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Bayesian Framework for Inversion of Second-Order Stress Glut Moments: Application to the 2019 Ridgecrest Sequence Mainshock

Atterholt, James and Ross, Zachary E. (2022) Bayesian Framework for Inversion of Second-Order Stress Glut Moments: Application to the 2019 Ridgecrest Sequence Mainshock. Journal of Geophysical Research. Solid Earth, 127 (4). Art. No. e2021JB023780. ISSN 2169-9313. doi:10.1029/2021JB023780. https://resolver.caltech.edu/CaltechAUTHORS:20220106-726694700

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Abstract

We present a fully Bayesian inverse scheme to determine second moments of the stress glut using teleseismic earthquake seismograms. The second moments form a low-dimensional, physically motivated representation of the rupture process that captures its spatial extent, source duration, and directivity effects. We determine an ensemble of second-moment solutions by employing Hamiltonian Monte Carlo and automatic differentiation to efficiently approximate the posterior. This method explicitly constrains the parameter space to be symmetric positive definite, ensuring the derived source properties have physically meaningful values. The framework accounts for the autocorrelation structure of the errors and incorporates hyperpriors on the uncertainty. We validate this methodology using a synthetic test and subsequently apply it to the 2019 M_w7.1 Ridgecrest earthquake using teleseismic data. The distributions of second moments determined for this event provide probabilistic descriptions of low-dimensional rupture characteristics that are generally consistent with results from previous studies. The success of this case study suggests that probabilistic and comparable finite source properties may be discerned for large global events regardless of the quality and coverage of local instrumentation.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1029/2021JB023780DOIArticle
https://doi.org/10.1002/essoar.10509325.1DOIDiscussion Paper
https://doi.org/10.7914/SN/IIDOIData
https://doi.org/10.7914/SN/IUDOIData
https://www.globalcmt.orgRelated ItemGlobal Centroid Moment Tensor (gCMT)
https://mondaic.comRelated ItemMondaic
https://www.generic-mapping-tools.orgRelated ItemGeneric Mapping Tools (GMT)
ORCID:
AuthorORCID
Atterholt, James0000-0003-1603-5518
Ross, Zachary E.0000-0002-6343-8400
Additional Information:© 2022. American Geophysical Union. Issue Online: 20 April 2022; Version of Record online: 20 April 2022; Accepted manuscript online: 25 March 2022; Manuscript accepted: 19 March 2022; Manuscript revised: 03 February 2022; Manuscript received: 06 December 2021. This study was partially funded by the National Science Foundation's (NSF) Graduate Research Fellowships Program (GRFP) under Grant no. DGE-1745301. The authors would like to thank Dr. Hiroo Kanamori for sharing his experience and providing perceptive comments. The authors would also like to thank Dr. Jeffrey McGuire and an anonymous reviewer for their insightful comments which greatly improved this manuscript, and the editor Dr. Rachel Abercrombie for facilitating the review process. Data Availability Statement: The teleseismic waveforms used in this study are from the Global Seismographic Network (GSN) operated by Scripps Institution of Oceanography (II: IRIS/IDA; https://doi.org/10.7914/SN/II; Scripps Institution Of Oceanography, 1986) and the Albuquerque Seismological Laboratory (IU: IRIS/USGS; https://doi.org/10.7914/SN/IU; Albuquerque Seismological Laboratory (ASL)/USGS, 1988). These waveforms and associated metadata used in this study were accessed through the IRIS Data Management Center (DMC). The centroid and moment tensor solutions used in this study were obtained from Global Centroid Moment Tensor (gCMT) catalog (Dziewonski et al., 1981; Ekström et al., 2012) at https://www.globalcmt.org/. The synthetic waveforms used in this study were generated using the software Salvus (Afanasiev et al., 2019), available at https://mondaic.com/. Figure 1 was generated using The Generic Mapping Tools (GMT), version 6 (Wessel et al., 2019), available at https://www.generic-mapping-tools.org/.
Group:Seismological Laboratory
Funders:
Funding AgencyGrant Number
NSF Graduate Research FellowshipDGE-1745301
Subject Keywords:source characterization; stress glut second moments; Bayesian inversion; Ridgecrest earthquake
Issue or Number:4
DOI:10.1029/2021JB023780
Record Number:CaltechAUTHORS:20220106-726694700
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220106-726694700
Official Citation:Atterholt, J., & Ross, Z. E. (2022). Bayesian framework for inversion of second-order stress glut moments: Application to the 2019 Ridgecrest sequence mainshock. Journal of Geophysical Research: Solid Earth, 127, e2021JB023780. https://doi.org/10.1029/2021JB023780
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112743
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:08 Jan 2022 22:24
Last Modified:25 Apr 2022 21:23

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