Graph-learning Approach to Combine Multiresolution Seismic Velocity Models

Zheng Zhou, Peter Gerstoft, & Kim B. Olsen

Under Review 2024, SCEC Contribution #13436

The resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations (e.g., Yeh and Olsen, 2023). Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probability graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Furthermore, by taking physical information (such as ray-path density) into consideration, we introduce physics-informed probability graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. By transferring (segmented) distribution information from the HR regions, the details in the LR regions are enhanced by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The efficacy of our model is underscored by the deviation between observed and calculated travel times along the boundaries between HR and LR regions, 38\% less than those obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation.

Zhou, Z., Gerstoft, P., & Olsen, K. B. (2024). Graph-learning Approach to Combine Multiresolution Seismic Velocity Models. Geophysical Journal International, (under review).