was located, and a wooden ramp was placed over

the silt material allowing vehicles to carefully drive up on top the load cells without

destroying the test bed. Figure 3.14 shows the layout of the array with the location of

the load cells and wooden ramp.

48

FIGURE 3.13: Load cells used for vehicle load testing (Longacre ® Accuset II)

FIGURE 3.14: Experimental layout

Vehicles carefully drove up on to the load cells, beginning with the lightest

vehicle. The resulting load, as displayed on the load cells, was recorded and impact

49

testing was performed. The vehicles were turned off during all impact testing so as not

to add to the noise observed in the DAS array. Vehicles carefully backed off the load

cells and impact testing was performed on the unloaded array. Table 3.1 presented the

loading progression from no-coupling (A) through to maximum loading (K) while the

fiber optic cable was still intact. The Surcharge presented in this table was calculated

by taken the load read on the load cell (in pounds) and dividing it by one square foot

(the surface area of the load cell)., and then converting the pressure from pounds per

square foot to kilopascals. All vehicles were relatively balanced, such that both load

cells read similar loadings. Figure 3.15 provides a photograph of one of the vehicles

associated with loading C stationed on top of the load cells. Results and discussion of

the data collected at this temporary test bed are presented in chapter 5 of this

dissertation. Figure 3.16 shows which vehicles are associated with each surcharge

shown in Table 3.1.

Table 3.1: CRREL test bed experiment loading sequence Surcharge Order Surcharge on DAS array

A No coupling B Initial Coupling C 45kPa D Unload after 45kPa E 57kPa F Unload after 57kPa G 68kPa H Unload after 68 kPa I 115kPa J Unload after 115 kPa K 180kPa

50

FIGURE 3.15: Vehicle for loading C on load cells

FIGURE 3.16: Vehicles associated with the surcharge (note: the F800 image

source is purplewave.com). The F800 used in this study looked like that pictured.

51

3.3 Data Processing

The performance of the DAS array was evaluated based on the detectability of

signals (vibration signature due to hammer impact events) above the ambient noise

level. Hence Signal-to-Noise Ratio (SNR) was used as the performance criteria

selected to monitor and evaluate DAS performance. Figures 3.17A, 3.17B, and 3.17C

show the typical response observed in Channel 123 (location near source E shown on

Figure 3.9) due to one hammer impact event. Note that the DAS response amplitude

has been normalized such that the maximum value is unity. The signal response from

the DAS is presently unquantified (Soga and Luo 2018, Lindsey et al. 2020), meaning

the amplitude of the response signal does not precisely correlate to a strain

measurement and is unique to each section of DAS array. Subplot 3.17A shows the

time series of the channel response whereas 3.17B shows the power spectral density

which highlights the frequency content in the signal. Figure 3.17C is a time-frequency

diagram showing the evolution of the spectral content over time. The color scale in

this figure is proportional to the energy in the measured signal as a function of time

and frequency (red indicating high energy and blue indicating low energy).

52

FIGURE 3.17: Typical DAS response to impact testing

SNR response corresponding to each hammer strike event was calculated for each

responsive channel. SNR is defined as a logarithmic measure of the ratio of the Root

Mean Square (RMS) values of the signal (RMSsignal) and noise (RMSnoise). The RMS

value is calculated using equation for RMS is shown in Equation 8. A capture (dt) of

the signal is used to calculate RMSsignal and the same capture (dt) of the noise

immediately following the signal is used to calculate RMSnoise. The equation for

SNR is presented in Equation 9. Figure 3.17(D) illustrates the time capture selection.

Let x[n] be the sampled version of the channel response x(t), sampled for a duration of

0.35 s (T) at a sampling rate (Fs) of 2500 Hz. The window corresponding to the signal

was measured starting from 1 second before the signal peak as shown in Figure 3.17D.

The number of samples (N) in the 0.35 seconds long time segment is,

The duration of the signal (T) is also equal to,

53

The RMS value in the continuous time domain is defined as,

In the discrete time, using the sampled signal x[n] the RMS value can be written as,

EQ 8

The SNR in logarithmic notation is defined as,

EQ 9 Figure 3.18 illustrates was the signal-to-noise looks like on a typical test day

presenting response to the nineth impact at source location E on January 2, 2020.

FIGURE 3.18: Signal-to-Noise visual example from January 2, 2020. Response

from the nineth impact at source location E.

54

The ambient noise observed in the permanent test bed is generally far less than

that observed at the CRREL test bed. The CRREL test bed’s proximity to a variety of

industrial systems elevated noise level when compared to the permanent test bed. The

capture time selected for the experiments in permanent test bed was 0.35 seconds. Due

to the increased ambient noise levels at the CRREL test bed obscuring the signal

within the duration of the signal, the capture time was increased to 0.5 seconds to

make sure the entire signal was captured.

55

CHAPTER 4

RESULTS AND DISCUSSION

The results of the year-long field study in which a DAS system was installed in

different soil types (silty sand, clean sand, gravel, and flowable fill) adjacent to an

existing, decade-old DAS array are presented in this chapter. The year-long study was

performed in a permanent DAS test bed. This chapter will also discuss the results from

a DAS vehicle load test performed at a temporary DAS test bed at CRREL.

The results are organized according to the effects of 1) soil type and time in-

situ, 2) vehicle loading, and 3) soil moisture and temperature. This chapter includes a

discussion of the results.

4.1 Soil Type and Time In-situ

As described in Chapter 3, the seismic source used was a standard proctor

hammer striking an aluminum plate. To evaluate the performance of the DAS array in

different soil types over time, the SNR response to source locations shown in Figure

4.1 was evaluated.

56

FIGURE 4.1: Source locations used to evaluate DAS performance in different soil

types.

DAS performance was evaluated from August 2019 through February 2020.

Impact source location No. 1 (see Figure 4.1) is between parallel portions of the

previously installed fiber optic cable and the new fiber optic cable, both in native silty

sand material. Figure 4.2 shows values of SNR in the new and prior installation with

distance away from impact location 1. The results in Figure 4.2 indicate that the new

portion of DAS array in native material generally yields higher peak SNR for a larger

range (i.e. distance from source) than that of the prior installation in native material.

The response in the new installation is approximately 5 dB greater than the prior

installation; however, the attenuation for both portions of the DAS array is similar (i.e.

SNR with distance form source).

57

FIGURE 4.2: SNR of prior installation vs. new installation in native material

At ten years old, the prior DAS installation still responds well to the impact

source, demonstrating the long-term viability of DAS monitoring systems. Differences

in DAS response between the new install and the prior install could be due to

differences in the installation technique.

A second impact source location (location No. 2 as shown in Figure 4.1) is

between parallel portions of the DAS array in sand and gravel, and flowable fill to

allow for assessment of soil type on DAS response. Figure 4.3 shows the variation of

SNR with distance away from source location 2 in each soil type for measurements

taken over the course of seven months. The results show that the SNR attenuates in all

materials at a similar rate, but that portions of the array in sand and gravel consistently

yield higher SNR (about 5dB more) than the flowable fill. The sand and gravel

58

perform similarly. A discussion of the role that soil stiffness and impedance may

contribute to these results is presented in Section 4.4.

FIGURE 4.3: SNR with distance: sand, gravel, and flowable fill

As shown in Figure 4.3 over the seven months of testing, the portion of DAS

array in gravel performed well suggesting that the impacts of any bending caused by

coupling with the gravel are insignificant. This is of interest because during routine

fiber optic cable installation for DAS use, larger pieces of gravel are removed from the

installation trenches to reduce bending losses. Not having to remove gravel from

trenches and/or installed fiber optic cable in gravel trenches could save time, reducing

cost and opens DAS technology to more civil engineering applications.

59

4.2 Vehicle Loading

To evaluate the effect of ground surface loading on DAS performance, a

temporary DAS test bed was constructed at CRREL. The same OTDR interrogator

used at the permanent test bed was used for this temporary study. Vehicle load testing

was conducted on 5 March 2020. Ten responsive channels were buried in dry silt at

the CRREL test bed. The two most responsive channels in the test bed (Channel A and

Channel B) were selected to follow through loading and unloading experiments.

Figure 4.4 illustrates the approximate locations of the channels selected for analysis.

FIGURE 4.4: Approximate location of channels analysed during DAS vehicle

load testing.

The CRREL vehicle load testing included data sets collected was with fiber optic

cable open to the atmosphere (i.e. before more dry silt was placed on top of the laid

fiber optic cable, “no coupling”) and after the fiber optic cable was buried with

another 15cm of silt (i.e. “coupled”). These initial tests (no coupling and coupling)

were performed before the vehicle load cell pads were placed on top of the DAS array.

Table 4.1 provides the surcharge loading pressures (beginning at A and progressing

60

through K) experienced beneath the load cell pads on the silt bedding. Figure 4.5

provides photos of loading C, loading E, and impact location.

Table 4.1: CRREL test bed experiment loading sequence Surcharge Order Surcharge on DAS array

A No coupling B Initial Coupling C 45kPa D Unload after 45kPa E 57kPa F Unload after 57kPa G 68kPa H Unload after 68 kPa I 115kPa J Unload after 115 kPa K 180kPa

FIGURE 4.5: Photos of loading C (A), loading E (B), and impact source location

(C) with respect to the temporary DAS array.

For each loading/unloading sequence, ten impacts were performed. Both the

average and range of SNR is shown in Figure 4.6.

61

FIGURE 4.6: SNR for loading (closed circles) and unloading (open circles)

during vehicle loading experiment. The error bars for loading (continuous line)

and unloading (dashed line) are also shown.

