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Mapping Geochemical Anomalies Through Integrating Random Forest and Metric Learning Methods

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Abstract

Extracting geochemical anomalies from geochemical exploration data is one of the most important activities in mineral exploration. Geochemical anomaly detection can be regarded as a binary classification problem. The similarity between geochemical samples can be measured by their distance. The key issue of this classification is to find the intrinsic relationship and distance between geochemical samples to separate geochemical anomalies from background. In this paper, a hybrid method that integrates random forest and metric learning (RFML) is used to identify geochemical anomalies related to Fe-polymetallic mineralization in Southwest Fujian Province of China. RFML does not require any specific statistical assumption on geochemical data, nor does it depend on sufficient known mineral occurrences as the prior knowledge. The geochemical anomaly map obtained by the RFML method showed that the known Fe deposits and the generated geochemical anomaly area have strong spatial association. Meanwhile, the receiver operating characteristic curves for the results of RFML and another method, namely maximum margin metric learning, indicated that the RFML method exhibited better performance, suggesting that RFML can be effectively applied to recognize geochemical anomalies.

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References

  • Baghshah, M. S., & Shouraki, S. B. (2010). Non-linear metric learning using pairwise similarity and dissimilarity constraints and the geometrical structure of data. Pattern Recognition, 43, 2982–2992.

    Article  Google Scholar 

  • Bar-Hillel, A., Hertz, T., Shental, N., & Weinshall, D. (2005). Learning a mahalanobis metric from equivalence constraints. Journal of Machine Learning Research, 6, 937–965.

    Google Scholar 

  • Breiman, L. (1984). Classification and regression trees. Rubber Company.

  • Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123–140.

    Google Scholar 

  • Breiman, L. (2001). Random forest. Machine Learning, 45, 5–32.

    Article  Google Scholar 

  • Cao, Q., Ying, Y., & Li, P. (2012). Distance metric learning revisited (pp. 283–298). Berlin: Springer.

    Google Scholar 

  • Carranza, E. J. M. (2009). Geochemical anomaly and mineral prospectivity mapping in GIS. Handbook of exploration & environmental geochemistry (Vol. 11). Amsterdam: Elsevier.

    Google Scholar 

  • Carranza, E. J. M. (2011). Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values. Journal of Geochemical Exploration, 110, 167–185.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Carranza, E. J. M., & Laborte, A. G. (2015b). Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm. Ore Geology Reviews, 71, 777–787.

    Article  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, 35–50.

    Article  Google Scholar 

  • Chen, Y., & An, A. (2016). Application of ant colony algorithm to geochemical anomaly detection. Journal of Geochemical Exploration, 164, 75–85.

    Article  Google Scholar 

  • Chen, Y., & Wu, W. (2017). 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 

  • Cheng, Q. (2007). Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geology Reviews, 32, 314–324.

    Article  Google Scholar 

  • Cheng, Q., & Agterberg, F. P. (2009). Singularity analysis of ore-mineral and toxic trace elements in stream sediments. Computers & Geosciences, 35, 234–244.

    Article  Google Scholar 

  • Cheng, Q., Agterberg, F. P., & Ballantyne, S. B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration, 51, 109–130.

    Article  Google Scholar 

  • Cheng, Q., Xu, Y., & Grunsky, E. (2000). Integrated spatial and spectrum method for geochemical anomaly separation. Natural Resources Research, 9, 43–52.

    Article  Google Scholar 

  • Cohen, D. R., Kelley, D. L., Anand, R., & Coker, W. B. (2010). Major advances in exploration geochemistry, 1998–2007. Geochemistry: Exploration, Environment, Analysis, 10, 3–16.

    Google Scholar 

  • Dong, Y., Du, B., & Zhang, L. (2015a). Target detection based on random forest metric learning. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 8, 1830–1838.

    Article  Google Scholar 

  • Dong, Y., Du, B., Zhang, L., & Hu, X. (2018). Hyperspectral target detection via adaptive information-theoretic metric learning with local constraints. Remote Sensing, 10, 1415.

    Article  Google Scholar 

  • Dong, Y., Zhang, L., Zhang, L., & Du, B. (2015b). Maximum margin metric learning based target detection for hyperspectral images. ISPRS Journal of Photogrammetry & Remote Sensing, 108, 138–150.

