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

Abstract

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...

Knowledge-driven mineral prospectivity modelling in areas with glacial overburden: porphyry Cu exploration in Quesnellia, British Columbia, Canada

“…Using these features, new proximity maps were generated to represent the distance to heat sources and fluid pathways while the orogenic mineral system was active. Almasi et al (2017) used a similar approach by employing Au assays of stream sediments as well as airborne magnetic and radiometric surveys to interpret various components of an orogenic mineral system in the Saqez-Shardaht Goldfield, Iran. Knowledge-based criteria extracted from these datasets include density and proximity to faults, contacts, and prospective lithologies.…”

Constraining of strain ellipsoid shape from sectional data in the Au bearing shear zone west of Iran

“…These shear zones in the earth crust provide a high permeability fractured zone, and facilitated the circulation of hydrothermal fluid that mostly accompanied with the mineralization. High concentration of Au in the ductile shear zone directly related to the deformation intensity in the study area (Aliyari et al, 2009;Alizadeh-Dinabad et al, 2013;Almasi et al, 2015;Almasi et al, 2017). Strain ellipsoid geometry provides important data about deformation history in the shear zone (Mookerjee and Nickleach, 2011).…”

“…The dynamo-thermal regional deformation followed by severe ductile deformation. This phase is the most important event in the study area and associated with the development of the high strain, Au-bearing ductile shear zone (Aliyari et al, 2009;Almasi et al, 2017). Event D2 is related to the postcollisional contraction in the Zagros suture zone.…”

“…Mesozoic rocks in the study area deposited after a long interval with older units; this contains a complex of volcanic-sedimentary. These units have widespread exposure, high thickness and metamorphosed to greenschist facies (Aliyari et al, 2009;Alizadeh-Dinabad et al, 2013;Almasi et al, 2015;Almasi et al, 2017) (Fig. 1b).…”

“…The circulations of the magmatic fluids convert this zone to be a host for mineralization and ore deposit. The orogenic gold deposit is the typical manifestation in the Zagros collisional Orogenic belt (Aliyari et al, 2009;Alizadeh-Dinabad et al, 2013;Almasi et al, 2015;Almasi et al, 2017). The crustal shear zone has…”