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Trans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains

Dettmer, Jan; Dosso, S.E.

Description

This paper develops a trans-dimensional approach to matched-field geoacoustic inversion, including interacting Markov chains to improve efficiency and an autoregressive model to account for correlated errors. The trans-dimensional approach and hierarchical seabed model allows inversion without assuming any particular parametrization by relaxing model specification to a range of plausible seabed models (e.g., in this case, the number of sediment layers is an unknown parameter). Data errors are...[Show more]

dc.contributor.authorDettmer, Jan
dc.contributor.authorDosso, S.E.
dc.date.accessioned2015-12-10T22:29:29Z
dc.identifier.issn0001-4966
dc.identifier.urihttp://hdl.handle.net/1885/54926
dc.description.abstractThis paper develops a trans-dimensional approach to matched-field geoacoustic inversion, including interacting Markov chains to improve efficiency and an autoregressive model to account for correlated errors. The trans-dimensional approach and hierarchical seabed model allows inversion without assuming any particular parametrization by relaxing model specification to a range of plausible seabed models (e.g., in this case, the number of sediment layers is an unknown parameter). Data errors are addressed by sampling statistical error-distribution parameters, including correlated errors (covariance), by applying a hierarchical autoregressive error model. The well-known difficulty of low acceptance rates for trans-dimensional jumps is addressed with interacting Markov chains, resulting in a substantial increase in efficiency. The trans-dimensional seabed model and the hierarchical error model relax the degree of prior assumptions required in the inversion, resulting in substantially improved (more realistic) uncertainty estimates and a more automated algorithm. In particular, the approach gives seabed parameter uncertainty estimates that account for uncertainty due to prior model choice (layering and data error statistics). The approach is applied to data measured on a vertical array in the Mediterranean Sea.
dc.publisherAcoustical Society of America
dc.sourceJournal of the Acoustical Society of America
dc.subjectKeywords: Acceptance rate; Auto regressive models; Automated algorithms; Autoregressive error model; Data errors; Error model; Geoacoustic inversion; Mediterranean sea; Model choice; Model specifications; Parameter uncertainty; Parametrizations; Sediment layers; Un
dc.titleTrans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume132
dc.date.issued2012
local.identifier.absfor040407 - Seismology and Seismic Exploration
local.identifier.absfor040599 - Oceanography not elsewhere classified
local.identifier.ariespublicationu4027924xPUB314
local.type.statusPublished Version
local.contributor.affiliationDettmer, Jan, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationDosso, S.E., University of Victoria
local.description.embargo2037-12-31
local.bibliographicCitation.issue4
local.bibliographicCitation.startpage2239
local.bibliographicCitation.lastpage2250
local.identifier.doi10.1121/1.4746016
local.identifier.absseo970104 - Expanding Knowledge in the Earth Sciences
local.identifier.absseo810108 - Navy
local.identifier.absseo969902 - Marine Oceanic Processes (excl. climate related)
dc.date.updated2016-02-24T10:29:30Z
local.identifier.scopusID2-s2.0-84867385349
CollectionsANU Research Publications

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