GIS-Based Identification of Promising Porphyry Copper Mineralization Areas in Shahre Babak (Kerman Province, Iran) using Machine Learning Method

Document Type : Research Article

Authors

• Moslem Jahantigh ¹
• Hamid Reza Ramazi ²
• Hosein Ferdowsi ³
• Zahra Jafari ⁴

¹ Ph.D. Student, Department of Mining Engineering‌, Faculty of Mine, AmirKabir University, Tehran, Iran

² Professor, Department of Mining Engineering‌, Faculty of Mine, AmirKabir University, Tehran, Iran

³ Ph.D. Student, Department of Mining Engineering‌, Faculty of Mine, Tehran University, Tehran, Iran

⁴ M.Sc., Department of Geology, Faculty of Basic Sciences, Shahid Bahonar University, Kerman, Iran

10.22067/econg.2023.79466.1057

Abstract

Producing mineral potential model using GIS software has been increased over the past years. In this study, predictive map consisted of argillic alteration, philic alteration, iron oxide alteration, reduction to pole of aeromagnetic data, lineaments, cu geochemistry anomaly, and principal component analysis (component 3) were prepared from Shahre Babak area. For training model, 37 mineralized points were used. Point pattern analysis was used as well for making non-deposit points and for training model, percepteron artificial neural network with two layers was applied. The training model was used to prepare the final mineral potential model. Based on the mentioned model, the main promising areas were identified to be in the northwest and eastern part of the studied area. Moreover, two areas in the northern and southwestern parts of this area were identified for additional studies. For evaluating the model, ROC curve was used. ROC curve shows high precision of the produced model. For more evaluating, sensitivity, specificity, positive predict value, negative predict value, accuracy, and kappa were computed. The coefficients confirm the high accuracy of the mineral potential model.
 
Introduction
Mineral prospectivity mapping (MPM) is a multicriteria decision-making task that aims to outline and prioritize prospective areas for exploring undiscovered mineral deposits of the type sought (Carranza and Laborte, 2015; Yousefi and Carranza, 2015; Sun et al., 2019). In the early stages of exploration, if there are enough known indices in an area, data-driven modeling is proper for mineral potential prospectivity. In this method, at first, all the characteristics of the known indices, of the type of mineralization sought, are collected and the relationship of these characteristics with evidence and spatial patterns is quantified. Then, points with similar characteristics are searched in those areas.
Shahre Babak as the studied area is a part of Urumieh-Dokhtar zone. Urumieh-Dokhtar zone is proper for porphyry copper deposits.
In this study, at the first stage, conceptual model was defined for porphyry copper modelling. Then, based on the model, some predictive layers were made ready and the data were imported to the trained model of artificial neural network in MATLAB 2021. At the next stage, final model was presented.
 
Material and methods
For constructing mineral potential model, a conceptual model was defined. Based on this model, some predictive layers consisted of argillic alteration, iron oxide alteration, phillic alteration, reduction to pole of airborne magnetic map, cu geochemistry anomaly, principal component geochemistry anomaly, intrusive units, lineaments structures, and digital elevation models were made in ARCGIS in raster formats. The pixel size of the raster files is 100m*100m. After fuzzification of raster files, these features were extracted to ASCII formats.
 
Geology data (Intrusive body, faults, and dykes)
Shahre Babak geology map in 1:250000 scale was used for extracting geological information. The intrusive bodies, faults, and dykes were extracted from Shahre Babak geology map. After extracting geological information, based on the Euclidean distance, the distance maps were made in ARCGIS. Then these maps became fuzzy.
Airborne magnetic data
The airborne magnetic data were surveyed by Atomic Energy Organization of Iran (AEOI) during 1977 and 1978. The flight lines distance and the sensor altitude were about 500 and 120 m, respectively. The reduction to pole filter was applied on total magnetic intensity map.
 
