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Modulating the Impacts of Stochastic Uncertainties Linked to Deposit Locations in Data-Driven Predictive Mapping of Mineral Prospectivity

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Abstract

The operation of large-scale ore-forming processes triggers the development of neighboring mineral deposits of the same or related types in a metallogenic province. While these deposits often bear striking similarities, variations in local geological settings cause differences in many deposit features. Therefore, in a metallogenic province, geochemical, geophysical, and geological signatures of local areas mineralized with a certain deposit type can show considerable inherent differences. The application of deposit-type locations as training sites, thus, introduces a type of stochastic uncertainty into data-driven mineral prospectivity mapping (MPM), impairing the predictive capability of this activity. This study delves into this type of uncertainty and applies an ensemble technique combining bootstrapping and naïve Bayes classifiers to measure this uncertainty and lessen its impact on the MPM-generated exploration targets. Two components, one representing the quantified uncertainty and the other a modulated predictive model, are retained by the proposed framework. This framework was applied to a suite of mineral-systems derived targeting criteria of skarn-type Cu mineralization in the Alborz–Azerbaijan magmatic belt of northern Iran. The predictive results derived by the proposed technique outperformed those derived using a single classifier, showcasing its efficacy. In addition, a novel approach is described and applied to demarcating exploration targets marked by low uncertainty.

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Figure 1

Modified after Jamali et al. (2010)

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Modified after Mehrpartou et al. (1992)

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References

  • Abdi, H. (2010). Coefficient of variation. Encyclopedia of Research Design, 1, 169–171.

    Google Scholar 

  • Agard, P., Omrani, J., Jolivet, L., & Mouthereau, F. (2005). Convergence history across Zagros (Iran): Constraints from collisional and earlier deformation. International Journal of Earth Sciences, 94, 401–419.

    Article  Google Scholar 

  • Agard, P., Omrani, J., Jolivet, L., Whitechurch, H., Vrielynck, B., Spakman, W., Monié, P., Meyer, B., & Wortel, R. (2011). Zagros orogeny: A subduction-dominated process. Geological Magazine, 148, 692–725.

    Article  Google Scholar 

  • Aghazadeh, M., Hou, Z., Badrzadeh, Z., & Zhou, L. (2015). Temporal–spatial distribution and tectonic setting of porphyry copper deposits in Iran: Constraints from zircon U-Pb and molybdenite Re–Os geochronology. Ore Geology Reviews, 70, 385–406.

    Article  Google Scholar 

  • Agterberg, F. P., & Cheng, Q. (2002). Conditional independence test for weights-of-evidence modeling. Natural Resources Research, 11, 249–255.

    Article  Google Scholar 

  • Alavi, M. (1994). Tectonics of the Zagros orogenic belt of Iran: New data and interpretations. Tectonophysics, 229, 211–238.

    Article  Google Scholar 

  • Arndt, N., Kesler, S., & Ganino, C. (2015). Metals and Society: An introduction to economic geology. Springer.

    Google Scholar 

  • Banerjee, A., Chitnis, U. B., Jadhav, S. L., Bhawalkar, J. S., & Chaudhury, S. (2009). Hypothesis testing, type I and type II errors. Industrial Psychiatry Journal, 18, 127.

    Article  Google Scholar 

  • Bárdossy, G., & Fodor, J. (2004). Evaluation of uncertainties and risks in geology. Springer.

    Book  Google Scholar 

  • Barley, M. E., & Groves, D. I. (1992). Supercontinent cycles and distribution of metal deposits through time. Geology, 20, 291–294.

    Article  Google Scholar 

  • Berberian, M., & King, G. C. P. (1981). Towards a paleogeography and tectonic evolution of Iran: Reply. Canadian Journal of Earth Sciences, 18, 1764–1766.

    Article  Google Scholar 

  • Bonham-Carter, G. F. (1994). Geographic information systems for geoscientists: Modelling with GIS. Pergamon.

    Google Scholar 

  • Bonham-Carter, G. F., Agterberg, F. P., & Wright, D. F. (1990). Weights of evidence modelling: A new approach to mapping mineral potential. In Statistical Applications in the Earth Sciences. Geological Survey of Canada Paper, 89, 171–183.

