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No Access Submitted: 25 March 2019 Accepted: 27 August 2019 Published Online: 30 September 2019

  • Environmental information content of ocean ambient noise

The Journal of the Acoustical Society of America 146, 1824 (2019); https://doi.org/10.1121/1.5126520
Martin Sideriusa)
  • Department of Electrical and Computer Engineering, Portland State University, Portland, Oregon 97201, USA
    • John Gebbie
  • Metron Inc., 2020 SW 4th Avenue, Suite 170, Portland, Oregon 97201, USA
    • more...View Affiliations
    View Contributors
    • Martin Siderius
    • John Gebbie
    ABSTRACT
    In recent years, methods have been developed to estimate a variety of environmental parameters based on measurements of the ocean ambient noise. For example, noise has been used to estimate water depth using the passive fathometer technique and bottom loss estimated and used to invert for seabed parameters. There is also information in the noise about the water column sound speed, volume attenuation, and the sea-state. The Fisher information can be used to quantify the basic information available in the noise measurements and its inverse, the Cramér–Rao lower bound (CRLB), provides the lower limit on the variance of an unbiased estimator of a particular parameter. The CRLB can be used to study the feasibility of various measurement configurations and parameter sensitivities. In this paper, the CRLB is developed for ocean ambient noise and the environmental information contained in the measurements is determined. The CRLBs provide an estimate of the underlying information in the data, however, it is independent of the estimation methodology. This is useful to determine if a given estimation method is reaching the lower bound. Results illustrating the bounds as well as sensitivities and performance of estimators are demonstrated using both simulations and data.

    ACKNOWLEDGMENTS

    The authors would like to gratefully acknowledge support for this research by the Office of Naval Research Ocean Acoustics Program under Grant No. N00014-13-1-0632. We also would like to acknowledge the Marine Physical Laboratory of the Scripps Institution of Oceanography, University of California, San Diego for providing the Noise'09 experimental data.

