An innovative seismic and statistical approach to understand 3D magmatic structures and ore deposits in the western Bushveld Complex, South Africa

Abstract

In this paper, we demonstrate the effectiveness of a novel approach to identify and quantify structural features that disrupt ore-bearing horizons using 3D seismic attributes and a horizon-surface processing technique (difference-of-two-surfaces) on high-resolution 3D reflection seismic data from the Lonmin Platinum Mine in the Bushveld Complex, South Africa. We demonstrate that seismic attribute analysis is an effective tool to enhance the intelligibility of the UG-2, a PGE-enriched chromitite layer, as well as potholes (slump structures), faults, iron-rich ultramafic pegmatite bodies (IRUPs) and other subtle geological features. Furthermore, the horizon-surface processing technique is able to quantify the dimensions and geometry of potholes. In total, we identify 66 potholes and show that they are probably randomly distributed in 2D on the UG-2 horizon with some extent of clustering. Roughly two classes of potholes are identifiable by size. The larger potholes are clustered around a major normal fault (the Marikana Fault) and its branches in the area. Using a first-order pothole structure/geometry statistical analysis, we demonstrate that: (1) the diameters and surface areas of the potholes are strongly related to their depths; and (2) the volume of the potholes is substantially log-normally distributed. We interpret these findings to implicate a coherent and pervasive pothole formation mechanism. Furthermore, we show that statistically-derived pothole structure/geometry knowledge can be used to constrain the validity of several hypotheses. Our findings indicate the most probable pothole formation hypothesis is a widespread extensional regime in the Bushveld Complex magma chamber. Therefore, our study provides important, large-scaled and highly data-driven insights with an approach that can be applied to a wide range of magmatic structures. Furthermore, relationships identified between various pothole characteristics, in addition to their academic value, also allows for predictive modelling that can guide mine planning and resource extraction.