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Regionalized Classification of Geochemical Data with Filtering of Measurement Noises for Predictive Lithological Mapping

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Abstract

A method for predictive lithological mapping is proposed, which combines geostatistical simulation of geochemical concentrations with coregionalization analysis and decision-tree classification algorithm. The method consists of classifying each target point based on simulated values of the geochemical concentrations, filtered from the short-scale spatial components corresponding to noise and measurement errors. The procedure is repeated over many simulations to give finally as a result the most probable lithology at each target point. An application to a set of geochemical samples of soils and surface rocks is presented, in which lithology is recorded from an interpretive geological field map. It shows significant classification improvement when pre-processing the sampling data through geostatistical simulation with filtering of the nugget effect, with rates of correctly classified data increased by 3.5 to 11 percentage points depending on whether training or testing data subset is considered. The lithological prediction allows generating geological maps as complementary activities to exploration of mineral resources to be able to forecast and/or to validate the geology mapped at each point of explored areas.

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Source: Mining Company Cornerstone Ecuador S.A. 2012

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References

  • Adeli, A., Emery, X., & Dowd, P. (2018). Geological modelling and validation of geological interpretations via simulation and classification of quantitative covariates. Minerals, 8(1), 7.

    Google Scholar 

  • Baldock, J. W. (1982). Geología del Ecuador. Boletín de Explicación del Mapa Geológico (1:1.000.000) de la República del Ecuador (p. 54). Quito: Resource document. Ministerio de Recursos Naturales y Energéticos.

    Google Scholar 

  • Barbosa, P., Oliveira, T., & Silva, J. (2010). Regionalized classification of multivariate geochemical data from Jacupiranga Alkaline Complex (Ribeira de Iguape Valley/Sao Paulo, Brazil). Revista Brasileira de Geociencias, 40(2), 212–219.

    Google Scholar 

  • Barnett, R. M., Manchuk, J. G., & Deutsch, C. V. (2013). Projection pursuit multivariate transform. Mathematical Geosciences, 46(3), 337–359.

    Google Scholar 

  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

    Google Scholar 

  • Carranza, E. J. M. (2009). Geochemical anomaly and mineral prospectivity mapping in GIS. Amsterdam: Elsevier.

    Google Scholar 

  • Carrasco, P. (2010). Nugget effect, artificial or natural? Journal of the Southern African Institute of Mining and Metallurgy, 110(6), 299–305.

    Google Scholar 

  • Castillo, P. I. C., Townley, B. K., Emery, X., Puig, A. F., & Deckart, K. (2015). Soil gas geochemical exploration in covered terrains of northern Chile: Data processing techniques and interpretation of contrast anomalies. Geochemistry Exploration, Environment, Analysis, 15(2–3), 222–233.

    Google Scholar 

  • Chilès, J. P., & Delfiner, P. (2012). Geostatistics: Modeling spatial uncertainty. New York: Wiley.

    Google Scholar 

  • Darsow, A., Schafmeister, M. T., & Hofmann, T. (2009). An ArcGIS approach to include tectonic structures in point data regionalisation. Ground Water, 47(4), 591–597.

    Google Scholar 

  • Emery, X. (2008). A turning bands program for conditional co-simulation of cross-correlated Gaussian random fields. Computers and Geosciences, 34(12), 1850–1862.

    Google Scholar 

  • Emery, X. (2010). Iterative algorithms for fitting a linear model of coregionalization. Computers and Geosciences, 36(9), 1150–1160.

    Google Scholar 

  • Emery, X., Arroyo, D., & Porcu, E. (2016). An improved spectral turning-bands algorithm for simulating stationary vector Gaussian random fields. Stochastic Environmental Research and Risk Assessment, 30(7), 1863–1873.

    Google Scholar 

  • Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179–188.

    Google Scholar 

  • Galli, A., Gerdil-Neuillet, F., & Dadou, C. (1984). Factorial kriging analysis: A substitute to spectral analysis of magnetic data. In G. Verly, M. David, A. G. Journel, & A. Maréchal (Eds.), Geostatistics for natural resources characterization (pp. 543–557). Dordrecht: Reidel.

