Fault network reconstruction using agglomerative clustering: applications to southern Californian seismicity

Kamer, Yavor; Ouillon, Guy; Sornette, Didier

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 southern 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 to 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 spatiotemporal evolution of seismicity.

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Kamer, Yavor / Ouillon, Guy / Sornette, Didier: Fault network reconstruction using agglomerative clustering: applications to southern Californian seismicity. 2020. Copernicus Publications.

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