Secret Geochemical Tools You’ve Never Seen

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Timestamps
00:00 Intro – why new geochemical tools
​00:40 Old prospecting vs blind deposits
​01:20 Portable XRF and LIBS
​02:50 DNA, microbes, and hidden ore
​04:15 Hyperspectral satellites and alteration
​05:10 Deep‑sea vents and seafloor metals
​06:50 AI – the brain of exploration
08:00 Autonomous drilling optimization
09:00 Robots mapping caves and mines
10:00 Automated core scanning
11:00 Probabilistic ore models and bias
12:00 Human–AI exploration and green metals
13:10 Closing – every rock has a story

Modern techniques of exploration and secret geochemical tools you’ve never seen are revolutionizing how junior and senior geologists perform geochemical analysis and mineral prospecting using machine learning and AI. This comprehensive guide for the geological community, students of earth science, and ore prospectors investigates the use of ML and AI alongside advanced geochemistry to identify hidden deposits of critical minerals like lithium, cobalt, and rare earth elements. Current field strategies utilize the portable X-ray fluorescence (XRF) analyzer for non-destructive elemental analysis and laser-induced breakdown spectroscopy (LIBS) for detecting light elements such as beryllium and aluminum that traditional tools often miss (Axiom Group, n.d.; Farmonaut, 2025). The integration of hyperspectral analysis, including the TerraSpec Halo mineral identifier and satellite-based multispectral imaging, allows geologists to delineate alteration zones and map surface mineralogy with surgical precision (Axiom Group, n.d.; Farmonaut, 2025). On the cutting edge, genomic analysis and molecular biology are transforming prospecting by using high-throughput sequencing to profile soil microbial communities as biological markers for deeply buried mineralization (Winterburn et al., 2017). Exploration is also pushing into the deep sea, where diving robots like the ROV KIEL 6000 investigate massive sulphides at black smokers and manganese nodules that could secure the global supply of battery metals (GEOMAR, 2020). Artificial intelligence further reshapes the industry through the use of multi-channel convolutional neural networks for seismic interpretation and reinforcement learning for autonomous drilling, effectively reducing non-productive time and optimizing well placement (Adjei et al., 2025; Liu, 2017; Weng & Lee, 2025). By fusing these geochemical signatures with structural information and spectral signals, the modern prospector can generate predictive models that falsify geological hypotheses and minimize the risk of false positives (Caers, n.d.; Montero Mining, 2025; Zeng et al., 2024).

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P. Geo. Ricardo A Valls, M. Sc. and Geo Gadfly
Valls Geoconsultant
ORCID ID- https://orcid.org/0000-0002-5421-0914
Scopus Author ID: 7003369619/35335510700
ResearcherID: S-6604-2018

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Some references
Adjei, K. Y., Odor, G. E., Nurein, S. A., Ogu-Opara, P. C., Ugwu, O. C., Owunna, I. B., & Virginia, E. O. (2025). Optimizing well placement and reducing costs using AI-driven automation in drilling operations. World Journal of Advanced Research and Reviews, 25(02), 1029-1038.
Dodgson, J. (2025, December 12). How AI is reshaping early-stage mineral exploration. Global Mining Review. https://www.globalminingreview.com/mi...
Farmonaut. (2025, July 13). Geochemical analysis in prospecting: 2025 top innovations. https://farmonaut.com/geochemical-ana...
Modern geochemical exploration and multimodal sensor integration. (n.d.).
Modern geochemical methods and innovative mineral exploration techniques. (n.d.).
Winterburn, P. A., Noble, R. R. P., & Lawie, D. (2017). Advances in exploration geochemistry, 2007 to 2017 and beyond. In V. Tschirhart & M. D. Thomas (Eds.), Proceedings of Exploration 17: Sixth Decennial International Conference on Mineral Exploration (pp. 495-505). Mineral Deposit Research Unit.