Moment-based method to statistically categorize rock outcrops based on their topographical features

Gauss, Roger C.; Fialkowski, Joseph M.; Calvo, David; Menis, Richard; Olson, Derek R.; Lyons, Anthony P.

doi:10.23919/oceans.2015.7401845

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…a spatial map of measured backscattering) is typically characterized by a random process 7 . There are a variety of metrics, or features, that can be used to describe this random process, including the autocorrelation function, power spectrum 8 , wavelet decomposition 9 , gray-level co-occurance matrix 6,10 , the mean intensity (scattering cross section) 11,12 and in general, the intensity probability density function (pdf) [13][14][15] .…”

Section: Introductionmentioning

confidence: 99%

Comparison of Model Selection Techniques for Seafloor Scattering Statistics

OLSON,

GEILHUFE

2023

Synthetic Aperture Sonar and Synthetic Aperture Radar 2023

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Section: Introductionmentioning

confidence: 99%

Comparison of Model Selection Techniques for Seafloor Scattering Statistics

OLSON,

GEILHUFE

2023

Synthetic Aperture Sonar and Synthetic Aperture Radar 2023

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“…For parts of the rock outcrop consisting of fractured surfaces, the image intensity changes rapidly over a variety of scales, and results in a heavytailed distribution. One of these outcrops was studied by (Gauss et al, 2015) in terms of moments of the scattered field and bathymetry; Heavy-tailed statistics were also observed.…”

Section: Introductionmentioning

confidence: 99%

Scattering statistics of rock outcrops: Model-data comparisons and Bayesian inference using mixture distributions

Olson

Lyons

Abraham

et al. 2019

The Journal of the Acoustical Society of America

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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.

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