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 e...
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| Format: | Article |
| Language: | English |
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McGill University
2023-12-01
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| Series: | Seismica |
| Online Access: | https://seismica.library.mcgill.ca/article/view/388 |
| _version_ | 1797378832299196416 |
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| author | Jack Muir |
| author_facet | Jack Muir |
| author_sort | Jack Muir |
| collection | DOAJ |
| 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.
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| first_indexed | 2024-03-08T20:13:19Z |
| format | Article |
| id | doaj.art-b01cf0c2f2ca4d1095539794746643a7 |
| institution | Directory Open Access Journal |
| issn | 2816-9387 |
| language | English |
| last_indexed | 2024-03-08T20:13:19Z |
| publishDate | 2023-12-01 |
| publisher | McGill University |
| record_format | Article |
| series | Seismica |
| spelling | doaj.art-b01cf0c2f2ca4d1095539794746643a72023-12-22T22:54:52ZengMcGill UniversitySeismica2816-93872023-12-012210.26443/seismica.v2i2.388Bayesian eikonal tomography using Gaussian processesJack Muir0University of Oxford 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. https://seismica.library.mcgill.ca/article/view/388 |
| spellingShingle | Jack Muir Bayesian eikonal tomography using Gaussian processes Seismica |
| 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 |
| url | https://seismica.library.mcgill.ca/article/view/388 |
| work_keys_str_mv | AT jackmuir bayesianeikonaltomographyusinggaussianprocesses |