Limitations

Comments

Reliance of row detection and depth registration

The Custom Segmentation model is based on predicting and masking geological features within a row of drill core. The dependency on the depth registered rows being identified means if a row is missed by the row model during Image Preparation, this row will not have the segmentation modelled applied.

Training is dependant dependent on what can be seen within a row image

Datarock’s Custom Segmentation model relies on features being segmented using visually identifiable RGB features some of which are too subtle or fine to predict from a photo, in particular if resolution is poor.

If segmentation classes are not identified, the resulting segmentation model will generally be lower than expectations based on any available site logging.

Training data must be representative of whole area segmentation is to be applied

If new imagery or segmentation class is introduced to the model, the performance may decline as these examples were not trained during onboarding.

An initial model evaluation will need to be undertaken to see the suitability of the model in particular against any new imagery. Ideally, a new model version is trained to incorporate the new untrained drill core or segmentation class.