24-MMP-A4 Mine Valuation and Mineral Resource Estimation · May 2018
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
EGBC National Exam — Mining and Mineral Processing Engineering, 09-MMP-A4 Mine Valuation and Mineral Resource Estimation, 2018-May. 3 hours duration; closed book, with one handwritten 8.5×11 in. reference sheet (both sides) permitted; only an approved Sharp or Casio calculator allowed. Question 1 is compulsory (40 marks, parts 1.1–1.9); candidates then select THREE of the five optional Questions 2–6 (20 marks each) to complete the paper.
Reference texts: Isaaks & Srivastava, An Introduction to Applied Geostatistics (variogram modelling, kriging estimators); Hustrulid, Kuchta & Martin, Open Pit Mine Planning and Design (mine valuation, cut-off grade theory, incremental analysis); Gentry & O'Neil, Mine Investment Analysis (Canadian mining taxation, cash flow/risk, smelter contract terms, NSV/NSR); SME Mining Engineering Handbook, 3rd ed. (ore deposit models, mineral exploration/evaluation stages, equipment utilization); O'Hara, T.A., “Quick Guides to the Evaluation of Orebodies,” CIM Bulletin, Feb. 1980 (parametric capital-cost estimating); CIM Definition Standards for Mineral Resources and Mineral Reserves / National Instrument 43-101 (resource/reserve classification and reporting).
Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.
Method of sections, inverse-distance and polygonal (nearest-neighbour) estimation all assign weights to nearby samples using a rule chosen a priori — a fixed power-law falloff, an arbitrary area/volume of influence, or manual interpretation between sections — without ever asking how the deposit itself actually varies in space. Geostatistics replaces the a-priori rule with one derived from the data: an experimental semi-variogram is computed from the sample pairs themselves, fitted with a model (spherical, exponential, Gaussian), and that model then supplies the spatial-covariance structure used to solve a kriging system at each estimation point or block. The practical gains are threefold: (1) kriging weights are the statistically OPTIMAL (minimum-variance, unbiased) linear combination of the actual samples used, rather than an assumed inverse-distance exponent; (2) the same variogram model yields an estimation (kriging) VARIANCE for every block, giving a quantified, defensible confidence measure that inverse-distance and polygonal methods cannot produce; and (3) the variogram can be fitted anisotropically, honouring directional continuity (e.g. along strike vs. across a vein) that a symmetric inverse-distance-squared weighting ignores. Geostatistics therefore does not change WHAT is being estimated, only how objectively and defensibly the weighting and confidence are derived.
