Curvature Attribute from Surface-Restoration as Predictor Variable in Kupferschiefer Copper Potentials

Abstract

International audienceThis work explains a procedure to predict Cu potentials in the ore-Kupferschiefer using structural surface-restoration and logistic regression (LR) analysis. The predictor in the assessments are established from the restored horizon that contains the ore-series. Applying flexural-slip to unfold/unfault the 3D model of the Fore-Sudetic Monocline, we obtained curvature for each restored time. We found that curvature represents one of the main structural features related to the Cu mineralization. Maximum curvature corresponds to high internal deformation in the restored layers, evidencing faulting and damaged areas in the 3D model. Thus, curvature may highlight fault systems that drove fluid circulation from the basement and host the early mineralization stages. In the Cu potential modeling, curvature, distance to the Fore-Sudetic Block and depth of restored Zechstein at Cretaceous time are used as predictors and proven Cu-potential areas as targets. Then, we applied LR analysis establishing the separating function between mineralized and non-mineralized locations. The LR models show positive correspondence between predicted probabilities of Cu-potentials and curvature estimated on the surface depicting the mineralized layer. Nevertheless, predicted probabilities are particularly higher using curvatures obtained from Late Paleozoic and Late Triassic restorations

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).…”

Mineral potential mapping with a restricted Boltzmann machine

“…The logistic regression methods (Agterberg, 1974(Agterberg, , 1989Agterberg and Bonham-Carter, 1999;Carranza and Hale, 2002b;Carranza et al, 2008a,b;Chen et al, 2011;Mejía-Herrera et al, 2014;Nykänen et al, 2008) seek to model the deposit-bearing probability as a function of numeric and/or categorical evidence map patterns. The regression coefficients can be interpreted as measures of relative importance of evidence map patterns.…”

“…Ultimately multiple quantified relationships are combined into a single potential map. Several mineral potential mapping methods belong to data-driven methods, such as weights of evidence or WofE (Agterberg, 1990(Agterberg, , 1992Agterberg et al, 1990;Bonham-Carter et al, 1988Brown et al, 2000;Carranza, 2004;Carranza and Hale, 2002a;Nykänen et al, 2008;Tangestani and Moore, 2001;Xu et al, 1992), extended weights of evidence (Mansour et al, 2009;Pan, 1996;Porwal et al, 2001), fuzzy weights of evidence (Cheng et al, 2007;Porwal et al, 2006a), logistic regression (Agterberg, 1974(Agterberg, , 1989Agterberg and Bonham-Carter, 1999;Carranza and Hale, 2001b;Carranza et al, 2008a;Chen et al, 2011;Mejía-Herrera et al, 2014;Nykänen et al, 2008), feed-forward neural networks (Brown et al, 2000;Skabar, 2003), multilayer perceptrons (Skabar, 2007), Bayesian networks (Porwal et al, 2006b), radial basis functional link net (Leite et al, 2009a;Nykänen, 2008;Porwal et al, 2003), probabilistic neural networks (Leite et al, 2009b), certainty factor (Chen, 2003), evidence belief functions Carranza, 2014;Carranza and Hale, 2003;Carranza et al, 2005Carranza et al, , 2008bChen, 2004;Moon, 1989…”

Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling

“…For this, maps of spatially continuous evidential values (e.g., distance to structures, geological complexity, magnetic susceptibility, etc.) are usually firstly discretized into some classes using arbitrary intervals and then weights are assigned to every evidential class based on either expert judgment or locations of known mineral occurrences, or a combination of both, or using functions to calculate weights of classes of discretized evidential values to MPM (e.g., Luo, 1990;Bonham-Carter, 1994;Cheng and Agterberg, 1999;Luo and Dimitrakopoulos, 2003;Carranza, 2008bCarranza, , 2014Porwal et al, 2003aPorwal et al, ,b,c, 2004Porwal et al, , 2006Mejía-Herrera et al, 2014). The aforementioned existing practice of MPM to discretize spatially continuous evidential values is sensitive to the choice of class interval, and the relative importance of every value in a map is evaluated imprecisely because of the approximation involved in classification of continuous evidential values, defining class intervals, and their weights as evidence of mineral prospectivity.…”