ProMine Project WP1. PART 2: Mineral Potential and Predictive Mapping. ProMine final conference. Levi, April 23rd 2013

Transcription

1 ProMine Project WP1 PART 2: Mineral Potential and Predictive Mapping ProMine final conference Levi, April 23rd 213 G. Bertrand, D. Cassard, M. Billa, B. Tourlière, and the WP1 Team

2 Introduction WP1 has produced a huge homogeneous dataset of primary and secondary mineral resources that covers the whole EU. The objective of WP1 was to go farther, and to valorize this dataset by producing added value layers. Two types of added value layers : Mineral potential maps, to assess geographic distribution of known ores Mineral predictive maps, to identify areas that could favorably contain targeted commodities

3 Introduction Summary Introduction Main commodity associations/deposit types Mineral potential mapping Methodology Results and examples Mineral predictive mapping with the Weight of Evidence method Methodology Results and examples Mineral predictive mapping with the Database Querying method Methodology Results and examples Conclusion

4 Main commodity associations Number Association Name Commodity Association 2 Alkaline & Peralkaline intrusions Epithermal 3 Igneous Felsic Igneous Intermediate Igneous Replacement IOCG Mafic intrusion 8 Mafic or UltraMafic 9 Orogenic Gold Au, (Ag, As, W, Cu, Sb, Bi) Pegmatites Carbonatehosted deposits Sandstone and shalehosted deposits Sedimentary deposits 14 VMS 15 Residual deposits Nb, Ta, Sn, Li, Be, (U, REE) Zn, Pb, Ag, Ba Cu, U, Pb, (Ni, Co, Zn, V, PGE, Re) Fe, Mn, Ba,K,Na,Sr Cu, Zn, Pb, (Ag, Au, Te, Sn, In) Fe, Al, Ni, Cu, (Mn, Au, P, REE) 16 Base metals veins 1 12 ProMine final meeting Levi 23rd Nb, REE, P, (Ta, Zr, Sc, F, U, Fe) Au, Ag, Sb, Hg, Te, Cu, In Sn, W, Ta, Nb, (Mo, Li, Be, B, In, F) Cu, Mo, Au, (Re) Fe, W, Pb, Zn, Cu, Au Fe, Cu, Au, (P, REE, U, Co) Fe, Ti, V Ni, Cr, Cu, PGE, (Co, Bi, U, Ag) Pb, Zn, Cu, U, (Ba, F) April 213 'Type codes queried in MD database (including sons ) C1, C2 D C4 C5 C7 K B3 B, except B3 A, plus 'commodity Gold' C6 F4 F2, F3, F6 F5 E H2, H3 A, without 'commodity Gold' In order to analyse homogeneous mineralization, a list of 16 most characteristic commodity associations or deposit types has been established by WP1 partners. The ProMine MD database was then queried to extract all deposits of these 16 major types. As a result, 16 homogeneous deposit populations were obtained, that were processed for potential and predictive mapping.

5 Mineral potential mapping methodology Principles: The goal of potential mapping is to identify areas of high mineral resources potential, based on the distribution and size of known deposits of a given type. The principle of the method was to combine: a statistical study of the spatial distribution of deposits (kernel density in a first stage); the introduction, as a weight, of the size (class of the main commodity) of the deposits. geological constrains, by selecting lithology polygons containing deposits of the selected type. The interest of such a methodical approach is that it is reproducible, whatever the type and the density of deposits.

6 Mineral potential mapping methodology Density grid Density coverage grid Potential map calculation: For each population (i.e. commodity association), the potential map is the sum of: 1) A density grid: kernel density analysis using the ninth decile of proximity statistics as search radius and weighting deposits according to their class (from 1 for class E or 'no class', to 25 for class A, based on the highest class of the deposit) 2) A density coverage grid: contouring of the weighted density grid (interval equal to one decile of the density statistics) and extraction of the surface inside a buffer equal to the mean value of the density statistics 3) A geology coverage grid: extraction of the surface covered by geology polygons containing at least one deposit of the population. Map of geologycontrolled deposit density Geology coverage grid

