Short-Term Earthquake Forecasting via Self-Supervised Learning

Due to the infrequency of major earthquakes (magnitude 5 or above) and the highly nonlinear nature of seismic activities, short-term earthquake forecasting (hours to weeks) faces the challenge of lacking seismic samples and struggling with acquiring seismic-related knowledge. To address these issues, we propose a novel self-supervised learning for earthquake forecasting (SSL-EF) approach, namely, self-supervised learning (SSL) for earthquake forecasting, which has two distinctive characteristics: 1) an efficient pretext task leveraging temporal signal patterns for knowledge acquisition and 2) the knowledge transfer mitigating the constraints posed by limited seismic samples. Specifically, we first design the prediction task as a pretext task, leveraging the past week’s observational data to predict the coming week’s data. Subsequently, we set the classification task as a downstream task, focusing on whether a major earthquake occurs in the coming week. Moreover, transferring knowledge from the prediction task facilitates training the classification model on a small-scale and balanced dataset obtained through undersampling. Compared with ten competing methods, our SSL-EF yields state-of-the-art area under the curve (AUC), with a 21.98% relative improvement on the real-world dataset.