Thank you, Federico Arboleda, for reopening this important discussion on the accuracy of Alpha and Beta measurements – in your observation, the error is likely to fall within +/- 5 degrees – referring to the instrument-level measurement deviation. Core-reading goniometers, usually with 5 degrees marks, may typically provide, a +/- 2.5 decimal degrees readings by interpolation, which sets their precision; however, accuracy requires assessment of the real value. Furthermore, electronic devices would have other reading scales based on their internal components and calibrations. Nevertheless, it represents the instrument-level deviation, not the measurement-net deviation. Yet even the most accurate and precise structural reading methods cannot compensate for poorly oriented core, a persistent and often overlooked issue in the industry.
Indeed, your post has sparked a range of insightful responses from the community, spanning concerns about potential misuses to the challenges of ensuring robust QA/QC in core orientation programs and related structural readings. The emphasis on assessing measurement-net deviation – encompassing all steps from core orientation to the final application of the structural dataset, is both relevant and critical. As highlighted by Brett Davis and others, this issue extends far beyond the technicality of structural readings if we consider the final and usable product for the three-dimensional modeling of mineral targets (measurement-net accuracy and precision). These reactions highlight longstanding worries over the reliability and value of structural data, particularly since the introduction of the BallMarkTM core orientation tool, in the late 1990s. This innovation not only advanced the core orientation industry but also introduced a range of new workflows and challenges, including the critical issue of core misorientation. Effectively addressing these challenges is crucial for establishing stronger industry standards and reinforcing trust in the structural datasets that underpin three-dimensional modeling of mineral targets and orebodies.
The reliability of the core orientation program itself emerges as the foundation upon which all subsequent structural data depends on. Without robust, end-to-end QA/QC measures, even the most sophisticated structural reading method can fall short of providing the dependable datasets essential for geological modeling and mineral resource assessments. Therefore, as an industry, we must address the measurement-net accuracy and precision of structural readings in oriented core programs to overcome these significant and impactful issues. Key works like Ragan (1973), Nelson et al. (1987), Vearncombe & Vearncombe (1998), Marjoribanks (2010), Holcombe (2013), Stigsson and Munierand (2013), Davis (2014), and Myers et al. (2016), among others, address some these challenges and serve as essential reading for professionals in the field, and we highly recommend reading them.
In 2002, while working in the Thompson Nickel Belt in Canada, I encountered significant challenges measuring out-of-plane lineations of pentlandite, biotite, and inclusions within massive sulfides, including fault kinematics and fold asymmetry studies from oriented core (Figure 1). My objective was simple but crucial: correlate such lineations and fold asymmetry domains with nickel grade distribution to determine the mineralization structural controls and thus enhance predictability in short-term and near-mine drilling. Existing structural extraction methods, such as readings from core restoration rigs (commonly known as ‘rocket launchers’), which require adjustments for the influence of magnetic pyrrhotite, or Alpha-Beta-Gamma readings, which are inherently incapable of capturing out-of-plane lineations, have proven inadequate for meeting the stated requirements. While these limitations extend beyond your question, Federico, they remain fundamentally important in achieving reliable structural analysis and establishing connections with the spatial distribution of grades and the characterization of ore shoots (Figure 2). For a deeper understanding of the complexities of the nickel sulphide deposits in the Thompson Nickel Belt and the importance of developing a robust, lineation-rich structural database, I recommend consulting McDowell et al. (2007), Lightfoot et al. (2017), and Monteiro (2017).
In response to this challenge, I developed a cylindrical coordinate-based reading system to independently measure lines and planes with high precision (Monteiro, 2002), now known as the Structural Vectoring® Log or SVL. By leveraging vector calculus and creating a custom Visual Basic code for Excel®, this system offered a robust solution to address the structural reading inconsistencies of other methods. It provided a reliable and straightforward way to collect kinematic and asymmetry data for a more complete structural readings and analysis of mineral targets. Simply put, any structural feature – planar or linear – and associated attributes that can be measured in an outcrop with a compass, can be also measured using the SVL method, whether from oriented and non-oriented core. This innovation and its intellectual property were safeguarded under Inco Ltd and Vale until their transfer to Vektore in 2012, following my retirement. Since then, we have been improving its readings and associated processes, and over time, this innovation evolved into the Structural Vectoring Log (SVL) – a module of the Ore.node software [Link to Ore.node], which has been adopted by leading companies to streamline their structural measurements (Vektore 2, 2012-2025). Building upon the SVL, we developed in 2023 the vSTAR™, an augmented-reality structural reader that prioritizes instrument-level accuracy, precision, speed, visualization and processing in real-time [Link to vSTAR]. These tools have the potential to transform how structural data is collected, analyzed, and integrated into mineral exploration workflows, driving significant efficiency and decision-making confidence (Vektore 1, 2012-2025).
