Greenfields exploration is normally conceptual, relying on the predictive power of the ore genesis models, to search for the mineral resource in an unexplored virgin ground. Brownfields exploration is conducted within geological terrains which are closer to known ore deposits.
Brownfields is less risky, as the geology is better understood and the exploration methodology is well known, since large deposits have already been found. In such cases, the rewards are less.
Greenfields, on the other hand, has a lower strike rate, since the geology is less understood at the conception of exploration programme, but rewards are greater because it is easier to find a bigger resource with concerted exploration effort. Exploration done to expand a mineral resource that has already been found and developed into a mine is called “on-mine-site” exploration (Gandhi & Sarkar, 2016).
For greenfields exploration, geological analysis is a key component. A territory could have been explored and drilled earlier without the requisite success. But application of new exploration concept or new exploration technology, it is possible to find the area prospective for a mineral resource, not sought there before.
Detailed mapping and application of advanced visualization of structural data are essential for brownfields exploration projects for success. Advances in 3D modelling enhance the probability of success in brownfields.
Exploration targeting from geophysical data and geochemical data and their interpretation will help to design drilling programme and also search for buried resources.
Rock outcrops, detailed drill core logs, evaluation of aerial photos, and geophysical data help an explorationist to decipher the tectonic structure of the area of interest. 3D modelling and visualization will give an idea of the resource and its projection (Gandhi & Sarkar, 2016).
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Reference
Gandhi, S. M., & Sarkar, B. C. (2016). Chapter 7—Geological Exploration. In S. M. Gandhi & B. C. Sarkar (Eds.), Essentials of Mineral Exploration and Evaluation (pp. 159–198). Elsevier. https://doi.org/10.1016/B978-0-12-805329-4.00014-4