7 Mineral potential mapping results 1 Alkalineperalkaline intrusions 2 Epithermal and volcanic systems 3 Igneous felsic 4 Igneous intermediate 5 Igneous replacement or skarn 6 IOCG (Iron Oxide Copper Gold) 7 Mafic intrusions 8 Maficultramafic 9 Orogenic gold 1 Pegmatites 11 Carbonate hosted 12 Sandstone and shale hosted 13 Sedimentary deposits 14 VMS (Volcanogenic Massive Sulfides) 15 Residual deposits 16 Base metals veins One map for each commodity association (or deposit type)

8 Mineral potential mapping results Orogenic gold: Distribution of potential is guided by a single main commodity (Au) and a well constrained type of mineralization. Major districts belong to two groups: Paleoproterozoic orogenic deposits related to greenstones in the Fennoscandian shield; Hercynian goldbearing districts related to late Hercynian (~3 Ma) deformation belts (N. Iberian peninsula, French Massif Central, Bohemian Massif). Additional more scattered deposits can be found in other Hercynian (Salsigne) or Caledonian (Great Britain and Norway) domains and the BalkanCarpathian region.

9 Mineral potential mapping results Igneous intermediate: Potential of igneous intermediate mineralization clearly highlights the western Tethyan suture, with large porphyrytype districts: Kremnica in Slovakia, Telkibanya in Hungary, The Apuseni mounts in Romania, Bor in Serbia, Assarel in Bulgaria, Bucim in Macedonia. Other domains have porphyrytype mineralization, such as the Fennoscandian shield (Aitik, Tallberg, Kopsa), the Caledonian belt (Coed y Brenin in Wales, Black Stockarton Moor in Scotland), and the upper Paleozoic Hercynian magmatism (Beauvin, Sibert, AuxellesleHaut in France)

10 Mineral predictive mapping Overview: The goal of predictive mapping is to identify areas where unknown mineral raw materials could favourably be discovered. Methodologies used for the calculation of mineral predictive maps vary depending on the fact that the studied commodity is a main element in the deposit or a byproduct. Up to now, a very large majority of predictive studies dealt with a relatively low number of elements which are the main commodities in their deposits and/or which belong to the main paragenesis (e.g. Cu or Au in porphyries). However, ProMine project bears a great attention to strategic and critical commodities, and especially to the 14 critical raw materials identified by the European Commission. In fact, these critical commodities may either be the main commodities with a proper mineral expression in mined ores (e.g. W, Sb, Fl or Sn), or commodities which are byproducts from mining (e.g. Ge, Ga, In or Ta). As a consequence, predictive methods needed to be adapted to consider both possibilities. In the present work, we used, for the first case, a geographic prediction method (Weight of Evidence, or WofE) and, for the second case, a database querying method.

11 Mineral predictive mapping, Weight of Evidence methodology principles: The WofE method is a probabilitybased approach that uses Bayes rule to combine evidence with an assumption of conditional independence. Where sufficient data are available, it can be applied to estimate the relative importance of evidence by statistical means. For details of the method, see BonhamCarter (1994) and Kemp et al. (21). The spatial analysis was performed on the deposits (training points) and geology (evidential theme) layers, using the ArcSDM (Spatial Data Modeller) extension, developed for ArcView 3.x / Spatial Analyst (ESRI ). The same process was used for all targeted commodities. Wh was calculated separately for deposits (i.e. class A, B, C, D, E) and showings (i.e. class N/A) in the database. Results were added as follow: (Wh deposits) +.5 (Wh showings)

12 Mineral predictive mapping, Weight of Evidence results Copper Fluorite Antimony Tin Tungsten Zinc in carbonate hosted deposits One predictive map was calculated for each of the 6 studied commodities. Deposits containing the targeted commodity are also placed on the maps to easily identify new favourable areas.

