AMARs on VENUS Autonomous Multichannel Acoustic Recorders on the VENUS Ocean Observatory

John Moloney, Craig Hillis, Xavier Mouy, and Ildar Urazghildiiev

JASCO Applied Sciences Dartmouth, Canada

john.moloney@jasco.com

Tom Dakin Ocean Networks Canada

University of Victoria Victoria, Canada

Abstract—The Autonomous Multichannel Acoustic Recorder

(AMAR, JASCO Applied Sciences) is a sophisticated precision instrument for passive acoustic monitoring and accurate underwater sound level measurements. It can be integrated with small hydrophone arrays and non-acoustic oceanographic sensors. To date, AMARs have typically been used autonomously and deployed for a few months to a year on oceanographic moorings; however, AMARs are also capable of real-time data streaming when connected to a data telemetry system. This paper describes the capabilities and functionality of the AMAR through the example of its integration within Ocean Networks Canada’s VENUS Ocean Observatory deployed off the coast of British Columbia, Canada. The recent deployment of two AMAR-based hydrophone arrays and associated non-acoustic and oceanographic sensors within the VENUS system is presented in detail. The planned research and development within the AMARs on VENUS program, as well as preliminary results on the real-time automatic detection, classification, localization, and tracking of marine mammals, are presented. The two AMARs deployed on the VENUS Ocean Observatory demonstrate that, unlike traditional underwater acoustic recorders, the AMAR can act as a hub for mini ocean observatories, capturing and transmitting both acoustic and non-acoustic sensor data in real-time. It is demonstrated that the AMAR is an effective technology that can be used in near-shore, small-scale, low-cost ocean observatories.

Keywords—low-cost ocean observatory; real-time passive acoustic monitoring; detection; classification; localization; marine mammals.

I. INTRODUCTION Recent advances in technology allow cabled ocean

observatories to collect more data at higher resolution over longer time periods and in more remote locations (i.e., deeper waters, further offshore), making them a more attractive and powerful tool for oceanographers. Ocean observatories have three main advantages over autonomous time-limited moored instruments: (1) Access to power from land allows collection of data continuously and simultaneously from numerous sensors ranging from current meters, conductivity-temperature-depth sensors, and hydrophones, to video cameras, which allows a global and more comprehensive view of oceanographic and climatological phenomena; (2) Data can be sent to shore in real- or near-real time, which allows real-time monitoring for extended periods (e.g., earthquake monitoring);

(3) The data can be made accessible online for free, allowing the worldwide scientific community to access and analyze high quality data at little or no cost, and consequently favoring and democratizing collaborative scientific research.

Several ocean observatories are being funded worldwide. The European Seafloor Observatory Network, started in 2007, is a network of long-term multidisciplinary ocean observatories in deep waters around Europe (http://www.esonet-noe.org). The ALOHA is a cabled observatory deployed off Hawaii for studying deep-sea biology, abyssal circulation, and acoustic signatures of earthquakes, ships, marine mammals, wind waves, and rain (http://aco-ssds.soest.hawaii.edu). The National Science Foundation funded Ocean Observatories Initiative (OOI) deployed several coastal sensor arrays on the east and west coast of the United States of America (http://oceanobservatories.org). In Canada, Ocean Networks Canada (ONC) deployed large cabled observatories including the North East Pacific Time-series Underwater Networked Experiment (NEPTUNE) in the Northeast Pacific, the Victoria Experimental Network Under the Sea (VENUS) in the Salish Sea and a mini-observatory in the Arctic Ocean offshore of Nunavut (http://www.oceannetworks.ca). The VENUS ocean observatory has operated continuously since February 2006. Deployed in the coastal waters of southern British Columbia, VENUS provides long-term oceanographic data on physical, chemical, biological, and sediment conditions in Saanich Inlet and in the Strait of Georgia near Vancouver, BC.

This paper describes the implementation of two innovative ocean observing systems in the Salish Sea as part of the VENUS ocean observatory. One of the goals of this project is to demonstrate how Autonomous Multichannel Acoustic Recorders (AMARs, JASCO Applied Sciences, https://jasco.squarespace.com/amar/), typically used as autonomous instruments deployed on regular oceanographic moorings, can be integrated in ocean observatories to collect and stream acoustic and non-acoustic data from various sensors in real-time. Another goal of this project is to develop and implement efficient methods for analyzing in near-real time the acoustic data streamed ashore by the AMARs, including ambient noise measurements, automated detection, classification, localization, and tracking (DCLT), and automated density estimation (DE) for the marine mammals that inhabit Canada’s Pacific coastal region.

