Abstract
The important goals of mineral exploration geochemistry are detection and identification of underlying mineralization. This paper deals with element concentration data analyzed of surface geochemical samples acquired from soil horizons or plants. A new unsupervised procedure is proposed for this purpose when the samples have been taken on a regular or irregular grid in the area under investigation. The methodology is based on Generalized Additive Model fits on element concentration data. Then new data points are taken of the surface of the smooth fits across the entire sampling area as a regular grid. Pairwise log-ratios of elements are then calculated of these points, and curvature of the log-ratio pairs is computed. High curvature indicates abrupt spatial changes, which could point at locations of mineralized zones. A measure called c-value evaluates the overall curvature and thus serves as an importance measure of a log-ratio pair. The methodology is tested on two real surface geochemical data sets collected in areas with known underlying mineralization, and the results confirm existing knowledge of the underlying mineralization.
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Acknowledgements
The authors thank Jukka Va\(\ddot{\mathrm{l}}\)imaa and Jyrki Korteniemi (Agnico-Eagle Finland Ltd., Kittilä Mine, Finland) for allowing access to drill core geochemical data and plant sampling at Tiira, and Sami Lepistö and Tero Niiranen (Geological Survey of Finland, GTK) for 3D and lithogeochemical expertise. Päivi Heikkilä, Matti Piekkari, Petro Oravalahti and Markku Virtanen (GTK) performed the plant sampling and sample preparation. The sample collection and analytics was funded by the GTK project Postglacial faults (# 50402-20086, 2016–2019). Many thanks go to Simon M. Thaarup and Anders Risbjerg (Geological Survey of Denmark and Greenland, GEUS), for the sample collection and useful information related to data analytics. This activity received funding from the European Institute of Innovation and Technology (EIT), a body of the European Union, under the Horizon 2020, the EU Framework Programme for Research and Innovation. Data analyses was funded by the EIT Raw Materials project UpDeep - Upscaling deep buried geochemical exploration techniques into European business (2017–2020) (Grant No. 16329). This research of Christopher Rieser was supported by the Austrian Science Fund (FWF) under the Grant No. P 32819 Einzelprojekte.
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Mikšová, D., Rieser, C., Filzmoser, P. et al. Identification of Mineralization in Geochemistry for Grid Sampling Using Generalized Additive Models. Math Geosci 53, 1861–1880 (2021). https://doi.org/10.1007/s11004-021-09929-x
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DOI: https://doi.org/10.1007/s11004-021-09929-x