Application of staged factor analysis and logistic function to create a fuzzy stream sediment geochemical evidence layer for mineral prospectivity mapping

Abstract

Stream sediment geochemical data are usually subjected to methods of multivariate analysis (e.g. factor analysis) in order to extract an anomalous geochemical signature (factor) of the mineral deposit-type sought. A map of anomalous geochemical signature can be used as evidence, in combination with other layers of evidence, for mineral prospectivity mapping (MPM). Because factor analysis may yield more than one factor in a stream sediment dataset, it raises the challenge of how to recognize the factor that best indicates presence of the mineral deposit-type sought. In addition, MPM is faced with the challenge of how to assign weights to classes in a geochemical evidence map. Accordingly, a new approach is discussed in this paper for the extraction of significant anomalous geochemical signature of the mineral deposit-type sought and for assigning weights to anomaly classes in a geochemical evidence map. In this approach, we used a staged factor analysis and then applied a logistic function to transform factor scores representing an anomalous geochemical signature in order to derive a map of geochemical mineralisation prospectivity indices (GMPI) as a spatial evidence layer for MPM based on the theory of fuzzy sets and fuzzy logic. The GMPI is a fuzzy weight in the [0,1] range. We demonstrate the application of the GMPI for mapping prospectivity for Mississippi valley-type fluorite deposits in the Mazandaran province, north of Iran, which is a greenfield area.

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

“…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.…”

“…The geophysical data were used to generate two evidence layers, namely: (a) structural evidence layer and (b) evidence layer of hydrothermal activities. Each of the evidence maps was generated by using a suitable logistic function (Tsoukalas and Uhrig, 1997; Nykänen et al, 2008a,b;Yousefi et al, 2012Yousefi et al, , 2013Yousefi et al, , 2014 and Yousefi and Carranza, 2015a,b) …”

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

“…The compromise legible cell size can be determined according to traditional cartographic concept (Hengl, 2006, Zuo, 2011, Yousefi et al, 2014, as follows:…”

“…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).…”

“…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.…”