LKI Consulting defines geological systems that reflect how they behave, rather than how they are simplified for interpretation or modeling.
The industry often relies on hierarchical and visually interpreted datasets, which provide essential context but can underrepresent variability present in the data.
We take a data-driven approach that integrates geology, geochemistry, mineralogy, and spatial datasets in both two and three dimensions. The work focuses on identifying meaningful variation within the system and ensuring that interpretations reflect that variation.
The result is a clearer representation of the system, supporting stronger domain definition, more informed targeting, and better alignment across technical workstreams.
Exploration
In exploration settings, this work focuses on defining lithology, alteration, and mineralization in a more quantitative and integrated way, particularly where legacy datasets form the foundation of current interpretation.
Lithogeochemistry is used to distinguish lithological units based on consistent chemical signatures rather than visual interpretation alone. Alteration systems are defined through geochemical and mineralogical patterns, providing a clearer understanding of fluid pathways and overprint relationships. Mineralization is then interpreted within this framework, supporting vectoring based on how these components relate to one another.
The outcome is a more coherent geological model that improves targeting by distinguishing meaningful signal from background variation and aligning interpretation with the underlying data.
Development and Operations
In development and operational settings, this work is applied across brownfields and generative exploration, as well as in refining geological models within producing assets.
The focus is on integrating legacy and current datasets to build coherent lithogeochemical and alteration frameworks that improve understanding of the system. Where appropriate, mineralization is interpreted within this context to support vectoring and ongoing targeting.
A key component is the development of domain models that reflect meaningful variation present in the data. These domains are designed to align with how the system behaves and can be used by technical teams to support resource modeling and other downstream workflows.