SCEC Award Number 24148 View PDF
Proposal Category Collaborative Research Project (Multiple Investigators / Institutions)
Proposal Title Fault Zone Structure from High-Resolution Seismicity Recorded by a Dense Nodal Array in the San Fernando Valley
Investigator(s)
Name Organization
Patricia Persaud University of Arizona Alan Juárez-Zúñiga University of Arizona
SCEC Milestones A1-3, A2-3, A3-2, B2-1, D1-1 SCEC Groups Seismology, PBS, CEM
Report Due Date 03/15/2025 Date Report Submitted 04/07/2025
Project Abstract
 The San Fernando Valley is a densely populated area in Southern California prone to large damaging earthquakes such as the 1994 magnitude 6.7 Northridge earthquake that occurred on a blind fault beneath the valley. With the aim of improving the structural characterization of the San Fernando Valley basin and the seismicity in the area, a team of 29 volunteers deployed an array of 140 three-component seismic nodal instruments covering the valley from October 21 to December 2, 2023. This SCEC award supported a student-led research project that leverages a machine learning algorithm, and the seismic data collected by the dense nodal array to detect and analyze microseismicity that could help to identify unknown active faults. In particular, the proposed research produced a high-resolution earthquake catalog of the local seismicity recorded by our nodal array and 10 permanent stations of the Southern California Seismic Network. The project characterized the spatiotemporal noise and seismicity patterns in an urban area in Southern California and developed a new approach for evaluating event detectability that can be useful in other high noise settings. It also combined earthquake locations and the focal mechanism from a microearthquake to map a previously unidentified active fault zone that is concealed beneath the southern part of the valley. These findings contribute to improving seismic hazard evaluation in the San Fernando Valley and other high seismic hazard urban areas.
SCEC Community Models Used Community Fault Model (CFM)
Usage Description 3D fault representations from the SCEC CFM 6.1
Intellectual Merit Our microearthquake detection (MUnet) algorithm is publicly available (Omojola & Persaud, 2024), and the additional codes needed to reproduce the results in this study will accompany the peer-review publication. Insights from this research can be used by the regional network to locate additional seismic stations for hazard monitoring purposes and to set event detection thresholds. This method can also be applied to previously collected datasets to help refine 3D fault plane representations by harvesting additional small events.
Broader Impacts This project involved a PhD student and a postdoctoral researcher. The results from this research have been used in presentations and lectures given by the PI and other project participants. The MUnet algorithm combined with nodal arrays provide a low-cost, effective method to characterize urban seismicity in populated regions with observational data gaps.
Project Participants Joses Omojola
Patricia Persaud
Alan Juarez-Zuniga
Exemplary Figure Figure 1. Topographic map of the study area in the San Fernando Valley and the surround region showing the preliminary locations of the 62 events in our catalog. Traces of faults from the SCEC Community Fault Model (CFM) 6.1 are shown as black lines (Marshall et al., 2023), and the outline of the San Fernando Valley (Juárez-Zúñiga & Persaud, 2025) is highlighted with the yellow polygon.
Linked Publications

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