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Change-point detection for recursive Bayesian geoacoustic inversions

TitleChange-point detection for recursive Bayesian geoacoustic inversions
Publication TypeJournal Article
Year of Publication2015
AuthorsTan B.A, Gerstoft P, Yardim C, Hodgkiss WS
JournalJournal of the Acoustical Society of America
Date Published2015/04
Type of ArticleArticle
ISBN Number0001-4966
Accession NumberWOS:000353653500051
Keywordsinference; model selection; monte-carlo; noise; tracking; uncertainty

In order to carry out geoacoustic inversion in low signal-to-noise ratio (SNR) conditions, extended duration observations coupled with source and/or receiver motion may be necessary. As a result, change in the underlying model parameters due to time or space is anticipated. In this paper, an inversion method is proposed for cases when the model parameters change abruptly or slowly. A model parameter change-point detection method is developed to detect the change in the model parameters using the importance samples and corresponding weights that are already available from the recursive Bayesian inversion. If the model parameters change abruptly, a change-point will be detected and the inversion will restart with the pulse measurement after the change-point. If the model parameters change gradually, the inversion (based on constant model parameters) may proceed until the accumulated model parameter mismatch is significant and triggers the detection of a change-point. These change-point detections form the heuristics for controlling the coherent integration time in recursive Bayesian inversion. The method is demonstrated in simulation with parameters corresponding to the low SNR, 100-900 Hz linear frequency modulation pulses observed in the Shallow Water 2006 experiment [Tan, Gerstoft, Yardim, and Hodgkiss, J. Acoust. Soc. Am. 136, 1187-1198 (2014)]. (c) 2015 Acoustical Society of America.

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