Computerized identification of stress tensors determined from heterogeneous fault-slip data by combining the multiple inverse method and k-means clustering

Abstract

The multiple inverse method is a resampling technique that can separate stresses from heterogeneous fault-slip data. Numerous optimal stresses are determined for each extracted subset of data, and the clusters of these stresses are thought to represent significant solutions. Hitherto, the clusters have had to be visually recognized on stereonets. This study computerized the identification of the clusters by using the k-means clustering technique. We tested the technique using artificial datasets with known solutions. As a result, it was found that the present method detected objectively the correct solutions. In addition, the spread of each cluster was evaluated to indicate the confidence levels of the identified stresses that were represented by the cluster centers.