Real-time Seismic Intensity Prediction using Self-supervised Contrastive GNN for Earthquake Early Warning

Seismic intensity prediction in a geographical area from early or initial seismic waves received by a few seismic stations is a critical component of an effective Earthquake Early Warning (EEW) system. State-of-the-art deep learning-based techniques for this task suffer from limited accuracy in the prediction and, more importantly, require input waveforms of a large time window from a handful number of seismic stations, which is not practical for EEW systems. To overcome the above limitations, in this paper, we propose a novel deep learning approach, Seismic Contrastive Graph Neural Network (SC-GNN) for highly accurate seismic intensity prediction using a small portion of initial seismic waveforms received by a few seismic stations. The SC-GNN comprises two key components: (i) a graph neural network (GNN) to propagate spatiotemporal information through the nodes of a graph-like structure of seismic station distribution and wave propagation, and (ii) a self-supervised contrastive learning component to train the model with larger time windows and make predictions using shorter initial waveforms. The efficacy of our proposed model is thoroughly evaluated through experiments on three real-world seismic datasets, showing superior performance over existing state-of-the-art techniques. In particular, the SC-GNN model demonstrates a substantial reduction in mean squared error (MSE) and the lowest standard deviation of the error, indicating its robustness, reliability, and a strong positive relationship between predicted and actual values. More importantly, the model maintains superior performance even with 5s input waveforms, making it particularly efficient for EEW systems.