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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Abedi, M., Norouzi, G. H., & Bahroudi, A. (2012). Support vector machine for multi-classification of mineral prospectivity areas. Computers & Geosciences,46, 272–283.
Agterberg, F. P. (1974). Automatic contouring of geological maps to detect target areas for mineral exploration. Journal of the International Association for Mathematical Geology,6(4), 373–395.
Agterberg, F. P. (1981). Application of image analysis and multivariate analysis to mineral resource appraisal. Economic Geology,76, 1016–1031.
Agterberg, F. P. (1988). Application of recent developments of regression analysis in mineral resource evaluation. In C. F. Chung, et al. (Eds.), Quantitative analysis of mineral and energy resources (pp. 1–28). Dordrecht: D. Reidel Publishing Company.
Agterberg, F. P. (1989). LOGDIA-FORTRAN 77 program for logistic regression with diagnostics. Computers & Geosciences,15(4), 599–614.
Agterberg, F. P. (1990). Combining indicator patterns for mineral resource evaluation. In China University of Geosciences (Eds.). Proceedings of international workshop on statistical prediction of mineral resources (Vol. 1, pp. 1–15).
Agterberg, F. P. (1992). Combining indicator patterns in weights of evidence modelling for resource evaluation. Nonrenewable Resources,1(1), 39–50.
Allek, K., Boubaya, D., Bouguern, A., & Hamoudi, M. (2016). Spatial association analysis between hydrocarbon fields and sedimentary residual magnetic anomalies using weights of evidence: An example from the Triassic province of Algeria. Journal of Applied Geophysics,135(S1), 100–110.
Behnia, P. (2007). Application of radial basis functional link networks to exploration for Proterozoic mineral deposits in Central Iran. Natural Resources Research,16(2), 147–155.
Bergmann, R., Ludbrook, J., & Spooren, P. J. M. W. (2000). Different outcomes of the Wilcoxon–Mann–Whitney test from different statistics packages. The American Statistician,54(1), 72–77.
Biswas, A. (2018). Inversion of source parameters from magnetic anomalies for mineral/ore deposits exploration using global optimization technique and analysis of uncertainty. Natural Resources Research,27(1), 77–107.
Bonham-Carter, G. F., Agterberg, F. P., & Wright, D. F. (1989). Weights of evidence modelling: A new approach to mapping mineral potential. In F. P. Agterberg & G.F. Bonham-Carter (Eds.), Statistical applications in the Earth sciences, Paper 89-9 (pp. 171–183). Geological Survey of Canada.
Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition,30, 1145–1159.
Brown, W. M., Gedeon, T. D., Groves, D. I., & Barnes, R. G. (2000). Artificial neural networks: A new method for mineral potential mapping. Australian Journal of Earth Sciences,47(4), 757–770.
Carranza, E. J. M., & Hale, M. (2001). Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines. Exploration and Mining Geology,10(3), 165–175.
Carranza, E. J. M., & Hale, M. (2002). Where are porphyry copper deposits spatially localized? A case study in Benguet province, Philippines. Natural Resources Research,11(1), 45–59.
Carranza, E. J. M., & Laborte, A. G. (2015a). Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of random forests algorithm. Ore Geology Reviews,71, 777–787.
Carranza, E. J. M., & Laborte, A. G. (2015b). Random forest predictive modelling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines). Computers & Geosciences,74, 60–70.
Carranza, E. J. M., & Laborte, A. G. (2016). Data-driven predictive modeling of mineral prospectivity using random forests: A case study in Catanduanes Island (Philippines). Natural Resources Research,25(1), 35–50.
Chen, Y. L. (2015). Mineral potential mapping with a restricted Boltzmann machine. Ore Geology Reviews,70(S1), 749–760.
Chen, Y. L., & Li, X. B. (2011). Mineral target prediction based on kernel minimum square error. Journal of Jilin University (Earth Science Edition),41(3), 937–944. (In Chinese with English Abstract).
Chen, Y. L., Lu, L. J., & Li, X. B. (2014). Kernel mahalanobis distance for multivariate geochemical anomaly recognition. Journal of Jilin University (Earth Science Edition),44(1), 396–408. (In Chinese with English Abstract).
Chen, Y. L., & Wu, W. (2016). A prospecting cost-benefit strategy for mineral potential mapping based on ROC curve analysis. Ore Geology Reviews,74, 26–38.
Chen, Y. L., & Wu, W. (2017a). Mapping mineral prospectivity using an extreme learning machine regression. Ore Geology Reviews,80, 200–213.