The average DAS response under vehicle loading increased as the loading

increased. This is consistent with the findings of Zhang et al. 2014, 2015, and Zhu et

al. 2015 who that observed overburden pressure increased cable-to-soil coupling

(particularly in dry soils) and suggested this would increase sensor performance. As

shown in Figure 4.6, this study indicates that an increase in loading generally leads to

an increase in SNR (designated by the solid-line data sets). This study also indicates

that the performance level was retained during unloading (designated by the dashed-

line data sets). While Figure 4.6 indicates that there may have been a slight increase in

62

SNR upon unloading, the variation in SNR response is too large to suggest SNR

increase with unloading is true. The variation in response is likely due to the

significant irregular ambient noise at the site. Figure 4.7 illustrates the magnitude of

the noise at the CRREL test bed. The red dotted lines are the noise in the active

CRREL test bed channel, over laying the noise and signal response at the permanent

test bed. The magnitude and the fluctuation of this noise likely contributed to the

variance observed in the load test data. Despite this variability, the results of this

loading experiment suggest that loading the ground above where a fiber optic DAS

array is located can increase array performance.

FIGURE 4.7: Noise in the CRREL test bed (red dashed lines) overlaying noise

and signal from the permanent test bed to illustrate the magnitude of the ambient

noise at CRREL.

63

4.3 Soil Moisture and Temperature

The permanent test bed was used to study the effect of seasonal environmental

changes (i.e. soil temperature and soil moisture) and aging (over one year from

installation) on DAS response. Soil temperature and moisture sensors (CS650) were

placed in the different soil types (native silty sand, sand, and gravel). Figure 4.8

indicates where the CS650 sensors are located and which channels are evaluated for

performance changes with moisture and temperature changes.

FIGURE 4.8: Permanent test bed layout with Impact and CS650 locations

Impact test responses were collected at the permanent test bed from the time of

installation in August 2019 through September 2020. The SNR was calculated in the

64

prior installation (Channel 100), gravel (Channel 121), sand (Channel126), and the

new native material (Channel 129), as shown in Figure 4.8. The temperature and

moisture content data collected from the CS650 sensors is provided in Figure 4.9.

Note that volumetric water content for “gravel” is not plotted in Figure 4.9 because the

CS650 in the gravel did not detect moisture. However, the CS650 sensor in gravel

provides temperature data that agrees with the temperatures recorded in the CS650

sensors, installed in the other materials therefore we assume that the sensor in the

gravel is functioning properly and that the gravel remained dry.

FIGURE 4.9: Permanent test bed layout with Impact and CS650 locations

The SNR results from ten impacts were averaged to calculate the mean signal to

noise ratio. The variation of SNR with soil volumetric water content and soil

temperature are provided in Figure 4.10 and Figure 4.11, respectively.

65

Figures 4.10 and 4.11 show that soil moisture and temperature do not

significantly affect the SNR performance of the DAS sensor. There is a slight trend of

decreasing SNR with increasing water content for all three soil types, but more data is

needed to substantiate this finding. In this study soil moisture and temperature are

paired data, meaning that the soil moisture and soil temperature were collected occur

at the same time and cannot be independently separated or varied. Future testing may

include a controlled in-situ study where one variable (e.g. soil moisture) can be held

constant, while another variable (e.g. temperature) is fluctuated.

FIGURE 4.10: Variation of SNR with moisture or prior install, new native

material and sand.

66

FIGURE 4.11: Variation of SNR with temperature.

While Figure 4.10 does not indicate a trend between DAS performance and

soil moisture, work from Zhang et al. 2014, 2015 and Zhu et al. 2015 infers that soil

water content and density affect the physical and mechanical properties of soils. Due

to the gap in testing from the Covid-19 pandemic, it is possible that with more test

data at different water contents, a better trend with soil water content and DAS

performance would emerge.

While this research indicates soil moisture and temperature alone do not

significantly affect with sensor performance, Figure 4.12 indicates that time in-situ

does have an effect on performance over time. Figure 4.12 follows one channel per

material (i.e. existing installation, new native material, sand, and gravel). These are the

same channels used to plot results in Figures 4.10 and 4.11. It appears that both the

prior and new portions of DAS array in native material maintain response levels and

67

seem to be performing similarly. The response in portion of the array in gravel has

dropped a little after 11 months in-situ. The most significant change in observed in the

response of the portion of the array in the sand. Unfortunately, no DAS response data

could be collected between March 2020 and July 2020 due to the Covid 19 pandemic

and we were unable to observe the decline in performance. There does not appear to

be a consistent effect of time on the DAS response in this study.

Figure 4.12: SNR in one channel for each material from August 2019 to

September 2020.

Initially we suspected that the fiber optic cable may have been fractured near the

sand-gravel transition in the trench. However, OTDR power measurements indicate

that there is no power loss through this section of fiber optic cable, i.e. the fiber optic

cable is intact and not broken or fractured. Portions of the DAS array beyond the sand

68

are still performing well, which confirms the fiber optic cable is not fractured or

broken. Additionally, there is no discernable change on the ground surface above the

sand portion of the array. There was no activity onsite between March 2020 and July

2020, so we considered that the drop of SNR in the sand was attributed to overgrown

vegetation, damping the impact. However, follow-up impact tests with freshly mowed

grass, confirmed that the nearly 10dB drop in response is true and not due to

overgrown vegetation.

Spring 2020 was very wet at the test bed, and summer 2020 was very dry. The

sand is in proximity to a wetland and it is possible that the sand was near fully

saturated at some point in the spring, and the dry summer desiccated the sand, possibly

causing the sand to lose coupling with the fiber optic cable. Zhang et al. 2016

proposed that water infiltrations could affect the coupling of a fiber optic cable to the

soil, this study indicates that desiccation may have changed the cable to soil coupling

affecting the sensor performance.

Our results agree with the hypotheses of Wu et al. 2015 and Friedli et al. 2019 ,

who observed DAS response changing over time, suggesting that the coupling

between the fiber optic cable and the host medium may change due to aging and

seasonal environmental changes.

69

4.4 Discussion of Results: Impedance Ratio, Soil Stiffness, and DAS response

Sections 4.1, 4.2, and 4.3 of this dissertation present the DAS response results for

portions on a DAS array in different soil types through seasonal fluctuations. Native

silty sand material, sand fill, and gravel fill were placed and compacted in an

excavated trench and excavatable cementitious flowable fill was placed in a trench in

two lifts such that the flowable fill beneath the fiber optic cable firm before the second

lift of flowable fill was placed. Table 4.2 provides the in-situ nuclear gauge (ASTM

D6938) dry density and water content, the calculated total unit weight, the optimum

dry density and optimum water content (Modified Proctor, Method B (ASTM

D1557)), and the calculated dry density percent compaction.

Table 4.2: In-situ average soil density and water content

Soil type Dry Unit Weight (pcf)

Water Content (%)

Calc. Total Unit Weight (pcf)

Max Dry Unit Weight (pcf)

Opt. Water Content (%)

Percent Comp, %

Native Material (Impact Location) 99 25 124 132.5 7.5 75

Native Material (In Trench) 106 15 122 132.5 7.5 80

Sand Fill 108 2 110 114.5 13.6 94

For impact source locations offset from the fiber optic cable array, the impact-

generated seismic wave would travel from the ground surface of the undisturbed

native silty sand material towards the trench material containing the fiber optic cable.

Figure 4.13 indicates the location of an impact source and the estimated ray path the

seismic wave would follow to reach the fiber optic cable in the trenched materials. As

shown in Table 4.2 the in-situ density and water content of the undisturbed native

material (where the impact source is located) is different than the native silty sand and

70

sand in the trenches. Differences in these soil properties indicate that perhaps the soil

stiffnesses are different enough to affect the propagating waves.

Figure 4.13 shows that the DAS surrounded by flowable fill performed differently

than the portions of DAS in sand and gravel for the source location shown in Figure

4.13. To evaluate why this difference is observed, we can estimate the impedance ratio

between the undisturbed native material and the trenched materials.

71

FIGURE 4.13. Impact source wave rays to DAS channels and resulting SNR

differences

72

The impedance ratio of a seismic (incident) wave traveling from one soil to

another where the soil stiffnesses are different can provide information on how much

of the wave energy (and stress and displacement) will be transmitted into the next

material and how much will be reflected back into the origin material. An impedance

ratio of less than 1 indicates that the incident wave is approaching a softer, less stiff

material (Kramer, 1996). An impedance ratio of zero would mean that the incident

wave is approaching a free end (Kramer, 1996). Along the same lines, when an

impedance ratio is greater than one, the incident wave is approaching a stiffer material

such that an impedance ratio of infinity implies that the incident wave is approaching a

fixed end where no displacement can occur (Kramer, 1996). The impedance ratio

provides information on the expected displacement (i.e. or strain) amplitude as the

incident wave travels from one material to another. DAS response is proportional to

strain experienced along the fiber optic cable. In this study seismic strain/displacement

in the soil surrounding the fiber optic cable is transferred to the fiber optic cable

interior causing a DAS response.

The impedance ratio (α) can be calculated using the in-situ density (ρ) and

shear wave velocity (Vs) of the material the wave is in (material 1) and the in-situ

density and shear wave velocity of the material the wave is traveling towards (material

2). The equation for impedance ratio is presented in Equation 10. For this study,

material 1 is the undisturbed native silty sand on which the impact is occurring.

EQ 10

73

The in-situ densities of the native material and sand fill are known from

nuclear density gage testing during test bed installation (Table 4.2). There is no water

in the gravel and the gravel unit weight is assumed to be approximately 135pcf

(Ryden, 2004) and the excavatable cementitious flowable fill is assumed 140pcf

(Ryden, 2004) which is less than typical concrete and similar to the low end of asphalt.

be. Shear wave velocities in near surface material at a depth of 2 feet are difficult to

evaluate. Shear wave velocity measurements are often used in geotechnical

engineering to assess soil stiffness and associated liquefaction potential (Andrus and

Stokoe, 1998). The small strain shear modulus (Gmax) relates to shear wave velocity

with the relationship shown in Equation 11, and an empirical relationship (Equation

12) was used to estimate Gmax for each soil (Seed and Idriss, 1970). The K2

coefficient in Equation 12 is estimated using guidance from Seed and Idriss (1970) for

imperial units. By setting Equation 11 equal to Equation 12, shear wave velocity can

be solved for and placed in Equation 10. The Seed and Idriss, 1970 relationship

(Equation 12) relies on mean effective stress (ϭ’m) at a depth (d) of about 2 feet.

