Data-driven fuzzy analysis in quantitative mineral resource assessment

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

“…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).…”

“…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).…”

Orogenic Gold Prospectivity Mapping Using Geospatial Data Integration, Region of Saqez, Nw of Iran

“…The spatially referenced geosciences data such as airborne geophysical data, geochemical data, geological and structural data are especially suitable for quantitative analysis using a Geographic Information System (GIS), in order to derive information that is useful in mineral exploration (Jafarirad, 2009;Jafarirad and Busch, 2011;Magalhaes and Souza Filho, 2012;Nykanen et al, 2008). As stated by Luo and Dimitrakopoulos (2003), these quantitative methods are often used to: (1) extract the maximum amount of information from the data; (2) effectively combine data from diverse information sources; (3) rank potential targets (mineral sites); and (4) reduce data processing and evaluation time. In this paper, a reconnaissance scale mineral prospectivity map is presented for orogenic gold mineralization in Saqez area, northern part of Sanandaj-Sirjan metamorphic zone, NW of Iran (Figure 1).…”

Evaluation of the reserve of a granite deposit by fuzzy kriging

“…Nefeslioglu, Gokceoglu and Sonmez 5 (2006) obtain some statistical and fuzzy models for predicting weighted joint density to evaluate block size. Luo and Dimitrakopoulos (2003) and Bardossy, Szabo and Varga (2003) have applied the fuzzy set theory in reserve estimation and mathematically evaluated the spatial continuity of ore bodies by fuzzy sets.…”