What is a multivariate simulation MVS? Iron, silica, manganese, alumina, phosphorus
7
Multiple drilling grids from a simulation
8
Case study - Copper-nickel mineralisation
9
Multivariate simulation workflow
10
Simulation - Raw data inputs
11
Simulation - Gaussianisation
12
Simulation - Scatterplot of simulated factors
13
Simulation - Reproduction of sample histograms
14
Simulation resampling - Chosen realisation
15
Simulation resampling - Pseudo drillholes
16
Post-processing of OK models and value comparison
17
Misclassification maps - Example
18
Results - Drilling cost difference for various grids
19
Results - Misclassification example
20
Results - Drilling revenue to a drilling pattern
Description:
Explore a comprehensive presentation on drillhole spacing analysis using simulated information delivered by Ian Glacken, Executive Consultant at Snowden Optiro, during the Geology Symposium 2022 in Perth. Delve into the importance of optimizing drill spacing for grade control and minimizing drilling costs to achieve indicated resources. Examine the principles of value optimization, including revenue, planned mining cost, ore loss, and dilution. Understand the consequences of misclassification and the benefits of using simulation techniques. Learn about multivariate simulation (MVS) and its application to various minerals. Follow a detailed case study on copper-nickel mineralization, exploring the multivariate simulation workflow from raw data inputs to post-processing of OK models. Analyze misclassification maps and interpret results showing drilling cost differences for various grids, misclassification examples, and drilling revenue patterns.
Drillhole Spacing Analysis Using Simulated Information for Grade Control Optimization