Assuming the horizontal effective stress is axisymmetric (ϭ’3 = ϭ’2) and the at rest

earth pressure coefficient (K0) is 0.5., then the impedance ratio, Equation 14, can be

estimated.

EQ 11

EQ 12

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EQ 13

EQ 14

Based on Equation 14, Table 4.2 provides the approximate impedance ratio values for

an incident wave traveling from the undistributed native silty sand to the sand, gravel,

and flowable fill trenches. The impedance ratios of the undisturbed native material to

native material in the trench and sand fill are approximately one, meaning there would

be relatively little change in the wave energy, stress, and displacement due to the

stiffness of different materials. According to Table 5-1 in Kramer (1996) when the

impedance ratio is one, the displacement and energy continuing into the new material

is relatively unchanged. However, when the impedance ratio is 1.5, the displacement

(strain) amplitude of the incident wave will be about 3/4s of the originating

displacement.

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Table 4.3: Impedance ratio calculations

In-situ Unit Weight (pcf)

K2 α

Native Material (Impact Location) 124 60 1 Native Material (In Trench) 122 65 1 Sand Fill 110 70 1 Gravel Fill 135 (130-140) 70-80 1.1-1.25 Flowable Fill 140 90-120 1.35-1.55

The difference in SNR responses shown in Figure 4.13 due to the source

location shown in Figure 4.1 of portions of the fiber optic DAS array sand, gravel and

flowable fill can be explained, in part, by the impedance ratios shown in Table 4.3. In

Figure 4.14, the response of portions of DAS array in the sand and gravel seem to be

similar, and likewise their impedance ratios are similar, essentially one. In Figure

4.13, the flowable fill response is approximately 60 to 75% of that observed in the

sand and gravel, which agrees with the calculated impedance ratio of approximately

1.5.

Looking at DAS response when the impact source is in-line with the fiber optic

cable and the trenched in material, shown in Figure 4.14, it appears that the array in

gravel and in flowable fill performs similarly with distance. Once again, the

impedance ratio can help explain why this is. The source locations shown in Figure

4.14 deliver the seismic wave energy directly to the trench material (gravel and

flowable fill in this case), therefore there is no impedance contrast causing the wave

energy, stress, and strain to change. The impact travels from the ground surface

through similar amount of native fill (overlaying the trench material, about 2 to 4

inches) before entering the trench material.

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FIGURE 4.14: DAS response in different material types due to in-line above

trench material source location.

As shown in Equation 10, the impedance ratio depends on the density and

shear wave velocity of material 1 and material 2. Zhou and Xia (2006) show that slight

changes on soil saturation can have a significant effect on shear wave velocity and

Rayleigh wave velocity, typically increasing velocity with saturation. This means that

the soil stiffness (Gmax) may also change with saturation. Although, Figure 4.10

indicates that SNR did not change significantly with water content that may be

because testing was halted during significant water content fluctuations of the 2020

spring and summer (March 2020 through August 2020) due to the Covid-19 pandemic.

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FIGURE 4.15: Observed volumetric water content changes in trench materials

over time.

To review the significance that water content fluctuations may have had on the

impedance ratio between the native silty sand where the impact occurred and the sand

trench, where the most affected portion of DAS array is, consider the following: the

undisturbed native silty sand and sand fill dry densities in Table 4.2, we can use the

water content fluctuation from the moisture and temperature sensors (as shown in

Figure 4.15). The volumetric water content measured in the sand fill was around 0.05

in September 2019, then about 0.25 from December 2019 through April 2020, and

then down to nearly zero by August 2020. The native silty sand material in the trench

went from a volumetric water content of about 0.28 in September 2019 to about 0.42

from December 2019 through April 2020, and then back down to about 0.25 by

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August 2020. Assuming the dry densities stay the same and the undistributed native

silty sand material water content fluctuates comparably to the trenched-in material, the

approximate impedance ratios of a wave traveling from undisturbed native material to

the sand between September 2019 and August 2020 can be calculated. Table 4.3

provides a breakdown of the calculations and water content selected. Note that the

relationship between water content (w), saturation (S), and volumetric water content

(θ) is provided in Frelund and Rahrdjo, 1993 as Equation 15. Assume specific gravity

(Gs) remains constant.

Equation 15

The water contents presented in Table 4.4 are approximate and based on

Fredlund and Rahrdjo (1993) volumetric water content, saturation, and soil density in

conjunction with the volumetric water content information shown in Figure 15. The

resulting impedance ratios suggest that the sand becomes slightly softer than the native

silty sand overtime due to water content fluctuations. An impedance ratio of 0.95

indicates that the displacement would be very, slightly amplified through the stiffness

transition. However, a significant effect of impedance ratio on displacement is not

anticipated to be observed until the impedance ratio is less than ½ or greater than 1 ½.

Therefore, it is unlikely that the impedance ratio between the native material near the

impact source and the material in the DAS array trenches would change with seasonal

water fluctuations. This agrees with the lack of trend observed between SNR and

water content in the native material and the sand material.

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Table 4.4: Impendence ratio fluctuations with water content

Dry Unit Weight (pcf)

Water Content

(%)

Calculated Total Unit

Weight (pcf) K2 α

Native Material (September 2019) 99 25 124 60

1 Sand Fill (September 2019) 108 2 110 70

Native Material (Winter 2020) 99 30 129 60 0.95 Sand Fill (Winter 2020) 108 14 123 70 Native Material (August 2020) 99 15 114 60 0.96 Sand Fill (August 2020) 108 0 108 70

The results in Figure 4.16, which follows the SNR performance of the DAS

array in different trench materials over time, cannot be explained with impedance ratio

is still below 1, which means that the material from which the seismic wave originates

and to which the seismic wave is traveling are of similar stiffnesses. From the water

content fluctuation and impedance ratio thought experiment, the impedance ratio

between the native material where the impact source is and the sand trench material

where the DAS array is not estimated to change significantly, per the source location

shown in Figure 4.13. This is in part because both materials experience water

fluctuations at the same time. Thus, there must be another mechanism at work causing

the performance in the sand portion of the array to change over time. Perhaps there is a

slight de-coupling of the fiber optic cable to the sand due to water infiltration

fluctuations or drought. This hypothesis would be supported by the relatively

insignificant change in performance or the portion of the array in gravel. Perhaps the

performance in the gravel remains relatively constant because the material is free

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draining and relatively heavy and angular, and there is less chance for changes in

stiffness.

FIGURE 4.16: DAS performance over time.

Impedance ratio (relative stiffness between the material through which a

seismic wave travels) explains why the portion of the DAS array in flowable fill

under-performs when compared to the other materials for off-set sources. The

impedance ratio between native material and sand or gravel seemed was close to one

and agrees with the pre-pandemic DAS performance results. Though impedance ratio

cannot explain the change in performance of the portion of array in sand over time,

impedance contrast and seismic wave travel path should be considered when installing

a DAS array.

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CHAPTER 5

CONCLUSIONS

Distributed Acoustic Sensing (DAS) is a fiber optic sensing system that is used

for vibration monitoring. At a minimum, DAS is composed of a fiber optic cable and

an optic analyzer, i.e. an interrogator. The oil and gas industry has used DAS for over

a decade to monitor infrastructure such as pipelines for leaks. In recent years, changes

in DAS performance have been observed for DAS arrays that have been buried in the

ground for long periods of time. This dissertation investigates the effect that soil type,

soil temperature, soil moisture, time in-situ, and overburden pressure have on DAS

performance for fiber optic cable buried in soil. To explore this problem, a new

portion of DAS array was added to an existing DAS array installed a decade prior. The

design of the new portion of DAS array includes native silty sand material, sand,

gravel, and excavatable flowable fill. Soil moisture and temperature sensors were

buried adjacent to the fiber optic cable to monitoring seasonal soil moisture and

temperature. Periodic impact testing was performed at set locations along the DAS

array for over one year. A separate, temporary DAS array was used to study the effect

of vehicle loading. The signal response of the DAS array to the impact testing was

processed for Signal to Noise Ratio (SNR), which was the performance criteria used to

evaluate the system performance.

The results of the impact testing program indicate that portions of the array in

gravel performed the most consistently over the year-long monitoring period. Neither

soil moisture nor soil temperature appear to have a significant effect on DAS

performance. DAS performance was observed to increase with increased vehicle

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loading. Over the course of one year in-situ, DAS performance was observed to

slightly lower in the native material and significantly lower in the sand and flowable

fill material, while the variance performance was observed to increase all materials.

A significant change in performance occurred while testing halted from March

2020 to August 2020 due to the Covid-19 pandemic. Though it is unfortunate that the

data was not collected while these changes in DAS performance developed, the

observed changes emphasize the potential of DAS to be used for infrastructure

change-detection monitoring. While changes in performance were observed in the new

portion of test bed over the course of one year, the performance of the decade-old

portion of test bed remained consistent and strong. Though response in the flowable

fill portion of the DAS array was observed to be less for offset source locations, this is

likely due to the impedance contrast between the native material on which the source

is located and the stiffer flowable fill material. Overall, this dissertation provides

guidance to the civil engineering community for installation design recommendations

related to DAS used for infrastructure monitoring.

5.1 Impact of research on civil engineering

The results of this research will influence civil engineering DAS installation

recommendations. DAS arrays installed in silty sand (native material for this study)

and gravel have long-term viability and continue to be sensitive to vibrations

regardless of soil moisture, soil temperature, and time in-situ. Portions of the DAS

array in excavatable flowable fill did not perform as well as portions of the array in

other soil types, likely due to the stiffness difference between the native material and

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the flowable fill (i.e. impedance contrast). Initially, portions of the array in the sand

performed well from August 2019 through February 2020. Due to the Covid-pandemic

testing was not performed from March 2020 until the end of July 2020, and when

testing resumed the SNR in the portions of the array in dams and flowable fill

significantly. Fiber optic cable breakage or bending was ruled out through OTDR

testing. Field inspection of the test bed did not provide any clues to the cause of the

SNR drop. The spring of 2020 was very wet and the summer of 2020 was very dry,

which may have caused the sand to desiccate and de-coupled from the fiber optic

cable, or purpose material piped along the fiber optic cable during a significant rain

event and de-coupled the cable and sand. The continued performance of the portions

of the DAS array installed in native material (both new and existing) and the portion

of the array installed in gravel strongly suggest that DAS systems can provide long-

term quality performance.