Geochemistry data
Geochemistry data in 1:250000 scale was used for geochemical interpretations. The cu geochemistry anomaly was drawn from the data. Principal component analysis method was applied on geochemical data. Component 3 was extracted from the data.
 
Aster data
Band ratio method was used for extracting the alterations. Iron oxide alteration, philic alteration, and argillic alteration were drawn in ENVI software in raster format. The iron oxide, argillic, and philic alteration files were imported to ARCGIS software and transformed to shapefile format. The distance maps were drawn based on the Euclidean distance. Then these maps became fuzzy.
 
Digital Elevation Model
Digital Elevation Model (DEM) was extracted from Aster data. The data became fuzzy.
 
Training dataset
For training model, 37 deposit points were selected. Point pattern analysis was used for non-deposit points. Based on this method, 37 non-deposit points were extracted of the Shahre Babak (the studied area). Each of the labels was located in a unique pixel. The features of these points were extracted from the predictive maps. Then these points were imported to artificial neural network (perceptron neural network with two layers). 70% of data were used for training model and 30% were used for testing model. Then the trained model was applied on the ASCII format. The resulting model was drawn using ARCGIS.
 
Artificial neural network
ANN is a modelling approach that simulates human brain system inspired by biological neural networks (Celik and Basarir, 2017). ANN can be effectively applied for pattern recognition in a wide variety of geoscience investigations. In this network, the neurons of different layers are interconnected to exchange information in a unidirectional way starting from the input layer through hidden layers to the output layer (Rodriguez-Galiano et al., 2015; Celik and Basarir, 2017). The flow of information is performed by assigning weights to the connections of different neurons (Rodriguez-Galiano et al., 2015).
The back-propagation algorithm is employed to ensure the learning capability of ANN. This algorithm computes the error between the outputted value and real target value, then feeds back it to ANN in order to adjust the weights and biases (Celik and Basarir, 2017).
Results
Mineral potential map of studied area was produced by artificial neural network. Based on resulting model, the first-class promising areas were detected in north western and eastern parts of the studied area. Moreover, two areas in north and south western parts of studied area were identified. For evaluating the model, ROC curve was used. This curve shows model accuracy with high precision. For further evaluation, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and kappa were calculated with 94.7%, 91.8%, 92.3%, 94.4%, 93.3%, and 89%, respectively. These coefficients also confirm the high accuracy of the mineral potential model.

Keywords

• prospectivity
• Artificial neural network
• Point pattern analysis
• Kappa
• Share-Babak

20.1001.1.20087306.1402.15.2.3.3

---

References

Abedi, M., Norouzi, G.H. and Torabi, S.A., 2013. Clustering of mineral prospectivity area as an unsupervised classification approach to explore copper deposit. Arabian Journal of Geosciences, 6(10): 3601–3613. https://doi.org/10.1007/s12517-012-0615-5

Aghanabati, A., 2004. The Geology of Iran. Geological Survey of Iran, Tehran, 586 pp. (in Persian)

Ayati, F., Yavuz, F., Noghreyan, M., Haroni, H.A. and Yavuz, R., 2008. Chemical characteristics and composition of hydrothermal biotite from the Dalli porphyry copper prospect, Arka, central province of Iran. Mineralogy and Petrology, 94(1): 107–122. https://doi.org/10.1007/s00710-008-0006-5

Carranza, E.J.M., 2008. Geochemical Anomaly and Mineral Prospectivity Mapping in GIS, Handbook of Exploration and Environmental Geochemistry. Vol. 11, Elsevier, Amsterdam, 351 pp. Retrieved November 21, 2022 from https://www.elsevier.com/books/geochemical-anomaly-and-mineral-prospectivity-mapping-in-gis/carranza/978-0-444-51325-0

Carranza, E.J.M., 2009. Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity. Computer Geoscience, 35(10): 2032–2046. https://doi.org/10.1016/j.cageo.2009.02.008

