Rapid earthquake magnitude classification via P-wave strains from borehole strainmeters and Distributed Acoustic Sensing

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Abstract
Distributed Acoustic Sensing (DAS) offers a promising approach for earthquake early warning (EEW) in settings where seismic networks are costly to maintain. By repurposing fiber optic cables as dense strainmeter arrays, DAS enables real-time earthquake detection wherever those fibers are accessible. However, poor azimuthal coverage and challenges in estimating magnitude from strain measurements remain key hurdles in applying DAS to earthquake monitoring. Here, we develop a machine learning method to distinguish moderate-to-large (defined here as M ≥ 5.4) earthquakes from smaller ones within the first 4 sec of a strain waveform after a P-wave arrival without determining the earthquake location. Using ensemble decision tree models trained on borehole strainmeter data (3.5 ≤ M ≤ 7.1) and tested on onshore DAS waveforms (including the 2024 M7 Offshore Cape Mendocino earthquake), we find that low-frequency (0.2–0.5 Hz) continuous wavelet transform coefficients are the strongest predictors of magnitude, in addition to strain amplitude. Both DAS and borehole strainmeters effectively capture long-period strain signals, making these results valuable for EEW systems. Our method shows high precision compared to the real-time EEW system, ShakeAlert®, supporting the position that DAS is a viable technology for earthquake monitoring and magnitude classification. Fiber optic cables plus machine learning could aid earthquake early warning by estimating earthquake size within the first few seconds of shaking.

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