Automatic fracture detection from the images of electrical image logs using Mask R-CNN

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This paper presents a novel approach for automatically detecting fractures from electrical image logs using a state-of-the-art object detection and segmentation method, i.e., the Mask Regional Convolutional Neural Network (Mask R-CNN). First, we apply image processing algorithms of inpainting and augmentation to generate the basic dataset. The fractures in those processed images are properly identified and labeled. The processed images with the annotated fractures will be used as input data for the training and testing of the Mask R-CNN model. Subsequently, the input data are randomly split into training dataset, validation dataset, and testing dataset. The Mask R-CNN model is trained using the training and validation datasets. The performance and accuracy of the trained model is tested by comparing the predicted fractures from the testing dataset to the actual fractures that are identified manually. The testing results demonstrate that the trained Mask R-CNN model has precision and recall values of 96% and 92% in detecting fractures from the image logs. Last, we further validate the proposed approach by comparing the fractures detected using the developed Mask R-CNN model against those identified from three core samples. The comparison also indicates a high accuracy of the Mask R-CNN model.