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100+ Free Advanced Mine Valuation Certificate Exam Practice Questions
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Sample Advanced Mine Valuation Certificate Exam Practice Questions
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1In spatial geostatistics, what does the experimental variogram gamma(h) measure for a sample pair separation distance (lag) h?
A.One-half of the average squared difference between sample values separated by distance h
B.The total variance of all sample grades across the entire orebody
C.The correlation coefficient between grade and channel width
D.The standard deviation of sample grades at zero distance
Explanation: The experimental variogram gamma(h) is defined as one-half the expected squared difference between values separated by distance h: gamma(h) = (1 / 2N(h)) * sum[(Z(x_i) - Z(x_i + h))^2]. It measures spatial variability as a function of separation distance.
2Which component of a variogram model represents unresolvable micro-scale grade variation and measurement errors?
A.Range of influence (a)
B.Nugget effect (C0)
C.Spatial variance / Sill contribution (C)
D.Lag distance (h)
Explanation: The nugget effect (C0) represents the apparent discontinuity at the origin (h = 0). It is caused by micro-structure variability occurring at scales smaller than the sampling interval, as well as sampling and assaying errors.
3An evaluator calculates a block variance (BV) of 4,000 (cm.g/t)^2 and an Ordinary Kriging variance (KV) of 1,000 (cm.g/t)^2 for a selective mining unit (SMU). What is the Kriging Efficiency (KE)?
A.25.0%
B.75.0%
C.80.0%
D.300.0%
Explanation: Kriging Efficiency is calculated using the formula: KE = [(BV - KV) / BV] * 100%. Substituting the given values: [(4000 - 1000) / 4000] * 100% = (3000 / 4000) * 100% = 75.0%. Higher Kriging Efficiency indicates less smoothing and greater reliability in block grade estimates.
4Why is a Lagrange multiplier (mu) introduced into the system of linear equations for Ordinary Kriging?
A.To minimize the variance of estimation while ensuring the kriging weights sum to 1.0 (unbiasedness constraint)
B.To force the kriging weights to always remain positive
C.To convert non-stationary grade trends into stationary Gaussian distributions
D.To eliminate the nugget effect from the sample covariance matrix
Explanation: Ordinary Kriging requires the estimator to be unbiased, meaning the sum of kriging weights must equal 1.0. The Lagrange multiplier (mu) is a mathematical parameter used in constrained optimization to minimize estimation variance subject to this weight sum constraint.
5In a spherical variogram model gamma(h) = C0 + C * [1.5(h/a) - 0.5(h/a)^3] for h <= a, what happens to gamma(h) when the lag distance h exceeds the range of influence (a)?
A.gamma(h) drops back to the nugget effect C0
B.gamma(h) continues to rise linearly with slope 1.5
C.gamma(h) equals the total sill (C0 + C) and remains constant for all h > a
D.gamma(h) becomes negative due to spatial anti-correlation
Explanation: For distances h greater than or equal to the range a, the spherical variogram model reaches its maximum value, known as the total sill (C0 + C), and stays constant. At distances beyond the range, sample pairs are spatially uncorrelated.
6According to Krige's Relationship (D^2(v/V) = D^2(v/D) - D^2(V/D)), how does increasing the volume/support size of a mining block affect the variance of block grades?
A.Block grade variance increases because larger blocks capture more regional variability
B.Block grade variance decreases (smoothing effect) while the mean grade remains unchanged
C.Block grade variance remains identical regardless of block support size
D.Block grade variance becomes zero because large blocks exhibit no grade uncertainty
Explanation: The support effect dictates that as the volume of the sample or block increases (from point support v to block support V), the dispersion variance of the block grades decreases. This smoothing effect reduces overall block grade variance while preserving the overall mean grade of the population.
7Why is Sichel's t-estimator preferred over the arithmetic mean when estimating mean reef accumulation (cm.g/t) in gold deposits of the Witwatersrand Basin?
A.Gold grades in Wits reefs follow a highly skewed lognormal distribution, where arithmetic means are overly sensitive to high-grade outliers
B.Sichel's t-estimator automatically converts gold grades to platinum group element equivalents
C.Arithmetic means cannot handle sample sizes smaller than 100 observations
D.Sichel's t-estimator produces an un-smoothed block model suitable for direct pit optimization
Explanation: Witwatersrand gold reefs typically exhibit highly positively skewed, lognormal grade distributions. The arithmetic mean of small sample numbers tends to be biased by rare high-grade samples; Sichel's t-estimator provides the Minimum Variance Unbiased Estimator (MVUE) for lognormal populations.
8Under the SAMREC Code (2016), what distinguishes an Indicated Mineral Resource from a Measured Mineral Resource?
A.Indicated Resources can be converted into Proved Reserves, whereas Measured Resources cannot
B.Measured Resources have a higher level of geological and grade confidence derived from detailed and reliable exploration/sampling, enabling high-confidence mine planning
C.Indicated Resources are based purely on geophysical anomalies without physical channel or core samples
D.Measured Resources require a completed Bankable Feasibility Study (BFS), whereas Indicated Resources do not
Explanation: Under SAMREC, a Measured Mineral Resource has sufficient high-density, reliable exploration and sampling data to assume geological and grade continuity with high confidence. Indicated Resources have sufficient confidence to assume continuity, but at a lower confidence level than Measured Resources.
9Given sample pairs separated by a lag distance of h = 20 m with grade differences (Z(x_i) - Z(x_i + h)) of: +2.0, -4.0, +3.0, and -1.0 cm.g/t (in thousands), calculate the experimental variogram value gamma(20 m).
A.3.75 (cm.g/t)^2 * 10^6
B.7.50 (cm.g/t)^2 * 10^6
C.15.00 (cm.g/t)^2 * 10^6
D.30.00 (cm.g/t)^2 * 10^6
Explanation: The experimental variogram formula is gamma(h) = [1 / (2 * N)] * sum(delta^2). The squared differences are: 2^2 = 4, (-4)^2 = 16, 3^2 = 9, (-1)^2 = 1. Sum of squared differences = 4 + 16 + 9 + 1 = 30. With N = 4 pairs: gamma(20) = 30 / (2 * 4) = 30 / 8 = 3.75.
10What is the primary difference between Simple Kriging (SK) and Ordinary Kriging (OK)?
A.Simple Kriging assumes a known constant global mean across the domain, while Ordinary Kriging re-estimates the local mean implicitly within the search neighborhood
B.Simple Kriging can only be applied to 2D orebodies, whereas Ordinary Kriging requires 3D coordinates
C.Simple Kriging uses linear variograms, whereas Ordinary Kriging uses spherical variograms
D.Simple Kriging yields zero estimation variance for all estimated points
Explanation: Simple Kriging assumes the stationary population mean (mu) is known exactly across the spatial domain and incorporates it directly into the kriging estimator. Ordinary Kriging treats the local mean as unknown, constraining the weights to sum to 1 to implicitly estimate the local mean.
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