978-1-4799-4918-2/14/$31.00 ©2014 IEEE This is a DRAFT. As such it may not be cited in other works. The citable Proceedings of the Conference will be published in

IEEE Xplore shortly after the conclusion of the conference.

A technical description of AMAR and itpresented and the integration and deploymenthe VENUS cabled observatory is examinresults from the data collected thus far are givefuture related research and development are pr

II. OVERVIEW OF THE DEPLOYED

Between March 7 and 10th, 2014, hydrophones and several non-acoustic sensorsAMAR were deployed at two VENUS node Strait of Georgia–East Node (SoG East) and Delta Dynamics Laboratory Mini-Node (DDL approximately 4.6 km apart in the Strait of GFig. 1).

The AMAR on the SoG East node has yaw, combined pressure and temperature, dand turbidity sensors. The AMAR on the DDaxis roll-pitch-yaw sensor. The DDL node AMdeployed with a conductivity-temperature-depwhich operated successfully, but the sensor remove its operational acoustic emissions as noise interference. The non-acoustic sensors are described in Table II.

At each node, four hydrophones were corners of a tetrahedral aluminum frame, whito the ocean bottom. The distance betweehydrophones is between 1.4 and 1.9 m (Fhydrophones are sampled synchronously at per second) and the non-acoustic sensors are sa

III. SPECIFICATIONS OF THE AUTONOMOUSACOUSTIC RECORDERS

The AMAR is an underwater data collecfunctions in one of two modes. In the first mfunctions as a long-term, autonomous recorrecords acoustic and non-acoustic data to intesolid-state memory. In the second mode, the Aas a real-time data acquisition and transmisssecond mode supports data analysis applicatiocontinuous, real-time data stream. It is this swas employed within the VENUS observatory

TABLE II. SENSORS CONNE

Sensor Model

4 × Current driven hydrophone M8E-51C-35dB Geo

Inc

Roll, pitch, yaw 3DM Orientation Sensor LO

Conductivity, temperature, depth CTD-NH Tel

Dissolved oxygen UT-O2-MK1 AMGm

Turbidity STM/AG306 Sea

Pressure, temperature ATM.1ST/N/T STS

TABLE I. LOCATION AND WATER DEPON THE VENUS OCEAN OBSER

VENUS node Latitude Lo

SoG East 49°02.559ƍ N 123°

DDL 49°04.862ƍ N 123°2

s functionality is nt of AMARs on ned. Preliminary en and targets for roposed.

SYSTEM two arrays of

s connected to an locations: at the the Fraser River node), which are

Georgia (Table I,

3-axis roll-pitch-issolved oxygen,

DL node has a 3-MAR was initially pth (CTD) sensor,

was removed to a source of self-on the AMARs

attached to the ich was deployed en each pair of

Fig. 2). All four 64 kHz (samples ampled at 1 Hz.

S MULTICHANNEL

ction device that mode, the AMAR rding device that ernal non-volatile AMAR functions sion device. This ons that require a second mode that y.

The AMAR provides many intepower sources, and other computing

• Two 24-bit analog-to-digitafour channels each samplingThis lets the AMAR supportwith up to eight elemeconfigurable gain between 0

• One 16-bit ADC sampling atinput gain can be set to 0 or 2

ECTED TO THE AMARS DEPLOYED ON THE VENUS OCEAN OBSERVATO

Manufacturer Interface

oSpectrum Technologies . Current loop Omnidirectiona

í164 dB re 1V/

ORD MicroStrain RS-232

ledyne RD Instruments RS-232/485 Power: 8 to 35 VBio-fouling resi

MT Analysenmesstechnik mbH Voltage 0–5 V Power: 9–30 V

aPoint Sensors, Inc. Voltage 0–5 V Power: 7–20 V,Gain settings: 1

S Sensors Analog current loop, 4–20 mA standard

Depth range: 0–Temperature ran

Fig. 1. Location of the AMARs on the DVENUS Ocean Observatory in the Strait of

PTH OF THE AMARS DEPLOYED RVATORY NODES.

ongitude Water depth (m)

19.065ƍ W 172

20.433ƍ W 144

erfaces to sensors, storage, g equipment:

al converters (ADCs) with g at speeds up to 128 kHz. t volumetric or linear arrays ents. Each channel has and 42 dB.

t speeds up to 688 kHz. The 21 dB.

