This website uses cookies to enhance browsing experience. Read below to see what cookies we recommend using and choose which to allow.
By clicking Accept All, you'll allow use of all our cookies in terms of our Privacy Notice.
Essential Cookies
Analytics Cookies
Marketing Cookies
Essential Cookies
Analytics Cookies
Marketing Cookies
Read authoritative articles, technical papers, and presentations on a wide range of resource development and environmental issues.
This challenge is best addressed through the development of an absolute prospectivity model that quantifies the estimated probability of undiscovered mineralisation at the cell level.
Updating models is key to ultimate success, and an ongoing cycle of mapping, modelling, predicting, and validating ensures that interpretations remain grounded in reality—even as exploration pushes into deeper terranes.
For prospectivity analysis, SRK typically uses a hybrid approach or develops both model types for comparison. The approaches are considered complementary, as demonstrated in a recent prospectivity- mapping project across Western Australia (WA).
Feature engineering guided by geology is a key step in applying machine learning to mineral exploration.
The study provided a revised interpretation of the Arabian Shield’s geological history, integrating multidisciplinary datasets and expert knowledge.