Parametric Bayesian level set approach with radial basis expansion and Metropolis-Hastings sampling reconstructs acoustic sources from multiple frequency data, proving posterior well-posedness and showing competitive numerical results.
Fast acoustic source imaging using multi-frequency sparse data
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abstract
We consider the acoustic source imaging problems using multiple frequency data. Using the data of one observation direction/point, we prove that some information (size and location) of the source support can be recovered. A non-iterative method is then proposed to image the source for the Helmholtz equation using multiple frequency far field data of one or several observation directions. The method is simple to implement and extremely fast since it only computes an indicator function on the interested domain using only matrix vector multiplications. Numerical examples are presented to validate the effectiveness of the method.
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math.NA 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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A parametric Bayesian level set approach for acoustic source identification using multiple frequency information
Parametric Bayesian level set approach with radial basis expansion and Metropolis-Hastings sampling reconstructs acoustic sources from multiple frequency data, proving posterior well-posedness and showing competitive numerical results.