title: Estimating seabed scattering mechanisms via Bayesian model selection.
abstract: A quantitative inversion procedure is developed and applied to determine the dominant scattering mechanism (surface roughness and/or volume scattering) from seabed scattering-strength data. The classification system is based on trans-dimensional Bayesian inversion with the deviance information criterion used to select the dominant scattering mechanism. Scattering is modeled using first-order perturbation theory as due to one of three mechanisms: Interface scattering from a rough seafloor, volume scattering from a heterogeneous sediment layer, or mixed scattering combining both interface and volume scattering. The classification system is applied to six simulated test cases where it correctly identifies the true dominant scattering mechanism as having greater support from the data in five cases; the remaining case is indecisive. The approach is also applied to measured backscatter-strength data where volume scattering is determined as the dominant scattering mechanism. Comparison of inversion results with core data indicates the method yields both a reasonable volume heterogeneity size distribution and a good estimate of the sub-bottom depths at which scatterers occur.
authors.affiliation: School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 3P6, Canada.
authors.forename: Gavin
authors.initials: G
authors.lastname: Steininger
authors.affiliation: School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 3P6, Canada.
authors.forename: Stan E
authors.initials: SE
authors.lastname: Dosso
authors.affiliation: Applied Research Laboratory, The Pennsylvania State University, State College, Pennsylvania 16804-0030.
authors.forename: Charles W
authors.initials: CW
authors.lastname: Holland
authors.affiliation: School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 3P6, Canada.
authors.forename: Jan
authors.initials: J
authors.lastname: Dettmer