تعداد نشریات | 161 |
تعداد شمارهها | 6,532 |
تعداد مقالات | 70,504 |
تعداد مشاهده مقاله | 124,123,078 |
تعداد دریافت فایل اصل مقاله | 97,231,187 |
Investigating the performance of continuous weighting functions in the integration of exploration data for mineral potential modeling using artificial neural networks, geometric average and fuzzy gamma operators | ||
International Journal of Mining and Geo-Engineering | ||
مقاله 7، دوره 57، شماره 4، اسفند 2023، صفحه 405-412 اصل مقاله (786.35 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/ijmge.2023.361593.595080 | ||
نویسندگان | ||
Esmaeil Bahri* 1؛ Andisheh Alimoradi1؛ Mahyar Yousefi2 | ||
1Department of Mining and Petroleum Engineering, Faculty of Engineering, Imam Khomeini International University | ||
2Department of Mining Engineering, Faculty of Engineering, Malayer University | ||
چکیده | ||
In mineral exploration programs, reducing uncertainty and increasing exploration success have always been challenging issues. To modulate the above-mentioned uncertainty and increase exploration accomplishment, integration, and prospectivity analysis techniques are used for mineral exploration targeting. This paper aims to model the mineral potential of porphyry copper deposits in the Jiroft region, Kerman province. To achieve this goal and overcome the aforementioned issues resulting from the operation of complex ore-forming geological processes, continuous weighting methods through logistic functions were used while training points and analyst’s opinions were not contributed to the weighting procedure. Then, to generate exploration targets, the weighted layers were combined with three different integration methods namely, artificial neural network, geometric average, and fuzzy gamma operators. The comparison of the model obtained from the application of an artificial neural network with those obtained by the geometric average and the fuzzy gamma operators using prediction rate-area plots indicated that all the models have good overall performance and acceptable prediction rate. However, the performance of the artificial neural network model is slightly less than that of the other two models. Thus, the targets generated using the geometric average and fuzzy gamma operators are more reliable for planning further exploration programs. | ||
کلیدواژهها | ||
Artificial neural network؛ Exploration targets؛ Fuzzy gamma؛ Geometric average؛ Porphyry copper deposits | ||
مراجع | ||
[1] Yousefi, M., Kreuzer, O.P., Nykänen, V., Hronsky, J.M.A., 2019. "Exploration information systems―a proposal for the future use of GIS in mineral exploration targeting". Geology Reviews 111, 103005.
[2] Afzal, P., Yousefi, M., Mirzaei, M., Ghadiri-Sufi, E., Ghasemzadeh, S., Daneshvar Saein, L., 2019. "Delineation of podiform-type chromite mineralization using Geochemical Mineralization Prospectivity Index (GMPI) and staged factor analysis in Balvard area (southern Iran). Journal of Mining and Environment 10: 705-715.
[3] Yousefi, M., Kreuzer, O.P., Nykänen, V., Hronsky, J.M.A., 2019. "Exploration information systems―a proposal for the future use of GIS in mineral exploration targeting". Ore Geology Reviews 111, 103005.
[4] Yousefi, M., E.J.M., Carranza, Kreuzer, O.P., Nykänen, V., Hronsky, J.M.A., Mihalasky, M., J., 2021. "Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: State-of-the-Art and Outlook". Journal of Geochemical Exploration 229, 106839.
[5] Carranza, E. J. M. 2008. “Geochemical Anomaly and Mineral Prospectivity Mapping in GIS” Handbook of Exploration and Environmental Geochemistry. Vol. 11, Elsevier, Amesterdam.
[6] Yousefi, M., Carranza, M. J. M. 2015. “Fuzzification of Continouse- value spatial evidence for mineral prosprctivity mapping” Computers & Geosciences 74: 97-109.
[7] Yousefi, M., Carranza, M. J. M. 2017. “Union score and fuzzy logic mineral prospectivity mapping using discretized and continuous spatial evidence values” Journal of African Earth Sciences 128: 47-60.
[8] Berberian, F., Muir, I.D., Pankhurst, R.J., Berberian, M., 1982. "Late cretaceous and early miocene andean-type plutonic activity in northern Makran and central Iran". J. Geol. Soc. Lond. 139, 605e614.
[9] Badrzadeh, Z., Aghazadeh, M., 2014. “Geochemistry and Structural Geology of Intrusive Masses of South-Western Part of Jiroft” Geochemistry Journal, 2, Payame Noor University
[10] Hezarkhani, A. 2006. “Mineralogy and fluid inclusion investigations in the Reagan Porphyry System, Iran, the path to an uneconomic porphyry copper deposit” Journal of Asian Earth Sciences 27: 598–612.
