Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-231-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-231-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jan 2025
Research article |
| 14 Jan 2025
| 14 Jan 2025
Analysis of borehole strain anomalies before the 2017 Jiuzhaigou Ms 7.0 earthquake based on a graph neural network
Chenyang Li
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
Changfeng Qin
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
Jie Zhang
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
Yu Duan
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
Chengquan Chi
CORRESPONDING AUTHOR
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
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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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This study advances the field of earthquake prediction by introducing an extraction method for pre-seismic anomalies based on the structure of Graph WaveNet networks. We believe that our study makes a significant contribution to the literature as it not only demonstrates the effectiveness of this innovative approach in integrating borehole strain data from multiple stations but also reveals distinct temporal and spatial correlations preceding earthquake events.
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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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This study advances the field of earthquake prediction by introducing an extraction method for pre-seismic anomalies based on the structure of Graph WaveNet networks. We believe that our study makes a significant contribution to the literature as it not only demonstrates the effectiveness of this innovative approach in integrating borehole strain data from multiple stations but also reveals distinct temporal and spatial correlations preceding earthquake events.
Cited articles
Abu Bakar, M. A., Mohd Ariff, N., Abu Bakar, S., Goh, P. C., and Rajendran, R.: Peramalan Kualiti Udara menggunakan Kaedah Pembelajaran Mendalam Rangkaian Perlingkaran Temporal (TCN), Sains Malays., 51, 3785–3793, https://doi.org/10.17576/jsm-2022-5111-22, 2021.
Akhoondzadeh, M., De Santis, A., Marchetti, D., and Shen, X.: Swarm-TEC Satellite Measurements as a Potential Earthquake Precursor Together With Other Swarm and CSES Data: The Case of Mw 7.6 2019 Papua New Guinea Seismic Event, Front. Earth Sci., 10, 820189, https://doi.org/10.3389/feart.2022.820189, 2022.
Bai, S., Kolter, J. Z., and Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, arXiv [preprint], https://doi.org/10.48550/arXiv.1803.01271, 2018.
Barino, F. O., Silva, V. N. H., Lopez-Barbero, A. P., De Mello Honorio, L., and Santos, A. B. D.: Correlated Time-Series in Multi-Day-Ahead Streamflow Forecasting Using Convolutional Networks, IEEE Access, 8, 215748–215757, https://doi.org/10.1109/access.2020.3040942, 2020.
Bilal, M. A., Ji, Y., Wang, Y., Akhter, M. P., and Yaqub, M.: Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN), Appl. Sci.-Basel, 12, 7548, https://doi.org/10.3390/app12157548, 2022.
Bufe, C. G. and Varnes, D. J.: Predictive Modeling of the Seismic Cycle of the Greater San-Francisco Bay-Region, J. Geophys. Res.-Sol. Ea., 98, 9871–9883, https://doi.org/10.1029/93JB00357, 1993.
Bufe, C. G., Nishenko, S. P., and Varnes, D. J.: Seismicity Trends and Potential for Large Earthquakes in the Alaska-Aleutian Region, Pure Appl. Geophys., 142, 83–99, https://doi.org/10.1007/BF00875969, 1994.
Chen, L., Yan, X., Su, X., Jiang, Z., Wang, Z., and Hu, Y.: The Coseismic Comparative Analysis of Gaotai YRY Borehole Strain Gauge and BBVS-120 Seismometer of Maduo Ms 7.4 Earthquake in Qinghai, Journal of Geodesy and Geodynamics, 44, 539–544, https://doi.org/10.14075/j.jgg.2023.08.172, 2024.
Chen, Y., Shu, T., Zhou, X. K., Zheng, X. Z., Kawai, A., Fueda, K., Yan, Z., Liang, W., and Wang, K. I. K.: Graph Attention Network With Spatial-Temporal Clustering for Traffic Flow Forecasting in Intelligent Transportation System, IEEE T. Intell. Transp., 24, 8727–8737, https://doi.org/10.1109/TITS.2022.3208952, 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.
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, Sci. Rep.-UK, 13, 20095, https://doi.org/10.1038/s41598-023-47387-z, 2023.
Chi, S.: Strain Anomalies Before Wenchuan and Lushan Earthquakes Recorded by Component Borehole Strainmeter, Science and Technology Review, 31, 27–30, https://doi.org/10.3981/j.issn.1000-7857.2013.12.004, 2013.
