034 - Data error covariance matrix for vertical array data in an ocean waveguide
Huang C.
Abstract
Information about the data errors is essential for solving any inverse problem. The likelihood function plays a critical role in describing the data uncertainties in geoacoustic inversion. The choice of likelihood function depends on the statistics of the errors (the difference between observed and estimated fields). In all work to date, the likelihood function has been derived based on an assumption of Gaussian errors. Typically, the errors are assumed to be independent, identically distributed with equal variance (referred to as the error variance) and the error variance is estimated as part of the optimization. Recently, there has been interest in estimating a more full data error covariance matrix. To estimate a truly full data error covariance matrix, we adopt a maximum likelihood approach based on ensemble averages using the observed errors over many inversions. The approach is illustrated using data obtained during the ASIAEX 2001 East China Sea experiment. The parameter uncertainties resulting from incorporating the full data error covariance matrix are compared with those obtained from the simplified data error covariance matrix characterized by the error variance alone.
Citation
Huang C.: Data error covariance matrix for vertical array data in an ocean waveguide, CD-ROM Proceedings of the Thirtheenth International Congress on Sound and Vibration (ICSV13), July 2-6, 2006, Vienna, Austria, Eds.: Eberhardsteiner, J.; Mang, H.A.; Waubke, H., Publisher: Vienna University of Technology, Austria, ISBN: 3-9501554-5-7
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