Tag Archives: Datarock

New IMDEX rig count shows strong growth in major mining regions

New mineral exploration rig use figures released by IMDEX have revealed strong growth in major mining regions across the globe.

The rig use snapshot, which account for seasonal factors, were contained in an IMDEX presentation to the Macquarie WA Forum on December 2. They were taken in October and updated a similar assessment conducted by IMDEX earlier this year.

The figures, of surface and underground coring and RC rigs, show the fleet is close to capacity in Australia and New Zealand at 81%, up from 72% in April.

European rig use in October was 50%, up from 39%, South America 48% (39%), Africa 57% (54%), Canada 65% (46%), Mexico and Central America 48% (44%).

Globally, rig use increased from 46% in April to 55% in October.

The USA, at 64%, was down from 72% but North America was up from 49% to 59% utilisation.

IMDEX Chief Executive Officer, Paul House, said delivery times for new rigs had increased, and the sector was facing skilled labour shortages and mobility restrictions — but these were short-term constraints.

“We’re seeing recovery and growth in all key IMDEX regions,” he said. “This is flowing through to revenue, and is reflected in increasing demand for IMDEX HUB IQ™ connected sensors and software as companies continue to embrace innovation and new digital ways of working.”

House said there was continuing strong demand for gold, copper and other base metals, but that demand for critical minerals was expected to increase at a faster rate because of the push for decarbonisation.

The profile of exploration spending is shifting, through a combination of targeting, compliance, and drilling at depth, he added.

House said the company’s recent acquisition of Mineportal and investment in Datarock had added strength to IMDEX’s geoscience analytics, artificial intelligence, and computer visualisation capabilities as part of its integrated rock knowledge expertise.

“IMDEX technologies enable us to provide critical insights right through the mining value chain,” he said. “Our global presence is unrivalled and provides a compelling opportunity to embed real value for clients.”

IMDEX bolsters real-time rock knowledge with Datarock investment

IMDEX says it has boosted its rock knowledge capabilities with a deal to acquire an initial 30% stake in image analysis company Datarock for A$5.5 million ($4 million).

Datarock has, IMDEX says, extensive geoscience and data science expertise that has led to the development of a cloud-based platform which applies artificial intelligence and machine learning to automate the extraction of geological and geotechnical information from core imagery, videos, and point clouds. This automation creates high value datasets that drive efficiency within mining operations, IMDEX added.

IMDEX has an exclusive option to acquire the remaining interest in Datarock over the next four years in a two-tranche process, subject to Datarock achieving agreed strategic milestones.

The partnership will enable IMDEX and Datarock to work together to accelerate growth plans, including product development and market expansion, it said.

IMDEX Chief Executive Officer, Paul House, said Datarock’s existing and planned products complemented IMDEX’s existing software including ioGAS™, aiSIRIS, MinePortal, and its cloud-based platform IMDEXHUB-IQ™, and strengthened the company’s ability to deliver real-time rock knowledge answer products.

“The Datarock team and the products they have built are strongly aligned with our strategy, our existing product offering and our value proposition for clients,” House said. “Data collected by geologists and engineers inform operational and financial decisions throughout a mine’s life cycle. This data is commonly collected manually, which is slow, laborious and can be prone to human error. Datarock aims to eliminate this error and deliver high quality and auditable data that provides value for the entire life of the mine.

“We are looking forward to working with the Datarock team. Its members are experts in the field of geoscience, data science and AI, and like IMDEX, have a drive for developing technologies to solve the mining industry’s biggest challenges.”

Datarock is an Australia-based mining technology company servicing the global exploration and mining sector. It is owned by two private companies, Solve Geosolutions Pty Ltd and DiUS Computing Pty Ltd. Solve Geosolutions and Datarock recently combined to both operate under the Datarock name. Solve is one of Australia’s leading geoscience machine learning and data science consulting businesses. DiUS is an Australia-based consultancy that helps organisations build the future using its expertise in AI, machine learning, IoT, cloud computing and product development.

Datarock’s products are applicable across the mining value chain, from geotechnical analysis of drill core during drill out, through to the mining and extraction phase, according to IMDEX. It has an existing customer base with major mining companies globally.

