Natural Resources Research

, Volume 24, Issue 3, pp 275–290 | Cite as

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

An Example from the Fore-Sudetic Region
  • Pablo Mejía-Herrera
  • Jean-Jacques Royer
  • Guillaume Caumon
  • Alain Cheilletz


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.


Structural restoration Fault system Fault activity Logistic regression Predictive modeling 



This works has been performed in the frame of the Gocad research project. We thank the industry and academic members of the Gocad Research Consortium ( for supporting this research. We thank Paradigm for the Gocad software. We appreciate the collaboration of Clementine Fourrier (Université de Lorraine). We thank Piotr Krzemiński (Mozów Copper SP z o.o.) for his comments and remarks which helped improve this paper. Finally, our special thanks go to Laurent Ailleres (Monash University) and Tobias Bauer (Luleå University of Technology) for very helpful observations and discussions. Part of this research received funding from the European Union’s Seventh Framework Program under grant agreement \(\hbox {n}^{\circ } 228559\) (ProMine Project), and was performed in the framework of the of Investissements d’avenir Labex RESSOURCES21 (ANR-10-LABX-21).


  1. Agterberg, F., Bonham-Carter, G., Cheng, Q., & Wright, D. (1993). Weights of evidence modeling and weighted logistic regression for mineral potential mapping. In Proceedings of the Computers in Geology-25 Years of Progress (pp. 13–32). Oxford: Oxford University Press Inc.Google Scholar
  2. Bechtel, A., Elliott, W. C., Wampler, J. M., & Oszczepalski, S. (1999). Clay mineralogy, crystallinity, and K-Ar ages of illites within the Polish Zechstein basin; implications for the age of Kupferschiefer mineralization. Economic Geology, 94, 261–272.CrossRefGoogle Scholar
  3. Bechtel, A., Gratzer, R., Püttmann, W., & Oszczepalski, S. (2001). Variable alteration of organic matter in relation to metal zoning at the Rote Fäule front (Lubin–Sieroszowice mining district, SW Poland). Organic Geochemistry, 32(3), 377–395.CrossRefGoogle Scholar
  4. Blundell, D. J., Karnkowski, P. H., Alderton, D. H. M., Oszczepalski, S., & Kucha, H. (2003). Copper mineralization of the Polish Kupferschiefer: A proposed basement fault-fracture system of fluid flow. Economic Geology, 98(7), 1487–1495.CrossRefGoogle Scholar
  5. Bond, C., Gibbs, A., Shipton, Z., & Jones, S. (2007). What do you think this is? “Conceptual uncertainty” in geoscience interpretation. GSA Today, 17(11), 4–10.CrossRefGoogle Scholar
  6. Botella, A., Lévy, B., & Caumon, G. (2013). Indirect hex-dominant mesh generation using a matching tetrahedra method. In Proceedings of the 33rd Gocad Meeting. Nancy: ASGA.Google Scholar
  7. Brown, A. C. (2011). Adding geochemical rigor to the general basin-scale genetic model for sediment-hosted stratiform copper mineralization. In Proceedings of the 11th SGA Biennial Meeting. Antofagasta: SGA.Google Scholar
  8. Caumon, G. (2010). Towards stochastic time-varying geological modeling. Mathematical Geosciences, 42(5), 555–569.CrossRefGoogle Scholar
  9. Caumon, G., Collon-Drouaillet, P., Carlier, Le, de Veslud, C., Viseur, S., & Sausse, J. (2009). Surface-based 3D modeling of geological structures. Mathematical Geosciences, 41, 927–945.CrossRefGoogle Scholar