    Article  Google Scholar 

  • Egozcue, J., Pawlowskyglahn, V., Mateufigueras, G., & Barcelóvidal, C. (2003). Isometric logratio transformations for compositional data analysis. Mathematical Geology, 35, 279–300.

    Article  Google Scholar 

  • Fabrigar, L., Wegener, D., MacCallum, R., & Strahan, E. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272.

    Article  Google Scholar 

  • Filzmoser, P., Hron, K., Reimann, C., & Garrett, R. (2009). Robust factor analysis for compositional data. Computers & Geosciences, 35, 1854–1861.

    Article  Google Scholar 

  • Franc, V., & Sonnenburg, S. (2009). Optimized cutting plane algorithm for large-scale risk minimization. Journal of Machine Learning Research, 10, 2157–2192.

    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.

    Article  Google Scholar 

  • Ge, C., Han, F., Zhou, T., & Chen, D. (1981). Geological characteristics of the Makeng iron deposit of marine volcano-sedimentary origin. Acta Geosicientia Sinica, 3, 47–69. (In Chinese with English Abstract).

    Google Scholar 

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

    Article  Google Scholar 

  • Grunsky, E. C. (2010). The interpretation of geochemical survey data. Geochemistry: Exploration, Environment, Analysis, 10, 27–74.

    Google Scholar 

  • Hu, R., Bi, X., Jiang, G., Chen, H., Peng, J., Qi, Y., et al. (2012). Mantle-derived noble gases in ore-forming fluids of the granite-related Yaogangxian tungsten deposit, Southeastern China. Mineralium Deposita, 47, 623–632.

    Article  Google Scholar 

  • Kirkwood, C., Cave, M., Beamish, D., Grebby, S., & Ferreira, A. (2016). A machine learning approach to geochemical mapping. Journal of Geochemical Exploration, 167, 49–61.

    Article  Google Scholar 

  • Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th international joint conference on artificial intelligence, Montreal, Canada (pp. 1137–1145).

  • Liaw, A., & Wiener, M. (2002). Classification and regression by random Forest. R News, 2, 18–22.

    Google Scholar 

  • Mao, J., Pirajno, F., & Cook, N. (2011). Mesozoic metallogeny in East China and corresponding geodynamic settings-An introduction to the special issue. Ore Geology Reviews, 43, 1–7.

    Article  Google Scholar 

  • Mao, J., Tao, K., Xie, F., Xu, N., & Chen, S. (2001). Rock-forming and ore-forming processes and tectonic environments in Southwest Fujian. Acta Petrologica Et Mineralogica, 20, 329–336. (In Chinese with English abstract).

    Google Scholar 

  • Parsa, M., Maghsoudi, A., & Yousefi, M. (2018). Spatial analyses of exploration evidence data to model skarn-type copper prospectivity in the Varzaghan district, NW Iran. Ore Geology Reviews, 92, 97–112.

    Article  Google Scholar 

  • Peng, J., Zhou, M., Hu, R., Shen, N., Yuan, S., et al. (2006). Precise molybdenite Re–Os and mica Ar–Ar dating of the Mesozoic Yaogangxian tungsten deposit, central Nanling district, South China. Mineralium Deposita, 41, 661–669.

    Article  Google Scholar 

  • Reimann, C., Filzmoser, P., & Garrett, R. G. (2002). Factor analysis applied to regional geochemical data: Problems and possibilities. Applied Geochemistry, 17, 185–206.

    Article  Google Scholar 

  • Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, 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, 804–818.

    Article  Google Scholar 

  • Rose, A. W., Hawkes, H. E., & Webb, J. S. (1979). Geochemistry in mineral exploration (2nd ed.). London: Academic Press.

    Google Scholar 

  • Roshanravan, B., Aghajani, H., Yousefi, M., & Kreuzer, O. (2018). An improved prediction-area plot for prospectivity analysis of mineral deposits. Natural Resources Research, 1–17.

  • Shu, L., Faure, M., Wang, B., Zhou, X., & Song, B. (2008). Late Palaeozoic-early Mesozoic geological features of South China: Response to the Indosinian collision events in Southeast Asia. Comptes Rendus Geoscience, 340, 151–165.

