Core logging in exploration: Practices, data management and workflow integration

Core logging represents one of the most critical control points in mineral exploration, serving as the stage in which geological observations are transformed into structured data that underpin interpretation, modeling and economic decision-making. Every geological model and resource estimate ultimately depends on how accurately drill core is logged, recorded and managed. Despite its importance, core logging is frequently treated as a routine operational activity rather than a data-critical process. In practice, inconsistencies in logging approaches, subjective interpretation and weaknesses in data management can introduce errors that propagate throughout the exploration workflow. These errors often remain undetected until later stages, where they can significantly affect model reliability and project evaluation. As exploration programs become increasingly data-driven, there is a growing need to improve not only how geological data are collected, but also how they are structured and integrated into downstream workflows. This article presents a practical overview of core logging practices, examines the evolution of logging systems and highlights key challenges and opportunities for improving data reliability from field logging through to resource estimation. The role of core logging in exploration Core logging involves the systematic description of the geological, structural and geotechnical characteristics of drill core. Observations typically include lithology, mineralization, alteration and structural features, along with geotechnical parameters such as rock quality designation (RQD) and core recovery. These observations form the primary data set used in geological interpretation and resource estimation. The reliability of resource models is therefore directly dependent on the quality and consistency of logging data. Errors introduced during logging do not remain isolated; they propagate through geological domain interpretation, influence grade estimation and ultimately affect resource classification and project economics. In this context, core logging should be understood not merely as a field activity but as a critical component of data integrity within the exploration process. Evolution of core logging workflows The practice of core logging has evolved significantly over time, reflecting broader changes in data management and technological capability within the mining industry. Historically, logging was conducted using paper-based systems in which geological observations were recorded manually on standardized sheets. While these systems provided a structured format for data collection, they were inherently limited by their reliance on manual transcription. The process of transferring handwritten records into digital systems introduced errors and delays, reducing overall data reliability. The introduction of spreadsheet-based workflows marked a significant improvement, allowing geological data to be stored in structured formats and linked through drillhole identifiers. This facilitated integration with modeling software and improved accessibility. However, these systems remained heavily dependent on manual data entry, and issues such as inconsistent formatting, variable terminology and limited validation controls continued to affect data quality. More recently, modern digital logging systems have transformed the workflow by integrating core imaging, real-time data capture and automated validation into a unified platform. These systems enable geologists to log directly on high-resolution images of core, ensuring that observations remain spatially accurate and aligned with depth intervals. In addition, automated tools assist with depth marking, lithological classification and sample planning, improving both efficiency and consistency. The shift toward integrated digital logging represents a fundamental change in exploration workflows, not only increasing efficiency but also enhancing data traceability and reducing the likelihood of error. Core logging workflow in practice The core logging process begins with the retrieval and handling of drill core. After drilling, the core is placed sequentially into core boxes, with depth markers ensuring accurate spatial reference. Proper handling at this stage is essential, as any misplacement or loss of material can compromise the integrity of the data set. Core orientation and meter marking follow, providing a reference framework for structural measurements and verifying depth accuracy. Orientation lines allow structural features to be measured relative to a consistent reference, enabling three-dimensional interpretation. Lithological logging is then carried out, involving the identification and description of rock units based on observable characteristics such as color, mineral composition, grain size and texture. This step forms the basis for defining geological domains and interpreting subsurface conditions. Structural logging focuses on features such as fractures, veins and bedding planes. Measurements are typically recorded using alpha and beta angles, which allow the orientation of structures to be reconstructed in three dimensions. This information is critical for understanding controls on mineralization and structural geometry. Alteration logging identifies mineralogical changes associated with hydrothermal processes, often providing key indicators of mineralization. These observations are essential for targeting and interpreting ore zones. Geotechnical logging evaluates rock mass properties, including RQD, fracture frequency and rock strength. This information is particularly important for mine design and assessing ground stability. Core photography is conducted using standardized methods to create a permanent visual record of the core. These images serve as an important reference for verification, auditing and remote collaboration. Sampling represents the transition from qualitative observation to quantitative analysis. Sample intervals are defined based on geological boundaries to ensure that results are representative. Quality assurance and quality control procedures, including the use of blanks, standards and duplicate samples, are implemented to monitor accuracy and precision before assay data are used in modeling. End-to-end data flow in exploration A critical aspect of modern exploration workflows is the integration of logging data into downstream processes. Core logging does not exist in isolation; it forms part of a continuous data pipeline that extends from field observation to resource estimation.Logging data is first organized into structured geological databases, typically comprising lithological, structural, assay and survey data sets linked through drillhole identifiers and depth intervals. A well-designed database ensures that data are