Explainable artificial intelligence for short-term data-driven aftershock forecasts

The catastrophic nature of earthquakes drives the need for understanding seismic events, as well as for providing forecasts of when these are likely to occur. Due to the clustering nature of earthquakes, large magnitude events often trigger aftershocks that occur close to the mainshock in both space and time. In this study, we use a convolutional neural network to develop a data-driven spatiotemporal model to forecast next-day seismicity in an attempt to provide information that can contribute to answering one of the most pressing questions: whether a larger magnitude earthquake is to be expected after an intermediate magnitude event. We design our test to estimate expected seismicity within one day after earthquakes of magnitude four and above. We assemble a comprehensive dataset of earthquake catalogues from diverse tectonic regions to achieve a representative sample of input data and use it to create weekly spatiotemporal sequences of seismicity consisting of daily maps. Leveraging the predictive power of deep learning, our model uncovers complex patterns within this large dataset to produce next-day expected seismicity rate and magnitude forecasts in regions of interest. We use gradient-weighted class activation mapping (Grad-CAM) to provide visual explanations of the produced forecasts. We evaluate the performance of our forecasting model using data science and earthquake forecasting metrics and compare against persistence, which assumes no change between consecutive days, echoing typical experimental setups of forecasting models. Furthermore, we use a time series forecasting foundation model to generate next-day aftershock forecasts on the same dataset and compare these results against those produced by the convolutional neural network. We find that deep learning approaches are a promising solution for producing short-term aftershock forecasts, providing valuable insights that can contribute to better earthquake preparedness and response and be integrated with disciplinary statistics and physics-based forecasts.