[11] Roberts, R. G., Sheahan, P., Cherry, M. E. 1998. “ Ore Deposit Models” Geoscience Canada Reprint Series 3, Geological Association of Canada, Newfoundland.
[12] Berger, B. R., Drew, L. J. 2002. “Mineral – deposit models: new developments in: A.G. Fabbri, Gaal, G., Mccammon, R.B. (Eds.), Deposit and Geoenvironmental models for Rsource Exploration and Environmental Security” NATO Science Series 2, 80: 121-134.
[13] Pirajno, F. 1992. “Hydrothermal Mineral Deposits, Principles and Fundamental Concepts for the Exploration Geologists” Springer- verlag, Berlin.
[14] Robb, L. 2004. “Introduction to Ore- foming Processes” Blackwell, Oxford.
[15] Carlson, C. A. 1991. “Spatial distribution of ore depisits” Geology 19: 111-114.
[16] Vearcombe, J., Vearcombe, S. 1999. “The spatial distribution of mineralization: applications of Fry analysis” Economic Geology 94: 475- 486.
[17] Bonham – Carter, G. F. 1985. “Statistical association of gold occurrences with Landsat – derived lineaments, Timmins-Kirkland Lake area, Ontario, Canadian” Journal of Remote sensing 11: 195-211.
[18] Carranza, E. J. M, Hale, M. 2002. “Spatial association of mineral occurrence and curvi-linear geological features” Mathematical Geology 34: 199-217.
[19] Sillitoe, R. H. 1972. “A plate tectonic model for the origin of porphyry copper deposits” Econ. Geol. 67: 184-197.
[20] Sillitoe, R. H. 1997. “Characteristics and controls of the largest porphyry copper-gold and epithermal gold deposits in the circum-Pacific region” Aust. J. Earth Sci. 44: 373-388.
[21] Sillitoe, R. H., 2010.” Porphyry copper systems” Econ. Geol. 105: 3-41.
[22] Arribas, A. J. 1995. “Contemporaneous formation of adjacent porphyry and epithermal Cu-Au deposits over 300 ka in northern Luzon, Philippines” Geology, 23: 337–340.
[23] Singer, D. A., Berger, V. I., Moring, B. C. 2005. “Porphyry copper deposits of the world: Database, map, grade and tonnage models” U.S. Geological Survey. Open-File Report : 1005–1060.
[24] Hezarkhani, A. 2006. “Mineralogy and fluid inclusion investigations in the Reagan Porphyry System, Iran, the path to an uneconomic porphyry copper deposit” Journal of Asian Earth Sciences 27: 598–612.
[25] Guillou-Frottier, L., Burov, E. 2003. “The development and fracturing of plutonic apexes: Implications for porphyry ore deposits” Earth and Planetary Science Letters 214: 341–356.
[26] Qu, X., Hou, Z., Zaw, K., Youguo, L. 2007. “Characteristics and genesis of Gangdese porphyry copper deposits in the southern Tibetan Plateau: Preliminary geochemical and geochronological results” Ore Geology Reviews 31: 205–223.
[27] Ghasemi, A., Talbot, C. J. 2006. “A new tectonic scenario for the Sanandaj–Sirjan Zone (Iran)” Journal of Asian Earth Sciences 26: 683–693.
[28] Meshkani, S. A., Mehrabi, B., Yaghubpur, A., Sadeghi, M. 2013. “Recognition of the regional lineaments of Iran: Using geospatial data and their implications for exploration of metallic ore deposits” Ore Geology Reviews 55: 48–63.
[29] Zare Chahooki, M. A., 2010. “Moltivariate Analysis in SPSS” University of Tehran.
[30] Yousefi, M., Kamkar-Rouhani, A., Carranza, M. 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.
[31] Yousefi, M., Carranza, E. J. M., Kamkar-Rouhani, A. 2014. “Weighted drainage catchment basin mapping of stream sediment geochemical anomalies for mineral potential mapping”.Journal of Geochemical Exploration 128:88–96.
[32] 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.
[33] Yousefi, M., Nykänen, V. 2016. “Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping” Journal of Geochemical Exploration 164: 94–106.
[34] Nykanen, 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 Resource Research 17: 29-48.
[35] Zadeh, L. A. 1997. “Introduction to hybrid artificial intelligence systems” in Tsoukalas, L. H., and Uhrig, R. E., eds., fuzzy and Neural Approaches in Engineering: john and sons. Inc., New York, p. 1-7.
[36] Porwal, A., 2006. “Mineral Potantial Mapping with Mathematical Geological Models” ph.D. Thesis, University of Utrecht, The Netherlands, ITC(International Institute for Geo-Information Science and Netherlands, ITC(International Institute for Geo-Information Science and Earth Observation) Publication No. 130, Enschede, 289pp.