RY NODES

Notes

al, 35 dB preamp gain /µPa sensivity, 1 Hz to 150 kHz

VDC @ 40 mA istant copper screens

, 3.5 mA avg. 1×, 5×, 20×, and 100× –250 m, Accuracy: 0.625 m nge: -5 to 50 °C, Accuracy: < 1 °C

DDL and SoG East nodes of theGeorgia.

• Eight low-speed ADC channels that These channels are DC coupled with a-10 to +10 Volts.

• Ten digital I/O lines, which can conexternal devices.

• Two RS-232 ports and one RS-485 communicate with external digital sens

• One USB 2.0 OTG connection, confighost or device, which can connect to cstorage devices.

• A dual port 10/100 Ethernet interface. used to stream real-time data from thdownload data recorded by the autonomous operation. This is the primenables data streaming.

• An onboard proprietary interface tovolatile, solid-state storage memory record the data during autonomous dAMAR can hold up to seven 256 GB mfor a total storage capacity or 1792 GB

• Eight external power connectors to sensors. Two are directly connected input power supply, and the six are poand switchable from software.

• Two DC power inputs (7–16 VDCvoltage protection to power the AMARVarious alkaline and lithium battery pato power the AMAR, or the AMAR from any other stable DC source.

• Power-over-Ethernet to power the AEthernet connection instead of the DC i

• An onboard interface to an optiona(RPY) sensor whose data can be stream

Through the addition of current-to-voboards, the various ADC channels can also loop-based hydrophones and non-acoustic sens

At the heart of the AMAR is the i.MX27

Fig. 2. The AMAR and hydrophone array deployed at Observatory node. (Left) Hydrophone separation (mm) fbold) and the SoG East node. (Right) Deployment of tharray.

operate at 1 Hz. a voltage range of

ntrol or monitor

port, which can sors.

urable to act as a commercial mass

This interface is he AMAR and to

AMAR during mary interface that

o onboard non-modules, which

deployments. The memory modules .

power external to the AMAR’s

ower-conditioned

C) with reverse R from batteries. acks are available can be powered

AMAR over an inputs.

al roll-pitch-yaw med or recorded.

oltage converter support current-

sors.

7 microprocessor

(Freescale Semiconductor, Inc.). Tdevice provides the AMAR with a and an onboard processing capabideployment durations under extremeIt performs essential operations configuration, onboard sensor monitoring (temperature, volcommunication with off-board devrecording based on the configured re

IV. AMAR INTEGRATION A

Fig. 3 shows the AMAR and hyVENUS node. Each AMAR has foloop hydrophones (GeoSpectrum quad current-to-voltage converter bsampled at 64 kHz using a four simultaneous sampling. The hyfrequency high-pass pole at 17 Hz ahas a low frequency high-pass pole in the ADC has a -3 dB bandwidth(31.36 kHz). The channels were coon the AMAR circuit board. The nless than 35 dB re 1 µPa/¥Hz at 1 hydrophones and current-to-voltage re 1 V/µPa at 250 Hz.

The analog sensors on the SoG Ealow speed ADC inputs through a 4buffer board. The control signals flevel shifted from the 3 V of the AM5 V using the level shift function voltage buffers on the daughter cathe turbidity and dissolved oxygen

each VENUS Ocean for the DDL node (in he DDL hydrophone Fig. 3. Integration of the AMARs and hyd

East and (bottom) DDL nodes of the VENUS

This extremely low-power large number of interfaces lity, while delivering long e environmental conditions.

including input channel control, onboard sensor

ltage, and humidity), vices, and duty-cycling of ecording schedule.

AND CONFIGURATION drophone array within each

our M8E-51C-35dB current Technologies Inc.) and a

board. The hydrophones are channel ADC to ensure

ydrophones have a low nd the AMAR circuit board at 1.7 Hz. The digital filter

h of 0.49 of the sample rate nfigured with 0 dB of gain

noise floor of the system is kHz. The sensitivity of the converter board is -165 dB

st AMAR are connected to 4–20 mA current loop quad for the turbidity sensor are MAR board to the required of the daughter card. The

ard buffer the outputs from sensors. The outputs of the

drophone arrays on the (top) SoGS Ocean Observatory.

temperature and depth sensors are converted tthe 4–20 mA current loop receivers on the daAMAR boards on each node monitor their heavoltage, supply current, humidity, etc.) with connected to the low speed ADCs. The onboaalso monitored. The sensors with digital ouCTD) are connected through the AMAR boaRS-485 serial ports.

