High-grade ore zones in low-sulfidation epithermal deposits are commonly associated with the occurrence of banded quartz veins. The ore minerals in these veins are heterogeneously distributed and are mostly confined to ginguro bands, which can be identified in hand specimen based on their distinct dark gray to black color. Micro-X-ray fluorescence element maps obtained on representative samples of banded quartz veins show that Au occurs together with Ag minerals in some of the ginguro bands, but Au can also be present in quartz bands that are light gray to white and cannot be macroscopically distinguished from barren bands. The occurrence of compositionally distinct ginguro and gankin bands, the latter being a new term coined here for colloform quartz bands containing primarily electrum or native gold, can be explained by temporal changes in the composition of the ore-forming thermal waters or variations in the conditions of ore deposition. Textural relationships, including the dendritic shape of ore minerals that appear to have grown in a matrix of silica microspheres, suggest that the ginguro and gankin bands have formed as a result of rapid deposition associated with vigorous boiling or flashing of the thermal waters.

Understanding the mineralogy and geochemistry of the subsurface is key when assessing and exploring for mineral deposits. To achieve this goal, rapid acquisition and accurate interpretation of drill core data are essential. Hyperspectral shortwave infrared imaging is a rapid and non-destructive analytical method widely used in the minerals industry to map minerals with diagnostic features in core samples. In this paper, we present an automated method to interpret hyperspectral shortwave infrared data on drill core to decipher major felsic rock-forming minerals using supervised machine learning techniques for processing, masking, and extracting mineralogical and textural information. This study utilizes a co-registered training dataset that integrates hyperspectral data with quantitative scanning electron microscopy data instead of spectrum matching using a spectral library. Our methodology overcomes previous limitations in hyperspectral data interpretation for the full mineralogy (i.e., quartz and feldspar) caused by the need to identify spectral features of minerals; in particular, it detects the presence of minerals that are considered invisible in traditional shortwave infrared hyperspectral analysis.

The Mollehuaca and San Juan de Chorunga deposits are hosted in the poorly explored gold and copper trends of the Nazca-Ocoña metallogenic belt in Arequipa, Perú, which extends from Trujillo (9 °S) to Nazca-Ocoña (14 °S). The aim of this study is to characterize the age, occurrence, and distribution of quartz vein-hosted Au-Ag mineralization and associated trace elements (e.g., Hg, Pb, Cu, Zn, and Bi) in these deposits. Here, we present geological mapping, geochemical whole rock inductively coupled plasma (ICP)-MS data of the veins, petrographic observations, backscattered electron images, quantitative SEM-based automated mineralogy, and electron microprobe analyses (EMPA). Despite the fact that there are numerous small-scale gold mines in the Nazca-Ocoña metallogenic belt, there have been few studies that document the origin and geological evolution of these deposits or the implications for decision-making in exploration, metallurgical processing, and environmental management. In this research, we document the host rock age of the mineralized veins (129.2 ± 1.0 Ma; U-Pb in zircon), the mineralization age (95.86 ± 0.05 Ma; ⁴⁰Ar/³⁹Ar in secondary biotite), the occurrence and distribution of Au-Ag in the veins, the mineral zonation present in the vein system, and the zircon geochemistry, in order to provide tools for natural resource management in the metallogenic belt.