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.
A well-drilled, regularly-spaced porphyry grid with a smoothly varying, roughly isotropic grade field produces a variogram that itself rises close to an inverse-power-of-distance shape over the sampled range — under those conditions the OPTIMAL (kriging) weights end up numerically close to a simple inverse-distance-squared weighting, so the two estimates converge. Kriging remains preferable even when the point ESTIMATE barely differs, because: (1) it supplies an estimation (kriging) variance for every block, letting confidence/risk be quantified and reported, which IDS cannot do; (2) it correctly de-clusters preferentially-drilled areas (a cluster of close-spaced holes is down-weighted relative to an isolated hole at the same distance, something a fixed power-law cannot do); and (3) it can honour anisotropy and any local trend directly through the variogram model, rather than through an ad-hoc adjustment to the distance exponent.
