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3D, 4D and Predictive Modelling of Major Mineral Belts in Europe pp 9–58Cite as

ProMine Mineral Databases: New Tools to Assess Primary and Secondary Mineral Resources in Europe

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Part of the book series: Mineral Resource Reviews ((MIRERE))

Abstract

A major objective of the ProMine project was to develop a Pan-EU GIS data management and visualization system for natural and man-made mineral endowment and the implementation of a Pan-EU predictive resource assessment, and thus to provide a renewed picture of European metallogeny. To reach this objective, ProMine work package 1 produced pan-European databases of primary and secondary mineral resources, the ProMine Mineral Deposit (MD) and Anthropogenic Concentration (AC) databases. The present version of the MD database contains 12,979 records (mines, deposits, occurrences or showings) and covers 34 European countries. The total number of records of the AC database is 3408. As an exhaustive inventory of mineral wastes in Europe was far beyond the scope of the project, ProMine focused on major anthropogenic concentrations (i.e. mining and ore processing wastes) and on the most interesting in terms of volume/tonnage and content (e.g. possible presence of critical metals). After briefly presenting the databases—their structure, the way they were fed and their content—the present chapter focuses on how they can allow (i) geological approaches such as the spatial and temporal distributions of commodities and/or deposit types (and, in turn, the identification of metallogenic epochs), as well as (ii) statistics calculation on the main commodities and metallogenic types present in Europe and their contribution to the EU mineral budget. In addition, it is shown that the thorough and homogeneous data contained in the MD database also allows calculation of mineral potential and predictive maps at European scale. Given the limited number of parameters—present in an homogeneous way—which can be used when working at continental scale, different methods of calculation have been adapted: for the calculation of potential, kernel density and weighting have been used, and for predictivity mapping, besides the use of the well-known Weight-of-Evidence (based on lithostratigraphy) for main commodities present in an ore deposit, a new method using metals associations has been set up for by-product commodities in formerly known deposits. Working at European scale, one should however keep in mind that such studies cannot be used for targeting. The aim is more realistically to precise or to redefine ‘district’ contours and in the best case to enhance ‘some less obvious’ areas. In order to display and to deliver data through the Internet, a web portal was developed. The ProMine web portal architecture is based, especially for metadata and web services, on OGC [Open Geospatial Consortium (http://www.opengeospatial.org/)] principles related to open architecture and interoperability. A mapping between the data stored in the ProMine databases and standard data models like GeoSciML for geological information and EarthResourceML for mineral deposits, mines and mining wastes has been implemented to deliver the data according to these international standards.

Gabor Gaál: Deceased.

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Notes

  1. 1.

    High-tech metals are engaged in the major theme sets of “climatic change”, at the level of “renewable energies” and reducing emission of “greenhouse gases”. The CO2 battle is involving a growing number of these “minor” (often by-products) metals which, in this case, can be qualified as ecological “green metals”.

  2. 2.

    Tonnage of commodity (metric tons of metal) in the ore body, based on its grade and the tonnage of ore.

  3. 3.

    IndexMundi is a platform containing various data concerning selected attributes and characteristics of countries, including detailed statistics on commodities compiled from multiple sources (http://www.indexmundi.com/en/commodities/minerals/).

  4. 4.

    Tonnage of commodity (metric tons of metal) in the anthropogenic concentration, based on its grade and the tonnage of waste.

  5. 5.

    Kernel density is a method to calculate the density of point (or line) features (deposits in our case) per unit area using a kernel function to fit a smoothly tapered surface to each point (or line).

  6. 6.

    Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE) http://inspire.jrc.ec.europa.eu.

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Acknowledgments

The authors wish to thanks all WP1 ProMine partners for their fruitful collaboration and contributions: Soile Aatos (GTK, Finland), Vassiliki Aggelatou (IGME, Greece), Nikolaos Arvanitidis (IGME, Greece; now at SGU, Sweden), Anne-Sophie Audion (BRGM, France), Dimitrios Ballas (Hellas Gold S.A., Greece), Christos Christidis (IGME, Greece), Alexandros Demetriadis (IGME, Greece), Dimitrina Dimitrova (Bulgarian Academy of Sciences, Bulgaria), Pasi Eilu (GTK, Finland), Augusto Filipe (LNEG, Portugal), Emmy Gazea (Hellas Gold S.A., Greece), Philippe Gentilhomme (BRGM, France), Eric Gloaguen (BRGM, France), Jérome Gouin (BRGM, France), Dimitrios Iliopoulos (IGME, Greece), Carlos Inverno (LNEG, Portugal), Christian Joannes (BRGM, France), Maria João Batista (LNEG, Portugal), Tuomo Karinen (GTK, Finland; now at Mustavaaran Kaivos Oy, Finland), Teemu Karlsson (GTK, Finland), Esa Kauniskangas (GTK, Finland), Panu Lintinen (GTK, Finland), Timo Mäki (Pyhäsalmi Mine Oy, Finland), Frédérik Maldan (BRGM, France), Ioannis Marantos (IGME, Greece), Santiago Martin Alfageme (IGME, Spain), João Matos (LNEG, Portugal), Maël Meliani (BRGM, France), Constantinos Michael (IGME, Greece), Wojciech Mizera (KGHM Cuprum, Poland), Vassilka Mladenova (Sofia University, Bulgaria), Javier Navas (IGME, Spain), Mateusz Niedbal (KGHM Cuprum, Poland), Ewan Pelleter (BRGM, France; now at IFREMER, France), George Perantonis (Hellas Gold S.A., Greece), Jean-Claude Picot (BRGM, France), Jacek Pyra (KGHM Cuprum, Poland), Francis Ralay (BRGM, France), Ignace Salpeteur (BRGM, France), Helena Santana (LNEG, Portugal), Todor Serafimovski (Goce Delčev University, FYROM), Juha Strengell (GTK, Finland), Michal Strzelecki (KGHM Cuprum, Poland), Goran Tasev (Goce Delčev University, FYROM), François Tertre (BRGM, France), Fernando Tornos (IGME, Spain) and George Tudor (Institutul Geologic al României, Romania). The authors also want to kindly thank ProMine’s Project Leader Juha KAIJA (GTK, Finland) for his constant support. Detailed reviews by Gus GUNN and Martiya SADEGHI are kindly acknowledged and have substantially contributed to improve the initial manuscript. The ProMine project was funded by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 228559.

