Analysis and Investigation of Babakoohi Anticline Fractures Based on Clustering Technique Using K-means and Genetic Algorithm, and Paleostress Determination Using MIM Method, Shiraz, Iran

Geological structural data related to joints and faults can be effectively analysed using statistical methods and techniques to improve the quality of the data and achieve accurate analysis regarding the applied stress in an area. Clustering is considered one of the ideal methods of organizing data or information, with the purpose of optimizing, analyzing the data, identifying accurate patterns consistent with the data, and establishing effective connections between data for precise analysis. K-means and genetic algorithms are two of the most important and practical fuzzy algorithms in statistical methods that have been used to cluster data from Babakohi anticline valleys located in the north of Shiraz, Iran. The results of applying this technique and geological structural studies show the Babakohi anticline data as two distinct gatherings or clusters. In order to investigate the accuracy of the number of clusters identified, VW, ICC, VMPC, and VPBMF validation indices were also analysed. These indicators also confirm the two clusters. The specified stress phases in the region by the MIM method (multiple inverse techniques) based on clustering using fault scratch slip data show two stress phases: tensile and strike-slip. The integration of the joint clustering results using the mentioned algorithms and determining the stress phases indicates the action of the two stress phases in the Babakohi anticline.