An overview of how AI and machine learning can be applied across oil and gas, mining, water, geotechnical, carbon and non-terrestrial resource questions.
Exploration disciplines share a recurring challenge: high-dimensional datasets, incomplete observations and costly decisions made under uncertainty. Machine learning can help identify patterns across seismic, geological, geochemical and spatial information that are difficult to resolve through isolated analysis.
GeonatIQ develops models around the geological setting and the decision at hand. That may mean screening regional opportunities, ranking targets or testing relationships between subsurface properties and observed signals.
Cross-sector research is particularly valuable because methods developed in one domain can often be adapted carefully to another, from conventional resources and groundwater to carbon storage and planetary exploration.