    REFERENCES

    1. 1. C. H. Harrison and D. G. Simons, “ Geoacoustic inversion of ambient noise: A simple method,” J. Acoust. Soc. Am. 112(4), 1377–1389 (2002). https://doi.org/10.1121/1.1506365, Google ScholarScitation, ISI
    2. 2. 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(3), 1315–1323 (2006). https://doi.org/10.1121/1.2227371, Google ScholarScitation, ISI
    3. 3. M. Siderius, H. C. Song, P. Gerstoft, W. S. Hodgkiss, P. Hursky, and C. H. Harrison, “ Adaptive passive fathometer processing,” J. Acoust. Soc. Am. 127(4), 2193–2200 (2010). https://doi.org/10.1121/1.3303985, Google ScholarScitation, ISI
    4. 4. C. H. Harrison and M. Siderius, “ Bottom profiling by correlating beam-steered noise sequences,” J. Acoust. Soc. Am. 123(3), 1282–1296 (2008). https://doi.org/10.1121/1.2835416, Google ScholarScitation, ISI
    5. 5. P. Gerstoft, W. S. Hodgkiss, M. Siderius, C. F. Huang, and C. H. Harrison, “ Passive fathometer processing,” J. Acoust. Soc. Am. 123(3), 1297–1305 (2008). https://doi.org/10.1121/1.2831930, Google ScholarScitation, ISI
    6. 6. S. Vagle, W. G. Large, and D. M. Farmer, “ An evaluation of the WOTAN technique of inferring oceanic winds from underwater ambient sound,” J. Atmos. Ocean. Tech. 7, 576–595 (1990). https://doi.org/10.1175/1520-0426(1990)007<0576:AEOTWT>2.0.CO;2, Google ScholarCrossref
    7. 7. J. A. Nystuen, “ Rainfall measurements using underwater ambient noise,” J. Acoust. Soc. Am. 79(4), 972–982 (1986). https://doi.org/10.1121/1.393695, Google ScholarScitation, ISI
    8. 8. H. L. Van Trees, Detection, Estimation, and Modulation Theory, Optimum Array Processing ( John Wiley & Sons, New York, 2004). Google Scholar
    9. 9. S. Walker, Y. Caglar, A. Thode, and A. C. Ery, “ Using Fisher information to quantify uncertainty in environmental parameters estimated from correlated ambient noise,” J. Acoust. Soc. Am. 133(4), EL228–EL234 (2013). https://doi.org/10.1121/1.4792836, Google ScholarScitation, ISI
    10. 10. A. B. Baggeroer, W. A. Kuperman, and H. Schmidt, “ Matched field processing: Source localization in correlated noise as an optimum parameter estimation problem,” J. Acoust. Soc. Am. 83(2), 571–587 (1988). https://doi.org/10.1121/1.396151, Google ScholarScitation, ISI
    11. 11. A. B. Baggeroer, “ The stochastic Cramer-Rao bound for source localization and medium tomography using vector sensors,” J. Acoust. Soc. Am. 141(5), 3430–3449 (2017). https://doi.org/10.1121/1.4981398, Google ScholarScitation, ISI
    12. 12. A. B. Baggeroer and H. Schmidt, “ Cramer-Rao bounds for matched field tomography and ocean acoustic tomography,” in Full Field Inversion Methods in Seismo-Acoustic Inversion, edited by O. Diachok , A. Caiti , P. Gerstoft , and H. Schmidt ( Kluwer Academic Publishers, 1995). Google ScholarCrossref
    13. 13. H. Schmidt and A. B. Baggeroer, “ Physics-imposed resolution and robustness issues in seismo-acoustic parameter inversion,” in Full Field Inversion Methods in Seismo-Acoustic Inversion, edited by O. Diachok , A. Caiti , P. Gerstoft , and H. Schmidt ( Kluwer Academic Publishers, 1995). Google ScholarCrossref
    14. 14. L. Muzi, M. Siderius, and C. M. Verlinden, “ Passive bottom reflection-loss estimation using ship noise and a vertical line array,” J. Acoust. Soc. Am. 141(6), 4372–4379 (2017). https://doi.org/10.1121/1.4985122, Google ScholarScitation, ISI
    15. 15. D. Battle, P. Gerstoft, W. A. Kuperman, W. S. Hodgkiss, and M. Siderius, “ Geoacoustic inversion of tow-ship noise via near-field-matched-field processing,” IEEE J. Ocean. Eng. 28(3), 454–467 (2003). https://doi.org/10.1109/JOE.2003.816679, Google ScholarCrossref
    16. 16. 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(4), 2658–2667 (2012). https://doi.org/10.1121/1.3688482, Google ScholarScitation, ISI
    17. 17. R. A. Koch and D. P. Knobles, “ Geoacoustic inversion with ships as sources,” J. Acoust. Soc. Am. 117(2), 626–637 (2005). https://doi.org/10.1121/1.1848175, Google ScholarScitation, ISI
    18. 18. A. Thode, G. L. D'Spain, and W. A. Kuperman, “ Matched-field processing, geoacoustic inversion, and source signature recovery of blue whale vocalizations,” J. Acoust. Soc. Am. 107(3), 1286–1300 (2000). https://doi.org/10.1121/1.428417, Google ScholarScitation, ISI
    19. 19. D. R. Barclay and M. J. Buckingham, “ Depth dependence of wind-driven, broadband ambient noise in the Philippine Sea,” J. Acoust. Soc. Am. 133(1), 62–71 (2013). https://doi.org/10.1121/1.4768885, Google ScholarScitation, ISI
    20. 20. K. G. Sabra, P. Roux, A. M. Thode, G. L. D'Spain, W. S. Hodgkiss, and W. A. Kuperman, “ Using ocean ambient noise for array self-localization and self-synchronization,” IEEE J. Ocean. Eng. 30(2), 338–347 (2005). https://doi.org/10.1109/JOE.2005.850908, Google ScholarCrossref
    21. 21. MATLAB and Signal Processing Toolbox Release 2017b ( The MathWorks, Inc., Natick, Massachusetts). Google Scholar
    22. 22. W. G. Carrara, R. M. Majewski, and R. S. Goodman, Spotlight Synthetic Aperture Radar: Signal Processing Algorithms ( Artech House, Boston, MA, 1995), Appendix D. 2. Google Scholar
    23. 23. E. Brookner, Practical Phased Array Antenna Systems ( Artech House, Boston, MA, 1991). Google Scholar
    24. 24. H. Schmidt, Oases 3.1 User Guide and Reference Manual, Massachusetts Institute of Technology, Cambridge, MA, http://acoustics.mit.edu/faculty/henrik/oases.html, 2004 (Last viewed August 1, 2019). Google Scholar
    25. 25. W. A. Kuperman and F. Ingenito, “ Spatial correlation of surface generated noise in a stratified ocean,” J. Acoust. Soc. Am. 67(6), 1988–1996 (1980). https://doi.org/10.1121/1.384439, Google ScholarScitation, ISI
    26. 26. F. B. Jensen, W. A. Kuperman, M. B. Porter, and H. Schmidt, Computational Ocean Acoustics ( Springer Science & Business Media, Berlin, 2011). Google ScholarCrossref
    27. 27. V. Young, “ Using a vertical line array and ambient noise to obtain measurements of seafloor reflection loss,” Centre for Maritime Research and Experimentation Report SR-410 (2005). Google Scholar
    28. 28. J. I. Arvelo, Jr. “ Robustness and constraints of ambient noise inversion,” J. Acoust. Soc. Am. 123(2), 679–686 (2008). https://doi.org/10.1121/1.2828205, Google ScholarScitation, ISI
    29. 29. L. Muzi, M. Siderius, and J. Gebbie, “ An analysis of beamforming algorithms for passive bottom reflection-loss estimation,” J. Acoust. Soc. Am. 144(5), 3046–3054 (2018). https://doi.org/10.1121/1.5080258, Google ScholarScitation, ISI
    30. 30. B. F. Cron and C. H. Sherman, “ Spatial-correlation functions for various noise models,” J. Acoust. Soc. Am. 34, 1732–1736 (1962). https://doi.org/10.1121/1.1909110, Google ScholarScitation, ISI
    31. 31. B. F. Cron and C. H. Sherman, “ Addendum: Spatial correlation functions for various noise models [J. Acoust. Soc. Am. 34, 1732–1736 (1962)],” J. Acoust. Soc. Am. 38, 885 (1965). https://doi.org/10.1121/1.1909826, Google ScholarScitation
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