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Clusty is a new open source toolbox dedicated to earthquake clustering based on waveforms recorded across a network of seismic stations. Its main application is the study of active faults and the detection and characterization of faults and fault networks. By using a density-based clustering approach, earthquakes pertaining to a common fault can be recognized even over long fault segments, and the first-order geometry and extent of active faults can be inferred. Clusty implements multiple techniques to compute a waveform based network similarity from maximum cross-correlation coefficients at multiple stations. The clustering procedure is designed to be transparent and parameters can be easily tuned. It is supported by a number of analysis visualization tools which help to assess the homogeneity within each cluster and the differences among distinct clusters. The toolbox returns graphical representations of the results. A list of representative events and stacked waveforms facilitate further analyses like moment tensor inversion. Results obtained in various frequency bands can be combined to account for large magnitude ranges. Thanks to the simple configuration, the toolbox is easily adaptable to new data sets and to large magnitude ranges. To show the potential of our new toolbox, we apply Clusty to the aftershock sequence of the M-w 6.9 25 October 2018 Zakynthos (Greece) Earthquake. Thanks to the complex tectonic setting at the western termination of the Hellenic Subduction System where multiple faults and faulting styles operate simultaneously, the Zakynthos data set provides an ideal case-study for our clustering analysis toolbox. Our results support the activation of several faults and provide insight into the geometry of faults or fault segments. We identify two large thrust faulting clusters in the vicinity of the main shock and multiple strike-slip clusters to the east, west and south of these clusters. Despite its location within the largest thrust cluster, the main shock does not show a high waveform similarity to any of the clusters. This is consistent with the results of other studies suggesting a complex failure mechanism for the main shock. We propose the existence of conjugated strike-slip faults in the south of the study area. Our waveform similarity based clustering toolbox is able to reveal distinct event clusters which cannot be discriminated based on locations and/or timing only. Additionally, the clustering results allows distinction between fault and auxiliary planes of focal mechanisms and to associate them to known active faults.
The structure and organization of river networks has been used for decades to investigate the influence of climate and tectonics on landscapes. The majority of these studies either analyze rivers in profile view by extracting channel steepness or calculate planform metrics such as drainage density. However, these techniques rely on the assumption of homogeneity: that intrinsic and external factors are spatially or temporally invariant over the measured profile. This assumption is violated for the majority of Earth's landscapes, where variations in uplift rate, rock strength, climate, and geomorphic process are almost ubiquitous. We propose a method for classifying river profiles to identify landscape regions with similar characteristics by adapting hierarchical clustering algorithms developed for time series data. We first test our clustering on two landscape evolution scenarios and find that we can successfully cluster regions with different erodibility and detect the transient response to sudden base level fall. We then test our method in two real landscapes: first in Bitterroot National Forest, Idaho, where we demonstrate that our method can detect transient incision waves and the topographic signature of fluvial and debris flow process regimes; and second, on Santa Cruz Island, California, where our technique identifies spatial patterns in lithology not detectable through normalized channel steepness analysis. By calculating channel steepness separately for each cluster, our method allows the extraction of more reliable steepness metrics than if calculated for the landscape as a whole. These examples demonstrate the method's ability to disentangle fluvial morphology in complex lithological and tectonic settings.
We suggest a new clustering approach to classify focal mechanisms from large moment tensor catalogues, with the purpose of automatically identify families of earthquakes with similar source geometry, recognize the orientation of most active faults, and detect temporal variations of the rupture processes. The approach differs in comparison to waveform similarity methods since clusters are detected even if they occur in large spatial distances. This approach is particularly helpful to analyse large moment tensor catalogues, as in microseismicity applications, where a manual analysis and classification is not feasible. A flexible algorithm is here proposed: it can handle different metrics, norms, and focal mechanism representations. In particular, the method can handle full moment tensor or constrained source model catalogues, for which different metrics are suggested. The method can account for variable uncertainties of different moment tensor components. We verify the method with synthetic catalogues. An application to real data from mining induced seismicity illustrates possible applications of the method and demonstrate the cluster detection and event classification performance with different moment tensor catalogues. Results proof that main earthquake source types occur on spatially separated faults, and that temporal changes in the number and characterization of focal mechanism clusters are detected. We suggest that moment tensor clustering can help assessing time dependent hazard in mines.