Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms

Cavur, Mahmut; Yu, Yu-Ting; Demir, Ebubekir; Duzgun, Sebnem

doi:10.3390/rs16071223

Title: Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms

Journal Article · 30 March 2024 · Remote Sensing

DOI: https://doi.org/10.3390/rs16071223 · OSTI ID:2331359

Cavur, Mahmut;

;

Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration.

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Sponsoring Organization:: USDOE

OSTI ID:: 2331359

Journal Information:: Remote Sensing, Journal Name: Remote Sensing Vol. 16 Journal Issue: 7; ISSN 2072-4292

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

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