Group A, Poster #267, SCEC Community Models (CXM)

Multi-resolution seismic tomography maps fusion with probability graphical models near the 2019 Ridgecrest area

Zheng Zhou, Peter Gerstoft, & Kim B. Olsen
Poster Image: 

Poster Presentation

2022 SCEC Annual Meeting, Poster #267, SCEC Contribution #12558 VIEW PDF
The resolution of velocity models obtained by tomography varies due to the inversion approach, ray coverage, etc. Fusing such tomography models with different resolutions is desired when updating community models, to enable more accurate ground motion simulations. Toward this goal, we propose a novel approach to fuse multi-resolution seismic tomography maps with probability graphical models (PGMs). The PGMs are employed to provide segmentation results on the seismic tomography images with various resolutions, and the segmentation results here correspond to various velocity intervals. We present the relation between subdomains with multiple resolutions, in terms of high-resolution (HR) and lo...w-resolution (LR) components. By transferring the distribution information from the HR parts, the details in the LR areas can be enhanced by solving a maximum likelihood problem with prior knowledge from HR models informed. While updating the neighboring areas between the HR and LR parts, a patch-scanning policy is adopted to consider the local patterns and avoid a sharp boundary. To evaluate the efficacy of the proposed PGM fusion method, we employ the model on both synthetic checkerboard models and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of shallow-scale (top 1 km) high-resolution surface wave models obtained from ambient noise tomography, which is embedded into the SCEC Community Velocity Model (CVM) version S4.26-M01. Our model is evaluated by the misfit between observed and calculated travel times. The proposed PGM fusion method can merge any type of gridded multi-resolution velocity model and has the potential to become a valuable tool for computational seismology.
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Citation: Zhou, Z., Gerstoft, P., & Olsen, K. B. (2022, 09). Multi-resolution seismic tomography maps fusion with probability graphical models near the 2019 Ridgecrest area. Poster Presentation at 2022 SCEC Annual Meeting.
SCEC Award: 22053, 21133
Relevant SCEC Themes:
Special Fault Study Areas: Focus on Earthquake Gates
Community Models
Data-Intensive Computing