  10. Chamberlin, R. T. (1910). The Appalachian folds of central Pennsylvania. The Journal of Geology, 18(3), 228–251.CrossRefGoogle Scholar
  11. Cheng, Q. (2008). Non-linear theory and power-law models for information integration and mineral resources quantitative assessments. In Progress in Geomathematics, (pp. 195–225). Berlin: Springer.Google Scholar
  12. Cherpeau, N., Caumon, G., & Lévy, B. (2010). Stochastic simulations of fault networks in 3D structural modeling. Comptes Rendus Geoscience, 342(9), 687–694.CrossRefGoogle Scholar
  13. Dadlez, R., Marek, S., & Pokorski, J. (2000). Geological map of Poland without Cainozoic deposits. Warszawa: Państwowy Instytut Geologiczny.Google Scholar
  14. Dahlstrom, C. (1969). Balanced cross sections. Canadian Journal of Earth Sciences, 6(4), 743–757.CrossRefGoogle Scholar
  15. de Araújo, C. C., & Macedo, A. B. (2002). Multicriteria geologic data analysis for mineral favorability mapping: Application to a metal sulphide mineralized area, Ribeira valley metallogenic province Brazil. Natural Resources Research, 11, 29–43.CrossRefGoogle Scholar
  16. de Quadros, T., Koppe, J., Strieder, A., & Costa, J. (2006). Mineral-potential mapping: A comparison of weights-of-evidence and fuzzy methods. Natural Resources Research, 15(1), 49–65.CrossRefGoogle Scholar
  17. De Santi, M., Campos, J., & Martha, L. (2002). A finite element approach for geological section reconstruction. In Proceedings of the 22th Gocad Meeting. Nancy: ASGAGoogle Scholar
  18. Duffy, O. B., Gawthorpe, R. L., Docherty, M., & Brocklehurst, S. H. (2013). Mobile evaporite controls on the structural style and evolution of rift basins: Danish Central Graben North Sea. Basin Research, 25(3), 310–330.CrossRefGoogle Scholar
  19. Durand-Riard, P., Caumon, G., & Muron, P. (2010). Balanced restoration of geological volumes with relaxed meshing constraints. Computers & Geosciences, 36, 441–452.CrossRefGoogle Scholar
  20. Durand-Riard, P., Guzofski, C. A., Caumon, G., & Titeux, M. O. (2013). Handling natural complexity in 3D geomechanical restoration, with application to the recent evolution of the outer fold-and-thrust belt, deepwater Niger Delta. AAPG Bulletin, 97(1), 87–102.CrossRefGoogle Scholar
  21. Finch, E., Hardy, S., & Gawthorpe, R. (2004). Discrete-element modelling of extensional fault-propagation folding above rigid basement fault blocks. Basin Research, 16(4), 467–488.CrossRefGoogle Scholar
  22. Forster, C., & Smith, L. (1989). The influence of groundwater flow on thermal regimes in mountainous terrain: A model study. Journal of Geophysical Research: Solid Earth (1978–2012), 94(B7), 9439–9451.CrossRefGoogle Scholar
  23. Gao, D. (2013). Integrating 3D seismic curvature and curvature gradient attributes for fracture characterization: Methodologies and interpretational implications. Geophysics, 78(2), O21–O31.CrossRefGoogle Scholar
  24. Gouin, J. (2008). Mode de genése et valorisation des minerais de type black shales: cas du Kupferschiefer (Pologne) et des schistes noirs de Talvivaara (Finlande). Ph.D. thesis report., Université d’Orléans, Orléans.Google Scholar
  25. Gratier, J. P., & Guillier, B. (1993). Compatibility constraints on folded and faulted strata and calculation of total displacement using computational restoration (UNFOLD program). Journal of structural geology, 15(3), 391–402.CrossRefGoogle Scholar
