GoldSpot spots multiple exploration targets
AI identifies 31 potential mineral targets for Critical Elements Metal Tech News - September 8, 2021
Last updated 9/7/2021 at 1:20pm
GoldSpot Discoveries Corp. Sept. 7 announced the results of its property-wide comprehensive data review, compilation, and target generation on Critical Elements Lithium Corp.s' New Blocks 1-6 and 7 claims within the prolific Nemiscau greenstone belt in James Bay, Quebec.
Aspiring to be a large-scale, responsible supplier of lithium to the burgeoning EV and energy storage systems industries, Critical Elements Lithium is advancing its wholly-owned, high-purity Rose lithium-tantalum project in Quebec.
A feasibility study conducted for Rose in 2017 is based on 26.8 million metric tons of probable reserves averaging 0.85% lithium and 133 parts per million tantalum, this is enough reserves to produce 236,532 tons of lithium and 429 tons of tantalum concentrates over a 17 year mine life.
Located a little over 40 kilometers (25 miles) from its Rose project, with a land package of 700 square kilometers (270 square miles), New Blocks 1-6 and 7 is the latest project Critical Elements is advancing in its highly prospective land portfolio.
GoldSpot's study of these new claims hinged on the digital extraction from an exhaustive collection of complied data, including assessment files, government data, and academic studies. This dataset provided outcrop and sample descriptions, bedrock geology, geochemical analyses, and geochemical surveys.
Using its proprietary machine learning algorithms, GoldSpot generated lithium-tantalum, copper-nickel, and gold targets. As a result, a total of 19 lithium-tantalum exploration targets were identified, with five being copper-nickel and seven gold.
"We are very pleased with the results of the combined AI targeting the outcrop detection conducted by GoldSpot," said Jean-Sébastien Lavallée, CEO of Critical Elements. "These cutting-edge approaches enabled us to quickly generate several promising targets. These tools are extremely useful to reduce exploration cost and time, in particular the large portfolio of 700 square kilometers owned by Critical Elements."
In addition to the targeting, GoldSpot provided a map of probable outcrop zones to support future field programs, with more than 75% of the existing outcrops being found by its machine learning model, further highlighting the predictive accuracy of this method.
"I'm thrilled to announce the results of our investigation and analysis of Critical Elements' claims sourced from our team's extensive digital extraction of assessment files, government data and academic studies," said GoldSpot Discoveries CEO Vincent Dubé-Bourgeois. "This dataset provided outcrop/sample description, bedrock geology, geochemical analyses, and geophysical surveys which generated lithium-tantalum, copper-nickel, and gold-focused targets, using our geological and machine learning methods. We look forward to working with Critical Elements exploration team to validate these targets and further advance the claims."