EGU21-15874, updated on 04 Mar 2021
https://doi.org/10.5194/egusphere-egu21-15874
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Hybrid mineral predictive mapping with self-organizing maps and a multilayer perceptron applied to tin deposits in the Erzgebirge, Germany

Andreas Brosig, Andreas Barth, Peggy Hielscher, Claus Legler, Stefan Schäfer, Peter Bock, and Andreas Knobloch
Andreas Brosig et al.
  • Beak Consultants GmbH, Freiberg, Germany (andreas.brosig@beak.de)

Self-organizing maps (SOM) are a useful tool to analyze and interpret gridded datasets like potential field or stream sediment geochemistry data. The data are transformed from geographic space to SOM space where they can be clustered according to overall similarity. By transforming the clusters back to geographic space, geological interpretation of the clusters is facilitated. We present the application of a multilayer perceptron (MLP) in SOM space to produce mineral predictive maps. The reduced number of grid cells in SOM space greatly enhances the performance of the MLP and the tolerance to noise in the input data, compared to an application of the MLP to the original data. The method is applied to tin skarn deposits in the German part of the Erzgebirge. The training and validation data required for the MLP are compiled from mining and exploration records. The input data for the SOM are reprocessed gravimetric, magnetic, stream sediment geochemistry, geologic and tectonic data sets. Potentially ore-controlling spatial relationships, such as the distance to different types of partly covered granite intrusions, are derived from a regional scale 3D geological model. The resulting mineral prediction map allows the definition of exploration zones for detailed studies.

The paper has been compiled in the frame of "NEXT - New EXploration Technologies" project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776804.

How to cite: Brosig, A., Barth, A., Hielscher, P., Legler, C., Schäfer, S., Bock, P., and Knobloch, A.: Hybrid mineral predictive mapping with self-organizing maps and a multilayer perceptron applied to tin deposits in the Erzgebirge, Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15874, https://doi.org/10.5194/egusphere-egu21-15874, 2021.

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