Light Detection and Ranging (LiDAR) data plays a major role in detecting terrain anomalies related to morphogenetic processes. In this study, a multi-temporal LiDAR analysis was carried out to identify structurally controlled alteration zones related to karstification within carbonate rocks that may be related to Copper-Cobalt-Nickel mineralisation within a study area located in Asturias, Northwest Spain. The style of alteration can cause nivo-karst dolines and niches, which can be accelerated by the annual accumulation and melting of snow on the Aramo Plateau, which forms the core of the study area. Three LiDAR datasets were used to compare terrain alterations during an 11-year period (2012-2023). The first two campaigns were conducted by the Spanish National Geographic Institute (IGN). The third campaign was performed for a geological exploration survey within the scope of the S34I Horizon Europe Project – Secure and Sustainable Supply of Raw Materials for EU Industry. The original point cloud datasets were processed in order to generate a Digital Terrain Model (DTM) with one-meter spatial resolution. A raster math operation was then carried out on the products to detect anomalous height differences, considering the vertical root-mean-square-error (RMSE) of each product. Several height differences were identified, which require validation in the field to check and verify the nature of these anomalous occurrences. In the future, further studies are being developed based on the Copernicus Sentinel-1 mission data, using the Differential Interferometry Synthetic Aperture Radar (DInSAR) technique to detect more subtle subsidence of the terrain.
The S34I (Secure Sustainable Supply of Raw Materials for EU Industry) project aims to enhance European autonomy in raw materials production by prototyping Earth Observation (EO) methods, which can support the multiple-phase approach of the exploration and mining industry. The present study explores two ensemble machine learning algorithms based on decision trees, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), to predict alteration zones associated with Copper-Cobalt-Nickel mineralisation in northwest Spain. The study site is located in Asturias, Spain, within the Saint Patrick License area, which encompasses the Aramo Plateau adjacent to the historical AramoTexeo Mine. The training dataset was extracted using bands from the Landsat-9 and PRISMA satellites referencing lithogeochemical alteration zones and associated anomalous mineralisation previously identified during active exploration programmes conducted by Aurum Global Exploration. Independent Component Analysis (ICA) was applied to the satellite bands to reduce the dimensionality and increase computational efficiency. As a result, the pixels of the image have been classified as either host rock or alteration zone. The RF algorithm achieved a mean classification accuracy of 0.97 for the PRISMA image. The accuracy for the Landsat-9 image was at 0.90. The XGBoost algorithm demonstrated an accuracy of 0.95 for the PRISMA image and 0.82 for the Landsat 9 image, indicating reduced overfitting. The results enable the creation of predictive mineral maps that can support exploration programmes for CRMs, establish the resource potential of new areas in Europe, and ultimately lead to sustainable and ethical European-based mining practices.
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