Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9536139 | Journal of Structural Geology | 2005 | 8 Pages |
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.
Related Topics
Physical Sciences and Engineering
Earth and Planetary Sciences
Geology
Authors
Yehua Shan, Norman Fry,