| 摘要: |
| 近年来,机器学习的快速发展在计算机视觉、自然语言处理和数据挖掘等领域取得前所未有的成绩。地震研究学科众多,包括地震学、大地测量学、地球化学、地球电磁学和地质学等,研究产生的多源、复杂、海量数据高度符合机器学习对于训练数据的要求,因此许多学者将机器学习方法引入到地震预测中。本文基于机器学习背景、地震预测应用流程和评价方法等方面,回顾了近年来基于机器学习方法,利用不同学科数据进行地震预测的应用概况和主要进展,并对机器学习在地震预测中的应用进行总结和讨论。 |
| 关键词: 机器学习 多源数据 地震预测 研究进展 |
| DOI： |
| 分类号:P315 |
| 基金项目:国家自然科学基金联合基金项目(U2039202)资助 |
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| Recent Progress of Multi-source Data-based Machine Learning in Earthquake Prediction |
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Zhu Junqing, Sun Ke
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Institute of Earthquake Forecasting, CEA, Beijing 100036, China
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| Abstract: |
| In recent years,the rapid development of machine learning has made unprecedented achievements in the fields of computer vision,natural language processing and data mining. There are many disciplines of earthquake research,including seismology,geodesy,geochemistry,geomagnetism and geology. Therefore,the multi-source,complex and massive data generated by the earthquake research highly meet the requirements of machine learning for training data. In fact,many scholars have applied machine learning methods into earthquake prediction. In the view of the background of machine learning,the application process and evaluation methods of earthquake prediction,in this paper we reviewed the application and main progress of earthquake prediction based on machine learning methods and data from different disciplines in recent years,and summarized and discussed the application of machine learning in earthquake prediction. |
| Key words: Machine learning Multi-source data Earthquake prediction Research progress |
