[1] G. F. Bonham-Carter, 1994, “Geographic information systems for geoscientists-modeling with GIS,” Comput. methods Geosci., vol. 13, p. 398.
[2] V. F. Rodriguez-Galiano, M. Chica-Olmo, and M. Chica-Rivas, 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,” Int. J. Geogr. Inf. Sci., vol. 28, no. 7, pp. 1336–1354.
[3] E. J. M. Carranza and A. G. Laborte, 2015, “Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines),” Comput. Geosci., vol. 74, pp. 60–70.
[4] Y. Gao, Z. Zhang, Y. Xiong, and R. Zuo, 2016, “Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China,” Ore Geol. Rev., vol. 75, pp. 16–28.
[5] H. Hu, Y. Wen, T. S. Chua, and X. Li, 2014, “Toward scalable systems for big data analytics: A technology tutorial,” IEEE Access, doi: 10.1109/ACCESS.2014.2332453.
[6] H. Moeini and F. M. Torab, 2017, “Comparing compositional multivariate outliers with autoencoder networks in anomaly detection at Hamich exploration area, east of Iran,” J. Geochemical Explor., doi: 10.1016/j.gexplo.2017.05.008.
[7] Y. Xiong, R. Zuo, and E. J. M. Carranza, 2018, “Mapping mineral prospectivity through big data analytics and a deep learning algorithm,” Ore Geol. Rev., doi: 10.1016/j.oregeorev.2018.10.006.
[8] M. F. Goodchild, 2008, “The use cases of digital earth,” Int. J. Digit. Earth, doi: 10.1080/17538940701782528.
[9] M. Deng and L. Di, 2009, “Building an online learning and research environment to enhance use of geospatial data,” …Journal Spat. Data Infrastructures Res., doi: 10.2902/1725-0463.2009.04.art4.
[10] A. Gore, 1998, “The Digital Earth: Understanding our planet in the 21st Century,” Open GIS Consortium.
[11] R. Zuo and Y. Xiong, 2018, “Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods,” Nat. Resour. Res., doi: 10.1007/s11053-017-9357-0.
[12] C. Wang, X. Ma, J. Chen, and J. Chen, 2018, “Information extraction and knowledge graph construction from geoscience literature,” Comput. Geosci., doi: 10.1016/j.cageo.2017.12.007.
[13] V. Mayer-Schönberger and K. Cukier, 2013, Big Data: A Revolution That Will Transform How We Live, Work, and Think.
[14] X.-X. Liu, Y.-C. Chen, D.-H. Wang, F. Huang, and Z. Zhao, 2016, “The metallogenic geomorphic condition analysis of the ion-absorbing type rare earths ore in the Eastern Nanling region based on DEM data,” Acta Geosci. Sin., doi: 10.3975/cagsb.2016.02.05.
[15] J. M. Luo et al., 2017, “Application of integrated geophysical and geochemical data processing to metallogenic target zone quantitative prediction and optimization,” Bull. Mineral. Petrol. Geochemistry, vol. 36, no. 6, pp. 886–891.
[16] M. Chen, S. Mao, and Y. Liu, 2014, “Big data: A survey,” in Mobile Networks and Applications, doi: 10.1007/s11036-013-0489-0.
[17] A. Porwal, E. J. M. Carranza, and M. Hale, 2003, “Artificial neural networks for mineral-potential mapping: a case study from Aravalli Province, Western India,” Nat. Resour. Res., vol. 12, no. 3, pp. 155–171.
[18] D. Harris and G. Pan, 1999, “Mineral favorability mapping: a comparison of artificial neural networks, logistic regression, and discriminant analysis,” Nat. Resour. Res., vol. 8, no. 2, pp. 93–109,.
[19] H.-J. Oh and S. Lee, 2010, “Application of artificial neural network for gold--silver deposits potential mapping: a case study of Korea,” Nat. Resour. Res., vol. 19, no. 2, pp. 103–124,.
[20] M. Abedi, G.-H. Norouzi, and A. Bahroudi, 2012, “Support vector machine for multi-classification of mineral prospectivity areas,” Comput. Geosci., vol. 46, pp. 272–283,.