    Google Scholar 

  • Goovaerts, P. (1992). Factorial kriging analysis: A useful tool for exploring the structure of multivariate spatial soil information. Journal of Soil Science, 43, 597–619.

    Google Scholar 

  • Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Oxford: Oxford University Press.

    Google Scholar 

  • Goulard, M., & Voltz, M. (1992). Linear coregionalization model: Tools for estimation and choice of cross-variogram matrix. Mathematical Geology, 24(3), 269–286.

    Google Scholar 

  • Gringarten, E., & Deutsch, C. V. (2001). Teacher’s aide: Variogram interpretation and modelling. Mathematical Geology, 33(4), 507–534.

    Google Scholar 

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

    Google Scholar 

  • Grunsky, E. C., Corrigan, D., Mueller, U. A., & Bonham-Carter, G. F. (2012). Predictive geologic mapping using lake sediment geochemistry in the Melville Peninsula. Geological Survey of Canada, Open File. https://doi.org/10.4095/291901.

    Article  Google Scholar 

  • Grunsky, E. C., Mueller, U. A., & Corrigan, D. (2014). A study of the lake sediment geochemistry of the Melville Peninsula using multivariate methods: Applications for predictive geological mapping. Journal of Geochemical Exploration, 141, 15–41.

    Google Scholar 

  • Hassan, A. E., Bekhit, H. M., & Chapman, J. B. (2009). Using Markov Chain Monte Carlo to quantify parameter uncertainty and its effect on predictions of a groundwater flow model. Environmental Modelling and Software, 24(6), 749–763.

    Google Scholar 

  • Hastie, T., Tibshirani, R., & Friedman, J. (2008). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York: Springer.

    Google Scholar 

  • Hofmann, T., Darsow, A., & Schafmeister, M. T. (2010). Importance of the nugget effect in variography on modeling zinc leaching from a contaminated site using simulated annealing. Journal of Hydrology, 389(1–2), 78–89.

    Google Scholar 

  • Ibarguren, I., Lasarguren, A., Pérez, J. M., Muguerza, J., Arbelaitz, O., & Gurrutxaga, I. (2016). BFPART: Best-first PART. Information Sciences, 367–368, 927–952.

    Google Scholar 

  • Jaquet, O. (1989). Factorial kriging analysis applied to geological data from petroleum exploration. Mathematical Geology, 21(7), 683–691.

    Google Scholar 

  • Jenny, H. (1941). Factors of soil formation: A system of quantitative pedology. New York: McGraw-Hill.

    Google Scholar 

  • Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29(2), 119.

    Google Scholar 

  • Kuhn, S., Cracknell, M. J., & Reading, A. M. (2019). Lithological mapping in the Central African Copper Belt using random forests and clustering: Strategies for optimised results. Ore Geology Reviews, 112, 103015.

    Google Scholar 

  • Larocque, G., Dutilleul, P., Pelletier, B., & Fyles, J. W. (2006). Conditional Gaussian co-simulation of regionalized components of soil variation. Geoderma, 134, 1–16.

    Google Scholar 

  • Leuangthong, O., & Deutsch, C. V. (2003). Stepwise conditional transformation for simulation of multiple variables. Mathematical Geology, 35(2), 155–173.

    Google Scholar 

  • Liu, Y., Carranza, E. J. M., Zhou, K. F., & Xia, Q. L. (2019). Compositional balance analysis: An elegant method of geochemical pattern recognition and anomaly mapping for mineral exploration. Natural Resources Research, 28, 1269–1283.

    Google Scholar 

  • Matheron, G. (1962). Traité de Géostatistique Appliquée. Paris: Technip.

    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 

  • Merian, E., Anke, M., Ihnat, M., & Stoeppler, M. (2004). Elements and their compounds in the environment-occurrence, analysis and biological relevance. New York: Wiley.

