Search

GeoERA Minerals projects have produced data aimed at supporting Europe’s minerals sector and to assist the European Commission to realise its goals for raw materials. Data has been compiled on mineral occurrences and mineral provinces across Europe, in particular, areas with potential to host Critical Raw Materials. Anecdotal evidence from the minerals sector provides an indication of the likelihood of exploration leading to mine development. For every 1,000 mineral showings examined, only 100 may receive further exploration work and of those 100, only 10 may warrant more detailed sampling either through trenching, drilling or other means and of those 10 only 1 may proceed to an evaluation through a full feasibility study which itself has only 50% chance of being positive. Following this, any project for which a mine proposal is made must undergo a full evaluation and permitting by authorities including full public consultation. The proposal may or may not pass this scrutiny. In terms of a schedule, the generally accepted minimum time frame from discovery to production is 10 years and usually much more, up to 20 years. info:eu-repo/semantics/publishedVersion

MICA develops the EU-RMICP with an innovativevisualization interface that guidesthe user to a ‘recipe’ for how to find answersto particular mineral raw material relatedquestions. This is different from a databaseproviding pre-formulated answers toquestions: MICA offers an expert-designedpathway towards answers by means of an exhaustive catalogue of data sources andpeer-reviewed information with relevance to the user question. Thus, MICA enables theuser to explore data, acquire information and build up knowledge,rather than beinggiven narrow, pre-formulated answers. The MICA Platform endeavours to caterfor professionalas well as general publicstakeholders.

International audience; IntroductionRare earth elements (REE) are a group of 17 metallic chemical elements that have relatively similar chemical and physical properties. They comprise the 15 lanthanides, plus scandium (sometimes excluded) and yttrium. REE are used in a wide range of manufactured products, especially in green energy and digital technologies, and are considered as critical raw materials by the European Commission1. China has the most abundant resources and production of REE in the world, although significant resources, either known or inferred, exist in other countries. The goal of the present study is to assess REE occurrences in Europe, using a predictivity methodology developed by BRGM to evaluate the favorability of known mineralization to host a targeted commodity, the DataBase Querying (or DBQ) method2. Its purpose is not to evaluate the REE endowment of Europe, but to pinpoint mineral occurrences that may contain REE, in order to identify favorable areas and help guiding further exploration works.Data sources and preparationData that was used for this work came from both the EURare IKMS (see presentation from Cassard et al. in this conference) and the ProMine Mineral Deposit (MD) database2. Datasets required by the DBQ method have to include a large number of occurrences or deposits, and to list their type and the presence/absence of a wide panel of commodities. The EURare IKMS dataset was extracted and completed to list all contained commodities (deduced from the mineralogy mostly) and to classify deposits in 17 distinct metallogenic families (16 from the ProMine project, plus placers, even though it is not a metallogenic type sensu stricto), in order to apply subsequent processing to coherent deposit populations. Additional information from publication3,4,5,6 was also included. The resulting ‘augmented’ dataset contained 158 REE deposits, amongst which 104 were sufficiently documented (in terms of deposit type and contained commodities) to be processed with the DBQ method. The ProMine Mineral Deposit database was also queried for all deposits belonging to the identified metallogenic families, with their type and contained commodities. This ProMine dataset was merged with the 104 IKMS deposits. After removal of duplicates, the final dataset contained 8326 occurrences and deposits, classified in 17 metallogenic families and described for their content in 52 commodities. 244 deposits out of the 8326 contain REE.Predictive assessmentThe DBQ method was developed within the ProMine project2, to perform predictive assessment on datasets that are not big enough to provide reliable results with “usual” methods (e.g., Weight of Evidence), which is often the case when targeted commodities are “rare” elements and/or by-products. The first step of DBQ is to calculate, for each metallogenic family, an enrichment ratio (ER, or ratio of frequency of REE in the metallogenic family vs. frequency of REE in the whole dataset). ER>1 indicates a metallogenic family enriched in REE, while ER

EU-Raw Materials Intelligence Capacity Platform (or EU-RMICP) integrates metadata on data sources related to primary and secondary mineral resources and brings the end users an expertise on the methods and tools used in mineral intelligence. The system is capable of bringing relevant user ‘answers’ of the type 'how to proceed for …' on almost any question related to mineral resources, on the whole supply chain, from prospecting to recycling, taking into account the environmental, political and social dimensions. EU-RMICP is based on an ontology of the domain of mineral resources (coupled with more generic cross-functional ontologies, relative to commodities, time and space), which represents the domain of the questions of the users (experts and non-experts). The user navigates in the ontology by using a Dynamic Graph of Decision (DDG), which allows him/her to discover the solutions which he/she is looking for without having to formulate any question. The system is coupled with a 'RDF Triple Store' (a database storing the ontologies), factSheets, doc-Sheets and flowSheets (i.e., specific formatted forms) related to methods and documentation, scenarios and metadata., JRC.B.6-Digital Economy

National audience

International audience; In this paper, we propose QUANTMINER, a mining quantitative association rules system. This system is based on a genetic algorithm that dynamically discovers "good" intervals in association rules by optimizing both the support and the confidence. The experiments on real and artificial databases have shown the usefulness of QUANTMINER as an interactive, exploratory data mining tool.

Deux milliards d'années nous contemplent lorsqu'il est question de la Guyane. Ce sous-sol précambrien du bouclier des Guyanes possède une histoire riche, complexe et des ressources minérales que les géologues scrutent attentivement depuis des décennies. L'or, le fer, la bauxite, le diamant et d'autres substances sont présentes du Venezuela au Brésil en passant par la Guyane. Comprendre, rechercher, identifier pour mieux exploiter sont les objectifs qui s'inscrivent dans les attentes de la société tout en prenant en compte les contraintes environnementales.

