Bring the power of machine learning to your drilling data with Imago’s AutoCrop

Drilling can be a lengthy and expensive process, so you need to make the most of the data collected from those investments, and ensure it remains well managed, easy to find, and available long after the core is sourced.

Information that is inconsistent, siloed, or poorly integrated poses a significant barrier to those who rely on accurate and timely data to make informed decisions.

At Seequent, we are always looking for ways to enable our customers to operate more effectively, minimise manual data handling, and drive better decision-making, so stepping into the Artificial Intelligence (AI) and Machine Learning (ML) space was an easy decision to make.

To start this journey, we have released AutoCrop into Imago. AutoCrop uses ML technology to automatically crop and linearise core tray imagery, removing the need to manually crop images, expediting the image capture and cataloguing process.

More precise cropping and linearisation of downhole data improves visualisation and analysis of diamond core imagery. The image data is clean, consistent, and organised, ready to be used for data validation, to support decision-making, and to enrich your geological models.

Imago’s integration with Leapfrog, for example, enables users to visualise downhole imagery next to drillholes in Leapfrog, validating model data as you build, and supporting deeper orebody analysis. Core imagery supports modelling decisions surrounding structure and lithology and is a key part of the model validation workflow where data is interrogated in 3D alongside the 2D drillhole correlation tool.

With AutoCrop speeding up the capture and cropping of your images, the images are available more quickly for modelling Geologists to bring in to their models. With high-quality image data available more rapidly, the journey from drillhole to decision becomes far more efficient.

The power of AutoCrop also extends to historic data, both from current project archives, but also when existing projects are taken over.

Collating all your historic project data into a standardized, centralised location is a huge time saver for project teams – upon upload, AutoCrop will automatically crop, linearise, and catalogue your historic images, accelerating their accessibility by syncing them into your cloud-based Portal where they are available to view and share in seconds. This allows your historical images to be visualised, analysed, and managed in a consistent way, and prevents historic data from going to waste.

Equally, inheriting historic information could be incredibly valuable as a shortcut towards the best exploration sites, while steering you away from mistakes past owners may have made. But trying to extract value from old imagery of poor quality and varied consistency is time consuming and unreliable.

AutoCrop removes data silos by converging all your core imagery into an accessible, centralised location.

Adopting a solution like Imago, with powerful machine learning capability, standardises and centralises your data while also reducing the time from data collection to modelling, enabling you to make quicker, more informed decisions that save money and improve results on the drill campaign, and ultimately maximise your investment and outcomes.