4.7 Article

Systematic Detection of Clustered Seismicity Beneath the Southwestern Alps

期刊

JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
卷 124, 期 11, 页码 11531-11548

出版社

AMER GEOPHYSICAL UNION
DOI: 10.1029/2019JB018110

关键词

-

资金

  1. IGGCAS (Institute of Geology and Geophysics, China Academy of Sciences, Beijing)
  2. Labex OSUG@2020(France)
  3. RvdH's Schlumberger chair
  4. European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program [789742335]

向作者/读者索取更多资源

We present a new automated earthquake detection and location method based on beamforming (or back projection) and template matching and apply it to study the seismicity of the Southwestern Alps. We use beamforming with prior knowledge of the 3-D variations of seismic velocities as a first detection run to search for earthquakes that are used as templates in a subsequent matched-filter search. Template matching allows us to detect low signal-to-noise ratio events and thus to obtain a high spatiotemporal resolution of the seismicity in the Southwestern Alps. We describe how we address the problem of false positives in energy-based earthquake detection with supervised machine learning and how to best leverage template matching to iteratively refine the templates and the detection. We detected 18,754 earthquakes over 1 year (our catalog is available online) and observed temporal clustering of the earthquake occurrence in several regions. This statistical study of the collective behavior of earthquakes provides insights into the mechanisms of earthquake occurrence. Based on our observations, we infer the mechanisms responsible for the seismic activity in three regions of interest: the Ubaye valley, the Brianconnais, and the Dora Maira massif. Our conclusions point to the importance of fault interactions to explain the earthquake occurrence in the Brianconnais and the Dora Maira massif, whereas fluids seem to be the major driving mechanism in the Ubaye valley.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据