Preprints
https://doi.org/10.5194/egusphere-2023-2855
https://doi.org/10.5194/egusphere-2023-2855
11 Dec 2023
 | 11 Dec 2023
Status: this preprint is open for discussion.

Extraction of Pre-earthquake Anomalies in Borehole Strain Data Using Graph WaveNet: A Case Study of the Lushan Earthquake

Chenyang Li, Yu Duan, Ying Han, Zining Yu, Chengquan Chi, and Dewang Zhang

Abstract. On 20 April 2013, Lushan experienced a magnitude 7.0 earthquake. In seismic assessments, borehole strain meters, recognized for their remarkable sensitivity and inherent reliability in tracking crustal deformation, are extensively employed. However, traditional data processing methods encounter challenges when handling massive datasets. This study proposes using a graph wavenet graph neural network to analyze borehole strain data from multiple stations near the earthquake epicenter and establishes a node graph structure using data from four stations near the Lushan epicenter, covering years 2010–2013. After excluding the potential effects of pressure, temperature, and rainfall, we statistically analyzed the pre-earthquake anomalies. Focusing on the Guza, Xiaomiao, and Luzhou stations, which are the closest to the epicenter, the fitting results revealed two accelerations of anomalous accumulation before the earthquake. Approximately four months before the earthquake event, one acceleration suggests the pre-release of energy from a weak fault section. Conversely, the acceleration a few days before the earthquake indicated a strong fault section reaching an unstable state with accumulating strain. We tentatively infer that these two anomalous cumulative accelerations may be related to the preparation phase for a large earthquake. This study highlights the considerable potential of graph neural networks in con-ducting multi-station studies of pre-earthquake anomalies.

Chenyang Li, Yu Duan, Ying Han, Zining Yu, Chengquan Chi, and Dewang Zhang

Status: open (until 03 Apr 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2855', Anonymous Referee #1, 10 Jan 2024 reply
    • AC1: 'Reply on RC1', Chenyang Li, 17 Jan 2024 reply
    • AC2: 'Reply on RC1', Chenyang Li, 17 Jan 2024 reply
  • EC1: 'Comment on egusphere-2023-2855', Michal Malinowski, 13 Feb 2024 reply
    • AC3: 'Reply on EC1', Chenyang Li, 28 Feb 2024 reply
Chenyang Li, Yu Duan, Ying Han, Zining Yu, Chengquan Chi, and Dewang Zhang
Chenyang Li, Yu Duan, Ying Han, Zining Yu, Chengquan Chi, and Dewang Zhang

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Short summary
This study advances the field of earthquake prediction through the introduction of a novel pre-earthquake anomaly extraction method based on a graph wavelet network structure. We believe that our study makes a significant contribution to the literature because it not only demonstrates the efficacy of this innovative approach in integrating borehole strain data from multiple stations but also sheds light on distinct temporal and spatial correlations preceding seismic events.