Spatiotemporal Magnitude Classification: A New Approach Using a Graph Convolution LSTM Model

ABSTRACT Earthquake prediction remains one of the most challenging tasks in natural hazard research due to the complexity and heterogeneity of seismic processes in space and time. To address this, we propose a deep learning (DL) framework based on graph convolutional and recurrent neural networks (RNNs) to forecast the maximum seismic magnitude in different regions of Chile. Using a cleaned and spatially segmented catalog of seismic events, we construct a graph where each node represents a seismic cluster derived from K‐means clustering, with edges reflecting spatial proximity. Two models are evaluated: a standard Long Short‐Term Memory (LSTM) network and a hybrid Graph Convolutional Network‐LSTM (GCN‐LSTM), which incorporates both temporal dynamics and spatial dependencies. Our results show that the GCN‐LSTM model significantly outperforms the simple LSTM in terms of F1‐score and recall, especially in regions with complex seismic activity. This demonstrates the advantage of graph‐based neural models in capturing spatial correlations and improving earthquake magnitude prediction at a regional scale.