Hyperspectral outcrop characterization for structural mapping


Hyperspectral outcrop characterization for structural mapping

Thiele, S. T.; Kirsch, M.; Lorenz, S.; Gloaguen, R.

Digital outcrop models have become a powerful tool for detailed structural mapping (Bemis et al., 2014), as they allow geological exposures to be characterized in unprecedented detail while simultaneously mitigating access limitations that hinder conventional mapping approaches. In this contribution we present an emerging workflow that fuses digital outcrop data with high resolution ground- and UAV- based hyperspectral imaging products to better discriminate key lithological units (marker horizons) and alteration trends (Lorenz et al., 2018; Kirsch et al., 2019). In some settings, hyperspectral data allows key mineral abundances to be mapped directly to create qualitative mineral maps (e.g., Thiele et al., 2022), however for structural mapping purposes the identification of distinctive marker horizons can be sufficient (e.g., Thiele et al., 2021). We illustrate this workflow with several examples from the Iberian Pyrite Belt (Spain), where the hyperspectral data helped constrain the geometry of deformed volcanic units hosting massive sulphide mineralization. Finally, a preliminary approach for combining (hyperspectral) digital outcrop data and 3-D interpolation algorithms to derive 3-D structural models of open-pit mines is discussed.

Acknowledgements: This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 776487.

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Lorenz, S., Salehi, S., Kirsch, M., Zimmermann, R., Unger, G., Sørensen, E.V., & Gloaguen, R. (2018): Radiometric Correction and 3D Integration of Long-Range Ground-Based Hyperspectral Imagery for Mineral Exploration of Vertical Outcrops. Remote Sensing 10, no. 2:176. https://doi.org/10.3390/rs10020176.
Thiele, S.T., Lorenz, S., Kirsch, M., Acosta, I.C.C., Tusa, L., Hermann, E., Möckel, R., & Gloaguen, R. (2021): Multi-Scale, Multi-Sensor Data Integration for Automated 3-D Geological Mapping Using Hylite. Ore Geology Reviews 136. https://doi.org/10.1016/j.oregeorev.2021.104252.
Thiele, S.T., Bnoulkacem, Z., Lorenz, S., Bordenave, A., Menegoni, N., Madriz, Y., Dujoncquoy, E., Gloaguen, R., & Kenter, J. (2022): Mineralogical Mapping with Accurately Corrected Shortwave Infrared Hyperspectral Data Acquired Obliquely from UAVs. Remote Sensing 14, no. 1 https://doi.org/10.3390/rs14010005.

  • Invited lecture (Conferences)
    European Society for Deformation Mechanisms, Rheology and Tectonics, 04.-07.07.2022, Catania, Italy

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Publ.-Id: 35842