PDF(5677 KB)
Research progress in artificial intelligence earthquake prediction
Chen LI, LianQing ZHOU, MengQiao DUAN, ZiYi LI, Na ZHANG, MaoFa WANG
Prog Geophy ›› 2026, Vol. 41 ›› Issue (3) : 997-1018.
PDF(5677 KB)
PDF(5677 KB)
Research progress in artificial intelligence earthquake prediction
This article provides an overview of recent advances in conventional earthquake prediction research alongside the current status of artificial intelligence applications in seismology. It explores the role of AI in the extraction of precursory anomalies, its application in short-term and imminent earthquake forecasting, its utility in medium-to long-term seismic hazard assessment, and its use in aftershock prediction. Research indicates that artificial intelligence technology can rapidly analyze real-time data and effectively identify seismic precursor signals. By integrating spatiotemporal features, AI can recognize subtle anomalous seismic patterns that are difficult to capture with non-AI methods, thereby improving the accuracy of earthquake prediction. However, current earthquake prediction research is still limited by factors such as data quality, model interpretability, computational cost, and cross-regional generalization ability. Future research should focus on exploring physics-informed artificial intelligence learning methods, enhancing multi-modal data fusion, and improving model interpretability. Emphasis should also be placed on translating prediction outcomes into practical applications for disaster early warning and risk assessment. Furthermore, AI technology shows significant potential in aftershock prediction research and, by leveraging the regular patterns of post-mainshock sequences, is poised to become a breakthrough area for the broader field of earthquake prediction.
Short-term prediction / Medium- to long-term prediction / Aftershock prediction / Artificial intelligence
|
|
|
|
|
Al Banna M H, Ghosh T, Taher K A, et al. 2021. An earthquake prediction system for Bangladesh using deep long short-term memory architecture. //Intelligent Systems. Singapore: Springer, 465-476, doi: 10.1007/978-981-33-6081-5_41.
|
|
|
|
|
|
|
|
|
|
Andalib A, Zare M, Atry F. 2017. A fuzzy expert system for earthquake prediction, case study: the Zagros range. arXiv: 1610.04028, doi: 10.48550/arXiv.1610.04028.
|
|
Apostol B F. 2020. Bath's law, correlations and magnitude distributions. arXiv: 2006.07591, doi: 10.48550/arXiv.2006.07591.
|
|
|
|
|
|
|
|
|
|
|
|
Asim K M, Idris A, Martinez-Alvarez F, et al. 2016. Short term earthquake prediction in Hindukush region using tree based ensemble learning. //2016 International Conference on Frontiers of Information Technology (FIT). Islamabad, Pakistan: IEEE, 365-370, doi: 10.1109/FIT.2016.073.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Celik E, Atalay M, Kondiloǧlu A. 2016. The earthquake magnitude prediction used seismic time series and machine learning methods. //Ⅳ. International Energy Technologies Conference. Istanbul, Turkey.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Dehbozorgi L, Farokhi F. 2010. Notice of Retraction: Effective feature selection for short-term earthquake prediction using Neuro-Fuzzy classifier. //2010 Second IITA International Conference on Geoscience and Remote Sensing. Qingdao, China: IEEE, 165-169, doi: 10.1109/IITA-GRS.2010.5602504.
|
|
|
|
|
|
Dong X N. 2011. Research and application of artificial neural network based on rough set in earthquake prediction [Master's thesis] (in Chinese). Ji'nan: Shandong Normal University.
|
|
|
|
Engdahl E R, Villaseñor A. 2002. 41-Global seismicity: 1900-1999. //International Geophysics. Elsevier, 81: 665-690, doi: 10.1016/S0074-6142(02)80244-3.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Huang S Z. 2010. The prediction of the earthquake based on neutral networks. //2010 International Conference on Computer Design and Applications. Qinhuangdao, China: IEEE: V2-517-V2-520, doi: 10.1109/ICCDA.2010.5541341.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Kanamori H. 1981. The nature of seismicity patterns before large earthquakes. //Simpson D W, Richards P G eds. Earthquake Prediction: An International Review. Washington, D.C.: American Geophysical Union, 4: 1-19, doi: 10.1029/ME004p0001.
|
|
Kanamori H. 1983. Global seismicity. //Earthquakes: Observation, Theory, and Interpretation. Amsterdam: Societa Italiana di Fisica, 596-608.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Lakkos S, Hadjiprocopis A, Comley R, et al. 1994. A neural network scheme for Earthquake prediction based on the seismic electric signals. //Proceedings of IEEE Workshop on Neural Networks for Signal Processing. Ermioni, Greece: IEEE, 681-689, doi: 10.1109/NNSP.1994.365997.
