Fracture recognition in ultrasonic logging images via unsupervised segmentation network

• Published in: Earth science informatics Vol. 14; no. 2; pp. 955 - 964
• Main Authors: Zhang, Wei, Wu, Tong, Li, Zhipeng, Liu, Shiyuan, Qiu, Ao, Li, Yanjun, Shi, Yibing
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer Nature B.V
• Subjects: Artificial neural networks; Domains; Earth and Environmental Science; Earth Sciences; Earth System Sciences; Fractures; Image quality; Image segmentation; Information Systems Applications (incl.Internet); Neural networks; Object recognition; Oil and gas exploration; Oil exploration; Ontology; Research Article; Simulation and Modeling; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Ultrasonic testing; Fracture recognition; Unsupervised domain adaptation; Semantic segmentation; Ultrasonic image well logging
• ISSN: 1865-0473; 1865-0481
• DOI: 10.1007/s12145-021-00605-6

Image well logging is an intuitive approach to identify fractures of reservoir for oil and gas exploration. However, these logging images are rare and nonannotated. A method of unsupervised segmentation network based on convolutional neural network is adopted to automatically extract pixels pertaining to fracture information in this paper. We propose a modified model to accomplish domain adaptation from the source domain with similar fractures information to the target domain, which can improve the accuracy of fracture recognition. The network is trained in the source domain with ground truth and tested in the target domain without any labels. Compared with the experimental results of other classical methods, this method has demonstrated satisfactory performances in terms of accuracy and visual quality even if the logging image dataset is insufficient.