GoldSpot Discoveries (TSXV: SPOT) and Gran Colombia Gold (TSX: GCM) announced that they have established a partnership to use machine learning to identify new drilling targets at the Segovia operation located in the Segovia-Remedios mining district of Antioquia, northwestern Colombia.
“With the recent closing of a C$20-million private placement, Gran Colombia plans to dramatically accelerate the exploration programs at Segovia and has engaged GoldSpot to minimize exploration risk and increase discovery rates by leveraging machine learning,” a press release issued by the companies reads.
According to the media brief, Goldspot will use its geoscience and machine science expertise to clean, unify and analyse Gran Colombia’s Segovia exploration data and produce 2D and 3D targets for the exploration program.
Gran Colombia is looking to expand Segovia’s mineral reserves through an accelerated drilling campaign
The raw data will also allow the tech company to deliver newly constructed lithological and mineralization models, new geophysical products, and new structural interpretations and models.
“We are excited to work with Gran Colombia at its Segovia operations as we aim to address the big data challenge in mining. Segovia has produced over 5 million ounces of gold in the last 150 years and has a library of data just waiting to be tapped,” Denis Laviolette, CEO and President of Goldspot, said in the media statement.
Gran Colombia, which is the largest underground gold and silver producer in the South American country, said the company is eager to focus on Segovia’s exploration potential, in particular that of the untouched 24 known veins in the mining title.
“We are excited to work with Goldspot to leverage their machine learning capabilities to increase our potential for success in expanding our mineral reserves through the accelerated drilling campaign we will be launching this year with the proceeds from our recently completed financing,” the Canadian miner’s Executive Chairman, Serafino Iacono, said in the release.
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