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Advanced Block Modelling and Resource Estimation
Rating: 4.4 out of 5(135 ratings)
757 students

Advanced Block Modelling and Resource Estimation

From Drillholes to Resource classification
Last updated 6/2021
English

What you'll learn

  • Create a Geological database
  • Visualize drillholes in 3D and create custom legend
  • Analyse assay statistical data
  • Soft and hard boundary analysis
  • Grade Population count
  • Sample compositing
  • Calculating a reference global grade/tonnage estimate
  • Creating an empty block model
  • Defining a search ellipsoid
  • Grade interpolation
  • Resource reporting / Classification

Course content

2 sections14 lectures2h 17m total length
  • Creating a Geological Database17:35

    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.

  • Assay Quick Statistical Summary5:30
  • Histogram and grade population7:42
  • Domaining and boundry analysis15:45
  • Volume report and domain assign9:56
  • Filtring populations8:18
  • Applying a top cut7:26

    Apply a top cut to cap outliers and nugget effects, using a threshold to constrain data for more accurate block modeling.

Requirements

  • Basic mining engineering or Geology

Description

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.

Who this course is for:

  • Geologist
  • Mining engineer
  • Geoscientist
  • GIS Students