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Title: Fracture identification in well logging images: two-stage adaptive network
Authors: Zhang, Wei
Li, Zhipeng
Wu, Tong
Yao, Zhenqiu
Qiu, Ao
Li, Yanjun
Shi, Yibing
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Zhang, W., Li, Z., Wu, T., Yao, Z., Qiu, A., Li, Y. & Shi, Y. (2021). Fracture identification in well logging images: two-stage adaptive network. IEEE Transactions On Instrumentation and Measurement, 71, 5003112-.
Journal: IEEE Transactions on Instrumentation and Measurement
Abstract: 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
DOI: 10.1109/TIM.2021.3130671
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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