An AHP–TOPSIS Predictive Model for District-Scale Mapping of Porphyry Cu–Au Potential: A Case Study from Salafchegan Area (Central Iran)

Asadi, Hooshang H.; Sansoleimani, Atefeh; Fatehi, Moslem; Carranza, Emmanuel John M.

doi:10.1007/s11053-016-9290-7

Cited by 47 publications

(13 citation statements)

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“…In this study, to apply knowledge of critical processes of mineralization in an orogenic gold system to prospectivity analysis (e.g., Groves et al, 2000;McCuaig et al, 2010;Lisitsin et al, 2010;Herbert et al, 2014;Asadi et al, 2016;Duarte Campos, 2017), district-scale mapping of prospectivity for the deposit-type sought was considered appropriate to delineate target areas for further exploration at camp-scale. Following the discussion in Carranza et al (2015), district-scale of mapping was selected considering the sorts of data available in the study area (see next sub-section), which is covered by the 1:100,000 scale geological map ( Fig.…”

Section: General Characteristics Of Orogenic Gold Depositsmentioning

confidence: 99%

“…The explanation for that is that deformation causes the less competent metasedimentary country rocks to fail by shearing, but given that these rocks are very tight due to previous shortening and deformation no, or only very little, new space is created. The granitoid bodies, on the other hand, fail by fracturing creating low stress sites and space for gold deposition.The fracturing event triggers increased permeability and suction and thus focuses fluid flow into the newly created facture network / stockwork, centered upon granitoid intrusions.Orogenic-gold deposits are associated with indicative geochemical anomalies, which have been widely used as a reconnaissance-scale exploration tool (Nykänen, 2008; Nykänen et al, 2008a,b;Nykänen and Salmirinne, 2007;Bierlein and McKnight, 2005;Fu et al, 2012;Groves et al, 2000;Goldfarb et al, 2001;.In this study, to apply knowledge of critical processes of mineralization in an orogenic gold system to prospectivity analysis (e.g., Groves et al, 2000;McCuaig et al, 2010;Lisitsin et al, 2010;Herbert et al, 2014;Asadi et al, 2016;Duarte Campos, 2017), district-scale mapping of prospectivity for the deposit-type sought was considered appropriate to delineate target areas for further exploration at camp-scale. Following the discussion in Carranza et al (2015), district-scale of mapping was selected considering the sorts of data available in the study area (see next sub-section), which is covered by the 1:100,000 scale geological map ( Fig.…”

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confidence: 97%

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Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran

Almasi

Yousefi

Carranza

2017

Ore Geology Reviews

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Diverse deposit-types or mineral systems form by diverse geological processes, so translation of knowledge about the controls of mineralization acquired from the 4D geological modeling into 2D spatial predictor maps is a major challenge for prospectivity analysis. In this regard, mathematical functions have been used to model the conceptual or perceived spatial relationships between geological variables and targeted type or system of mineralization. In this paper, due to the different models of spatial relationships between predictors and mineral deposits, we investigated the performance of different fuzzification functions to quantify the relationships. We demonstrated that various types of relationships between exploration features and a mineralization-type sought could be quantified using different fuzzification functions for prospectivity analysis. We illustrated the process of the prospectivity analysis by using a data set of orogenic gold deposits in Saqez-Sardasht Goldfield, Iran. Prospectivity modeling of orogenic gold mineralization in the study area showed that the NE-SW trending targets have priority for further prospecting of the deposits. Saqez-Sardasht Goldfield, Zagros Orogen, Iran (by A. Almasi, M. Yousefi, E. J.M. Carranza) Manuscript submitted to Ore Geology Reviews 2 Prospectivity analysis of orogenic gold deposits in IntroductionFor mineral prospectivity mapping (MPM) of a deposit-type sought in an area, mathematical functions have been used to model spatial relationships between geological variables system of mineralization (e.g., Bonham-Carter, 1994;Luo and Dimitrakopoulos, 2003; Porwal et al., 2003a,b,c;Carranza, 2008Carranza, , 2017. In MPM, mathematical functions, have been widely used to assign weights to discretized spatial evidence values as fuzzified evidential maps in the [0,1] range or to rank target areas as fuzzy prospectivity models (e.g., Bonham-Carter, 1994;Carranza and Hale, 2002;Luo and Dimitrakopoulos, 2003;Porwal et al., 2003c;Carranza, 2008Carranza, , 2009Carranza, , 2017Lisitsin et al., 2013;Mutele et al., 2017;Nykänen et al., 2017). The weights assigned to classes of discretized evidential values may be based on (a) expert judgment directly, (b) locations of known mineral occurrences (KMOs), (c) a combination of (a) and (b), or (d) subjectively-defined functions, so indirectly-assigned by analyst (e.g., Luo, 1990;Bonham-Carter, 1994;Cheng and Agterberg, 1999;Luo and Dimitrakopoulos, 2003;Porwal et al., 2003a Porwal et al., ,b,c, 2004Porwal et al., , 2006Carranza et al., 2005;Carranza, 2008Carranza, , 2014Porwal and Kreuzer, 2010;Mejía-Herrera et al., 2014;Carranza and Laborte, 2016;McKay and Harris, 2016). All these methods impart bias due to discretization of continuous spatial values, use of subjective expert judgments, and sparse or incomplete data on locations of KMOs in knowledge-and data-driven MPM (Coolbaugh et al., 2007;Lusty et al., 2012;Ford et al., 2016).To reduce bias in the assignment of weights to continuous-value spatial evidence, various re...

