Articles | Volume 25, issue 9
https://doi.org/10.5194/nhess-25-3603-2025
https://doi.org/10.5194/nhess-25-3603-2025
Research article
 | 
25 Sep 2025
Research article |  | 25 Sep 2025

Research on the extraction of pre-seismic anomalies in borehole strain data of the Maduo earthquake based on the SVMD-Informer model

Shanzhi Dong, Jie Zhang, Changfeng Qin, Yu Duan, Chenyang Li, Chengquan Chi, and Zhichao Zhang

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Cited articles

Barbour, A. J. and Agnew, D. C.: Detection of Seismic Signals Using Seismometers and Strainmeters, B. Seismol. Soc. Am., 102, 2484–2490, https://doi.org/10.1785/0120110298, 2012. 
Bilal, M. A., Ji, Y., Wang, Y., Akhter, M. P., and Yaqub, M.: Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN), Applied Sciences, 12, 7548, https://doi.org/10.3390/app12157548, 2022. 
Chen, C., Chen, X., Guo, C., and Hang, P.: Trajectory Prediction for Autonomous Driving Based on Structural Informer Method, IEEE T. Autom. Sci. Eng., 22, 17452–17463, https://doi.org/10.1109/tase.2023.3342978, 2023. 
Chi, C., Li, C., Han, Y., Yu, Z., Li, X., and Zhang, D.: Pre-earthquake anomaly extraction from borehole strain data based on machine learning, Scientific Reports, 13, 20095, https://doi.org/10.1038/s41598-023-47387-z, 2023. 
Chi, C., Zhu, K., Yu, Z., Fan, M., Li, K., and Sun, H.: Detecting Earthquake-Related Borehole Strain Data Anomalies With Variational Mode Decomposition and Principal Component Analysis: A Case Study of the Wenchuan Earthquake, IEEE Access, 7, 157997–158006, https://doi.org/10.1109/access.2019.2950011, 2019. 
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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.
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