While DAS performance did not correlate to soil temperature or soil moisture,

over time the performance was observed to drop in sand and flowable fill portions of

the array, indicating that there may be other long-term variables that affect DAS

performance. Although soil moisture may not directly correlate with performance,

perhaps wetting-drying cycles or freeze-thaw cycles affect performance. After nearly a

year in-situ, it appears that the new portion of DAS array in native material may

perform comparably to the portion of array installed a decade prior.

The vehicle load testing performed on the temporary DAS test bed at CRREL

indicate that DAS performance increases with increased loading. The significant

variation in DAS response is likely due to various industrial activities at CRREL and

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some disturbance to the array while on-boarding and off-boarding vehicles. The

results of this experiment indicate that the presence of load on less than 10% of an

array channel length may improve DAS performance.

The results of this dissertation have already had an impact on the installation of

DAS arrays. Prior to this study, gravel pieces were removed from installation trenches.

Gravel was avoided as a trench material as it was thought to cause micro-bends in the

fiber optic cable, which would increase attenuation and lower performance. This

research demonstrates that DAS will perform well in gravel, which supports

infrastructure monitoring applications such as roadway subgrade, foundations,

mechanically stabilized walls, and more.

Similar performance between the new and previously installed portions of the

permanent DAS test bed support the long-term viability of the use of DAS as a

vibration monitoring system to the civil engineering community. An initial investment

in a DAS interrogator and installation of fiber optic cable can provide infrastructure

monitoring capabilities for years to come. The ability to monitor 40 or more

kilometers of fiber optic cable along infrastructure at 10-meter resolution could

transform the way infrastructure aging is understood and the way maintenance is

performed. DAS could help the civil engineering community localize repairs before

they become more significant, prioritize infrastructure repairs on a national scale, and

capture the impact of seismic events on infrastructure.

85

5.2 Engineering recommendations

This research has filled some existing knowledge gaps regarding DAS arrays in

soil and will hopefully increase confidence in using DAS technology in civil

engineering projects. The research provides long term feedback on how DAS will

perform which may help guide engineering recommendations for fiber optic

installation.

Based on the results of this research program the following recommendations are

indicated:

• The prior DAS array installation still performed well after ten years in-situ

and performs comparably to the new portion of array one year after

installation.

• DAS arrays perform well in gravel.

• Until further investigation, it might be prudent to avoid intentionally

removing native material to replace with sand material.

• Review the impedance contract and consider the directionality of the

event seismic waves to be monitored.

• Consider using DAS to supplement existing vibration monitoring

instruments. What DAS might lack in SNR, it makes up for in response

data density.

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5.3 Future work

While this research effort has filled in some of the knowledge gaps regarding soil-

embedded DAS array, the results have also presented more uncertainties to explore in

future research. Testing at the permanent test bed will continue for the foreseeable

future. Observing and documenting the performance of a DAS array as it experiences

multiple seasonal cycles will help the engineering community set performance

expectations. Additionally, conventional and phase-sensitive OTDR DAS

interrogators will be used on the same cable to compare SNR response. Future

research will include below 0°C temperatures testing and capture potential

performance changes during the freezing and thawing process. The performance loss

in the portions of the array in sand will continue to be monitored. Perhaps performance

will return during seasonal moisture and temperature fluctuations and/or we will

observe DAS performance cycles.

Additionally, testing at a similar DAS test bed in a different geographic location

connected to an existing array will demonstrate how and if seasonal changes and soil

conditions effect DAS performance in a different native materials with different

seasonal environmental factors.

87

APPENDIX A

GEOTECHNIQUE LETTERS

Title: Distributed Acoustic Sensing in Soil for Infrastructure Monitoring SNR

Evaluation

A.1 Abstract

Fibre optic Distributed Acoustic Sensing (DAS) systems provide vibration

response information comparable to accelerometers, geophones, and seismometers and

have the potential to become widely used for infrastructure monitoring. DAS can be

used to monitor earthquake activity, carbon sequestration, pipelines, and

roadway/railway subgrade integrity, however little is known about the effect of soil

type and burial method on DAS response. The objective of this paper is to present the

results of a field study in which a DAS system was installed in different soil types

(silty sand, clean sand, gravel, and a controlled density, cementitious excavatable

flowable fill) adjacent to an existing, decade-old DAS array. Impact tests were

performed such that the DAS response in the different soil types and a portion of DAS

array installed a decade prior could be evaluated and compared. Signal-to-Noise Ratio

(SNR) was determined to be the most effective performance metric for comparing

DAS response. Results over a seven-month monitoring program indicate that portions

of the array in sand, gravel, and native material (a silty sand) had good response with

comparable SNR, whereas the portion of the array in flowable fill did not perform

well. The newer installation in native material performed approximately five decibels

better than the portion of the array installed ten years prior in the same soil. However,

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the ten-year old array still performs with adequate SNR, which should provide

confidence to the civil engineering community about the longevity of DAS systems

used for infrastructure vibration monitoring.

Keywords

Monitoring; Vibration.

List of notations

RMS Root Mean Square

T period

fs sampling frequency

SNR Signal to Noise Ratio

A.2. Introduction

Distributed Acoustic Sensing (DAS) is a relatively new commercially available

vibration sensing system. DAS is currently used for monitoring vibrations associated

with pipelines, seismic activity, CO2 sequestration, railway subgrades and more

(examples provided in Daley et al., 2016; Dou et al., 2017; Mateeva et al., 2014; Soga

and Lou, 2018). It has the potential to become a widely used infrastructure-monitoring

tool due to its high data resolution, long sensor length, and ease of installation when

compared to point sensors.

89

DAS typically consists of a fibre optic cable and a fibre optic analyzer for

transmission, data acquisition, processing, and storage (Soga et al., 2015). The fibre

optic cable serves as both the sensor and the means of returning vibration information

to the fibre optic analyzer, which is called an interrogator. An Optical Time-Domain

Reflectometer (OTDR) interrogator houses a laser that pulses light into the fibre optic

cable core and measures the light scattering back towards the interrogator as the laser

pulse proceeds down the fibre. The scattering is characterized by Rayleigh scattering,

which occurs where there are density changes in the core of the optical fibre, termed a

scattering centre. This is an elastic process, meaning that the return time provides

information about how far down the cable the scattering occurred (Sang, 2011;

Schenato, 2017; Soga and Luo, 2018; Wang et al., 2019). DAS detects changes in

Rayleigh scattering resulting from strain along the fibre optic cable length, and this

scattering reflects back along the fibre to the interrogator where the power of the

backscatter is monitored (Krohn et al., 2014). Rayleigh scattering is collected by the

OTDR interrogator, summed, and “binned” by time of return, which corresponds to

distance down the fibre optic cable form the interrogator (Owen et al., 2012).

Vibrational strains along the fibre optic cable change the Rayleigh scattering centres in

the optical fibre core, and this allows for sensing of the vibrational strain field acting

on the fibre (Lindsey et al., 2020).

The “distributed” aspect of DAS allows for the capture of a continuous

strain/vibration profile at varying spatial resolution (typically 2 to 10 metres) over

long distances (i.e. several kilometres) at a high sampling rate (e.g. 2500Hz). The

sampling rate achievable in DAS makes its response comparable to accelerometers,

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geophones, and seismometers. Studies such as Daley et al. (2016) and Egorov et al.

(2018) compare DAS to geophones and the studies concluded that DAS response

could be processed to yield results comparable to geophones. Martin et al. (2018)

provides a comprehensive review for processing DAS data.

As described in Soga and Luo (2018), the transfer of strain from the

surrounding media to the fibre core is caused by shearing along the tightly bonding

interfaces between series of materials within the cable from the cable jacket to the

cladding to the core. Different coupling between the fibre optic cable jacket and the

host medium (e.g. grout versus soil) will change the way strain is transferred to the

fibre optic cable. Mateeva et al. (2014) and Lindsey et al. (2020) observed this effect

in vertical seismic profiling surveys where the way the fibre optic cable was fixed to

the oil and gas wells significantly affected the DAS response. Studies by Wu et al.

(2015) and Friedli et al. (2019) observed response changes over time, suggesting that

the coupling between the fibre optic cable and the host medium may change due to

aging or other effects.

Achieving strong coupling between the fibre optic cable and the surrounding

media remains a challenge for the DAS community. Coupling is a critical component

to acquire efficient and meaningful data (Miah and Potter, 2017), and the method of

coupling depends on the application. In addition, the particulate nature of soil makes

the cable-soil coupling susceptible to changes in the surrounding environment that

affects measured data (Zhang et al., 2016).

The objective of this paper is to present the results of a field study in which a

DAS system was installed in different soil types (silty sand, clean sand, gravel, and

91

flowable fill) adjacent to an existing, decade-old DAS array. Impact tests were

performed such that the response in the different soil types and prior installation could

be evaluated and compared.

A.3. Methodology

To study the effect of soil type and in-situ aging on DAS response, a fibre

optic cable was installed in a trench and was added on to an existing DAS array that

was installed a decade earlier (circa 2010). The same fibre optic cable was used for

the new portion of test bed as with the prior installation (i.e. the cable came from the

same spool). The loose-tube cable is a silica single mode fibre with reflective coating

surrounded by a waterproof buffer tube, corrugated steel armour, and a polyethylene

jacket. A conventional, incoherent, not phase sensitive OTDR interrogator was used

to generate and receive signals throughout the array. Although a conventional OTDR

is an older version of the phase-coherent optical time domain reflectometry (ф-OTDR)

used in studies such as Lindsey et al. (2020), this study focusses on array amplitude

performance as a function of soil type. The native material on site is a silty sand with

gravel and some cobbles such that about 40% of the silty sand by weight is finer than

0.074mm. Below the fill is one to two meters of glacial till underlain by bedrock.

