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Mineral Potential Mapping Using a Conjugate Gradient Logistic Regression Model

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Abstract

Mineral potential prediction is a process of establishing a statistical model that describes the relationship between evidence variables and mineral occurrences. In this study, evidence variables were constructed from geological, remote sensing, and geochemical data collected from the Lalingzaohuo district, Qinghai Province, China. Based on these evidence variables, a conjugate gradient logistic regression (CG-LR) model was established to predict exploration targets in the study area. The receiver operating characteristic (ROC) and prediction–area (P-A) curves were used to evaluate the effectiveness of the CG-LR model in mineral potential mapping. The difference between the vertical and horizontal coordinates of each point on the ROC curve was used to determine the optimal threshold for classifying the exploration targets. The optimal threshold corresponds to the point on the ROC curve where the difference between the vertical coordinate and the horizontal coordinate is the largest. In exploration target prediction in the study area, the CG algorithm was used to optimize iteratively the LR coefficients, and the prediction effectiveness was tested for different epochs. With increasing iterations, the prediction performance of the model becomes increasingly better. After 60 iterations, the LR model becomes stable and has the best performance in exploration target prediction. At this point, the exploration targets predicted by the CG-LR model occupy 14.39% of the study area and contain 93% of the known mineral deposits. The exploration targets predicted by the model are consistent with the metallogenic geological characteristics of the study area. Therefore, the CG-LR model can effectively integrate geological, remote sensing, and geochemical data for the study area to predict targets for mineral exploration.

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Acknowledgments

The authors are very grateful to the two anonymous reviewers for their insightful comments, which greatly improved the manuscript. This work was supported by the National Natural Science Foundation of China (Grant Nos. 41702357 and 41672322).

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  1. College of Earth Science, Jilin University, Changchun, 130026, Jilin, China

    Nan Lin & Laijun Lu

  2. College of Surveying and Mapping Engineering, Jilin Jianzhu University, Changchun, 130018, Jilin, China

    Nan Lin

  3. Institute of Mineral Resources Prognosis on Synthetic Information, Jilin University, Changchun, 130026, Jilin, China

    Yongliang Chen

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Lin, N., Chen, Y. & Lu, L. Mineral Potential Mapping Using a Conjugate Gradient Logistic Regression Model. Nat Resour Res 29, 173–188 (2020). https://doi.org/10.1007/s11053-019-09509-1

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