Dauphin, Y. N., Fan, A., Auli, M., and Grangier, D.: Language modeling with gated convolutional networks, International conference on machine learning, Sydney, Australia, 933–941, arXiv [preprint], https://doi.org/10.48550/arXiv.1612.08083, 2017.
Deng, G., Liang, F., Li, X., Zhao, J., Liu, H., and Wang, X.: S-transform spectrum decomposition technique in the application of the extraction of weak seismic signals, Chinese J. Geophys.-Ch., 58, 4594–4604, 2015.
De Santis, A.: Geosystemics, Entropy and Criticality of Earthquakes: A Vision of Our Planet and a Key of Access, Nonlinear Phenomena in Complex Systems: From Nano to Macro Scale, WOS: 000345122100001, Springer, https://doi.org/10.1007/978-94-017-8704-8_1, 2014.
De Santis, A., Balasis, G., Pavón-Carrasco, F. J., Cianchini, G., and Mandea, M.: Potential earthquake precursory pattern from space: The 2015 Nepal event as seen by magnetic Swarm satellites, Earth Planet. Sc. Lett., 461, 119–126, https://doi.org/10.1016/j.epsl.2016.12.037, 2017.
Dobrovolsky, I. P., Zubkov, S. I., and Miachkin, V. I.: Estimation of the size of earthquake preparation zones, Pure Appl. Geophys., 117, 1025–1044, https://doi.org/10.1007/BF00876083, 1979.
Dragomiretskiy, K. and Zosso, D.: Variational Mode Decomposition, IEEE T. Signal Proces., 62, 531–544, https://doi.org/10.1109/tsp.2013.2288675, 2014.
Gan, Z., Li, C., Zhou, J., and Tang, G.: Temporal convolutional networks interval prediction model for wind speed forecasting, Electr. Pow. Syst. Res., 191, 106865, https://doi.org/10.1016/j.epsr.2020.106865, 2021.
Gopali, S., Abri, F., Siami-Namini, S., and Namin, A. S.: A Comparison of TCN and LSTM Models in Detecting Anomalies in Time Series Data, in: 2021 IEEE International Conference on Big Data (Big Data), Electr. Network, 15–18 December 2021, Orlando, FL, USA, 2415–2420, https://doi.org/10.1109/BigData52589.2021.9671488, 2021.
Guo, Y., Zhuo, Y., Liu, P., Chen, S., and Ma, J.: Experimental Study of Observable Deformation Process in Fault Meta-Instability State before Earthquake Generation, Geodynamics and Tectonophysics, 11, 417–430, https://doi.org/10.5800/gt-2020-11-2-0483, 2020.
Hafeez, A., Ehsan, M., Abbas, A., Shah, M., and Shahzad, R.: Machine learning-based thermal anomalies detection from MODIS LST associated with the Mw 7.7 Awaran, Pakistan earthquake, Nat. Hazards, 111, 2097–2115, https://doi.org/10.1007/s11069-021-05131-8, 2022.
Kipf, T. N. and Welling, M.: Semi-supervised classification with graph convolutional networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1609.02907, 2016.
Kirisci, M. and Cagcag Yolcu, O.: A New CNN-Based Model for Financial Time Series: TAIEX and FTSE Stocks Forecasting, Neural Process. Lett., 54, 3357–3374, https://doi.org/10.1007/s11063-022-10767-z, 2022.
Kitagawa, Y., Koizumi, N., Takahashi, M., Matsumoto, N., and Sato, T.: Changes in groundwater levels or pressures associated with the 2004 earthquake off the west coast of northern Sumatra (M 9.0), Earth Planets Space, 58, 173–179, https://doi.org/10.1186/BF03353375, 2006.
Lei, Q., Zhai, S., Mao, Y., and Zhu, Z.: Analysis and research on baicheng Ms 5.4 earthquake based on Hilbert-Huang transform, Inland Earthquake, 36, 265–272, https://doi.org/10.16256/j.issn.1001-8956.2022.03.009, 2022.
Li, F., Zhang, B., Verma, S., and Marfurt, K. J.: Seismic signal denoising using thresholded variational mode decomposition, Explor. Geophys., 49, 450–461, https://doi.org/10.1071/eg17004, 2018.
Li, X., Ren, X., Lou, Y., Gao, S., Ge, X., and Liao, X.: An SG-ClM model mapping technology study via knowledge graph and graph attention network, Science and Technology Review, 41, 124–132, 2023.
Liang, A.: Research on Social Network Node Classification Based on Graph Neural Networks, Master's degree, Chongqing University of Technology, China, https://doi.org/10.27753/d.cnki.gcqgx.2023.001129, 2023.
