Scattering statistics of rock outcrops: Model-data comparisons and Bayesian inference using mixture distribution
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Authors
Olson, Derek R.
Lyons, Anthony P.
Abraham, Douglas A.
Sæbø, Torstein O.
Subjects
EM algorithm
Probability theory
Sonar
Geophysical techniques
Bayesian inference
Acoustic field
Geoacoustic inversion
Statistical mechanics models
Statistical analysis
Probability theory
Sonar
Geophysical techniques
Bayesian inference
Acoustic field
Geoacoustic inversion
Statistical mechanics models
Statistical analysis
Advisors
Date of Issue
2019
Date
Publisher
Acoustical Society of America (ASA)
Language
Abstract
The probability density function of the acoustic field amplitude scattered by the seafloor was measured in a rocky environment off the coast of Norway using a synthetic aperture sonar system, and is reported here in terms of the probability of false alarm. Interpretation of the measurements focused on finding the appropriate class of statistical models (single versus two-component mixture models), and on appropriate models within these two classes. It was found that two-component mixture models performed better than single models. The two mixture models that performed the best (and had a basis in the physics of scattering) were a mixture between two K distributions, and a mixture between a Rayleigh and generalized Pareto distribution. Bayes’ theorem was used to estimate the probability density function of the mixture model parameters. It was found that the K-K mixture exhibits a significant correlation between its parameters. The mixture between the Rayleigh and generalized Pareto distributions also had a significant parameter correlation, but also contained multiple modes. It is concluded that the mixture between two K distributions is the most applicable to this dataset.
Type
Article
Description
The article of record as published may be found at https://doi.org/10.1121/1.5089892
Series/Report No
Department
Oceanography
Organization
Identifiers
NPS Report Number
Sponsors
This work was supported by the U.S. Office of Naval Research under Grant Nos. N00014-18-WX00776, N00014-16-1-2335, N00014-13-1-0056, and N00014-12-1-0546.
Funder
This work was supported by the U.S. Office of Naval Research under Grant Nos. N00014-18-WX00776, N00014-16-1-2335, N00014-13-1-0056, and N00014-12-1-0546.
Format
14 p.
Citation
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.