Datarock Chief Executive Officer and Director, Liam Webb, said there were clear synergies between Datarock’s products and several of IMDEX’s offerings.

“By working together, we will add considerable value to both companies,” Webb said. “When we started seeking investment our primary goal was to align ourselves with a company who saw the future the same way we did and could help us achieve our goals. I feel by entering into this agreement with IMDEX, who we believe are one of the world’s leading mining technology companies, we have achieved this.”

Datarock machine learning drill core analysis tool hits major milestone

DiUS and Solve Geosolutions have leveraged PyTorch-based image analysis techniques to help automate the analysis of drill core imagery and provide greater insight that can be used to influence decision making on mine sites.

Mine sites often produce between 100 and 1,000 m of drill core per day, generating hundreds of images a week at a single drill site, according to PyTorch, an open source machine learning platform.

Historically, these images have been kept as a record of the job and a resource for geologists to refer to, rather than being used as a quantitative dataset that adds value to a mining operation.

Tapping into this rarely used data source of drill core imagery, DiUS – an Australia-based technology services organisation with a strong focus on machine learning and deep learning image segmentation analysis – joined forces with Solve Geosolutions – a mining-focused data science and machine learning consultancy – to build a machine learning-powered, cloud-based platform to automate the analysis of this drill core imagery using image segmentation technology.

Together, DiUS and Solve Geosolutions worked on applying a range of PyTorch-based image analysis techniques, including image classification, object detection and both semantic and instance segmentation, to a range of geological problems.

In particular, the team wanted to understand how different models performed in terms of training and inference speed, training requirements and overall accuracy of prediction to inform how they could be deployed in a production environment.

One model they used extensively is Mask R-CNN.

“This model can be applied to a range of segmentation tasks, however it can also demand large training datasets that are sometimes not available,” PyTorch said. “To support this, the team developed novel ways to increase the initial, often sparse training dataset through data augmentation techniques such as rotation, flipping, contrast, saturation, lighting and cropping.”

Following the initial discovery period, the team set about combining techniques to create an image processing workflow for drill core imagery. This involved developing a series of deep learning models that could process raw images into a structured format and segment the important geological information, according to PyTorch.

Their first productionised process was a metric referred to as RQD, otherwise known as rock quality designation. “RQD is a difficult and monotonous dataset to collect manually,” PyTorch said. “It’s also well suited to automation, and of high value to a mining operation. RQD is used by engineers to understand the strength of a rock and is used in the design and engineering of a mine.”

With the release of Detectron2 – a PyTorch-based computer vision library released by Facebook in October 2019 – the team made the decision to switch from the previous model implementation on TensorFlow to the next-generation platform to help improve instance segmentation tasks, PyTorch said.

The team found Detectron2 to be four times faster in training the models (using GPUs) and three times faster in inference (using CPUs) than the previous model implementation, PyTorch said.

Building the models on PyTorch-based frameworks meant the team was able to reduce valuable training time across the board. This increased the number of experiments and, as a consequence, improved model accuracy on an identical dataset. The PyTorch Dynamic Graph also made it much easier for the team to debug and investigate any issues that arose, PyTorch said.

The resultant Datarock platform is a software as a service offering that applies machine learning – image segmentation technologies – to drill core imagery and delivers information about a mineral deposit’s geology at scale, and at a resolution that’s not been previously economically viable, PyTorch says.

Since launching Datarock in 2019, the team has extended the platform to turn drill core imagery into high-quality datasets to support decision making throughout the entire mining cycle.

“The models perform optical character recognition, instance and semantic segmentation, as well as geological statistical analysis on a dataset,” PyTorch said. “This allows a geologist to inspect the model prediction and check for quantity and quality in unmatched datasets.”

Mining and exploration companies can now get consistent geological information from their rock core imagery in a matter of minutes, according to PyTorch.

“This near real-time power is enabling more intelligent decisions to be made further down the mining chain – saving time and money that can be put towards other business-critical projects – and freeing up geologists to do higher value tasks,” PyTorch said.

To date, the Datarock platform has processed more than 1 million metres of drill core images – that’s enough core to cover the distance between Sydney and Melbourne – over 800 km.