Mineral exploration modeling and singularity analysis for geological feature recognition and mineral potential mapping in southeastern Yunnan mineral district, China

Abstract

Nowadays, with the development in construction of geo-exploratory datasets and data processing techniques, mineral exploration modeling for recognition of mineralization associated geological features and mapping of mineral potentials become more dependent on GIS-based analysis and geo-information from multi-source datasets. Geological, geochemical and geophysical data as three main sources of geo-information in support of mineral exploration have long been employed in many researches. Spatial distributions of geological bodies or controlling factors associated with mineralization were frequently interpreted from these datasets. However, former characterizations of the controlling factors were simply focused on their location information; concerns on spatial variations of their geological signatures and controlling effects on mineralization were not sufficiently emphasized. Therefore, through a series of newly developed GIS-based manipulations, current study intends to demonstrate a comprehensive mineral exploration modeling process for more explicit recognition of controlling factors and their interactions on mineralization and delineation of hydrothermal mineral potentials in southeastern Yunnan mineral district, China. The hydrothermal mineralization as a nonlinear geo-process is accompanied with anomalous energy release and material accumulation in a narrow spatial-temporal interval. Simultaneously, it is a cascade process associated with various geological activities (e.g., magmatism, tectonism, etc.). Knowledge of these associated geo-activities is consequently beneficial to the exploration of hydrothermal mineralization. As the key point of this study, the singularity index mapping method in the context of fractal/multifractal efficient in separating geo-anomalies from both strong and weak background is applied to characterize variations of geological signatures of three controlling factors (i.e., granitic intrusions, faults and the Gejiu formation). With the guidance of multidisciplinary approaches, these geo-information derived from multi-source datasets is further integrated to produce the potential map. In comparison with traditionally used methods, the newly depicted predictor maps enhance weak geo-anomalies hidden within a strong variance of background. In addition, three geo-information integration methods including RGB composition, the principal component analysis and the weights of evidence method are implemented. By the weights of evidence method, the qualitatively and quantitatively interpretable result possessing advantages of the other two methods, simultaneously, is accepted as the final result of currently proposed mineral exploration modeling. Summarized experiences through this study will not only support future exploration in the study area, but also benefit the work in other areas