Geology and mine‑planning packages (e.g., GEOVIA Surpac) integrate the full workflow from data to classified resources. Their tools do not “decide” the classification for you, but they calculate the quantitative metrics and provide visual controls that underpin Measured–Indicated–Inferred categories under JORC/NI 43‑101.

Core modelling and estimation tools

Surpac and similar systems provide an integrated sequence:

  • Geological database management: import and validate collar, survey, assay, and lithology tables in a structured database (Huang and Xie, 2019; Kumar, 2021; Song and An, 2018). This supports QA/QC and ensures that only verified data feed classification.
  • 3D geological and wireframe modelling: build solid orebody and lithology models that honour structure and stratigraphy (Smirnova, Chen and Mikhaylova, 2022; Huang and Xie, 2019; Kumar, 2021; Song and An, 2018). Reliable geology is a prerequisite for higher‑confidence resources (Lindi et al., 2024).
  • Block model creation: define block and sub‑block sizes, attributes (grades, density, classification flags), and constraints; blocks are the units to which resource categories are ultimately assigned (Smirnova, Chen and Mikhaylova, 2022; Kumar, 2021; Song and An, 2018).
  • Geostatistics and variography: Surpac‑type software implements experimental variograms, model fitting and anisotropy analysis to quantify spatial continuity (Huang and Xie, 2019; Kumar, 2021; Song and An, 2018). Variogram ranges are then used to design search neighbourhoods and help define classification distances (Silva and Boisvert, 2014; Lindi et al., 2024).

These steps generate the geostatistical framework needed for confidence evaluation.

Kriging and geostatistical confidence metrics

Surpac and comparable tools implement ordinary kriging and other geostatistical estimators directly on the block model (Huang and Xie, 2019; Kumar, 2021; Song and An, 2018). Along with estimated grade, they output per‑block uncertainty and data‑support metrics that feed classification:

  • Kriging variance (KV) and kriging standard deviation
  • Kriging efficiency (KE) or related measures of estimation quality (Rocha and Bassani, 2023; Marwanza et al., 2025)- Slope of regression and other diagnostics where implemented (Rocha and Bassani, 2023)- Number of informing samples and drillholes and average distance to samples (Kumar, 2021; Marwanza et al., 2025; Lindi et al., 2024)These are the same variables used in formal classification schemes and multi‑criteria scorecards for resource categories (Rocha and Bassani, 2023; Silva and Boisvert, 2014; Marwanza et al., 2025; Lindi et al., 2024). For example, a laterite nickel study used Surpac modelling and ordinary/“efficiency” kriging, then classified blocks into Measured, Indicated, and Inferred purely from kriging efficiency thresholds, implemented via Surpac’s estimation outputs (Marwanza et al., 2025). Blocks with KE > 0.5 were Measured, 0.3–0.5 Indicated, and <0.3 Inferred, then spatial constraints were built to report tonnage by class (Marwanza et al., 2025).

Similarly, studies using Surpac for gold, graphite, and other deposits exploit kriging‑based metrics and block‑model cross‑plots to verify model quality and support classification tables for reporting (Huang and Xie, 2019; Song and An, 2018).

Linking Surpac outputs to formal classification methods

Independent resource‑classification research relies heavily on the same types of metrics that Surpac computes:

  • Multi‑layer scorecard workflows (MLSW) combine number of samples, slope of regression, kriging efficiency, kriging variance, and composite “risk indices” in a transparent scoring system for Measured/Indicated/Inferred (Rocha and Bassani, 2023). Surpac provides the underlying KV, KE, SoR and sample statistics needed to populate such scorecards.
  • Comparative studies of classification techniques show that industry practice is dominated by drill‑hole spacing, search neighbourhood definitions and kriging‑variance thresholds, all of which are implemented through the block‑modelling and estimation tools in packages like Surpac (Osburn et al., 2014; Silva and Boisvert, 2014).
  • Other methods classify resources using relative kriging measures, conditional variance, or search‑neighbourhood criteria, again requiring per‑block kriging outputs and search statistics that software computes automatically (Silva and Boisvert, 2014; Lindi et al., 2024).
  • A Cu–Mo porphyry study classified Measured, Indicated and Inferred resources by combining estimated variance and minimum numbers of informing samples per voxel; those quantities are standard kriging outputs in block‑modelling software (Yasrebi and Hezarkhani, 2019).

In a Surpac‑based nickel laterite example, the workflow was:

  1. Build geological model and block model in Surpac.
  2. Perform variography and ordinary/efficiency kriging in Surpac.
  3. Export per‑block KE, number of samples and drillholes.
  4. Apply documented KE thresholds to assign each block a resource class, then generate constraints and reports for each class (Marwanza et al., 2025).

This matches the general guidance that resource classification should use quantitative uncertainty measures and data distribution to reflect confidence (Rocha and Bassani, 2023; Silva and Boisvert, 2014; Marwanza et al., 2025; Lindi et al., 2024).