Carranza, E.J.M., 2011. Geocomputation of mineral exploration targets. Computer Geoscience, 37(12): 1907–1916. https://doi.org/10.1016/j.cageo.2011.11.009

Carranza, E.‌J.‌M. and Laborte, A.‌G., 2015. Data driven predictive mapping of gold prospectivity, Baguio district, Philippines: application of random forestsalgorithm. Ore Geology Reviews, 71: 777–787. https://doi.org/10.1016/j.oregeorev.2014.08.010

Celik, U. and Basarir, C., 2017. The Prediction of Precious Metal Prices via Artificial Neural Network by Using RapidMiner. Alphanumeric Journal, 5(1): 45–54. https://doi.org/10.17093/alphanumeric.290381

Chen, J.L., Xu, J.F., Wang, B.D., Yang, Z.Y., Ren, J.B., Yu, H.X., Liu, H. and Feng, Y., 2015. Geochemical differences between subduction and collision-related copper bearing porphyries and implications for metallogenesis. Ore Geology Reviews, 70: 424-437. https://doi.org/10.1016/j.oregeorev.2015.01.011

Dehghani, Z., 2019. Easy to learn neural network. Ati-negar publisher, Tehran‌, 123 pp. (in Persian)

Ghaderi, M., Yasemi, N. and Booyeri, M., 2018. Porphyry copper deposits of Iran. Tarbiat Modares publisher, Tehran, 645 pp. (in Persian)

Hassanpour, S., Alirezaei, S. and Selby, D., 2015. SHRIMP zircon U–Pb and biotite and hornblende Ar–Ar geochronology of Sungun, Haftcheshmeh, Kighal, and Niaz porphyry Cu–Mo systems: evidence for an early Miocene porphyry-style mineralization in northwest Iran. International Journal of Earth Sciences, 104: 45–59. https://doi.org/10.1007/s00531-014-1071-0

Hengl, T., 2006. Finding the right pixel size. Computer Geoscience, 32(9): 1283–1298. https://doi.org/10.1016/j.cageo.2005.11.008

Ibrahim, O.M., 2013. A Comparison of Methods for Assessing the Relative Importance of Input Variables in Artificial Neural Networks. Journal of Applied Sciences Research, 9: 5692–5700. Retrieved November 14, 2022 from https://www.scirp.org/(S(351jmbntvnsjt1aadkozje))/reference/referencespapers.aspx?referenceid=1345433

Li, S., Chen, J. and Liu, C., 2022. Overview on the Development of Intelligent Methods for Mineral Resource Prediction under the Background of Geological Big Data. Minerals, 12(5): 616. https://doi.org/10.3390/min12050616

Mars, J.C. and Rowan, L.C., 2006. Regional mapping of phyllic- and argillicaltered rocks in the Zagros magmatic arc, Iran, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and logical operator algorithms. Geosphere, 2: 161–186. Retrieved November 14, 2022 from https://www.scirp.org/(S(351jmbntvnsjt1aadkposzje))/reference/ReferencesPapers.aspx?ReferenceID=1191514

Nathan, M., Bokuik, A., Petterson, M. and Holm, R., 2021. Stream Sediment Datasets and Geophysical Anomalies: A Recipe for Porphyry Copper Systems Identification-The Eastern Papuan Peninsula Experience. Geosciences, 11(7): 299. https://doi.org/10.3390/geosciences11070299

Nykänen, V. and Ojala, V.J., 2007. Spatial analysis techniques as successful mineral-potential mapping tools for orogenic gold deposits in the Northern Fennoscandian Shield, Finland. Natural Resources Research, 16(2): 85–92. https://doi.org/10.1007/s11053-007-9046-5

Olden, J.D. and D.A., Jackson, 2002. Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modeling, 154(1–2): 135–150. https://doi.org/10.1016/S0304-3800(02)00064-9 

