3D Multiresolution Velocity Model Fusion With Probability Graphical Models

Zheng Zhou, Peter Gerstoft, & Kim B. Olsen

Submitted October 27, 2023, SCEC Contribution #13337

The variability in spatial resolution of seismic velocity models obtained via tomographic methodologies is attributed to many factors, including inversion strategies, ray path coverage, and data integrity. Integration of such models, with distinct resolutions, is crucial during the refinement of community models, thereby enhancing the precision of ground motion simulations. Toward this goal, we introduce the Probability Graphical Model (PGM), combining velocity models with heterogeneous resolutions and non-uniform data point distributions. The PGM integrates data relations across varying-resolution subdomains, enhancing detail within low-resolution domains by utilizing information and a priori knowledge from high-resolution subdomains through a maximum likelihood problem. Assessment of efficacy, utilizing both 2D and 3D velocity models—consisting of synthetic checkerboard models and a fault zone model from Ridgecrest, CA—demonstrates noteworthy improvements in accuracy. Specifically, we find reductions of 30% and 44% in travel-time residuals for 2D and 3D models, respectively, as compared to conventional smoothing techniques. Unlike conventional methods, the PGM's adaptive weight selection facilitates preserving and transfering details from complex, non-uniform high-resolution models into the low-resolution background domain.

Key Words
multiresolution, seismic velocity models, machine learning

Zhou, Z., Gerstoft, P., & Olsen, K. B. (2023). 3D Multiresolution Velocity Model Fusion With Probability Graphical Models. Bulletin of the Seismological Society of America, (submitted).

Related Projects & Working Groups
seismology, community velocity models