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

Abstract : This 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.
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Article dans une revue
Natural Resources Research, Springer Verlag, 2015, 24 (3), 〈10.1007/s11053-014-9247-7〉
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https://hal.univ-lorraine.fr/hal-01304796
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Soumis le : mercredi 20 avril 2016 - 12:54:42
Dernière modification le : mardi 29 mai 2018 - 12:51:03

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Pablo Mejía-Herrera, Jean-Jacques Royer, Guillaume Caumon, Alain Cheilletz. Curvature Attribute from Surface-Restoration as Predictor Variable in Kupferschiefer Copper Potentials. Natural Resources Research, Springer Verlag, 2015, 24 (3), 〈10.1007/s11053-014-9247-7〉. 〈hal-01304796〉

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