    Google Scholar 

  • Brandmeier, M., Zamora, I. G. C., Nykänen, V., & Middleton, M. (2020). Boosting for mineral prospectivity modeling: A new GIS toolbox. Natural Resources Research, 29, 71–88.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Brodeur, Z. P., Herman, J. D., & Steinschneider, S. (2020). Bootstrap aggregation and cross-validation methods to reduce overfitting in reservoir control policy search. Water Resources Research, 56, e2020WR027184.

    Article  Google Scholar 

  • Burnhan, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference. Springer.

    Google Scholar 

  • Burt, D. M. (1982). Skarn deposits—Historical bibliography through 1970. Economic Geology, 77, 755–763.

    Article  Google Scholar 

  • Carranza, E. J. M. (2008). Geochemical anomaly and mineral prospectivity mapping in GIS. Elsevier.

    Google Scholar 

  • Carranza, E. J. M. (2009a). Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity. Computers & Geosciences, 35, 2032–2046.

    Article  Google Scholar 

  • Carranza, E. J. M. (2009b). Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features. Ore Geology Reviews, 35, 383–400.

    Article  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., 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.

    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 

  • Cheeseman, P. C., & Stutz, J. C. (1996). Bayesian classification (AutoClass): Theory and results. Advances in Knowledge Discovery and Data Mining, 180, 153–180.

    Google Scholar 

  • Cheng, Q. (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, 55–70.

    Article  Google Scholar 

  • Clechenko, C. C., & Valley, J. W. (2003). Oscillatory zoning in garnet from the Willsboro Wollastonite Skarn, Adirondack Mts, New York: A record of shallow hydrothermal processes preserved in a granulite facies terrane. Journal of Metamorphic Geology, 21, 771–784.

    Article  Google Scholar 

  • Coolbaugh, M. F., Raines, G. L., & Zehner, R. E. (2007). Assessment of exploration bias in data-driven predictive models and the estimation of undiscovered resources. Natural Resources Research, 16, 199–207.

    Article  Google Scholar 

  • Corbett, G. J., & Leach, T. M. (1998). Southwest Pacific Rim gold-copper systems: structure, alteration, and mineralization (Vol. 6, p. 237). Littleton, Colorado: Society of Economic Geologists.

    Book  Google Scholar 

  • Di Napoli, M., Carotenuto, F., Cevasco, A., Confuorto, P., Di Martire, D., Firpo, M., Pepe, G., Raso, E., & Calcaterra, D. (2020). Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability. Landslides, 17, 1897–1914.

    Article  Google Scholar 

  • Dietterich, T. G. (2002). Ensemble learning. In The handbook of brain theory and neural networks, Vol. 2, pp 110–125.

  • Efron, B. (1992). Bootstrap methods: another look at the jackknife. Breakthroughs in statistics. New York, NY: Springer.

    Google Scholar 

  • Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. CRC Press.

    Book  Google Scholar 

  • Einaudi, M., Meinert, L. D., & Newberry, R. J. (1981). Skarn deposits. In Economic Geology, 75th Anniversary Volume, pp. 317–391.

  • Faulkner, D. R., Jackson, C. A. L., Lunn, R. J., Schlische, R. W., Shipton, Z. K., Wibberley, C. A. J., & Withjack, M. O. (2010). A review of recent developments concerning the structure, mechanics and fluid flow properties of fault zones. Journal of Structural Geology, 32, 1557–1575.

    Article  Google Scholar 

  • Fumera, G., & Roli, F. (2005). A theoretical and experimental analysis of linear combiners for multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 942–956.

    Article  Google Scholar 

  • Ford, A. (2020). Practical implementation of random forest-based mineral potential mapping for porphyry Cu–Au mineralization in the Eastern Lachlan Orogen, NSW, Australia. Natural Resources Research, 29, 267–283.

    Article  Google Scholar 

  • Ford, A., & McCuaig, T. C. (2010). The effect of map scale on geological complexity for computer-aided exploration targeting. Ore Geology Reviews, 38, 156–167.

    Article  Google Scholar 

  • Ford, A., Miller, J. M., & Mol, A. G. (2016). 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, 19–33.

