Acoustic and topographic sediment classification using high-resolution multibeam echosounder: examples from the Colorado River in Marble Canyon

2nd Multibeam in Rivers Workshop, March 2015

Transcript

2. Multibeam echosounder measures depth & echo strengths High-resolution soundings ... Submerged aquatic vegetation Definition of physical habitats Bed substrate classification Appeal of backscatter = hardness + roughness (?) Topography, Acoustic Backscatter, or both? Bathymetry at RM 61, Marble Canyon, May 2014
3. Multibeam echosounder measures depth & echo strengths High-resolution soundings ... Submerged aquatic vegetation Definition of physical habitats Bed substrate classification Appeal of backscatter = hardness + roughness (?) Topography, Acoustic Backscatter, or both?
4. Multibeam echosounder measures depth & echo strengths High-resolution soundings ... Submerged aquatic vegetation Definition of physical habitats Bed substrate classification Appeal of backscatter = hardness + roughness (?) Topography, Acoustic Backscatter, or both?
5. Multibeam echosounder measures depth & echo strengths High-resolution soundings ... Submerged aquatic vegetation Definition of physical habitats Bed substrate classification Appeal of backscatter = hardness + roughness (?) Topography, Acoustic Backscatter, or both?
6. RM30, Marble Canyon, August 2013 11 million soundings 25 cm grid
7. RM30, Marble Canyon, August 2013 11 million soundings 25 cm grid
8. Median Backscatter
9. Stochastic geometry. 1) Spectral Strength P1(K) = ω1 (h0|K|)γ1 measure of power at low frequencies
10. Stochastic geometry. 2) Spectral Width P1(K) = ω1 (h0|K|) γ1 measure of the rate of decay in power as a function of increasing frequency. The range of frequencies necessary to describe the data
11. Stochastic geometry. 3) Spectral Variance σ2 1 = 2 K0 P1(K)dK overall power in the spectrum over all frequencies
12. Sediment Classification using Backscatter
13. Sediment Classification using Backscatter
14. Sediment Classification using Backscatter
15. Summary so far ... Power law: no significant concentration of spectral power in any particular frequency band Slopes range between -0.5 and -3.5. The scaling relationship between amplitude and wavelength does not hold over all wavelengths Rate at which backscatter amplitude declines relative to wavelength is smaller for hard, rough surfaces compared with relatively smooth and soft surfaces. Hypothesis 1: Backscatter responds to grain size which is more rapidly varying than surface topography. Hypothesis 2: These measures of backscatter respond to both grain size and surface topography.
16. Summary so far ... Power law: no significant concentration of spectral power in any particular frequency band Slopes range between -0.5 and -3.5. The scaling relationship between amplitude and wavelength does not hold over all wavelengths Rate at which backscatter amplitude declines relative to wavelength is smaller for hard, rough surfaces compared with relatively smooth and soft surfaces. Hypothesis 1: Backscatter responds to grain size which is more rapidly varying than surface topography. Hypothesis 2: These measures of backscatter respond to both grain size and surface topography.
17. Summary so far ... Power law: no significant concentration of spectral power in any particular frequency band Slopes range between -0.5 and -3.5. The scaling relationship between amplitude and wavelength does not hold over all wavelengths Rate at which backscatter amplitude declines relative to wavelength is smaller for hard, rough surfaces compared with relatively smooth and soft surfaces. Hypothesis 1: Backscatter responds to grain size which is more rapidly varying than surface topography. Hypothesis 2: These measures of backscatter respond to both grain size and surface topography.
18. Summary so far ... Power law: no significant concentration of spectral power in any particular frequency band Slopes range between -0.5 and -3.5. The scaling relationship between amplitude and wavelength does not hold over all wavelengths Rate at which backscatter amplitude declines relative to wavelength is smaller for hard, rough surfaces compared with relatively smooth and soft surfaces. Hypothesis 1: Backscatter responds to grain size which is more rapidly varying than surface topography. Hypothesis 2: These measures of backscatter respond to both grain size and surface topography.
19. Rest of this talk What’s the relationship between stochastic and deterministic measures of backscatter and topography? What’s their relationship to sediment type? Can we use this information to get at what scaling relationships exist between amplitude and lengthscales of riverbed patchiness?
20. Topography: deterministic geometry (amplitudes) Standard deviation of locally detrended elevations Well known to be a function of window size
21. RM30, Marble Canyon, May 2012
22. Classification using Backscatter and/or Topography
23. Classification using Backscatter and/or Topography
24. Classification using Backscatter and/or Topography
25. Topography power spectrum: ‘global’ versus ‘local’ detrend
26. Stochastic geometry. 1) Spectral Strength P1(K) = ω1 (h0|K|)γ1
27. Stochastic geometry. 2) Spectral Width P1(K) = ω1 (h0|K|) γ1
28. Stochastic geometry. 3) Spectral Variance σ2 1 = 2 K0 P1(K)dK
29. RM30, Aug. 2013. Wentworth sediment type
30. Relationship between sediment type & ‘roughness’ Variance in fluctuating part of both the topographic and backscatter signal increase with increasing clast size
31. Relationship between sediment type & spectral strength Low-frequency component increases with increasing clast size Strong relationships with roughness
32. Linear relationships on a continuum
33. Classification using Backscatter and/or Topography
34. Choice of detrending technique Pollyea and Fairley, 2011. Geology 39, 623 - 626. ODR - any benefit of smaller residuals?
35. Summary High-resolution MBES data from a non-cohesive riverbed Issue will be how to estimate sediment type when topography and backscatter are unrelated? Is it a problem? Lots of options for acoustic bed sediment classification Relative proportions of sand and gravel in mixtures? How would this change with silt/clay, or vegetated bottoms ?
36. Summary High-resolution MBES data from a non-cohesive riverbed Issue will be how to estimate sediment type when topography and backscatter are unrelated? Is it a problem? Lots of options for acoustic bed sediment classification Relative proportions of sand and gravel in mixtures? How would this change with silt/clay, or vegetated bottoms ?
37. Summary High-resolution MBES data from a non-cohesive riverbed Issue will be how to estimate sediment type when topography and backscatter are unrelated? Is it a problem? Lots of options for acoustic bed sediment classification Relative proportions of sand and gravel in mixtures? How would this change with silt/clay, or vegetated bottoms ?
38. Summary High-resolution MBES data from a non-cohesive riverbed Issue will be how to estimate sediment type when topography and backscatter are unrelated? Is it a problem? Lots of options for acoustic bed sediment classification Relative proportions of sand and gravel in mixtures? How would this change with silt/clay, or vegetated bottoms ? Sandy gravels in Glen Canyon, Dec 2014
39. Summary High-resolution MBES data from a non-cohesive riverbed Issue will be how to estimate sediment type when topography and backscatter are unrelated? Is it a problem? Lots of options for acoustic bed sediment classification Relative proportions of sand and gravel in mixtures? How would this change with silt/clay, or vegetated bottoms ?
40. Last word. Sand grain size and acoustic roughness
41. Thanks for listening