Rapid Earthquake Magnitude Estimation Using Deep Learning

Zhao, Sha; Xu, Yizhi; Luo, Zhiling; Liu, Jie; Song, Jindong; Li, Shijian; Panl, Gang

doi:10.1109/ijcnn55064.2022.9892141

2022 International Joint Conference on Neural Networks (IJCNN) 2022

DOI: 10.1109/ijcnn55064.2022.9892141

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Rapid Earthquake Magnitude Estimation Using Deep Learning

Sha Zhao

Yizhi Xu

Zhiling Luo

et al.

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2024

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Cited by 1 publication

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Real-Time On-Site Ground-Motion Prediction Using ResNet

Yang,

Chen,

Chin

2024

Seismological Research Letters

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On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the waveform of the initial P-wave. A ResNet architecture model is developed to address the degradation problem in multilayered models, thereby enhancing performance. The model exhibits high performance for a test dataset, achieving F1-scores higher than 90% over various time windows, and outperforms the traditional progressive displacement value (Pdv) method and newer convolutional neural network (CNN) methods. Ground motion of two actual earthquakes are applied to test the model and validate its prediction capabilities, and the results indicate that the proposed model has higher accuracy and speed than Pdv and CNN methods. In addition, the model is integrated with the Earthworm earthquake early warning system for real-time waveform analysis and prediction, extending the model’s applicability beyond experimental stages to online systems. The proposed method is also tested using data from earthquakes that occurred in March 2023.

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Real-Time On-Site Ground-Motion Prediction Using ResNet

Yang,

Chen,

Chin

2024

Seismological Research Letters

View full text Add to dashboard Cite

show abstract

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Rapid Earthquake Magnitude Estimation Using Deep Learning

Cited by 1 publication

References 26 publications

Real-Time On-Site Ground-Motion Prediction Using ResNet

Real-Time On-Site Ground-Motion Prediction Using ResNet

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