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Abstract Repeating earthquakes—sequences of colocated, quasi-periodic earthquakes of similar size—are widespread along California’s San Andreas fault (SAF) system. Catalogs of repeating earthquakes are vital for studying earthquake source processes, fault properties, and improving seismic hazard models. Here, we introduce an unsupervised machine learning-based method for detecting repeating earthquake sequences (RES) to expand existing RES catalogs or to perform initial, exploratory searches. We implement the “SpecUFEx” algorithm (Holtzman et al., 2018) to reduce earthquake spectrograms into low-dimensional, characteristic fingerprints, and apply hierarchical clustering to group similar fingerprints together independent of location, allowing for a global search for potential RES throughout the data set. We then relocate the potential RES and subject them to the same detection criteria as Waldhauser and Schaff (2021). We apply our method to ∼4000 small (ML 0–3.5) earthquakes located on a 10 km long segment of the creeping SAF and double the number of detected RES, allowing for greater spatial coverage of slip-rate estimations at seismogenic depths. Our method is novel in its ability to detect RES independent of initial locations and is complimentary to existing cross-correlation-based methods, leading to more complete RES catalogs and a better understanding of slip rates at depth.
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Using Deep Learning for Flexible and Scalable Earthquake Forecasting
Abstract Seismology is witnessing explosive growth in the diversity and scale of earthquake catalogs. A key motivation for this community effort is that more data should translate into better earthquake forecasts. Such improvements are yet to be seen. Here, we introduce the Recurrent Earthquake foreCAST (RECAST), a deep‐learning model based on recent developments in neural temporal point processes. The model enables access to a greater volume and diversity of earthquake observations, overcoming the theoretical and computational limitations of traditional approaches. We benchmark against a temporal Epidemic Type Aftershock Sequence model. Tests on synthetic data suggest that with a modest‐sized data set, RECAST accurately models earthquake‐like point processes directly from cataloged data. Tests on earthquake catalogs in Southern California indicate improved fit and forecast accuracy compared to our benchmark when the training set is sufficiently long (>104events). The basic components in RECAST add flexibility and scalability for earthquake forecasting without sacrificing performance.
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- Award ID(s):
- 1761987
- PAR ID:
- 10473631
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 50
- Issue:
- 17
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1029/2023GL103909
