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Europe as a Mining Investment Destination – Policies and Progress

Most mining industry observers would have noticed Finland achieving top global rank as mining investment destination in the 2014 mining survey by the Fraser Institute. However, overall the EU is not seen as a major mining region. This is true for the production of metallic minerals with production limited to only 3% of global supply while the EU consumes 25% – 30% of global production. On the other side it is self-sustaining in the aggregates and cement industry and ranks among the top three producers for many industrial minerals.

European Raw Materials Policies

The strong dependence on metal imports and resulting supply risks for key industries have led to a renewed political focus on raw materials in Europe. The European Union launched the Raw Materials Initiative in 2008. In 2011 European Commission adopted a new strategy document which sets out targeted measures to secure and improve access to raw materials for the EU. This new strategy consists of a 3 pillar-based approach to improving access to Raw Materials for Europe. These pillars are:

  1. Fair and sustainable supply of raw materials from international markets
  2. Fostering sustainable supply within the EU
  3. Boosting resource efficiency and promote recycling

The Strategic Implementation Plan (SIP) of the European Innovation Partnership on Raw Materials is structured into 3 domains; technology development, non-technology issues and international cooperation. The European Technology Platform on Sustainable Mineral Resources (ETP SMR) is providing a coordination function for the raw materials related research activities. Two of the priority areas have specific mining relevance.

Priority Areas of the SIP

The priority area Technologies for primary and secondary raw materials production addresses the mining value chain from exploration to processing and refining. Aims are to deliver pre-competitive geo-data for Europe and cost competitive technology to facilitate the exploration and exploitation of deeper and more complex deposits and ore types.

The priority area Improving Europe’s raw materials framework conditions addresses mining policies, land use planning policies and public awareness. Aims are to improve mining regulation and permitting procedures, introducing the concept of “mineral deposits of public importance” and supporting the building of stakeholder trust towards the raw materials industry.

Implementation Progress


Progress on the implementation of the SIP has been achieved with a number of commitments and projects such as ProMine, I2Mine, EuroGeoSource, Mineral4EU. An example of results is given in Figure 1. Public awareness initiatives such as the European Minerals Day have been conducted with the aim to educate the public about the European Minerals Industry and its relevance to stakeholders. Funding for initiatives is mainly provided through Horizon 2020, see for example this recent call that targets RTI actions in the priority areas. Further funding comes directly from member states, for example, the creation of Germany’s exploration funding programme.

EU metallic minerals deposits
Figure 1: Known EU deposits of metallic minerals (Generated using the ProMine portal)

Will these policies lead to success?

One of the objectives of the EU strategy outlined above is the attraction of investment to foster the domestic mining industry. Increasing EU competitiveness in a global industry and global markets is challenging on the background of generally dense population and 30 years of low investment in exploration.

Technology developments

Technology developments as encouraged through EU funding can improve the attractiveness of potential future deep mining operations in Europe compared to easier and cheaper shallow mines worldwide. However it is hard to imagine any commercialized technology would be only available in Europe; therefore potential future Europe-based mines would still have to compete globally. Benefits of such developments can be captured via sales of technology and services, which could further boost this already strong area of Europe’s involvement in the mining sector. Conversely Europe is not alone in advancing exploration and mining technologies with key government (co-)funded programmes, for example, in Canada and in Australia (DET CRC, CRCMining), and European companies will have to compete with any offerings evolving out of these international efforts.

Provision of geo-data

The provision of pre-competitive geo-data has good prospects to attract investment if minerals potential can be demonstrated; the annual global survey of mining companies by the Fraser Institute states that investment decisions are driven by this factor by 60%. Current views of minerals prospectivity in Europe are relatively low. The Fraser Institute survey gives Europe (excluding Russia and Greenland) an average ranking of 31% which is the lowest rank among the regions in the survey. Only 3 of the 14 jurisdictions (Finland, Ireland and Sweden) rank above the global median for minerals prospectivity.

Given the long history of mining in many European mineral belts and the lack of modern exploration of these belts the hidden potential could be very large. So far however only the top ranking countries have been improving over the last 5 years, see Figure 2. This might be a function of ongoing exploration (the more you look the more you find) but a consistent effect of EU initiatives cannot be detected at this early stage of implementation.

