The Digital Grain Size Project: grain size estimates from images of sediment
Transcript of The Digital Grain Size Project: grain size estimates from images of sediment
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The Digital Grain Size Project: grain size estimates from images of sediment
Daniel Buscombe
Grand Canyon Monitoring & Research Center
U.S. Geological Survey, Flagstaff, AZ.
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Collaborators:
Martin Austin, Daniel Conley, Gerd Masselink, Alex Nimmo-Smith (UoP)
Dave Rubin, Jessie Lacy, Jon Warrick, Chris Sherwood, Guy Gelfenbaum, Bruce Jaffe, Curt Storlazzi, Paul Grams, Scott Wright, Ted Melis (USGS)
Ian Miller (Wash. SeaGrant)
Jon Williams (ABPmer)
Dayton Dove (BGS)
Joe Wheaton (USU)
Technical Support: Hank Chezar (USGS) Gerry Hatcher (USGS) Robert Wyland (USGS) Bob Tusso (USGS)
Thanks
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Outline
• Why take pictures of sediment?
• How do you estimate grain size from those images?
• How do you take suitable pictures?
• Software (the Digital Grain Size Project)
• The future
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Why take pictures of sediment?
Huge increase in temporal resolution and/or spatial coverage No physical samples required You can’t always visit your field site
Temporal Resolution
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How do you estimate grain size
from those images?
• Deterministic versus statistical
• Evolution of methods
• Current method
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No 'background' intensity against which to threshold Subjective choice of filter sizes and operation sequences Difficult to design a 'universal‘ algorithm which works equally well Non-diffuse reflectance, particle overlap, marks/scratches,etc
Deterministic
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Statistical – Rubin (2004)
Rubin (2004) J. Sed. Res
Characterize features without directly measuring them Circumnavigate problem of detecting grains
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But reliant on calibration Errors introduced by calibration Buscombe (2008), Sedimentary Geology
Buscombe and Masselink (2009), Sedimentology
Statistical
Could also use spectra, fractals and variograms
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Grain size found as 2pi times typical grain-scale wavenumber
1. Requires neither calibration nor advanced image processing algorithms 2. Direct statistical estimate, grid-by-number style, of mean of all intermediate axes
Buscombe, Rubin & Warrick (2010) Journal of Geophysical Research
Rk 2
Statistical – Buscombe et al (2010)
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•Generic & transferable expressions for particle size mean and standard deviation •No calibration or tunable parameters •Supported using a simple theoretical model
Buscombe & Rubin (2012) Journal of Geophysical Research
Statistical – Buscombe and Rubin (2012)
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Global wavelet power spectrum
Short sequences
Non-stationarity, aperiodic
Non-Gaussian distributions
Statistical – Buscombe (2013)
Buscombe (2013) Sedimentology
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Tested with a wide range of sediments
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Skill Buscombe (2013) Sedimentology
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How do you take suitable pictures?
• Exposed sediment
• Submerged sediment
• Biogenic?
• Mud?
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Praa Sands, UK Dan Conley, Plymouth University
Dave Rubin, USGS
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Praa Sands, UK
Buscombe, Conley, Nimmo-Smith, Rubin (in prep)
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Colorado River in Grand Canyon
Dave Rubin, USGS
Paul Grams, USGS
Ted Melis, USGS
100 microns
300 microns
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Slapton Sands, UK Gerd Masselink, Plymouth University
Buscombe, PhD thesis (2008)
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Strait of Juan de Fuca
Elwha River Dungeness Spit Port Angeles
Jon Warrick, USGS
Ian Miller, UCSC
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How do you take suitable pictures?
• Exposed sediment
• Submerged sediment
• Biogenic?
• Mud?