Lin, J.-W.: An adaptive Butterworth spectral-based graph neural network for detecting ionospheric total electron content precursor prior to the Wenchuan earthquake on 12 May 2008, Geocarto Int., 37, 14292–14308, https://doi.org/10.1080/10106049.2022.2087752, 2022.
Liu, C., Wang, G., Shi, Z., and Zhao, D.: Groundwater precursor anomalies of the 7.0 Sichuan Lushan earthquake, in: Proceedings of the 2014 China Geoscience Joint Annual Conference-Topic 10: Fluid geoscience and the genesis of mega-mineralised zones and major natural disasters, 20–23 October 2014, Beijing, China, 2014.
Liu, J.: Research On Seismic Precursor Anomaly Detection, Master's degree, Southeast University, China, https://doi.org/10.27014/d.cnki.gdnau.2021.002788, 2022.
Liu, X., Yang, J., Chen, C., Guan, Y., Chen, G., Zhao, W., and Hong, M.: The argumentation of properties of borehole system at Linxia station, China, Chinese J. Geophys.-Ch., 59, 3343–3353, https://doi.org/10.6038/cjg20160918, 2016a.
Liu, Y., Yang, G., Li, M., and Yin, H.: Variational mode decomposition denoising combined the detrended fluctuation analysis, Signal Process., 125, 349–364, https://doi.org/10.1016/j.sigpro.2016.02.011, 2016b.
Lou, J. and Tian, J.: Review on seismic strain-wave observation based on high-resolution borehole strainmeters, Prog. Geophys., 37, 51–58, https://doi.org/10.6038/pg2022FF0050, 2022.
Ma, D., Niu, A., Yuan, S., Wang, X., and Wang, L.: Analysis on the influence of short-term atmospheric pressure fluctuation on body strain data based on spectrum characteristics, Inland Earthquake, 25, 255–262, https://doi.org/10.16256/j.issn.1001-8956.2011.03.010, 2011.
Manero, J., Béjar, J., and Cortés, U.: “Dust in the Wind …”, Deep Learning Application to Wind Energy Time Series Forecasting, Energies, 12, 2385, https://doi.org/10.3390/en12122385, 2019.
Nishimura, T.: Triggering of volcanic eruptions by large earthquakes, Geophys. Res. Lett., 44, 7750–7756, https://doi.org/10.1002/2017GL074579, 2017.
Qiu, Z., Kan, B., and Tang, L.: Conversion and application of 4-component borehole strainmeter data, Earthquake, 29, 83–89, 2009.
Qiu, Z., Yang, G., Tang, L., Guo, Y., and Zhang, B.: Abnormal Strain Changes Prior to the M 7.0 Lushan Earthquake Observed by a Borehole Strainmeter at Guzan, Journal of Geodesy and Geodynamics, 35, 158–161 + 166, https://doi.org/10.14075/j.jgg.2015.01.036, 2015.
Rao, D., Huang, M., Shi, X., Yu, Z., and He, Z.: A Microseismic Signal Denoising Algorithm Combining VMD and Wavelet Threshold Denoising Optimized by BWOA, CMES-Comp. Model. Eng., 141, 187–217, https://doi.org/10.32604/cmes.2024.051402, 2024.
Shan, F., Li, F., Wang, Z., Ji, P., Wang, M., and Sun, H.: Deep Learning Social Network Access Control Model Based on User Preferences, CMES-Comp. Model. Eng., 140, 1029–1044, https://doi.org/10.32604/cmes.2024.047665, 2024.
Shi, W., Peng, Z., Huang, Y., Zhang, G., and Wang, C.: Retracted: An unsupervised anomaly detection approach for pre-seismic ionospheric total electron content, Meas. Sci. Technol., 34, 055101, https://doi.org/10.1088/1361-6501/acb453, 2023.
Shu, G. and Zhang, Z.: Precursor observation with RZB Capacitance Borehole Strainmeter and its practice in earthquake prediction, Bulletin of the Institute of Crustal Dynamics, 22–29, 1997.
Shu, X., Ding, W., Peng, Y., Wang, Z., Wu, J., and Li, M.: Monthly Streamflow Forecasting Using Convolutional Neural Network, Water Resour. Manag., 35, 5089–5104, https://doi.org/10.1007/s11269-021-02961-w, 2021.
Solís, E., Noboa, S., and Cuenca, E.: Financial Time Series Forecasting Applying Deep Learning Algorithms, Information and Communication Technologies, Univ Politecnica Salesiana, Guayaquil, Ecuador, 24–26 Nov 2021, 46–60, https://doi.org/10.1007/978-3-030-89941-7_4, 2021.
