Presented to Coastal and Marine Geology Program, USGS, Feb 3rd 2015

1. 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.
dbuscombe@usgs.gov

2. 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

3. 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

4. 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

5. How do you estimate grain size
from those images?
• Deterministic versus statistical
• Evolution of methods
• Current method

6. 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

7. Statistical – Rubin (2004)
Rubin (2004) J. Sed. Res
Characterize features without directly
measuring them
Circumnavigate problem of detecting grains

8. 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

9. 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)

10. •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)

11. Global wavelet power
spectrum
Short sequences
Non-stationarity, aperiodic
Non-Gaussian distributions
Statistical – Buscombe (2013)
Buscombe (2013) Sedimentology

12. Tested with a wide range of sediments

13. Skill
Buscombe (2013) Sedimentology

14. How do you take suitable pictures?
• Exposed sediment
• Submerged sediment
• Biogenic?
• Mud?

15. Praa Sands, UK Dan Conley, Plymouth University
Dave Rubin, USGS

16. Praa Sands, UK
Buscombe, Conley, Nimmo-Smith, Rubin (in prep)

17. Colorado River in Grand Canyon
Dave Rubin, USGS
Paul Grams, USGS
Ted Melis, USGS
100 microns
300 microns

18. Slapton Sands, UK Gerd Masselink, Plymouth University
Buscombe, PhD thesis (2008)

19. Strait of Juan de Fuca
Elwha River
Dungeness SpitPort Angeles
Jon Warrick, USGS
Ian Miller, UCSC

20. How do you take suitable pictures?
• Exposed sediment
• Submerged sediment
• Biogenic?
• Mud?

21. 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

22. Grainsize(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)

23. 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

24. The Santa Cruz Seafloor Observatory
Dave Rubin, USGS
Jessie Lacy, USGS
Curt Storlazzi, USGS
Chris Sherwood, USGS

25. The Santa Cruz Seafloor Observatory

26. The Santa Cruz Seafloor Observatory

27. The Santa Cruz Seafloor Observatory

28. The Santa Cruz Seafloor Observatory
Buscombe et al (2014), Limnology & Oceanography
Methods

29. Colorado River in Grand Canyon
Rubin et al (2007),
Sedimentary Geology

30. Bars: Eddies: Channel:
> 0.5 mm~0.45 mm< 0.4mm
~500 m
Lower Marble
Canyon, 2009-12
Above LCR confluence, 2009 – 2014:

31. UK Continental Shelf Dayton Dove, BGS
Images courtesy of British Geological Survey

32. The Digital Grain Size Project
Matlab tools Python tools Web/cloud application

33. Resolution
Matlab tools https://github.com/dbuscombe-usgs/DGS

34. Resolution
Image courtesy of British Geological Survey
Matlab tools https://github.com/dbuscombe-usgs/DGS

35. Cropping
Image courtesy of British Geological Survey
Matlab tools https://github.com/dbuscombe-usgs/DGS

36. Cropping
Image courtesy of British Geological Survey
Matlab tools https://github.com/dbuscombe-usgs/DGS

37. Matlab tools https://github.com/dbuscombe-usgs/DGS
Select/cycle through images
Set resolution Save results
CalculateSelect ROI
Filter

38. IMG1931
Mean = 7.7 pixels
Median = 7.22
D75-D25 = 13.67
Skewness = 0.17
Image courtesy of British Geological Survey

39. IMG2008
Mean = 18.02 pixels
Median = 17.1
D75-D25 = 27.59
Skewness = 0.1
Image courtesy of British Geological Survey

40. IMG2016
Mean = 20.4 pixels
Median = 20.18
D75-D25 = 28.97
Skewness = 0.07
Image courtesy of British Geological Survey

41. IMG1936
Mean = 24.6 pixels
Median = 24.26
D75-D25 = 30.77
Skewness = 0.04
Image courtesy of British Geological Survey

42. 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

43. 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

44. What’s next?
Digital Grain Size in the web browser?
Current USGS-CDI proposal in review

45. 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?

46. Image courtesy Raleigh Martin, UCLA
Image courtesy Jon Warrick, USGS
Areal map of sediment sizes?
Size in pixels

47. 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.
dbuscombe@usgs.gov