Mineral exploration is fundamentally a search problem – finding valuable deposits hidden beneath surface rocks and soil. Artificial intelligence applied to geospatial data is improving how explorers identify where to focus their efforts.

The Exploration Challenge

Discovery of new ore deposits has become increasingly difficult:

Surface deposits found: Most easily discovered deposits at or near surface have been identified. Future discoveries will come from greater depths.

Rising discovery costs: Average discovery costs per ounce of gold or tonne of copper have increased substantially over recent decades.

Success rates: Exploration is inherently uncertain. Even well-funded programmes frequently fail to find economic mineralisation.

Data richness: Meanwhile, geoscience data volumes have grown enormously. Satellite imagery, geophysical surveys, geochemistry, and geological mapping generate petabytes of potentially useful information.

AI offers methods to extract value from this data abundance.

Geospatial Data Types

Exploration AI integrates multiple data types:

Geological mapping: Surface geology, structure, and alteration provide direct evidence of mineralising systems.

Geochemistry: Soil, stream sediment, and rock chip samples reveal element distributions that may indicate buried mineralisation.

Geophysics: Magnetic, gravity, electromagnetic, and seismic surveys detect physical property variations that distinguish ore from host rock.

Remote sensing: Satellite and airborne imagery capture surface characteristics including subtle alteration signatures.

Historical data: Drill results, historical workings, and past exploration records contain valuable information often underutilised.

Each data type provides partial information. Combining them effectively is the exploration challenge AI addresses.

Machine Learning Approaches

Several machine learning techniques apply to exploration targeting:

Supervised classification: Training models on known deposits to identify similar signatures elsewhere. This requires representative training examples.

Unsupervised clustering: Identifying natural groupings in data that may correspond to different geological environments or prospectivity levels.

Deep learning: Neural networks that learn complex feature combinations directly from raw data, potentially identifying patterns humans wouldn’t recognise.

Generative models: Systems that can synthesise data or simulate geological scenarios, potentially identifying what signatures undiscovered deposits might show.

Each approach has strengths and limitations. Effective exploration AI typically combines multiple techniques.

Practical Applications

Exploration companies are applying AI in various ways:

Regional targeting: Identifying prospective areas within large, underexplored regions. This prioritises where to focus reconnaissance efforts.

Drill target generation: Defining specific drill targets within known prospective areas. AI can identify anomalies warranting expensive drill testing.

Data integration: Combining diverse datasets into unified prospectivity maps that humans couldn’t produce manually.

Analog identification: Finding areas geologically similar to known deposits, anywhere in the world.

Drill programme optimisation: Designing drill programmes that optimally reduce exploration uncertainty.

A group we’ve worked with developing bespoke systems for exploration companies address specific geological and data contexts.

Success Stories

AI-assisted exploration has contributed to several discoveries:

Goldspot Discoveries: This AI-focused exploration company has applied machine learning to multiple projects, identifying targets that led to drill intersections.

OreMaps: Uses AI to process satellite imagery for alteration detection, contributing to exploration programmes in various countries.

Major miners: Rio Tinto, BHP, and others have invested in AI exploration capabilities, though specific discovery attributions are rarely disclosed.

The contribution of AI is often difficult to isolate – it augments traditional methods rather than replacing them entirely.

Data Challenges

Exploration AI faces significant data challenges:

Training data scarcity: Ore deposits are rare. Limited positive examples make model training difficult.

Spatial bias: Known deposits are not randomly distributed. Models can learn spurious correlations with exploration intensity rather than genuine prospectivity.

Data heterogeneity: Data collected at different times, with different methods, at different scales, must be harmonised for analysis.

Coverage gaps: Not all areas have all data types. Models must handle missing data gracefully.

Quality variability: Historical data quality varies. Poor data can mislead models.

Addressing these challenges requires careful data preparation and model design.

Integration with Geology

AI doesn’t replace geological expertise – it augments it:

Domain knowledge: Understanding of geological processes guides feature selection, model design, and result interpretation.

Contextual judgment: AI identifies statistical patterns. Geologists assess whether patterns make geological sense.

Hypothesis testing: AI can test specific geological hypotheses against data, supporting or refuting conceptual models.

Communication: Discoveries must be explained to stakeholders. Geological interpretation of AI results enables this communication.

The most effective exploration AI programmes combine strong data science with deep geological expertise.

Implementation Considerations

Companies implementing exploration AI should consider:

Data inventory: Understanding what data exists and its quality is a prerequisite for AI application.

Problem definition: Clearly defining what AI should address – regional targeting, drill targets, specific commodity – focuses effort productively.

Validation approach: How success will be measured affects model design. Ultimately, drill results validate exploration predictions.

Skills requirements: AI exploration requires both geological and data science expertise. Building or accessing these skills is essential.

Realistic expectations: AI won’t find every deposit. Improvements in targeting efficiency are valuable even if imperfect.

Future Directions

Exploration AI continues to evolve:

Foundation models: Large models trained on diverse geoscience data may enable transfer learning across geological contexts.

Real-time integration: AI that updates predictions as new data arrives, including during drilling programmes.

Uncertainty quantification: Better methods for expressing exploration uncertainty and designing programmes to reduce it.

Autonomous exploration: Systems that can design and execute exploration programmes with reduced human intervention.

The future of mineral exploration will involve closer human-AI collaboration. Geologists armed with AI tools will explore more effectively than either could alone.