[37] Fathi, M., Alimoradi, A., Hemati Ahooi, R. 2021 "optimizing extreme learning machine algorithm using particle swarm optimization to estimate iron ore grade"journal of Mining and Environment 12 (2), 397-411.
[38] 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.
[39] Chen, Y., Wu, W., 2017. “Mapping mineral prospectivity using an extreme learning machine regression” Ore Geology Reviews 80 : 200–213.
[40] Huang, G. B., Zhu, Q. Y., Siew, C. K., 2006. “Extreme learning machine: theory and applications” Neurocomputing 70 : 489–501.
[41] Carranza, E. J. M., Woladi, T., Chikambwe, E. M. 2005. “Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmaties, Lundazi District, Zambia” Natural Resource Deposits 14: 47-63.
[42] Bonham-Carter, G. F., Agterberg, F. P., Wright, D, F. 1989. “Weigh of evidence modeling: a new apprpach to mapping mineral potential In: Agterberg, F. P., Bonham-Carter, G. F. (Eds.), Statistical Applications in the Earth Science” Geological Survey of Canada 89: 171-183.
[43] Yousefi, M., Carranza, M. J. M. 2015. “Prediction – area (P- A) plot and C-A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling”Computers & Geosci Computers & Geosciences 83:72–79.
[44] Nezhad, S. G., Mokhtari, A. R., Rodsari, P. R. 2017. “The true sample catchment basin approach in the analysis of stream sediment geochemical data” Ore Geology Reviews 83; 127–134.
[45] Zhang, N., Zhou, K., Du, X. 2017. “Application of fuzzy logic and fuzzy AHP to mineral prospectivity mapping of porphyry and hydrothermal vein copper deposits in the Dananhu- Tousuquan island arc, Xinjiang, NW China” Journal of African Earth Sciences 128: 84–96.
[46] Du, X., Zhou, K., Cui, Y., Wang, J., Zhang, N., Sun, W. 2016. “Application of fuzzy Analytical Hierarchy Process (AHP) and Prediction-Area (PA) plot for mineral prospectivity mapping: A case study from the Dananhu metallogenic belt, Xinjiang, NW China” Arabian Journal of Geosciences 9: 298.
[47] Nykanen, 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.
[48] Parsa, M., Maghsoudi, A., Yousefi, M. 2017. “A receiver operating characteristics-based geochemical data fusion technique for targeting undiscovered mineral deposits” Natural Resources Research 27: 15–28.
[49] Zuo, R. 2018. “Selection of an elemental association related to mineralization using spatial analysis” Journal of Geochemical Exploration 184: 150–157.
[50] Roshanravan, B., Aghajani, H., Yousefi, M., Kreuzer, O. 2018b. “An Improved Prediction-Area Plot for Prospectivity Analysis of Mineral Deposits” Natural Resources Research.
[51] Mars, J. C., & Rowan, L. C. (2006). Regional mapping of phyllic-and argillic-altered rocks in the Zagros magmatic arc, Iran, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and logical operator algorithms. Geosphere, 2(3), 161-186.
[27] Ivanička J., Polák M., Hók J., Határ J., Greguš J., Vozár J., Nagy A., Fordinál K., Pristaš J., Konečný V., Šimon L., Geological map of the Tribeč Mountains (1:50000). GSSR, Bratislava, 1998.
[28] Bielik, M., Kováč, M., Kučera, I., Michalík, P., Šujan, M. & Hók, J., 2002: Neoalpine linear density boundaries (faults) detected by gravimentry. Geologica Carpathica 53, 235–255
[29] Staškovanová, Veronika and Minár, Jozef. Modelling the geomorphic history of the Tribeč Mountains and the Pohronský Inovec Mountains (Western Carpathians) with the CHILD model, Open Geosciences, 8(1), 2016, pp. 371-389. https://doi.org/10.1515/geo-2016-0038
[30] Zahorec P., Pašteka R., Mikuška J., Szalaiová V., Papčo J., Kušnirák D., Pánisová J., Krajňák M., Vajda P., Bielik M., Marušiak I., 2017: Chapter 7 – National Gravimetric Database of the Slovak Republic. In: Paˇsteka R., Mikuˇska J., Meurers B. (Eds.): Understanding the Bouguer Anomaly: A Gravimetry Puzzle. Elsevier, Amsterdam, 113–125, doi: 10.1016/B978-0-12-812913-5.00006-3. | ||
آمار تعداد مشاهده مقاله: 178 تعداد دریافت فایل اصل مقاله: 230 |