[21] M. Shabankareh and A. Hezarkhani, 2017, “Application of support vector machines for copper potential mapping in Kerman region, Iran,” J. African Earth Sci., vol. 128, pp. 116–126,.
[22] S. Hariharan, S. Tirodkar, A. Porwal, A. Bhattacharya, and A. Joly, 2017, “Random forest-based prospectivity modelling of greenfield terrains using sparse deposit data: An example from the Tanami Region, Western Australia,” Nat. Resour. Res., vol. 26, no. 4, pp. 489–507,.
[23] G. LeCun, Y., Bengio, Y., Hinton, 2015, “Deep learning. nature 521 (7553): 436,” Nat. 521 (7553), 436–444., doi: 10.1038/nature14539.
[24] Y. Chen, Z. Lin, X. Zhao, G. Wang, and Y. Gu, 2014, “Deep learning-based classification of hyperspectral data,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., doi: 10.1109/JSTARS.2014.2329330.
[25] L. Zhang, L. Zhang, and B. Du, 2016, “Deep learning for remote sensing data: A technical tutorial on the state of the art,” IEEE Geosci. Remote Sens. Mag., doi: 10.1109/MGRS.2016.2540798.
[26] N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, 2017, “Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data,” IEEE Geosci. Remote Sens. Lett., doi: 10.1109/LGRS.2017.2681128.
[27] A. P. Valentine and J. Trampert, 2012, “Data space reduction, quality assessment and searching of seismograms: Autoencoder networks for waveform data,” Geophys. J. Int., doi: 10.1111/j.1365-246X.2012.05429.x.
[28] Z. E. Ross, M. A. Meier, and E. Hauksson, 2018, “P Wave Arrival Picking and First-Motion Polarity Determination With Deep Learning,” J. Geophys. Res. Solid Earth, doi: 10.1029/2017JB015251.
[29] Y. Chen, 2015, “Mineral potential mapping with a restricted Boltzmann machine,” Ore Geol. Rev., vol. 71, pp. 749–760,.
[30] Y. Chen and W. Wu, 2018, “Isolation Forest as an Alternative Data-Driven Mineral Prospectivity Mapping Method with a Higher Data-Processing Efficiency,” Nat. Resour. Res., pp. 1–16,.
[31] M. Karimpour and others, 2011, “Review of age, Rb-Sr geochemistry and petrogenesis of Jurassic to Quaternary igneous rocks in Lut Block, Eastern Iran,” Geopersia, vol. 1, no. 1, pp. 19–54,.
[32] A. M. Shafaroudi and M. H. Karimpour, 2013, “Hydrothermal alteration mapping in northern Khur, Iran, using ASTER image processing: a new insight to the type of copper mineralization,” Acta Geol. Sin. Ed., vol. 87, no. 3, pp. 830–842,.
[33] R. M. Beydokhti, M. H. Karimpour, and S. A. Mazaheri, 2014, “Studies of remote sensing, geology, alteration, mineralization and geochemistry of Balazard copper-gold prospecting area, west of Nehbandan,” Iran. J. Crystallogr. Mineral., vol. 23, no. 3, pp. 459–470,.
[34] R. M. Beydokhti, M. H. Karimpour, S. A. Mazaheri, J. F. Santos, and U. Klötzli, 2015, “U--Pb zircon geochronology, Sr--Nd geochemistry, petrogenesis and tectonic setting of Mahoor granitoid rocks (Lut Block, Eastern Iran),” J. Asian Earth Sci., vol. 111, pp. 192–205,.
[35] A. M. Shafaroudi, M. H. Karimpour, and C. R. Stern, 2015, “The Khopik porphyry copper prospect, Lut Block, Eastern Iran: geology, alteration and mineralization, fluid inclusion, and oxygen isotope studies,” Ore Geol. Rev., vol. 65, pp. 522–544,.
[36] M. A. Akrami and N. Naderi Mighan, 2005, Geological map of Dehsalm(1:100,000). Geological Survey of Iran,.
[37] G. E. Hinton, S. Osindero, and Y.-W. Teh, 2006, “A fast learning algorithm for deep belief nets,” Neural Comput., vol. 18, no. 7, pp. 1527–1554,.
