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Machine learning predicts meter-scale laboratory earthquakes.

Reiju Norisugi¹, Yoshihiro Kaneko², Bertrand Rouet-Leduc³

¹Department of Geophysics, Kyoto University, Kyoto, Japan. norisugi.reiju.77e@st.kyoto-u.ac.jp.

Nature Communications

|October 31, 2025

Summary

This summary is machine-generated.

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Machine learning accurately predicts meter-scale laboratory quakes by analyzing acoustic emissions. This approach offers insights for forecasting natural earthquakes by tracking fault stress evolution.

Area of Science:

Geophysics
Earthquake Science
Machine Learning Applications

Background:

Growing interest in machine learning (ML) for predicting laboratory quakes (shear-slip failures) in rock friction experiments.
Uncertainty exists regarding ML applicability to larger-scale laboratory quakes and natural earthquakes due to vast timescale variations.

Purpose of the Study:

To apply an advanced ML approach to meter-scale laboratory quake data.
To assess ML's capability in predicting time-to-failure for larger-scale seismic events.

Main Methods:

Utilized an advanced ML approach on meter-scale laboratory quake data.
Employed a network representation of the event catalog for ML model training.
Compared ML predictions with a dynamic model of shear failures.

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Main Results:

Accurately predicted time-to-failure for meter-scale mainshocks, from seconds to milliseconds prior.
Demonstrated ML's ability to forecast events across timescales relevant to natural earthquakes (decades to weeks).
Identified tracking shear stress evolution on creeping faults as key to ML prediction.

Conclusions:

ML can effectively predict laboratory quakes by analyzing acoustic emission events.
Findings suggest ML can indirectly track fault stress, crucial for earthquake prediction.
Provides critical insights for short-term forecasting of natural earthquakes.