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An Automated Method for Developing a Catalog of Small Earthquakes Using Data of a Dense Seismic Array and Nearby Stations

Cheng, Yifang and Ben-Zion, Yehuda and Brenguier, Florent and Johnson, Christopher W. and Li, Zefeng and Share, Pieter-Ewald and Mordret, Aurélien and Boué, Pierre and Vernon, Frank (2020) An Automated Method for Developing a Catalog of Small Earthquakes Using Data of a Dense Seismic Array and Nearby Stations. Seismological Research Letters, 91 (5). pp. 2862-2871. ISSN 0895-0695. doi:10.1785/0220200134.

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We propose a new automated procedure for using continuous seismic waveforms recorded by a dense array and its nearby regional stations for P‐wave arrival identification, location, and magnitude estimation of small earthquakes. The method is illustrated with a one‐day waveform dataset recorded by a dense array with 99 sensors near Anza, California, and 24 surrounding regional stations within 50 km of the dense array. We search a wide range of epicentral locations and apparent horizontal slowness values (⁠0–15  s/km⁠) in the 15–25 Hz range and time shift the dense array waveforms accordingly. For each location–slowness combination, the average neighboring station waveform similarity (avgCC) of station pairs <150  m apart is calculated for each nonoverlapping 0.5 s time window. Applying the local maximum detection algorithm gives 966 detections. Each detection has a best‐fitting location–slowness combination with the largest avgCC. Of 331 detections with slowness <0.4  s/km⁠, 324 (about six times the catalog events and 98% accuracy) are found to be earthquake P‐wave arrivals. By associating the dense array P‐wave arrivals and the P‐ and S‐wave arrivals from the surrounding stations using a 1D velocity model, 197 detections (⁠∼4 times of the catalog events) have well‐estimated locations and magnitudes. Combining the small spacing of the array and the large aperture of the regional stations, the method achieves automated earthquake detection and location with high sensitivity in time and high resolution in space. Because no preknowledge of seismic‐waveform features or local velocity model is required for the dense array, this automated algorithm can be robustly implemented in other locations.

Item Type:Article
Related URLs:
URLURL TypeDescription
Cheng, Yifang0000-0002-6507-5607
Ben-Zion, Yehuda0000-0002-9602-2014
Li, Zefeng0000-0003-4405-8872
Additional Information:© 2020 Seismological Society of America. Manuscript received 3 April 2020; Published online 22 July 2020. The authors thank the Cahuilla Band of Mission Indians Reservation for graciously allowing us to deploy instruments on tribal land. The study was supported by the U.S. Department of Energy (Awards DE‐SC0016520 and DE‐SC0016527) and the European Research Council under Grant Number 817803, FaultScan project.
Group:Seismological Laboratory
Funding AgencyGrant Number
Department of Energy (DOE)DE‐SC0016520
Department of Energy (DOE)DE‐SC0016527
European Research Council (ERC)817803
Issue or Number:5
Record Number:CaltechAUTHORS:20201001-123520618
Persistent URL:
Official Citation:Cheng, Y., Y. Ben- Zion, F. Brenguier, C. W. Johnson, Z. Li, P.- E. Share, A. Mordret, P. Boué, and F. Vernon (2020). An Automated Method for Developing a Catalog of Small Earthquakes Using Data of a Dense Seismic Array and Nearby Stations, Seismol. Res. Lett. 91, 2862–2871, doi: 10.1785/0220200134
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105723
Deposited By: Tony Diaz
Deposited On:01 Oct 2020 20:26
Last Modified:16 Nov 2021 18:45

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