Bayesian eikonal tomography using Gaussian processes
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 eik...
| Main Author: | |
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| Format: | Journal article |
| Language: | English |
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Seismica
2023
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| _version_ | 1826311934906990592 |
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| author | Muir, J |
| author_facet | Muir, J |
| author_sort | Muir, J |
| collection | OXFORD |
| description | 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 predicted from data, a point which has not been thoroughly investigated. In this work, I provide a rigorous formulation for eikonal tomography using Gaussian processes (GPs) to smooth observed 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. |
| first_indexed | 2024-03-07T08:18:38Z |
| format | Journal article |
| id | oxford-uuid:63f4844b-3476-40a4-9195-11f89c0ad239 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:18:38Z |
| publishDate | 2023 |
| publisher | Seismica |
| record_format | dspace |
| spelling | oxford-uuid:63f4844b-3476-40a4-9195-11f89c0ad2392024-01-15T16:49:21ZBayesian eikonal tomography using Gaussian processesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:63f4844b-3476-40a4-9195-11f89c0ad239EnglishSymplectic ElementsSeismica2023Muir, JEikonal 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 predicted from data, a point which has not been thoroughly investigated. In this work, I provide a rigorous formulation for eikonal tomography using Gaussian processes (GPs) to smooth observed 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. |
| spellingShingle | Muir, J Bayesian eikonal tomography using Gaussian processes |
| title | Bayesian eikonal tomography using Gaussian processes |
| title_full | Bayesian eikonal tomography using Gaussian processes |
| title_fullStr | Bayesian eikonal tomography using Gaussian processes |
| title_full_unstemmed | Bayesian eikonal tomography using Gaussian processes |
| title_short | Bayesian eikonal tomography using Gaussian processes |
| title_sort | bayesian eikonal tomography using gaussian processes |
| work_keys_str_mv | AT muirj bayesianeikonaltomographyusinggaussianprocesses |