Automated core logging


Our machine learning tools have been developed to undertake rapid, automatic processing of core photo directories to extract important geological observations.

Core is often relogged when a new company takes over a deposit, or when there is a lot of historical core logged by numerous geologists over the years with limited control of consistency.

The core logging process is typically based on visual observations of core, which makes it an ideal candidate for automation through machine learning. Automating the logging process saves time, money and ensures an objective end result.

The re-logging process requires selecting a number of training points (typically several hundred). These points, and the associated core photos, are then analysed by GoldSpot’s proprietary algorithm to generate a data model. The model can then be applied to classify all core photos collected over time. Additional training data can be provided to improve accuracy.

Example of ML core logging being used to re-log diamond drill core from photographs, generate a point cloud of coordinates for the hanging wall and the foot wall, and finally the quartz vein model in 3D.