Ore body modelling software

Import assays, attributes categorical or continuous and geological structures including faults; Desurvey drill holes with choices of 5 desurvey algorithms; Composite and color-code drill holes by lithology, attributes or assays; Create custom DDH attributes as input to subsequent block model interpolations ; Create planned drill holes for drilling planning.

Create normal faults, reverse faults, strike-slip faults or combinations of the above fault types. Use fault networks for unfaulted interpolation of faulted deposits. Includes options to use unfaulting and dynamic anisotropy or unfolding for more accurate resource estimates. Skip to content. Geological Modelling and Resource Estimation. GeoModeler Key Benefits. Flexible: Includes a powerful Visual Formula Editor for customization, plus all common surface, solid and block model manipulation tools in the Foundation module of GeoMine.

GeoModeler Feature List. Foundation Feature List - included. DrillHole sub-module Import assays, attributes categorical or continuous and geological structures including faults; Desurvey drill holes with choices of 5 desurvey algorithms; Composite and color-code drill holes by lithology, attributes or assays; Create custom DDH attributes as input to subsequent block model interpolations ; Create planned drill holes for drilling planning.

Implicit Modeling sub-module: Automatically create lithology wireframes; Create real-time grade shells to quickly evaluate what-if scenarios.

Fault Network Modeling Create normal faults, reverse faults, strike-slip faults or combinations of the above fault types Create and manage fault networks with finite or infinite faults. GeoModeler - Fault Modeling. Translate PDF. Introduction A key point in the design and operation of a mine is the construction of what is called an ore body model.

The proper description of an ore body is the foundation upon which follow up mine decisions are taken. An ore body model has three distinct components, viz. The ore body model is constructed by interpolating between sample points and extrapolating onto the volume beyond sample limits. The modelling depends on considerations such as sampling methods, reliability of data, specific purpose of estimation and required accuracy.

The basic concept of ore body modelling is to conceive the entire ore body as an array of blocks arranged in a three dimensional X Y Z grid system X representing Easting, Y representing Northing and Z representing Elevation by making certain assumptions about the continuity of the ore body parameters.

Each block of uniform size represents a small volume of material to which the value of width, grade, tonnage and other geological entities are assigned. There are four conditions that an ore body model must satisfy, viz. As a prerequisite to ore body modelling, it is necessary to identify geological domains of homogeneity within which ore body modelling should be carried out. These techniques exist as computer packages in some form or other to provide a high-speed computation of a large number of blocks.

Conventional methods The conventional methods of modelling may be described as tools for quantitative and qualitative estimation of deposit based on its geometry and sample configuration. Methods under conventional techniques include polygonal, triangular, sectional, random stratified grid and contouring methods Table 1, and Fig. These methods utilise zonation of a deposit to which arithmetic calculations are applied for arriving at estimates of quality or quantity of the mineral property within the influence zone of each drill hole determined by the deposit geometry and drill hole configuration.

Aggregating the individual estimates of tonnage, for each influence zone, provides global estimates while individual tonnage weighted quality parameters yield estimate of quality parameters of a deposit.

These methods do not take into account spatial relationship among sample values and are unable to define the precision of estimates that leads to subjective mineral appraisal. The conventional methods are based on the rules given by Popoff , which govern delineation of block boundaries for the purpose of reserve estimation, viz. The rule of gradual changes points to a linear change in any property between two points where the value of property such as quality parameters, width, density etc.

The rule of nearest points expresses that in the absence of any other information, the value at any point nearer to the location can be presumed. The rule of influence range provides the reasonable distance for extending the value of a sample along a given 2 direction within its influence. Summary descriptions of conventional methods of reserve estimation Methods Descriptions i Polygonal method Polygons are constructed by drawing perpendicular bisectors to lines connecting sample points so that each polygon encloses one sample point Fig.

The area of each polygon measured by a planimeter, and the individual sample grades are then extended to the whole area of corresponding polygons. A global estimate of grade is obtained by summing the individual sample grades weighted by their respective polygonal areas. The grade of each triangle estimated from the arithmetic mean of the three corner samples, as a thickness weighted mean or by weighting samples as a function of their distance from the centre of the triangle.

A global estimate is obtained by summing the individual triangle grades weighted by their respective areas. An area of mineralisation in each section is outlined by joining the intersected thicknesses and measured by a planimeter. Estimation of average grade is carried out by summing the individual drill composite grades weighted by their respective thickness. An estimated global mean value of the deposit is the thickness weighted average of the individual panel values. Areas lying between each successive pair of contours are measured by a planimeter and multiplied by the average value of its confining contours.

A global estimate of grade is obtained by summing the individual contour confined values weighted by their respective areas. Hewlet had developed computer programs for polygons and triangles while numerous contouring packages are readily available today. Other computer methods include i estimation by linear or quadratic interpolation between known values, i. Inverse Distance; ii estimation by fitting a surface polynomial to known values, i. Trend Surface Analysis; and iii estimation by smoothing of 3 grade variation, i.