    Article  Google Scholar 

  • Singer, D. A., & Kouda, R. (2001). Some simple guides to finding useful information in exploration geochemical data. Natural Resources Research, 10, 137–147.

    Article  Google Scholar 

  • Tripathi, V. S. (1979). Factor analysis in geochemical exploration. Journal of Geochemical Exploration, 11, 263–275.

    Article  Google Scholar 

  • Wang, F. (2011). Semi-supervised metric learning by maximizing constraint margin. IEEE Transactions on Systems Man & Cybernetics, Part B (Cybernetics), 41, 931–939.

    Article  Google Scholar 

  • Wang, H., Cheng, Q., & Zuo, R. (2015a). Spatial characteristics of geochemical patterns related to Fe mineralization in the southwestern Fujian province (China). Journal of Geochemical Exploration, 148, 259–269.

    Article  Google Scholar 

  • Wang, Z., Dong, Y., & Zuo, R. (2019). Mapping geochemical anomalies related to Fe–polymetallic mineralization using the maximum margin metric learning method. Ore Geology Reviews, 107, 258–265.

    Article  Google Scholar 

  • Wang, S., Zhang, D., & Vatuva, A. (2015b). Zircon U–Pb geochronology, geochemistry and Hf isotope compositions and their implications of the Dayang and Juzhou Granite from Longyan Area in Fujian Province. Geochemica, 44, 440–468.

    Google Scholar 

  • Wang, H., & Zuo, R. (2015). A comparative study of trend surface analysis and spectrum–area multifractal model to identify geochemical anomalies. Journal of Geochemical Exploration, 155, 84–90.

    Article  Google Scholar 

  • Wang, J., & Zuo, R. (2018). Identification of geochemical anomalies through combined sequential Gaussian simulation and grid-based local singularity analysis. Computers & Geosciences, 118, 52–64.

    Article  Google Scholar 

  • Wang, J., Zuo, R., & Caers, J. (2017). Discovering geochemical patterns by factor-based cluster analysis. Journal of Geochemical Exploration, 181, 106–115.

    Article  Google Scholar 

  • Wang, Z., Zuo, R., & Zhang, Z. (2015c). Spatial analysis of Fe deposits in Fujian Province, China: Implications for mineral exploration. Journal of Earth Science, 26, 813–820.

    Article  Google Scholar 

  • Wong, T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48, 2839–2846.

    Article  Google Scholar 

  • Wu, G., Zhang, D., Chen, B., & Wu, J. (2000). Transformation of Mesozoic tectonic domain and its relation to mineralization in Southeastern China: An evidence of Southwestern Fujian Province. Earth Science, 25, 390–396. (In Chinese with English abstract).

    Google Scholar 

  • Xie, X., Mu, X., & Ren, T. (1997). Geochemical mapping in China. Journal of Geochemical Exploration, 60, 99–113.

    Article  Google Scholar 

  • Xiong, C., Johnson, D. M., & Corso, J. J. (2012). Efficient max-margin metric learning. In 6th International workshop on evolution and change in data management (pp. 1–9).

  • Xiong, Y., & Zuo, R. (2016). Recognition of geochemical anomalies using a deep autoencoder network. Computers & Geosciences, 86, 75–82.

    Article  Google Scholar 

  • Xiong, Y., & Zuo, R. (2018). GIS-based rare events logistic regression for mineral prospectivity mapping. Computers & Geosciences, 111, 18–25.

    Article  Google Scholar 

  • Xiong, Y., Zuo, R., Wang, K., & Wang, J. (2018). Identification of geochemical anomalies via local RX anomaly detector. Journal of Geochemical Exploration, 189, 64–71.

    Article  Google Scholar 

  • Yan, H., Lu, J., Deng, W., & Zhou, X. (2014). Discriminative multimetric learning for kinship verification. IEEE Transactions on Information Forensics and Security, 9, 1169–1178.

    Article  Google Scholar 

  • Yousefi, M. (2017). Recognition of an enhanced multi-element geochemical signature of porphyry copper deposits for vectoring into mineralized zones and delimiting exploration targets in Jiroft area, SE Iran. Ore Geology Reviews, 83, 200–214.

    Article  Google Scholar 

  • Yousefi, M., & Carranza, E. J. M. (2015a). Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Computers & Geosciences, 74, 97–109.

