| Autor: |
Bilal MA; College of Instrumentation & Electrical Engineering, Jilin University, Changchun 130061, China., Ji Y; College of Instrumentation & Electrical Engineering, Jilin University, Changchun 130061, China., Wang Y; College of Geoexploration Science & Technology, Jilin University, Changchun 130061, China.; Institute of Integrated Information for Mineral Resources Prediction, Jilin University, Changchun 130026, China., Akhter MP; Riphah College of Computing, Faisalabad Campus, Riphah International University, Faisalabad 38000, Pakistan., Yaqub M; Riphah College of Computing, Faisalabad Campus, Riphah International University, Faisalabad 38000, Pakistan.; Faculty of Information Technology, Beijing University of Technology, Beijing 100021, China. |
| Abstrakt: |
Earthquakes threaten people, homes, and infrastructure. Early warning systems provide prior warning of oncoming significant shaking to decrease seismic risk by providing location, magnitude, and depth information of the event. Their usefulness depends on how soon a strong shake begins after the warning. In this article, the authors implement a deep learning model for predicting earthquakes. This model is based on a graph convolutional neural network with batch normalization and attention mechanism techniques that can successfully predict the depth and magnitude of an earthquake event at any number of seismic stations in any number of locations. After preprocessing the waveform data, CNN extracts the feature map. Attention mechanism is used to focus on important features. The batch normalization technique takes place in batches for stable and faster training of the model by adding an extra layer. GNN with extracted features and event location information predicts the event information accurately. We test the proposed model on two datasets from Japan and Alaska, which have different seismic dynamics. The proposed model achieves 2.8 and 4.0 RMSE values in Alaska and Japan for magnitude prediction, and 2.87 and 2.66 RMSE values for depth prediction. Low RMSE values show that the proposed model significantly outperforms the three baseline models on both datasets to provide an accurate estimation of the depth and magnitude of small, medium, and large-magnitude events. |
