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A Bat Algorithm-Based Data-Driven Model for Mineral Prospectivity Mapping

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Abstract

In data-driven mineral prospectivity mapping, a statistical model is established to represent the spatial relationship between layers of metallogenic evidence and locations of known mineral deposits, and then, the former are integrated into a mineral prospectivity model using the established model. Establishment of a data-driven mineral prospectivity model can be regarded as a process of searching for the optimal integration of layers of metallogenic evidence in order to maximize the spatial relationship between mineral prospectivity and the locations of known mineral deposits. Mineral prospectivity can be simply defined as the weighted sum of layers of metallogenic evidence. Then, the optimal integration of the layers of evidence can be determined by optimizing weight coefficients of the layers of evidence to maximize the area under the curve (AUC) of the defined model. To this end, a bat algorithm-based model is proposed for data-driven mineral prospectivity mapping. In this model, the AUC of the model is used as the objective function of the bat algorithm, and the ranges of the weight coefficients of layers of evidence are used to define the search space of the bat population, and the optimal weight coefficients are then automatically determined through the iterative search process of the bat algorithm. The bat algorithm-based model was used to map mineral prospectivity in the Helong district, Jilin Province, China. Because of the high performance of the traditional logistic regression model for data-driven mineral prospectivity mapping, it was used as a benchmark model for comparison with the bat algorithm-based model. The result shows that the receiver operating characteristic (ROC) curve of the bat algorithm-based model is coincident with that of the logistic regression model in the ROC space. The AUC of the bat algorithm-based model (0.88) is slightly larger than that of the logistic regression model (0.87). The optimal threshold for extracting mineral targets was determined by using the Youden index. The mineral targets optimally delineated by using the bat algorithm-based model and logistic regression model account for 8.10% and 11.24% of the study area, respectively, both of which contain 79% of the known mineral deposits. These results indicate that the performance of the bat algorithm-based model is comparable with that of the logistic regression model in data-driven mineral prospectivity mapping. Therefore, the bat algorithm-based model is a potentially useful high-performance data-driven mineral prospectivity mapping model.

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References

  • Abedi, M., Norouzi, G. H., & Bahroudi, A. (2012). Support vector machine for multi-classification of mineral prospectivity areas. Computers & Geosciences,46(2), 272–283.

    Google Scholar 

  • Agterberg, F. P. (1974). Automatic contouring of geological maps to detect target areas for mineral exploration. Mathematical Geology,6(4), 373–395.

    Google Scholar 

  • Agterberg, F. P. (1989). LOGDIA-FORTRAN 77 program for logistic regression with diagnostics. Computers & Geosciences,15(4), 599–614.

    Google Scholar 

  • 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, Wuhan, China, 1, 1–15.

  • Agterberg, F. P. (1992). Combining indicator patterns in weights of evidence modeling for resource evaluation. Nonrenewable Resources,1(1), 39–50.

    Google Scholar 

  • Agterberg, F. P., & Bonham-Carter, G. F. (1999). Logistic regression and weights of evidence modeling in mineral exploration. In Proceedings of the 28th international symposium on applications of computer in the mineral industry (APCOM), Golden, Colorado, pp. 483–490.

  • Agterberg, F. P., Bonham-Carter, G. F., & Wright, D. F. (1990). Statistical pattern integration for mineral exploration. In G. Gaal & D. F. Merriam (Eds.), Computer applications for mineral exploration in resource exploration (pp. 1–21). Oxford: Pergamon Press.

    Google Scholar 

  • Bishop, C. M. (2006). Pattern recognition and machine learning. Berlin: Springer.

    Google Scholar 

  • Bonham-Carter, G. F., Agterberg, F. P., & Wright, D. F. (1988). Integration of geological datasets for gold exploration in Nova Scotia. Photogrammetric Engineering and Remote Sensing,54(11), 1585–1592.

    Google Scholar 

  • Brown, W., Gedeon, T., & Groves, D. I. (2003a). Use of noise to augment training data: a neural network method of mineral–potential mapping in regions of limited known deposit examples. Natural Resources Research,13(2), 141–152.

    Google Scholar 

  • Brown, W. M., Gedeon, T. D., Groves, D. I., & Barnes, R. G. (2000). Artificial neural networks: A new method for mineral prospectivity mapping. Australian Journal of Earth Sciences,47(4), 757–770.

    Google Scholar 

  • Brown, W., Groves, D., & Gedeon, T. (2003b). Use of fuzzy membership input layers to combine subjective geological knowledge and empirical data in a neural network method for mineral-potential mapping. Natural Resources Research,12(3), 183–200.

    Google Scholar 

  • Carranza, E. J. M., & Hale, M. (2002a). Where are porphyry copper deposits spatially localized? A case study in Benguet province, Philippines. Natural Resources Research,11(1), 45–59.

