P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California

Elif Ecem Bas; Elnaz Seylabi; Alan K. Yong; Hesam Tehrani; Domniki Asimaki

doi:10.1093/gji/ggac201

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P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California

Elif Ecem Bas·Elnaz Seylabi·Alan K. Yong·Hesam Tehrani·Domniki Asimaki

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Abstract

This study uses an ensemble Kalman method for near-surface seismic site characterization of 154 network earthquake monitoring stations in California to improve the resolution of S -wave velocity ( V S ) and P -wave velocity ( V P ) profiles—up to the resolution depth—coupled with better quantification of uncertainties compared to previous site characterization studies at this network. These stations were part of the Yong et al . site characterization project, with selected stations based on future recordings of ground motions that are expected to exceed 10 per cent peak ground acceleration in 50 yr. To estimate V S and V P from experimental dispersion data, Yong et al . investigated these stations using linearized (local search and iteration) routines, and Yong et al . later studied a subset of these stations using nonlinear (global search and optimization) routines. In both studies, the selection of model parameters—that is, discretization of the V S and V P profiles with only five fixed thickness layers—was mainly based on trial and error. In contrast, this paper uses an approximate Bayesian method to assimilate experimental dispersion data and sequentially update an ensemble of particle estimates that span the V S and V P parameter spaces. Doing so, we systematically determine the most probable profiles conditioned on the experimental dispersion data, the introduced noise levels, and a priori knowledge in the form of physical constraints. We consider two configurations to discretize the soil depth from the surface to half of the maximum discernible wavelength obtained from the experimental dispersion data, namely refined and coarse models, and two initial models for each configuration to study solution multiplicity. Our results suggest that using the refined model for the top surface layers improves the resolution of near-surface site characteristics and the model’s success rate in capturing dispersion data at high frequencies. All models result in similar V S but distinct V P profiles, with increasing uncertainty at deeper layers, suggesting that the fundamental mode of Rayleigh wave dispersion data is not adequate to constrain the P -wave velocity profile and the S -wave velocity close to the resolution depth.

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Geophysical Journal International Article

Source-reported bounding extent: 32.565333160841035° to 38.75408327579141° latitude; -122.684326171875° to -114.071044921875° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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BibTeX RIS

Elif Ecem Bas, Elnaz Seylabi, Alan K. Yong, Hesam Tehrani, Domniki Asimaki. 2022-05-30. P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California. https://doi.org/10.1093/gji%2Fggac201

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