AI for Resource Estimation: Accelerated Discovery

Artificial Intelligence (AI) models dramatically cut drilling costs, ensure early availability of geology data, reduce human error, increase prediction accuracy, slash overall costs, and shrink exploration lead time by as much as 90 percent.


Mining engineers, geologists and data scientists are developing Artificial Intelligence (AI) to find, evaluate, predict, and map subsurface rocks because these technologies do in weeks what traditional methods take years to achieve.

In our new Technical Post series on AI and mining resource estimation, we examine what the technology entails, how mining companies are developing it, and what it means for the industry.

What is AI?

Artificial intelligence contains many subfields, including machine learning, neural networks, deep learning, computer vision and natural language processing. AI generally refers to the broad idea that machines can execute tasks “intelligently.”

Machine learning refers to the concept that machines can learn and adapt through experience. A neural network is a computing system modeled after the human brain. Deep learning uses huge neural networks with many layers of processing networks. Computer vision uses pattern recognition and deep learning to recognize what’s in a picture or video. Natural language processing allows computers to analyze, understand and generate human language and speech.

It’s Happening Now

In mining resource estimation, machine learning is being used to locate and estimate mineral deposits by training algorithms with large data sets to identify patterns not detectable by humans alone. For example, …

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Min Liang is a Geostatistician and a Data Scientist. She holds a PhD in Geostatistics from the École Polytechnique de Montréal and records 6 publications on geospatial modelling and simulation. Before joining Dassault Systèmes in 2019, Min worked 1 year in Data Sciences and 3 years as an Environmental Consultant.