Chen, Y. L., & Wu, W. (2017b). Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data. Geochemistry -exploration Environment Analysis,17(3), 231–238.
Chen, Y. L., & Wu, W. (2017c). Mapping mineral prospectivity by using one class support vector machine to identify multivariate geological anomalies from digital geological survey. Australian Journal of Earth Sciences,44(5), 639–651.
Chen, Y. L., & Wu, W. (2019). Isolation forest as an alternative data-driven mineral prospectivity mapping method with a higher data-processing efficiency. Natural Resources Research,28(1), 31–46.
Chen, J., Xie, Z. Y., Li, B., Tan, S. X., Ren, H., Zhang, Q. M., et al. (2013). Petrogenesis of Devonian intrusive rocks in the Lalingzaohuo area, eastern Kunlun, and its geological significance. Journal of Mineralogy and Petrology,33(2), 26–34. (In Chinese with English Abstract).
Cheng, Q. M. (2011). Integration of adaboost and weights of evidence model for mineral potential probabilistic mapping. In IAMG2011, Salzburg.
Cheng, Q. M. (2012). Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas. Journal of Geochemical Exploration,122(S1), 55–70.
Cheng, Q. M. (2015). Boostwofe: A new sequential weights of evidence model reducing the effect of conditional dependency. Mathematical Geosciences,47(5), 591–621.
Cheng, Q. M., & Agterberg, F. P. (2009). Singularity analysis of ore-mineral and toxic trace elements in stream sediments. Computers & Geosciences,35(2), 234–244.
Chung, C. F., & Agterberg, F. P. (1980). Regression models for estimating mineral resources from geological map data. Mathematical Geology,12, 473–488.
Dai, M. F., & Wang, S. L. (2011). Metallogenic background and prospect analysis of Lalingzaohuo region in Qinghai Province. Qinghai Science and Technology,4, 11–14. (In Chinese with English Abstract).
Deng, J. K. (1992). Regional tectonic evolution of east Kunlun. Qinghai Geological,1(1), 15–25. (In Chinese with English Abstract).
Dragos, C. (2010). ROC curve for discrete choice models an application to the Romanian car market. Applied Economics Letters,17(1), 75–79.
Feizi, F., Karbalaei-Ramezanali, A., & Tusi, H. (2017). Mineral potential mapping via topsis with hybrid AHP–Shannon entropy weighting of evidence: A case study for porphyry-Cu, Farmahin Area, Markazi Province, Iran. Natural Resources Research,26(4), 533–570.
Fitzmaurice, G. M., Laird, N. M., Zahner, G., & Daskalakis, C. (1995). Bivariate logistics-regression analysis of childhood psychopathology ratings using multiple informants. American Journal of Epidemiology,142(11), 1194–1203.
Fletcher, R., & Reeves, C. (1964). Function minimization by conjugate gradients. The Computer Journal,7(2), 149–154.
Fong, Y. Y., Yin, S. X., & Huang, Y. (2016). Combining biomarkers linearly and nonlinearly for classification using the area under the ROC curve. Statistics in Medicine,35(21), 3792–3809. (In Chinese with English Abstract).
Gabr, S., Ghulam, A., & Kusky, T. (2010). Detecting areas of high-potential gold mineralization using ASTER data. Ore Geology Reviews,38(1), 59–69.
Gao, Y. B., Li, W. Y., & Tan, W. J. (2010). Metallogenic characteristics and analysis of the prospecting potential in the area of Qimantage. North Western Geology,43(4), 35–43. (In Chinese with English Abstract).
Gomez, Cécile, Delacourt, C., Allemand, P., Ledru, P., & Wackerle, R. (1999). Using aster remote sensing data set for geological mapping, in Namibia. Physics and Chemistry of the Earth,30(1), 97–108.
Harris, D., & Pan, G. (1999). Mineral favorability mapping: A comparison of artificial neural networks, logistic regression, and discriminant analysis. Natural Resources Research,8(2), 93–109.
Harris, D., Zurcher, L., Stanley, M., Marlow, J., & Pan, G. (2003). A comparative analysis of favorability mappings by weights of evidence, probabilistic neural networks, discriminant analysis and logistic regression. Natural Resources Research,12(4), 241–255.
Hestenes, M. R., & Stiefel, E. L. (1952). Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards,49(6), 409–435.
Koh, K. M., Kim, S. J., & Boyd, S. (2007). An interior-point method for large-scalel(1)-regularized logistic regression. Journal of Machine Learning Research,8, 1519–1555.