The new fibre optic cable was spliced into the existing array and installed in a

300-meter-long trench at a depth of 0.5 meters, with the test bed layout and trench

profile shown in Figure 1. For the portion of the array in native material, the trench

was excavated to a depth of 0.5 meters and the fibre optic cable was laid at the bottom

of the trench. The sand, gravel, and flowable fill trenches were excavated to a depth of

one meter so there would be 0.5 meters of non-native material above and below the

92

cable. All trenches were approximately 0.5-meters-wide, which was the width of the

excavator bucket used for the installation. The sand fill has a median grain size of

about 0.4mm. The gravel is uniform, angular stone about 20 to 40mm in size. The

native fill material and the sand fill were placed in 30cm lifts and compacted using a

plate compactor. In-situ densities collected via nuclear density gage were

approximately 1730kg/m3 in the native fill and sand fill, which corresponded to an

estimated relative compaction of 80%. The cementitious controlled density

excavatable flowable fill had a seven-day compressive strength of approximately

400kPa, a very weak concrete-like material.

Figure 1. DAS test bed layout showing the original array along with the new array

installed in the native material (silty sand), sand fill, gravel fill, and flowable fill.

Channel numbers are indicated along the length of the cable in addition to the

locations of the impact tests performed for this study.

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A standard Proctor hammer (24.5 N rammer with a 305mm drop generating

600kN-m/m3 of compactive effort according to ASTM D 698 (ASTM,2012)) that is

used in laboratory compaction testing was the impact source for this study. The

hammer was used to strike an aluminum plate at marked locations (Figure 1) for

repeatability. At each hammer location, ten hammer strikes were performed. The

hammer strike locations were approximately two meters offset from the buried fibre

optic cable. Figure 2 shows a typical response in Channel 131 (location shown on

Figure 1) to one hammer strike at Location No. 1. The DAS response amplitude has

been normalized such that the maximum value is unity. The amplitude of the DAS

response was normalized with the maximum response because the instrument response

is unquantified (Soga and Luo, 2018 and Lindsey et al., 2020), meaning that the

amplitude of the response signal does not precisely correlate to a strain measurement.

94

Figure 2. A typical DAS signal response in Channel 131 due to an impact test at

location No. 1, including a) normalized time series, b) the power spectrum of the

signal shown in a., and c) the spectrogram of the signal shown in a.

95

Signal to Noise Ratio (SNR), as defined in Equation 1, was used to evaluate

the performance of DAS. The OTDR interrogator used for this study is a conventional,

incoherent, not phase sensitive instrument; the response is proportional to the average

strain experienced along the cable channel length. The SNR of the response to each

strike was calculated for the channels near each strike location. SNR is defined as a

logarithmic measure of the ratio of the Root Mean Square (RMS) values of the signal

and noise. A 0.35 seconds capture of the signal is used to calculate RMSsignal

whereas a 0.35 second capture of the noise immediately following the signal time

window is used to calculate RMSnoise. The capture length (i.e. time window) was

selected as a consistently achievable signal capture time and subsequent noise capture

time that could be used across all data sets collected over time when series of ten or

more impacts are performed at each location. The optimal time interval for each signal

and noise will vary depending on the source of the vibrations to be measured. Figure

2(A) illustrates the time capture selection.

Equation 1

A.4. Results

The DAS response results presented herein are from impact test data collected

over a period of seven months. Impact source No. 1 (see Figure 1) was located

between parallel portions of the previously installed fibre optic cable and new cable,

both in native material. Figure 3 shows how the SNR of the received signals attenuates

away from the impact source in both cables. There is considerable scatter in the

response to individual hits, but the trend shows that the SNR is higher in the new

96

installation compared to the decade-old installation, which was compacted in a similar

method to the new portion of array. The shape of the attenuation curve is comparable

in both cables.

Figure 3. SNR of prior installation vs. new installation in native material

Results from a second impact source, located between parallel positions of the

sand, gravel, and flowable fill trenches are shown in Figure 4. These results show that

the SNR in both the sand and gravel are comparable and are consistently higher than

the SNR, and thus the signal response, in the controlled density excavatable flowable

fill.

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Figure 4. Comparison of SNR with distance for the fibre optic cable installed in sand,

gravel, and controlled density, cementitious excavatable flowable fill.

A.5. Discussion

The results shown in Figure 3 and 4 strongly suggest that soil type surrounding

the fibre optic cable affects the SNR performance of a DAS array. The new portion of

the DAS array in native material generally yields higher SNR values for a longer

distance than the prior install (Figure 3). Regardless, at ten years old, the prior

installation still responds well to the impact source, demonstrating the long-term

viability of DAS monitoring systems. Differences in DAS response between the new

install and the prior install could be due to aging effects and/or due to small

differences in the installation technique.

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DAS response to impact location No. 2 indicates that the portion of fibre optic

sensor in the sand and gravel had comparable responses and yields higher SNR values

than the portion of fibre optic sensor in the flowable fill (Figure 4). It is possible that

the small-strain stiffness contrast between the native material and the flowable fill

(with the flowable fill being stiffer) resulted in lower SNR values in the flowable fill.

Due to the shallow cable burial depth, and thus very low effective stresses, small strain

shear modulus was not evaluated in this study. The fibre optic sensor portions in sand

and gravel also appear to yield high SNR values than the portions of fibre optic sensor

in the native material (Figure 3). Often, larger gravel bits are removed from fibre-

optic cable DAS installation trenches so as not to cause bends in the fibre that may

reduce the power of the light pulsed into the fibre, and thus lower the performance.

However, this was not observed in any of the data over the seven months of testing,

suggesting that the impacts of any bending caused by gravel are insignificant.

This conventional OTDR DAS system is used for vibration monitoring. The

user is typically interested in events observed along the length of the fibre optic senor

that are multiples greater than the baseline noise. This study indicates that the array is

capable to responding to the impact source with an SNR of 5 dB at distances greater

than 30 meters.

A.6. Conclusions

The results presented herein indicate that DAS vibration monitoring systems

have long-term viability and perform well even after a decade of burial. Geotechnical

design considerations, such as installing the DAS fibre optic cable in gravel instead of

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a controlled density, cementitious excavatable flowable fill, have a positive impact on

the overall system performance. Common construction materials such as sand and

gravel performed well over a seven-month test period during which impact tests on the

ground surface were used to monitor performance of the DAS array. Even though

there was a clear improvement of the response in the gravel and sand over the

flowable fill and the native silty sand, all the SNR values were acceptable for

monitoring purposes. These results suggest that DAS will be highly responsive when

buried in readily available construction materials for more than a decade, which

supports using DAS as a geotechnical/structural health monitoring tool. Work

comparing the DAS array response in the test bed described herein will continue to

observe how the response of the new installations change with time and environmental

conditions.

A.7. Acknowledgements

The authors would like to acknowledge the U.S. Army Corps of Engineers

Engineering Research and Development Center funding, and Ms. Jennifer Picucci, Dr.

Katherine Winters, Mr. Josh McCleave; and our collaborators Mr. Darren Flynn, Mr.

Ryan Carlson, and Ms. Jen McGunigal.

A.8. References

ASTM (2012). D698-12e2. Standard Test Methods for Laboratory

Compaction Characteristics of Soil Using Standard Effort (12400 ft-lbf/ft3 (600kN-

m/m3)). ASTM International. West Conshohocken, PA, USA.

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Daley, T.M., Miller, D.E. , Dodds,K., Cook, P., Freifeld, B. M. (2016). “Field

testing of modular borehole monitoring with simultaneous distributed acoustic sensing

and geophone vertical seismic profiles at Citronelle, Alabama.” Geophysical

Prospecting, (64) 1318-1334. https://doi.org/10.1111/1365-2478.12324

Dou, S., Lindsey, N., Wagner, A.M., Daley, T., Thomas, M, Freifeld, B.,

Robertson, M., Peterson, J, Ulrich, C., Martin, E. R., Ajo-Franklin, J. B. (2017).

“Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-

Noise Interferometry Case Study.” Scientific Reports, (7)

https://doi.org/10.1038/s41598-017-11986-4

Egorov, A., Correa, J., Bóna, A., Pevzne, R., Tertyshnikov, K.,

Glubokovskikh, S., Puzyrev, V., Gurevich, B. (2018). “Elastic full-waveform

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104

APPENDIX B

INTERNATIONAL CONFERENCE ON SOIL MECHANICS AND

GEOTECHNICAL ENGINEERING CONFERNCE PAPER

Title: Fiber optic DAS monitoring acoustic emissions for geotechnical structure

performance in the field

Détection fibre optique DAS pour surveiller les émissions acoustiques pour la

performance des structures géotechniques sur le terrain

ABSTRACT: Geotechnical engineers can use Acoustic Emissions (AE) to

monitor the performance of geotechnical components of infrastructure. Changes in

measured AE have been hypothesized to reflect changes in the soil properties that can

affect infrastructure performance. Fiber optic Distributed Acoustic Sensing (DAS) is a

relatively new instrument to the civil engineering community that could be used to

monitor AE. DAS uses a fiber optic cable to measure strains along its length at

sampling rates close to geophones. This paper presents results of an on-going, 11-

month field study on the response of a buried DAS to impact tests on the ground

surface. The fiber optic cable was placed in a trench, with different sections backfilled

with sand, gravel, and flowable fill. Impact tests were performed by striking a standard

Proctor hammer on a aluminum plate, and the response in the DAS was recorded using

a conventional optical time-domain reflectometer interrogator. DAS response in each

backfill material was measured as a function of distance from the source and over

time. The primary results of this study suggest that a) Signal-to-Noise Ratio might be a

better metric by which to observe changes in the soil over time ; b) attenuation of DAS

105

response with distance was comparable among the three backfill materials ; and c)

there was a significant reduction in SNR for all materials over the 11-month

measurement period. More research is needed to better understand these findings for

increased acceptance of DAS for Civil Engineering infrastructure monitoring.

