DATA error estimation in matched-field geoacoustic inversion
Keywords:Algorithms, Amplitude modulation, Arrays, Data reduction, Frequencies, Gaussian noise (electronic), Inverse problems, Maximum likelihood estimation, Optimization, Parameter estimation, Sensors, Signal processing, Simulated annealing, Acoustic data, Error estimation, Geoacoustic inversion, Geoacoustic parameters
AbstractThe data error estimation in matched-field geoacoustic inversion was discussed. The matched-field inversion (MFI) is based on searching for the set of geoacoustic model parameters m that minimizes an objective function quantifying the misfit between measured and modeled acoustic fields. The objective function to be minimized was derived from the likelihood function corresponding to the assumed data uncertainty distribution. It is found that a hybrid optimization algorithm that combines local downhill simplex moves within a fast simulated annealing global search has proved highly effective for MFI.
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