Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data
| Autor: | Margret C. Fuchs, Mahdi Khodadadzadeh, Laura Tusa, Richard Gloaguen, Cecilia Contreras, Jens Gutzmer, Kasra Rafiezadeh Shahi |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Abundance estimation
010504 meteorology & atmospheric sciences hyperspectral imaging Science Analyser Hyperspectral imaging 010502 geochemistry & geophysics mineral association 01 natural sciences Data type Random forest drill-core SWIR Support vector machine mineral abundance mapping Data acquisition machine learning General Earth and Planetary Sciences Scale (map) Geology 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Remote Sensing, Vol 12, Iss 1218, p 1218 (2020) Remote Sensing Volume 12 Issue 7 Pages: 1218 Remote Sensing 12(2020)7, 1218 |
| ISSN: | 2072-4292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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