Parsimonious Velocity Inversion Applied to the Los Angeles Basin, CA
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 infers where local data provide meaningful constraints beyond those found in regional models - for example 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 are 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 toward 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.
Additional Information© 2022 American Geophysical Union. Issue Online: 28 January 2022; Version of Record online: 28 January 2022; Accepted manuscript online: 22 January 2022; Manuscript accepted: 18 January 2022; Manuscript revised: 10 January 2022; Manuscript received: 24 August 2021. The authors would like to thank Rob Graves (USGS) for providing synthetic seismograms for the Ridgecrest events. This study was supported by the United States National Science Foundation awards EAR-1520081, EAR-2105358 and EAR-2011079, and the Southern California Earthquake Center award 20024. JBM acknowledges the support of the General Sir John Monash Foundation and the Origin Energy Foundation for support during his graduate studies. Data Availability Statement: The CSN data used in this paper are freely available from http://csn.caltech.edu/data. The TEKS inversion code may be found at https://doi.org/10.5281/zenodo.5834927 (Muir, 2022). Data analysis codes can be found at https://doi.org/10.5281/zenodo.5823526 (Muir et al., 2022).
Published - 2021JB023103.pdf
Supplemental Material - 2021jb023103-sup-0001.pdf
Supplemental Material - 2021jb023103-sup-0002-movie_si-s01.mp4