    Google Scholar 

  • Mitchell, T. M. (1997). Decision tree learning. Singapore: WCB/McGraw-Hill Inc.

    Google Scholar 

  • Olea, R. A. (1999). Geostatistics for engineers and earth scientists. New York: Springer.

    Google Scholar 

  • Quinlan, J. R. (1993). C4.5, Programs for machine learning. San Mateo: Morgan Kaufmann.

    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.

    Google Scholar 

  • Salminen, R., Batista, M. J., Bidovec, M., Demetriades, A., De Vivo, B., De Vos, W., et al. (2005). Geochemical Atlas of Europe. Espoo: Geological Survey of Finland.

    Google Scholar 

  • Sandjivy, L. (1984). The factorial kriging analysis of regionalized data—Its application to geochemical prospecting. In G. Verly, A. G. Journel, & A. Maréchal (Eds.), Geostatistics for natural resources characterization (pp. 559–571). Dordrecht: Reidel.

    Google Scholar 

  • Simpson, E. H. (1949). Measurement of diversity. Nature, 163(4148), 688.

    Google Scholar 

  • Soares, A. (1992). Geostatistical estimation of multi-phase structures. Mathematical Geology, 24(2), 148–160.

    Google Scholar 

  • Stanley, C. R., & Sinclair, A. J. (1989). Comparison of probability plots and the gap statistic in the selection of thresholds for exploration geochemistry data. Journal of Geochemical Exploration, 32(1–3), 355–357.

    Google Scholar 

  • Sun, T., Chen, F., Zhong, L. X., Liu, W. M., & Wang, Y. (2019). GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China. Ore Geology Reviews, 109, 26–49.

    Google Scholar 

  • Talebi, H., Mueller, U., Tolosana-Delgado, R., Grunsky, E. C., McKinley, J. M., & de Caritat, P. (2019). Surficial and deep earth material prediction from geochemical compositions. Natural Resources Research, 28, 869–891.

    Google Scholar 

  • Tolosana-Delgado, R., Mueller, U., & van den Boogaart, K. G. (2019). Geostatistics for compositional data: An overview. Mathematical Geosciences, 51(4), 485–526.

    Google Scholar 

  • van den Boogaart, K. G., Mueller, U., & Tolosana-Delgado, R. (2017). An affine equivariant multivariate normal score transform for compositional data. Mathematical Geosciences, 49(2), 231–251.

    Google Scholar 

  • Wackernagel, H. (1988). Geostatistical techniques for interpreting multivariate spatial information. In C. F. Chung, A. G. Fabbri, & R. Sinding-Larsen (Eds.), Quantitative analysis of mineral and energy resources (pp. 393–409). Dordrecht: Reidel.

    Google Scholar 

  • Wackernagel, H. (2003). Multivariate geostatistics: An introduction with applications. Berlin: Springer.

    Google Scholar 

  • Xiang, J., Xiao, K. Y., Carranza, E. J. M., Chen, J. P., & Li, S. (2020). 3D mineral prospectivity mapping with random forests: A case study of Tongling, Anhui. China. Natural Resources Research, 29(1), 395–414.

    Google Scholar 

  • Zuo, R. G., & Xiong, Y. H. (2018). Big data analytics of identifying geochemical anomalies supported by machine learning methods. Natural Resources Research, 27(1), 5–13.

    Google Scholar 

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Acknowledgments

This research was funded by the National Agency for Research and Development of Chile, through grant ANID/CONICYT PIA AFB180004, by the Ministry of Higher Education, Science, Technology and Innovation of Ecuador (SENESCYT), through scholarship program “Open Call 2012 Second Phase” of the government of Ecuador, and by the Particular Technical University of Loja-Ecuador.

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Guartán, J.A., Emery, X. Regionalized Classification of Geochemical Data with Filtering of Measurement Noises for Predictive Lithological Mapping. Nat Resour Res 30, 1033–1052 (2021). https://doi.org/10.1007/s11053-020-09779-0

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