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all good today
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  • Norman Heckscher
  • Central Queensland, Australia
  • LinkedIn in/nheckscher

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normanheckscher/README.md

Norman Heckscher (MAusIMM)

Senior Coal Geologist | Mining Software Architect | Open-Source Advocate
Specialising in Stratigraphic Modelling, 3D Reconciliation, and Mathematical Geology.


🧭 Executive Summary

With over 20 years of continuous operational and consulting experience across the coal deposits of the Bowen Basin (Queensland, Australia), my career bridges field geology, structural interpretation, and high-performance computational systems.

My technical focus centers on dismantling the "black-box" limitations of legacy mining CAD suites. Through open-source architectures, rigorous peer-reviewed algorithms, and mathematically sound true-thickness calculations, I develop modern tooling to enforce open JORC reporting standards and verifiable geological reconciliation.


πŸ› οΈ Key Technical Projects

Incline β€” Open-Source Mining CAD (MIT)

  • Role: Domain Contributor & Core Geological Architect.
  • Focus Areas:
    • Extending open-pit CAD capabilities into stratigraphic coal geology.
    • Implementing drillhole composite parsing, horizon correlation, and slice-view section snapping.
    • Implementing robust true-thickness trigonometry to eliminate volume distortion in inclined and faulted seams.

GeoCAD β€” Depletion & Stratigraphic Reconciliation Engine

  • Role: Lead Architect & Developer.
  • Architecture:
    • Modular reconciliation engine built to audit short-term pit depletion against long-term resource models.
    • Custom stratigraphic surface generation using RBFs and exact structural dip vectors.
    • Fully transparent, open-license code base designed to provide an open alternative to commercial legacy packages.

πŸ› οΈ Key Technical Focus Areas

πŸ”„ Single-Model Reconciliation Systems (GeoCAD & Incline)

  • Eliminating the "Model Gap": Engineering software architectures that replace the traditional split between long-term resource models and short-term grade control models.
  • Continuous Conditioning: Building one mainline, running model continuously conditioned by active production data (survey depletion, dispatch, weightometers) to remove the tracking gaps where geological accountability goes to die.
  • Algorithmic Surface Foundations: Replacing biased Inverse Distance Weighting (IDW) methods with exact Radial Basis Functions (RBFs) to preserve precise faulted structures rather than outputting smoothed, unrealistic terrain grids.

πŸ“ Stratigraphic & Mathematical Geology

  • True Thickness Calculations: Crafting robust 3D trigonometric and geometric matrix solutions to model highly dipping, faulted, and complex layered coal deposits without introducing volume or tonnage distortions.
  • Structural Accountability: Ensuring sub-seismic faulting, oxidation zones, and ply loss are hard-coded into active modeling forecasts rather than masked as execution errors.
  • Geotechnical & Structural Integrity: Leveraging precise true-thickness and RBF surface datasets to improve mine safety, slope stability mapping, and structural slip-plane hazard identification at the coal face.

🌌 Agentic AI Coder Swarms

  • Deploying autonomous, containerized engineering workflows and multi-agent AI networks to process vast geophysics data streams.
  • Maximizing on-premise hardware environments to handle massive localized optimization and computation tasks.

πŸ“š Publications & Featured Writing

πŸ“‘ Professional Articles (LinkedIn)

  • Nobody Owns the Miss (August 2026)
    An architectural critique of mine site reconciliation reporting (F1, F2, F3 chains). Exposes how maintaining two separate, unaligned geological orebodies allows structural errors (like sub-seismic faulting) to disappear into the data shuffle. Proposes a version-controlled "single running model" system as the only viable mechanism for continuous optimization.

  • Inverting the Exploration Paradigm: A Radical Approach to Geological Training and Resource Extraction (December 2024)
    An operational manifesto arguing that discovery rates improve only when junior geologists build deep competency "at the coal face" before moving to greenfield programs. Outlines a structured, competency-based framework linking core mapping, structural dip predictions, and real-time mine reconciliation directly to technical advancement.

πŸŽ“ Selected Technical Papers

  • Precision in the dip: true thickness calculation for complex coal deposits (2025)
    Bowen Basin Symposium β€” Refined methodology applying 3D trigonometric equations to determine true thickness with precision. Focuses on isolating and correcting geometric surface variances native to legacy mining software by incorporating seam topography, bedding plane orientation, and borehole deviation.
  • Beyond Traditional Methods: Radial Basis Functions for Stratigraphic Modelling Innovation in Complex Coal Deposits (2024)
    Sydney Basin Symposium β€” A novel stratigraphic modeling methodology combining in-situ data, true thickness trigonometry, and RBFs for smooth interpolation of thickness grids. Demonstrates significantly improved volume and tonnage reconciliation over traditional Independent Surface or Stacking methods in folded strata.
  • Ambient seismic energy techniques: the high frequency limit and application to upper crustal imaging and geothermal exploration (2010)
    SEISMIX Abstract Volume β€” Investigating the potential for pushing the high-frequency limit of passive seismic data acquisition and array analysis to infer regional tectonic and crustal structures.
  • Characterizing the ambient seismic wavefield for upper crustal imaging: energy sources and station deployment protocols in an ocean island setting (2009)
    American Geophysical Union (AGU) Fall Meeting β€” Research mapping natural ambient wavefields in Eastern Tasmania to deduce regional 3D geological structure and establish constraints on structural crustal architecture.

πŸ’» Systems & Infrastructure Stack

  • Geological Domains: Stratigraphic Seam Correlation, Structural Fault Modelling, High-Wall Mapping, Grade Control, Mine Reconciliation, JORC Resource Estimation.
  • Geotechnical & Structural Integrity: Leveraging precise true-thickness and RBF surface datasets to improve mine safety, slope stability mapping, and structural slip-plane hazard identification at the coal face.
  • Core Languages: Rust, Python, C++, SQL.
  • Operating Environments: Vim on the command line, absolute proficiency with native Linux/Unix systems, shell provisioning, and distributed local clustering.
  • Hardware & Acceleration: High-density GPU computing environments (CUDA / SXM2 format) optimized for localized LLM execution, self-hosted framework tools, and local GitLab CI/CD pipelines.
  • Licensing Stance: Hardline commitment to copyleft software principles (GNU GPLv3) to ensure verifiable, peer-reviewed reproducibility in minerals industry computations.

πŸ“¬ Contact & Professional Credentials

  • Credentials: MAusIMM (Member of the Australasian Institute of Mining and Metallurgy)
  • Core Philosophy: Equally at home with a laptop terminal running over SSH on a remote site as I am on a high-wall auger miner.
  • Location: Moranbah, Queensland, Australia
  • GitHub: @normanheckscher

Pinned Loading

  1. Incline-Developers/Incline-Design Incline-Developers/Incline-Design Public

    Free Open Source Mine Design

    Rust 6 3

  2. Beyond-Traditional-Methods Beyond-Traditional-Methods Public

    Radial Basis Functions for Stratigraphic Modelling Innovation in Complex Coal Deposits

    Jupyter Notebook

  3. mnist-tensorboard-embeddings mnist-tensorboard-embeddings Public

    Initial commit. Tensorboard running with embeddings to utilise t-SNE

    Python 70 36

  4. mnist-multi-gpu mnist-multi-gpu Public

    MNIST ConvNet MultiGPU TensorFlow

    Python 47 21

  5. google-deepmind/learning-to-learn google-deepmind/learning-to-learn Public

    Learning to Learn in TensorFlow

    Python 4.1k 602

  6. tensorflow/tensorflow tensorflow/tensorflow Public

    An Open Source Machine Learning Framework for Everyone

    C++ 200k 77k