Evaluation of normalization methods applied to Short-Wavelength Infrared (SWIR) spectroscopy mineral databases from multiple instruments and for vectoring analysis exploration
Over the past decade, short-wave infrared (SWIR) spectroscopy has made significant advances in detecting geochemical variations in minerals like white mica, alunite, and chlorite for exploring hydrothermal ore deposits. These variations provide valuable clues, indicating changes in temperature, pH,...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | Spanish |
| Published: |
Universidad Nacional de Colombia
2024-12-01
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| Series: | Boletín de Ciencias de la Tierra |
| Subjects: | |
| Online Access: | https://revistas.unal.edu.co/index.php/rbct/article/view/113445 |
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| Summary: | Over the past decade, short-wave infrared (SWIR) spectroscopy has made significant advances in detecting geochemical variations in minerals like white mica, alunite, and chlorite for exploring hydrothermal ore deposits. These variations provide valuable clues, indicating changes in temperature, pH, and fluid oxidation state towards the mineralized center. However, small calibration differences among devices challenge data integration. This study evaluates the 2200 nm Al-OH absorption feature in four white mica SWIR spectroscopy databases collected by TerraSpec™ and OreXpress™ from samples at the Grasshopper porphyry prospect. It evaluates three normalization methodologies: rescaling, mean normalization, and Z-score, yielding p-values for successful data merging of up to 0.75. Findings suggest effective normalization methods across devices, reducing biases from uncalibrated spectrometers. This research offers a methodology to correct SWIR database biases, facilitating accurate data integration across instruments for vectoring analysis.
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| ISSN: | 0120-3630 2357-3740 |