Certified: ISO 9001:2015 & ISO 14001:2015
Official Partner Dassault Systèmes

Article

Applying Internal and External Dilution in a Block Model Using Surpac

16 May, 2026    Geovia, Surpac   
Internal vs External Grade Dilution in Block Models Concepts Determination and Modelling

Reliable mineral resource models must approximate what will actually be mined, not just the “pure” in-situ ore. That means explicitly accounting for internal dilution (waste inside ore blocks) and external/operational/contact dilution (waste mixed at ore–waste boundaries and during mining). [1] [2] [3] [4] [5]Below is a practical, step‑by‑step approach for an open‑pit nickel deposit, using the ideas from ACCOUNTING FOR DILUTION IN RESOURCE ESTIMATION and Surpac-based workflows. [1] [2] [3] [4] [6] [7] [8]

1. Key Concepts: Types of Dilution

Internal dilution (change of support)

  • Waste mixed inside what is treated as an ore block, due to block size vs. true ore geometry and grade variability. [1] [2] [3] [4] [9] [10]- In practice, internal dilution lowers block grade when block volumes are larger than the natural “selective mining unit.” [1] [2] [3] [4] [9] [6]

Geologic contact (external) dilution

  • Unavoidable mixing of ore and waste where the orebody contacts barren or low‑grade material (e.g., hangingwall/footwall contacts or barren dykes). [1] [2] [4] [9] [11]

Operational dilution

  • Extra dilution from loading, blasting, and dig line designs; depends on equipment accuracy, bench geometry, and contacts in the short‑term plan. [1] [2] [5] [12] [13] [14] [15]

Simple Dilution & Diluted Grade Calculations

QuantitySimple formula
Dilution (%)waste tonnes ÷ (ore + waste tonnes) × 100
Diluted grade(ore grade × ore t) ÷ (ore t + waste t)

Figure 1: Basic dilution and diluted grade relationships used in models

2. Building the Base Nickel Block Model in Surpac

A clean geologic and grade model is needed before adding dilution. Surpac workflows for iron ore, fluorite, nickel, and other deposits are similar. [6] [7] [8] [16] [17] [18]Main steps in Surpac [6] [7] [8] [16] [17] [18]:

  1. Database and QA/QC
    • Import collar, survey, lithology, and assay tables (including Ni).
    • Validate data, run basic statistics and checks.
  2. Geological interpretation
    • Create cross‑sections and wireframes for nickel ore, sub‑grade, and waste; define footwall and hangingwall contacts. [6] [8] [16] [17]3. Solid and block model creation
    • Build 3D solids for ore and waste.
    • Define a block model (block size, origin, extents; with sub‑blocking if needed near contacts). [2] [9] [6] [16]- Populate blocks with lithology and Ni grade using IDW or kriging. [6] [7] [16] [17] [18]This outputs an undiluted in‑situ Ni block model.

3. Applying Internal Dilution in the Block Model

Internal dilution is controlled by how grades are averaged to block scale and how blocks are classified as ore or waste. [1] [2] [3] [4] [9] [10]

3.1 Conceptual approach

  • Treat each block as a mix of ore and waste, based on the spatial distribution of grades or lithologies inside it. [1] [2] [3] [4]- Internal dilution is the waste portion inside the mining unit; it reduces the effective Ni grade. [1] [3] [4] [9]

3.2 Practical Surpac workflow

Option A – Lithology‑based internal dilution (useful for nickel with interbedded waste) [1] [3] [4] [6] [8]:

  • Sub‑block the model around ore–waste contacts so small blocks can carry pure ore or pure waste rock codes. [2] [9] [6] [16]2. For each parent block, compute:
    • Ore sub‑block volume and waste sub‑block volume.
    • Internal dilution (%) = waste volume / (ore + waste volume). [1] [3]3. Compute diluted Ni grade at the parent‑block scale:
    • Ni_diluted = (Σ(Ni_ore × ore tonnes)) ÷ (ore + internal waste tonnes). [1] [3] [15]4. Store INT_DIL% and NI_INTDIL as new block attributes.

Option B – Grade‑based support change

  • Use smaller “point” or small‑block simulations, then average to the mining block size to capture the support effect (loss of selectivity and grade smoothing). [1] [2] [3] [4] [9] [6]- In Surpac, this is approximated by:
    • Creating a finer temporary model, estimating Ni, then averaging into larger mining blocks (e.g., via scripts or reblocking). [2] [9] [6]

4. Applying External / Operational Dilution with Surpac

External and operational dilution mainly occur at ore–waste boundaries and depend on the short‑term mine design and equipment. [1] [2] [4] [5] [12] [13] [19]

4.1 Geologic contact dilution at block boundaries

  • Focus on blocks along ore–waste contacts and around barren intrusions or dyke zones. [1] [2] [4] [9] [11]- In Surpac:
    • Identify boundary blocks (e.g., those intersecting both ore and waste solids). [2] [9] [6] [16]2. Define a dilution skin thickness (e.g., half bucket width) on each side of the contact. [2] [5] [12]3. For each ore block in this skin, mix in a proportion of adjacent waste blocks:
    • External dilution (%) = waste in skin ÷ (ore + boundary waste). [2] [5] [12] [19]- Recalculate NI_EXTDIL using the diluted grade formula. [1] [3] [5] [15]

4.2 Operational dilution via mine plans

A more advanced approach links dilution to short‑term plans (dig polygons) and bench geometry. [2] [5] [12] [13] [19]In Surpac (conceptually similar to workflows used in open‑pit phosphate and other deposits) [5] [7] [12] [13]:

  1. Import or design dig polygons for each period on each bench.
  2. Intersect polygons with the block model to identify mined blocks/parts.
  3. For each period:
    • Compute planned ore tonnes and Ni grade from undiluted or internally diluted model.
    • Map nearby waste blocks along dig boundaries and bench faces. [2] [5] [12]- Estimate operational dilution by mixing these waste blocks into the mined set. [5] [12] [13] [19]- Store a DIL_GRADE (e.g., NI_OPDIL) per block or period. [5]

Output:

  • NI_INTDIL – Ni grade including internal dilution.
  • NI_TOTDIL – Ni grade after internal + external/operational dilution.

