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

Date

2012

Authors

Dettmer, Jan
Dosso, S.E.

Journal Title

Journal ISSN

Volume Title

Publisher

Acoustical Society of America

Abstract

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 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.

Description

Keywords

Keywords: 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

Citation

Source

Journal of the Acoustical Society of America

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

Restricted until

2037-12-31