Analysis of borehole strain anomalies before the 2017 Jiuzhaigou Ms 7.0 earthquake based on a graph neural network
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| Publicado en: | Natural Hazards and Earth System Sciences vol. 25, no. 1 (2025), p. 231 |
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Copernicus GmbH
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.5194/nhess-25-231-2025 |2 doi | |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Li, Chenyang |u 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 | |
| 245 | 1 | |a Analysis of borehole strain anomalies before the 2017 Jiuzhaigou Ms 7.0 earthquake based on a graph neural network | |
| 260 | |b Copernicus GmbH |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Earthquakes | ||
| 653 | |a Boreholes | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Strain analysis | ||
| 653 | |a Topography | ||
| 653 | |a Signal processing | ||
| 653 | |a Accumulation | ||
| 653 | |a Neural networks | ||
| 653 | |a Data analysis | ||
| 653 | |a Statistical methods | ||
| 653 | |a Statistical models | ||
| 653 | |a Case studies | ||
| 653 | |a Datasets | ||
| 653 | |a Seismic activity | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Graph neural networks | ||
| 653 | |a Earthquake prediction | ||
| 653 | |a Decomposition | ||
| 653 | |a Methods | ||
| 653 | |a Anomalies | ||
| 653 | |a Strain energy | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Qin, Changfeng |u 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 | |
| 700 | 1 | |a Zhang, Jie |u 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 | |
| 700 | 1 | |a Duan, Yu |u 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 | |
| 700 | 1 | |a Chi, Chengquan |u 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 | |
| 773 | 0 | |t Natural Hazards and Earth System Sciences |g vol. 25, no. 1 (2025), p. 231 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3154997984/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3154997984/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3154997984/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |