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The document defines key terms related to mineral resources, including different types of minerals, classifications of deposits and reserves, sampling techniques, and statistical concepts used in resource estimation. It provides definitions for over 50 terms covering topics like mineral types, resource/reserve classifications, sampling, grade estimation, modeling, and statistical analysis methods.
1 definitions
- 1. Definitions • Ore minerals: Naturally occurring compounds valued for their metal content that needs further processing and refining. • Industrial minerals: Compounds or elements used for their own specific physical/chemical properties. • Energy minerals: Coal, oil and gas • Incremental ore: The portion of ore that must be drilled, blasted and moved in the course of mining “bonafide ore” but contains sufficient value to pay the incremental costs (difference between delivery to the waste are, the feed bin, stockpile pad or crusher and cost of crushing, processing, royalties etc) for itself and provide some profit as well.
- 2. • Deposit: for the purpose of this document means: “a natural occurrence of mineral or mineral aggregate, in such quantity and quality to invite exploitation”. • Classification and Categorization: “a mineral deposit may be subdivided into two Classes, Mineral Resources and Mineral Reserves. Each of these Classes may be subdivided into Categories: Measured, Indicated and Inferred in the case of Mineral Resources and Proven and Probable in the case of Mineral Reserves”. • Uncertainty: Imprecision or inaccuracy, expressing the deviation of measurements from the true value.
- 3. • Resource: An in situ mineral occurrence quantified on the basis of geologic data and a geologic cutoff grade only. • Reserve: If the study of technical and economic criteria and data relating to the resource has been carried out and stated in terms of mineable tons of reserve or volume and grade. • Selective mining unit (SMU): The smallest block on which selection as ore or waste is commonly made. • Grade-tonnage curve: As the cutoff grade increases, the tonnage of ore decreases and the average grade of that tonnage increases. • Geological cutoff grade: A subjective quantity that define the boundary of mineralization in an ore body. • Anisotropic geology: When a particular geologic characteristic is directional in nature, and differ in their character as a function of direction in the space.
- 4. • Linear estimation: It uses a linear combination of surrounding sampled values to make predictions. • Cross correlation: Correlation between the paired values of two different variables, the paired values being separated in either time or space. • Bias: A systematic favouritism that is present in the data collection process resulting in misleading results. • Support: The mass shape and orientation of the sample volume that is sub sampled for assaying. • Error of Estimation: Errors of extensions made in extending the grades of samples to a much larger volume of rock. The estimation must be unbiased and random error must not exceed an acceptable quality criterion. • Global estimation: Average grade and tonnage of very large volume of a deposit, represents a justification for long term production planning. • Local estimation: Estimation of average grade values for each block in an array. commonly used for short term planning and medium range production planning both at feasibility stage and in operating mines.
- 5. • Block: A single entity in a discretized array of fixed dimension and volume in a given geological model of a mineral deposit. • Geologic continuity: Spatial continuity in physical occurrence of geologic features e.g. vein that control mineralization. • Domain: A geologic domain is a spatial entity that represents a well defined mineralized body.
- 6. • Frequency Distribution: Important characteristics of large mass of data can be readily assessed by grouping the data into different classes and then determining the number of observations that fall in each class. Such an arrangement in tabular form is called frequency distribution. • Histogram: A graph showing the frequency of a variable within contiguous value intervals (commonly a uniform class interval) that extend over the range of the variable. • Continuous distribution: Continuous distributions are used to describe data and it may be useful to fit such a distribution to a histogram. Probability density function (PDFs) are mathematical models used to describe the probability that random draws from populations defined by the function meets particular specifications (i.e. above or below a certain value, quantile).
- 7. • Normal distribution: Normal or Gaussian probability density function is the common bell shaped curve, symmetric about the mean value. • Distribution parameters: The parameters that define a certain continuous distribution function are called the distribution parameters for that distribution. • Accuracy: Nearness to the truth • Precision: Measure of reproducibility of a result by repeated attempts. • Isotropic geology: When a particular geologic character persists in much the same manner in all directions within a domain, a geological feature is isotropic. • Cut off grade: A grade below which the value of the contained metal/mineral in a volume of rock does not meet certain specified economic requirements.
- 8. • Estimation: Quantification of naturally occurring materials, estimated by a variety of empirically or theoretically based procedures. As a verb estimation is “to judge or approximate the value, worth, or significance of; to determine the size, extent, or nature of”. As a noun “an approximate calculation; a numerical value obtained from a statistical sample and assigned to a population parameter”.
- 9. • Models: Representation of Some Phenomenon that often describe relationships between variables • Deterministic Models: Hypothesize Exact Relationships. Suitable When Prediction Error is Negligible. Example: Force Is Exactly = m·a • Probabilistic Models: Hypothesize 2 Components Deterministic and Random Error Example: Sales Volume Is 10 Times Advertising Spending + Random Error =Y = 10X + e i.e. Random Error May Be Due to Factors Other Than Advertising • Nonlinear estimation: Instead of using linear combinations of the data, linear combinations of _functions_ of the data are used.
- 10. • Correlation: The measure of similarity between variables. • Autocorrelation: Correlation between the paired values of a variable, or correlation of the variable with itself, the paired values being separated in either time or space. • Dispersion: The measures of spread of data values e.g. range, variance.
- 11. • Compositing: Combining raw samples from a variety of supports in such a way as to produce combinations of such samples in order to obtain samples of approximately uniform support is called compositing. Specimen: Specimens are preferentially selected by geologists to exemplify particular features of mineralogy, texture, structure or geologic relations and are unlikely to be representative of ore grades in much larger volumes.
- 12. • Statistical Sample: n individual values combine to make a sample (n items) of a deposit. • Mining sample: A physical quantity of rock material, representative fraction of which can be analyzed to produce numeric measures of quality (e.g. grades) • Continuity: The state of being connected or unbroken in space
- 13. • Value continuity: Value continuity can be defined as the continuous mineralization within the part of the continuous geologic feature e.g. ore shoot that is mineralized with economically important minerals. • A measure of the spatial character of grades, mineral abundances, vein thickness or some other value measure throughout a specific domain.
- 14. • Sampling patterns: The spatial layout of the sampling scheme. • Representative Sample: A sample having minimum bias and random error. How well a given assay value represent a mass being estimated. A representative sample for each sampling scheme depends upon different factors like the size of individual sample, the number of individual samples, and the sampling pattern. • Sample reduction: Procedures used to extract a much smaller representative amount for actual analysis from a large sample volume. Series of alternating particle size reduction and mass reduction steps are involved in sample reduction.
- 15. • Salting: Surreptitious introduction of material into samples. • Univariate procedures for data evaluation: Histogram, probability plots • Outlier: An observation that appears to be inconsistent to the vast majority of data values. • Dilution: Mixing of non-ore-grade material with ore- grade material during production, generally leading to increase in tonnage and decrease in mean grade relative to original expectation.