Abstract
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 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.
Highlights
Owing to the continuing advances in instrumentation and improvement of seismic networks coverage, earthquake detection magnitude thresholds have been decreasing while the number of recorded events is increasing
The Gaussian kernels together with the uniform background kernel represent a mixture model where each kernel has a contributing weight proportional to the number of points that are associated with it (Bishop, 2007). This representation facilitates the calculation of an overall likelihood and allows us to compare models with different complexities using the Bayesian information criteria (BIC) (Schwarz, 1978) given by BIC = − log(L) + log(N ), (2)
We use the KaKiOS-16 catalog (Kamer et al, 2017) that was obtained by the probabilistic absolute location of nearly 479 000 southern Californian events spanning the time period 1981–2011
Summary
Owing to the continuing advances in instrumentation and improvement of seismic networks coverage, earthquake detection magnitude thresholds have been decreasing while the number of recorded events is increasing. Recent studies suggest that the Gutenberg–Richter law might hold down to very small magnitudes corresponding to interatomic-scale dislocations (Boettcher et al, 2009; Kwiatek et al, 2010) This implies that there is practically no upper limit on the amount of seismicity we can expect to record as our instrumentation capabilities continue to improve. Earthquake forecasting models are commonly based on the complete part of the catalogs In their forecasting model, Helmstetter et al (2007) use only M > 2 events, which corresponds to only ∼ 30 % of the recorded seismicity. Y. Kamer et al.: Fault network reconstruction using agglomerative clustering understanding of earthquakes, and due to this data censoring. We conclude with an outlook on future developments
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