Kottas, M., Kuss, O., & Apf, A. (2014). A modified Wald interval for the area under the ROC curve (AUC) in diagnostic case-control studies. BMC Medical Research Methodology,14(1), 26–30.
Kurum, E., Yildirak, K., & Weber, G. W. (2012). A classification problem of credit risk rating investigated and solved by optimisation of the ROC curve. Central European Journal of Operations Research,20(3), 529–557.
Leite, E. P., & Desouza, C. R. (2009a). Artificial neural networks applied to mineral potential mapping for copper–gold mineralizations in the Carajás Mineral Province, Brazil. Geophysical Prospecting,57(6), 1049–1065.
Leite, E. P., & Desouza, C. R. (2009b). Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Carajás Mineral Province, Brazil. Computers & Geosciences,35(3), 675–687.
Lin, C. J., Weng, R. C., & Keerthi, S. S. (2008). Trust region Newton method for large-scale logistic regression. Journal of Machine Learning Research,9, 627–650.
Liu, Y., Cheng, Q. M., Xia, Q. L., & Wang, X. Q. (2013). Application of singularity analysis for mineral potential identification using geochemical data—A case study: Nanling W-Sn-Mo polymetallic metallogenic belt, South China. Journal of Geochemical Exploration,134, 61–72.
Liu, Y., Xia, Q. L., Cheng, Q. M., & Wang, X. (2014). Application of singularity theory and logistic regression model for tungsten polymetallic potential mapping. Nonlinear Processes in Geophysics,20(4), 445–453.
Liu, Y., Zhou, K. F., & Xia, Q. L. (2018a). A MaxEnt model for mineral prospectivity mapping. Natural Resources Research,27(3), 299–313.
Liu, Y., Zhou, K. F., Zhang, N. N., & Wang, J. L. (2018b). Maximum entropy modeling for orogenic gold prospectivity mapping in the Tangbale-Hatu belt, Western Junggar, China. Ore Geology Reviews,100(S1), 133–147.
Lu, H. F., Li, J. Q., Yin, Z. H., & Li, Y. L. (2011). The distribution of mineral and the partition of ore prospective area in Qinghai Ge-ermu Lalingzaohuo region. China Mining Magazine,20(7), 66–69. (In Chinese with English Abstract).
McCarthy, M. A., Burgman, M. A., & Ferson, S. (1995). Sensitivity analysis for models of population viability. Biological Conservation,73, 93–100.
McKay, G., & Harris, J. R. (2016). Comparison of the data-driven random forests model and a knowledge-driven method for mineral prospectivity mapping: A case study for gold deposits around the Huritz Group and Nueltin Suite, Nunavut, Canada. Natural Resources Research,25(2), 125–143.
Nykänen, V., Groves, D. I., & Ojala, V. J. (2008). Combined conceptual/empirical prospectivity mapping for orogenic gold in the northern Fennoscandia Shield, Finland. Australian Journal of Earth Sciences,55(1), 39–59.
Obuchowski, N. A., & Bullen, J. A. (2018). Receiver operating characteristic (ROC) curves: Review of methods with applications in diagnostic medicine. Physics in Medicine & Biology,63(7), 1–28.
Oh, H. J., & Lee, S. (2008). Regional probabilistic and statistical mineral potential mapping of gold-silver deposits using GIS in the Gangreung area, Korea. Resource Geology,58(2), 171–187.
Oh, H. J., & Lee, S. (2010). Application of artificial neural network for gold–silver deposits potential mapping: A case study of Korea. Natural Resources Research,19(2), 103–124.
Pinsky, P. F. (2005). Scaling of true and apparent ROC AUC with number of observations and number of variables. Communications in Statistics-Simulation and Computation,34(3), 771–781.
Rajendran, S., Nasir, S., Kusky, T. M., Ghulam, A., Gabr, S., & El-Ghali, M. A. K. (2013). Detection of hydrothermal mineralized zones associated with listwaenites in central Oman using aster data. Ore Geology Reviews,53, 470–488.
Rigol-Sanchez, J. P., Chica-Olmo, M., & Abarca-Hernandez, F. (2003). Artificial neural networks as a tool for mineral potential mapping with GIS. International Journal of Remote Sensing,24(5), 1151–1156.
Rodriguez-Galiano, V., Chica-Olmo, M., & Chica-Rivas, M. (2014). Predictive modelling of gold potential with the integration of multisource information based on random forest: A case study on the Rodalquilar area, Southern Spain. International Journal of Geographic Information Science,28(7), 1336–1354.
