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/content/asa/journal/jasa/135/3/10.1121/1.4864792
1.
1. M. Siderius, C. H. Harrison, and M. B. Porter, “ A passive fathometer technique for imaging seabed layering using ambient noise,” J. Acoust. Soc. Am. 120, 13151323 (2006).
http://dx.doi.org/10.1121/1.2227371
2.
2. C. H. Harrison and D. G. Simons, “ Geoacoustic inversion of ambient noise: A simple method,” J. Acoust. Soc. Am. 112, 13771389 (2002).
http://dx.doi.org/10.1121/1.1506365
3.
3. D. L. Donoho, “ Compressed sensing,” IEEE Trans. Inf. Theory 52, 12891306 (2006).
http://dx.doi.org/10.1109/TIT.2006.871582
4.
4. E. J. Candès, J. Romberg, and T. Tao, “ Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489509 (2006).
http://dx.doi.org/10.1109/TIT.2005.862083
5.
5. M. Lustig, D. Donoho, and J. M. Pauly, “ Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magn. Reson. Med. 58, 11821195 (2007).
http://dx.doi.org/10.1002/mrm.21391
6.
6. R. G. Baraniuk, E. Candes, M. Elad, and Y. Ma, “ Applications of sparse representation and compressive sensing,” Proc. IEEE 98, 906909 (2010).
http://dx.doi.org/10.1109/JPROC.2010.2047424
7.
7. M. Elad, Sparse and Redundant Representations (Springer, New York, 2010), pp. 1376.
8.
8. H. Yao, P. Gerstoft, P. M. Shearer, and C. F. Mecklenbräuker, “ Compressive sensing of the Tohoku–Oki Mw 9.0 earthquake: Frequency-dependent rupture modes,” Geophys. Res. Lett. 38, L20310, doi:10.1029/2011GL049223 (2011).
http://dx.doi.org/10.1029/2011GL049223
9.
9. N. R. Chapman and I. Barrodale, “ Deconvolution of marine seismic data using the ℓ1 norm,” Geophys. J. Int. 72, 93100 (1983).
http://dx.doi.org/10.1111/j.1365-246X.1983.tb02806.x
10.
10. F. Fazel, M. Fazel, and M. Stojanovic, “ Random access compressed sensing for energy- efficient underwater sensor networks,” IEEE J. Sel. Areas Commun. 29, 16601670 (2011).
http://dx.doi.org/10.1109/JSAC.2011.110915
11.
11. W. U. Bajwa, J. Haupt, A. M. Sayeed, and R. Nowak, “ Compressed channel sensing: A new approach to estimating sparse multipath channels,” Proc. IEEE 98, 10581076 (2010).
http://dx.doi.org/10.1109/JPROC.2010.2042415
12.
12. C. R. Berger, S. Zhou, J. C. Preisig, and P. Willett, “ Sparse channel estimation for multicarrier underwater acoustic communication: From subspace methods to compressed sensing,” IEEE Trans. Signal Process. 58, 17081721 (2010).
http://dx.doi.org/10.1109/TSP.2009.2038424
13.
13. G. F. Edelmann and C. F. Gaumond, “ Beamforming using compressive sensing,” J Acoust. Soc. Am. 130, EL232EL237 (2011).
http://dx.doi.org/10.1121/1.3632046
14.
14. W. Mantzel, J. Romberg, and K. Sabra, “ Compressive matched-field processing,” J. Acoust. Soc. Am. 132, 90102 (2012).
http://dx.doi.org/10.1121/1.4728224
15.
15. D. Malioutov, M. Çetin, and A. S. Willsky, “ A sparse signal reconstruction perspective for source localization with sensor arrays,” IEEE Trans. Signal Process. 53, 30103022 (2005).
http://dx.doi.org/10.1109/TSP.2005.850882
16.
16. E. J. Candès and M. B. Wakin, “ An introduction to compressive sampling,” IEEE Signal Process. Mag. 25, 2130 (2008).
http://dx.doi.org/10.1109/MSP.2007.914731
17.
17. C. H. Harrison and M. Siderius, “ Bottom profiling by correlating beam-steered noise sequences,” J. Acoust. Soc. Am. 123, 12821296 (2008).
http://dx.doi.org/10.1121/1.2835416
18.
