Our paper “Ore-Waste Discrimination with Adaptive Sampling Strategy” has been published in Natural Resources Research.

From theory to practice

If the Mathematical Geosciences paper laid the theoretical foundation, this paper is where the rubber meets the road. Here we take the information-driven sampling framework and apply it directly to real mining problems – specifically, ore-waste discrimination for grade control and short-term mine planning.

The central question is practical: given a limited budget of samples, how should they be distributed to best distinguish ore from waste in a mining block? The standard industry practice relies on regular grid drilling patterns. Our work demonstrates that an adaptive, information-guided strategy can do significantly better.

Validation on real cases

What makes this paper particularly satisfying is the validation. We tested the approach on three real mining cases, and in each one the adaptive strategy showed clear improvement over the standard regular grid approach. This is not a marginal academic improvement – it translates to better classification of ore and waste blocks, which directly impacts operational decisions and economic outcomes.

For me, this paper closes a loop that started during my doctoral research. The theoretical framework needed to prove itself in realistic scenarios, and it did. Seeing the ideas work on actual mining data, with all the messiness and complexity that entails, was one of the most rewarding moments of my PhD journey. It confirmed that the years spent on formalization were not in vain.