|
|
Li A G, Kang L. 2009. KNN-based modeling and its application in aftershock prediction. //2009 International Asia Symposium on Intelligent Interaction and Affective Computing. Wuhan, China: IEEE, 83-86, doi: 10.1109/ASIA.2009.21.
|
|
Li C, Liu X Y. 2016. An improved PSO-BP neural network and its application to earthquake prediction. //2016 Chinese Control and Decision Conference (CCDC). Yinchuan, China: IEEE, 3434-3438, doi: 10.1109/CCDC.2016.7531576.
|
|
|
|
Li R, Lu X B, Li S W, et al. 2020. DLEP: A deep learning model for earthquake prediction. //2020 International Joint Conference on Neural Networks (IJCNN). Glasgow, UK: IEEE, 1-8, doi: 10.1109/IJCNN48605.2020.9207621.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Liu Y, Li Y, Li G Z, et al. 2005. Constructive ensemble of RBF neural networks and its application to earthquake prediction. //Second International Symposium on Neural Networks. Chongqing, China: Springer, 532-537, doi: 10.1007/11427391_85.
|
|
Lu Y, Sawada E, Chakraborty G. 2019. Earthquake aftershock prediction based solely on seismograph data. //2019 IEEE International Conference on Big Data and Smart Computing (BigComp). Kyoto, Japan: IEEE, 1-6, doi: 10.1109/BIGCOMP.2019.8679491.
|
|
|
|
Ma L L, Xu F Z, Wang X H, et al. 2010. Earthquake prediction based on Levenberg-Marquardt algorithm constrained back-propagation neural network using DEMETER data. //4th International Conference on Knowledge Science, Engineering and Management. Belfast, Northern Ireland, UK: Springer, 591-596, doi: 10.1007/978-3-642-15280-1_57.
|
|
|
|
|
|
|
|
|
|
Maya M, Yu W. 2019. Short-term prediction of the earthquake through Neural Networks and Meta-Learning. //2019 16th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). Mexico City, Mexico: IEEE, 1-6, doi: 10.1109/ICEEE.2019.8884562.
|
|
|
|
|
|
|
|
Mignan A, Broccardo M. 2019. A deeper look into 'Deep Learning of Aftershock Patterns Following Large Earthquakes': Illustrating first principles in neural network physical interpretability. //15th International Work-Conference on Artificial Neural Networks. Gran Canaria, Spain: Springer, 3-14, doi: 10.1007/978-3-030-20521-8_1.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Nishenko S P. 1989. Earthquakes: Hazards and predictions. //James D E ed. Encyclopedia of Solid Earth Geophysics. New York: Springer, 260-268, doi: 10.1007/0-387-30752-4_4.
|
|
|
|
|
|
|
|
|
|
|
|
Ohtake M, Matumoto T, Latham G V. 1981. Evaluation of the forecast of the 1978 Oaxaca, Southern Mexico Earthquake based on a precursory seismic quiescence. //Simpson D W, Richards P G eds. Earthquake Prediction: An International Review. Washington, D.C.: American Geophysical Union, 4: 53-61, doi: 10.1029/ME004p0053.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Shah H, Ghazali R. 2011. Prediction of earthquake magnitude by an improved ABC-MLP. //2011 Developments in E-systems Engineering. Dubai, United Arab Emirates: IEEE, 312-317, doi: 10.1109/DeSE.2011.37.
|
|
|
|
Shao X Y, Li X, Li L L, et al. 2012. The application of ant-colony clustering algorithm to earthquake prediction. //Jin D, Lin S eds. Proceedings of the EECM 2011 International Conference on Electronic Engineering, Communication and Management. Beijing, China: Springer, 145-150, doi: 10.1007/978-3-642-27296-7_23.
|
|
|
|
|
|
|
|
Shi X Y. 2021. Research on earthquake prediction based on machine learning regression algorithm and its application in China Seismic Experimental Site [Master's thesis] (in Chinese). Beijing: Institute of Earthquake Forecasting, China Earthquake Administration.
|
|
|
|
Shodiq M N, Kusuma D H, Rifqi M G, et al. 2017. Spatial analisys of magnitude distribution for earthquake prediction using neural network based on automatic clustering in Indonesia. //2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC). Surabaya, Indonesia: IEEE, 246-251, doi: 10.1109/KCIC.2017.8228594.
|
|
|
|
|
|
Si X. 2025. Earthquake monitoring and prediction based on graph neural network and foundation model [Ph. D. thesis] (in Chinese). Hefei: University of Science and Technology of China, doi: 10.27517/d.cnki.gzkju.2024.000450.
