Geologist in the Loop: A Hybrid Intelligence Model for Identifying Geological Boundaries from Augmented Ground Penetrating Radar

Abstract

Common industry practice means that geological or stratigraphic boundaries are estimated from exploration drill holes. While exploration holes provide opportunities for accurate data at a high resolution down the hole, their acquisition is cost-intensive, which can result in the number of holes drilled being reduced. In contrast, sampling with ground-penetrating radar (GPR) is cost-effective, non-destructive, and compact, allowing for denser, continuous data acquisition. One challenge with GPR data is the subjectivity and challenges associated with interpretation. This research presents a hybrid model of geologist and machine learning for the identification of geological boundaries in a lateritic deposit. This model allows for an auditable, probabilistic representation of geologists’ interpretations and can feed into exploration planning and optimising drill campaigns in terms of the density and location of holes.

Keywords

• ground-penetrating radar
• Gaussian processes
• uncertainty modelling
• hybrid intelligence
• optimised exploration