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Authors and Affiliations

  1. GeoResources Division, BRGM, French Geological Survey, 3 Avenue Claude Guillemin, BP 36009, 45060, Orléans Cedex 2, France

    Daniel Cassard, Guillaume Bertrand, Mario Billa, Bruno Tourlière & Jean-Michel Angel

  2. Information Systems Division, BRGM, French Geological Survey, 3 Avenue Claude Guillemin, BP 36009, 45060, Orléans Cedex 2, France

    Jean-Jacques Serrano

  3. Southern Finland Office, GTK, Geological Survey of Finland, Betonimiehenkuja 4, 02151, Espoo, Finland

    Gabor Gaál

Authors
  1. Daniel Cassard
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  2. Guillaume Bertrand
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  3. Mario Billa
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  1. Centre of Advanced Mining and Metallurgy, Luleå University of Technology, Division of Geosciences and Environmental Engineering, Luleå, Sweden

    Pär Weihed

Appendices

Appendix 1

Class Threshold Values for Selected Commodities

Commodity

Description

Class threshold values (in metric tons)

Super-large deposits (class A)

Large deposits (class B)

Medium deposits (class C)

Small deposits (class D)

Ag

Silver (metal)

10,000

2,500

500

100

Al

Aluminum (Bauxite ore)

1,000,000,000

100,000,000

10,000,000

1,000,000

Au

Gold (metal)

500

100

10

1

Be

Beryllium (BeO)

20,000

2,000

200

50

Bi

Bismuth (metal)

20,000

2,000

200

2

Brt

Barite (BaSO4)

5,000,000

1,000,000

200,000

50,000

Cd

Cadmium (metal)

10,000

2,000

500

100

Co

Cobalt (metal)

500,000

50,000

2,000

200

Cr

Chrome (Cr2O3)

25,000,000

5,000,000

1,000,000

200,000

Cu

Copper (metal)

10,000,000

1,000,000

100,000

10,000

Fe

Iron (metal)

1,000,000,000

100,000,000

10,000,000

1,000,000

Fl

Fluorite (CaF2)

5,000,000

1,000,000

200,000

50,000

Ga

Gallium (metal)

100

50

10

1

Ge

Germanium (metal)

500

100

20

5

Gr

Graphite (substance)

10,000,000

1,000,000

100,000

10,000

Hf

Hafnium (metal)

10,000

1,000

100

10

Hg

Mercury (metal)

50,000

5,000

500

100

In

Indium (metal)

500

100

25

5

Li

Lithium (Li2O)

1,000,000

100,000

50,000

5,000

Mg

Magnesium, magnesite (MgCO3)

100,000,000

10,000,000

1,000,000

100,000

Mn

Manganese (metal)

100,000,000

10,000,000

1,000,000

100,000

Mo

Molybdenum (metal)

500,000

100,000

5,000

1,000

Nb

Niobium–columbium (Nb2O5)

1,000,000

100,000

10,000

2,000

Ni

Nickel (metal)

2,000,000

500,000

20,000

2,000

PbZn

Lead + Zinc (metal)

10,000,000

1,000,000

100,000

10,000

Pltd

Platinoids, group (metal)

1,000

100

10

1

Rb

Rubidium (Rb2O)

1,000

100

10

1

Re

Rhenium (metal)

5,000

500

50

5

REE

Rare Earths (RE2O3)

1,000,000

100,000

10,000

1,000

Sb

Antimony (metal)

100,000

25,000

2,000

1,000

Se

Selenium (substance)

5,000

1,000

250

50

Sn

Tin (metal)

200,000

25,000

1,000

100

Ta

Tantalum (Ta2O5)

25,000

2,000

1,000

200

Ti

Titanium, general (TiO2)

20,000,000

2,000,000

200,000

20,000

V

Vanadium (metal)

2,000,000

200,000

20,000

2,000

W

Wolfram (WO3)

200,000

50,000

5,000

500

Zr

Zirconium (ZrO2)

1,000,000

100,000

10,000

1,000

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Cassard, D. et al. (2015). ProMine Mineral Databases: New Tools to Assess Primary and Secondary Mineral Resources in Europe. In: Weihed, P. (eds) 3D, 4D and Predictive Modelling of Major Mineral Belts in Europe. Mineral Resource Reviews. Springer, Cham. https://doi.org/10.1007/978-3-319-17428-0_2

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