The AMARs were built and the sensors wJASCO in Dartmouth, NS. The tetrahedrasupporting ONCCEE Interface Module (OIMthe ONC Innovation Centre. These twointegrated and tested at the University of VTechnology Centre in Saanich, BC. Once systems were loaded aboard the CCGS Johtransported to the deployment site. They werthe shipboard crane and a remotely operated ve

V. DATA TRANSMISSION AND PRO

All data collected by the AMARs on the Vstreamed to shore stations where they are coand CSV files and transferred to the Management and Archiving System (DMASDMAS are made publicly accessible through archive web interface (http://venus.uvic.ca/daand sent to the JASCO Victoria office foranalysis (Fig. 4).

JASCO receives the acoustic data in 5typical latency between 45 and 90 min due to VENUS cabled observatory. The incomautomatically processed in near real-time to dmarine mammal calls, localize and track thvocal animals, and analyze the ambient noise.

A. Detection and Classification of Marine MaAutomated algorithms to detect and

Fig. 4. Data processing workflow for the AMARs onObservatory nodes.

Fig. 5. Detection and classification of killer wAMARs on the VENUS Ocean Obsersegmentation process. Dark blue lines intransient events. (Bottom) Results of the cspecies identified by the random forest clasindicate the confidence of the classification r

to voltages using aughter card. The alth status (supply

onboard sensors ard temperature is utputs (RPY and ard’s RS-232 and

were integrated by al frames and a M) were built by o systems were Victoria’s Marine

tested, the two hn P. Tully and re deployed with ehicle.

OCESSING VENUS nodes are

nverted to WAV VENUS Data

). Data from the the VENUS data ata/data-archive/) r processing and

5 min files with constraints of the ming data are etect and classify he movement of

ammal Calls classify marine

mammal calls have been develocontinuously on the acoustic data cthe VENUS nodes. The detection execution of several detectors on tCurrently, a humpback whale (Megkiller whale (Orcinus orca) call deteand is being tested. Its process spectrogram is normalized from 50 window normalizer to attenuate vessels and increase the signal-ttransients [1]. The spectrogram is selocal variance of energy values onsize 0.1 s × 100 Hz. Transients arspectrogram with a local variance defined threshold. Each detected trarepresented by a set of 40 featuresto) the frequency and time confrequency modulation index, and time and frequency envelopes. Eapresented to a three-class randomdetermine if the detection corresponhumpback whale call, or to noise detections are sent by email to anclassification results. False alarclassified correctly) are added to dataset to continually improve classifier.

B. Localization of Marine MammaFor each call detected by th

azimuth and elevation is evaluatedtime-difference-of-arrival [3]. An eof azimuths in the time domain osided dolphin (Lagenorhynchus obl

n the VENUS Ocean

whale calls recorded by one of the rvatory. (Top) Results of the dicate the edge of the detected classification. Colors indicate the ssifier. Numbers above the boxes results in percent.

oped and are being run ollected by the AMARs on processing allows parallel

the incoming AMAR data. gaptera novaeangliae) and ector has been implemented

is as follows. First the Hz to 15 kHz using a split-long tones generated by

to-noise ratio of acoustic egmented by calculating the n a 2-dimensional kernel of re defined by areas of the greater than an empirically

ansient in the spectrogram is s including (but not limited ncentration, amplitude and

inter-quartile range of the ach set of features is then m forest classifier [2] to nds to a killer whale call, a (Fig. 5). Spectrograms of

n analyst who reviews the rms (i.e., detections not

the classification training the performance of the

l Calls e hydrophone arrays, the d by maximum likelihood

example of the distribution obtained for Pacific white-iquidens) whistles recorded

at the SoG East node is shown in Fig. 6. Thisobtained using a sliding time window 60 s in lby 30 s. The resolution in the azimuth domsources were detected and their angular evaluated. From these data, one can extract thewhich is unavailable from single-channel seninformation, the number of sources can be edensity can be estimated with accuracy.