18. P. Gerstoft, W. S. Hodgkiss, M. Siderius, C.-F. Huang, and C. H. Harrison, “ Passive fathometer processing,” J. Acoust. Soc. Am. 123, 12971305 (2008).
http://dx.doi.org/10.1121/1.2831930
19.
19. M. Siderius, H. Song, P. Gerstoft, W. S. Hodgkiss, P. Hursky, and C. Harrison, “ Adaptive passive fathometer processing,” J. Acoust. Soc. Am. 127, 21932200 (2010).
http://dx.doi.org/10.1121/1.3303985
20.
20. J. Traer, P. Gerstoft, and W. S. Hodgkiss, “ Ocean bottom profiling with ambient noise: A model for the passive fathometer,” J. Acoust. Soc. Am. 129, 18251836 (2011).
http://dx.doi.org/10.1121/1.3552871
21.
21. Z.-H. Michalopoulou, C. Yardim, and P. Gerstoft, “ Particle filtering for passive fathometer tracking,” J. Acoust. Soc. Am. 131, EL74EL80 (2012).
http://dx.doi.org/10.1121/1.3670004
22.
22. C. H. Harrison, “ Sub-bottom profiling using ocean ambient noise,” J. Acoust. Soc. Am. 115, 15051515 (2004).
http://dx.doi.org/10.1121/1.1645854
23.
23. M. Siderius and C. Harrison, “ High-frequency geoacoustic inversion of ambient noise data using short arrays,” AIP Conf. Proc. 728, 2231 (2004).
http://dx.doi.org/10.1063/1.1842993
24.
24. J. I. Arvelo, Jr., “ Robustness and constraints of ambient noise inversion,” J. Acoust. Soc. Am. 123, 679686 (2008).
http://dx.doi.org/10.1121/1.2828205
25.
25. J. E. Quijano, S. E. Dosso, J. Dettmer, L. M. Zurk, M. Siderius, and C. H. Harrison, “ Bayesian geoacoustic inversion using wind-driven ambient noise,” J. Acoust. Soc. Am. 131, 26582667 (2012).
http://dx.doi.org/10.1121/1.3688482
26.
26. J. E. Quijano, S. E. Dosso, J. Dettmer, L. M. Zurk, and M. Siderius, “ Trans-dimensional geoacoustic inversion of wind-driven ambient noise,” J. Acoust. Soc. Am. 133, EL47EL53 (2012).
http://dx.doi.org/10.1121/1.4771975
27.
27. R. G. Baraniuk, “ Compressive sensing,” IEEE Signal Process. Mag. 24, 118121 (2007).
http://dx.doi.org/10.1109/MSP.2007.4286571
28.
28. H. Monajemi, S. Jafarpour, M. Gavish, Stat 330/CME 362 Collaboration, and D. L. Donoho, “ Deterministic matrices matching the compressed sensing phase transitions of Gaussian random matrices,” Proc. Natl. Acad. Sci. U.S.A. 110, 11811186 (2013).
http://dx.doi.org/10.1073/pnas.1219540110
29.
29. S. Boyd and L. Vandenberghe, Convex Optimization (Cambridge University Press, New York, 2004), pp. 1701.
30.
30. W. A. Kuperman and F. Ingenito, “ Spatial correlation of surface generated noise in a stratified ocean,” J. Acoust. Soc. Am. 67, 19881996 (1980).
http://dx.doi.org/10.1121/1.384439
31.
31. J. Traer and P. Gerstoft, “ Coherent averaging of the passive fathometer response using short correlation time,” J. Acoust. Soc. Am. 130, 36333641 (2011).
http://dx.doi.org/10.1121/1.3654026
32.
32. M. Grant, S. Boyd, and Y. Ye, CVX: Matlab software for disciplined convex programming, http://cvxr.com/cvx (Last viewed October 1, 2013).
33.
33. N. M. Carbone, G. B. Deane, and M. J. Buckingham, “ Estimating the compressional and shear wave speeds of a shallow water seabed from the vertical coherence of ambient noise in the water column,” J. Acoust. Soc. Am. 103, 801813 (1998).
http://dx.doi.org/10.1121/1.421201
34.
34. L. Muzi, M. Siderius, J. Gebbie, and J. Paddock, “ On the use of adaptive beam forming techniques for geoacoustic inversion of marine ambient noise,” in Proceedings of the IEEE OCEANS Conference (Seattle, WA, 2010), pp. 16.