    Article  Google Scholar 

  • Ford, A., Peters, K. J., Partington, G. A., Blevin, P. L., Downes, P. M., Fitzherbert, J. A., & Greenfield, J. E. (2019). Translating expressions of intrusion-related mineral systems into mappable spatial proxies for mineral potential mapping: Case studies from the Southern New England Orogen, Australia. Ore Geology Reviews, 111, 102943.

    Article  Google Scholar 

  • Hagemann, S. G., Lisitsin, V. A., & Huston, D. L. (2016). Mineral system analysis: Quo vadis. Ore Geology Reviews, 76, 504–522.

    Article  Google Scholar 

  • Haldar, S. K. (2018). Mineral exploration: Principles and applications. Elsevier.

    Google Scholar 

  • Hassanpour, S. (2013). The alteration, mineralogy and geochronology (SHRIMP U-Pb and 40 Ar/39 Ar) of copper-bearing Anjerd skarn, north of the Shayvar Mountain, NW Iran. International Journal of Earth Sciences, 102, 687–699.

    Article  Google Scholar 

  • Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 832–844.

    Article  Google Scholar 

  • Hronsky, J. M., & Groves, D. I. (2008). Science of targeting: Definition, strategies, targeting and performance measurement. Australian Journal of Earth Sciences, 55, 3–12.

    Article  Google Scholar 

  • Hronsky, J. M., & Kreuzer, O. P. (2019). Applying spatial prospectivity mapping to exploration targeting: Fundamental practical issues and suggested solutions for the future. Ore Geology Reviews, 107, 647–653.

    Article  Google Scholar 

  • Jamali, H., Dilek, Y., Daliran, F., Yaghubpur, A., & Mehrabi, B. (2010). Metallogeny and tectonic evolution of the Cenozoic Ahar-Arasbaran volcanic belt, northern Iran. International Geology Review, 52, 608–630.

    Article  Google Scholar 

  • Jamali, H., & Mehrabi, B. (2015). Relationships between arc maturity and Cu–Mo–Au porphyry and related epithermal mineralization at the Cenozoic Arasbaran magmatic belt. Ore Geology Reviews, 65, 487–501.

    Article  Google Scholar 

  • Jurado, K., Ludvigson, S. C., & Ng, S. (2015). Measuring uncertainty. American Economic Review, 105, 1177–1216.

    Article  Google Scholar 

  • Knox-Robinson, C. M., & Wyborn, L. A. I. (1997). Towards a holistic exploration strategy: Using geographic information systems as a tool to enhance exploration. Australian Journal of Earth Sciences, 44, 453–463.

    Article  Google Scholar 

  • Kreuzer, O. P., Etheridge, M. A., Guj, P., McMahon, M. E., & Holden, D. J. (2008). Linking mineral deposit models to quantitative risk analysis and decision-making in exploration. Economic Geology, 103, 829–850.

    Article  Google Scholar 

  • Kuhn, M., & Johnson, K. (2013). Applied predictive modeling (Vol. 26). Springer.

    Book  Google Scholar 

  • Lindsay, M., Aitken, A., Ford, A., Dentith, M., Hollis, J., & Tyler, I. (2016). Reducing subjectivity in multi-commodity mineral prospectivity analyses: Modelling the west Kimberley, Australia. Ore Geology Reviews, 76, 395–413.

    Article  Google Scholar 

  • Lindsay, M. D., Betts, P. G., & Ailleres, L. (2014). Data fusion and porphyry copper prospectivity models, southeastern Arizona. Ore Geology Reviews, 61, 120–140.

    Article  Google Scholar 

  • Lisitsin, V. A., Porwal, A., & McCuaig, T. C. (2014). Probabilistic fuzzy logic modeling: Quantifying uncertainty of mineral prospectivity models using Monte Carlo simulations. Mathematical Geosciences, 46, 747–769.

    Article  Google Scholar 

  • Maepa, F., Smith, R. S., & Tessema, A. (2020). Support vector machine and artificial neural network modelling of orogenic gold prospectivity mapping in the Swayze greenstone belt, Ontario, Canada. Ore Geology Reviews, 103968.

  • McCuaig, T. C., Beresford, S., & Hronsky, J. (2010). Translating the mineral systems approach into an effective exploration targeting system. Ore Geology Reviews, 38, 128–138.

