Dynamic Australian tech startup, OreFox is using machine learning and deep learning systems to find hidden insights in geological data, to increase the efficiency and success at finding new mineral deposits.

OreFox utilises artificial intelligence to rapidly generate quality exploration targets for minerals. This unique system is capable of comparing data from thousands of known deposits against the data of areas to be explored.

The OreFox system is then able to show correlations in data that might otherwise have been missed, including hidden patterns, clusters and relationships; helping geologists to prioritise areas for exploration. 

Driven by discovery, the OreFox mission is to put clients at the centre of geological data science innovation, allowing them to make bigger mineral deposit discoveries, faster.

Mineral explorers are able to reap the benefits of using artificial intelligence and machine learning to analyse large amounts of geological data in identifying possible mineral deposits. 

OreFox has been working with Mining Projects Accelerator (MPX) to assess data identification of drill targets on prospective gold and copper targets in Australia. 

OreFox founder and CEO Warwick Anderson said,  

“We’ve developed a system that utilises a hybrid artificial intelligence capability to analyse huge amounts of geological data and then collate patterns in that data,” Mr Anderson said. 

“The patterns uncovered by this process are patterns that a human geologist could never see as the datasets are simply too large and complex for a person to make sense of”.

OreFox is able to analyse data and more accurately identify drill targets, helping companies to select suitable projects and drill sites in accelerated time frames, creating a more efficient operating model for mining companies across Australia. 

With mining investment rising and the need for new discoveries to meet future demand high, OreFox is uniquely positioned to provide expert services to market helping to fast-track and facilitate traditional exploration and accelerate discovery timelines.