35.
35. H. Schmidt, OASES: Version 3.1, user guide and reference manual, http://acoustics.mit.edu/faculty/henrik/oases.html (Last viewed October 1, 2013).
36.
36. C. H. Harrison, “ Formulas for ambient noise level and coherence,” J. Acoust. Soc. Am. 99, 20552066 (1996).
http://dx.doi.org/10.1121/1.415392
37.
37. C. F. Mecklenbrauker, P. Gerstoft, A. Panahi, and M. Viberg, “ Sequential Bayesian sparse source reconstruction using array data,” IEEE Trans. Signal Process. 61, 63446354 (2013).
http://dx.doi.org/10.1109/TSP.2013.2282919
38.
38. C. W. Holland, “ Mapping seabed variability: Rapid surveying of coastal regions,” J. Acoust. Soc. Am. 119, 13731387 (2006).
http://dx.doi.org/10.1121/1.2161439
39.
39. C. W. Holland, P. L. Nielsen, J. Dettmer, and S. Dosso, “ Resolving meso–scale seabed variability using reflection measurements from an autonomous underwater vehicle,” J. Acoust. Soc. Am. 131, 10661078 (2012).
http://dx.doi.org/10.1121/1.3672696
40.
40. C. W. Holland, “ Coupled scattering and reflection measurements in shallow water,” IEEE J. Ocean. Eng. 27, 454470 (2002).
http://dx.doi.org/10.1109/JOE.2002.1040930
41.
41. C. W. Holland, R. C. Gauss, P. C. Hines, P. Nielsen, J. R. Preston, C. H. Harrison, D. D. Ellis, K. D. LePage, J. Osler, R. W. Nero, D. Hutt, and A. Turgut, “ Boundary characterization experiment series overview,” IEEE J. Ocean. Eng. 30, 784806 (2005).
http://dx.doi.org/10.1109/JOE.2005.862133
42.
42. A. Turgut, “ Inversion of bottom/sub-bottom statistical parameters from acoustic backscatter data,” J. Acoust. Soc. Am. 102, 833852 (1997).
http://dx.doi.org/10.1121/1.419954
43.
43. J. R. Preston, D. D. Ellis, and R. C. Gauss, “ Geoacoustic parameter extraction using reverberation data from the 2000 Boundary Characterization Experiment on the Malta Plateau,” IEEE J. Ocean. Eng. 30, 709732 (2005).
http://dx.doi.org/10.1109/JOE.2005.862130
44.
44. M. R. Fallat, P. L. Nielsen, S. E. Dosso, and M. Siderius, “ Geoacoustic characterization of a range-dependent ocean environment using towed array data,” IEEE J. Ocean. Eng. 30, 198206 (2005).
http://dx.doi.org/10.1109/JOE.2004.838067
45.
45. J. Dettmer, S. E. Dosso, and C. W. Holland, “ Model selection and Bayesian inference for high-resolution seabed reflection inversion,” J. Acoust. Soc. Am. 125, 706716 (2009).
http://dx.doi.org/10.1121/1.3056553
46.
46. P. L. Nielsen and C. Harrison, “ Combined geoacoustic inversion of propagation and reverberation data,” IEEE J. Ocean. Eng. 34, 5162 (2009).
http://dx.doi.org/10.1109/JOE.2008.2008828
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/content/asa/journal/jasa/135/3/10.1121/1.4864792
2014-03-01
2016-08-31

Abstract

Surface generated ambient noise can be used to infer sediment properties. Here, a passive geoacoustic inversion method that uses noise recorded by a drifting vertical array is adopted. The array is steered using beamforming to compute the noise arriving at the array from various directions. This information is used in two different ways: Coherently (cross-correlation of upward/downward propagating noise using a minimum variance distortionless response fathometer), and incoherently (bottom loss vs frequency and angle using a conventional beamformer) to obtain the bottom properties. Compressive sensing is used to invert for the number of sediment layer interfaces and their depths using coherent passive fathometry. Then the incoherent bottom loss estimate is used to refine the sediment thickness, sound speed, density, and attenuation values. Compressive sensing fathometry enables automatic determination of the number of interfaces. It also tightens the sediment thickness priors for the incoherent bottom loss inversion which reduces the search space. The method is demonstrated on drifting array data collected during the Boundary 2003 experiment.

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