5 research outputs found
Scattering statistics of rock outcrops: Model-data comparisons and Bayesian inference using mixture distributions
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 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 significant correlation between its
parameters. The mixture between the Rayleigh and generalized Pareto
distributions also had significant parameter correlation, but also contained
multiple modes. We conclude that the mixture between two K distributions is the
most applicable to this dataset.Comment: 15 pages, 7 figures, Accepted to the Journal of the Acoustical
Society of Americ
Scattering statistics of rock outcrops: Model-data comparisons and Bayesian inference using mixture distribution
The article of record as published may be found at https://doi.org/10.1121/1.5089892The 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.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.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