Use of novel 3D seismic technology and machine learning for pothole detection, characterization, and classification — Case study in the Bushveld Complex (South Africa)

Abstract

We demonstrate that integrating 3D reflection seismics with machine learning (ML) can bring many benefits for the future development of the mining industry. We use a serial integration of reflection seismics, which identifies economic horizon-depression structures known as potholes within the western Bushveld Complex. Thereafter, agglomerative clustering is applied to the resulting data, using features engineered from the physical characteristics of the potholes. Our results indicate that potholes can be divided into several classes based on characteristic features; e.g., large potholes are substantially less steep than small potholes. Furthermore, we model this empirical relationship and show that it can be used to predict average sizes of potholes given their typical in-mine exposures. We also demonstrate that pothole formation is likely to have been initiated depth-wise, followed by lateral increases in size. Lastly, we demonstrate that our serial application of seismically based data generation and ML-based data analytics is a viable alternative to conventional geostastistical analysis, especially for the classification, prediction, and modeling of geologic structures such as potholes.

Moving towards deep underground mineral resources: Drivers, challenges and potential solutions

“…The journey from initial consideration, proof-of-concept, proof-of-value to adoption is long but constitutes almost half of the roadmap to a dominantly automated context (Global Mining Guidelines Group, 2019). The history of mining is extensive, and particularly in South Africa where underground mining has generated enormous amounts of irreplaceable data (e.g., Manzi et al, 2015;Westgate et al, 2020;Zhang et al, 2021b;Nwaila et al, 2022). However, legacy data can be difficult to comprehend, as metadata, reference data and other supplementary information are incomplete, missing or not machine readable.…”