EU Minerals Prospectivity Perception
Figure 2: Global ranking of EU countries' minerals prospectivity over the past 5 years (data from Fraser Institute 2014)

Raw material framework conditions

The raw material framework conditions are an area of that can be directly controlled or at least influenced by the EU and member states. The Fraser Institute survey gives 40% weight to these policy factors for exploration and mining investment decisions. Importantly these conditions are a key enabler for the advancement of modern exploration, see above. Some observations in the survey are;

  • Overall EU strength regarding available skills, infrastructure, geological database, security and trade policies, especially in western and northern EU countries.
  • Broad concern about uncertainty of environmental regulation and protected areas and issues due to regulatory duplication and inconsistencies.
  • Excellent policy rating of Finland, Ireland, Norway, Portugal and Sweden
  • Below median ranking for Bulgaria, Greece, and Romania

These perceptions are overall in line with the EU analysis and the plans to address mining and land use planning policies has good chances of improving the investment climate for exploration and mining ventures. The successful efforts of Ireland in improving it's regulatory regime over the past 5 years (Figure 3) should be applauded in this context. It can serve as a role model for lagging countries in this domain. Good progress is also being made with regard to guidance on dealing with Natura 2000 requirements, which concerns almost 18% of the EU’s land area.

Mining policy perception index in the EU
Figure 3: Policy Perception Index development over the past 5 years (data from Fraser Institute, 2014)


There are good arguments to believe the EU hosts significant undiscovered mineral deposits and the generation of pre-competitive geo-data by EU initiatives will support uncovering this potential, which is the foundation for any mining venture and the future growth of the industry in Europe. Success in this area will be strong leverage in both attracting business and creating a more inviting regulatory regime for the industry as well as acceptance by stakeholders.

Investment in technologies with focus on the challenges anticipated for mining in Europe can provide a more level playing field by making deeper deposits more attractive for investment; also exporting this technology can boost the EU's equipment manufacturing and services sectors.

Improving framework conditions is a crucial component of these policies as they facilitate and enable industry growth. Community  engagement will have to remain a strong focus here to overcome likely NIMBY (Not In My Back Yard) opposition in more densely populated areas.

It will be exciting to follow the impact of the renewed political focus on the raw materials sector in Europe!

Would you consider exploration or mining ventures in Europe? What would encourage you or hold you back? What have been good or bad experiences you have had in the sector in Europe? 

Please share your views and experiences so we can all learn from them!

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Exploratory Data Analysis in Practice: Iron Ore Geochemistry and Mineralogy

I recently have had the pleasure to perform some beta testing of a novel software, X10-Geo, by Phinar Software. As I was performing this testing I made notes to provide feedback to Rob de Bruin, who is behind Phinar. By the end I looked at these raw notes and thought the work would make a nice example illustrating the step by step digging through data during exploratory data analysis (EDA) and, I decided to write up the results as a little case study. So here it is.

The main geochemistry of the common BIF-hosted Iron Ore deposits can be described as a mixing system of Fe-oxides, Quartz, Kaolinite. The scatter plot of Al2O3 versus SiO2 in Figure 1 illustrates iron ore samples relative to key constituent minerals and mixing/enrichment trends.

Fe - Kaolinite - Quartz system
Figure 1: Al2O3 - SiO2 scatter plot with the Iron Ore key constituent minerals, Fe-oxides - Kaolinite - Quartz.

While the genesis of Iron Ore deposits continues to be debated, Quartz is a mobile mineral in the system and its relative depletion converts primary BIF into iron ore (“enrichment”). Kaolinite in these deposits is derived from shale bands and is geochemically immobile. In samples the shale bands get mixed with the Fe/SiO2 rich layers, see “mixing” trend in Figure 1 for enriched iron ore mixing with Kaolinite. Note that the “enrichment” trend can also get realized via the mixing of strongly enriched rocks with partially or non-enriched rocks.

Mineralogy model for Iron Ore Assays

Starting with this general model of the iron ore geochemistry it is possible to drill down into further particularities of a given data set. First screening of the data points show some “outlier” samples above the “mixing” line in Figure 1. These highlight the simplification of the model; the observed “excess” Al2O3 is caused by two factors firstly, the Al-mineral Gibbsite is known to occur in parts of these deposits and, secondly, the Fe-minerals show replacement of Fe(3+) by Al(3+).

