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A hierarchical cluster approach for forward separation of heterogeneous fault/slip data into subsets

Shan, Yehua and Fry, Norman 2005. A hierarchical cluster approach for forward separation of heterogeneous fault/slip data into subsets. Journal of structural geology 27 (5) , pp. 929-936. 10.1016/j.jsg.2005.02.001

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Abstract

A new simple method of stress inversion uses hierarchical cluster analysis for forward separation of heterogeneous fault/slip data into subsets. Fault/slip data are classified into homogeneous fault classes, and a clustering routine classifies these into subsets. The method includes a way of discarding some residual data at the first stage that makes it fairly easy to recognize and eliminate some spurious fault data. However, this method is a type of hard division that overlooks the indeterminate nature of fault data. The more heterogeneous the data, the larger the calculation needed to find from a K-data set the homogeneous fault class that agglomerates a pair of 5-data subsets, sampled in a binomial distribution, with the maximum similarity in estimated stress vector between them. The K-data set is a working data group successively taken from the whole data. Given P phases of different stress state, the minimum value of K is 5P+1. Results from applying the method to two examples, artificial and real, demonstrate the feasibility of the method.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Earth and Ocean Sciences
Uncontrolled Keywords: Heterogeneity ; Fault/slip data ; Stress inversion ; Hierarchical clustering
Publisher: Elsevier
ISSN: 01918141
Last Modified: 31 Jan 2020 06:17
URI: http://orca-mwe.cf.ac.uk/id/eprint/1326

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