The Art and Science of Geology: Resource Models – More than Just Grades Stuffed into Blocks

Resource estimation could be considered equal parts Art / Science / Math, due to its foundations in statistics and calculus, its incorporation of physical, chemical, and geological principles, and its requirement for subjectivity and professional opinion.

No geostatistical method, nor the most experienced professional, can account for or correct errors in underlying data or improper understanding of geological controls.

Resource estimation is based on the fundamentals of proper deposit sampling, digital database integrity, geological understanding of mineralization controls, use of appropriate estimation methods, and application of suitable confidence classification.

Resource estimation methodologies take traditional statistics and add spatial continuity relationship criteria to alter the process. The concept is that geological features are not random, and that measurable spatial continuity exists between sample points. The continuity that needs to be captured is equal parts geological and grade. If we want to appropriately utilize the principles of geostatistical methods, we must acknowledge that the spatial continuity we are trying to capture—and then understand and reproduce—is a function of the underlying geological controls.

So, why then is geology so often forgotten in resource estimation? What are the real-life implications for the resulting resource model, stope designs, pit optimization, mine plans, budgets, and investment decisions, if we get the geology wrong? How have advancements in implicit modeling changed the geological modeling scene? What are some common pitfalls in geological modeling, and what steps could be taken to appropriately validate and support the geological model that will form the foundation of the entire resource model?