  Most methods for automated full-bore rock core image analysis (description,
colour, properties distribution, etc.) are based on separate core column
analyses. The core is usually imaged in a box because of the significant amount
of time taken to get an image for each core column. The work presents an
innovative method and algorithm for core columns extraction from core boxes.
The conditions for core boxes imaging may differ tremendously. Such differences
are disastrous for machine learning algorithms which need a large dataset
describing all possible data variations. Still, such images have some standard
features - a box and core. Thus, we can emulate different environments with a
unique augmentation described in this work. It is called template-like
augmentation (TLA). The method is described and tested on various environments,
and results are compared on an algorithm trained on both 'traditional' data and
a mix of traditional and TLA data. The algorithm trained with TLA data provides
better metrics and can detect core on most new images, unlike the algorithm
trained on data without TLA. The algorithm for core column extraction
implemented in an automated core description system speeds up the core box
processing by a factor of 20.