Tang, L., Fan, J., Liu, G., and Qiu, Z.: The Reliability Analysis of Strain Seismic Waves Recorded by High Sampling Four-component Borehole Strain Meter, Earthquake Research In China, 39, 78–87, 2023.
van den Ende, M. P. and Ampuero, J. P. J. G. R. L.: Automated seismic source characterization using deep graph neural networks, Geophys. Res. Lett., 47, e2020GL088690, https://doi.org/10.1029/2020GL088690, 2020.
Van Den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K.: Wavenet: A generative model for raw audio, arXiv [preprint], https://doi.org/10.48550/arXiv.1609.03499, 2016.
Wang, X., Qi, G., and Zhao, Y.: Propagation before fault instability and resistivity precursor, Sci. Sinica, 11, 1026–1038, 1984.
Wu, L., Zhang, L., Li, G., and Guo, H.: The Relative Calibration and Its Application of 4-component Borehole Strain Observation in HaiYuan Station, Journal of Seismological Research, 33, 318–322, 2010.
Wu, Z., Pan, S., Long, G., Jiang, J., and Zhang, C.: Graph WaveNet for Deep Spatial-Temporal Graph Modeling, Proceedings of the Twenty-eighth International Joint Conference on Artificial Intelligence, 10–16 August 2019, Macao, Peoples Republic of China, 1907–1913, 2019.
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip, S. Y.: A comprehensive survey on graph neural networks, IEEE T. Neur. Net. Lear., 32, 4–24, https://doi.org/10.1109/TNNLS.2020.2978386, 2020.
Xu, K. and Li, Y.: The violent ground motion before the Jiuzhaigou earthquake Ms 7.0, Open Geosci., 12, 919–927, https://doi.org/10.1515/geo-2020-0184, 2020.
Xu, K., Li, Y., Li, X., Wan, W., and Ju, H.: Seismological Characteristics of Seismogenic Process of the Jiuzhaigou Ms 7.0 Earthquake, Journal of Geodesy and Geodynamics, 44, 497–502, https://doi.org/10.14075/j.jgg.2023.08.154, 2024.
Xu, X., Chen, G., Wang, Q., Chen, L., Ren, Z., Xu, C., Wei, Z., Lu, R., Tan, X., Dong, S., and Shi, F.: Discussion on seismogenic structure of jiuzhaigou earthquake and its implication for current strain state in the southeastern Qinghai-Tibet Plateau, Chinese J. Geophys.-Ch., 60, 4018–4026, https://doi.org/10.6038/cjg20171028, 2017.
Yadav, A., Kumar, N., Verma, S. K., Shukla, V., and Chauhan, V.: Imprint of Diurnal and Semidiurnal Cyclicity in Radon Time Series of MPGO, Ghuttu Garhwal Himalaya: Evidence Based on Singular Spectrum Analysis, Pure Appl. Geophys., 180, 1081–1097, https://doi.org/10.1007/s00024-023-03231-z, 2023.
Yang, G., Liu, S., and Li, X.: A Report on Borehole Strain Observation at Guzan Station for the 8.0 Wenchuan Earthquake of 2008, Bulletin of the Institute of Crustal Dynamics, 135–148, 2010.
Yang, X., Liu, C., He, B., Dou, M., Wang, D., and Zhang, L.: Analyzing the anomaly of the extensional strain steps recorded by borehole dilatometer at Qianling Seismic Station, Seismological and Geomagnetic Observation and Research, 35, 159–164, 2014.
Yao, X., Wang, W., and Teng, Y.: Detection of Geomagnetic Signals as Precursors to Some Earthquakes in China, Appl. Sci.-Basel, 12, 1680, https://doi.org/10.3390/app12031680, 2022.
Yi, G., Long, F., Liang, M., Zhang, H., Zhao, M., Ye, Y., Zhang, Z., Qi, Y., Wang, S., Gong, Y., Qiao, H., Wang, Z., Qiu, G., and Su, J.: Focal mechanism solutions and seismogenic structure of the 8 August 2017 M 7.0 Jiuzhaigou earthquake and its aftershocks, northern Sichuan, Chinese J. Geophys.-Ch., 60, 4083–4097, https://doi.org/10.6038/cjg20171033, 2017.