    Article  Google Scholar 

  • Yousefi, M., & Carranza, E. J. M. (2015b). 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.

    Article  Google Scholar 

  • Yousefi, M., & Carranza, E. J. M. (2016). Data-driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration. Natural Resources Research, 25, 3–18.

    Article  Google Scholar 

  • Yousefi, M., Kamkar-Rouhani, A., & Carranza, E. J. M. (2012). Geochemical mineralization probability index (GMPI): A new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping. Journal of Geochemical Exploration, 115, 24–35.

    Article  Google Scholar 

  • Zhang, Z., Cheng, Q., Yang, J., & Hu, X. (2018). Characterization and origin of granites from the Luoyang Fe deposit, southwestern Fujian Province, South China. Journal of Geochemical Exploration, 184, 119–135.

    Article  Google Scholar 

  • Zhang, D., Wu, G., Di, Y., Yu, X., Shi, Y., Zhang, X., et al. (2013). SHRIMP U–Pb zircon geochronology and Nd–Sr isotopic study of the Mamianshan Group: Implications for the Neoproterozoic tectonic development of southeast China. International Geology Review, 55, 730–748.

    Article  Google Scholar 

  • Zhang, Z., Zuo, R., & Cheng, Q. (2015a). The mineralization age of the Makeng Fe deposit, South China: Implications from U–Pb and Sm–Nd geochronology. International Journal of Earth Sciences, 104, 663–682.

    Article  Google Scholar 

  • Zhang, Z., Zuo, R., & Cheng, Q. (2015b). Geological features and formation processes of the Makeng F e Deposit, China. Resource Geology, 65, 266–284.

    Article  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, 556–572.

    Article  Google Scholar 

  • Zuo, R. (2011). Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum-area fractal modeling in the Gangdese Belt, Tibet (China). Journal of Geochemical Exploration, 111, 13–22.

    Article  Google Scholar 

  • Zuo, R. (2017). Machine learning of mineralization-related geochemical anomalies: A review of potential methods. Natural Resources Research, 26, 457–464.

    Article  Google Scholar 

  • Zuo, R., Carranza, E. J. M., & Wang, J. (2016). Spatial analysis and visualization of exploration geochemical data. Earth-Science Reviews, 158, 9–18.

    Article  Google Scholar 

  • Zuo, R., Cheng, Q., Agterberg, F. P., & Xia, Q. (2009). Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China. Journal of Geochemical Exploration, 101, 225–235.

    Article  Google Scholar 

  • Zuo, R., & Wang, J. (2016). Fractal/multifractal modeling of geochemical data: A review. Journal of Geochemical Exploration, 164, 33–41.

    Article  Google Scholar 

  • Zuo, R., Xia, Q., & Wang, H. (2013). Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization. Applied Geochemistry, 28, 202–211.

    Article  Google Scholar 

  • Zuo, R., & Xiong, Y. (2018). Big data analytics of identifying geochemical anomalies supported by machine learning methods. Natural Resources Research, 27, 1–9.

    Article  Google Scholar 

  • Zuo, R., Zhang, Z., Zhang, D., Carranza, E. J. M., & Wang, H. (2015). Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: A case study with skarn-type Fe deposits in Southwestern Fujian Province, China. Ore Geology Reviews, 71, 502–515.

    Article  Google Scholar 

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Acknowledgments

We thank Prof. John Carranza, Dr. M. Yousefi and an anonymous reviewer whose comments and suggestions helped us improve this study. This study was jointly supported by the National Natural Science Foundation of China (41772344, 61801444), the Natural Science Foundation of Hubei Province (China) (2017CFA053), the Hong Kong Scholars Program (XJ2018012), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (CUG170687) and the Most Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences (MSFGPMR03-3).

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  1. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074, China

    Ziye Wang & Renguang Zuo

  2. Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China

    Yanni Dong

  3. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China

    Yanni Dong

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  1. Ziye Wang
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Correspondence to Renguang Zuo or Yanni Dong.

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Wang, Z., Zuo, R. & Dong, Y. Mapping Geochemical Anomalies Through Integrating Random Forest and Metric Learning Methods. Nat Resour Res 28, 1285–1298 (2019). https://doi.org/10.1007/s11053-019-09471-y

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