VI. DISCUSSION AND CONCLU

Noise from an inductor in a power supplyelectronics, nearby sensors, or the VENequipment is easily detected above the AMAnoise floor. Noise from a power supply ininside the ONCCEE electronics pressure housAMAR with self-noise during the initial deplnoise problem has been resolved. Any and AMAR sources of audio noise that can be trathe water or through the hydrophone frameeliminated. Similarly, significant efforts went ielectrical noise was not received or induced into the AMAR hydrophone preamplifiers. removed from the DDL node AMAR transmissions as a potential source of interfThese efforts resulted in an extremely low sfloor. Since the deployment of the AMAR 2014, many acoustic events have been recvessel passages, killer whale calls, and Pacdolphin calls and high-frequency clicks (Fig. 7

JASCO and ONC plan to integrate the archiving, DCLT processing, and display coAMARs on VENUS into a near real-time system. Although it would be technically psuch a system real-time, it is anticipated that 5 min may be required due to operational coVENUS system. This 5 min delay from sensobe perhaps inconvenient when trying to cooobservations from other sensing systems. JAhope to promulgate near real-time marine output information before the end of the projto make the AMARs on VENUS detection ainformation available to the VENUS user conear future.

A number of experiments are planned to va

Fig. 6. Distribution of azimuths of Pacific white-sidrecorded by the AMAR hydrophone array on the SoVENUS Ocean Observatory, March 11, 2014.

s distribution was ength overlapped main is 1°. Five

positions were e source location, nsors. Using this

evaluated and the

SION y for the AMAR NUS electronics AR sensors’ self-nductor vibrating sing swamped the loyment, but this all VENUS and

ansmitted through e were carefully into ensuring that n power supplied The CTD was to remove its

fering self-noise. system self-noise arrays in March

corded including cific white-sided

7).

data acquisition, omponents of the

ocean observing possible to make a data latency of onstraints on the or to display will ordinate and fuse ASCO and ONC mammal DCLT ect. The intent is and classification ommunity in the

alidate the DCLT

algorithms, and efforts are planned tthe ambient noise environment, resmammals, and the effects of anthropSea. It is the intent of the research tdensity using the AMARs on Vdirectional and tracking capabilities.

JASCO and ONC are keen to eexploiting the AMARs on VENUS other research purposes.

The AMAR has been provdeployments as an efficient and csensor data recorder. The AMARs the AMAR is also capable of mstreaming in a cabled ocean obobserving capability of the AMAR band cabled and wireless telemcontinuous raw sensor data to dynamic range of the AMAR is a sigthe existing VENUS hydrophones anoise studies in the major shippinglocated.

ded dolphin whistlesoG East node of the

Fig. 7. Acoustic events recorded by the AVENUS Ocean Observatory. (Top) Vessel p14, 2014. (Middle) Pacific white-sided doclicks at the DDL node, March 11, 2014. (Bothe SoG East node, June 17, 2014.

to extract information about ident and transitory marine pogenic noise on the Salish team to examine population

VENUS with their unique .

engage other researchers in and their collected data for

ven through hundreds of capable autonomous multi-on VENUS project proves

multi-sensor real-time data bservatory. This real-time can be coupled with wide-

metry systems to provide users and scientists. The gnificant improvement over and now allows for ambient route in which VENUS is

AMAR hydrophone arrays on the passage at the SoG East node, July olphin whistles and echolocation ottom) Killer whale pulsed calls at

The AMAR also brings high-frequency acoustic array capabilities to VENUS for the first time. Initial algorithm development and data analysis indicate that AMARs can provide valuable detection, classification, localization, and tracking information in real-time. It is anticipated that significant improvements in population density estimation can be realized via the AMAR-based hydrophone arrays.

The two AMARs deployed on the VENUS Ocean Observatory demonstrate that the AMAR can act as a hub for mini ocean observatories, capturing and transmitting both acoustic and non-acoustic sensor data in real-time. The AMAR is a potential technical solution for real-time multi-sensor ocean observing and is especially applicable to small arrays of acoustic sensors. The AMARs on VENUS enable researchers to examine a diverse and continuous dataset from a unique environment and to collaborate in related research initiatives.

ACKNOWLEDGMENT The authors are grateful to the ONC scientific team and the

crew of the CCGS John P. Tully for deploying the AMAR hydrophone arrays at the SoG East and DDL nodes. John Dorocic and Marlene Jeffries from ONC provided support for accessing and transferring data from the DMAS to the JASCO Victoria office. Many thanks to the JASCO engineers and technicians who helped develop and execute this project. Thanks to Karen Hiltz and Nicole Chorney for the editorial review of this paper.

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