3D mineral prospectivity mapping of the Bockau tin deposit
Authors: Bohlender Paul , Brosig Andreas , De La Rosa Roberto , Knobloch Andreas , Barth Andreas 
Published: February 2023

To ensure Europe increases its domestic production of high quality and responsibly produced raw materials, the development of innovative technologies for 3D geological modeling in mineral exploration is paramount. The Erzgebirge in Germany provides an excellent framework to showcase the application of artificial intelligence and in particular Artificial Neural Networks (ANN) for 3D mineral prospectivity mapping. The Erzgebirge belongs to the Variscan Belt, withholding 800 years of mining history and it is also famous for Ag, Sn, W, Fe, Cu, Li mineralizations among others. The Bockau deposit is located at the western section of the Erzgebirge. The target area is a Paleozoic metasediment body that was formed during the Variscan orogeny. The metasediment body consists primarily of alternating micaschist, phyllite and quartzite and dips mostly 25&#176; to 240&#176; SW. The metasediment is surrounded by Late Variscan plutons which partly led to contact metamorphic zones. In addition there is a large Quartzite body which was mined near to the surface in the 17th century for Sn, following a stratiform tin anomaly which can reach up to 4000 ppm Sn.
Thanks to the long mining history, the Bockau deposit condenses a large amount of geological, geochemical, geophysical and mineral data. To increase mineralogical knowledge of the deposit and to help identify drilling targets, a hybrid approach for 3D mineral predictivity mapping is implemented. Potentially mineralisation-controlling factors are identified in knowledge-driven genetic exploration models, taking into account the borehole data, major faults, electromagnetic data, intrusive bodies, contact metamorphic zones and lithological borders, followed by data-driven weighted ANN predictive modelling implemented in the in-house developed advangeo&#174; 3D Prediction Software. The predictive model is guided by structural variables such as the euclidean distance to fault planes, lithological surfaces and to metamorphic contact zones. The model is also constrained by geophysical data by a magnetic susceptibility model obtained from an airborne magnetic data inversion. Finally, Sn anomaly data from boreholes is implemented as training data for the prediction.
The results show the probability distribution of Sn mineralisation occurrence in 3D over a voxel model formed by blocks of approximately 684 m3 13(x), 13.5(y) and 4(z), increasing the mineralogical knowledge of the deposit and guiding exploration efforts complementing the decision making process for drilling new targets. The results are validated by iteratively implementing the jackknife method, splitting the training data into validation and training subsets. The first prediction iteration is performed with a subset containing 77 % of the Sn content data from boreholes as training data, followed by 50 and 30 % subsets. Thus, allowing at each iteration to perform a quantitative evaluation of the prediction by comparing the validation subset with the Sn content of the borehole that was not used for the prediction.
The paper has been prepared in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through the Grant Agreement 869398.