title: Trans-dimensional geoacoustic inversion of wind-driven ambient noise.
abstract: This letter applies trans-dimensional Bayesian geoacoustic inversion to quantify the uncertainty due to model selection when inverting bottom-loss data derived from wind-driven ambient-noise measurements. A partition model is used to represent the seabed, in which the number of layers, their thicknesses, and acoustic parameters are unknowns to be determined from the data. Exploration of the parameter space is implemented using the Metropolis-Hastings algorithm with parallel tempering, whereas jumps between parameterizations are controlled by a reversible-jump Markov chain Monte Carlo algorithm. Sediment uncertainty profiles from inversion of simulated and experimental data are presented.
authors.affiliation: School of Earth and Ocean Sciences, University of Victoria, 3800 Finnerty Road, Victoria, British Columbia V8P 5C2, Canada. jorgeq@uvic.ca
authors.forename: Jorge E
authors.initials: JE
authors.lastname: Quijano
authors.forename: Stan E
authors.initials: SE
authors.lastname: Dosso
authors.forename: Jan
authors.initials: J
authors.lastname: Dettmer
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Zurk
authors.forename: Martin
authors.initials: M
authors.lastname: Siderius
mesh_terms: D000162:Acoustics; D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D019015:Geologic Sediments; D005844:Geology / Q000379:methods*; D008390:Markov Chains; D008962:Models, Theoretical; D009010:Monte Carlo Method; D009622:Noise; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013997:Time Factors; D035501:Uncertainty; D014867:Water; D014919:Wind
chemical_list: D014867:Water