Bearbmagovva

Analysis of borehole strain anomalies before the 2017 Jiuzhaigou Ms 7.0 earthquake based on a graph neural network

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Publikašuvnnas:Natural Hazards and Earth System Sciences vol. 25, no. 1 (2025), p. 231
Váldodahkki: Li, Chenyang
Eará dahkkit: Qin, Changfeng, Zhang, Jie, Duan, Yu, Chi, Chengquan
Almmustuhtton:
Copernicus GmbH
Fáttát:
Earthquakes
Boreholes
Wavelet transforms
Strain analysis
Topography
Signal processing
Accumulation
Neural networks
Data analysis
Statistical methods
Statistical models
Case studies
Datasets
Seismic activity
Fourier transforms
Graph neural networks
Earthquake prediction
Decomposition
Methods
Anomalies
Strain energy
Environmental
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Abstrákta:On 8 August 2017, a strong earthquake of magnitude 7.0 occurred in Jiuzhaigou, Sichuan Province, China. To assess pre-earthquake anomalies, we utilized variational mode decomposition to preprocess borehole strain observation data and combined them with a graph WaveNet neural network model to process data from multiple stations. We obtained 1-year data from four stations near the epicenter as the training dataset and data from 1 January to 10 August 2017 as the test dataset. For the prediction results of the variational mode decomposition–graph WaveNet model, the anomalous days were extracted using statistical methods, and the results of anomalous-day accumulation at multiple stations showed that an increase in the number of anomalous days occurred 15–32 d before the earthquake. The acceleration effect of anomalous accumulation was most obvious 20 d before the earthquake, and an increase in the number of anomalous days also occurred in the 1 to 3 d post-earthquake. We tentatively deduce that the pre-earthquake anomalies are caused by the diffusion of strain energy near the epicenter during the accumulation process, which can be used as a signal of pre-seismic anomalies, whereas the post-earthquake anomalies are caused by the frequent occurrence of aftershocks.
ISSN:1561-8633
1684-9981
DOI:10.5194/nhess-25-231-2025
Gáldu:Engineering Database