Rare metal-polymetallic mineralization of Koshmansay ore field (Eastern Uzbekistan)

The Koshmansai ore field is located in the southern part of the granitoid Chatkal batholith, in its apical ledge and exocontact zones, in the Koshmansai river basin. The host environment of the granitoids is Lower Carboniferous carbonate rocks, which were primarily affected by intensive skarnification. Sedimentary-metamorphic and volcanics rocks and granitoids constitute the geological structure of the skarn rare-metal-polymetallic Koshmansai deposit. In the distribution of ore-forming and associated elе- ments in the mineral phases of skarn orebodies, their morphogenetic type plays a certain role. Thus, in bimetasomatic skarns, minerals accumulate more Cu, Zn, Ni, Te, Tl, Ge. In infiltration skarns, these are Ag, Pb, Bi, Cd, Sb, Co. Sulfide polymetallic mineralization in skarns is associated with quartz and calcite. The Koshmansai ore field has a distinct geochemical zoning, which can be subdivided into the Koshmansai rare- metal-polymetallic deposit at the upper levels of the ore field and the Nizhnekoshmansai rare-metal-copper ore occurrence at its lower levels. Nevertheless, orebodies formation proceeded in a similar thermodynamic environment, in the conditions of upper shielding at low temperature gradients, which makes it possible to consider the ore field as a single geochemical anomaly. The vertical geochemical zoning of ore-forming element halos determined by their concentration at the lower section levels of the Koshmansai deposit skarn orebodies suggests the expansion of its prospects in depth.

Coupling Relationship Analysis of Gold Content Using Gaofen-5 (GF-5) Satellite Hyperspectral Remote Sensing Data: A Potential Method in Chahuazhai Gold Mining Area, Qiubei County, SW China

The gold (Au) geochemical anomaly is an important indicator of gold mineralization. While the traditional field geochemical exploration method is time-consuming and expensive, the hyperspectral remote sensing technique serves as a robust technique for the delineation and mapping of hydrothermally altered and weathered mineral deposits. Nonetheless, mineralization element anomaly detection was still seldomly used in previous hyperspectral remote sensing applications in mineralization. This study explored the coupling relationship between Gaofen-5 (GF-5) hyperspectral data and Au geochemical anomalies through several models. The Au geochemical anomalies in the Chahuazhai mining area, Qiubei County, Yunnan Province, SW China, was studied in detail. First, several noise reduction methods including radiometric calibration, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), Savitzky–Golay filter, and endmember choosing methods including Minimum Noise Fraction (MNF) transformation, matched filtering, and Fast Fourier Transform (FFT) transformation were applied to the Gaofen-5 (GF-5) hyperspectral data processing. The Spectrum-Area (S-A) method was introduced to build an FFT filter to highlight the spectral abnormal characteristics associated with Au geochemical anomaly information. Specifically, the Matched Filtering (MF) technique was applied to the dataset to find the Au geochemical anomaly abundances of endmembers with innovative large-sample learning. Then, Multiple Linear Regression (MLR), Partial Least Squares (PLS) regression, a Back Propagation (BP) network, and Geographically Weighted Regression (GWR) were used to reveal the coupling relationship between the spectra of the processed hyperspectral data and the Au geochemical anomalies. The results show that the GWR analysis has a much higher coefficient of determination, which implies that the Au geochemical anomalies and the spectral information are highly related to spatial locations. GWR works especially well for showing the regional Au geochemical anomaly trend and simulating the Au concentrated areas. The GWR model with application of the S-A method is applicable to the detection of Au geochemical anomalies, which could provide a potential method for Au deposit exploration using GF-5 hyperspectral data.

Regional Geochemical Anomaly Identification Based on Multiple-Point Geostatistical Simulation and Local Singularity Analysis—A Case Study in Mila Mountain Region, Southern Tibet

The smoothing effect of data interpolation could cause useful information loss in geochemical mapping, and the uncertainty assessment of geochemical anomaly could help to extract reasonable anomalies. In this paper, multiple-point geostatistical simulation and local singularity analysis (LSA) are proposed to identify regional geochemical anomalies and potential mineral resources areas. Taking Cu geochemical data in the Mila Mountain Region, southern Tibet, as an example, several conclusions were obtained: (1) geochemical mapping based on the direct sampling (DS) algorithm of multiple-point geostatistics can avoid the smoothing effect through geochemical pattern simulation; (2) 200 realizations generated by the direct sampling simulation reflect the uncertainty of an unsampled value, and the geochemical anomaly of each realization can be extracted by local singularity analysis, which shows geochemical anomaly uncertainty; (3) the singularity-quantile (S-Q) analysis method was used to determine the separation thresholds of E-type α, and uncertainty analysis was carried out on the copper anomaly to obtain the anomaly probability map, which should be more reasonable than the interpolation-based geochemical map for geochemical anomaly identification. According to the anomaly probability and favorable geological conditions in the study area, several potential mineral resource targets were preliminarily delineated to provide direction for subsequent mineral exploration.