Alumina - silica scatter plot coloured by Fe
Figure 2 Al2O3 - SiO2 scatter plot coloured by Fe grade showing a set of iso-Fe lines.

In Figure 2, the Al2O3 - SiO2 scatter plot is coloured by Fe grade which results in a regular “striping” apart from some “outlier” points in the central area of the plot (black ellipse). To gain a better understanding of those outliers in the data set it is desirable to isolate the “well-behaved” samples, which appear to adhere to the three-minerals mixing system introduced above.

The “well-behaved” samples can be characterized by constant Fe while Kaolinite and Quartz vary systematically. This variation follows the parallel lines in Figure 2, representing a set of Fe-isolines, which can be described with a linear function;

with the gradient m and  the intercept of the function with the y-axis. The gradient m can be calculated using the mass percentage differences of and  between Quartz and Kaolinite:

Using -3/4  as approximation for gradient m, Equation (1) can be solved for Al2O3(0) and gives an expression that captures the “striping” effect observed in Figure 2:

The equation (2) can be implemented in the data set to make it available as a derived variable. Figure 3 shows the implementation in the Data Loader of X10 Geo.

X10-Geo Data Loader
Figure 3 Derived variable implementation in X10 Geo Data Loader.

Testing the Mineralogy Model

In the next step the explanatory power of this derived variable, Al2O3(0), is analysed using its scatter plot with Fe, see Figure 4. The graph on the right in Figure 4 demonstrates that the previously observed outliers have been successfully delineated.

Outliers of the three-minerals system
Figure 4 Highlighting outliers of the three-minerals system using derived variable Al2O3(0)

As this little case study was compiled as part of beta testing for X10 geo the full capabilities were not understood. When I got to this point in twisting the data, I discovered the really powerful generic implementation of the 3D plot utility in X10 Geo. Primarily the axes defaults are geographic coordinates however, they can be set to any variable available in the data set. This way I could generate 3D plot shown in Figure 5 which I rotated such that the outliers delineated above stand out again.

3D-plot of iron-silica-alumina
Figure 5 3D plot of Fe - SiO2 - Al2O3.

How easy! And clearly, the data points that adhere to the three-minerals system sit close to a plane in this 3D space, while the outliers are some distance away from the plane. Note also that the variable reduction performed above through introducing the derived variable Al2O3(0) is equivalent to a projection of the data points in the 3D plot onto a plane with normal vector in Equation (2).

So the flexible 3D plotting feature facilitates the data analysis considerably yet, using variable Al2O3(0) is still useful when looking for further detail behind those outliers. Figure 6  displays the scatter plot Al2O3_0 – Fe, coloured by MgO concentration.  It can be seen that most outliers are related to a MgO component, likely to be a carbonate. The data points in the blue ellipse belong to yet another rock type (high sulphur).

Al2O3(0) - Fe scatter plot coloured by MgO
Figure 6 Al2O3(0) - Fe scatter plot coloured by MgO concentration.

In conclusion most samples in the data adhere to a simple mixing system between FE, Al2O3 and SiO2 plus other correlated elements (likely LOI), which have not been investigated here. On a mineralogy basis the system consists of {Fe-oxide, Kaolinite, Quartz}. Starting with this kind of model hypothesis gives clear guidance to the EDA process, which can enhance focus on parts of the data set that are not explained by the hypothesis and require other ideas and their testing.

When this type of data analysis is done with a spatial data set further analysis is required to investigate the potential spatial coherence of any identified population. Where a coherent and geologically reasonable region can be identified, it should be used as spatial domain which can greatly improve the quality of generated deposit model.

Darmstadt, December 2014

PS: I had first started looking into the X10 solution a while back, researching process and system improvements for resource modelling at BHP Billiton Iron Ore. In the meantime, Phinar started a period of beta releases and included me in beta user group. From the very beginning Phinar has been very open to user input and invited ideas for features, quite a few of which have been implemented.

Please note that I don’t have any formal affiliation with Phinar Software however, Phinar did issue me with a temporary free license to continue our mutually beneficial relationship. The data used is a publicly available data set of a BIF-hosted deposit.

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