Domain Delineation Using Geological Data, Variogram Analysis, and Clustering Algorithms
Domain Delineation Using Geological Data, Variogram Analysis, and Clustering Algorithms作者机构：Facultad de Ingeniera Universidad Andres Bello Santiago Chile Department of Mining Engineering Universidad de Chile Santiago Chile Advanced Mining Technology Center Universidad de Chile Santiago Chile Mining Engineering Department Yazd University Yazd Iran
出 版 物：《Journal of Geoscience and Environment Protection》 (地球科学和环境保护期刊(英文))
年 卷 期：2025年第13卷第1期
页 面：31-47页
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Domaining Hard and Fuzzy Clustering Spatial Anisotropy Kahang Deposit
摘 要：Domaining is a crucial process in geostatistics, particularly when significant spatial variations are observed within a site, as these variations can significantly affect the outcomes of spatial modeling. This study investigates the application of hard and fuzzy clustering algorithms for domain delineation, using geological and geochemical data from two exploration campaigns at the eastern Kahang deposit in central Iran. The dataset includes geological layers (lithology, alteration, and mineral zones), geochemical layers (Cu, Mo, Ag, and Au grades), and borehole coordinates. Six clustering algorithms—K-means, hierarchical, affinity propagation, self-organizing map (SOM), fuzzy C-means, and Gustafson-Kessel—were applied to determine the optimal number of clusters, which ranged from 3 to 4. The fuzziness and weighting parameters were found to range from 1.1 to 1.3 and 0.1 to 0.3, respectively, based on the evaluation of various hard and fuzzy cluster validity indices. Directional variograms were computed to assess spatial anisotropy, and the anisotropy ellipsoid for each domain was defined to identify the model with the highest level of anisotropic discrimination among the domains. The SOM algorithm, which incorporated both qualitative and quantitative data, produced the best model, resulting in the identification of three distinct domains. These findings underscore the effectiveness of combining clustering techniques with variogram analysis for accurate domain delineation in geostatistical modeling.
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