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

Yousefi, Mahyar; Kamkar‐Rouhani, Abolghasem; Carranza, Emmanuel John M.

doi:10.1016/j.gexplo.2012.02.002

Cited by 165 publications

(69 citation statements)

References 63 publications

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“…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 researchers (e.g., Nykänen et al, 2008a;Yousefi et al, 2012Yousefi et al, , 2013Yousefi et al, , 2014Yousefi and Carranza, 2015a, b, c, 2016a;Yousefi and Nykänen, 2016) have applied logistic functions to assign fuzzy weights to indicator features without using locations of KMOs and without discretization of evidential values into some arbitrary classes based on expert opinion. While this practice overcomes imprecise evaluation of the relative importance of evidential values, as portrayed by simplification and discretization of continuous-value evidential data into some arbitrary classes, it is also subjective because using a single logistic function for weighting spatial evidence values does not consider the fact that diverse deposit-types or mineral systems form by diverse geological processes.…”

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confidence: 99%

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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confidence: 99%

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

Almasi

Yousefi

Carranza

2017

Ore Geology Reviews

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“…Logistic function could be generator of fuzzy membership for spatially continuous weights (Bishop, 2006;Nykänen et al, 2008;Yousefi et al, 2012;2014;Yousefi and Carranza, 2015a, b;Yousefi and Nykänen, 2016). This function has been detected as following equation (Yousefi and Carranza, 2015a):…”

Section: -2-logistic Fuzzy Membership Functionmentioning

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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“…Prospectivity modeling methods have been used in mineral exploration (e.g., Zahiri et al, 2006;Porwal, 2006;Carranza, 2008;Yousefifar et al, 2011;Yousefi et al, 2012Yousefi et al, , 2014, groundwater resource exploration (e.g., Sener et al, 2005;van Beynen et al, 2012;Elez et al, 2013, Nampak et al, 2014 and environmental studies (e.g., Chang et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…For this sequentially we used fuzzy logic modeling approach (e.g., Yousefi et al, 2012;Ford et al, 2013;Yousefi et al, 2014;Beucher et al, 2014) for WPM in regional scale and ground-based geophysical surveys (gravity and electrical resistivity) to select drilling sites in local scales. To evaluate the sequential exploration approach we selected Tepal area, Iran as case study.…”

Section: Introductionmentioning

confidence: 99%

Prospectivity Modeling of Karstic Groundwater Using a Sequential Exploration Approach in Tepal Area, Iran

Sharifi

Arab-Amiri

Kamkar‐Rouhani

et al. 2017

Archives of Mining Sciences

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The purpose of this study is water prospectivity modeling (WPM) for recognizing karstic water-bearing zones by using analyses of geo-exploration data in Kal-Qorno valley, located in Tepal area, north of Iran. For this, a sequential exploration method applied on geo-evidential data to delineate target areas for further exploration. In this regard, two major exploration phases including regional and local scales were performed. In the first phase, indicator geological features, structures and lithological units, were used to model groundwater prospectivity as a regional scale. In this phase, for karstic WPM, fuzzy lithological and structural evidence layers were generated and combined using fuzzy operators. After generating target areas using WPM, in the second phase geophysical surveys including gravimetry and geoelectrical resistivity were carried out on the recognized high potential zones as a local scale exploration. Finally the results of geophysical analyses in the second phase were used to select suitable drilling locations to access and extract karstic groundwater in the study area. wodonośnych pochodzenia krasowego zamodelowano warstwy struktur skalnych dowodzące występowa-nia wód w oparciu o podejście logiki rozmytej. Po wytyczeniu obszarów docelowych, w drugim etapie badań przeprowadzono szczegółowe analizy geofizyczne z wykorzystanie grawimetrii i badań oporności geo-elektrycznej w strefach potencjalnego występowania wód, w aspekcie badania w skali lokalnej. W końcowym etapie, wyniki analiz geofizycznych otrzymane w drugim etapie procedury wykorzystane zostały do wyznaczenia miejsc wykonania odwiertów do uzyskania wód gruntowych pochodzenia krasowego w badanym terenie.Słowa kluczowe: modelowanie występowania wód, badanie sekwencyjne, logika rozmyta, wody gruntowe pochodzenia krasowego

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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

Cited by 165 publications

References 63 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

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

Prospectivity Modeling of Karstic Groundwater Using a Sequential Exploration Approach in Tepal Area, Iran

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