  26. Groshong, R. (2006). 3-D structural geology. A practical guide to quantitative surface and subsurface map interpretation (2nd ed.). Berlin Heidelberg: Springer-Verlag.Google Scholar
  27. Guzofski, C., Mueller, J., Shaw, J., Muron, P., Medwedeff, D., Bilotti, F., et al. (2009). Insights into the mechanisms of fault-related folding provided by volumetric structural restorations using spatially varying mechanical constraints. AAPG Bulletin, 93, 479–502.CrossRefGoogle Scholar
  28. Hitzman, M. W., Selley, D., & Bull, S. (2010). Formation of sedimentary rock-hosted stratiform copper deposits through Earth history. Economic Geology, 105(3), 627–639.CrossRefGoogle Scholar
  29. Hosmer, D. W, Jr, & Lemeshow, S. (2004). Applied logistic regression. London: John Wiley.Google Scholar
  30. Jarvis, A., Reuter, H., Nelson, A., & Guevara, E. (2008). Hole-filled SRTM for the globe version 4. Retrieved from the CGIAR-SXI SRTM 90m database
  31. Jowett, E. C. (1986). Genesis of Kupferschiefer Cu-Ag deposits by convective flow of Rotliegendes brines during triassic rifting. Economic Geology, 81(8), 1823–1837.CrossRefGoogle Scholar
  32. Jowett, E. C., Pearce, G. W., & Rydzewski, A. (1987). A mid-Triassic paleomagnetic age of the Kupferschiefer mineralization in Poland, based on a revised apparent polar wander path for Europe and Russia. Journal of Geophysical Research, 92(B1), 581–598.CrossRefGoogle Scholar
  33. Kane, K. E., Jackson, C. A. L., & Larsen, E. (2010). Normal fault growth and fault-related folding in a salt-influenced rift basin: South Viking Graben, offshore Norway. Journal of Structural Geology, 32(4), 490–506.CrossRefGoogle Scholar
  34. Karnkowski, P. H. (1999). Origin and evolution of the polish Rotliegend basin. Polish Geological Institute Special Papers, 3, 1–93.Google Scholar
  35. Kerrich, R. (1993). Perspectives on genetic models for lode gold deposits. Mineralium Deposita, 28(6), 362–365.CrossRefGoogle Scholar
  36. KGHM Polska Miedź, S. A. (2012). Report on the mining assets of KGHM Polska Miedź S.A. located within the Legnica–Głogów Copper Belt Area. Report prepared by an internal team of KGHM Polska Miedź S. A. 50, KGHM Polska Miedź S.A., Poland.Google Scholar
  37. Krzywiec, P. (2006). Triassic–Jurassic evolution of the pomeranian segment of the Mid-Polish Trough: Basement tectonics and subsidence patterns. Geological Quarterly, 50, 139–150.Google Scholar
  38. Lecour, M., Cognot, R., Duvinage, I., Thore, P., & Dulac, J. C. (2001). Modelling of stochastic faults and fault networks in a structural uncertainty study. Petroleum Geoscience, 7(S), S31–S42.CrossRefGoogle Scholar
  39. Lefebvre, J. (1989). Les gisements stratiformes en roche sédimentaire d’Europe centrale (Kupferschiefer) et de la Ceinture Cuprifère du Zaïre et de Zambie. Annales de la Societé Géologique de Belgique, 112(1), 121–135.Google Scholar
  40. Lisle, R. (1994). Detection of zones of abnormal strains in structures using Gaussian curvature analysis. AAPG Bulletin, 78(12), 1811–1819.Google Scholar
  41. Maerten, L., & Maerten, F. (2006). Chronologic modeling of faulted and fractured reservoirs using geomechanically based restoration: Technique and industry applications. AAPG Bulletin, 90(8), 1201–1226.CrossRefGoogle Scholar
  42. Mallet, J. L. (2002). Geomodeling. Oxford: Oxford University Press.Google Scholar
  43. Mazur, S., Scheck-Wenderoth, M., & Krzywiec, P. (2005). Different modes of the Late Cretaceous\(-\)Early Tertiary inversion in the North German and Polish basins. International Journal of Earth Sciences, 94(5), 782–798.CrossRefGoogle Scholar
