Geometrical properties of seismicity in California

Author:

Ross Zachary E1,Ben-Zion Yehuda2,Zaliapin Ilya3ORCID

Affiliation:

1. Seismological Laboratory, California Institute of Technology , Pasadena, CA 91125, USA

2. Department of Earth Sciences and Southern California Earthquake Center, University of Southern California , Los Angeles, CA 90089, USA

3. Department of Mathematics and Statistics, University of Nevada , Reno, NV 89557, USA

Abstract

SUMMARY The spatial geometry of seismicity encodes information about loading and failure processes, as well as properties of the underlying fault structure. Traditional approaches to characterizing geometrical attributes of seismicity rely on assumed locations and geometry of fault surfaces, particularly at depth, where resolution is overall quite poor. In this study, we develop an alternative approach to quantifying geometrical properties of seismicity using techniques from anisotropic point process theory. Our approach does not require prior knowledge about the underlying fault properties. We characterize the geometrical attributes of 32 distinct seismicity regions in California and introduce a simple four class classification scheme that covers the range of geometrical properties observed. Most of the regions classified as having localized seismicity are within northern California, while nearly all of the regions classified as having distributed seismicity are within southern California. In addition, we find that roughly 1 out of 4 regions exhibit orthogonal seismicity structures. The results of this study provide a foundation for future analyses of geometrical properties of seismicity and new observables to compare with numerical modelling studies.

Funder

National Science Foundation

Southern California Earthquake Center

NSF

Publisher

Oxford University Press (OUP)

Subject

Geochemistry and Petrology,Geophysics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A neural encoder for earthquake rate forecasting;Scientific Reports;2023-07-31

2. A deep Gaussian process model for seismicity background rates;Geophysical Journal International;2023-02-16

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