Retrospective estimation of earthquake magnitude based on a self-optimized bayesian BiLSTM with attention mechanism

Earthquake prediction has long been a profound challenge for earthquake science. The devastating consequences of earthquakes on human lives and property necessitate the continuous pursuit of more accurate prediction methods. This study employs and compares three deep learning techniques, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) to estimate earthquake magnitudes in the southeastern Tibetan Plateau. These models' performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results indicate that the BiLSTM model outperformed other two comparative models with the smallest prediction deviation. We then integrated multi-head attention mechanism and Bayesian self-optimization to enhance both the prediction accuracy and model generalization of the BiLSTM model. Retrospective accuracy of earthquake magnitude estimation using the seismic catalog (2014-2024) in the southeastern Tibetan Plateau achieves 92.1%, with MSE = 0.08, MAE = 0.22, and RMSE = 0.28, demonstrating high effectiveness of the model. The results highlight the effectiveness of deep learning techniques in earthquake magnitude estimation, and can promote the development of data-driven earthquake prediction.