Preprints
https://doi.org/10.5194/egusphere-2025-1130
https://doi.org/10.5194/egusphere-2025-1130
14 Mar 2025
 | 14 Mar 2025

Research on the Extraction of Pre-Seismic Anomalies in Borehole Strain Data of the Mado Earthquake Based on the SVMD-Informer Model

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

Abstract. Earthquake is a major natural disaster triggered by the accumulation and release of crustal stress, and the accurate extraction of pre-seismic anomaly signals is crucial to improve the earthquake prediction capability. In this study, an anomaly detection method for borehole strain data based on the combination of Segmented Variational Modal Decomposition (SVMD) and Informer network is proposed, and a pre-seismic anomaly extraction study is carried out for the 2021 Mado Ms7.4 earthquake in Qinghai. The SVMD method effectively solves the memory limitation problem of traditional Variational Modal Decomposition (VMD) when dealing with large-scale data through the sliding-window mechanism, and at the same time maintains the correlation between the data. The Informer network significantly reduces the computational complexity of the long-series prediction and realizes the high-precision one-time long-time series prediction by utilizing its ProbSparse self-attention mechanism and self-attention distillation. By analyzing the borehole strain data from the Mengyuan station, this study identifies the accelerated anomaly accumulation phenomenon in the two stages before the Mado earthquake: in the first stage, the number of anomalous days shows an accelerated growth starting from about three months before the earthquake (February 13, 2021); in the second stage, the anomalous accumulation tendency is further intensified since the second month before the earthquake (the end of March, 2021), and the accumulation curve shows a typical S-shape growth characteristic. The results are highly consistent with the time windows of the index of microwave radiation anomaly (IMRA), outward long-wave radiation (OLR) and geoelectric field anomalies, and with the subsurface-to-atmosphere multilayer anomalies (e.g., Benionff strain, CO concentration, electron concentration anomalies, etc.), which indicate that the borehole strain anomalies are closely related to the gestation process of the Mado earthquake. This study provides a new method for the extraction of pre-seismic anomalies based on machine learning, and provides an important basis for understanding earthquake precursors.

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Journal article(s) based on this preprint

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
Nat. Hazards Earth Syst. Sci., 25, 3603–3618, https://doi.org/10.5194/nhess-25-3603-2025,https://doi.org/10.5194/nhess-25-3603-2025, 2025
Short summary
Shanzhi Dong, Jie Zhang, Changfeng Qin, Yu Duan, Chenyang Li, Chengquan Chi, and Zhichao Zhang

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-1130', Anonymous Referee #1, 05 Apr 2025
    • AC2: 'Reply on RC1', shanzhi dong, 13 May 2025
  • RC2: 'Comment on egusphere-2025-1130', Anonymous Referee #2, 07 Apr 2025
    • AC1: 'Reply on RC2', shanzhi dong, 13 May 2025

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-1130', Anonymous Referee #1, 05 Apr 2025
    • AC2: 'Reply on RC1', shanzhi dong, 13 May 2025
  • RC2: 'Comment on egusphere-2025-1130', Anonymous Referee #2, 07 Apr 2025
    • AC1: 'Reply on RC2', shanzhi dong, 13 May 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (01 Jul 2025) by Filippos Vallianatos
AR by shanzhi dong on behalf of the Authors (04 Jul 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (05 Jul 2025) by Filippos Vallianatos
RR by Anonymous Referee #2 (17 Jul 2025)
ED: Publish subject to minor revisions (review by editor) (20 Jul 2025) by Filippos Vallianatos
AR by shanzhi dong on behalf of the Authors (25 Jul 2025)  Author's tracked changes   Manuscript 
EF by Polina Shvedko (25 Jul 2025)  Author's response 
ED: Publish as is (04 Aug 2025) by Filippos Vallianatos
AR by shanzhi dong on behalf of the Authors (06 Aug 2025)  Manuscript 

Journal article(s) based on this preprint

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
Nat. Hazards Earth Syst. Sci., 25, 3603–3618, https://doi.org/10.5194/nhess-25-3603-2025,https://doi.org/10.5194/nhess-25-3603-2025, 2025
Short summary
Shanzhi Dong, Jie Zhang, Changfeng Qin, Yu Duan, Chenyang Li, Chengquan Chi, and Zhichao Zhang
Shanzhi Dong, Jie Zhang, Changfeng Qin, Yu Duan, Chenyang Li, Chengquan Chi, and Zhichao Zhang

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Short summary
This study proposes an anomaly detection method for borehole strain data based on the combination of segmented variational mode decomposition (SVMD) and Informer network. We believe that this study is an important contribution in the literature because it introduces a new method for predicting seismicity by combining advanced signal processing and machine learning techniques, showing its promising application in seismic network data analysis.
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