Due to the extensive drilling performed every year in exploration campaigns for the discovery and evaluation of ore deposits, drill-core mapping is becoming an essential step. While valuable mineralogical information is extracted during core logging by on-site geologists, the process is time consuming and dependent on the observer and individual background. Hyperspectral short-wave infrared (SWIR) data is used in the mining industry as a tool to complement traditional logging techniques and to provide a rapid and non-invasive analytical method for mineralogical characterization. Additionally, Scanning Electron Microscopy-based image analyses using a Mineral Liberation Analyser (SEM-MLA) provide exhaustive high-resolution mineralogical maps, but can only be performed on small areas of the drill-cores. We propose to use machine learning algorithms to combine the two data types and upscale the quantitative SEM-MLA mineralogical data to drill-core scale. This way, quasi-quantitative maps over entire drill-core samples are obtained. Our upscaling approach increases result transparency and reproducibility by employing physical-based data acquisition (hyperspectral imaging) combined with mathematical models (machine learning). The procedure is tested on 5 drill-core samples with varying training data using random forests, support vector machines and neural network regression models. The obtained mineral abundance maps are further used for the extraction of mineralogical parameters such as mineral association.
The rapid mapping and characterization of specific porphyry vein types in geological samples represent a challenge for the mineral exploration and mining industry. In this paper, a methodology to integrate mineralogical and structural data extracted from hyperspectral drill-core scans is proposed. The workflow allows for the identification of vein types based on minerals having significant absorption features in the short-wave infrared. The method not only targets alteration halos of known compositions but also allows for the identification of any vein-like structure. The results consist of vein distribution maps, quantified vein abundances, and their azimuths. Three drill-cores from the Bolcana porphyry system hosting veins of variable density, composition, orientation, and thickness are analysed for this purpose. The results are validated using high-resolution scanning electron microscopy-based mineral mapping techniques. We demonstrate that the use of hyperspectral scanning allows for faster, non-invasive and more efficient drill-core mapping, providing a useful tool for complementing core-logging performed by on-site geologists.
La definición de las ecorregiones incide sobre el uso de la tierra ya que éstas son tomadas como base para la formulación de políticas de manejo y conservación. Los disturbios provocan variaciones de la vegetación entre sitios cercanos dificultando el establecimiento de límites entre regiones. Con el objetivo de analizar un gradiente geográfico en un área transicional entre el Espinal y el Monte, se caracterizaron estructura y composición de la vegetación leñosa en el noreste de la Patagonia y se evaluó en qué medida sitios cercanos con diferencias estructurales presentaban diferencias composicionales asimilables a cada ecorregión. La variación de la composición florística presentó cambios más importantes en dirección E-O que en dirección N-S, reflejando un amplio gradiente de transición Espinal-Monte. Los cambios estructurales más claros fueron la disminución de la altura y cobertura arbórea en oposición a la cobertura arbustiva. Tales cambios resultaron menos asociados con la variación geográfica que los cambios composicionales, exhibiendo mayor variación en dirección N-S y en menor medida en la dirección E-O. Los disturbios promueven cambios más notables en la estructura que en la composición florística por lo que resulta poco probable que provoquen un corrimiento de límites geográficos entre las ecorregiones Monte y Espinal.
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