Moving Average. A comprehensive view of these techniques has been given by Davis Geomathematical methods Geomathematical methods of exploration modelling involve fitting a mathematical function, f x to define adequately a mineral deposit with respect to the distribution of its size, shape, grade, density, thickness and other geological attributes of relevance with an aim to provide three dimensional representations of the deposit parameters with stated level of confidence.

The techniques aim at replicating the reality of a deposit as closely as possible using available sample information of exploration campaign.

Geomathematical representation of a minral deposit can be achieved either through estimation or simulation. While estimation enables only one realisation of reality, simulation provides a family of realisations.

Various geomathematical methods of modelling a mineral deposit can be grouped under two broad techniques, viz. Each of these techniques has its merits and limitations. However, recent advances in geomathematical modelling have established that the probabilistic methods are more useful and accurate than the deterministic methods. These methods generate a single realisation for qualitative and quantitative estimates of reserve parameters.

The more important techniques under the deterministic group include i distance weighting, ii moving average and iii trend surface analysis. Distance weighting These methods became more popular when computer assistance became available to perform a large number of repetitive calculations. The objective of distance weighting methods is to assign a value for the mineral quality assay value parameters i.

In general, it is assumed that the potential influence of a sample value say, grade at a point decreases as one moves away from that point.

The 5 grade change, thus, becomes a function of distance i. The grade change, thus, becomes a function of distance i. If linear distances are used to calculate the value grade of a block or at point, it is called Direct Linear Distance Method. Instead of using direct distance, the inverse of distance can also be used as a function.

In such case, the method is known as Inverse Distance Method. The function used for the weighting parameter in the inverse distance method for the estimation of a value at a point from the nearest neighbourhood sample value is sometimes considered as inversely proportional to some power n of the distances between the samples and the point to be estimated i. If the power of the inverse distance used is 2, it is known as Inverse Distance Squared IDS Method, which is the most common weighting method used by computers to calculate grade at a point or block that has been sampled.

This situation further increases with IDC weighting. Moving average Moving average technique produces a trend surface and represents a smooth picture of grade variation but not confined by a mathematical function. It was first used by krige in South Africa to establish block grades that provided the basis for the development of Geostatistics.

The method differs from others in that all data surrounding block is used to value it but once all the blocks have been valued, the point values are deleted from any further calculations. If in mineral deposit, there exits a systematic change in the expectation of attribute, then it is said that a trend exits. A trend surface is a mathematical surface expressible by polynomials, fitted to spatially distributed exploration data represented by geographic coordinates, by the method of least squares.

The surface may be linear, quadratic, cubic, quartic etc depending upon the degree and order of polynomial fit. Thus, a second order trend surface is a numerical analogy of an anticline or syncline.

Xn are the geographic coordinates; a1, a2, a3…….. This method attempts to fit a mathematical function-a polynomial, to the assay values in a deposit so that the value at any point can be estimated. Davis provides a comprehensive view of the trend analysis. On removing the trend component m x , from the data set one obtains the residuals R x for which experimental semi-variograms can be constructed and fitted to suitable models.

After analysis, m x is added back to get g x i. The strategy in the trend surface analysis should be initiated by i evaluating the reliability of the fitted trend surface ii selecting right geological and statistical models and iii considering the pertinence of trend surface analysis of geological data Koch and Link, Trend surface of a part can be patched with each other. It is a mathematical technique and unless it can be geologically explained, it has no relevance in geological data analysis or in modelling of a mineral deposit.

Since most exploration geological data influenced by a multiplicity of causes i. Developments in geomathematics have led to a number of probabilistic methods. However, the most frequently used methods are: i Classical statistical methods; and ii Geostatistical methods. It involves random observations of independent individuals of a given population, regardless of their spatial position. It provides i the nature of frequency distribution; ii 9 estimates associated within specified confidence limits; iii average deviations of observations from the mean; and iv a check on the sampling and analytical biases.

Various other distributions are known but the assumption of either normality or log normality can be made for most mineral deposits and the use of a more complex distribution is not justified David, ; Rendu, The cumulative probability density function c.

N 0,1. Numerical value of skewness should be zero or close to zero and that of kurtosis should Ore Body Modelling- Concepts and Techniques DR. Estimation of parameters Once the fit of a normal distribution is established to a sample distribution, the theory of distribution can be applied to estimate mean, variance and confidence limits of mean.