  44. Mazur, S., Aleksandrowski, P., Turniak, K., Krzemiński, L., Mastalerz, K., Górecka-Nowak, A., et al. (2010). Uplift and late orogenic deformation of the Central European Variscan belt as revealed by sediment provenance and structural record in the Carboniferous foreland basin of western Poland. International Journal of Earth Sciences, 99(1), 47–64.CrossRefGoogle Scholar
  45. McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (monographs on statistics and applied probability 37). London: Chapman Hall.Google Scholar
  46. Mejia, P., & Royer, J. J. (2012). Explicit surface restoring-decompacting procedure to estimate the hydraulic fracturing: Case of the Kupferschiefer in the Lubin region, Poland. In Proceedings of the 32nd Gocad Meeting. Nancy: ASGA.Google Scholar
  47. Michalik, M. (1997). Mineral deposits, research and exploration. Chlorine containing illites, copper chlorides and other chloride bearing minerals in the fore-sudetic copper deposit (Poland) (pp. 543–546). Rotterdam: Balkema.Google Scholar
  48. Moretti, I. (2008). Working in complex areas: New restoration workflow based on quality control, 2D and 3D restorations. Marine and Petroleum Geology, 25(3), 205–218.CrossRefGoogle Scholar
  49. Moretti, I., Lepage, F., & Guiton, M. (2006). KINE3D: a new 3D restoration method based on a mixed approach linking geometry and geomechanics. Oil & Gas Science and Technology - Revue d’IFP Energies nouvelles, 61(2), 277–289.CrossRefGoogle Scholar
  50. Moretti, I., Delos, V., Letouzey, J., Otero, A., & Calvo, J. C. (2007). The use of surface restoration in foothills exploration: Theory and application to the sub-Andean zone of Bolivia. In O. Lacombe, F. Roure, J. Lavé, & J. Vergés (Eds.), Thrust belts and foreland basins, frontiers in earth sciences (pp. 149–162). Berlin Heidelberg: Springer.CrossRefGoogle Scholar
  51. Muchez, P., Heijlen, W., Banks, D., Blundell, D., Boni, M., & Grandia, F. (2005). 7: Extensional tectonics and the timing and formation of basin-hosted deposits in Europe. Ore Geology Reviews, 27(1), 241–267.CrossRefGoogle Scholar
  52. Narkiewicz, M., Resak, M., Littke, R., & Marynowski, L. (2010). New constraints on the middle palaeozoic to cenozoic burial and thermal history of the holy cross mts. Central Poland: Results from numerical modelling. Geologica Acta, 8, 189–205.Google Scholar
  53. Oszczepalski, S. (1999). Origin of the Kupferschiefer polymetallic mineralization in Poland. Mineralium Deposita, 34, 599–613.CrossRefGoogle Scholar
  54. Oszczepalski, S., & Speczik, S. (2011). Prospectivity analysis of the polish Kupferschiefer: New insight. In Proceedings of the 11th SGA Biennial Meeting. Antofagasta: SGA.Google Scholar
  55. Oszczepalski, S., Rydzewski, A., & Geologiczny, I. (1997). Metallogenic atlas of Zechstein copper-bearing series in Poland. Wydawnictwo Kartograficzne Polskiej Agencji Ekologicznej.Google Scholar
  56. PARADIGM. (2012). Training guide: modeling reservoir architecture. SKUA—-Paradigm. PARADIGM.Google Scholar
  57. Pawlak, W., Aniol-Kwiatkowska, J., Pawlak, J., Nowak-Ferdhus, E., Migoń, P., & Malicka, A. et al. (2008). Atlas Ślaska Dolnego i Opolskiego. Uniwersytet Wrocławski. Pracownia Atlasu Dolnego Ślaska.Google Scholar
