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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/nhess-2020-231
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-231
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Jul 2020

23 Jul 2020

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This preprint is currently under review for the journal NHESS.

Fault Network Reconstruction using Agglomerative Clustering: Applications to South Californian Seismicity

Yavor Kamer1, Guy Ouillon2, and Didier Sornette1 Yavor Kamer et al.
  • 1ETH Zurich, Switzerland
  • 2Lithophyse, Nice, France

Abstract. In this paper we introduce a method for fault network reconstruction based on the 3D spatial distribution of seismicity. One of the major drawbacks of statistical earthquake models is their inability to account for the highly anisotropic distribution of seismicity. Fault reconstruction has been proposed as a pattern recognition method aiming to extract this structural information from seismicity catalogs. Current methods start from simple large scale models and gradually increase the complexity trying to explain the small scale features. In contrast the method introduced here uses a bottom-up approach, that relies on initial sampling of the small scale features and reduction of this complexity by optimal local merging of substructures.

First, we describe the implementation of the method through illustrative synthetic examples. We then apply the method to the probabilistic absolute hypocenter catalog KaKiOS-16, which contains three decades of South Californian seismicity. To reduce data size and increase computation efficiency, the new approach builds upon the previously introduced catalog condensation method that exploits the heterogeneity of the hypocenter uncertainties. We validate the obtained fault network through a pseudo prospective spatial forecast test and discuss possible improvements for future studies. The performance of the presented methodology attests the importance of the non-linear techniques used to quantify location uncertainty information, which is a crucial input for the large scale application of the method. We envision that the results of this study can be used to construct improved models for the spatio-temporal evolution of seismicity.

Yavor Kamer et al.

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Yavor Kamer et al.

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Latest update: 14 Aug 2020
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Short summary
Earthquakes cluster in space highlighting fault structures in the crust. We introduce a method to identify such patterns. The method follows a bottom-up approach that starts from many small clusters and by repeated mergings produces a larger, less complex structure. We test the resulting fault network model by investigating its ability to forecast the location of earthquakes that were not used in the study. We envision that our method can contribute to future studies relying on fault patterns.
Earthquakes cluster in space highlighting fault structures in the crust. We introduce a method...
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