Hydroacoustic signatures of riverbed sediments using multibeam sonar

Presented at SEDHYD 2015, Reno, Nevada

Transcript

2. Aim: mapping of Colorado River bed sediments. Why? Where is the sand? Habitats Geomorphology
3. Aim: mapping of Colorado River bed sediments. Why? How much sand is there? Budgets Sediment transport models
4. Aim: mapping of Colorado River bed sediments. Why? How does that vary in time? Dam operations Experimental high flows
5. Bed sediments sampled using video ... ... which has revealed enormous bed sediment heterogeneity Fine sand through to boulders Abrupt transitions
6. Riverbed bathymetry is mapped using multibeam ...
7. Objectives. Infer bed sediments using high-frequency backscatter and topography Develop a data-driven approach using patches of known sediment type Has to fit into existing channel mapping protocol
8. We always have topography and backscatter
9. Backscatter to infer grain size
10. Acoustics of a heterogeneous river bed Patchiness, abrupt transitions, & covariation of bedforms with grain size. Backscatter must be considered at smaller spatial scales
11. Acoustics of a heterogeneous river bed
12. Stochastic geometry. 1) Spectral Strength P1(K) = ω1 (h0|K|)γ1 measure of power at low frequencies
13. 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.
14. Stochastic geometry. 3) Spectral Variance σ2 1 = 2 K0 P1(K)dK overall power in the spectrum over all frequencies
15. Sediment Classification using Backscatter
16. Sediment Classification using Backscatter
17. Sediment Classification using Backscatter
18. Sediment Classification using Backscatter
19. RM30, Marble Canyon, May 2012
20. Relationship between sediment type & spectral strength Low-frequency component increases with increasing clast size Strong relationships with roughness
21. Relationship between sediment type & ‘roughness’ Variance in fluctuating part of both the topographic and backscatter signal increase with increasing clast size
22. Classification using Backscatter and/or Topography
23. Summary Hydroacoustic sedment signatures on non-cohesive riverbed Backscatter statistics sensitive, in part, to form roughness Want to formalise relation between backscatter and topography Relative proportions of sand and gravel in mixtures? Silt/clay, or vegetated bottoms?
24. Summary Hydroacoustic sedment signatures on non-cohesive riverbed Backscatter statistics sensitive, in part, to form roughness Want to formalise relation between backscatter and topography Relative proportions of sand and gravel in mixtures? Silt/clay, or vegetated bottoms?
25. Summary Hydroacoustic sedment signatures on non-cohesive riverbed Backscatter statistics sensitive, in part, to form roughness Want to formalise relation between backscatter and topography Relative proportions of sand and gravel in mixtures? Silt/clay, or vegetated bottoms?
26. Summary Hydroacoustic sedment signatures on non-cohesive riverbed Backscatter statistics sensitive, in part, to form roughness Want to formalise relation between backscatter and topography Relative proportions of sand and gravel in mixtures? Silt/clay, or vegetated bottoms? Sandy gravels in Glen Canyon, Dec 2014
27. Summary Hydroacoustic sedment signatures on non-cohesive riverbed Backscatter statistics sensitive, in part, to form roughness Want to formalise relation between backscatter and topography Relative proportions of sand and gravel in mixtures? Silt/clay, or vegetated bottoms?
28. Thanks for listening
29. This slide is intentionally blank.
30. Why not just use high-resolution topography? Want to go sub-’pixel’ Softness/ hardness (habitats, vegetation) Because we always have both
31. Acoustics of a River Bed Fall between two scattering regimes depending on grain size
