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Urban Basin Structure Imaging Based on Dense Arrays and Bayesian Array‐Based Coherent Receiver Functions

Wang, Xin and Zhan, Zhongwen and Zhong, Minyan and Persaud, Patricia and Clayton, Robert W. (2021) Urban Basin Structure Imaging Based on Dense Arrays and Bayesian Array‐Based Coherent Receiver Functions. Journal of Geophysical Research. Solid Earth, 126 (9). Art. No. e2021JB022279. ISSN 2169-9313. doi:10.1029/2021jb022279.

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Urban basin investigation is crucial for seismic hazard assessment and mitigation. Recent advances in robust nodal-type sensors facilitate the deployment of large-N arrays in urban areas for high-resolution basin imaging. However, arrays typically operate for only one month due to the instruments' battery life, and hence, only record a few teleseismic events. This limits the number of available teleseismic events for traditional receiver function (RF) analysis-the primary method used in sediment-basement interface imaging in passive source seismology. Insufficient stacking of RFs from a limited number of earthquakes could, however, introduce significant biases to the results. In this study, we present a novel Bayesian array-based Coherent Receiver Function (CRF) method that can leverage datasets from short-term dense arrays to constrain basin geometry. We cast the RF deconvolution as a sparsity-promoted inverse problem, in which the deconvolution at a single-station involves the constraints from neighboring stations and multiple events. We solve the inverse problem using a trans-dimensional Markov chain Monte Carlo Bayesian algorithm to find an ensemble of RF solutions, which provides a quantitative way of deciding which features are well resolved and warrant geological interpretation. An application in the northern Los Angeles basin demonstrates the ability of our method to produce reliable and easy-to-interpret RF images. The use of dense seismic networks and the state-of-the-art Bayesian array-based CRF method can provide a robust approach for subsurface structure imaging.

Item Type:Article
Related URLs:
URLURL TypeDescription seismic data set seismic data set seismic data set
Wang, Xin0000-0002-6180-0058
Zhan, Zhongwen0000-0002-5586-2607
Zhong, Minyan0000-0002-1382-7061
Persaud, Patricia0000-0003-3462-7023
Clayton, Robert W.0000-0003-3323-3508
Additional Information:© 2021. American Geophysical Union. Issue Online: 29 August 2021; Version of Record online: 29 August 2021; Accepted manuscript online: 16 August 2021; Manuscript accepted: 07 August 2021; Manuscript revised: 26 July 2021; Manuscript received: 21 April 2021. The nodal Basin Amplification Seismic INvestigation (BASIN) project is a joint effort between Louisiana State University and California Institute of Technology. We thank IRIS Portable Array Seismic Studies of the Continental Lithosphere (PASSCAL), Louisiana State University, University of Utah, and University of Oklahoma for providing the nodes used to collect the data, and a large number of volunteers for their participation in the seismic surveys. We are also grateful to editor Michael Bostock, the associate editor, and reviewers Karen Lythgoe and Thomas Bodin for their comments. This research is supported by the National Science Foundation (grant 1722879). Dr. Xin Wang is supported by National Natural Science Foundation of China (grants 91958209 and 41774058), and the Young Elite Scientists Sponsorship Program by CAST (grant 2020QNRC001). The BASIN project was partly supported by U.S. Geological Survey awards GS17AP00002 and G19AP00015, Southern California Earthquake Center awards 18029 and 19033, and NSF award 2105320 and 2105358. Data Availability Statement: The BASIN seismic data set is archived in IRIS Data Management Center (;; The data set will be fully released in December 2021, which is two years after the last data collection. For the codes developed in this study, they are available from Dr. Xin Wang ( upon request.
Group:Seismological Laboratory
Funding AgencyGrant Number
National Natural Science Foundation of China91958209
National Natural Science Foundation of China41774058
China Association for Science and Technology2020QNRC001
Southern California Earthquake Center (SCEC)18029
Southern California Earthquake Center (SCEC)19033
Subject Keywords:Coherent Receiver Function; basin structure; large-N arrays; trans-dimensional inversion; array-processing
Issue or Number:9
Record Number:CaltechAUTHORS:20210910-174637755
Persistent URL:
Official Citation:Wang, X., Zhan, Z., Zhong, M., Persaud, P., & Clayton, R. W. (2021). Urban basin structure imaging based on dense arrays and Bayesian array-based Coherent Receiver Functions. Journal of Geophysical Research: Solid Earth, 126, e2021JB022279.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:110805
Deposited By: Tony Diaz
Deposited On:10 Sep 2021 20:14
Last Modified:10 Sep 2021 20:14

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