Análise geoestatística e mapeamento geoquímico de zonas potenciais em mineralizações de ouro - Posto Administrativo de Machipanda, Província de Manica

Abstract

Soils have been widely used in geochemical mapping as one of the primary means of sample collection. This is essential for geological mapping, mineral prospecting, and metallogenic studies, as soils reflect the composition of the underlying rocks. The geochemical data to be processed and interpreted resulted from a soil geochemical survey in the Machipanda region, conducted in 2016 by the National Mining Institute (INAMI). In this process, soil samples (402), stream sediments (41), rocks (21), and soils from gold mining pits (28) were collected, totaling 492 samples in all seven designated study areas. For this report, only soil sample results will be processed. The main objective of this work is to use geochemical, geological, and remote sensing data with the application of geostatistical methods and GIS techniques to assess the gold potential in the Machipanda region. From the available data, first, geochemical data were processed through the application of statistical methods in soil samples. Univariate statistics were applied to understand the studied variables, including background selection and geochemical anomalies, resulting in the creation of concentration distribution maps. Bivariate statistics allowed grouping variables based on their geochemical affinity, distinguishing three groups with weak, moderate, and strong positive correlations. Factor analysis in multivariate studies identified four factors, and geochemical thresholds were determined using probability plot methods. Geo-statistical analysis was used for the interpolation of single-element geochemical data through ordinary kriging, and indicator kriging was applied to indicate the zones of occurrence of geochemical associations defined by factor analysis, determining the spatial distribution of element concentrations. It allowed the distinction of four factors: the Mn-Cr-Ni-As association forming factor 1, located in Metagrauvaques (A3MVgy) and Felsic Metavolcanic Rocks (A3MMfv); the Ti-Na-Au-Pb association representing factor 2, related to Metagrauvaques (A3MVgy), Felsic Metavolcanic Rocks (A3MMfv), and Intermediate Metavolcanic Rocks (A3MMiv); the K-Ca-V association representing factor 3, related to Tufaceous Breccia (A3MMfv), Felsic Metavolcanic Rocks (A3MMfv), and Intermediate Metavolcanic Rocks (A3MMiv); and the Co-Cu association representing factor 4, which is related to Metagrauvaques (A3MVgy), Felsic Metavolcanic Rocks (A3MMfv), and Intermediate Metavolcanic Rocks (A3MMiv). Remote sensing data were utilized using Alos Spalsal images, and the multidirectional hillshade technique was employed for manual extraction of lineaments and their density. The Weighted Overlay data integration method was employed to create the final favorability map, integrating three thematic maps: a map of gold-hosting lithologies, geochemical maps, and a map of lineament density. Each map was assigned a weight based on its influence in the study area. Theintegration resulted in two predictive maps, indicating that the south (S) and southeast (SE) zones of the study area predominantly exhibit some favorability for gold occurrence.