机器学习在地震检测与震相识别的应用综述

在地震学研究中地震检测与震相识别是最基础的环节,其拾取速度和精度直接影响其在地震精确定位以及地震层析成像中的应用效率和精度。近年来,机器学习在地震学领域中引起广泛关注。机器学习可以改进传统地震检测和震相识别方法,使它们能达到更加准确,识别率更高的效果。把机器学习方法按照监督学习和无监督学习分类介绍,并对机器学习方法流程进行总结,并对目前在地震检测与震相识别方面应用较为广泛的机器学习方法(卷积神经网络、指纹和相似性阈值、广义相位检测、PhaseNet、模糊聚类)进行综述。结果表明:机器学习在地震事件检测和震相识别将会是主要的手段。数据驱动的机器学习在地震学中的应用和物理模型的联合运用将是未来的发展趋势。

Review of the Application of Machine Learning in Seismic Detection and Phase Identification

In seismological research, picking speed and accuracy of seismic detection and phase identification directly affect their application efficiency and accuracy in precise seismic positioning and tomography. In recent years, machine learning has attracted wide attention in the field of seismology. Machine learning can improve upon traditional seismic detection and phase identification methods, thus achieving more accurate and higher recognition rates. In this paper, we introduced a machine learning method according to the classification of supervised learning and unsupervised learning, then summarized the flow of the machine learning method. Finally, we reviewed those machine learning methods widely used in seismic detection and phase identification, i.e., convolution neural network, fingerprint and similarity threshold, generalized phase detection, PhaseNet, and fuzzy clustering. Results showed that machine learning will be the primary means of seismic event detection and seismic phase identification. Application of data-driven machine learning in seismology combined with the physical model will be the development trend of the future.