Automation of Mineral Resource Model Development Using Machine Learning and High-Performance Computing
Overview
Creating mineral resources models is a labor-intensive task that requires inputs from geology, mining, metallurgical and commercial disciplines. It requires thousands of samples from hundreds of drill holes to be verified, and then grouped into geological domains. Subsequently, to model the resource a block model is created, blocks estimated, uncertainty quantified, and then the resource is valued. This session explores how MATLAB’s Machine Learning, and High-Performance Computing capabilities can automate and speed-up parts of this process.
This session will:
- Automate mineral resource domaining using unsupervised machine learning techniques.
- Estimate blocks and quantify uncertainty using conditional simulation.
- Accelerate the computationally intensive simulations by using high-performance computing techniques such as parallel computing and GPU.
- Validate estimated blocks using supervised machine learning techniques.
About the Presenters
Recorded: 10 Nov 2020
Featured Product
Statistics and Machine Learning Toolbox
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)
Asia Pacific
- Australia (English)
- India (English)
- New Zealand (English)
- 中国
- 日本Japanese (日本語)
- 한국Korean (한국어)