    Google Scholar 

  • Carranza, E. J. M., & Hale, M. (2002b). Wildcat mapping of gold potential, Baguio district, Philippines. Transactions of the Institutions of Mining and Metallurgy (Applied Earth Science),111(2), 100–105.

    Google Scholar 

  • Carranza, E. J. M., Hale, M., & Faassen, C. (2008). Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping. Ore Geology Reviews,33, 536–558.

    Google Scholar 

  • 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.

    Google Scholar 

  • Carranza, E. J. M., & Laborte, A. G. (2015b). Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines). Computers & Geosciences,74(1), 60–70.

    Google Scholar 

  • 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.

    Google Scholar 

  • Chen, Y. L. (2015). Mineral potential mapping with a restricted Boltzmann machine. Ore Geology Reviews,71, 749–760.

    Google Scholar 

  • Chen, C. H., Dai, H. Z., Liu, Y., & He, B. B. (2011). Mineral prospectivity mapping integrating multisource geology spatial data sets and logistic regression modeling. In Proceedings of IEEE international conference on spatial data mining and geographical knowledge services (ICSDM), pp. 214–217.

  • 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.

    Google Scholar 

  • Chen, Y. L., & Wu, W. (2017a). Mapping mineral prospectivity using an extreme learning machine regression. Ore Geology Reviews,80, 200–213.

    Google Scholar 

  • Chen, Y. L., & Wu, W. (2017b). Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data. Australian Journal of Earth Sciences,44, 639–651.

    Google Scholar 

  • Chen, Y. L., & Wu, W. (2017c). Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data. Geochemistry: Exploration, Environment, Analysis,17, 231–238.

    Google Scholar 

  • 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.

    Google Scholar 

  • Chen, Y. L., Wu, W., & Zhao, Q. Y. (2019). A bat-optimized one-class support vector machine for mineral prospectivity mapping. Minerals,9, 317. https://doi.org/10.3390/min9050317.

    Article  Google Scholar 

  • Ford, A., Miller, J. M., & Mol, A. G. (2015). A comparative analysis of weights of evidence, evidential belief functions, and fuzzy logic for mineral potential mapping using incomplete data at the scale of investigation. Natural Resources Research,25(1), 19–33.

    Google Scholar 

  • Gao, Y., Zhang, Z., Xiong, Y., & Zuo, R. (2016). Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China. Ore Geology Reviews,75, 16–28.

    Google Scholar 

  • Geranian, H., Tabatabaei, S. H., Asadi, H. H., & Carranza, E. J. M. (2016). Application of discriminant analysis and support vector machine in mapping gold potential areas for further drilling in the Sari-Gunay gold deposit, NW Iran. Natural Resources Research,25(2), 145–159.

    Google Scholar 

  • Gonbadi, A. G., Tabatabaei, S. H., & Carranza, E. J. M. (2015). Supervised geochemical anomaly detection by pattern recognition. Journal of Geochemical Exploration,157, 81–91.

    Google Scholar 

  • Goyal, S., & Patterh, M. S. (2013). Wireless sensor network localization based on bat algorithm. International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS),4, 507–512.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Lin, N., Chen, Y. L., & Lai, J. L. (2019). Mineral potential mapping using a conjugate gradient logistic regression model. Natural Resources Research. https://doi.org/10.1007/s11053-019-09509-1.

    Article  Google Scholar 

  • Liu, F. S., & Zhang, M. L. (1999). Complete quality management of the new-round land resources survey. Chinese Geology,267, 20–21. (in Chinese with English Abstract).

    Google Scholar 

  • 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.

    Google Scholar 

  • Mejia-Herrera, P., Royey, J. J., Caumon, G., & Cheilletz, A. (2015). Curvature attribute from surface-restoration as predictor variable in Kupferschiefer copper potentials. Natural Resources Research,24(3), 275–290.

    Google Scholar 

  • Nykänen, V., Groves, D. I., Ojala, V. J., & Gardoll, S. J. (2008). Combined conceptual/ empirical prospectivity mapping for orogenic gold in the northern Fennoscandian Shield, Finland. Australian Journal of Earth Sciences,55(1), 39–59.

    Google Scholar 

  • 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.

    Google Scholar 

  • Pan, Y. D., Xu, B. J., Sun, Y., & Hou, L. (2016). Geological features of the Jinchengdong gold deposit in Helong City, Jilin Province, China. Jilin Geology,35, 30–35. (In Chinese with English Abstract).

    Google Scholar 

  • Rodriguez-Galiano, V. F., 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 Geographical Information Science,28(7), 1336–1354.

    Google Scholar 

  • Sharawi, M., Emary, E., Saroit, I. A., & El-Mahdy, H. (2012). Bat swarm algorithm for wireless sensor networks lifetime optimization. International Journal of Science and Research (IJSR),3, 655–664.

