Seequent Imago AI core imagery: workflow and modelling impacts for mine geologists
Reviewed by Tom Sullivan
First reported on International Mining – News
30 Second Briefing
Seequent, The Bentley Subsurface Company, has launched Imago image analysis, an AI-powered add-on to its cloud-based Imago platform that extracts geological information directly from drill core imagery. The tool processes high-resolution core photos in minutes rather than the days or weeks typical of manual logging, automatically classifying features such as lithology, structures and alteration. Faster, consistent image-derived logging can tighten geological models, speed resource estimation workflows and reduce reliance on time-intensive core shed work.
Technical Brief
- Imago image analysis runs directly within Seequent’s existing cloud-hosted Imago core imagery environment.
- The add-on targets geoscientific imagery broadly, not only drill core (e.g. chips, outcrop photos).
- Outputs are designed to feed straight into Seequent’s modelling ecosystem, reducing manual data transfer.
- Image analysis is performed server-side, avoiding the need for local GPU hardware in core sheds.
- Workflows remain accessible through a browser interface, aligning with remote logging and distributed teams.
- Similar image-based classification could extend to pit wall mapping, stockpile characterisation and plant feed reconciliation.
Our Take
In our database, Seequent features across multiple 2026 items for extending subsurface data into design tools (OpenGround–Civil 3D, Leapfrog updates), so using Imago for AI on imagery looks like a logical move to close the loop from raw visual data through to modelling and design environments.
The 7th Geoprofessionals survey referenced in a February 2026 piece flagged that users struggle to extract value from complex subsurface datasets; AI-driven image workflows with Imago give Seequent a way to target that pain point without forcing customers to overhaul existing Leapfrog or OpenGround-centric workflows.
Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.
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