    Article  Google Scholar 

  • McCuaig, T. C., Kreuzer, O. P., & Brown, W. M. (2007). Fooling ourselves – dealing with model uncertainty in a mineral systems approach to exploration. In Mineral Exploration and Research: Digging Deeper. Proceedings of the 9th Biennial SGA Meeting, Dublin, pp. 1435–1438.

  • Mehrpartou, M., Aminifazl, A., & Radfar, J. (1992). Geological map of Iran 1: 100,000 series. Varzaghan: Geological Survey of Iran.

    Google Scholar 

  • Meinert, L. D. (1992). Skarns and skarn deposits. Geoscience Canada, 19, 145–162.

    Google Scholar 

  • Meinert, L. D. (2000). Gold in skarns related to epizonal intrusions. Reviews in Economic Geology, 13, 47–75.

    Google Scholar 

  • Meinert, L. D., Dipple, G. M., & Nicolescu, S. (2005). World Skarn deposits. In Economic geology, 100th Anniversary Volume, pp. 299–336.

  • Meinert, L. D., Hedenquist, J. W., Satoh, H., & Matsuhisa, Y. (2003). Formation of anhydrous and hydrous skarn in Cu-Au ore deposits by magmatic fluids. Economic Geology, 98, 147–156.

    Article  Google Scholar 

  • Meyer, C. (1981). Ore-forming processes in geologic history. In Economic geology, 75th Anniversary Volume, pp. 6–41.

  • Meyer, J. S., Ingersoll, C. G., McDonald, L. L., & Boyce, M. S. (1986). Estimating uncertainty in population growth rates: Jackknife vs. bootstrap techniques. Ecology, 67, 1156–1166.

    Article  Google Scholar 

  • Mollai, H., Pe-Piper, G., & Dabiri, R. (2014). Genetic relationships between skarn ore de-posits and magmatic activity in the Ahar region, Western Alborz, NW Iran. Geologia Carpathica, 65, 209–227.

    Article  Google Scholar 

  • Mooney, C. F., Mooney, C. Z., Mooney, C. L., Duval, R. D., & Duvall, R. (1993). Bootstrapping: A nonparametric approach to statistical inference. Sage University Press.

    Book  Google Scholar 

  • Naghibi, S. A., Moghaddam, D. D., Kalantar, B., Pradhan, B., & Kisi, O. (2017). A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping. Journal of Hydrology, 548, 471–483. https://doi.org/10.1016/j.jhydrol.2017.03.020

    Article  Google Scholar 

  • Nykänen, V. (2008). Radial basis functional link nets used as a prospectivity mapping tool for orogenic gold deposits within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield. Natural Resources Research, 17, 29–48.

    Article  Google Scholar 

  • Nykänen, V., Lahti, I., Niiranen, T., & Korhonen, K. (2015). Receiver operating characteristics (ROC) as validation tool for prospectivity models—A magmatic Ni–Cu case study from the Central Lapland Greenstone Belt, Northern Finland. Ore Geology Reviews, 71, 853–860.

    Article  Google Scholar 

  • Pan, G. C., & Harris, D. P. (2000). Information synthesis for mineral exploration. Oxford University Press Inc.

    Google Scholar 

  • Parsa, M. (2021). A data augmentation approach to XGboost-based mineral potential mapping: An example of carbonate-hosted Zn–Pb mineral systems of Western Iran. Journal of Geochemical Exploration, 228, 106811. https://doi.org/10.1016/j.gexplo.2021.106811

    Article  Google Scholar 

  • Parsa, M., & Maghsoudi, A. (2018). Controls on Mississippi valley-type Zn-Pb mineralization in Behabad district, Central Iran: Constraints from spatial and numerical analyses. Journal of African Earth Sciences, 140, 189–198.

    Article  Google Scholar 

  • Parsa, M., Maghsoudi, A., Yousefi, M., & Sadeghi, M. (2016a). Recognition of significant multi-element geochemical signatures of porphyry Cu deposits in Noghdouz area, NW Iran. Journal of Geochemical Exploration, 165, 111–124.

    Article  Google Scholar 

  • Parsa, M., Maghsoudi, A., Yousefi, M., & Sadeghi, M. (2016b). Prospectivity modeling of porphyry-Cu deposits by identification and integration of efficient mono-elemental geochemical signatures. Journal of African Earth Sciences, 114, 228–241.

