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- W4320560282 abstract "Eikonal tomography has become a popular methodology for deriving phase velocity maps from surface wave phase delay measurements. Its high efficiency makes it popular for handling datasets deriving from large-N arrays, in particular in the ambient-noise tomography setting. However, the results of eikonal tomography are crucially dependent on the way in which phase delay measurements are interpolated, a point which has not been thoroughly investigated. In this work, I provide a rigorous formulation for eikonal tomography using Gaussian processes (GPs) to interpolate phase delay measurements, including uncertainties. GPs allow the posterior phase delay gradient to be analytically derived. From the phase delay gradient, an excellent approximate solution for phase velocities can be obtained using the saddlepoint method. The result is a fully Bayesian result for phase velocities of surface waves, incorporating the nonlinear wavefront bending inherent in eikonal tomography, with no sampling required. The results of this analysis imply that the uncertainties reported for eikonal tomography are often underestimated." @default.
- W4320560282 created "2023-02-15" @default.
- W4320560282 creator A5022221498 @default.
- W4320560282 date "2023-02-10" @default.
- W4320560282 modified "2023-09-23" @default.
- W4320560282 title "Bayesian eikonal tomography using Gaussian processes" @default.
- W4320560282 doi "https://doi.org/10.48550/arxiv.2302.05267" @default.
- W4320560282 hasPublicationYear "2023" @default.
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