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3.Technologies ahead of the Face

Technologies ahead of the face

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Technologies ahead of the face

Electric Resistance Tomography (ERT)
Statistical Analysis and Machine Learning
Tunnel Seismic Profiling (TSP)

Mining operations require advanced technologies to detect geological structures and ore distribution ahead of the mining face. The Advanced Orebody Knowledge (AOK) Programme focuses on improving subsurface exploration using geophysical, computational, and AI-driven techniques to enhance safety, efficiency, and mine planning.

The Challenge

By detecting geological structures, orebody variations, and potential hazards in advance, technologies ahead of the face enable proactive decision-making, reducing safety risks, minimising operational disruptions, and optimising resource extraction efficiency in underground mining. The challenge:

  • Difficulty in accurately understanding the underground orebody.
  • Hazards due to insufficient geological data.
  • Inefficient resource management leading to wastage.
  • High operational costs due to uncertainties.
  • Limited mine life from suboptimal extraction.
  • Environmental and community impacts.
Tunnel Seismic Profiling (TSP):

The Smart Drill is being developed under the Advanced Orebody Knowledge (AOK) programme in collaboration with the University of Pretoria, to enhance underground diamond drilling through automation and AI-driven optimisation. 

  • Utilizes seismic methods to map geological structures deep ahead of mining faces.
  • Helps detect geological hazards like faults or rock-type variations.
  • Proven to be beneficial for deep-level gold and platinum mines in South Africa, providing valuable geological information ahead of operations and significantly enhancing safety and productivity.
Electric Resistance Tomography (ERT):
  • Employs electrical resistivity measurements to detect geological disruptions, such as potholes and reef disturbances in tunnels.
  • Offers detailed, non-invasive imaging of subsurface geological features.
  • Supports informed decision-making by reducing risks and operational uncertainties, resulting in safer and more efficient mining operations.
Statistical Analysis and Machine Learning:
  • Utilizes data-driven predictive techniques to forecast geological hazards, such as potholes, faults, and variations in ore grade, ahead of the mining face.
  • Machine learning models, trained using historical and current geological data, enable accurate predictions and proactive management of geological risks.
Tunnel Seismic Profiling (TSP):
  • Effectively identified geological structures, faults, and ore distributions ahead of mining faces.
  • Provided high-resolution seismic imaging for better geological understanding.
Electric Resistance Tomography (ERT):
  • Successfully detected reef disruptions, potholes, and geological irregularities in mining tunnels.
  • Developed procedures and guidelines, enabling routine surveys within one shift.
Statistical Analysis and Machine Learning:
  • Achieved accurate predictions of geological hazards and ore variations.
  • Developed improved workflows incorporating deep learning for reliable geological predictions.
Tunnel Seismic Profiling (TSP):
  • Proven and actively implemented (TRL 9 – fully operational).
  • Successfully tested and validated in deep-level platinum and gold mining environments.
Electric Resistance Tomography (ERT):
  • Advanced stage of development and testing completed.
  • Operational guidelines and standardized procedures developed for routine mine use.
Statistical Analysis and Machine Learning:
  • Successfully applied in predicting geological hazards and ore variations.
  • These technologies have reached advanced development stages, with some fully operational and others nearing routine implementation​.
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Electric Resistance Tomography (ERT)

Statistical Analysis and Machine Learning
Tunnel Seismic Profiling (TSP)

Electrical Resistance Tomography (ERT) is a geophysical technique used in mining and geosciences to map subsurface structures by measuring variations in electrical resistivity. It enables the detection of geological disruptions, such as reef disturbances and potholes, ahead of mining tunnels. By providing detailed, non-invasive imaging of subsurface geological features, ERT plays a crucial role in early geological risk identification, improving safety, mine planning, and resource optimization.

Challenge
  • Geological Uncertainty – Detects reef disruptions, faults, and potholes ahead of tunnels, improving geological confidence.
  • Safety Risks – Identifies hazardous zones early, reducing rockfall and water ingress risks.
  • Inefficient Mine Planning – Provides real-time subsurface imaging for better mine design and extraction strategies.
  • High Operational Costs – Minimizes delays and equipment damage by enhancing predictive capabilities.
  • Limited Visibility – Maps geology beyond the mining face, aiding proactive decision-making.
Research and development

The Smart Drill is being developed under the Advanced Orebody Knowledge (AOK) programme in collaboration with the University of Pretoria, to enhance underground diamond drilling through automation and AI-driven optimisation. 

  • Extensive testing was conducted between 2018–2023.
  • Research was conducted collaboratively by the Council for Scientific and Industrial Research (CSIR) and mining industry partners.
  • ERT has progressed through feasibility studies, field trials, and industry adoption to enhance reef disruption detection in mining tunnels.
  • Testing confirmed its effectiveness in identifying geological disruptions like potholes.
  • Trials successfully demonstrated detection of geological structures and significant improvements in logistics, enabling surveys within a single shift.
  • Field trials optimised survey logistics, data acquisition, and processing.
Findings
  • Effectively resolved geological hazards, significantly reducing uncertainties associated with mining operations.
  • Established potential as a routine reconnaissance tool due to improved efficiency in logistics and data management.
  • ERT has been successfully tested and validated for detecting reef disruptions, potholes, and geological anomalies ahead of mining tunnels.
  • Guidelines and standardised procedures for in-mine ERT surveys have been developed.

