Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping

Yousefi, Mahyar; Carranza, Emmanuel John M.

doi:10.1016/j.cageo.2014.10.014

Cited by 176 publications

(90 citation statements)

References 125 publications

(235 reference statements)

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“…Carranza) Manuscript submitted to Ore Geology Reviews 17 presenting of geological features in exploration datasets (Lisitsin et al, 2013). Fuzziness, probability, similarity, vagueness, randomness, ambiguity, possibility, and imprecision are other types or sources of uncertainty as well (Celikyilmaz and Burhan Türksen, 2009;Yousefi and Carranza, 2015a). In this paper, we modulated the exploration bias resulting from improper selection of logistic function to be used for assigning evidential weights.…”

Section: Discussionmentioning

confidence: 99%

“…As Nykänen et al (2008a), Yousefi et al (2012Yousefi et al ( , 2013Yousefi et al ( , 2014, Yousefi and Carranza (2015a,b,c), and Yousefi and Nykänen (2016) demonstrated, evidential maps with spatially continuous weights can be generated by application of a logistic function without using locations of known mineral occurrences and without discretization of evidential values into some arbitrary classes. Yousefi and Carranza (2015a) …”

Section: Fuzzification Functionsmentioning

confidence: 99%

“…Therefore, understanding mineral system processes of the deposit-type sought in the area facilitates eliciting of exploration features to be used for MPM. In this regard, diversity of relationships between various mineral deposits and their corresponding spatial evidence values results in different conceptual models of mineral prospectivity.Thus, translation of the parameters of a conceptual model, which derived from understanding mineral system of the corresponding deposit-type sought, to weighted evidence layers is a critical undertaking There are various type of uncertainties that adversely affect prospectivity analysis of mineral deposits (e.g., McCuaig et al, 2010;Yousefi and Carranza, 2015a), of which the noteworthy are: (a) uncertainty due to missing evidence (Zuo et al, 2015); (b) uncertainty resulting from imprecise weighting to spatial evidence values (e.g., Nykänen et al 2008a;Yousefi and Nykänen, 2016;Yousefi, 2017); (c) uncertainty in selection of subjectively-defined functions to be used for weighting spatial values (Luo, 1990;Luo and Dimitrakopoulos, 2003); (d) uncertainty due to complexity of geological setting (Van Loon, 2002) and anomaly patterns (Cheng, 2007;Yousefi et al, 2013); (e) uncertainty due to dissimilarities of geological settings (McCuaig et al, 2010;Lisitsin et al, 2013); (f) uncertainties due to incomplete knowledge in the understanding of mineral system processes (McCuaig et al, 2010); (f) uncertainty due to complex relationships between indicator features and mineral deposits (e.g., Yousefi et al, 2013), and (g) uncertainty due to inaccurately and imprecisely Prospectivity analysis of orogenic gold deposits in Saqez-Sardasht Goldfield, Zagros Orogen, Iran (by A. Almasi, M. Yousefi, E. J.M. Carranza) Manuscript submitted to Ore Geology Reviews 17 presenting of geological features in exploration datasets (Lisitsin et al, 2013).…”

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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: Discussionmentioning

confidence: 99%

Section: Fuzzification Functionsmentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…In knowledge-driven techniques, few known mineral deposits are predicted in areas of interest, so expert experience and judgments are required. Analysts apply expert opinions to assess the relative importance of spatial evidence as meaningful decision support [19]. Several mineral potential mapping methods are classified as knowledge-driven techniques, including Boolean logic, index overlay, fuzzy analytical hierarchy process [3], and fuzzy logic [29].…”

Section: Introductionmentioning

confidence: 99%

Mineral prospectivity mapping with weights of evidence and fuzzy logic methods

Zhang

Zhou

2015

IFS

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Abstract. Knowledge-and data-driven approaches are two major methods used to integrate various evidential maps for mineral prospectivity mapping (MPM). Geological maps, geochemical samples and data from known gold deposits were collected in the western Junggar area, Xinjiang Province. The geological and a spatial database for geological and mineral occurrences were constructed for the studied region. A weights-of-evidence model and a fuzzy logic model were employed for MPM, and the results were compared. Results indicate that favorable sedimentary rocks, fault density, fault distance and concentration of Au were the primary factors affecting Au mineralization. Arsenic (AS), Stibium (Sb), fault direction, quartz veins and intrusive rocks were secondary factors affecting Au mineralization. Conditional independence exerted a major influence on the weights-of-evidence model. However, posterior probability would be very high if the conditional independence was disregarded, which impaired results. Combining the quantification results provided by weights-of-evidence and the fuzzy membership values determined by expert knowledge, the mineral prospectivity mapping according to the fuzzy logic method was proved to be valid. For the study area, which had a large number of deposits, data-driven approaches for MPM are generally considered to be appropriate. However, if sufficient data are not collected, the knowledge-driven approaches, for example, the fuzzy logic method used in the present study, usually achieves a better result.

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“…Mineral prospectivity mapping (MPM) is carried out based on integration of evidence layers especially at reconnaissance and prospecting scales in mineral exploration (Carranza, 2009 a,b;Jafarirad, 2009;Porwal & Carranza, 2015;Yousefi & Carranza, 2014, 2015a. Each scale is characterized based on the accuracy of evidence maps, where level of accuracy increases with the acceleration of exploration scale.…”

Section: Introductionmentioning

confidence: 99%

Porphyry Copper Prospectivity Mapping Using Fuzzy and Fractal Modeling in Sonajeel Area, Nw Iran

Yazdi

Rad

Aghazadeh

et al. 2018

Bulletin of the Mineral Research and Exploration

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Main purpose of this research is to present a local scale GIS-based mineral prospectivity model for prospecting Cu porphyry mineralization, and to validate the produced model by field observation, surface sampling and drilling data. Sonajeel area which is the subject of this study is a part of Arasbaran mineralization belt, NW of Iran.Constructing a mathematical exploratory algorithm based on a mineralization type is a complicated and interdisciplinary task. For this purpose, results from processing and interpreting different data sets including geology, geochemistry and remote sensing were considered. A comprehensive exploratory integration model was built up considering the exploration stage and the descriptive porphyry mineralization model suggested by Sillitoe, 2010. In order to prepare inputs for GIS-based exploration model, value assigned grids or evidence layers were produced using fuzzy membership curves and then integrated via gamma fuzzy function. In addition, for defuzzification and prioritizing the mineral prospectivity map, a Concentration-Area (C-A) fractal model was applied on the pixel values of the prospectivity map. Finally, the results were confirmed via field observation, surface sampling and drilling. Borehole logs at the first priority displayed a Cu mineralization zone with an average grade of 0.5%.

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Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping

Cited by 176 publications

References 125 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

Mineral prospectivity mapping with weights of evidence and fuzzy logic methods

Porphyry Copper Prospectivity Mapping Using Fuzzy and Fractal Modeling in Sonajeel Area, Nw Iran

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