SCEC Award Number 25260 View PDF
Proposal Category Collaborative Research Project (Multiple Investigators / Institutions)
Proposal Title Using both waveform coherence and complexity to cluster microearthquakes
Investigator(s)
Name Organization
Wenyuan Fan University of California, San Diego Peter Shearer University of California, San Diego
SCEC Milestones A2-3, A3-2, D1-1, D2-1 SCEC Groups Seismology, RC, FARM
Report Due Date 03/15/2026 Date Report Submitted 03/27/2026
Project Abstract
 Earthquakes are often grouped into clusters from catalogs based on the spatial and temporal behavior. When waveforms are considered, these clusters can be further refined, with subdivisions carrying critical information about detailed fault architecture, the complexity of the stress field, and the evolution of fault-zone characteristics. Traditionally, waveform properties are primarily evaluated based on their coherence. However, such simple metrics cannot fully characterize the complexities of waveforms, especially the subtle, late­arriving waveforms that are contaminated by noise or coda waves. In this proposal, we aim to develop a new earthquake clustering procedure that combines wave­form coherence (e.g., cross correlation) and complexities identified from a machine learning method—a waveform sequencing algorithm—to cluster microearthquakes. This approach will offer new metrics to analyze the stress and strength of complex fault zones and their relation to earthquakes. We will then apply the procedure to two connected but distinctly different fault segments in central California: the central San Andreas Fault (CSAF) and the Parkfield segment. Our algorithm can be integrated with a range of high-resolution fault-zone imaging techniques, such as relative earthquake locations for identifying fault networks and in-situ Vp/Vs measurements for resolving fault properties. Our identified clusters can also be used to invert for relative earthquake focal mechanisms. We will release our algorithm through open-source platforms, such as GitHub, upon publication of our study, ensuring it serves as a clear deliverable for the project.
Intellectual Merit The central goal of this project was to develop a machine-learning-enabled clustering procedure that leverages both waveform coherence and complexity to group microearthquakes. A key advantage of the method is that the identified clusters yield high-quality differential arrival time measurements for both P- and S-waves, which can be directly used for resolving relative earthquake locations, estimating relative focal mechanisms, and inverting for in-situ VP/VS ratios. These capabilities make the clustering procedure a versatile tool that integrates with a range of existing earthquake analysis workflows.
Broader Impacts A manuscript presenting the full results of the Noto Peninsula application is currently in preparation. This work constitutes a core component of the Ph.D. thesis of Nicolas DeSalvio, a graduate student supported by this project. We plan to standardize the workflow and release the algorithm as an open-source software package, ensuring broad accessibility and encouraging community-driven applications.
Project Participants Nicolas DeSalvio, Wenyuan Fan, and Peter Shearer from SIO/UCSD.
Exemplary Figure Figure 3: In-situ VP/VS estimates near the Mw 7.5 Noto earthquake region, showing high anomalies prop-
agating from deep toward shallower depths. The results imply the presence of possible fluid pathways in the
complex fault network.
Linked Publications

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