Group B, Poster #004, Seismology
Fusion of Multiresolution Seismic Tomography Maps Using Physics-informed Probability Graphical Models
Poster Image:
![](https://files.scec.org/s3fs-public/publication_13244.jpg?qQaUt9F978rO6S81pWfj9S9Vx0IJSwZc)
Poster Presentation
2023 SCEC Annual Meeting, Poster #004, SCEC Contribution #13244 VIEW PDF
By leveraging information from the HR subdomain, the PIPGM enhances details within LR areas through a maximum likelihood problem that incorporates prior knowledge from the HR regions. We assess the efficacy of the proposed methodology using both 2D and 3D velocity models, including synthetic checkerboard models as well as a fault zone model derived from the 2019 Ridgecrest, CA, earthquake sequence. Our findings demonstrate a ~38% reduction in travel time residuals compared to conventional Gaussian kernel smoothing in the 2D experiments, with similar reductions expected in 3D. These improvements stem from the PIPGM's adaptive weight selection, which effectively accommodates the complex structure of the Ridgecrest model. In contrast, traditional techniques struggle to handle nonuniformly distributed data uniformly. Our proposed PIPGM holds significant potential for enhancing our understanding of Earth's structure and offers promising advancements in other seismic research applications, such as earthquake ground motion prediction.
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