Visualisation and reporting to support decisions

Beyond calculations, Surpac‑type systems assist classification by:

  • 3D visualisation of classification criteria: users can colour blocks by kriging variance, KE, number of samples, or final category, quickly spotting over‑optimistic or inconsistent areas (Huang and Xie, 2019; Marwanza et al., 2025; Song and An, 2018).
  • Section and swath plots: comparison of drill data and estimated blocks along sections or swaths helps validate that blocks classified as Measured/Indicated have realistic continuity and no strong bias (Huang and Xie, 2019).
  • Cut‑off grade, tonnage–grade curves and classification tables: Surpac can generate grade–tonnage curves and tabulate tonnage and contained metal by class and cut‑off, mirroring what must be disclosed under JORC/NI 43‑101 (Huang and Xie, 2019; Song and An, 2018; Lindi et al., 2024).
  • Interoperability with mine‑planning modules: because classification is stored as a block attribute, mine‑design and scheduling tools (e.g., MineSched, Whittle) can constrain pits and schedules using only Measured+Indicated resources, directly linking classification to planning (Smirnova, Chen and Mikhaylova, 2022; Kumar, 2021; Melnikov, Cherkashin and Lebedev, 2022; Sobko, Lozhnikov and Tretyakov, 2021).
Interoperability with mine‑planning modules
Figure 1: Interoperability with mine‑planning modules

Summary

Software such as GEOVIA Surpac assists resource classification by:

  • Managing and validating geological data and 3D models (Huang and Xie, 2019; Kumar, 2021; Song and An, 2018).
  • Providing variography, kriging, and block‑model estimation tools that output key uncertainty and support metrics (KV, KE, SoR, sample counts, distances) (Rocha and Bassani, 2023; Huang and Xie, 2019; Kumar, 2021; Marwanza et al., 2025; Song and An, 2018).
  • Enabling quantitative, standards‑consistent classification workflows (e.g., kriging‑efficiency thresholds, scorecards, variance‑ and distance‑based methods) aligned with JORC/NI 43‑101 guidance (Rocha and Bassani, 2023; Osburn et al., 2014; Silva and Boisvert, 2014; Marwanza et al., 2025; Lindi et al., 2024).
  • Offering powerful 3D visualisation and reporting that help practitioners check that assigned categories genuinely reflect confidence and geological understanding (Huang and Xie, 2019; Marwanza et al., 2025; Song and An, 2018).

In practice, the competent person defines the rules; Surpac supplies the measurements and visual tools to apply them consistently across the block model.

References

Smirnova, A., Chen, S., & Mikhaylova, T., 2022. Geological Mathematical Block Modelling in Kuzbass Mining Industry. 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), pp. 1970-1973. https://doi.org/10.1109/sibircon56155.2022.10017106

Rocha, V., & Bassani, M., 2023. Practical application of a multi-layer scorecard workflow (MLSW) for comprehensive mineral resource classification. Applied Earth Science, 132, pp. 216 – 226. https://doi.org/10.1080/25726838.2023.2244775

Osburn, K., Pretorius, H., Kock, D., King, N., Pillaye, R., & Hlangwane, M., 2014. Enhanced geological modelling of the Upper Elsburg reefs and VCR to optimize mechanized mine planning at South Deep Gold Mine. Journal of The South African Institute of Mining and Metallurgy, 114, pp. 265-273.

Huang, S., & Xie, Y., 2019. Geostatistical estimation analysis of typical Carlin gold deposit in Hunan province, China. IOP Conference Series: Earth and Environmental Science, 267. https://doi.org/10.1088/1755-1315/267/2/022033

Kumar, C., 2021. Resource Modelling of Iron Ore Deposit using Surpac Software. Journal of the Geological Society of India, 97, pp. 559. https://doi.org/10.1007/s12594-021-1724-0

Silva, D., & Boisvert, J., 2014. Mineral resource classification : a comparison of new and existing techniques by. **.

Melnikov, V., Cherkashin, V., & Lebedev, A., 2022. Computer-aided tactical mine planning. Gornyi Zhurnal. https://doi.org/10.17580/gzh.2022.06.03

Yasrebi, A., & Hezarkhani, A., 2019. Resources classification using fractal modelling in Eastern Kahang Cu-Mo porphyry deposit, Central Iran. Iranian Journal of Earth Sciences, 11, pp. 0-0.

Sobko, B., Lozhnikov, O., & Tretyakov, V., 2021. RESEARCH OF THE EFFICIENCY OF USING «GEOVIA SURPAC» AND «MINESCHED» WHEN PLANNING MINING WORKS IN IRON QUARRY. SCIENTIFIC PAPERS OF DONNTU Series: “The Mining and Geology”. https://doi.org/10.31474/2073-9575-2021-1(25)-2(26)-7-15

Marwanza, I., Putra, D., Azizi, M., Dahani, W., Gumay, R., & Sahetapy, S., 2025. Geostatistical Modeling using Ordinary Kriging for Estimating Nickel Resources in Sulawesi Indonesia. Journal of Multidisciplinary Applied Natural Science. https://doi.org/10.47352/jmans.2774-3047.252

Song, H., & An, H., 2018. APPLICATION OF GEOSTATISTICS IN THE ESTI MATION OF SUJISHAN GRAPHITE DEPOSITS, MONGOLIA. **, 27, pp. 487-499. https://doi.org/10.14311/cej.2018.04.0039

Lindi, O., Aladejare, A., Ozoji, T., & Ranta, J., 2024. Uncertainty Quantification in Mineral Resource Estimation. Natural Resources Research, 33, pp. 2503 – 2526. https://doi.org/10.1007/s11053-024-10394-6