Further development will focus on refining AI models, conducting additional field trials, and advancing towards Autonomous Drilling.

Status
  • ERT is at an advanced stage of development with clear operational guidelines, ready for routine implementation in mining environments.
  • Mines have been slow to adopt ERT as the process remains tedious and time-consuming for routine use.
  • Despite its potential, logistical challenges and data processing complexities have had limited widespread industry uptake.
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Statistical Analysis and Machine Learning

Electric Resistance Tomography (ERT)
Tunnel Seismic Profiling (TSP)

Statistical analysis and machine learning are used to predict geological hazards such as potholes and faults, as well as ore-grade variations ahead of mining faces. These techniques enable accurate and timely predictions, allowing for proactive management of geological uncertainties to enhance safety, efficiency, and resource optimization in mining operations.

Challenge
  • Statistical analysis and machine learning aim to address key geological and operational challenges in mining, including:
  • Geological Uncertainty – Difficulty in accurately predicting faults, potholes, and ore-grade variations ahead of mining faces, leading to inefficient extraction.
  • Safety Risks – Unanticipated geological hazards increase the risk of rockfalls, equipment damage, and worker injuries
  • Inefficient Mine Planning – Traditional methods rely on limited geological data, resulting in suboptimal resource allocation and increased operational costs.
  • Time-Consuming Data Processing – Manual geological assessments can be slow and inconsistent, delaying critical mining decisions.
  • High Resource Wastage – Poor ore-body delineation leads to unnecessary dilution and inefficient ore recovery.
  • Limited Predictive Capabilities – Conventional techniques struggle to forecast geological disruptions in real time, affecting production continuity.
Research and development  
  • Predicting Geological Variables: Deep learning models predict potholes in PGM mines, though challenges remain beyond that range.
  • Gold & PGM Resource Prediction: Machine learning models refine ore grade and fault predictions using spatial weighting.
  • Seismic Data & AI Integration: 3D seismic data and CNN models improve geological structure identification in Tunnel Seismic Tomography.
Findings
  • Geological Prediction: ML models identify potholes and faults in PGM and gold mines.
  • Orebody Insights: AI predicts ore grades and geological structures with improved accuracy.
  • Seismic Integration: CNN models enhance 3D seismic imaging for better subsurface mapping.
Status
  • Currently pending outcomes from ongoing comparative analyses to confirm the final effectiveness and operational readiness.

Tunnel Seismic Profiling (TSP)

Tunnel Seismic Profiling (TSP) is an advanced geophysical technique used in mining to predict geological conditions ahead of the mining face. It helps in reducing drilling costs, improving safety, and optimizing orebody extraction.

Challenge
  • Limited Orebody Knowledge - Improves 3D subsurface imaging for better planning.
  • Safety Risks - Detects faults and unstable rock ahead of mining.
  • Resource Waste - Optimizes extraction, reducing losses.
  • High Costs - Lowers drilling and exploration expenses.
  • Short Mine Life - Supports long-term sustainability.
  • Environmental Impact - Enables precision mining, reducing waste​
Research and development

The Smart Drill is being developed under the Advanced Orebody Knowledge (AOK) programme in collaboration with the University of Pretoria, to enhance underground diamond drilling through automation and AI-driven optimisation. 

  • Technology Evaluation: Compared TSP with passive seismic monitoring, confirming its suitability for deep-level mining.
  • Field Trials: Conducted active and passive seismic surveys at platinum and gold mines.
  • Innovation & Integration: Combined TSP with Ground Penetrating Radar (GPR) for enhanced geological mapping.
  • 3D Seismic Modelling: Used broadband sensors, fiber optics, and GPS synchronization to improve data accuracy.
  • Extended Testing: Applied in deep gold mines to evaluate performance at greater depths with the research led by the University of the Witwatersrand, in collaboration with numerous partners from the European Consortium, as part of the parallel Innovative Mineral Exploration (FUTURE) project, a joint European Union and South African consortium.
Findings
  • Successfully tested at platinum and gold mines in South Africa between 2018 and 2023.
  • Active-source and passive seismic surveys conducted, integrating surface and underground seismic data.
  • Improved imaging resolution using advanced seismic instruments, broadband nodal sensors, and fiber-optic cables.
  • High-resolution images successfully identified geological structures and major orebody intersections.
  • Provided valuable insights into subsurface geology, enhancing mine planning and hazard management.

Further development will focus on refining AI models, conducting additional field trials, and advancing towards Autonomous Drilling.

Status
  • TSP is at Technology Readiness Level (TRL) 9, fully operational and proven in mining environments.
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