Ore genesis and critical metal enrichment using deep learning algorithms in Gunga Pb-Zn deposit, Southern Pakistan: Constraints from sphalerite geochemistry and isotopic compositions

Abstract

The Kirthar Fold and Thrust Belt in southern Pakistan is widely recognized as a major metallogenic province owing to the extensive presence of Pb-Zn deposits within the Jurassic sedimentary shelf sequence. One of these deposits, the Gunga Pb-Zn deposit, is a significant economic ore deposit situated within the Anjira Formation and comprises two mineralization zones, the Upper Mineralization Zone (UMZ) and Lower Mineralization Zone (LMZ). Despite the scarcity of documentation on the metallogenic source, origin, mineralization temperature, and other physicochemical conditions, our research is the first to explore the metallogenic differentiation and ore genesis of the deposit by examining minor and trace elements in sphalerite and isotopic analyses (S-Pb). The Gunga Pb-Zn deposit, amounting to 6.86 Mt. with 2.1 % and 11.4 % Pb and Zn, respectively, is linked to the interbedded limestone and shale unit of the Anjira Formation, and approximately below the depth of 200–250 m of the Jurassic-Cretaceous contact, the mineralization is believed to occur in the upper section of the Formation. Sphalerite is the dominant ore mineral, galena is a subordinate mineral, and pyrite is present in the main ore minerals. Sphalerite in the LMZ was found in a vein style, whereas it was disseminated in the UMZ and enriched in (medians) Ge (166 ppm), Cd (541 ppm), Pb (139 ppm), Ag (42 ppm), and Hg (68 ppm), lacking In (0.17 ppm), Co (6 ppm), and Mn (19). Based on trace element concentrations, the LMZ has a reduced environment, higher sulfur fugacity, and higher mineralization temperature (180–200 °C) than the UMZ, which has a lower mineralization temperature (130–180 °C). The presence of black shales, low temperatures, and high sulfur fugacity are the key parameters for Ge enrichment in the Gunga deposit. The δ³⁴S values range from −10 to +5.6 ‰, suggesting that reduced sulfur was produced by both the BSR and TSR mechanisms, with basinal brines and seawater being the main sources of sulfur. The Pb isotope results suggest that the upper crust is the main source of the metals. In this study, Deep Learning algorithms based on sphalerite trace elements were employed for metallogenic discrimination of Pb-Zn deposits. The classifiers were trained on a dataset comprising approximately 3800 data points from 99 mineral deposits with published trace element compositions. The performance of the classifiers was assessed using a cross-validation with a k-fold variant. This study employed well-established classifiers on newly acquired geochemical data obtained from sphalerite samples collected from the Gunga Pb-Zn deposit. Based on the findings of this study, low temperature, lack of magmatic source elements, and clastic sedimentary sequence in a passive margin environment, a CD-type mineralization is proposed for the Gunga Pb-Zn deposit.