[38] Y. Xiong and R. Zuo, 2018, “GIS-based rare events logistic regression for mineral prospectivity mapping,” Comput. Geosci., doi: 10.1016/j.cageo.2017.10.005.
[39] G. E. Hinton, 2002, “Training products of experts by minimizing contrastive divergence,” Neural Comput., doi: 10.1162/089976602760128018.
[40] G. Hinton, 2010, “A Practical Guide to Training Restricted Boltzmann Machines A Practical Guide to Training Restricted Boltzmann Machines,” Computer (Long. Beach. Calif)., doi: 10.1007/978-3-642-35289-8_32.
[41] E. J. M. Carranza and M. Hale, 2002, “Where are porphyry copper deposits spatially localized? A case study in Benguet province, Philippines,” Nat. Resour. Res., vol. 11, no. 1, pp. 45–59,.
[42] A. C. Philip, 2005, “Magmatic processes in the development of porphyry-type ore systems,” Econ. Geol., vol. 100, pp. 25–38,.
[43] R. H. Sillitoe, 2010, “Porphyry copper systems,” Econ. Geol., vol. 105, no. 1, pp. 3–41,.
[44] B. R. Berger, R. A. Ayuso, J. C. Wynn, and R. R. Seal, 2008, “Preliminary model of porphyry copper deposits,” US Geol. Surv. open-file Rep., vol. 1321, p. 55,.
[45] D. R. Cooke, P. Hollings, and J. L. Walshe, 2005, “Giant porphyry deposits: characteristics, distribution, and tectonic controls,” Econ. Geol., vol. 100, no. 5, pp. 801–818,.
[46] R. H. Sillitoe, 1972, “A plate tectonic model for the origin of porphyry copper deposits,” Econ. Geol., vol. 67, no. 2, pp. 184–197,.
[47] R. H. Sillitoe, 2000, “Gold-rich porphyry deposits: descriptive and genetic models and their role in exploration and discovery,” Rev. Econ. Geol., vol. 13, pp. 315–345,.
[48] E.-J. Holden, S. C. Fu, P. Kovesi, M. Dentith, B. Bourne, and M. Hope, 2011, “Automatic identification of responses from porphyry intrusive systems within magnetic data using image analysis,” J. Appl. Geophys., vol. 74, no. 4, pp. 255–262,.
[49] Z. Hou, H. Zhang, X. Pan, and Z. Yang, 2011, “Porphyry Cu (--Mo--Au) deposits related to melting of thickened mafic lower crust: examples from the eastern Tethyan metallogenic domain,” Ore Geol. Rev., vol. 39, no. 1–2, pp. 21–45,.
[50] M. Parsa, A. Maghsoudi, M. Yousefi, and M. Sadeghi, 2016, “Prospectivity modeling of porphyry-Cu deposits by identification and integration of efficient mono-elemental geochemical signatures,” J. African Earth Sci., vol. 114, pp. 228–241,.
[51] B. Roshanravan, H. Aghajani, M. Yousefi, and O. Kreuzer, 2019, “Particle Swarm Optimization Algorithm for Neuro-Fuzzy Prospectivity Analysis Using Continuously Weighted Spatial Exploration Data,” Nat. Resour. Res., pp. 1–17,.
[52] M. Yousefi and E. J. M. Carranza, 2016, “Data-driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration,” Nat. Resour. Res., vol. 25, no. 1, pp. 3–18,.
[53] V. Pawlowsky-Glahn and A. Buccianti, 2011, Compositional data analysis: Theory and applications. John Wiley & Sons,.
[54] E. J. M. Carranza, 2008, Geochemical anomaly and mineral prospectivity mapping in GIS, vol. 11. Elsevier,.
[55] M. Yousefi, A. Kamkar-Rouhani, and E. J. M. Carranza, 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,” J. Geochemical Explor., vol. 115, pp. 24–35,.
[56] D. A. Clark, 2014, “Magnetic effects of hydrothermal alteration in porphyry copper and iron-oxide copper--gold systems: a review,” Tectonophysics, vol. 624, pp. 46–65,.
