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A list of trending Geology AI tools for Exploration Teams

December 23, 2023

A list of trending Geology AI tools for Exploration Teams

Digital Transformation
Exploration Software
Written by
Megan Gammie

Geologists are always looking for new ways to improve their exploration operations. Artificial intelligence (AI) is emerging as a powerful tool to help achieve this goal. 

Exploration is such a data-intensive process, so it’s the perfect match for AI. This is because more (accurate) information you can feed AI, the better it ‘learns’ and the more value you are able to extract from the tool. 

So you might be wondering, what AI tools currently exist that geologists can start using right now to get closer to discovery?

Here’s a list of geology AI tools and companies in this space worth knowing about. Keep watching as this exciting space emerges!

AI for desk analysis

RadiXplore

It’s like google, but for tenements. RadiXplore is an AI platform that’s been specifically built to understand geological terminology. The software has ‘read’ all publicly available mineral exploration reports in Australia, including scanned images of handwritten core logs, so that geologists can search millions of pages of exploration reports in only a few seconds.

Every report is spatially tagged allowing users to perform AI-assisted exploration in a manner which was not possible before. And the results have been profound. CEO of RadiXplore, Russell Menezes, says “our customers have either made discoveries or identified opportunities which have increased their market caps between $20-30 million.”

AI for drillhole planning and target generation

OreFox

OreFox’s AI uses machine learning and cognitive analysis to rapdily generate exploration targets by comparing data from known deposits against data from the exploration area. The system can identify patterns and correlations in the data, and reveal relationships hiding deep within big data.

AI for corelogging

Datarock

Datarock is a cloud-based software that augments and automates core logging using computer vision and machine learning to analyse core imagery. Datarock has a ‘geo-in-loop’ process that brings the geologist along for the ride so they can spend more time flexing their observation and interpretation skills by outsourcing the processing to the AI.

Goldspot LithoLens

Goldspot is on the forefront of geology AI with it’s core imaging photography. The LithoLens uses deep learning algorithms to examine old core images and create autonomous core logs. It can even extract information from drill core videos, outcrop photography, and within-borehole sensors to create a comprehensive view of the geological landscape.

AI for geology reporting

ChatGPT

ChatGPT can analyse large amounts of data and help you to quickly and easily generate outlines and summaries for reporting, presentations and more.

Here’s a list of creative ways geologists can use ChatGPT to spend more time on geology.

AI for geotech

Datarock

Datarock also has a geotech product that generates geotech parameters for mine planning purposes. Geologists and engineers can use it to maximise the value of their existing datasets by being able to access more data volumes and consistencies than what’s currently possible with traditional methods.

AI for geomodelling

Driver AI (Seequent)

Driver AI uses cognitive learning to compare your identified vectors against global mine data and generate matches to accelerate the 3D modelling process. It can also evaluate directional continuity, create block models and generate enhanced zones for all 51 elements - something that was not previously possible due to time constraints. 

Tl;dr?

AI has been around for a while in the production space, but it’s only just beginning to grow in exploration.

Good data management is essential to the success of using AI. Concepts like data standardisation and interoperability are only going to become more important in the lead up to Mining 4.0. Going digital for all of your data capture and incorporating processes that facilitate good data will be an important step in preparing for the transition.

Here are 5 free best practice tips for data capture and management using digital tools to help you start prepping for an AI future.

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