Abstract
It has been shown that, in ocean acoustics, Gaussian processes can predict a densely sampled field on a receiving array, when only sparse samples of the sound in the water column are initially available. In a similar manner, a Gaussian process approach can be designed to densely sample signals in the time domain, resulting from the transmission and propagation of broadband waveforms. A mean waveform can be obtained that allows the high-resolution estimation of multipath arrival times. These can then be used for source localization and geoacoustic inversion. Uncertainty quantification in the time-series characterization, readily available from the Gaussian process modeling, facilitates uncertainty quantification in inversion, obviating the need for onerous computations. We investigate the potential of different kernels in aiding the inversion process. [Work supported by ONR.]
Published Version
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