摘要
理解并预测多尺度、高维度和非线性的地震学现象是一个极具挑战性的科学任务.与日俱增的海量观测数据降低了信息收集和信息解读之间的耦合程度,增加了信息解读的抽象性和不确定性.然而,伴随大数据一同来临的还有人工智能计算机技术——机器学习.机器学习突出的隐式关系提取和复杂任务处理能力推动着研究学者们不断将机器学习的应用推向更广阔的领域.本文介绍了地震学中常用的机器学习算法及其应用范围,讨论了人工智能与地震数据相结合的发展方向.
It is an inherently challenging scientific endeavor to understand and predict multi-scale,high-dimensional and nonlinear seismological phenomena.The increasing amount of observational big data breaks the linkage between data collection and interpretation,and increases the obscurity and uncertainty in data analysis.However,there is also artificial intelligent computer technology,i.e.machine learning in the era of big data.The excellent capability of machine learning for implicit relation extraction and complex task processing has enabled it to be applied to a variety of fields.In this article,we introduce some of the commonly used machine learning algorithms in seismology as well as their applications,and discuss the future directions of integrating artificial intelligence with seismic data interpretation.
作者
杨旭
李永华
盖增喜
Yang Xu;Li Yonghua;Ge Zengxi(Institute of Geophysics,China Earthquake Administration,Beijing 100081,China;School of Earth and Space Sciences,Peking University,Beijing 100871,China;Key Laboratory of Earthquake Source Physics,China Earthquake Administration,Beijing 100081,China)
出处
《地球与行星物理论评》
2021年第1期76-88,共13页
Reviews of Geophysics and Planetary Physics
基金
中国地震局地球物理研究所基本业务专项资助项目(DQJB19A0111)
国家自然科学基金资助项目(U1839210,41874108)。
关键词
地震学
机器学习
特征提取
深度学习
神经网络
seismology
machine learning
feature extraction
deep learning
neural network
