
Overview of progress in research on artificial intelligence for earthquake classification
TianRan LU, MengQiao DUAN, ZiYi LI, LianQing ZHOU
Prog Geophy ›› 2025, Vol. 40 ›› Issue (1) : 25-47.
Overview of progress in research on artificial intelligence for earthquake classification
The correct classification of earthquake events is of great significance to regional seismic hazard assessment and the reduction of natural or artificial earthquake disasters. Over the years, many scientific and technological personnel have conducted a large amount of research on this topic. This article systematically summarizes the current mainstream understanding of the classification characteristics and difficulties of various types of natural and artificial earthquake events in China and abroad, as well as the application status of artificial intelligence in earthquake classification research. The results show that: (1) the classification of blasting earthquakes and tectonic earthquakes has made relatively rapid progress; (2) the identification and classification of volcanic earthquakes and landslide events generally face the problems of insufficient sample size and data imbalance in the dataset; (3) the recognition of induced earthquakes remains a difficult and controversial subject in classification research; (4) artificial intelligence methods, with their high accuracy, efficiency, and potential for future automation, have become the mainstream method for earthquake classification at present. Based on the current application status of artificial intelligence technology in earthquake classification, this paper discusses and proposes corresponding suggestions and development trends.
Natural earthquakes / Blasting / Induced earthquakes / Volcanic earthquakes / Earthquake classification / Artificial intelligence
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|
|
|
Ando R K, Zhang T. 2006. Learning on graph with Laplacian regularization. //Proceedings of the 19th International Conference on Neural Information Processing Systems. Vancouver: ACM, 25-32.
|
|
|
|
|
Bengio Y, Delalleau O, Le Roux N. 2006. Label propagation and quadratic criterion. //Chapelle O, Schölkopf B, Zien A eds. Semi-Supervised Learning. Cambridge: The MIT Press.
|
|
|
|
|
|
Brémaud P. 1999. Discrete-time Markov models. //Brémaud P ed. Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues. New York: Springer, 53-93.
|
|
|
|
|
|
|
|
|
|
Chen T Q, Guestrin C. 2016. XGBoost: a scalable tree boosting system. //Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 785-794, doi: 10.1145/2939672.2939785.
|
|
Chen Y T, Wu Z L, Lü W W. 2003. Classification of earthquakes. City and Disaster Reduction (in Chinese), (1): 13-15.
|
|
|
|
Cramer J S. 2002. The origins of logistic regression. SSRN Electronic Journal, doi: 10.2139/ssrn.360300.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
He K M, Zhang X Y, Ren S Q, et al. 2016. Deep residual learning for image recognition. //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 770-778.
|
Ho T K. 1995. Random decision forests. //Proceedings of 3rd International Conference on Document Analysis and Recognition. Montreal: IEEE, 278-282, doi: 10.1109/ICDAR.1995.598994.
|
|
Hosmer Jr D W, Lemeshow S, Sturdivant R X. 2013. Applied Logistic Regression. 3rd ed. Hoboken: John Wiley & Sons.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Li J, Zhou Y J. 2020. Microseismic detection based on transfer learning: Practical methods for deep learning models——Taking the data from Wenchuan earthquake aftershocks and Oklahoma induced earthquake as examples. //Proceedings of the 2020 annual meeting of chinese geoscience union. Chongqing: CGU, 2619.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
McGarr A, Simpson D. 1997. Keynote lecture: A broad look at induced and triggered seismicity, "Rockbursts and Seismicity in Mines". //Proceedings of 4th International Symposium on Rockbursts and Seismicity in Mines Poland. Rotterdam: A. A. Balkema Press, 385-396.
|
|
|
|
|
|
|
|
|
Ohrnberger M. 2001. Continuous automatic classification of seismic signals of volcanic origin at Mt. Merapi, Java, Indonesia[Ph. D. thesis]. Germany: University of Potsdam.
|
|
|
|
|
Pascanu R, Mikolov T, Bengio Y. 2013. On the difficulty of training recurrent neural networks. //Proceedings of the 30th International Conference on International Conference on Machine Learning. Atlanta: ACM, Ⅲ-1310-Ⅲ-1318.
|
|
|
Petersen M D, Mueller C S, Moschetti M P, et al. 2015. Incorporating induced seismicity in the 2014 United States national seismic hazard model—results of 2014 workshop and sensitivity studies: U.S. Geological Survey Open-File Report 2015-1070, 69p.
|
|
|
|
|
|
|
|
|
|
Reynolds D. 2009. Gaussian mixture models. //Li S Z, Jain A eds. Encyclopedia of Biometrics. New York: Springer, 659-663.
|
Rish I. 2001. An empirical study of the naive Bayes classifier. //IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 3(22): 41 -46.
|
Rosenberg C, Hebert M, Schneiderman H. 2005. Semi-supervised self-training of object detection models. //7th IEEE Workshops on Applications of Computer Vision. Breckenridge: IEEE, 29-36.
|
|
Schapire R E. 2013. Explaining AdaBoost. //Schölkopf B, Luo Z Y, Vovk V eds. Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik. Berlin, Heidelberg: Springer, 37-52, doi: 10.1007/978-3-642-41136-6_5.
|
|
Settles B. 2009. Active learning literature survey. Madison: University of Wisconsin Madison.
|
|
|
|
|
Simonyan K, Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition. //3rd International Conference on Learning Representations. San Diego: ICLR.
|
|
Гадьперин Е. И. 1989. Seismic Exploration Polarization Method (in Chinese). He Q D, Yang B J Trans. Beijing: Petroleum Industry Press.
|
|
Tang L L. 2018. Induced seismicity detection and discrimination of seismic source[Ph. D. thesis](in Chinese). Hefei: University of Science and Technology of China.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Zhu X, Ghahramani Z. 2002. Learning from labeled and unlabeled data with label propagation.
|
|
Zhuang J, Werner M J, Hainzl S, et al. 2011. Basic models of seismicity: Spatiotemporal models. Community Online Resource for Statistical Seismicity Analysis, doi: 10.5078/corssa-07487583.
|
|
|
|
|
|
|
|
|
|
李君, 周一剑. 2020. 基于迁移学习的微震检测: 深度学习模型的实用化方法——以汶川余震与俄克拉荷马诱发地震数据为例. //2020年中国地球科学联合学术年会论文集. 重庆: 中国地球物理学会, 2619.
|
|
|
|
|
|
|
|
|
|
|
|
|
Гадьперин Е. И. 1989. 地震勘探偏振法. 何樵登, 杨宝俊译. 北京: 石油工业出版社.
|
唐兰兰. 2018. 诱发地震检测及地震震源分类[博士论文]. 合肥: 中国科学技术大学.
|
|
|
|
|
|
|
|
|
|
|
|
|
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感谢审稿专家提出的修改意见.
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