Mineralization potential from vein statistics at regional scale – an example from the Dharwar Craton (Southern India)

Hydrothermal veins can form when pre-existing anisotropy gets dilated by increase in fluid pressure (P_f). In addition fluctuation in P_f can result in the deposition of economically important deposits, such as gold in veins. Therefore, statistical analysis of dilational quartz veins can help to identify the mineralization potential of an area. This present study is focused on this aspect. The metavolcanic rocks of Dharwar Craton (Southern India) are replete with quartz veins, but mineralization is restricted to certain domains in the vicinity of Gadag, Hutti, and Kolar. To study the topological importance in mineral exploration, an experiment is carried out in different parts (e.g., Gadag, Hutti, Raichur, Gadwal) of Dharwar Craton. Orientation of dilational quartz veins, thickness, and spacing data are collected from different transects of the above-mentioned areas. Variations in driving pressure ratio (ΔRʹ), fractal dimension (Dc), Weibull modulus (α), vein intensity (Vi), and coefficient of vein spacing (C_v(S)) are calculated from vein data of each transect. These parameters are integrated to plot a three-axes “mineralization potential plot”, also known as P-D-F plot (Lahiri et al., 2020). The distance between the origin (0,0,0) and the point representing the transect in the P-D-F plot gives the “mineralization potential parameter” (Md). It is established that a transect in mineralized zone is represented by a point that lies closer to origin than non-mineralized zone. However, it is our observation that the points representing mineralized zone show more clustering on the P-D-F plot, whereas the points representing non-mineralized zone are dispersed.  We infer that in order to identify a zone as having high mineralization potential, the Md value should not only be lower but also the clustering should be higher.