If mp were the confidence limit of the true mean 'm' such that the probability of 'm' being less than mp is p, then m1-p is the confidence limit such that the probability that 'm' being larger than m1-p is 1-p, then the probability of 'm' falls between mp and m1-p is p confidence limits of mean. Once optimum solution for 'm' has been determined, it is desirable to check for the goodness of fit for the normal distribution to the sample distribution. For an ideal normal distribution curve, the degree of skewness should be zero or close to zero and the kurtosis should be equal to or close to three Rendu, Fitting a normal distribution The first step in fitting a normal distribution to sample values involves grouping of the sample values in different classes and calculation of frequencies corresponding to each class.

The individual class frequencies when divided by the total number of sample frequencies provide relative frequencies and enable construction of histogram that reflects whether or not the sample values are symmetrically distributed. The normal distribution can also be checked by a graphical method by using an arithmetic-probability paper provided the number of samples is large enough. The upper class limits are plotted in arithmetic scale whereas the corresponding percentage cumulative frequencies are plotted along the probability scale.

The assumptions of normal distribution are valid provided a straight line can be assumed to fit the plotted 12 points. But if the points plotted cannot be fitted through any sort of straight line, the distribution may be considered as 'non- normal'. These graphical estimates of mean and standard deviation are used to calculate the other statistics skewness, kurtosis, and confidence limits of mean. Lognormal distribution model Theory of lognormal distribution When the distribution curve is fairly skewed and its kurtosis value is either significantly greater than or less than three, the distribution may be represented by a 2- parameter or a 3-parameter lognormal distribution Krige, and ; Rendu, and ; Sichel, and Let xi be a variate with skewed distribution.

If ln xi is a variate with normal distribution, then the distribution of xi is said to be a 2-parameter lognormal distribution 2 PLND. The value of additive constant c is usually positive for low-grade deposits and negative for high- grade deposits.

In the case of a 3- parameter lognormal distribution, the plot is a curve either convex up or convex down and a straight line is fitted to it as described in the following steps David, ; Rendu, ; and Sarkar et al.

The upper class value limits are 14 plotted on the ordinate in log scale while the corresponding percent cumulative frequencies are plotted on the abscissa in probability scale.

However, theoretically any value of 'p' may be used. The value of 'p' is altered till a best-fit value of the additive constant is approached. If the plot fits or approximates well to a straight line, it is said to conform to a 3-parameter lognormal distribution. It is assumed that the samples taken from an unknown population are randomly distributed and are independent of one another.

In case of mineral deposits, this implies that all the samples in the deposit have an equal probability of being selected. The likely presence of trends, zones of enrichment or pay shoots in the mineralisation all may get neglected Rendu, The geostatistical modelling techniques are based on a set of theoretical concepts known as the theory of Regionalised Variables developed by Matheron based on empirical work carried out by Krige , and Any variable, which is related to its position i.

In fact, almost all variables encountered in earth sciences can be regarded as regionalised variables Kim, Most regionalised variables, in ore reserve estimation, display two aspects; viz. The two-fold purposes of the theory of regionalised variables are, i to express the spatial properties of regionalised phenomena in adequate form; and ii to solve the problems of estimating regionalised variables from sample data Kim, To achieve these, George Matheron introduced a probabilistic interpretation to regionalised variables that led to the emergence of Geostatistics as an ore reserve estimation technique in early s in France and spread worldwide.

On a global scale, Geostatistics has been successfully applied to metallic and non-metallic minerals, precious metals and fossil fuel while in India its application has been made mainly to base metals, BIFs, coal, oil, phosphorite and to some extent to bauxite as well. Geostatistics, if properly understood and appropriately applied does derive from the raw data, the best possible estimates of ore body parameters. The Conventional methods of estimation of mineral reserves and grades, in practice, do not provide any objective way of measuring the reliability of the estimates Sarkar, O' Leary and Mill, The Classical statistical techniques provide an error of estimation stated by confidence limits but ignore the spatial relations within a set of sample values Royle et al.

Trend surface analysis and Moving average methods take into account the spatial relationships 16 but ignore the error of estimations Davis, Geostatistics overrides these limitations by providing estimates together with a minimum error variance Matheron, Based on these quantifications, geostatistics produces: i estimation with a minimum variance; and ii an error of estimation, both in local and global scales.

None of these properties are taken into account of the conventional or classical methods. Geostatistics, thus, marks a major advance in ore body modelling, resource assessment and its appraisal provided that they exhibit a definite regionalised phenomenon.

A comprehensive account of recent methods of geostatistical modelling to mineral inventory estimation has been given by Sinclair and Blackwell A brief description of some the important geostatistical techniques are given in Table 2.

Of these kriging techniques, the Ordinary Kriging is the most simple and widely used kriging technique. Table 3. Various techniques of kriging Kriging techniques Description 1.

Ordinary kriging Linear kriging of a variable with unknown mean is called as Journel and Huijbregts, ordinary kriging OK. The OK technique accounts for local ; Goovaerts, variation of mean by limiting the domain of stationarity of and Olea, mean to neighbourhood samples.

This technique imposes a constraint that the sum of the kriged weights must be equal to unity.