ML in Geological Mapping | Sponsored by NRCan
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ML in Geological Mapping | Sponsored by NRCan

When happened: March - June 2022
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Advancing a framework to complement cognitive analysis / interpretation with ML methods to support regional to national scale geological mapping.
 

Leads / Advisors


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Hazen Russell | Lead Sedimentologist GS
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~Nicolas Benoit | Lead Hydrogeologist, GSC
 
 
 

Overview


  • Challenge - mapping geological materials that can have overlapping spectral signals and landscape positions. Extraction of irregular landform shapes and sizes that have inconsistent composition, moisture regime, and vegetation.
  • Tools - Machine Learning in Python using scikit-learn, TensorFlow or PyTorch algorithms (classification, regression, clustering, Convolutional Neural Network and more) and Geostatistic (spatial covariance, interpolation, simulation, scaling and uncertainty characterization).
  • The goal is to build an understanding of the challenge of replacing abstract cognitive analysis with ML analysis.
 
 

Top Contributors


~ Winning $2500 in prizes for notable progress
 
 
 

A bit about our experience


📎Details
 
 
 

Sponsored by our friends at


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