    Article  Google Scholar 

  • Parsa, M., Maghsoudi, A., & Ghezelbash, R. (2016c). Decomposition of anomaly patterns of multi-element geochemical signatures in Ahar area, NW Iran: A comparison of U-spatial statistics and fractal models. Arabian Journal of Geosciences, 9, 260.

    Article  Google Scholar 

  • Parsa, M., Maghsoudi, A., Yousefi, M., & Sadeghi, M. (2017a). Multifractal analysis of stream sediment geochemical data: Implications for hydrothermal nickel prospection in an arid terrain, eastern Iran. Journal of Geochemical Exploration, 181, 305–317.

    Article  Google Scholar 

  • Parsa, M., Maghsoudi, A., & Yousefi, M. (2017b). An improved data-driven fuzzy mineral prospectivity mapping procedure; cosine amplitude-based similarity approach to delineate exploration targets. International Journal of Applied Earth Observation and Geoinformation, 58, 157–167.

    Article  Google Scholar 

  • Parsa, M., Maghsoudi, A., Carranza, E. J. M., & Yousefi, M. (2017c). Enhancement and mapping of weak multivariate stream sediment geochemical anomalies in Ahar Area, NW Iran. Natural Resources Research, 26, 443–455.

    Article  Google Scholar 

  • Parsa, M., Maghsoudi, A., & Yousefi, M. (2018a). 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 

  • Parsa, M., Maghsoudi, A., & Yousefi, M. (2018b). A receiver operating characteristics-based geochemical data fusion technique for targeting undiscovered mineral deposits. Natural Resources Research, 27, 15–28.

    Article  Google Scholar 

  • Petrascheck, W. E. (1965). Typical features of metallogenic provinces. Economic Geology, 60, 1620–1634.

    Article  Google Scholar 

  • Pham, B. T., Bui, D. T., Dholakia, M. B., Prakash, I., Pham, H. V., Mehmood, K., & Le, H. Q. (2017). A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS. Geomatics, Natural Hazards and Risk, 8, 649–671.

    Article  Google Scholar 

  • Porwal, A., & Carranza, E. J. M. (2008). Classifiers for modelling of mineral potential. In O. Pourret, P. Naïm, & B. Marcot (Eds.), Bayesian networks: A practical guide to applications (pp. 149–171). Wiley.

    Chapter  Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2003a). Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping. Natural Resources Research, 12, 1–25.

    Article  Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2003b). Artificial neural networks for mineral-potential mapping: A case study from Aravalli Province, Western India. Natural Resources Research, 12, 155–171.

    Article  Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2004). A hybrid neuro-fuzzy model for mineral potential mapping. Mathematical Geology, 36, 803–826.

    Article  Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2006a). A hybrid fuzzy weights-of-evidence model for mineral potential mapping. Natural Resources Research, 15, 1–14.

    Article  Google Scholar 

  • Porwal, A., Carranza, E. J. M., & Hale, M. (2006b). Bayesian network classifiers for mineral potential mapping. Computers & Geosciences, 32, 1–16.

    Article  Google Scholar 

  • Pour, A. B., & Hashim, M. (2011). Identification of hydrothermal alteration minerals for exploring of porphyry copper deposit using ASTER data, SE Iran. Journal of Asian Earth Sciences, 42, 1309–1323.

    Article  Google Scholar 

  • Robb, L. (2005). Introduction to ore-forming processes. Wiley.

    Google Scholar 

  • Rodriguez, J. J., Kuncheva, L. I., & Alonso, C. J. (2006). Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 1619–1630.

    Article  Google Scholar 

  • Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. J. O. G. R. (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 

  • Rossi, M., Guzzetti, F., Reichenbach, P., Mondini, A. C., & Peruccacci, S. (2010). Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology, 114, 129–142.

    Article  Google Scholar 

  • 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, 403–416.

    Google Scholar 

  • Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5, 197–227.

    Article  Google Scholar 

  • Seward, T. M., & Barnes, H. L. (1997). Metal transport by hydrothermal ore fluids. In H. L. Barnes (Ed.), Geochemistry of hydrothermal ore deposits (pp. 435–486). Wiley.

