Articles | Volume 25, issue 9
https://doi.org/10.5194/nhess-25-3603-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-3603-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research on the extraction of pre-seismic anomalies in borehole strain data of the Maduo earthquake based on the SVMD-Informer model
Shanzhi Dong
School of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, 571158, China
Jie Zhang
School of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, 571158, China
Changfeng Qin
School of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, 571158, China
Yu Duan
School of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, 571158, China
Chenyang Li
School of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, 571158, China
School of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, 571158, China
Zhichao Zhang
CORRESPONDING AUTHOR
School of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, 571158, China
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Chenyang Li, Changfeng Qin, Jie Zhang, Yu Duan, and Chengquan Chi
Nat. Hazards Earth Syst. Sci., 25, 231–245, https://doi.org/10.5194/nhess-25-231-2025, https://doi.org/10.5194/nhess-25-231-2025, 2025
Short summary
Short summary
In this study, we advance the field of earthquake prediction by introducing a pre-seismic anomaly extraction method based on the structure of a graph WaveNet model, which reveals the temporal correlation and spatial correlation of the strain observation data from different boreholes prior to the occurrence of an earthquake event.
Chenyang Li, Yu Duan, Ying Han, Zining Yu, Chengquan Chi, and Dewang Zhang
Solid Earth, 15, 877–893, https://doi.org/10.5194/se-15-877-2024, https://doi.org/10.5194/se-15-877-2024, 2024
Short summary
Short summary
This study advances the field of earthquake prediction by introducing an extraction method for pre-seismic anomalies based on the structure of Graph WaveNet networks. We believe that our study makes a significant contribution to the literature as it not only demonstrates the effectiveness of this innovative approach in integrating borehole strain data from multiple stations but also reveals distinct temporal and spatial correlations preceding earthquake events.
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Short summary
This paper proposes a method for extracting anomalies in borehole strain data by combining segmented variational modal decomposition (SVMD) and the Informer network. We believe this study makes an important contribution to the literature because it introduces a new method for predicting seismic activity by combining advanced signal processing and machine learning techniques, demonstrating its potential in seismic network data analysis.
This paper proposes a method for extracting anomalies in borehole strain data by combining...
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