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The scientific process of earthquake forecasting involves estimating theprobability and intensity of earthquakes in a specific area within a certaintimeframe, based on seismic activity laws and observational data. Epidemic-TypeAftershock Sequence (ETAS) models, which rely on seismic empirical laws, is oneof the most commonly used methods for earthquake forecasting. However, itunderestimates feature in short-term time scale and overestimates in long-termtime scale. Convolutional Long Short-Term Memory (ConvLSTM), has emerged as apromising approach capable of extracting spatio-temporal features. Hence, wepropose a novel composite model named CL-ETAS model, which combines thestrengths of ConvLSTM and ETAS model. We conduct experimental verification onreal seismic data in Southern California. Our results show that CL-ETAS modeloutperforms both ETAS model and ConvLstm in accurately forecasting the numberof earthquake events, their magnitude, and spatial distribution. Additionally,CL-ETAS model demonstrates notable enhancements in forecast stability andinterpretability. These results offer valuable insights and recommendations forfuture earthquake forecasting endeavors.

A combining earthquake forecasting model between deep learning and Epidemic-Type Aftershock Sequence (ETAS) model