RÉSUMÉ : Les ingénieurs géotechniques peuvent utiliser les émissions

acoustiques (AE) pour surveiller les performances des structures géotechniques telles

que les culées de ponts. Les changements de l'AE mesuré peuvent être corrélés à des

changements dans l'état du contact structure-sol. La détection acoustique distribuée

par fibre optique (DAS) est un instrument relativement nouveau pour la communauté

du génie civil qui pourrait être utilisé pour surveiller l'EA. Le DAS utilise un câble à

fibre optique pour mesurer les déformations sur sa longueur à une fréquence

d'échantillonnage proche des géophones. Le DAS donne une réponse tous les 1 à 10

mètres sur sa longueur, chaque réponse distribuée remplace un capteur ponctuel.

Ainsi, une matrice DAS pourrait remplacer des centaines ou des milliers de capteurs

ponctuels pour la surveillance AE en fonction de la longueur du câble à fibre optique

et de la résolution de distribution des données. L'intégration du DAS dans la

conception des fondations ou dans la conception des culées de pont pourrait

révolutionner la surveillance intelligente des infrastructures. Une étude de suivi sur le

terrain DAS à long terme montre comment la performance du DAS dans le remblai

structurel sableux et le gravier n'est pas affectée par les changements saisonniers.

KEYWORDS: Distributed Acoustic Sensing, Instrumentation, Monitoring, Structural

Health Monitoring, Acoustic Emission

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1 INTRODUCTION.

Fiber optic Distributed Acoustic Sensing (DAS) systems are comprised of a

fiber optic cable and an interrogator. The fiber optic cable can be as simple as

telecommunication fiber optic cable or as complex as a specially fabricated cable with

unique materials and fiber orientation. The cable can be embedded in soil, placed in a

conduit, grouted in a borehole, or otherwise attached to the infrastructure to be

monitored.

A DAS interrogator contains one or more lasers which pulses light (photons)

into the fiber core. Light propagates down the fiber core and scatters due to density

anomalies in the fiber core material (Krohn et al. 2014); the location of these

anomalies are called scattering centers. Some of the scattered light returns to the

interrogator as backscatter, and Rayleigh scattering is measured using an optical time-

domain reflectometer (OTDR) located within the interrogator. Rayleigh scattering is

an elastic process such that the velocity of the light outbound from the laser is the

same as the velocity of the light reflected back towards the interrogator. This allows

for determination of the distance along the fiber where scattering centers are located

(Sang 2011, Owen et al. 2012; Schenato 2017; Soga and Luo 2018; Wang et al. 2019).

Vibrational strains acting on the fiber induce changes to the scattering centers.

This, in turn, changes the power of backscattered light which is proportional to the

magnitude of the vibrations (Lindsey et al. 2020). A typical sampling rate greater than

2000Hz allows DAS to detect vibrational strains acting along the fiber optic cable to

produce observations similar to that of geophones or seismometers. While the newest

and more expensive DAS systems claim 1-meter distributed response, this is under the

107

most optimal conditions with other trade-offs such as shorter DAS array length (Krohn

et al. 2014). The DAS community often uses 10-meter channel spacing and the DAS

fiber optic cable lengths at this channel spacing can exceed 20-kilometers (i.e. 2,000

responses evenly distributed along the cable length).

1.1 DAS Applications

For over a decade, DAS has been used in the oil and gas industry for both

security and leak detection along remote pipelines. Current infrastructure monitoring

research using distributed fiber optic sensing includes monitoring mining activities,

highway subgrade, railway ballast and ties, and movement in earth embankment dams

(Luo et al. 2016, Soga and Luo 2018, Li et al. 2018, Luo et al. 2019). There are several

studies showing how DAS can be used for vertical seismic profiling (e.g., Mateeva et

al., 2014; Olofsson and Martine, 2017; Egorov et al., 2018; Miller et al. 2018). Several

research efforts (including Daley et al., 2013; Bakulin et al., 2017; Castongia et al.,

2017; Dou et al., 2017; Hornman, 2017; Jreij et al., 2017; Costley et al., 2018, Spikes

2018; Miller et al., 2018) show that DAS can also be used to estimate the shear wave

velocity of soil profiles by multichannel analysis of surface waves (MASW). Parker et

al. 2018 indicated that seismometers provide a higher signal to noise ratio and wider

range of frequencies than DAS, while DAS provided more point data due to its

distributed nature and the long lengths of sensor that are achievable. Lindsey et al.

2020 demonstrated that DAS response is comparable to a high-quality broadband

seismometer and DAS was able to measure similar broadband frequencies as the

seismometer.

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DAS monitoring can be either active or passive in nature. For example,

roadway subgrade monitoring and railway ballast and tie monitoring is active,

meaning that engineers use the seismic response induced by vehicle traffic and trains

to evaluate subsurface conditions. Changes in the way a portion of the DAS array

performs along a roadway or railway indicate that further engineering investigation is

needed in that zone of the array. DAS in dams or other earthen embankments act as a

passive sensor. The DAS system remains in-situ and is used for change detection

(changes DAS response due to the movement of seeping water through a dams). DAS

use for earthquake monitoring is also a passive system.

1.2 Acoustic Emission

Changes in Acoustic Emissions (AE) can be used to monitor changes in the

condition of infrastructure. For geotechnical engineering applications of AE, there is

ongoing research in the laboratory attempting to correlate AE with soil strength and

deformation (Smith and Dixon 2018). Work from Heather-Smith et al., 2018; Smith et

al., 2017a; and Smith and Dixon 2018 indicate that changes in wave propagation and

attenuation measured via AE might be caused by changes in internal friction and other

soil properties. These researchers suggest that attenuation change is a function of the

soil layering and distance between measurements. Smith and Dixon 2018 indicate that,

theoretically, AE will increase with loading and unloading cycles and observed AE

increases with increasing strain. Strain, frequency content, soil density, soil Young’s

modulus, soil Poisson’s ratio, and both the internal and changes in the external

environment all impact AE.

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AE has been used to monitor slope stability (Tanimoto and Tanaka 1986,

Smith et al. 2014, Dixon et al. 2015, Smith et al. 2017b, Dixon et al. 2018). Smith and

Dixon 2018 discuss how AE changes would correspond to earthen slope movements.

Mao et al., 2020 identified variables that influence AE attenuation in soil including

propagating mode, depth, soil density, Young’s modulus, Poisson’s ratio, subsurface

environment, and the above-ground environment.

2 EXPERIMENTAL SETUP

The objective of this paper is to present the results of a field study that was

conducted to evaluate the effects of burial material on AE of a DAS array. A new

portion of fiber optic cable was installed in a trench and spliced into an existing DAS

array; Figure 1 illustrates the new test bed layout. Portions of the test bed were filled

and compacted with a sand fill, gravel, and an excavatable flowable fill. The fiber

optic cable is located at a depth of 0.5 meters with 0.5 meters of fill placed above and

below it. The cable consists of single mode silica fibers with a water-proof buffer tube

and polyethylene jacket. The interrogator used for this study is a conventional OTDR

with a sampling frequency of 2500Hz and 10-meter-long channels.

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Figure 1. DAS Test bed layout where each rectangle indicates a DAS channel.

3 METHODOLOGY

A standard proctor hammer impacting a metal plate was used to generate

repeatable seismic waves for the DAS test bed to record. At least ten impacts were

delivered per source location shown in Figure 1. Impact testing was conducted onsite

from August 2019 through September 2020.

AE is quantified by the Root Mean Square (RMS) value of the signal induced

in the DAS channel from the impact source (Smith and Dixon 2018). The RMS value

(xrms) of the signal x(t) measured using the DAS channel is defined as shown in

Equation 1.

(1)

111

Where T is the signal duration over which the RMS value is evaluated. The DAS

signal was sampled at 2500 Hz with a sampling interval (t) of 0.4 milliseconds. The

RMS calculations were made with T=0.35 seconds, yielding 875 samples (N) in the

analyzed time window. Using the discrete values sampled (x[n]), with n=1,2,3,…,N,

Equation 1 can be re-written in the discrete form as shown in Equation 2.

(2)

RMS values were calculated for DAS response in channels located in sand, gravel,

and flowable fill materials and used to quantify the AE as described earlier.

It was found in this work that Signal-to-Noise-Ratio (SNR) provided a better

measure than RMS for observing changes in DAS response over time. SNR

incorporates the RMS value xrms, as shown in equation 3. Note that both xrms_signal

and xrms_noise were made with T=0.35 such that the noise capture was the 0.35

seconds following 0.35 seconds of signal using the Equation 2.

(3)

4 RESULTS AND DISCUSSION

Impact tests were performed on four days between October 2019 and

September 2020 and the response of the DAS was recorded. DAS response in the three

materials to impulse events occurring at source location No. 1 is provided in Figure 2

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and Figure 3, where Figure 2 presents results in terms of AE and Figure 3 presents

results in terms of SNR.

Figure 2. AE response from DAS in sand, gravel, and flowable fill between

October 2019 and September 2020 in response to source location No. 1.

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Figure 3. SNR response from DAS in sand, gravel, and flowable fill between

October 2019 and September 2020 in response to source location No. 1.

DAS response in the gravel and flowable fill to impulse events occurring at

source locations No. 2 and No. 3 is shown in Figure 4 and Figure 5, where Figure 4

presents results in terms of AE and Figure 5 presents results in terms of SNR.

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Figure 4. AE DAS response in gravel (source location No. 2) and flowable fill

(source location No. 3) between October 2019 and September 2020.

115

Figure 5. SNR DAS response in gravel (source location No. 2) and flowable fill

(source location No. 3) between October 2019 and September 2020.

The differences in Figure 2 versus Figure 3, and in Figure 4 versus Figure 5

show the importance of the metric by which monitoring is being performed. Figures 2

and 4 present results in terms of AE and indicate a large response variation in portions

of the array closest to the source. Figures 3 and 5 present the same results in terms of

SNR; there is still variability in the results but much less that using AE. We suggest

that SNR is a better way to use DAS to perform long-term infrastructure monitoring as

it normalizes the response to the ambient noise conditions that might be specific to the

date or time of testing (e.g. day-time activity versus night-time activity or a windy

day).