Misoriented cores remains a major challenge, undermining entire datasets, rendering a very low measurement-net accuracy and precision. Aware of this issue, we began developing a suite of processes in 2005 to enhance the strength of both oriented and non-oriented core programs – the Structural Inversion and Structural Convergence methods (Monteiro, 2005 and Vektore 1&2, 2012-2025), which are part of the Quality Optimization process we are deploying in the industry.
Leading companies like Ero Copper, Centaurus Metals, Alvo Minerals, and OZ Minerals have embraced these workflows, by certifying their teams as Structural Optimization Specialists [Link to Post]. Through a hands-on 40-hour course, these Vektore-certified specialist gain the skills and knowledge needed to address core orientation misorientation challenges and elevate the measurement-net accuracy and precision of the structural data delivered to the geological modeling and resources teams.
We look forward to sharing more about these integrated solutions in future posts, where we will dive deeper into the techniques and open the conversation about how they can improve and simplify workflows to deliver robust datasets for analysis and integration to 3D models.
Consulted References
Davis, B. (2014). Use and abuse of oriented core; in: Mineral Resource and Ore Reserve Estimation. Second Edition;Publisher: AusIMM.
Holcombe, R. J. (2014). Oriented Drillcore: Measurement, Conversion, and QA/QC Procedures for Structural and Exploiration Geologists – last updated in May 2023. https://www.holcombe.net.au/downloads/HCOVG_oriented_core_procedures.pdf.
Lightfoot, P. C. et al. (2017) Relative contribution of magmatic and post-magmatic processes in the genesis of the Thompson Mine Ni-Co sulfide ores, Manitoba, Canada. Ore Geology Reviews 83 (2017).
Marjoribanks, R. (2010) Geological Methods in Mineral Exploration and Mining. Second Edition, Springer-Verlag, Berlin, 238 pp. https://link.springer.com/book/10.1007/978-3-540-74375-0.
McDowell, G. M., Stewart, R. & Monteiro, R. M. (2007) In-mine Exploration and Delineation Using an Integrated Approach. Advances in Mine Site Exploration and Ore Delineation in: Proceedings of Exploration 07: Fifth Decennial International Conference on Mineral Exploration edited by B. Milkereit, 2007, p. 571-589.
Monteiro, R. N. (2002). Structural Analysis of Borehole Data and Structural Scenario Design. Inco Internal Peer Reviewed Report.
Monteiro, R.N. (2005) Structural Inversion: Concepts, Procedures and Implications to Mineral Exploration/Exploitation. Internal ITSL Memorandum. December 20, 2005.
Monteiro, R. N. (2017) Structural Controls of the Thompson Nickel Belt Mineral Deposits. SIMEXMIN 2017 (VIII Simpósio Brasileiro de Exploração Mineral) – 34 slides.
Myers, R. at al (2016) An Inexpensive Way to Maximize and Preserve the Value of Oriented Core: The Orientation Log. SEG Discovery (107): 1–19.
Nelson, R. A.; Lenox, L. C.; Ward, B. J. (1987). Oriented Core: Its Use, Error, and Uncertainty1AAPG Bulletin 71 (4): 357–367.
Ragan, D. (1973) Structural Geology: An Introduction to Geometrical Techniques. Second Edition, published by John Wiley & Sons Inc. 232 pages.
Stigsson, M. and Munierand, R. (2013) Orientation uncertainty goes bananas: An algorithm to visualise the uncertainty sample space on stereonets for oriented objects measured in boreholes. Computers & Geosciences, Volume 56, July 2013, Pages 56-61.
Vearncombe, J. and Vearncombe, S. (1998). Structural data from drill core. Australian Institute of Geoscientists, Bulletin. 22. 67-82.
Vektore 1 (2012-2025) Vektore Webpage at www.vektore.com
Vektore 2 (2012-2025) Best Practices in Structural Geology applied to Mineral Exploration.