[57] H. H. Asadi, A. Porwal, M. Fatehi, S. Kianpouryan, and Y.-J. Lu, 2015, “Exploration feature selection applied to hybrid data integration modeling: Targeting copper-gold potential in central Iran,” Ore Geol. Rev., vol. 71, pp. 819–838,.
[58] A. H. Ansari and K. Alamdar, 2009, “Reduction to the pole of magnetic anomalies using analytic signal,” World Appl. Sci. J., vol. 7, no. 4, pp. 405–409,.
[59] A. R. Bansal, G. Gabriel, V. P. Dimri, and C. M. Krawczyk, 2011, “Estimation of depth to the bottom of magnetic sources by a modified centroid method for fractal distribution of sources: An application to aeromagnetic data in Germany,” Geophysics, vol. 76, no. 3, pp. L11--L22,.
[60] M. H. Zadeh, M. H. Tangestani, F. V. Roldan, and I. Yusta, 2014, “Sub-pixel mineral mapping of a porphyry copper belt using EO-1 Hyperion data,” Adv. Sp. Res., vol. 53, no. 3, pp. 440–451,.
[61] M. Yousefi and E. J. M. Carranza, 2017, “Union score and fuzzy logic mineral prospectivity mapping using discretized and continuous spatial evidence values,” J. African Earth Sci., vol. 128, pp. 47–60,.
[62] H. Ranjbar, F. Masoumi, and E. J. M. Carranza, 2011, “Evaluation of geophysics and spaceborne multispectral data for alteration mapping in the Sar Cheshmeh mining area, Iran,” Int. J. Remote Sens., doi: 10.1080/01431161003745665.
[63] J. R. Gozzard, 2006, Image processing of ASTER multispectral data. Geological Survey of WA,.
[64] T. Hengl, 2006, “Finding the right pixel size,” Comput. Geosci., vol. 32, no. 9, pp. 1283–1298,.
[65] M. Yousefi and E. J. M. Carranza, 2015, “Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping,” Comput. Geosci., vol. 74, pp. 97–109,.
[66] M. Yousefi and E. J. M. Carranza, 2015, “Geometric average of spatial evidence data layers: A GIS-based multi-criteria decision-making approach to mineral prospectivity mapping,” Comput. Geosci., doi: 10.1016/j.cageo.2015.07.006.
[67] A. Porwal, E. J. M. Carranza, and M. Hale, 2006, “Bayesian network classifiers for mineral potential mapping,” Comput. Geosci., vol. 32, no. 1, pp. 1–16,.
[68] B. Roshanravan, H. Aghajani, M. Yousefi, and O. Kreuzer, 2019, “An Improved Prediction-Area Plot for Prospectivity Analysis of Mineral Deposits,” Nat. Resour. Res., doi: 10.1007/s11053-018-9439-7.
[69] M. Yousefi and E. J. M. Carranza, 2015, “Prediction--area (P--A) plot and C--A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling,” Comput. Geosci., vol. 79, pp. 69–81,.
[70] X. Glorot and Y. Bengio, 2010, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the 13th International Conference On Artificial Intelligence and Statistics,.
[71] H. Larochelle, Y. Bengio, J. Louradour, and P. Lamblin, 2009, “Exploring Strategies for Training Deep Neural Networks,” J. Mach. Learn. Res.,.
[72] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, 1986, “Learning representations by back-propagating errors,” Nature, doi: 10.1038/323533a0.
[73] L. Li and Y. Wang, 2014, “What drives the aerosol distribution in Guangdong - The most developed province in Southern China?,” Sci. Rep., doi: 10.1038/srep05972.
[74] J. Vesanto, 1999, “SOM-based data visualization methods,” Intell. Data Anal., doi: 10.3233/IDA-1999-3203.
[75] A. Joly, A. Porwal, and T. C. McCuaig, 2012, “Exploration targeting for orogenic gold deposits in the Granites-Tanami Orogen: Mineral system analysis, targeting model and prospectivity analysis,” Ore Geol. Rev., doi: 10.1016/j.oregeorev.2012.05.004.
[76] M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, 2015, “Deep learning applications and challenges in big data analytics,” J. Big Data, doi: 10.1186/s40537-014-0007-7.