Amplified intelligence: How AI and expertise solve exploration challenges
In mineral exploration, the debate often centers on whether human intelligence or artificial intelligence (AI) provides the best solutions to complex challenges. While these opposing perspectives are interesting, at SRK Consulting we believe that combining both provides the most powerful results.
When human expertise and machine learning work together, they deepen understanding, accelerate discovery, and enable breakthroughs that were once out of reach.
SRK has pioneered the integration of domain knowledge and advanced AI tools to address exploration challenges. The example below shows how this combined approach improves our clients' exploration workflows.
First, SRK integrated its expertise in exploration geochemistry with data science and machine learning to extract geological insights from complex datasets. By analyzing data across river sediments, soils, and rocks from the national scale to the deposit scale, we identify anomalies in lithology, alteration zones, and multielement metals. Data science tools reveal patterns, which geochemists interpret and integrate into exploration models.
This approach goes beyond traditional geochemical methods to efficiently handle large datasets and reduce data noise. The result is a sharper, more practical understanding of the geochemical patterns that lead to exploration success.
Geological mapping has also advanced through the integration of deep learning and satellite imagery. SRK has developed an AI model that is trained to recognize geological features such as known mineral deposits, gossins, and alteration signatures of artisanal operations. Based on information obtained from field observations, these algorithms can be applied to large areas, enabling exploration campaigns on a national scale. This method saves time and accurately detects key geological features even in remote areas.
Interpretation of the structural features of drill cores has long been difficult and subjective. SRK addresses this problem by combining core photography, deep learning computer vision, and structural geology expertise to map features such as veins, rubble, gouges, foliation, and breccia. Labeling approaches produce reproducible, descriptive output rather than interpretation. Structural geologists can use these data to model the 3D structure of faults and shear zones that control mineralization. Automating this step produces consistent, detailed data that enhances expert interpretation.
These examples demonstrate that human expertise and AI are much more effective together than separately. By leveraging the strengths of both, SRK is solving exploration problems that once seemed insurmountable.
This partnership between humans and artificial intelligence is not only shaping the future of exploration, but defining its present.
