Fracture Identification in Well Logging Images: Two-Stage Adaptive Network

Automatic fracture identification and segmentation in well logging images is increasingly critical but arduous because of extensive exploration for oil and gas. Domain adaptation for semantic segmentation is an appealing alternative to the lack of semantic fracture annotations. Nevertheless, previou...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2022, Vol.71, p.1-12
Main Authors: Zhang, Wei, Li, Zhipeng, Wu, Tong, Yao, Zhenqiu, Qiu, Ao, Li, Yanjun, Shi, Yibing
Format: Article
Language:English
Subjects:
Adaptation
Algorithms
Computer architecture
Datasets
Domain adaptation
Domains
fracture identification
Generators
Image annotation
Image segmentation
Oil exploration
Oils
Semantic segmentation
Semantics
Training
two-stage adaptation
Ultrasonic imaging
ultrasonic well logging image
Visualization
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Summary:Automatic fracture identification and segmentation in well logging images is increasingly critical but arduous because of extensive exploration for oil and gas. Domain adaptation for semantic segmentation is an appealing alternative to the lack of semantic fracture annotations. Nevertheless, previous domain adaptation strategies merely focus on a single level (e.g., input, intermediate features, or output), which could result in a large generalization error on new well logging images. In this article, a two-stage network architecture is proposed. In stage 1, a pattern transfer network (PTN) model is utilized to carry out a transformation from one domain to the other domain, making images visually similar between two datasets. Sequentially, in stage 2, an adversarial learning model with a generator and discriminator is introduced to make two domain images generated via PTN producing similar semantic segmentation. Furthermore, an attention module was embedded in the generator to filter irrelevant noise in semantic segmentation results. Real logging images were collected as the tested datasets by an ultrasonic imaging logging instrument. Compared to earlier conventional algorithms and domain adaptation methods, the proposed method is shown to obtain better visual quality and accurate segmentation outcomes.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2021.3130671