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Section: General Characteristics Of Orogenic Gold Depositsmentioning

confidence: 99%

mentioning

confidence: 97%

Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran

Almasi

Yousefi

Carranza

2017

Ore Geology Reviews

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“…The integration of AHP and TOPSIS in a fuzzy environment has not been studied in the field of mineral prospectivity modeling in the initial exploration of the natural bitumen deposits. However, there have been several studies on implementing these methods and their combination for exploration of ore deposits such as copper, gold, iron, etc., (Pazand and Hezarkhani, 2015;Asadi et al, 2016;Feizi et al, 2017a;2017b). This paper presents a new technique of combining the proposed methods using MPM in a fuzzy environment and examines this method in this case study.…”

Section: Final Sorting Using Mcdm For Ftopsis Rankingmentioning

confidence: 99%

“…The typical of AHP is to make a set of criteria and weight methodically based on their importance. In this procedure, parameters measured according to the aforementioned produced criteria and a final score is attributed based on the weight (Abedi and Norouzi, 2016;Asadi et al, 2016;Feizi et al, 2017a).…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) is another method that is usually used as a comparing approach in parameters (Dagdeviren et al, 2009;Awasthi et al, 2010;Abedi and Norouzi, 2016;Asadi et al, 2016;Feizi et al, 2017b). To achieve the advantages of these two methods, this paper applies a combination of both AHP-FTOPSIS with a focus on mapping the high-potential zones of natural bitumen mineralization in the Zagros fold-thrust belt, which is a few known for natural bitumen deposit occurrences.…”

Section: Introductionmentioning

confidence: 99%

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New Approach in Application of the AHP–Fuzzy TOPSIS Method in Mineral Potential Mapping of the Natural Bitumen (Gilsonite): A Case Study from the Gilan-e-Gharb Block, the Kermanshah, West of Iran

Rahimi¹,

Shekarian²,

Farahani³

et al. 2020

American Journal of Engineering and Applied Sciences

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The Gilan-e-Gharb block is known as a prospective area for hydrocarbon resources in the form of oil, gas in deep potential and natural bitumen (Gilsonite) on the surface. Natural bitumen is not clearly detectable by geochemical or geophysical methods. Hereupon, identifying high potential areas for further exploration, attempted with the help of AHP-Fuzzy and TOPSIS methods. The comprehensive database of geological and geostructural records, satellite imagery analysis by remote sensing and mine indexes counted as the inputs for this method. First, the lithological unit as the main mineralization hosts determined with respect to the dominant geological structures as a factor of controlling natural bitumen placement (fold, fracture and faults) in the Gilan-e-Gharb block. The Gachsaran, Asmari, Pabdeh and Gurpi Formations identified as the most important lithological units for mineralization. Placement and distribution of natural bitumen mineralization in the form of mine indexes are added to the geology database. Finally, we assigned appropriate weights to applied information layers using Analytical Hierarchy Processing (AHP) based on knowledgeable information and field studies to synthesize exploratory data. Then, we used the FTOPSIS method to define the Positive Ideal Solution (FPIS, A+) that allows maximizing the beneficial characteristics and minimizing the impediment characteristics and the Negative Ideal Solution (FNIS, A-) that minimizes the beneficial characteristics and maximizes the impediment characteristics. This method, as a new approach in the exploration of minerals with a shortage of data, is applicable to other mineral deposits.

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Mineral Potential Mapping Via TOPSIS with Hybrid AHP–Shannon Entropy Weighting of Evidence: A Case Study for Porphyry-Cu, Farmahin Area, Markazi Province, Iran

2017

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An AHP–TOPSIS Predictive Model for District-Scale Mapping of Porphyry Cu–Au Potential: A Case Study from Salafchegan Area (Central Iran)

Cited by 47 publications

References 59 publications

Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran

Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran

New Approach in Application of the AHP–Fuzzy TOPSIS Method in Mineral Potential Mapping of the Natural Bitumen (Gilsonite): A Case Study from the Gilan-e-Gharb Block, the Kermanshah, West of Iran

Mineral Potential Mapping Via TOPSIS with Hybrid AHP–Shannon Entropy Weighting of Evidence: A Case Study for Porphyry-Cu, Farmahin Area, Markazi Province, Iran

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