  58. Pieczonka, J., Piestrzyński, A., Mucha, J., Głuszek, A., Kotarba, M., & Wiecław, D. (2008). The red-bed-type precious metal deposit in the Sieroszowice–Polkowice copper mining district, SW Poland. Annales Societatis Geologorum Poloniae, 78, 151–280.Google Scholar
  59. Piestrzyński, A., Pieczonka, J., & Głuszek, A. (2002). Redbed-type gold mineralisation, Kupferschiefer, south-west Poland. Mineralium Deposita, 37(5), 512–528.CrossRefGoogle Scholar
  60. Rentzsch, J., & Franzke, H. (1997). Regional tectonic control of the Kupferschiefer mineralization in Central Europe. Zeitschrift für Geologische Wissenschaften, 25, 141–150.Google Scholar
  61. Rentzsch, J., Franzke, H., & Friedrich, G. (1997). Die laterale Verbreitung der Erzmineralassoziationen im deutschen Kupferschiefer. Zeitschrift für Geologische Wissenschaften, 25, 121–140.Google Scholar
  62. Resak, M., Narkiewicz, M., & Littke, R. (2008). New basin modelling results from the Polish part of the Central European Basin system: Implications for the late cretaceous—early paleogene structural inversion. International Journal of Earth Sciences, 97(5), 955–972.CrossRefGoogle Scholar
  63. Roberts, A. (2001). Curvature attributes and their application to 3D interpreted horizons. First Break, 19(2), 85–100.CrossRefGoogle Scholar
  64. Rouby, D. (1994). Restauration en carte des domaines faillés en extension. Méthode et applications. PhD thesis, Université Rennes 1.Google Scholar
  65. Schaeben, H. (2012). Comparison of mathematical methods of potential modeling. Mathematical Geosciences, 44, 101–129.CrossRefGoogle Scholar
  66. Schaeben, H. (2013). Bits of mathematics of potential modelling. In Proceedings of the 12th Biennial SGA Meeting on Mineral Deposits Research for a High-Tech World, (vol. 2, pp. 489–491). Uppsala: SGA.Google Scholar
  67. Schaeben, H. (2014). Potential modeling: Conditional independence matters. GEM—International Journal on Geomathematics, 5(1), 99–116.CrossRefGoogle Scholar
  68. Schaeben, H., & Schmidt, S. (2013). Theoretical and practical comparison of weights-of-evidence and logistic regression models based on the notion of Markov random fields. In Proceedings of the 33rd Gocad Meeting. Nancy: ASGA.Google Scholar
  69. Scheck-Wenderoth, M., & Lamarche, J. (2005). Crustal memory and basin evolution in the Central European Basin system: New insights from a 3D structural model. Tectonophysics, 397(1–2), 143–165.CrossRefGoogle Scholar
  70. Schmidt Mumm, A., & Wolfgramm, M. (2004). Fluid systems and mineralization in the north German and Polish basins. Geofluids, 4(4), 315–328.CrossRefGoogle Scholar
  71. Sclater, J. G., & Christie, P. A. F. (1980). Continental stretching: An explanation of the post-mid-cretaceous subsidence of the Central North Sea basin. Journal of Geophysical Research, 85(B7), 3711–3739.CrossRefGoogle Scholar
  72. Scutari, M. (2010). Learning Bayesian networks with the bnlearn R package. Journal of Statistical Software, 35(3), 1–22.Google Scholar
  73. Speczik, S. (1995). The Kupferschiefer mineralization of Central Europe: New aspects and major areas of future research. Ore Geology Reviews, 9(5), 411–426.CrossRefGoogle Scholar
  74. Speczik, S., Oszczepalski, S., Karwasiecka, M., & Nowak, G. (2007). Kupferschiefer: A hunt for new reserves. In Proceedings of the 9th Biennial SGA Meeting on Digging Deeper (pp. 237–240). Dublin: Irish Association for Economic Geology.Google Scholar
