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Bayesian acoustic prediction assimilating oceanographic and acoustically inverted data

2009Journal article Open access
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dc.contributor.authorMartins, N.
dc.contributor.authorJesus, S. M.
dc.date.accessioned2014-10-06T12:56:00Z
dc.date.available2014-10-06T12:56:00Z
dc.date.issued2009
dc.description.abstractThe prediction of the transmission loss evolution on a day to week frame, in a given oceanic area, is an important issue in modeling the sonar performance. It relies primarily on acoustic propagation models, which convert water column and geometric/ geoacoustic parameters to ‘instantaneous’ acoustic field estimates. In practice, to model the acoustic field, even the most accurate acoustic models have to be fed with simplified environmental descriptions, due to computational issues and to a limited knowledge of the environment. This is a limitation, for example, in acoustic inversion methods, in which, by maximizing the proximity between measured and modeled acoustic signals, the estimated environmental parameters are deviated from reality, forming what is normally called an ‘acoustically equivalent environment’. This problem arises also in standard acoustic prediction, in which, the oceanographic forecasts and bottom data (typically from archives) are fed directly to an acoustic model. The claim in the present work is that, by converting the oceanographic prediction and the bottom properties to ‘acoustically equivalent’ counterparts, the acoustic prediction can be obtained in an optimal way, adapted to the environmental model at hand. Here, acoustic prediction is formulated as a Bayesian estimation problem, in which, the observables are oceanographic forecasts, a set of known bottom parameters, a set of acoustic data, and a set of water column data. The predictive posterior PDF of the future acoustic signal is written as a function of elementary PDF functions relating these observables and ‘acoustically equivalent’ environmental parameters. The latter are obtained by inversion of acoustic data. The concept is tested on simulated data based on water column measurements and forecasts for the MREA’03 sea trial.por
dc.description.sponsorshipWe thank the partial funding of Funda¸c˜ao para a Ciˆencia e Tecnologia - FCT under POSI, POCTI and POCI programs, and scholarship no. SFRH/BD/9032/2002. Acknowledgements are addressed also to Emanuel Coelho, for conducing the MREA’03 sea trial, and to Peter Gerstoft, for prompt help and improvements of the SAGA inversion package.por
dc.identifier.citationN.E. MARTINS and S.M. JESUS, ''Bayesian acoustic prediction assimilating oceanographic and acoustically inverted data'', Journal of Marine Systems, Vol.78, Supplement 1, pp. S349-S358, November, doi:10.1016/j.jmarsys.2009.01.033.por
dc.identifier.doihttp://dx.doi.org/10.1016/j.jmarsys.2009.01.033
dc.identifier.issn0924-7963
dc.identifier.otherAUT: SJE00662;
dc.identifier.urihttp://hdl.handle.net/10400.1/5188
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherElsevierpor
dc.subjectAcoustic predictionpor
dc.subjectOceanographic forecastpor
dc.subjectAcoustic inversionpor
dc.subjectRapid environmental assessmentpor
dc.titleBayesian acoustic prediction assimilating oceanographic and acoustically inverted datapor
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBD%2F9032%2F2002/PT
oaire.citation.endPageS358por
oaire.citation.issueSup. 1por
oaire.citation.startPageS349por
oaire.citation.titleJournal of Marine Systemspor
oaire.citation.volume78por
oaire.fundingStreamSFRH
person.familyNameJesus
person.givenNameSergio
person.identifier.orcid0000-0002-6021-1761
person.identifier.scopus-author-id7003729485
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspor
rcaap.typearticlepor
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relation.isAuthorOfPublication.latestForDiscoverye0226ece-3767-4beb-ab80-868e8897c14a
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