
Create a drill hole database in Micro Mine by importing color, survey, geology, and assay tables from CSV, set delimiters, and color-code by geology and iron assay.
Visualize drill data in 3d with geology and assay color coding, then use Micromine quick summary to weight by interval and assess mean, variance, and histogram.
Learn to build and interpret histograms for block modelling and resource estimation, choose bin size and breakpoints, decompose data into Gaussian populations, and extract mean and standard deviation.
Create high and low grade domains using wireframes and outer shells, analyze boundaries with histogram-driven colors, and compute downhole coordinates to constrain block models.
Compute volumes and tonnage using wireframe sets, then validate block model estimates against wireframe results by assigning density and domain values from wireframes to assay data.
Import the drill hole database, compute summary statistics, and assign high and low grade domains with grade shells. Filter the histogram to reveal high and low grade populations, excluding waste.
Apply a top cut to cap outliers and nugget effects, using a threshold to constrain data for more accurate block modeling.
Move from basic block modeling to drillhole compositing, learning how to choose compositing length using statistics and histograms, apply filters, and generate density-weighted downhole composites for block modeling.
Execute wireframe grade tonnage to estimate preliminary tonnage by transferring composited assay data into two wireframes, assign grades and density, and compare results with block models.
Create a search ellipsoid to estimate a point's grade from surrounding data, then configure its three-dimensional axes, scale, rotation, and sectors, and save it for use in estimation.
Create a blank block model, constrain it with wire frames, and assign domain and density attributes to capture high and low grade variability before estimation.
Validate the black model by generating a volumetrics report and comparing it to the wireframe results, adjusting density and domain categories for high and low grade.
Explore filling blank block model by interpolating data with inverse distance weighting, using four by four by four blocks and a search ellipsoid to classify blocks as measured or inferred.
Generate block model estimation reports by applying domain and class classifications, then configure grade-based estimates in tons using measured, indicated, and inferred categories.
This block modelling course will teach you to create an efficient block model and estimate the grade and tonnage of a mineral resource using one of the best software in the mining industry which is Micromine. This advanced course outlines the main steps needed to perform the estimation and provides background information where needed to explain the underlying statistical concepts. This course Begins by statistically describing the data (mean, median, histograms…), and then we move to the steps required to construct a block model like Drillhole compositing, blank model creation and the classical Inverse Distance Weighted (IDW) interpolation for estimation. Lastly, we will classify the block model into Measured, Indicated, and Inferred categories based on the level of confidence in the assay data and how close the estimated block to the assay data and then report on the ore grade and tonnage within each category. Even that this course might look like it is for experienced geologists, mining engineers, or geoscientists in general it’s delivered in an easy to follow methodology where beginners can follow along too. Any questions related to the course subject or the software used will be answered and more lectures will be added if needed. A part 2 of this course will be created soon that will get into geostatistical methods and variogram modelling.