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DOE OSTI · 2580224

3D Multiresolution Velocity Model Fusion with Probability Graphical Models

Zhou, Zheng (ORCID:0000000296164215)·Gerstoft, Peter (ORCID:000000020471062X)·Olsen, Kim (ORCID:000000023078485X)
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Abstract

ABSTRACT 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 nonuniform data point distributions. The PGM integrates data relations across varying resolution subdomains, enhancing detail within low-resolution (LR) domains by utilizing information and prior knowledge from high-resolution (HR) subdomains through a maximum posterior problem. Assessment of efficacy, utilizing both 2D and 3D velocity models—consisting of synthetic checkerboard models and a fault-zone model from Ridgecrest, California—demonstrates noteworthy improvements in accuracy, compared to state-of-the-art fusion techniques. Specifically, we find reductions of 30% and 44% in computed 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 learning details from complex, nonuniform HR models and applies the enhancements to the LR background domain.

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Zhou, Zheng (ORCID:0000000296164215), Gerstoft, Peter (ORCID:000000020471062X), Olsen, Kim (ORCID:000000023078485X). 2024-04-25. 3D Multiresolution Velocity Model Fusion with Probability Graphical Models. https://doi.org/10.1785/0120230271

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