Forecasting surface velocity fields associated with laboratory seismic cycles using Deep Learning.

It has been recently demonstrated that Machine Learning (ML) can predict laboratory earthquakes. Here we propose a prediction framework that allows forecasting future surface velocity fields from past ones for analog experiments of megathrust seismic cycles. Using data from two types of experiments, we explore the prediction performances of multiple Deep Learning (DL) and ML algorithms. In such a self-supervised regression, no feature extraction is required and the entire seismic cycle is forecasted. The onset, magnitude, and propagation of analog earthquakes can thus be predicted at different prediction horizons. From all architectures tested in this study, convolutional recurrent neural networks (CNN-LSTM and CONVLSTM) provide the best predictions although their performances depend on experiment characteristics and hyperparameters tuning. Analog earthquakes can be successfully anticipated up to a horizon of the order of their duration. This laboratory-based study may open new avenues for transfer learning applications with data from natural subduction zones.