Corel is a smart computer vision model that identifies facies and performs rock typing on core images. It is based on YOLOv8-Segmentation model that is trained to classify different facies and sedimentary features from core images. The facies include:
- Bioturbated mudstone/sandstone
- Massive mudstone/sandstone
- Parallel-laminated mudstone/sandstone
- Cross-bedded/graded-bedded sandstone
- Current-rippled sandstone
- Conglomerate
- Fissile shale
- Heterolithic
🤖 Try Corel --> HERE <-- Upload your own core image (or use example image in folder core_images)
The following is example of result of Corel facies classification on core image which can accurately make segmentation for every sedimentary changes such as parallel-laminated sandstone, massive sandstone, etc.
Corel uses simple data architecture as follows. Corel model is stored in Azure Storage which is then deployed when detection is run on images.
The medium-sized YOLOv8-Segmentation model is trained with tuned hyperparameters using Ray Tune algorithm. The fitness after 14 iterations is 0.64, which gives the hyperparameter that we use for training Corel model.
Current average F1-score for all facies is 0.654. The matrix confusion is as follows
- Depth information added to the computer vision model such as using named entity recognition to recognize the depth figures on core image
- Implementation of data architecture in cloud (in context of core image stream / almost realtime)
- Improvement on class accuracy for all sedimentary facies