    Google Scholar 

  • Somarin, A. K. (2004). Garnet composition as an indicator of Cu mineralization: Evidence from skarn deposits of NW Iran. Journal of Geochemical Exploration, 81, 47–57.

    Article  Google Scholar 

  • Stöcklin, J. (1974). Possible ancient continental margins in Iran. In The geology of continental margins. Berlin: Springer.

  • Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240, 1285–1293.

    Article  Google Scholar 

  • Tessema, A. (2017). Mineral systems analysis and artificial neural network modeling of chromite prospectivity in the Western Limb of the Bushveld Complex, South Africa. Natural Resources Research, 26, 465–488.

    Article  Google Scholar 

  • Tien Bui, D., Pradhan, B., Lofman, O., & Revhaug, I. (2012). Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and Naive Bayes Models. Mathematical Problems in Engineering. https://doi.org/10.1155/2012/974638

    Article  Google Scholar 

  • Tsangaratos, P., & Ilia, I. (2016). Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. CATENA, 145, 164–179.

    Article  Google Scholar 

  • Van Ravenzwaaij, D., Cassey, P., & Brown, S. D. (2018). A simple introduction to Markov Chain Monte-Carlo sampling. Psychonomic Bulletin & Review, 25, 143–154.

    Article  Google Scholar 

  • Veizer, J., Laznicka, P., & Jansen, S. L. (1989). Mineralization through geologic time; Recycling perspective. American Journal of Science, 289, 484–524.

    Article  Google Scholar 

  • Wang, X., & Tang, X. (2006). Random sampling for subspace face recognition. International Journal of Computer Vision, 70, 91–104.

    Article  Google Scholar 

  • Wang, Z., Yin, Z., Caers, J., & Zuo, R. (2020). A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping. Geoscience Frontiers, 11, 2297–2308.

    Article  Google Scholar 

  • Xiong, Y., & Zuo, R. (2021). A positive and unlabeled learning algorithm for mineral prospectivity mapping. Computers & Geosciences, 147, 104667.

    Article  Google Scholar 

  • Zhang, S., Xiao, K., Carranza, E. J. M., & Yang, F. (2019). Maximum entropy and random forest modeling of mineral potential: Analysis of gold prospectivity in the Hezuo-Meiwu district, west Qinling Orogen, China. Natural Resources Research, 28, 645–664.

    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. (2012). Exploring the effects of cell size in geochemical mapping. Journal of Geochemical Exploration, 112, 357–367.

    Article  Google Scholar 

  • Zuo, R. (2018). Selection of an elemental association related to mineralization using spatial analysis. Journal of Geochemical Exploration, 184, 150–157.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Zuo, R., & Wang, Z. (2020). Effects of random negative training samples on mineral prospectivity mapping. Natural Resources Research. https://doi.org/10.1007/s11053-020-09668-6

    Article  Google Scholar 

  • Zuo, R., Xiong, Y., Wang, J., & Carranza, E. J. M. (2019). Deep learning and its application in geochemical mapping. Earth-Science Reviews, 192, 1–14.

    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 

  • Zuo, R., Kreuzer, O. P., Wang, J., Xiong, Y., Zhang, Z., & Wang, Z. (2021). Uncertainties in GIS-based mineral prospectivity mapping: Key types, potential impacts and possible solutions. Natural Resources Research. https://doi.org/10.1007/s11053-021-09871-z.

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Acknowledgments

Special thanks are due to Prof. Renguang Zuo for handling this manuscript and to two anonymous reviewers for their critical comments on an earlier version of this work.

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Authors and Affiliations

  1. Department of Mining Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Kurdistan, Iran

    Mohammad Parsa

  2. Geological Sciences, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Durban, South Africa

    Emmanuel John M. Carranza

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Correspondence to Mohammad Parsa.

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Parsa, M., Carranza, E.J.M. Modulating the Impacts of Stochastic Uncertainties Linked to Deposit Locations in Data-Driven Predictive Mapping of Mineral Prospectivity. Nat Resour Res 30, 3081–3097 (2021). https://doi.org/10.1007/s11053-021-09891-9

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  • DOI: https://doi.org/10.1007/s11053-021-09891-9

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