Leapfrog Geo-Edge Copper porphyry modelling and estimation

Modeling Statistics and Geostatistics using Leapfrog Geo + Leapfrog Edge applied to Copper and Moly porphyry estimation
What you'll learn

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Modeling sedimentary, intrusive and brecciated units related to Cu-Mo porphyry mineralization
Statistic analysis to outlier detection, estimation domains merge and grade capping
Geostatistical analysis in each estimation domain and executing an estimation plan for Cu-Mo estimation
Calculate the probability to be above a cut-off grade, using a critical grade Indicator and Ordinary Kriging
Requirements

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Geochemistry zonation and description of copper porphyry knowledge
Intermediate knowledge in statistics
Basic knowledge in geostatistics
Basic knowledge in resource estimation
Description

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Global copper (Cu) demand continues to grow while supply shrinks, as fewer mineral deposits are discovered each year worldwide. A fundamental outcome of mineral exploration is delivering to the market a reliable estimate of Cu tonnage in the subsurface, one that provides confidence and minimizes uncertainty.
This nearly 14-hour course offers a series of high-quality videos, carefully designed to take participants from the core concepts of geological modeling and resource estimation all the way to the industry best practices that today's market demands.
The course walks step by step through a complete resource estimation workflow applied to copper porphyry deposits, covering
Geological modeling
Statistical analysis, outlier detection, and compositing
Resource modeling and contact analysis
Gaussian anamorphosis
Geostatistical analysis and variography
Resource estimation using Ordinary Kriging, Inverse Distance Weighting, and Nearest Neighbor - with a multi-pass estimation plan
Resource classification and reporting
Another point to consider during resource estimation is the uncertainty quantification, this is not possible if you don't have a conditional simulation module, but to top it all off, the scorecard methodology (from Rocha and Bassani, 2023) is applied to identify blocks of higher and lower confidence, giving you a complete, market-ready resource estimation workflow. This methodology is used in the course to give more insights about resource confidence
Who this course is for

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Geologist and Miners working with modeling and resource estimation
People in general who wants to learn modeling and resource estimationwith cutting-edge software
Homepage

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