Forecasting Earthquake Magnitude and Epicenter by Incorporating Spatiotemporal Priors Into Deep Neural Networks

Liu, Jie; Gao, Chulin; Wang, Peixiao

doi:10.1109/tgrs.2023.3281784

IEEE Trans. Geosci. Remote Sensing

2023

DOI: 10.1109/tgrs.2023.3281784

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Forecasting Earthquake Magnitude and Epicenter by Incorporating Spatiotemporal Priors Into Deep Neural Networks

Jie Liu

Chulin Gao

Peixiao Wang

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2024

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Cited by 5 publications

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A Ghost-Attention Network for Discriminating Tectonic and Non-Tectonic Events on a Small and Imbalanced Dataset

Liu,

Ren,

Chen

et al. 2024

IEEE Access

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Discrimination between tectonic and non-tectonic events is crucial to assess seismic hazards and manage associated risks. However, the discrimination process is challenging due to the imbalanced distribution of tectonic and non-tectonic events. In this paper, we propose a ghost-attention network (GA-Net) consisting of multiple ghost modules and convolutional block attention modules (CBAMs) to solve this problem. Ghost module allows the network to extract feature maps using cost effective operations, which are suitable for small and unbalanced training sets. CBAM emphasizes meaningful features along channel and spatial axes, effectively learning which information to emphasize or suppress. We train the proposed GA-Net using seismic data from Shaanxi Province in China. The evaluation shows that on the test set, the proposed GA-Net achieves sensitivity, specificity, and accuracy of 96.40%, 93.24%, and 95.75%, respectively. In addition, a comparative analysis with several state-of-the-art networks establishes its superiority. Our GA-Net exhibits robust generalized performance on several training sets with different imbalanced levels and an independent dataset.

show abstract

A Ghost-Attention Network for Discriminating Tectonic and Non-Tectonic Events on a Small and Imbalanced Dataset

Liu,

Ren,

Chen

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Advances in Earthquake Prevention and Reduction Based on Machine Learning: A Scoping Review

Zhao,

Lv,

Liu

2024

IEEE Access

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Short-Term Earthquake Forecasting via Self-Supervised Learning

Jiang,

Yu,

Zheng

2024

IEEE Geosci. Remote Sensing Lett.

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Forecasting Earthquake Magnitude and Epicenter by Incorporating Spatiotemporal Priors Into Deep Neural Networks

Cited by 5 publications

References 50 publications

A Ghost-Attention Network for Discriminating Tectonic and Non-Tectonic Events on a Small and Imbalanced Dataset

A Ghost-Attention Network for Discriminating Tectonic and Non-Tectonic Events on a Small and Imbalanced Dataset

Advances in Earthquake Prevention and Reduction Based on Machine Learning: A Scoping Review

Short-Term Earthquake Forecasting via Self-Supervised Learning

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