José Luis García-Lobón

Modelling of the Río Tinto Area

The Rio Tinto project area is located in the South Portuguese Zone, in the eastern part of the Iberian Pyrite Belt. The Iberian Pyrite Belt (IPB) is one of the world’s best-known ore provinces hosting volcanogenic massive sulphide deposit, formed in the latest Famennian (ca. 360 Ma) and subsequently folded and metamorphosed during the Variscan orogeny (330–300 Ma). The study area is located in the Rio Tinto syncline, with Carboniferous metasediments (Culm) in its core. The volcanic sedimentary complex (VSC) is overthrusted in the central part of the syncline forming the Rio Tinto anticline outcrop (an antiformal stack). The aim of this work was to construct a 3D geological model of the Rio Tinto mine area. To achieve this data compilation has been done including new geological mapping and structural interpretations, petrological and petrophysical sampling, drill hole logging, and geophysical data interpretation (gravimetric, magnetic and radiometric data). Complex surfaces were constructed using large data sets analysed by suitable geometrical techniques. The obtained 3D model shows the relationships between several lithologies, tectonic surfaces and mineralization zones, and is an example of reconstruction of complex geological units within the Iberian Pyrite Belt. In addition, in the Rio Tinto area it was possible to derive a predictive model defining four areas of high ore potential based on detailed geological field work, fracture analyses and geophysical studies related to the possible presence of massive sulphides and stockwork zones.

Modelling of the Cala area (Ossa-Morena Zone)

The Cala project area is a region ca. 400 km2 in size that comprises several mines and prospects hosted by Palaeozoic rocks. The Cala area is located in the southern segment of the Iberian Massif that forms the pre-Mesozoic basement in most of the Iberian Peninsula and constitutes the westernmost extent of the European Variscan orogeny. More precisely, it is situated in the south-western limb of the Monesterio Antiform, within the Ossa-Morena Zone (OMZ), which exposes a complex geological evolution. The current structure of the OMZ is mainly due to the Variscan orogeny. This study is focused on Variscan plutons that were emplaced into Late Proterozoic and Paleozoic sediments. Igneous and metamorphic activity of the Variscan Orogeny led to the formation of various types of mineralization. Two of these are studied in this paper: the iron oxide replacement and skarn in the Cala mine and the Ni-(Cu-PGE) deposit in the Aguablanca mafic to ultramafic intrusion. The aim of this work is to build three 3D geological models: one at regional scale (Cala regional model) and two at local scale (Cala mine and Aguablanca deposit). To achieve this we have improved the previous geological mapping and carried out two regional gravity surveys and one detailed survey in the neighborhood of Cala mine. Moreover, in order to study the Aguablanca deposit the geological information supplied by Lundin Mining was very useful. The use of geophysical data provide a tool to check the final models. Fully honoring the geological data and starting cross-sections, best processing practices, model properties based on petrophysical data, and the use of a profile mesh providing a great number of intersections where profile consistencies are proven in a 3D environment was the working scheme. The final 3D geological models give a new insight into the Cala and Aguablanca Variscan plutons concerning depth geometry, volume of mineralization and geological environment, not previously know. The regional model provides the geological context of the complex geological evolution that took place in the southern segment of the Iberian Massif. Furthermore, a predictive model has been constructed, including three areas of high potential for mineralization, based on geophysical studies. In addition occurrences of magnetite deposits related to replacement or skarn formation and possible uranium enrichment would be expected based on the predictive models.