| Abstrakt: |
In this paper, sequential Gaussian simulation (SGS) and number–size (N–S) fractal modeling were used for copper geochemical anomaly mapping in the western part (training area) of Kuhsiah-e-Urmak area, Iran. Then, according to the generated anomaly model in the training area, mineral potential mapping (MPM) was performed for the entire study area based on a well-fitted regression model as a data-driven method. In order to select the best model, six multivariate regression models including two linear, two quadratic, and two cubic functions were examined. For developing a mineral potential map of the study area, the geochemical anomaly raster map of the training area was utilized to create six models based on the values of geo-data sets. According to the results of R 2 , R adj 2 , and EF , the fourth model, generated using a quadratic function, was found to be the superior model compared with the rest of regression models defined in this paper. Based on the mathematical formula derived for the superior model, the geo-exploration data sets were synthesized to generate a potential map showing favorable areas for prospecting copper. The potential map generated was verified by the results of lithogeochemical sampling conducted in the training area and field observations in the entire study area. [ABSTRACT FROM AUTHOR] |
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