116

While Figure 3 indicates that portions of the array in flowable fill do not

perform as well as portions of the array in sand and gravel, Figure 5 suggests that

portions of the array in flowable fill perform as well as portions of the array in gravel.

The performance shown in Figure 5 is possibly due to the location of the source being

axially aligned with both the fiber topic cable and the trench material. The impact

source for the data in Figure 3 is located offset from the trench material and fiber optic

cable. The differing results in Figures 3 and 5 highlight the importance of

understanding the intent and goals of monitoring program to optimize the design of a

DAS array to yield quality, consistent results. The fact that the signal response and

attenuation is comparable in gravel and flowable fill can inform those who are burying

fiber optic cables for infrastructure monitoring.

To observe changes in DAS response over time, the results from tests

performed on 4 dates over an 11-month period are shown in Figure 6. Source location

1 was used for all the results shown in this figure.

117

Figure 6. Changes in SNR between October 2019 and September 2020 as

observed in sand (A), gravel (B), and flowable fill (C).

118

The DAS response in Figure 6 highlights the potential power and challenges of

using DAS as a change-detection monitoring tool for infrastructure. Figure 6 shows

that for readings from October 2019 through February 2020 DAS response was

relatively consistent in all material, with the response in the sand having the highest

SNR. Attenuation with distance from the source was comparable for all three

materials.

The DAS response for data collected in September 2020 is very different from

the earlier readings. For example, the close-to-source response for portions of the

array in sand dropped from roughly 25dB to 10dB with greater variance in the

September 2020 data (Figure 6A). Similarly, the portions of the array in flowable fill

closest to the source dropped from approximately 15dB to less than 5dB (Figure 6C).

While Figure 6B indicates that portions of the array in gravel continue to perform

consistently, though there is a significant increase in variability of the response in the

September 2020 data.

If using the DAS array in this study for infrastructure monitoring, the drop in

SNR observed in the September 2020 (Figure 6) data would trigger site inspection to

the affected portions of the DAS array. As indicated by Mao et al., 2020, a change in

AE (and as shown herein, a change in SNR) in soil could be due to changes in soil

density, subsurface environment, and the above-ground environment. Due to the

Covid-19 pandemic, further investigation on the cause of the AE/SNR changes have

not yet occurred, but preliminary observations indicate no change to the ground

surface above the DAS array. More investigation is needed to understand the

significant reduction in SNR for the 11-month readings.

119

5 CONCLUSIONS

The objective of this paper was to present the results of an on-going field study

on the response of a fiber optic DAS array. buried in different materials, to repeated

impact tests on the ground surface. The fiber optic cable was placed in a trench and

different sections were backfilled with sand, gravel, and flowable fill. Impact tests

were performed by striking a standard Proctor hammer on an aluminum plate, and the

response in the DAS was recorded over an 11-month period.

The results were assessed in terms of both Acoustic Emissions (AE) and

Signal-to-Noise Ratio (SNR). SNR exhibited less variability and is recommended for

in situ monitoring where on-site noise can be highly variable. Significant finding of

the field study included the following:

The response of the DAS in sand yielded the highest SNR but also the

largest amount of scatter in results;

The initial response in the gravel and flowable fill was comparable in

terms of SNR and attenuation away from the source;

This initial response in the gravel and flowable fill was comparable in

terms of SNR and attenuation away from the source;

There was a significant change in SNR between the 3- and 11-month

readings in all three backfill materials. Intermediate readings were not possible due to

COVID-19 travel and access restrictions. The reduction in SNR was most pronounced

in the flowable fill.

Changes in DAS response might be caused by water infiltration, water table

fluctuation, freeze-thaw activity, desiccation, or another seasonal phenomena;

120

however, more research is needed to better understand the reasons for the significant

reduction in SNR with time. Understanding these effects will lead to more acceptance

of DAS for Civil Engineering infrastructure monitoring.

6 ACKNOWLEDGEMENTS

We would like to thank our team members at the test site and funding from the

U. S. Army Corps of Engineers Engineering Research and Development Center.

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Géotechnique 4 (1), 6-15.

o find the test sequence within the recorded data set initialDAS was used. This

function loads the data, allows the user to clip the data to a specific time window, and

creates the data matrix used for anaylsis. Here is the associated code:

127

APPENDIX C

MATLAB CODE FOR DATA PROCESSING

MatLab was used to process the DAS data collected throughout this research

effort. This appendix includes the MatLab code used to process the data set.

To find the test sequence within the recorded data set “initialDAS” was used.

This function loads the data, allows the user to clip the data to a specific time window,

and creates the data matrix used for anaylsis. Here is the associated code:

function initialDAS(varargin) global hdr; getDataFile(varargin) channels = 1:10; %provide channel range of interest sig_start = 0; %start data of interest, time in seconds * sampling rate sig_end = 10000; %end of data of interest data = data_for_matrix(channels,sig_start,sig_end); %define data matrix save DataForProcessing data %save data end

Note initalDAS calls data_for_matrix. This code converts the raw binary data

provided by the DAS unit to a version we can use for data processing. Here is the

associated code for data_for_matrix:

128

function [data] = data_for_matrix(channels,sig_start,sig_end) global hdr; if isempty(hdr.input_fullname) [input_fname,input_pname] = uigetfile('*.dat'); hdr.input_fullname = [input_pname input_fname]; else [input_pname,input_fname_tmp,ext] = fileparts(hdr.input_fullname); end filename = strcat(input_pname,['/' input_fname_tmp]); hdr.filename = strrep(filename,'_','-'); hdr.filename = filename; fidin = fopen(hdr.input_fullname,'r','ieee-le'); fseek(fidin,0, 'eof'); numch_per_block_str = [num2str(length(channels)) '*int16=>int16']; num_datatype_bytes = 2; hdr.timeSampsAvail = hdr.nDataBytes/(hdr.num_datatype_bytes*hdr.NChansApert); if sig_start == -1 hdr.sig_start = 1; hdr.sig_end = hdr.timeSampsAvail; else hdr.sig_start = sig_start; hdr.sig_end = sig_end; end numpointstoread = hdr.sig_end-hdr.sig_start+1; fseek(fidin, hdr.HeaderBytes, 'bof'); fseek(fidin, ((hdr.sig_start-1).*hdr.NChansApert).*num_datatype_bytes, 'cof'); fseek(fidin, (channels(1)-1).*hdr.num_datatype_bytes, 'cof'); data = fread(fidin, numpointstoread*length(channels), numch_per_block_str, ((hdr.NChansApert-length(channels)))*hdr.num_datatype_bytes); data = double(data); if length(channels) >1 data_temp = reshape(data,length(channels),[])'; data = data_temp; end fclose(fidin); end

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To view the data matrix defined in initialDAS, processDAS was used:

% Program to process the DAS data clear close all % load DataForProcessing Fs=2500; % sampling frequency t=(1:length(data(:,1)))/Fs; % time series: for ii=1:length(data(1,:)) data(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*2000; % offset value is only for visual aid plot(t,offset+data(:,ii),'linewidth',1) hold on end xlabel('Time (sec)') ylabel('Voltage') title('<<Date>> <<Channels>>')

From processDAS, the ten impact responses were isolated and the 0.35 second

time window for check signal response and subsequent noise was defined (recall 0.5

second window for the CRREL data). This time values were manually selected

(rounding to the nearest 0.05seconds) and entered into the initialDAS_SNR_Event

function. This function saves the matrices for each signal response and subsequent

noise that will be used to calculate RMS and SNR. An example of the code used for

this process follows:

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function initialDAS_SNR_Event(varargin) global hdr; getDataFile(varargin) channels = 120:130; %<<What Soil type>> << what event>> %Signal Hit 1 sig_start = (2354.1)*2500; sig_end = (2354.45)*2500; data = read_data(channels,sig_start,sig_end); save dataSig1 data %Noise Hit 1 sig_start = (2354.45)*2500; sig_end = (2354.8)*2500; data = read_data(channels,sig_start,sig_end); save dataNoise1 data %Signal Hit 2 sig_start = (2355.6)*2500; sig_end = (2355.95)*2500; data = read_data(channels,sig_start,sig_end); save dataSig2 data %Noise Hit 2 sig_start = (2355.95)*2500; sig_end = (2356.3)*2500; data = read_data(channels,sig_start,sig_end); save dataNoise2 data %Signal Hit 3 sig_start = (2357.4)*2500; sig_end = (2357.75)*2500; data = read_data(channels,sig_start,sig_end); save dataSig3 data %Noise Hit 3 sig_start = (2357.75)*2500; sig_end = (2358.1)*2500; data = read_data(channels,sig_start,sig_end); save dataNoise3 data

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%Signal Hit 4 sig_start = (2358.75)*2500; sig_end = (2359.1)*2500; data = read_data(channels,sig_start,sig_end); save dataSig4 data %Noise Hit 4 sig_start = (2359.1)*2500; sig_end = (2359.45)*2500; data = read_data(channels,sig_start,sig_end); save dataNoise4 data %Signal Hit 5 sig_start = (2360.25)*2500; sig_end = (2360.6)*2500; data = read_data(channels,sig_start,sig_end); save dataSig5 data %Noise Hit 5 sig_start = (2360.6)*2500; sig_end = (2360.95)*2500; data = read_data(channels,sig_start,sig_end); save dataNoise5 data %Signal Hit 6 sig_start = (2362.2)*2500; sig_end = (2362.55)*2500; data = read_data(channels,sig_start,sig_end); save dataSig6 data %Noise Hit 6 sig_start = (2362.55)*2500; sig_end = (2362.9)*2500; data = read_data(channels,sig_start,sig_end); save dataNoise6 data

