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Data-driven study of mineral mapping for geothermal exploration, A
Yu, Yu-Ting
Yu, Yu-Ting
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2025
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2026-11-11
Abstract
Geothermal energy has emerged to play a pivotal role in the green energy transition. It is considered a critical renewable energy source due to its reliability as a baseload energy source. Geothermal exploration and development are challenging due to geological complexities. Mineral mapping is a powerful tool that supports geological surveys. However, making unbiased decisions during mapping procedures and efficiently interpreting mineral maps can be challenging. A data-driven study was introduced to evaluate mineral mapping approaches and explore the relations between mapped indicator minerals and other geothermal exploration parameters, including faults and land surface temperatures (LST). The study established a local Coso Spectral Library based on the sample of the Coso geothermal field in the USA. The library offers spectra for mapping tasks and provides ground truth information for accuracy assessment for mineral maps.
To identify the influence of mapping methods in mineral mapping, mineral maps were evaluated based on varied implementations of mapping algorithms, fusion combinations, and splice correction. The result identified that different mapping algorithms offered different mapping accuracies. Additionally, the fusion of mineral maps and splice correction increases mapping accuracy.
The study further assesses the influence of spectra sources on mineral maps, including spectral variabilities and spectral libraries. The result identified that spectral variability could significantly impact the accuracy of specific mapping algorithms. The local spectra library also proved to offer mineral maps with higher accuracy levels.
With the refined mineral mapping strategy based on the study of Coso, the coverage of study areas was expanded to Brady geothermal fields and Desert Peak geothermal fields to discover relations between the maps of indicator minerals, faults, and LSTs. The study integrated non-spatial and spatial techniques to explore significant relations between the selected geothermal exploration parameters. The results have discovered that different indicator minerals present varied tendencies toward faults and elevated LST. The finding elucidates the ability of data analysis to identify subtle patterns for geological interpretation and geothermal exploration.
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