Illuminating the Hierarchical Segmentation of Faults Through an Unsupervised Learning Approach Applied to Clouds of Earthquake Hypocenters

IF 2.9 3区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS
E. Piegari, G. Camanni, M. Mercurio, W. Marzocchi
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Abstract

We propose a workflow for the recognition of the hierarchical segmentation of faults through earthquake hypocenter clustering without prior information. Our approach combines density-based clustering algorithms (DBSCAN and OPTICS), and principal component analysis (PCA). Given a spatial distribution of earthquake hypocenters, DBSCAN identifies first-order clusters, representing regions with the highest density of connected seismic events. Within each first-order cluster, OPTICS further identifies nested higher-order clusters, providing information on their number and size. PCA analysis is applied to first- and higher-order clusters to evaluate eigenvalues, allowing discrimination between seismicity associated with planar features and distributed seismicity that remains uncategorized. The identified planes are then geometrically characterized in terms of their location and orientation in the space, length, and height. This automated procedure operates within two spatial scales: the largest scale corresponds to the longest pattern of approximately equally dense earthquake clouds, while the smallest scale relates to earthquake location errors. By applying PCA analysis, a planar feature outputted from a first-order cluster can be interpreted as a fault surface while planes outputted after OPTICS can be interpreted as fault segments comprised within the fault surface. The evenness between the orientation of illuminated fault surfaces and fault segments, and that of the nodal planes of earthquake focal mechanisms calculated along the same faults, corroborates this interpretation. Our workflow has been successfully applied to earthquake hypocenter distributions from various seismically active areas (Italy, Taiwan, and California) associated with faults exhibiting diverse kinematics.

Abstract Image

通过应用于地震中心云的无监督学习方法阐明断层的分层分割
我们提出了一种工作流程,用于在没有先验信息的情况下,通过地震次中心聚类识别断层的分层分割。我们的方法结合了基于密度的聚类算法(DBSCAN 和 OPTICS)和主成分分析(PCA)。给定地震次中心的空间分布后,DBSCAN 会识别一阶聚类,代表连接地震事件密度最高的区域。在每个一阶群集内,OPTICS 进一步识别嵌套的高阶群集,提供有关其数量和规模的信息。对一阶和高阶震群进行 PCA 分析,以评估特征值,从而区分与平面特征相关的地震和未分类的分布式地震。然后,根据平面在空间中的位置和方向、长度和高度,对识别出的平面进行几何特征描述。这一自动程序在两个空间尺度内运行:最大尺度对应于近似等密度地震云的最长模式,而最小尺度则与地震位置误差有关。通过应用 PCA 分析,一阶聚类分析输出的平面特征可解释为断层面,而 OPTICS 后输出的平面可解释为断层面内的断层段。被照亮的断层面和断层段的方位与沿同一断层计算的地震焦点机制的节点平面的方位之间的均匀性证实了这一解释。我们的工作流程已成功应用于多个地震活跃地区(意大利、台湾和加利福尼亚)的地震震中分布,这些地区的断层具有不同的运动学特征。
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来源期刊
Earth and Space Science
Earth and Space Science Earth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
5.50
自引率
3.20%
发文量
285
审稿时长
19 weeks
期刊介绍: Marking AGU’s second new open access journal in the last 12 months, Earth and Space Science is the only journal that reflects the expansive range of science represented by AGU’s 62,000 members, including all of the Earth, planetary, and space sciences, and related fields in environmental science, geoengineering, space engineering, and biogeochemistry.
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