32. Balancing the acoustic budget Raw echo (what we measure) BS(θc ) = EL(θc ) − SL(θc ) + 2TL(θc ) − 10 log Af (θc ) Source level [MEASURED] Transmission losses [ESTIMATED] True area of beam footprint [ESTIMATED] Amiri-Simkooei et al., Journal of the Acoustic Society of America, 2009
33. Balancing the acoustic budget Raw echo (what we measure) BS(θc ) = EL(θc ) − SL(θc ) + 2TL(θc ) − 10 log Af (θc ) Source level [MEASURED] Transmission losses [ESTIMATED] True area of beam footprint [ESTIMATED] Amiri-Simkooei et al., Journal of the Acoustic Society of America, 2009
34. Balancing the acoustic budget Raw echo (what we measure) BS(θc ) = EL(θc ) − SL(θc ) + 2TL(θc ) − 10 log Af (θc ) Source level [MEASURED] Transmission losses [ESTIMATED] True area of beam footprint [ESTIMATED] Amiri-Simkooei et al., Journal of the Acoustic Society of America, 2009
35. Balancing the acoustic budget Raw echo (what we measure) BS(θc ) = EL(θc ) − SL(θc ) + 2TL(θc ) − 10 log Af (θc ) Source level [MEASURED] Transmission losses [ESTIMATED] True area of beam footprint [ESTIMATED] Amiri-Simkooei et al., Journal of the Acoustic Society of America, 2009
36. Transmission, amplification & sampling BS(θc ) = EL(θc ) − SL(θc ) + 2TL(θc ) − 10 log Af (θc ) Function of: Output power [MEASURED] Input amplification [MEASURED] Ping rate [MEASURED] Pulse length [MEASURED]
37. Water & sediment attenuation BS(θc ) = EL(θc ) − SL(θc ) + 2TL(θc ) − 10 log Af (θc ) Sediment attenuation [ESTIMATED] using Urick (1948): Particle size and concentration [MEASURED] Particle density [ESTIMATED] Homogeneous mixing [ASSUMED] Water attenuation [ESTIMATED] using Fisher & Simmons (1977): Temperature [MEASURED] Salinity & pH [ESTIMATED] Homogeneous mixing [ASSUMED] Fisher and Simmons. Journal of the Acoustic Society of America, 1977. Urick. Journal of the Acoustic Society of America, 1948.
38. Beam footprint BS(θc ) = EL(θc ) − SL(θc ) + 2TL(θc ) − 10 log Af (θc ) Function of: Across- & along-track slopes [ESTIMATED] Angular range [ESTIMATED] Ping rate [MEASURED] Grazing angle (ψc ) [ESTIMATED]
39. Pre-Processing Roll and pitch bias, offset depths Slope ‘spikes’ Remove “rails”
40. Pre-Processing Correct by mean amplitude per grazing angle Outside nadir region, averaging over neighbouring bins Subtracted then value at reference angle (30o) is added back in
41. Software Open-source software MB-system: I/O, cleaning, initial processing GMT: Filtering, gridding Python/Cython for everything else (e.g. spectral analysis, classification, plotting) Efficient. Parallelised where possible. Modular set-up Backscatter needs a community-based open-source tool. Caress and Chayes. Proceedings of the IEEE Oceans 95 Conference, 1995. Caress and Chayes. Marine Geophysical Research, 2006.
42. Software Open-source software MB-system: I/O, cleaning, initial processing GMT: Filtering, gridding Python/Cython for everything else (e.g. spectral analysis, classification, plotting) Efficient. Parallelised where possible. Modular set-up Backscatter needs a community-based open-source tool. Wessel et al. EOS Trans. AGU, 1991, 1995, 1998, 2013
43. Software Open-source software MB-system: I/O, cleaning, initial processing GMT: Filtering, gridding Python/Cython for everything else (e.g. spectral analysis, classification, plotting) Efficient. Parallelised where possible. Modular set-up Backscatter needs a community-based open-source tool.
44. Software Open-source software MB-system: I/O, cleaning, initial processing GMT: Filtering, gridding Python/Cython for everything else (e.g. spectral analysis, classification, plotting) Efficient. Parallelised where possible. Modular set-up Backscatter needs a community-based open-source tool.
45. Software Open-source software MB-system: I/O, cleaning, initial processing GMT: Filtering, gridding Python/Cython for everything else (e.g. spectral analysis, classification, plotting) Efficient. Parallelised where possible. Modular set-up Backscatter needs a community-based open-source tool.