When DiUS partnered with Solve Geosolutions on a project, the two companies successfully reinvented the process for geologists to analyse drill core samples to generate geological information but also discovered a new area of opportunity for a new machine-learning powered software product to improve the mineral discovery process. The new SaaS product, DataRock, will change the way mining companies worldwide work, from exploration through to extraction.
A more responsive and accurate way to plan and manage operations
In today’s mining industry, fast access to reliable data about the geology of a deposit is critical when making operational decisions about field work at a mine. Even small improvements to the geological process can have a significant effect on efficiency and productivity—and ultimately profitability.
A key part of the exploration process involves drilling core samples at different locations and getting a geologist to manually inspect the samples and report on the geological features such as veins and textures. This task can be tedious and error prone and conclusions can be highly subjective. Additionally, the lag between drilling a sample and receiving the results impacts the numerous interrelated processes and decisions on how economically important metals can be efficiently extracted from a mineral deposit.
Solve Geosolutions (Solve) provides specialist computational analysis skills to mineral exploration companies. The team of mathematical geologists identified an opportunity to automate the analysis of drill core samples using an unexploited datasource: the store of high resolution digital photos that are taken of the core samples as a record of the job.
Geology from imagery reinvents discovery process
In order to create better outcomes from core image analysis, Solve needed assistance from a partner that had specialist expertise with deep neural networks to help with the complex image processing — so they reached out to DiUS.
Starting small with a project to detect veins in half-core, DiUS fast tracked the project by leveraging an open source deep neural network architecture suitable for object detection and segmentation problems. In addition, the team was able to further speed time to results by working closely with the Solve geoscientists, using a small dataset and heavily relying on augmenting images to train the model.
The results were impressive. The machine-learning powered solution detected veins faster, more consistently and often at a higher or equal quality to a geologist. More importantly, it provided the ability to more completely investigate the geology of a mineral deposit, as until now it has not been feasible for a geologist to dedicate the time to analyse the data at the same resolution.
This success led to a family of equally-successful neural network models developed with Solve for other Australian mining clients built to tackle other geological use cases, including rock texture analysis, rock tray fragmentation, rock quality designation, and grain analysis.