  75. Stephenson, R. A., Narkiewicz, M., van Dadlez, R., Wees, J. D., & Andriessen, P. (2003). Tectonic subsidence modelling of the Polish basin in the light of new data on crustal structure and magnitude of inversion. Sedimentary Geology, 156, 59–70.CrossRefGoogle Scholar
  76. Symons, D., Kawasaki, K., Walther, S., & Borg, G. (2011). Paleomagnetism of the Cu-Zn-Pb-bearing Kupferschiefer black shale (Upper Permian) at Sangerhausen Germany. Mineralium Deposita, 46(2), 137–152.CrossRefGoogle Scholar
  77. Titeux, M. O. (2009). Restauration et incertitudes structurales: Changement d’échelles des propriétés mécaniques et gestion de la tectonique salifïère. PhD thesis, Institut National Polytechnique de Lorraine.Google Scholar
  78. Vaughan, D. J., Sweeney, M. A., Friedrich, G., Diedel, R., & Haranczyk, C. (1989). The Kupferschiefer: An overview with an appraisal of the different types of mineralization. Economic Geology, 84(5), 1003–1027.CrossRefGoogle Scholar
  79. Verrall, P. (1981). Structural interpretation with application to North Sea problems. Course note no. 3. Joint Association for Petroleum Exploration courses (UK).Google Scholar
  80. Vidal-Royo, O., Cardozo, N., Muoz, J. A., Hardy, S., & Maerten, L. (2012). Multiple mechanisms driving detachment folding as deduced from 3D reconstruction and geomechanical restoration: the Pico del Águila anticline (External Sierras, southern Pyrenees). Basin Research, 24(3), 295–313.CrossRefGoogle Scholar
  81. Wagner, T., Okrusch, M., Weyer, S., Lorenz, J., Lahaye, Y., Taubald, H., et al. (2010). The role of the Kupferschiefer in the formation of hydrothermal base metal mineralization in the Spessart ore district, Germany: insight from detailed sulfur isotope studies. Mineralium Deposita, 45(3), 217–239.CrossRefGoogle Scholar
  82. Wedepohl, K., & Rentzsch, J. (2006). The composition of brines in the early diagenetic mineralization of the Permian Kupferschiefer in Germany. Contributions to Mineralogy and Petrology, 152(3), 323–333.CrossRefGoogle Scholar
  83. Wellmann, J. F., Horowitz, F. G., Schill, E., & Regenauer-Lieb, K. (2010). Towards incorporating uncertainty of structural data in 3D geological inversion. Tectonophysics, 490(34), 141–151.CrossRefGoogle Scholar
  84. Withjack, M. O., & Callaway, S. (2000). Active normal faulting beneath a salt layer: an experimental study of deformation patterns in the cover sequence. AAPG Bulletin, 84, 627–651.Google Scholar
  85. Withjack, M. O., Olson, J., & Peterson, E. (1990). Experimental models of extensional forced folds (1). AAPG Bulletin, 74, 1038–1054.Google Scholar
  86. Wodzicki, A., & Piestrzyński, A. (1994). An ore genetic model for the Lubin–Sieroszowice mining district Poland. Mineralium Deposita, 29(1), 30–43.CrossRefGoogle Scholar
  87. Ziegler, P. (1982). Geological atlas of Western and Central Europe. Singapore: Shell International Petroleum Maatschappij B.V.Google Scholar
  88. Zuo, R., & Carranza, E. J. M. (2011). Support vector machine: A tool for mapping mineral prospectivity. Computers & Geosciences, 37(12), 1967–1975.CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geosciences 2014

Authors and Affiliations

  • Pablo Mejía-Herrera
    • 1
  • Jean-Jacques Royer
    • 1
  • Guillaume Caumon
    • 1
  • Alain Cheilletz
    • 1
  1. 1.Université de Lorraine-ENSG, CNRS-CREGU, Géoressources, UMR 7359Vandoeuvre-lès-Nancy CedexFrance

Personalised recommendations