Rodriguez-Galiano, V., Sanchez-Castillo, M., & Chica-Olmo, M. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews,71(S1), 804–818.
Sadr, M. P., & Nazeri, M. (2018). Random forests algorithm in podiform chromite prospectivity mapping in Dolatabad area, SE Iran. Journal of Mining and Environment,9(2), 403–416.
Saljoughi, B. S., & Hezarkhani, A. (2016). A comparative analysis of artificial neural network (ANN), wavelet neural network (WNN), and support vector machine (SVM) data-driven models to mineral potential mapping for copper mineralizations in the Shahr-e-Babak region, Kerman, Iran. Applied Geomatics,10(3), 229–256.
Shabankareh, M., & Hezarkhani, A. (2017). Application of support vector machines for copper potential mapping in Kerman region, Iran. Journal of African Earth Sciences,128, 116–126.
Shariff, M. A. (1995). Constrained conjugate-gradient method and the solution of linear equations. Computers & Mathematics with Applications,30(11), 25–37.
Tayebi, M. H., Tangestani, M. H., & Vincent, R. K. (2014). Alteration mineral mapping with ASTER data by integration of coded spectral ratio imaging and SOM neural network model. Turkish Journal of Earth Sciences,23(6), 627–644.
Tiwari, P. S., Garg, R. D., & Sen, A. K. (2014). Spectral delineation of albite zone using ASTER data in Khetri Copper Belt. Arabian Journal of Geosciences,7(10), 4163–4173.
Tukey, J. W. (1972). Discussion of paper by FP Agterberg and SC Robinson. Bulletin of the International Statistical Institute,44(1), 596.
Wolfe, P. (1969). Convergence conditions for ascent methods. II: Some Corrections. Siam Review,11, 226–235.
Xiong, Y. H., & Zuo, R. G. (2018). GIS-based rare events logistic regression for mineral prospectivity mapping. Computers & Geosciences,111, 18–25.
Yabe, H., & Sakaiwa, N. (2005). A new nonlinear conjugate gradient method for unconstrained optimization. Journal of the Operations Research Society of Japan,48(4), 284–296.
Yousefi, M., & Carranza, E. J. M. (2015a). Prediction-area (P-A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences,79, 69–81.
Yousefi, M., & Carranza, E. J. M. (2015b). Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Computers & Geosciences,74, 97–109.
Yousefi, M., & Carranza, E. J. M. (2016). Data-driven index overlay and boolean logic mineral prospectivity modeling in greenfields exploration. Natural Resources Research,25(1), 3–18.
Yousefi, M., & Nykänen, V. (2016). Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping. Journal of Geochemical Exploration,164(S1), 94–106.
Yu, W., Chang, Y. C. I., & Park, E. (2014). A modified area under the ROC curve and its application to marker selection and classification. Journal of the Korean Statistical Society,43(2), 161–175.
Zeghouane, H., Allek, K., & Kesraoui, M. (2016). GIS-based weights of evidence modeling applied to mineral prospectivity mapping of Sn-Wand rare metals in Laouni area, Central Hoggar, Algeria. Arabian Journal of Geosciences,9(5), 373.
Zhang, D. J., Agterberg, F., Cheng, Q. M., & Zuo, R. G. (2014). A Comparison of modified fuzzy weights of evidence, fuzzy weights of evidence, and logistic regression for mapping mineral prospectivity. Mathematical Geosciences,46(7), 869–885.
Zhang, D. J., Ren, N., & Hou, X. H. (2018a). An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping. Geoscientific Model Development,11(6), 2525–2539.
Zhang, D. D., Zhou, X. H., Freeman, D. H., & Freeman, J. L. (2002). A non-parametric method for the comparison of partial areas under ROC curves and its application to large health care data sets. Statistics in Medicine,21(5), 701–715.
Zhang, N. N., Zhou, K. F., & Li, D. (2018b). Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China. Earth Science Informatics,11(4), 553–566.
Zuo, R. G., & Carranza, E. J. M. (2011). Support vector machine: A tool for mapping mineral prospectivity. Computers & Geosciences,37(12), 1967–1975.
Zuo, R. G., & Xia, Q. L. (2009). Evaluation of the uncertainty in mineral resource potential assessment. Progress in Geophysics,24(1), 315–320.
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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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
- Logistic regression
- Conjugate gradient
- Parameter optimization
- Youden index
- ROC curve analysis
- Mineral potential mapping