The OpportunityFinder Python based Natural Language Processing (NLP) algorithm has been extended to detect clues for porphyry copper in text.
Launched in early 2020 and used by organisations for petroleum and native hydrogen exploration, the algorithm uses hundreds of thousands of lexicons, taxonomies and labelled data for machine learning models. The novel Patented method combines these, placing a geological lens over unstructured information – turning it into structured information which can be visualised.
This can assist the Geoscientist ‘read’ hundreds of thousands/millions of notes, papers, reports, presentations, logs, maps and sketches for clues to potentially new hidden opportunities. Some ideas and opportunities may only be apparent by combining clues from many documents.
Why now? New approaches may be needed to make a contribution to the change that is likely needed. Limiting global warming to 2 degrees means more electronics, renewable energy such as wind turbines, solar panels and electric vehicles. Wood Mackenzie estimate this means an 85% increase of copper is needed by 2030. Source https://www.woodmac.com/press-releases/next-commodities-supercycle-will-be-driven-by-global-energy-transition/
A Geodyssey - Enterprise Search & Discovery and Text Mining Research 
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