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Praa Sands, UK A paddle constructed from a dive fin (1) is pushed back and forth by waves, turning a ratcheting speed-reducer in an oil-filled cylinder (2). The rotating output wheel of the speed-reducer (3) pulls down on the chain (4), which raises the video camera (5). When the chain on the wheel (3) passes its the lowest position, the ratchet allows the camera to fall to the bed …
… and a tilt sensor turns on a battery-powered video camera (5) and solid-state recorder (6) to collect a video
Buscombe et al (2014), Limnology & Oceanography Methods
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Gra
in s
ize
(mm
)
Grain size (mm)
Inverse relationship between flow speed and bed grain size • Weak flow, preferential selection of fines, leaving coarse lag • Stronger flow, more equal mobilisation, lag appears finer
Bottom orbital velocity
Praa Sands, UK Buscombe, Conley, Nimmo-Smith, Rubin (in prep)
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Decreasing vertical gradient with increasing shear (less selective resuspension with increasing shear)
Buscombe, Conley, Nimmo-Smith, Rubin (in prep)
Praa Sands, UK
Image from holographic camera
High energy
Low energy
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The Santa Cruz Seafloor Observatory Dave Rubin, USGS
Jessie Lacy, USGS
Curt Storlazzi, USGS
Chris Sherwood, USGS
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The Santa Cruz Seafloor Observatory
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The Santa Cruz Seafloor Observatory
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The Santa Cruz Seafloor Observatory
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The Santa Cruz Seafloor Observatory
Buscombe et al (2014), Limnology & Oceanography Methods
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Colorado River in Grand Canyon Rubin et al (2007), Sedimentary Geology
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Bars: Eddies: Channel:
> 0.5 mm ~0.45 mm < 0.4mm
~500 m
Lower Marble Canyon, 2009-12
Above LCR confluence, 2009 – 2014:
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UK Continental Shelf Dayton Dove, BGS
Images courtesy of British Geological Survey
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The Digital Grain Size Project
Matlab tools Python tools Web/cloud application
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Resolution
Matlab tools https://github.com/dbuscombe-usgs/DGS
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Resolution
Image courtesy of British Geological Survey
Matlab tools https://github.com/dbuscombe-usgs/DGS
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Cropping
Image courtesy of British Geological Survey
Matlab tools https://github.com/dbuscombe-usgs/DGS
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Cropping
Image courtesy of British Geological Survey
Matlab tools https://github.com/dbuscombe-usgs/DGS
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Matlab tools https://github.com/dbuscombe-usgs/DGS
Select/cycle through images
Set resolution Save results
Calculate Select ROI
Filter
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IMG1931
Mean = 7.7 pixels Median = 7.22 D75-D25 = 13.67 Skewness = 0.17
Image courtesy of British Geological Survey
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IMG2008
Mean = 18.02 pixels Median = 17.1 D75-D25 = 27.59 Skewness = 0.1
Image courtesy of British Geological Survey
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IMG2016
Mean = 20.4 pixels Median = 20.18 D75-D25 = 28.97 Skewness = 0.07
Image courtesy of British Geological Survey
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IMG1936
Mean = 24.6 pixels Median = 24.26 D75-D25 = 30.77 Skewness = 0.04
Image courtesy of British Geological Survey
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pip install pyDGS
git clone https://github.com/dbuscombe-usgs/pyDGS.git
python setup.py install
import DGS
density = 10 # process every 10 lines
res = 0.01 # mm/pixel
doplot = 0 # don't make plots
image_folder = '/home/sed_images'
DGS.dgs(image_folder,density,doplot,res)
image_file = '/home/sed_images/my_image.png'
mnsz, srt, sk, kurt, pd = DGS.dgs(image_file,density,doplot,res)
Python tools https://github.com/dbuscombe-usgs/pyDGS
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Used by (at least) 47 institutions in 12 countries US Geological Survey, USA
Dept. of Ecology, State of Washington, USA
Northwest Hydraulic Consultants, Canada
Northern Arizona University, USA
Dartmouth College, USA
Johns Hopkins University, USA
University of California Santa Cruz, USA
Franklin and Marshall College, USA
University of California Los Angeles, USA
Utah State University, USA
Southwest Research Institute, Boulder, USA
Universidad EAFIT, Colombia
University of Washington, USA
Oregon State University, USA
University of California Davis, USA
University of Pennsylvania, USA
Brigham Young University, USA
University of Calgary, Canada
University of Texas at Austin, USA
Geoengineers Inc. USA
University of Delaware, USA
Western Washington University, USA
River Design Group Inc., USA
GMA Hydrology Inc. USA
Iowa State University, USA
U.S. Forest Service, USA
Queens University Belfast, UK
Freie Universitat Berlin, Germany
Instituto Superior Technico, Portugal
Plymouth University, UK
Institut de Physique du Globe du Paris, France
Deltares, the Netherlands
Imperial College London, UK
Durham University, UK
Technical University Delft, the Netherlands
University of Queensland, Australia
University of Sydney, Australia
University of Auckland, New Zealand
Tsinghua University, China
Zhejiang University, China
University of Liverpool, UK
Centre Européen de Recherche et d'Enseignement des Géosciences de l'Environnement, France
Heriot-Watt University, UK
Instituto de Ciencias Agrarias, Spain
Université de Caen Basse Normandie, France
British Geological Survey, UK
University of Leicester, UK
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What’s next?
Digital Grain Size in the web browser?
Current USGS-CDI proposal in review
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What’s next?
Images courtesy of Gary Barton,USGS Idaho Water Science Center Glen Canyon, AZ Dec 2014
mixed sand/gravel/veg
Areal coverage of sediment types?
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Image courtesy Raleigh Martin, UCLA
Image courtesy Jon Warrick, USGS
Areal map of sediment sizes?
Size in pixels
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Thanks for listening
• Python: https://pypi.python.org/pypi/pyDGS
pip install pyDGS
https://github.com/dbuscombe-usgs/pyDGS
python setup.py install
• Matlab: https://github.com/dbuscombe-usgs/DGS
• Web application … watch this space
Daniel Buscombe
Grand Canyon Monitoring & Research Center
U.S. Geological Survey, Flagstaff, AZ.