13 Mineral predictive mapping, Weight of Evidence results Geological Code mp_vi m_va Ophjc t_vi t23 Plth2 Pltd3 PltGnko2 PltGnb2k hr h3 d3_vb h2_va Glako s1 s os o12 ko b2k bk bo kr Comment Mioc Mioc Mesoz. Mesoz. Mesoz. Psup_Mag Psup_Mag Pinf_Mag Pinf_Mag Psup Psup Psup Psup Pinf Pinf Psup Pinf Pinf Pinf Pinf Pinf Pinf P WofE WofE WofE (deposits) (showings) (combined) 1,8 2,14 2,87 1,33,57 1,61 1,88 1,13 2,45 4,87, 4,87,75 1,38 1,44 1,86 1,79 2,76 2,53 3,16 4,11 2,67 2,83 4,9 3,79 1,24 4,41 1,34 1,27 1,97 2,56 2,5 3,81 4,3 3,27 5,66 3,25 3,18 4,84 2,63 3,25 4,25 1,74 1,74, 1,4 1,38 1,73,69 1,72 1,55 1,18 1,52 1,94 3,97 3,71 5,82 2,31 2,24 3,43 2,53 2,36 3,71 3,11 2,7 4,47 1,68,22 1,79 code Antimony (Sb), table of results: 342 records from the ProMine MD database contain Sb: 19 deposits, 233 showings, Dominant deposit types are: Base metals veins (55,5%) Epithermals (17,8%) Orogenic gold (8,78%) Carbonate hosted (<5%) VMS (<5%) Dominant associated commodities are Au and As with, in smaller proportion (Pb, Zn, Cu, Ag) and Hg in epithermal parageneses.

14 Mineral predictive mapping, Weight of Evidence results Antimony (Sb), predictive map: Most favourable area is the French Hercynian domain (Massif Central and Brittany) and correspond to late orogenic veins (~3 Ma) on Paleozoic basement. Another favourable area is the BalkanCarpathian region, in relation to Mezosoic and Cenozoic series. Predictive results, however, allow to extend Sbfavourability zones to other areas of the Paleozoic domain (e.g. Wales, the Alps, eastern Bohemian massif, ) and the BalkanCarpathian domain.

15 Mineral predictive mapping, Database Querying methodology Principles: The objective of this approach is to assess favourability for rare metals commodities (i.e. byproducts of other commodities) in deposits of the MD ProMine database. To achieve this objective, deposits of the database are ranked according to their similarity to deposits containing the targeted commodity. Step 1: frequency of the targeted commodity is evaluated per deposit type and then compared to the whole database, in order to calculate an enrichment ratio (ER) per deposit type; Step 2: In all deposits that contain the targeted commodity, for each favourable deposit type, a list of associated commodities and their frequency is calculated. The result is a table of characteristic polymetallic association, or signature favouring the presence of the targeted commodity. This characteristic polymetallic signature will be search in all deposits of favourable types. Step 3: deposits are ranked relatively to their degree of similarity with the polymetallic signatures. The ranking is the sum, for all commodities in the polymetallic association of the product of a Boolean value (i.e. commodity is present = 1 and commodity is not present = ) and the frequency of the commodity in the signature. Step 4: to give a more global and synthetic view of results, ranks of deposits are weighted with the ER of the type they belong to.

16 Mineral predictive mapping, Database Querying results Cobalt Gallium Germanium Indium Tantalum One predictive map was calculated for each of the 5 studied commodities. Results are mapped as density. Deposits containing the targeted commodity are also placed on the maps to easily identify new favourable areas.

17 Mineral predictive mapping, Database Querying results N Metallogenic Type Base metals veins CarbonateHosted Epithermal VMS Igneous Felsic Igneous Intermediate Igneous Replacement Orogenic Gold Residual deposits SandStone and ShaleHosted Sedimentary deposits Enrichment ratio 1,14 3,35 1,5 1,78,69,59,98,2,13 1,9,24 Germanium (Ge), enriched deposit types: 111 deposits in the ProMine MD database contain germanium. Enrichment ratios show that germanium is preferably present in: Carbonate hosted deposits (ER=3,35) VMS (ER=1,78) Epithermals (ER=1,5) Base metals veins (ER=1,14) These 4 types contains 79% of Gebearing deposits.

