24-MMP-A4 Mine Valuation and Mineral Resource Estimation · December 2014
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, 2014-Dec. 3 hours duration; one handwritten 8.5×11 in reference sheet permitted (not an open-book exam); only approved Sharp or Casio calculators allowed. Question 1 is compulsory (40 marks, parts 1.1–1.7); candidates then select FOUR of the six optional Questions 2–7 (15 marks each) to complete the paper.
Reference texts: Isaaks & Srivastava, An Introduction to Applied Geostatistics (variogram modelling, kriging estimators, volume–variance relations); Hustrulid, Kuchta & Martin, Open Pit Mine Planning and Design (mine valuation, NPV and cut-off grade methodology, mineable reserves); Gentry & O'Neil, Mine Investment Analysis (Canadian mining taxation, smelter/refining contract terms, net smelter return); SME Mining Engineering Handbook, 3rd ed. (mineral exploration/evaluation stages, ore reserve classification); CIM Best Practice Guidelines and NI 43-101 (Canadian Securities Administrators).
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.
Each sample–block value is the average of the sample–point covariance computed between that sample and each of the four pseudo-samples (quadrant centroids) from Question 4.2.1: $$C_{i,B}=\frac{1}{4}\sum_{j=1}^{4}C(x_i,\ pseudo_j)=\frac{1}{4}\sum_{j=1}^{4}\left[\text{Sill}-\gamma(h_{i,j})\right]$$ For each pseudo-point j, the distance hi,j from sample i is measured from the actual geometry, γ(hi,j) is read off the fitted model exactly as in Question 4.2.2, converted to covariance via Sill−γ, and the four resulting covariances are simply averaged. This is exactly why a finer discretization grid gives a more accurate sample-to-block covariance: more pseudo-points means the average better approximates the true continuous integral of covariance over the block's full area. Applying this procedure with the four quadrant centroids of Question 4.2.1 produces the given table, C(1,B)=0.425 and C(2,B)=0.455 — sample 2 (closer to the block, at 30 m vs. sample 1's 50 m) carries a slightly higher average covariance to the block, as expected since covariance decreases with separation distance.