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%Signal Hit 7 sig_start = (2364.25)*2500; sig_end = (2364.6)*2500; data = read_data(channels,sig_start,sig_end); save dataSig7 data %Noise Hit 7 sig_start = (2364.6)*2500; sig_end = (2364.95)*2500; data = read_data(channels,sig_start,sig_end); save dataNoise7 data %Signal Hit 8 sig_start = (2365.85)*2500; sig_end = (2366.2)*2500; data = read_data(channels,sig_start,sig_end); save dataSig8 data %Noise Hit 8 sig_start = (2366.2)*2500; sig_end = (2366.55)*2500; data = read_data(channels,sig_start,sig_end); save dataNoise8 data %Signal Hit 9 sig_start = (2367.25)*2500; sig_end = (2367.6)*2500; data = read_data(channels,sig_start,sig_end); save dataSig9 data %Noise Hit 9 sig_start = (2367.6)*2500; sig_end = (2367.95)*2500; data = read_data(channels,sig_start,sig_end); save dataNoise9 data

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%Signal Hit 10 sig_start = (2369)*2500; sig_end = (2369.35)*2500; data = read_data(channels,sig_start,sig_end); save dataSig10 data %Noise Hit 10 sig_start = (2369.35)*2500; sig_end = (2369.7)*2500; data = read_data(channels,sig_start,sig_end); save dataNoise10 data end

The initialDAS_SNR_Event function saves dataSig and dataNoise matrices

that are then processed using processDAS_SNR_10. This code performs the RMS

calculation for each signal capture and each subsequent noise capture. Additionally the

signal and noise captures are plotted which allows for a quick visual check on the

capture. While processing the huge quantity of data involved in this research effort,

some minor mistakes are inevitable, but adding checks into the system such as this

visual check to make sure the signal was captured was key. The “_10” at the end of

the file refers to the ten hammer hits. On rare occasion, more and fewer hits were

collected. Files for nine hits or eleven hits were saved with “_9” and “_11.”

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Here is the code used for processDAS_SNR_10:

% Program to process the DAS data clear close all %% % Hit 1 %Signal load dataSig1 Fs=2500; tS=(1:length(data(:,1)))/Fs; T=0.35 %time capture of signal / noise % time series Signal: for ii=1:length(data(1,:)) dataS(:,ii)=data(:,ii)-mean(data(:,ii)); %save loaded matrix for the signal offset=(ii-1)*3000; %offset to see channels separately plot(tS,offset+dataS(:,ii),'linewidth',1) % RMS value rms_signal1(ii) = calcRMS(dataS(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 1') % Noise 1 load dataNoise1 tN=(1:length(data(:,1)))/Fs; % time series Noise: figure for ii=1:length(data(1,:)) dataN(:,ii)=data(:,ii)-mean(data(:,ii));

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offset=(ii-1)*3000; plot(tN,offset+dataN(:,ii),'linewidth',2,'LineStyle',':') % RMS value rms_noise1(ii) = calcRMS(dataN(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 1') SNRdb1=20*log10(rms_signal1./rms_noise1) %% % Hit 2 %Signal load dataSig2 Fs=2500; tS=(1:length(data(:,1)))/Fs; T=0.35 % time series Signal: for ii=1:length(data(1,:)) dataS(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tS,offset+dataS(:,ii),'linewidth',1) % RMS value rms_signal2(ii) = calcRMS(dataS(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 2') % Noise 2 load dataNoise2 tN=(1:length(data(:,1)))/Fs; % time series Noise:

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figure for ii=1:length(data(1,:)) dataN(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tN,offset+dataN(:,ii),'linewidth',2,'LineStyle',':') % RMS value rms_noise2(ii) = calcRMS(dataN(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 2') SNRdb2=20*log10(rms_signal2./rms_noise2) %% %Hit 3 %Signal load dataSig3 Fs=2500; tS=(1:length(data(:,1)))/Fs; T=0.35 % time series Signal: for ii=1:length(data(1,:)) dataS(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tS,offset+dataS(:,ii),'linewidth',1) % RMS value rms_signal3(ii) = calcRMS(dataS(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 3')

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% Noise 3 load dataNoise3 tN=(1:length(data(:,1)))/Fs; % time series Noise: figure for ii=1:length(data(1,:)) dataN(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tN,offset+dataN(:,ii),'linewidth',2,'LineStyle',':') % RMS value rms_noise3(ii) = calcRMS(dataN(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 3') SNRdb3=20*log10(rms_signal3./rms_noise3) %% %Hit 4 %Signal load dataSig4 Fs=2500; tS=(1:length(data(:,1)))/Fs; T=0.35 % time series Signal: for ii=1:length(data(1,:)) dataS(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tS,offset+dataS(:,ii),'linewidth',1) % RMS value rms_signal4(ii) = calcRMS(dataS(:,ii),T,Fs); hold on end

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xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 4') % Noise 4 load dataNoise4 tN=(1:length(data(:,1)))/Fs; % time series Noise: figure for ii=1:length(data(1,:)) dataN(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tN,offset+dataN(:,ii),'linewidth',2,'LineStyle',':') % RMS value rms_noise4(ii) = calcRMS(dataN(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 4') SNRdb4=20*log10(rms_signal4./rms_noise4) %% %Hit 5 %Signal load dataSig5 Fs=2500; tS=(1:length(data(:,1)))/Fs; T=0.35 % time series Signal: for ii=1:length(data(1,:)) dataS(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tS,offset+dataS(:,ii),'linewidth',1)

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% RMS value rms_signal5(ii) = calcRMS(dataS(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 5') % Noise 5 load dataNoise5 tN=(1:length(data(:,1)))/Fs; % time series Noise: figure for ii=1:length(data(1,:)) dataN(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tN,offset+dataN(:,ii),'linewidth',2,'LineStyle',':') % RMS value rms_noise5(ii) = calcRMS(dataN(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 5') SNRdb5=20*log10(rms_signal5./rms_noise5) %% %Hit 6 %Signal load dataSig6 Fs=2500; tS=(1:length(data(:,1)))/Fs; T=0.35

140

% time series Signal: for ii=1:length(data(1,:)) dataS(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tS,offset+dataS(:,ii),'linewidth',1) % RMS value rms_signal6(ii) = calcRMS(dataS(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 6') % Noise 6 load dataNoise6 tN=(1:length(data(:,1)))/Fs; % time series Noise: figure for ii=1:length(data(1,:)) dataN(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tN,offset+dataN(:,ii),'linewidth',2,'LineStyle',':') % RMS value rms_noise6(ii) = calcRMS(dataN(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 6') SNRdb6=20*log10(rms_signal6./rms_noise6) %% % Hit 7 %Signal load dataSig7 Fs=2500; % sampling freq. : needs to confirm the tS=(1:length(data(:,1)))/Fs;

141

T=0.35 % time series Signal: for ii=1:length(data(1,:)) dataS(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tS,offset+dataS(:,ii),'linewidth',1) % RMS value rms_signal7(ii) = calcRMS(dataS(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 7') % Noise 7 load dataNoise7 tN=(1:length(data(:,1)))/Fs; % time series Noise: figure for ii=1:length(data(1,:)) dataN(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tN,offset+dataN(:,ii),'linewidth',2,'LineStyle',':') % RMS value rms_noise7(ii) = calcRMS(dataN(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 7') SNRdb7=20*log10(rms_signal7./rms_noise7) %%

142

%Hit 8 %Signal load dataSig8 Fs=2500; tS=(1:length(data(:,1)))/Fs; T=0.35 % time series Signal: for ii=1:length(data(1,:)) dataS(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tS,offset+dataS(:,ii),'linewidth',1) % RMS value rms_signal8(ii) = calcRMS(dataS(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 8') % Noise 8 load dataNoise8 tN=(1:length(data(:,1)))/Fs; % time series Noise: figure for ii=1:length(data(1,:)) dataN(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tN,offset+dataN(:,ii),'linewidth',2,'LineStyle',':') % RMS value rms_noise8(ii) = calcRMS(dataN(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 8') SNRdb8=20*log10(rms_signal8./rms_noise8)

143

%% %Hit 9 %Signal load dataSig9 Fs=2500; tS=(1:length(data(:,1)))/Fs; T=0.35 % time series Signal: for ii=1:length(data(1,:)) dataS(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tS,offset+dataS(:,ii),'linewidth',1) % RMS value rms_signal9(ii) = calcRMS(dataS(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 9') % Noise 9 load dataNoise9 tN=(1:length(data(:,1)))/Fs; % time series Noise: figure for ii=1:length(data(1,:)) dataN(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tN,offset+dataN(:,ii),'linewidth',2,'LineStyle',':') % RMS value rms_noise9(ii) = calcRMS(dataN(:,ii),T,Fs); hold on end xlabel('Time (sec)')

144

ylabel('Relative Voltage Amplitude') title('Hit 9') SNRdb9=20*log10(rms_signal9./rms_noise9) %% % Hit 10 %Signal load dataSig10 Fs=2500; tS=(1:length(data(:,1)))/Fs; T=0.35 % time series Signal: for ii=1:length(data(1,:)) dataS(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tS,offset+dataS(:,ii),'linewidth',1) % RMS value rms_signal10(ii) = calcRMS(dataS(:,ii),T,Fs); hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 10') % Noise 10 load dataNoise10 tN=(1:length(data(:,1)))/Fs; % time series Noise: figure for ii=1:length(data(1,:)) dataN(:,ii)=data(:,ii)-mean(data(:,ii)); offset=(ii-1)*3000; plot(tN,offset+dataN(:,ii),'linewidth',2,'LineStyle',':') % RMS value rms_noise10(ii) = calcRMS(dataN(:,ii),T,Fs);

145

hold on end xlabel('Time (sec)') ylabel('Relative Voltage Amplitude') title('Hit 10') SNRdb10=20*log10(rms_signal10./rms_noise10) %% SNR for all hits SNR_all=[SNRdb1;SNRdb2;SNRdb3;SNRdb4;SNRdb5;SNRdb6;SNRdb7;SNRdb8;SNRdb9;SNRdb10] The code presented above provides a calculated SNR matrix for all channels of

interest and for each impact. This matrix was copied from MatLab and pasted into

Microsoft Excel. Microsoft Excel was used plot the data as shown in the results

figures presented herein.

146

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