Panda, L. and Tripathy, S.K., 2014. Performance prediction of gravity concentrator by using artificial neural network-a case study. International Journal of Mining Science and Technology, 24(4): 461–465. https://doi.org/10.1016/j.ijmst.2014.05.007

Porwal, A., Carranza, E.J.M. and Hale, M., 2003. Artificial neural networks for mineral potential mapping. Nature Resources Research, 12: 155–171. https://doi.org/10.1023/A:1025171803637  

Rao., Z., 2000. Artificial Neural Networks in Hydrology. Water Science and Technology Library, volume 36, Springer Dordrecht, USA, 332 pp. Retrieved November 20, 2022 from https://link.springer.com/book/10.1007/978-94-015-9341-0

Richards, J.P., Boyce, A.J. and pringle, M.S., 2001. Geological evolution of the Escondida area, northern Chile: A model for spatial and temporal localization of porphyry Cu mineralization. Economic Geology, 96(2): 271–305. https://doi.org/10.2113/gsecongeo.96.2.271

Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M. and 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. https://doi.org/10.1016/j.oregeorev.2015.01.001

Shahabpour, J., 1999. The role of deep structures in the distribution of some major ore deposits in Iran, NE of Zagros thrust zone. Journal of Geodynamics, 28(2–3): 237–250. https://doi.org/10.1016/S0264-3707(98)00040-4

Shokri, B., Ramazi, H., Doulati Ardejani, F. and Sadeghiamirshahidi, M., 2014. Prediction of Pyrite Oxidation in a Coal Washing Waste Pile Applying Artificial Neural Networks (ANNs) and Adaptive Neuro-fuzzy Inference Systems (ANFIS), Mine Water and the Environment, 33: 146–156. https://doi.org/10.1007/s10230-013-0247-3

Sillitoe, R.H., 1994. Erosion and collapse of volcanoes: Causes of telescoping in intrusion-centered ore deposits. Geology, 22 (10): 945–948. https://doi.org/10.1130/0091-7613(1994)022<0945:EACOVC>2.3.CO;2

Sillitoe, R.H., 1998. Major regional factors favouring large size, high hypogene grade, elevated gold content and supergene oxidation and enrichment of porphyry copper Deposits: PGC Publishing, Adelide, 230 pp. Retrieved October 24, 2022 from http://portergeo.com.au/publishing/porphyry98/Abstract2p.asp

Smola, A.‌J. and Schölkopf, B., 2004. A tutorial on support vector regression, Statistics and computing. 14(3): 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88

Sun, T., Chen, F., Zhong, L.‌X., Liu, W.‌M. and 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. https://doi.org/10.1016/j.oregeorev.2019.04.003

Tien Bui, D., Tuan, T.A., Klempe, H., Pradhan, B. and Revhaug, I., 2015. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides, 13: 361–378. https://doi.org/10.1007/s10346-015-0557-6

Wang, S.C., 2003. Interdisciplinary Computing in Java Programming language. V. 743, Springer, 743 pp. Retrieved November 15, 2022 from https://link.springer.com/chapter/10.1007/978-1-4615-0377-4_5

Waterman, G.C. and Hamilton, R.L., 1975. The Sar-Cheshmeh porphyry copper deposit. Economic Geology, 70(3): 568–576. https://doi.org/10.2113/gsecongeo.70.3.568

Yang, N., Zhang, Z., Yang, J., Hong, Z. 2022. Mineral Prospectivity Prediction by Integration of Convolutional Autoencoder Network and Random Forest. Natural Resources Research, 31: 1103–1119. https://doi.org/10.1007/s11053-022-10038-7

Yousefi, M. and Carranza, E.J.M., 2015. 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.  https://doi.org/10.1016/j.cageo.2015.03.007

Zuo, R. and Carranza, E.‌J.‌M., 2011. Support vector machine: a tool for mapping mineral prospectivity. Computers & Geosciences, 37(12): 1967–1975. https://doi.org/10.1016/j.cageo.2010.09.014