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Category: Mining and Resources

  • Automated First-Break Picking for Seismic Surveys

    Automated First-Break Picking for Seismic Surveys

    Automated First-Break Picking for Seismic Surveys

    Automated First-Break Picking for Seismic Surveys

    Overview

    We worked with HiSeis to automate first-break picking using deep learning that detects refracted wavelets across millions of geophone traces, achieving accuracy within 4 milliseconds of an expert.

    Problem

    Manual first-break picking is slow and error-prone in noisy data. Existing tools and published techniques at the time were not accurate enough for HiSeis to reliably identify wavelets refracting from different rock formations.

    Solution

    • Deep-learning approach: Applied computer-vision techniques to learn patterns in audio-vibration and geophone sensor data to predict target wavelets among large volumes of traces.
    • Expert-grade accuracy: September AI’s method performed within a 4 millisecond error of an expert technician and, in many cases, outperformed the human.

    Result

    • Significantly improved accuracy and consistency than manual picking.
    • Efficiency gains that reduce time and cost associated with manual first-break picking.

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  • SAG Mill Throughput Optimisation

    SAG Mill Throughput Optimisation

    SAG Mill Throughput Optimisation

    SAG Mill Throughput Optimisation

    Overview

    We helped BHP to improve Semi-autogenous grinding (SAG) mill throughput by deploying a machine learning model that ingests real-time, site-specific data across drilling, blasting, crushing, and SAG operations. Its explainable machine learning outputs show which factors drive each prediction, so operators can trust and act on recommended set-points.

    Problem

    Physics-based and commercial simulation tools were not accurate enough for real-time control. Just as importantly, they lacked explainability — operators couldn’t see which inputs influenced predictions, making it hard to adjust set-points with confidence.

    Solution

    • Integrated ML model: Uses live plant data from drilling, blasting, crushing, and SAG mill operations to estimate throughput and recommend set-points (e.g., crusher gap settings, SAG mill speed, percentage of solids) for the current operating context and constraints.
    • Explainable outputs: The model’s inbuilt explanatory power highlights the features influencing each prediction, helping operators understand why recommendations are made and increasing adoption in live plant operations.

    Result

    • Prediction uplift: 42% better than the existing physics-based model and 27% better than commercial simulation software on average.
    • Throughput gain: Applying the optimal set-points increased throughput by 12% on average.
    • Operator confidence: Clear explanations supported decision-making in live operations.

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