Relationship between ore genesis and plate tectonics are now well established as evidenced by the strong correlation between plate boundaries and ore deposit distribution. Three main stages of metallogenic evolution seem to be related to Neothethys closure and induced subduction dynamics changes. The striking feature of Hellenic slab is a narrow convex arc shape. This curvature is more likely the response of the slab retrograde motion in a toroidal component of the 3D return flow. Here, we explore the idea where the heat source is provided by a 3D asthenospheric mantle flow associated with the retreating Hellenic slab since at least 20 Ma. We propose to identify toroidal component such as one of parameters responsible of the subduction zone dynamics and ore deposits distribution related.

International audience; Recent advances in geosciences make more and more multidisciplinary data available for mining exploration. This allowed developing methodologies for computing forecast ore maps from the statistical combination of such different input parameters, all based on an inverse problem theory. Numerous statistical methods (e.g. algebraic method, weight of evidence, Siris method, etc) with varying degrees of complexity in their development and implementation, have been proposed and/or adapted for ore geology purposes. In literature, such approaches are often presented through applications on natural examples and the results obtained can present specificities due to local characteristics. Moreover, though crucial for statistical computations, "minimum requirements" needed for input parameters (number of minimum data points, spatial distribution of objects, etc) are often only poorly expressed. From these, problems often arise when one has to choose between one and the other method for her/his specific question. In this study, a direct statistical modeling approach is developed in order to i) evaluate the constraints on the input parameters and ii) test the validity of different existing inversion methods. The approach particularly focused on the analysis of spatial relationships between location of points and various objects (e.g. polygons and /or polylines) which is particularly well adapted to constrain the influence of intrusive bodies - such as a granite - and faults or ductile shear-zones on spatial location of ore deposits (point objects). The method is designed in a way to insure a-dimensionality with respect to scale. In this approach, both spatial distribution and topology of objects (polygons and polylines) can be parametrized by the user (e.g. density of objects, length, surface, orientation, clustering). Then, the distance of points with respect to a given type of objects (polygons or polylines) is given using a probability distribution. The location of points is computed assuming either independency or different grades of dependency between the two probability distributions. The results show that i)polygons surface mean value, polylines length mean value, the number of objects and their clustering are critical and ii) the validity of the different tested inversion methods strongly depends on the relative importance and on the dependency between the parameters used. In addition, this combined approach of direct and inverse modeling offers an opportunity to test the robustness of the inferred distribution point laws with respect to the quality of the input data set.

International audience; A comprehensive overview of the French Guiana's gold-mining activities along with a reliable gold predictivity map has been obtained as part of the 'Mineral Resources of French Guiana' GIS (3396 gold sites listed). The gold mining is almost exclusively in alluvial and eluvial deposits, with primary deposits representing only 2% of the inventoried gold sites. Most of the deposits being allochthonous, a preliminary processing of the data was necessary to draw up a set of 'in-situ' deposits sufficient for a) calculating, correlations between the primary mineralization and the controlling geologic parameters, and b) assessing the favorabilities of the different criteria -- i.e., lithostratigraphy, faults and lithostratigraphic contacts, geochemical and geophysical signatures. The processing, aimed at identifying and selecting sites close to primary sources, was based on ranking the catchment basins according to the Strahler classification. The resulting gold predictivity map combines a classical approach based on algebraic methods for processing polygon and line data, and an approach using cluster analysis for processing geochemical and radiometric data and identifying groups as marking mineralization signatures, then looking for and scoring grid pixels showing a high level of similarity. The map appears realistic. It confirms the value of the known metallotects and locates the main primary deposits; some deposits with a different geochemical and geophysical signature, however, would probably not have been picked up -- hence the importance of the model used for establishing the map. The dual approach is easily transposable to other equatorial and poorly accessible countries.

Colloque avec actes et comité de lecture. internationale.; International audience; To obtain a good modeling of a phenomenon, data and methodology are as important as the modeling technique used if this technique is an inductive machine learning one such as artificial neural networks or decision trees. We are interested to build gold predictive maps using a Geographical Information System. As for any classification task, we have to discriminate patterns of a desired class from other ones. The choice of counter examples is a crucial stage for a deposit classification. The first idea to have counter examples is to define barrens. This approach introduces skews because the barrens are usually far away from deposits with very different characteristics or close enough but without any certainty that they really are barren deposits. The goal of this study is to propose a new more general subjective approach to build dataset for classification based on the main properties of geology : type of rocks, geologic time scale, geographic zone, substances,? The main idea is to consider a deposit like a pattern characterized by a certain number of properties. Then, any pattern with at least one different property can be used as a counter example. Then, we apply on a GIS Andes these various approaches to create efficient datasets aim to obtain predictive maps on gold deposits using artificial neural networks. Thus, we first try to discriminate neogene gold deposits and old gold deposits. Then, we try to discriminate neogene deposits containing gold vs neogene deposits without gold. We obtain a minimum rate of good classification of 85% using 31 attributes describing geography, geology (lithology, faulting, recent volcanism), geometry of the subduction zone, geothermy, geophysics (seismic, gravimetry), and ore deposit. Finally, we compare the different maps obtained and discuss how to combine them using e.g. a bagging method.

Extraction et gestion des connaissances EGC 2003