Geometric average of spatial evidence data layers: A GIS-based multi-criteria decision-making approach to mineral prospectivity mapping

Yousefi, Mahyar; Carranza, Emmanuel John M.

doi:10.1016/j.cageo.2015.07.006

Cited by 113 publications

(22 citation statements)

References 59 publications

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“…(ii) geometric average function (Yousefi and Carranza, 2015c). Either of these two functions can be used to combine fuzzified evidence maps regardless of how weights of evidence values were given.…”

Section: Integration Of Weighted Evidence Mapsmentioning

confidence: 99%

“…For this, regarding to the weighting methods, several mathematical functions can be used to combine evidence layers (Bonham-Carter, 1994;Carranza, 2008). In this paper, we used two combination functions, namely (i) fuzzy gamma operator (An et al, 1991;Bonham-Carter, 1994) and(ii) geometric average function (Yousefi and Carranza, 2015c). Either of these two functions can be used to combine fuzzified evidence maps regardless of how weights of evidence values were given.…”

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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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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: Integration Of Weighted Evidence Mapsmentioning

confidence: 99%

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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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“…The parameters of equation () can be generalized for MPM by assuming v i = S i . Thus, the geometric average function of porphyry Cu mineralization, G ACu‐porphyry , is written as

\begin{matrix} true \\ left G_{A_{Cu - porphyry}} ((), S_{M B}, S_{M L}, S_{K S}, S_{A A}, S_{P h A}, S_{P A}, S_{I O}, S_{G L}) \\ left = {false(\prod_{i = 1}^{8} S_{i} false)}^{1 / 8} = \sqrt[8]{S_{M B} S_{M L} S_{K A} S_{A A} S_{P h A} S_{P A} S_{I O} S_{G L}}, \end{matrix}

where S i is a corresponding i th indicator value in the unit cell that geometric average is computed and S MB , S ML , S KA , S AA , S PhA , S PA , S IO and S GL are the transformed indicator scores of related weighted evidential layer calculated using the logistic function (Yousefi and Carranza, 2015c and 2016). After computing the values of G A for all pixels in the study area, they were mapped to produce a model of geometric average prospectivity of the Cu porphyry mineralization.…”

Section: Integration Of Indicator Layersmentioning

confidence: 99%

“…The weights of geological features are multiplied in this applied method, and the structure of the spatial data is not altered. The geometric average method is a multi‐criteria decision‐making method based on geographic information system, which is the statistically accurate average when calculating a single average from several heterogeneous sources (here various geo exploration indicator maps) with geometrical support (Yousefi and Carranza, 2015c).…”

Section: Introductionmentioning

confidence: 99%

Integration of airborne geophysics and satellite imagery data for exploration targeting in porphyry Cu systems: Chahargonbad district, Iran

Riahi

Bahroudi

Abedi

et al. 2021

Geophysical Prospecting

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This study illustrates the application of a geometric average integration of aeromagnetic, radiometric and satellite imagery data over a region prone to Cu‐bearing mineralization at Chahargonbad area in Kerman province of Iran. Processing aeromagnetic, radiometric and Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite data can provide exploratory insights about favourable zones in association with porphyry‐type ore occurrences, which can be synthesized through a combination of knowledge‐ and data‐driven approaches as a geometric average and be represented in a mineral prospectivity map. The existence of known deposits in a prospect region can facilitate the investigation of significant exploratory footprints extracted from airborne data by calculating each indicator layer's weight by plotting a prediction–area curve accompanied by a concentration–area fractal curve. Among various indicators, the most important ones are determined based on derived weights from the prediction–area plots to be synthesized in a single Cu favorability map. To fulfil this aim, indicator layers from airborne geophysics (magmatic bodies, magnetic lineaments and potassium radiometry) and remote‐sensing data (alterations such as argillic, phyllic, propylitic and iron oxide along with geological lineaments) were prepared and evaluated using the known porphyry Cu mineralization by the simultaneous plot of the concentration–area fractal model and the prediction–area curve to attain the ore prediction rate and the relevant occupied area of each map for weight assignment of indicators. The geometric average prospectivity model was applied to synthesize the leading indicators, and the result was compared with a multi‐class index overlay map. This study's significance lies in improvement of the performance of the mineral prospectivity/potential mapping after running a geometric average by a higher ore prediction rate of 79%, which has occupied 21% of the area as potential zones for further mining investigations.

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“…One approach to assist in this is the development of semiautomated systems which seek to aggregate and refine data into a reduced model. As an example, a variety of techniques can be applied in order to generate models of mineral resource potential (Knox-Robinson, 2000, Porwal et al , 2003, Rodriguez-Galiano et al , 2015, Yousefi and Carranza, 2015b.…”

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A role for data richness mapping in exploration decision making

Aitken

Occhipinti

Lindsay

et al. 2018

Ore Geology Reviews

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Exploration programs involve long, multi-branched decision pathways with many potential outcomes from a single starting point. Critical decisions along this pathway include the choice of the area(s) in which to explore, and the choice to acquire new data sets to assist in exploration. Included in this is a factoring of risks, including the technical risks associated with imperfect data and the value relative to cost of collecting more data. In our experience it is not common practice to explicitly assess and quantify the impact of data on exploration decisions. A better understanding of the opportunity provided by data has the potential to improve the quality and/or ease of decision making. In this study we explore the potential role of data richness mapping as part of a decision making workflow. We outline a method to quantitatively assess data richness following a four level hierarchy, namely presence of data (L1), quality of data (L2) and attributes of data (L3). Measures of these are combined across several data sets to give an overall estimate of data richness (L4). Using this data richness map, along with past exploration records and a prospectivity model, we conduct an illustrative case study for gold exploration in the Bryah Sub-basin (Yerrida Basin) region of Western Australia. The first application is to produce a simple data based classification of regions with different technical risk characteristics. The second application is to combine this with a prospectivity model to better understand exploration potential. The third application uses data richness mapping to understand which data sets are most likely to have an impact on regional exploration. In each case we show how an understanding of data richness can help support exploration decision making. Introduction: Modern exploration often demands the integration of concepts that operate across a variety of scales

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Geometric average of spatial evidence data layers: A GIS-based multi-criteria decision-making approach to mineral prospectivity mapping

Cited by 113 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

Integration of airborne geophysics and satellite imagery data for exploration targeting in porphyry Cu systems: Chahargonbad district, Iran

A role for data richness mapping in exploration decision making

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