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Deep learning for laboratory earthquake prediction and autoregressive forecasting of fault zone stress
We demonstrate that DL models based on Long-Short Term Memory (LSTM) and Convolution Neural Networks predict labquakes under several conditions, and that fault zone stress can be predicted with fidelity, confirming that acoustic energy is a fingerprint of fault zone stress.
Attention network forecasts time-to-failure in laboratory shear experiments
Our data were generated in a laboratory setting using a biaxial shearing device with granular fault gouge intended to mimic the conditions of tectonic faults.
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