A combining earthquake forecasting model between deep learning and epidemic-type aftershock sequence (ETAS) model

The scientific process of earthquake forecasting involves estimating the probability and intensity of earthquakes in a specific area within a certain timeframe, based on seismic activity features and observational data. Among the various methodologies, epidemic-type aftershock sequence (ETAS) models, rooted in seismic empirical laws, stand as widely used tools for earthquake forecasting. In this study, we introduce the CL-ETAS model, a novel approach that integrates convolutional long short-term memory (ConvLSTM), a deep learning model, with the ETAS model. Specifically, we leverage the forecasting outputs of ETAS to enhance both the training and forecasting processes within the ConvLSTM framework. Through forecasting tests, our findings illustrate the effectiveness of the CL-ETAS model in capturing the trends observed in earthquake numbers ($M \ge 3$) in Southern California following three main shocks. Overall, our model outperforms both a simple ETAS model and ConvLSTM in this context.