    Google Scholar 

  • Skabar, A. A. (2003). Mineral potential mapping using feed-forward neural networks. In Proceedings of the international joint conference on neural networks, 3, 1814–1819, Portland, OR, the United States, IEEE Press.

  • Sun, J. G., Zhang, Y., Xing, S. W., Zhao, K. Q., Zhang, Z. J., Bai, L. A., et al. (2012). Genetic types, ore-forming age and geodynamic setting of endogenic molybdenum deposits in the eastern edge of Xing-Meng orogenic belt. Acta Petrologica Sinica,28(4), 1317–1332. (In Chinese with English Abstract).

    Google Scholar 

  • Tangestani, M. H., & Moore, F. (2001). Porphyry copper potential mapping using the weights-of-evidence model in a GIS, northern Shahr-e-Babak, Iran. Australian Journal of Earth Sciences,48(5), 913–927.

    Google Scholar 

  • Wan, W. Z., Wang, J. B., Feng, X. Y., Zhang, H., Jia, N., & Zhang, Y. L. (2010). Geological features and prospecting directions of the Heanhe gold deposit in the Helong area, Jilin Province, China. Jilin Geology,29, 71–75. (in Chinese with English Abstract).

    Google Scholar 

  • Wu, F., Lin, J., Wilde, S. A., Zhang, Q., & Yang, J. (2005). Nature and significance of early Cretaceous giant igneous event in eastern China. Earth and Planetary Science Letters,233, 103–119.

    Google Scholar 

  • Wu, P. F., Sun, D. Y., Wang, T. H., Gou, J., Li, R., Liu, W., et al. (2013). Chronology, geochemical characteristic and petrogenesis analysis of diorite in Helong of Yanbian area, northeastern China. Geological Journal of China Universities,19, 600–610. (in Chinese with English Abstract).

    Google Scholar 

  • Xiong, Y., & Zuo, R. (2017). Effects of misclassification costs on mapping mineral prospectivity. Ore Geology Reviews,82, 1–9.

    Google Scholar 

  • Yan, D., Li, N., Xu, M., & Miao, M. M. (2015). Mineralization characteristics and genesis of the Bailiping silver deposit in Helong City, Jilin Province. Jilin Geology,34, 36–41. (in Chinese with English Abstract).

    Google Scholar 

  • Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In J.R. González, D.A. Pelta, C. Cruz, G. Terrazas, N. Krasnogor, (Eds.) Proceedings of the nature inspired cooperative strategies for optimization (NICSO 2010), Granada, Spain, 12–14 May 2010. Springer: Berlin, pp. 65–74.

  • Yang, X. S., & Gandomi, A. H. (2012). Bat algorithm: A novel approach for global engineering optimization. Engineering Computations,29, 464–483.

    Google Scholar 

  • Yang, X. S., Karamanoglu, M., & Fong, S. (2012). Bat algorithm for topology optimization in microelectronic applications. In Proceedings of the first international conference on future generation communication technologies, London, UK, 12–14 December 2012, pp. 150–155.

  • Yu, J. J., Wang, F., Xu, W. L., Gao, F. H., & Pei, G. P. (2012). Early Jurassic mafic magmatism in the Lesser Xing’an-Zhangguangcai Range, NE China, and its tectonic implications: Constraints from zircon U-Pb chronology and geochemistry. Lithos,142–143, 256–266.

    Google Scholar 

  • Zhang, Y. B., Wu, F. Y., Wilde, S. A., Zhai, M. G., Lu, X. P., & Sun, D. Y. (2004). Zircon U-Pb ages and tectonic implications of Early Paleozoic granitoids at Yanbian, Jilin Province, northeast China. Island Arc,13, 484–505.

    Google Scholar 

  • Zhang, Z., Zuo, R., & Xiong, Y. (2016). A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China. Science China Earth Sciences,59(3), 556–572.

    Google Scholar 

  • Zuo, R., & Carranza, E. J. M. (2011). Support vector machine: a tool for mapping mineral potential. Computers & Geosciences,37(12), 1967–1975.

    Google Scholar 

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Acknowledgments

The authors are grateful to the two anonymous reviewers for their constructive comments, which greatly improved the manuscript. The authors thank Sheli Chai of Jilin University for his assistance in the collection of geological and geochemical data. This study was supported by National Natural Science Foundation of China (Grant Nos. 41672322 and 41872244).

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Authors and Affiliations

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

    Yongliang Chen

  2. Changchun Institute of Urban Planning and Design, Changchun, 130033, China

    Wei Wu

  3. College of Earth Sciences, Jilin University, Changchun, 130061, China

    Qingying Zhao

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  1. Yongliang Chen
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Correspondence to Yongliang Chen.

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Chen, Y., Wu, W. & Zhao, Q. A Bat Algorithm-Based Data-Driven Model for Mineral Prospectivity Mapping. Nat Resour Res 29, 247–265 (2020). https://doi.org/10.1007/s11053-019-09589-z

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