Parsimonious velocity inversion applied to the Los Angeles Basin, CA

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Jack Broderick Muir , Robert W Clayton , Victor C. Tsai, Quentin Brissaud 


The proliferation of dense arrays promises to improve our ability to image geological structures at the scales necessary for accurate assessment of seismic hazard. However, combining the resulting local high-resolution tomography with existing regional models presents an ongoing challenge. We developed a framework based on the level-set method that provides a means to infer where local data provides meaningful constraints beyond those found in regional models - e.g. the Community Velocity Models (CVMs) of southern California. This technique defines a volume within which updates are made to a reference CVM, with the boundary of the volume being part of the inversion rather than explicitly defined. By penalizing the complexity of the boundary, a minimal update that sufficiently explains the data is achieved.

To test this framework, we use data from the Community Seismic Network, a dense permanent urban deployment. We inverted Love wave dispersion and amplification data, from the Mw 6.4 and 7.1 2019 Ridgecrest earthquakes. We invert for an update to CVM-S4.26 using the Tikhonov Ensemble Sampling scheme, a highly efficient derivative-free approximate Bayesian method. We find the data is best explained by a deepening of the Los Angeles Basin with its deepest part south of downtown Los Angeles, along with a steeper northeastern basin wall. This result offers new progress towards the parsimonious incorporation of detailed local basin models within regional reference models utilizing an objective framework and highlights the importance of accurate basin models when accounting for the amplification of surface waves in the high-rise building response band.



Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics


Seismology, Tomography, Surface Wave Amplification


Published: 2021-08-24 07:22

Last Updated: 2021-08-24 11:22


CC BY Attribution 4.0 International

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