Talk · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications · August 7, 2025
CNN-LSTM-Based Forecasting of Peak Ground Acceleration from Early Seismic Waveforms
Marat Nurtas, Aizhan Altaibek, Ayazhan Kumarkhanova, Tahmina Nessipbay
Abstract
Accurate and rapid peak ground acceleration (PGA) forecasting is vital for effective earthquake early warning (EEW) systems. This study presents a deep learning approach that combines convolutional and bidirectional recurrent layers with an attention mechanism to estimate PGA using the first three seconds of three-component seismic waveforms. The model is trained on strong-motion records from Japan’s K-NET network, covering events from March 2022 to March 2025. We compare three architectures: a fully connected neural network (ANN), a plain LSTM model, and our final CNN–BiLSTM model with attention mechanism. The proposed model outperforms both baselines, achieving a validation Mean Absolute Error of 2.61 gal and R2of 0.714. These results highlight the effectiveness of combining convolutional and sequential modeling with an attention mechanism for real-time seismic forecasting.
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