18 Mineral predictive mapping, Database Querying results Commodity Global Base metals % veins Zn Pb Ag Cd 4 48 Cu Ga In Au 15 3 Ba As Sb 1 6 Bi 9 6 Fe 8 Sn 7 1 Tl Ni 6 Te 6 Co 5 3 U 5 6 F 4 1 Hg 4 Se 4 CarbonateHosted Epithermal VMS Germanium (Ge), polymetallic signatures: The frequency of commodities associated with germanium is calculated for each enriched type. The resulting table shows, per deposit type, the commodities that are preferentially associated with germanium, or characteristic polymetallic associations. For instance, zinc that is the most frequent element is in 87% of base metals veins deposits that contain germanium, while Pb and Ag are in 74% and 68% (respectively) of these deposits.

19 Mineral predictive mapping, Database Querying results Ranking calculation for germanium: Rank 1 (base metals veins): ((.87 * [Comm.Zn]) + (.74 * [Comm.Pb]) + (.68 * [Comm.Ag]) + (.48 * [Comm.Cd]) + (.19 * [Comm.Cu]) + (.32 * [Comm.Ga]) + (.16 * [Comm.In_]) + (.3 * [Comm.Au]) + (.13 * [Comm.Ba]) + (.13 * [Comm.As_]) + (.6 * [Comm.Sb]) + (.6 * [Comm.Bi])) Rank 2 (carbonatehosted): ((.79 * [Comm.Zn]) + (.71 * [Comm.Pb]) + (.43 * [Comm.Ag]) + (.32 * [Comm.Cd]) + (.25 * [Comm.Cu]) + (.18 * [Comm.Ga]) + (.7 * [Comm.In_]) + (.4 * [Comm.Au]) + (.14 * [Comm.Ba]) + (.11 * [Comm.As_]) + (.7 * [Comm.Sb]) + (.4 * [Comm.Bi])) Rank 3 (epithermal): ((.4 * [Comm.Zn]) + (.3 * [Comm.Pb]) + (.7 * [Comm.Ag]) + (.2 * [Comm.Cd]) + (.8 * [Comm.Cu]) + (.1 * [Comm.Ga]) + (.3 * [Comm.In_]) + (.7 * [Comm.Au]) + (.2 * [Comm.Ba]) + (.3 * [Comm.As_]) + (.3 * [Comm.Sb]) + (.1 * [Comm.Bi])) Rank 4 (VMS): ((.89 * [Comm.Zn]) + (.68 * [Comm.Pb]) + (.37 * [Comm.Ag]) + (.32 * [Comm.Cd]) + (.26 * [Comm.Cu]) + (.37 * [Comm.Ga]) + (.16 * [Comm.In_]) + (.11 * [Comm.Au]) + (.11 * [Comm.Ba]) + (.5 * [Comm.Sb]) + (.5 * [Comm.Bi])) Germanium (Ge), ranks calculation: For each deposit in each favorable type, a rank is calculated, in order to measure its degree of similarity with deposits of the same type that contain the targeted commodity. Calculation is based on the polymetallic signature of the type. Ranks are then weighted with the ER of the type the deposits belong to. Global ranking for germanium = (1.14 [RANK1]) + (3.35 [RANK2]) + (1.58 [RANK3]) + (1.78 [RANK4])

20 Mineral predictive mapping, Database Querying results Germanium (Ge), predictive map: The resulting map highlights a germanium province in southern Europe with various types of mineralization emplaced in lower Paleozoic, Mesozoic (in relation to carbonates of the Tethyan margin), and upper CretaceousCenozoic (porphyries and epithermal deposits). Also, some areas, such as massive sulfides domains, show relatively high favourability, and should be investigated in more details by future studies.

21 Conclusion 16 maps of mineral potential have been produced, for the 16 main commodity associations (or deposit types) 6 predictive maps have been produced for 6 main elements (W, Sn, Sb, Fl, carbonate hosted Zn and Cu), using the Weight of Evidence method 5 predictive maps have been produced for 5 byproducts elements (Ge, Ga, In, Ta and Co), using a newly developped database querying method These 27 maps and their comments are available on the ProMine web portal. The ProMine databases will provide data for numerous future researches on mineral resources in Europe!

22 Thank you for your attention!