consistent, traceable and readily accessible. These data sets are then imported into modeling software such as Vulcan, where geologists construct three-dimensional models, define geological domains and estimate mineral resources. The accuracy of these models is directly dependent on the quality of the underlying data. Any inconsistency introduced during logging, whether through subjective interpretation or data entry error, can affect model geometry and grade distribution. As a result, maintaining data quality at the point of collection is essential for ensuring reliable outcomes throughout the workflow. Challenges in core logging Despite advances in technology, several persistent challenges continue to affect the reliability of core logging data. One of the most significant is subjectivity in interpretation. Different geologists may describe the same interval differently, particularly in cases where geological boundaries are subtle or transitional. This variability can lead to inconsistencies in domain definition and introduce bias into resource models. Data-entry and formatting errors also remain a concern, particularly in workflows that rely on manual input. Inconsistent coding systems and variations in terminology can create difficulties in database management and complicate data integration. Another key challenge is the lack of seamless workflow integration. In many operations, logging, database management and modeling are conducted in separate systems, requiring data to be transferred multiple times. This increases the risk of error, reduces traceability and can delay decision-making. These challenges highlight the need for a more integrated and standardized approach to core logging, where data quality is maintained throughout the entire workflow. Real-world implications of data quality in exploration Although core logging issues are rarely identified as the sole cause of project failure, evidence from industry case studies highlights the broader consequences of poor geological data quality. In several well-documented mining incidents, including slope failures and operational disruptions, investigations have pointed to incorrect assumptions about rock mass properties, geological continuity or structural conditions. These inaccuracies are often linked to limitations in the quality, consistency or integration of upstream geological data. For example, failures associated with mischaracterized ground conditions demonstrate how errors in geotechnical interpretation can propagate into design and operational decisions. Broader analyses of mining-related failures have shown that deficiencies in geological and geotechnical data, including data collection and management practices, are recurring contributing factors in risk and uncertainty across mining projects. Similarly, discrepancies between predicted and actual resource performance in some operations have been attributed to limitations in geological modeling, which relies heavily on the quality of logging data. While such outcomes cannot be attributed to a single stage in the workflow, they reinforce the critical role of accurate and standardized core logging as the foundation of reliable geological interpretation. Ensuring data integrity at the point of collection is therefore essential not only for resource estimation but also for minimizing technical risk and improving confidence in project outcomes. Improving core logging reliability Based on field experience and discussions with industry professionals, it is evident that many of the challenges associated with core logging are not solely technical in nature but are rooted in operational practices. A recurring issue is the lack of standardization across projects. Even within the same exploration program, differences in logging codes, terminology and interpretation approaches can lead to inconsistencies that are difficult to reconcile during modeling. This suggests that greater emphasis should be placed on developing and enforcing standardized logging protocols. Another important observation is the limited adoption of fully integrated digital systems. While many operations have transitioned away from paper-based logging, the use of spreadsheets or partially digital workflows continues to introduce inefficiencies and increase the risk of error. The implementation of systems that combine real-time data capture with automated validation would significantly improve data quality. Training also plays a critical role. Early-career geologists often develop logging practices through informal learning, which can result in variability in data recording. Structured training programs and ongoing quality control are therefore essential for ensuring consistency. Ultimately, improving core logging reliability requires a shift in perspective. Logging should not be viewed as a routine task, but as a data-critical process that underpins all downstream analysis and decision-making in mineral exploration. Conclusion As mineral exploration becomes increasingly reliant on data-driven decision-making, the importance of core logging continues to grow. The focus is no longer simply on collecting data but on ensuring that it is accurate, consistent and integrated into the broader workflow. Improving logging practices, reducing subjectivity and adopting integrated digital systems will be essential for enhancing confidence in geological models and resource estimates. The quality of any exploration outcome is ultimately determined by the quality of the underlying data. The future of mineral exploration will depend not only on the volume of data collected but on how effectively that data is captured, managed and interpreted. In this context, core logging remains one of the most important processes in ensuring the reliability and success of exploration programs. References Core Logging Committee of the South Africa Section of The Association of Engineering Geologists. 1976. A guide to core logging for rock engineering. Proceedings of the Symposium on Exploration for Rock Engineering, 71–86.Davis, C.R., Anderson, J.M., Tavchandjian, O., and Cochrane, L.B. 2003. Quality assurance/quality control for resource estimation at Inco Technical Services Limited. Exploration and Mining Geology, 10(4), 303–312.Isaaks, E.H., and Srivastava, R.M. 1989. An Introduction to Applied Geostatistics. Oxford University Press, London.Goulet, A., Grenon, M., & Hadjigeorgiou, J. (2022). Understanding the impact of alteration on rock mass strength. Geotechnical and Geological Engineering, 40(5), 2533–2552. https://doi.org/10.1007/s10706-021-02044-yKORE Geosystems. 2026. Modernizing Core Logging: Improving Data Quality, Consistency, and Productivity. Company presentation.

Core logging in exploration: Practices, data management and workflow integration

Authored by: Ben Kudakwashe Mperekwa

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