Seismic imaging of the gold deposit and geological structures through reprocessing of legacy seismic profiles near Kloof–Driefontein Complex East Mine, South Africa

Abstract

Two legacy reflection seismic profiles were acquired in 1988, north of the Kloof–Driefontein Complex East Mine in the West Rand goldfield (South Africa), for the purpose of gold exploration and mine planning. These legacy 2D seismic data have been reprocessed using the latest processing tools to improve imaging. Special interest is given to the Black Reef Formation, which hosts a known gold orebody. The original legacy data are of poor quality, especially in areas that are dominated by dolomitic outcrops. To improve the quality of the data, special attention was given to the refraction static correction to enhance the continuity of the reflections below dolomitic rocks. Refraction seismic tomograms from both profiles exhibit three‐layer P‐wave velocity models: (1) topsoil (1000–2000 m/s), (2) a weathered layer ranging from ca. 100 to 300 m in thickness (2000–5000 m/s) and (3) bedrock (> 5000 m/s). Seismic profile OK‐212 shows poor imaging of the Black Reef Formation because of the scattering of seismic energy in the near‐surface due to dolomites from the Transvaal Supergroup, while seismic profile OK‐213 exhibits south‐dipping reflections that are associated with the Black Reef Formation. To improve the structural imaging resolution, we tried pre‐stack time migration, pre‐stack depth migration and post‐stack time migration using the Kirchhoff algorithm. PreSDM most improved the imaging of deeper reflections due to its ability to honour complex lateral variations in the velocity field. Both pre‐stack time migration and post‐stack time migration enhanced the continuity of the near‐surface reflections below the dolomitic rocks.

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

“…This is a product of geoscientific evolution, as data generation methods changed over the history of resource exploration and extraction. Nonetheless, such issues may be remediated using a combination of modern data science and discipline-specific practices (e.g., Nwaila et al, 2019;Zhang et al, 2021b, c;Mutshafa et al, 2022). The benefits of legacy data cannot be understated.…”

“…In addition, digital-and data-driven insights that are derived from legacy data provide a competitive advantage for deep underground mining. Scientists routinely discover new insights from legacy data that ranges from characterisation, prediction and hypothesis testing of deep geological structures to geometallurgical properties of various ores (e.g., Cowan, 2020;Mutshafa et al, 2022). From an engineering perspective, legacy data has been successfully used to create machine learning algorithms that are highly specific to currently relevant data (e.g., Wu and Zhou, 1993;Jafrasteh et al, 2018;Zhao and Niu, 2020).…”