These diluted grades feed cut‑off grade, reserve reporting, and economic evaluation. [1] [2] [5] [10] [20] [15]

Conclusion

To realistically model a nickel deposit in Surpac, start with a clean undiluted geologic and grade model, then:

  • Quantify internal dilution by mixing ore and waste at the block (support) scale using sub‑blocks or fine‑to‑coarse averaging.
  • Add external/contact and operational dilution by explicitly modeling ore–waste contacts and short‑term dig polygons, mixing in boundary waste.

Storing dilution percentages and corresponding diluted Ni grades directly in the block model lets mine planners design pits, schedules, and cut‑offs that better match actual plant feed and financial performance.

References

1. Rossi M. ACCOUNTING FOR DILUTION IN RESOURCE ESTIMATION. . 2017.

2. Jara J, C, E, M, Ortíz EJ. Block size selection and its impact on open-pit design and mine planning. Journal of The South African Institute of Mining and Metallurgy. 2006;106:205-211.

3. Masoumi I, Kamali G, Asghari O. Assessment of an ore body internal dilution based on multivariate geostatistical simulation using exploratory drill hole data. Journal of Mining and Environment. 2019;10:271-286. doi:10.22044/jme.2019.7622.1618

4. Masoumi I, Kamali G, Asghari O, Emery X. Assessing the Impact of Geologic Contact Dilution in Ore/Waste Classification in the Gol-Gohar Iron Ore Mine, Southeastern Iran. Minerals. 2020;10:336. doi:10.3390/min10040336

5. Câmara TR, Leal RS, De Lemos Peroni R, Capponi L. Controlling operational dilution in open-pit mining. Mining Technology. 2019;128:1 – 8. doi:10.1080/25726668.2018.1470275

6. Verly G, Parker HM. Conditional Simulation for Mineral Resource Classification and Mining Dilution Assessment from the Early 1990s to Now. Mathematical Geosciences. 2021;53:279-300. doi:10.1007/s11004-021-09924-2

7. Kumar CTS. Resource Modelling of Iron Ore Deposit using Surpac Software. Journal of the Geological Society of India. 2021;97:559. doi:10.1007/s12594-021-1724-0

8. Zeqiri R. Nickel discretization and quality review in Gllavica mine, Kosovo. Mining of Mineral Deposits. 2021;15:35-41. doi:10.33271/mining15.01.035

9. Khorram F, Asghari O, Memarian H, Morshedy A, Emery X. Adaptive Multi-Size Block Modeling for Mineral Resources and Ore Reserves Evaluation. . 2021.

10. Pressacco R, Roscoe W. The checkerboard effect and mineral resource reporting of underground mineral resources. . 2021. doi:10.1080/19236026.2021.1902203

11. Alfaro R, Maleki M, Madani N, Soltani-Mohammadi S. Comparing the accuracy of two approaches to account for internal dilution: A case study from a porphyry copper deposit. International Journal of Mining, Reclamation and Environment. 2023;37:441 – 459. doi:10.1080/17480930.2023.2192030

12. Naik H, Das P. Optimization study of a Surface Mine and Grade Monitoring using SURPAC. . 2018.

13. Mussin A, Kydrashov A, Asanova Z, Abdrakhman Y, Ivadilinova D. Ore dilution control when mining low-thickness ore bodies using a system of sublevel drifts. Mining of Mineral Deposits. 2024. doi:10.33271/mining18.02.018

14. Mariz JLV, Ferraz TSG, Lima MP, Silva RMA, Jang H. Predicting Dilution in Underground Mines with Stacking Artificial Intelligence Models and Genetic Algorithms. Applied Sciences. 2025. doi:10.3390/app15115996

15. Torres-Sánchez V, Olivas-Maldonado P, Diaz-Huaina G, Raymundo-Ibañez C, Perez M. Optimized Method of Estimating Mineral Reserves Using the O’Hara Mathematical Model for Underground Mining in Peru. IOP Conference Series: Materials Science and Engineering. 2020;758. doi:10.1088/1757-899x/758/1/012016

16. Cheng J. Construction and visualization of a three-dimensional model of an engineering geological body. Arabian Journal of Geosciences. 2021;14. doi:10.1007/s12517-021-06600-x

17. Cherai M, Rddad L, Talbi F, Carranza E. 3D modeling for mineral resource assessment of fluorite ore and its industrial application in Jbel Tirremi, northeast Morocco. Modeling Earth Systems and Environment. 2023. doi:10.1007/s40808-022-01684-0

18. Liu F. Enabling data conversion between Micromine and Surpac – enhancing efficiency in geological exploration. Cogent Education. 2024;11. doi:10.1080/2331186x.2024.2438591

19. Bilin A, Kornienko A. A tool for optimizing mining units for surface block mining models. Mining Industry Journal (Gornay Promishlennost). 2023. doi:10.30686/1609-9192-2023-5s-113-118

20. Kumral M. Optimizing ore-waste discrimination and block sequencing through simulated annealing. Appl. Soft Comput.. 2013;13:3737-3744. doi:10.1016/j.asoc.2013.03.005

Did you like this? Share it!