DEEPBEAT
What is DeepBEAT?
A cutting-edge research project developing innovative exploration methods to detect deep-seated ore deposits on land — with minimal disruption for environment and local communities.
The Challenge
Valuable mineral deposits hidden deep underground can leave tiny chemical clues at the surface, but these clues are often weak, altered or difficult to recognise. DeepBEAT aims to make these hidden signals easier to detect by combining smart sampling, advanced geochemical tools, 3D modelling and AI-based analysis.
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Deep-seated ore deposits are often difficult to detect because the chemical signals they leave at the surface can be very faint, altered or hidden by natural background variation. Traditional exploration methods may therefore miss important clues, especially when deposits are located far below the surface.
DeepBEAT addresses this challenge by testing how different surface materials — including plants, mushrooms, stream sediments and soils — can help reveal hidden signals from deep mineral systems. The project compares which materials work best in different geological and ecological settings, and how they can be used more effectively in mineral exploration.
To improve detection, DeepBEAT combines portable and laboratory-based geochemical tools with smart sampling strategies. These strategies are guided by existing geological, ecological and geochemical knowledge, helping researchers choose the most promising sampling locations and the most suitable analytical methods.
The collected surface geochemical data are then integrated with 3D geological models and AI-based prospectivity mapping. This helps researchers better understand where hidden ore deposits may be located and improves the prediction of critical raw material resources beneath the surface.
Surface indicators: Plants, mushrooms, stream sediments, and soils are tested as fit-for-purpose material.
Informed sampling & analytical strategies: Geological, ecological, and geochemical prior knowledge is used to target efficient sampling locations and optimize the selection of analytical tools for mineral exploration.
Optimal ecological and geological environments for detection: Understanding which set of conditions improve exploration efficiency.
AI-Powered 3D prospectivity modelling: Surface geochemical data are processed with AI-driven techniques to generate regional 3D models, improving probability of mineral deposit predictions.