Yin, H., Zhong, J., Li, R., Shang, J., Wang, C., and Li, X.: High-Order Neighbors Aware Representation Learning for Knowledge Graph Completion, IEEE T. Neur. Net. Lear., https://doi.org/10.1109/tnnls.2024.3383873, in press, 2024.
Yu, H., Yu, C., Ma, Z., Zhang, X., Zhang, H., Yao, Q., and Zhu, Q.: Temporal and Spatial Evolution of Load/Unload Response Ratio Before the M 7.0 Jiuzhaigou Earthquake of Aug. 8, 2017 in Sichuan Province, Pure Appl. Geophys., 177, 321–331, https://doi.org/10.1007/s00024-019-02101-x, 2019.
Yu, Z., Zhu, K., Hattori, K., Chi, C., Fan, M., and He, X.: Borehole Strain Observations Based on a State-Space Model and ApNe Analysis Associated With the 2013 Lushan Earthquake, IEEE Access, 9, 12167–12179, https://doi.org/10.1109/access.2021.3051614, 2021.
Yue, C., Niu, A., Yu, H., Ji, P., Jiang, X., Wang, Y., and Ma, W.: Evolutionary Characteristics of Ground Strain LURR Anomaly before Jiuzhaigou Ms 7.0 Earthquake, Earthquake Research In China, 36, 267–275, 2020.
Zhang, A. and Li, Y.: Analysis of Thermal Infrared Brightness Temperature Anomaly Characteristics Before Aksai Ms 5.5 Earthquake on August 26, 2021, Inland Earthquake, 37, 137–144, https://doi.org/10.16256/j.issn.1001-8956.2023.02.003, 2023.
Zhang, J. and He, X.: Earthquake magnitude prediction using a VMD-BP neural network model, Nat. Hazards, 117, 189–205, https://doi.org/10.1007/s11069-023-05856-8, 2023.
Zhang, L. and Niu, F.: Component borehole strain observations coupling coefficients calculation, Chinese J. Geophys.-Ch., 56, 3029–3037, https://doi.org/10.6038/cjg20130916, 2013.
Zhang, T.: Research on the Surface Deformation of Jiuzhaigou Earthquake Based on the Technology of D-InSAR, Scientific and Technological Innovation, Heilongjiang Association for Science and Technology, 14–17, https://kns.cnki.net/kcms2/article/abstract?v=uQDmaVEYwcx6 (last access: 15 November 2024), 2023.
Zhang, W.: Analysis of borehole strain earthquake precursory observation data based on Wavelet Transform, bachelor's degree, Jilin University, https://kns.cnki.net/kcms2/article/abstract?v=ZkvsKdWJ3SQc (last access: 15 November 2024), 2018.
Zhang, X., Song, Z., and Li, G.: Temporal and Spatial Evolution of Precursory Anomalies of the Jiuzhaigou Ms 7.0 Earthquake and its Analysis, Earthquake Research in China, 34, 772–780, 2018.
Zhang, X., Cao, L., Chen, Y., Jia, R., and Lu, X.: Microseismic signal denoising by combining variational mode decomposition with permutation entropy, Appl. Geophys., 19, 65–80, https://doi.org/10.1007/s11770-022-0926-6, 2022.
Zhong, M., Shan, X., Zhang, X., Qu, C., Guo, X., and Jiao, Z.: Thermal Infrared and Ionospheric Anomalies of the 2017 Mw 6.5 Jiuzhaigou Earthquake, Remote Sens.-Basel, 12, 2843, https://doi.org/10.3390/rs12172843, 2020.
Zhu, K., Chi, C., Yu, Z., Zhang, W., Fan, M., Li, K., and Zhang, Q.: Extracting borehole strain precursors associated with the Lushan earthquake through principal component analysis, Ann. Geophys., 61, SE447, https://doi.org/10.4401/ag-7633, 2018.
Zhu, K., Fan, M., He, X., Marchetti, D., Li, K., Yu, Z., Chi, C., Sun, H., and Cheng, Y.: Analysis of Swarm Satellite Magnetic Field Data Before the 2016 Ecuador (Mw=7.8) Earthquake Based on Non-negative Matrix Factorization, Front. Earth Sci., 9, 621976, https://doi.org/10.3389/feart.2021.621976, 2021.
Short summary
In this study, we advance the field of earthquake prediction by introducing a pre-seismic anomaly extraction method based on the structure of a graph WaveNet model, which reveals the temporal correlation and spatial correlation of the strain observation data from different boreholes prior to the occurrence of an earthquake event.
In this study, we advance the field of earthquake prediction by introducing a pre-seismic...
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