|
|
|
|
|
|
|
|
|
|
|
|
Suratgar A A, Setoudeh F, Salemi A H, et al. 2008. Magnitude of earthquake prediction using neural network. //2008 Fourth International Conference on Natural Computation. Jinan, China: IEEE, 448-452, doi: 10.1109/ICNC.2008.781.
|
|
Tan K, Cai X S. 2010. Prediction of earthquake in Yunnan region based on the AHC over sampling. //2010 Chinese Control and Decision Conference. Xuzhou, China: IEEE, 2449-2452, doi: 10.1109/CCDC.2010.5498782.
|
|
|
|
|
|
|
|
|
|
Van Der Hilst R D, Bass J D, Matas J, et al. 2005. Earth's deep mantle: Structure, composition, and evolution: An introduction. //Van Der Hilst R D, Bass J D, Matas J, et al eds. Earth's Deep Mantle: Structure, Composition, and Evolution. Washington, D.C.: American Geophysical Union, 160: 1-7, doi: 10.1029/160GM02.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Wang Y, Chen Y, Zhang J K. 2009. The application of RBF neural network in earthquake prediction. //2009 Third International Conference on Genetic and Evolutionary Computing. Guilin, China: IEEE, 465-468, doi: 10.1109/WGEC.2009.81.
|
|
|
|
|
|
|
|
Xie J, Qiu J F, Li W, et al. 2011. The application of neural network model in earthquake prediction in East China. //Advances in Computer Science, Intelligent Systems and Environment. Berlin, Heidelberg: Springer, 79-84, doi: 10.1007/978-3-642-23753-9_13.
|
|
|
|
|
|
Xu F Z, Song X F, Wang X H, et al. 2010. Neural network model for earthquake prediction using DMETER data and seismic belt information. //2010 Second WRI Global Congress on Intelligent Systems. Wuhan, China: IEEE, 180-183, doi: 10.1109/GCIS.2010.237.
|
|
|
|
|
|
|
|
|
|
|
|
Zhang C J. 2021. Artificial intelligence and earthquake hazard cloud maps. //Proceedings of the 2021 Annual Meeting of Chinese Geoscience Union (in Chinese). Zhuhai: China Earthquake Networks Center, 490-491, doi: 10.26914/c.cnkihy.2021.074123.
|
|
|
|
|
|
Zhang Q W, Wang C. 2008. Using genetic algorithm to optimize artificial neural network: A case study on earthquake prediction. //2008 Second International Conference on Genetic and Evolutionary Computing. Jinzhou, China: IEEE, 128-131, doi: 10.1109/WGEC.2008.96.
|
|
|
|
|
|
|
|
Zhou F Y, Zhu X F. 2014. Earthquake prediction based on LM-BP neural network. //Proceedings of the 9th International Symposium on Linear Drives for Industry Applications, Volume 1. Berlin, Heidelberg: Springer, 13-20, doi: 10.1007/978-3-642-40618-8_2.
|
|
|
|
|
|
|
|
|
|
Zhou W Z, Kan J S, Sun S. 2017. Study on seismic magnitude prediction based on combination algorithm. //2017 9th International Conference on Modelling, Identification and Control (ICMIC). Kunming: IEEE: 539-544, doi: 10.1109/ICMIC.2017.8321703.
|
|
|
|
|
|
|
|
|
|
陈章立, 刘蒲雄, 黄德瑜, 等. 1981. 大震前的区域地震活动性特征. //国际地震预报讨论会论文选. 北京: 地震出版社, 121-131.
|
|
董晓娜. 2011. 粗糙集支持的人工神经网络在地震预测中的应用研究[硕士论文]. 济南: 山东师范大学.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
史翔宇. 2021. 基于机器学习回归算法的地震预测研究及其在中国地震科学实验场的应用[硕士论文]. 北京: 中国地震局地震预测研究所.
|
|
|
|
司旭. 2025. 基于图神经网络和基础模型的地震监测与预测[博士论文]. 合肥: 中国科学技术大学, doi: 10.27517/d.cnki.gzkju.2024.000450.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
张晁军. 2021. 人工智能与地震危险性云图. //中国地球科学联合学术年会2021. 珠海: 中国地震台网中心, 490-491, doi: 10.26914/c.cnkihy.2021.074123.
|
|
|
|
|
|
|
|
|
|
|
|
|
感谢两位匿名审稿专家对稿件提出的修改建议和意见.
/
| 〈 |
|
〉 |