Integration of Sparse Geologic Information in Gold Targeting Using Logistic Regression Analysis in the Hutti–Maski Schist Belt, Raichur, Karnataka, India—A Case Study


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Abstract

Logistic regression has been used in the study to integrate indicator patterns for estimation of the probability of occurrence of gold deposits in a part of the auriferous Archaean Hutti–Maski schist belt. Data used consist of categorical and continuous variables obtained from a coded lineament map and geochemical anomaly maps of the pathfinder elements of gold in soil and groundwater. Main effects and interactions of the variables studied were used in formulating the logistic regression model. Regression models using lineament-proximity data, combined with soil and groundwater geochemical anomalies were tested on parts of the schist belt with data not used in estimation of model parameters. Predicted probabilities greater than 0.9 identified known deposit locations in the area.

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