date: 2017-05-17T10:31:52Z
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pdf:docinfo:title: Data-driven earthquake focal mechanism cluster analysis
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subject:  Earthquake focal mechanism solutions (FMS) form the basic data input for many applications, e.g. stress tensor inversion or ground-motion prediction equation estimation. In these applications the FMS data is usually binned spatially or in predetermined ranges of rake and dip based on expert elicitation. However, due to the significant increase of FMS data in the past decade an objective data-driven cluster analysis is now possible. Here we present the method ACE (Angular Classification with Expectation-Maximization) that identities clusters of FMS without a priori information. The identified clusters can be used for the classification of the Style-of- Faulting and as weights for FMS data binning in the aforementioned applications. As an application example we use ACE to identify FMS clusters according to their Style-of- Faulting that are related to certain earthquake types (e.g. subduction interface) in northern Chile, the Nazca Plate and in Kyushu (Japan). We use the resulting clusters and weights as a priori information for a stress tensor inversion for these regions and show that uncertainties of the stress tensor estimates are reduced significantly. 
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dc:title: Data-driven earthquake focal mechanism cluster analysis
modified: 2017-05-17T10:31:52Z
cp:subject:  Earthquake focal mechanism solutions (FMS) form the basic data input for many applications, e.g. stress tensor inversion or ground-motion prediction equation estimation. In these applications the FMS data is usually binned spatially or in predetermined ranges of rake and dip based on expert elicitation. However, due to the significant increase of FMS data in the past decade an objective data-driven cluster analysis is now possible. Here we present the method ACE (Angular Classification with Expectation-Maximization) that identities clusters of FMS without a priori information. The identified clusters can be used for the classification of the Style-of- Faulting and as weights for FMS data binning in the aforementioned applications. As an application example we use ACE to identify FMS clusters according to their Style-of- Faulting that are related to certain earthquake types (e.g. subduction interface) in northern Chile, the Nazca Plate and in Kyushu (Japan). We use the resulting clusters and weights as a priori information for a stress tensor inversion for these regions and show that uncertainties of the stress tensor estimates are reduced significantly. 
pdf:docinfo:subject:  Earthquake focal mechanism solutions (FMS) form the basic data input for many applications, e.g. stress tensor inversion or ground-motion prediction equation estimation. In these applications the FMS data is usually binned spatially or in predetermined ranges of rake and dip based on expert elicitation. However, due to the significant increase of FMS data in the past decade an objective data-driven cluster analysis is now possible. Here we present the method ACE (Angular Classification with Expectation-Maximization) that identities clusters of FMS without a priori information. The identified clusters can be used for the classification of the Style-of- Faulting and as weights for FMS data binning in the aforementioned applications. As an application example we use ACE to identify FMS clusters according to their Style-of- Faulting that are related to certain earthquake types (e.g. subduction interface) in northern Chile, the Nazca Plate and in Kyushu (Japan). We use the resulting clusters and weights as a priori information for a stress tensor inversion for these regions and show that uncertainties of the stress tensor estimates are reduced significantly. 
pdf:docinfo:creator: Specht
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dc:description:  Earthquake focal mechanism solutions (FMS) form the basic data input for many applications, e.g. stress tensor inversion or ground-motion prediction equation estimation. In these applications the FMS data is usually binned spatially or in predetermined ranges of rake and dip based on expert elicitation. However, due to the significant increase of FMS data in the past decade an objective data-driven cluster analysis is now possible. Here we present the method ACE (Angular Classification with Expectation-Maximization) that identities clusters of FMS without a priori information. The identified clusters can be used for the classification of the Style-of- Faulting and as weights for FMS data binning in the aforementioned applications. As an application example we use ACE to identify FMS clusters according to their Style-of- Faulting that are related to certain earthquake types (e.g. subduction interface) in northern Chile, the Nazca Plate and in Kyushu (Japan). We use the resulting clusters and weights as a priori information for a stress tensor inversion for these regions and show that uncertainties of the stress tensor estimates are reduced significantly. 
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dc:creator: S.
description:  Earthquake focal mechanism solutions (FMS) form the basic data input for many applications, e.g. stress tensor inversion or ground-motion prediction equation estimation. In these applications the FMS data is usually binned spatially or in predetermined ranges of rake and dip based on expert elicitation. However, due to the significant increase of FMS data in the past decade an objective data-driven cluster analysis is now possible. Here we present the method ACE (Angular Classification with Expectation-Maximization) that identities clusters of FMS without a priori information. The identified clusters can be used for the classification of the Style-of- Faulting and as weights for FMS data binning in the aforementioned applications. As an application example we use ACE to identify FMS clusters according to their Style-of- Faulting that are related to certain earthquake types (e.g. subduction interface) in northern Chile, the Nazca Plate and in Kyushu (Japan). We use the resulting clusters and weights as a priori information for a stress tensor inversion for these regions and show that uncertainties of the stress tensor estimates are reduced significantly. 
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title: Data-driven earthquake focal mechanism cluster analysis
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