دورية أكاديمية
CONSS: Contrastive Learning Method for Semisupervised Seismic Facies Classification
| العنوان: | CONSS: Contrastive Learning Method for Semisupervised Seismic Facies Classification |
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| المؤلفون: | Kewen Li, Wenlong Liu, Yimin Dou, Zhifeng Xu, Hongjie Duan, Ruilin Jing |
| المصدر: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 7838-7849 (2023) |
| بيانات النشر: | IEEE, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Ocean engineering LCC:Geophysics. Cosmic physics |
| مصطلحات موضوعية: | Contrastive learning, deep learning, seismic facies classification, seismic interpretation, semisupervised learning, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809 |
| الوصف: | Recently, convolutional neural networks (CNNs) have been widely applied in the seismic facies classification. However, even state-of-the-art CNN architectures often encounter classification confusion distinguishing seismic facies at their boundaries. In addition, the annotation is a highly time-consuming task, especially when dealing with 3-D seismic data volumes. While traditional semisupervised methods reduce dependence on annotation, they are susceptible to interference from unreliable pseudolabels. To address these challenges, we propose a semisupervised seismic facies classification method called CONSS, which effectively mitigates classification confusion through contrastive learning. Our proposed method requires only 1% of labeled data, significantly reducing the demand for annotation. To minimize the influence of unreliable pseudolabels, we also introduce a confidence strategy to select positive and negative sample pairs from reliable regions for contrastive learning. Experimental results on the publicly available seismic datasets, the Netherlands F3 and SEAM AI challenge datasets, demonstrate that the proposed method outperforms classic semisupervised methods, including self-training and consistency regularization, achieving exceptional classification performance. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2151-1535 |
| العلاقة: | https://ieeexplore.ieee.org/document/10230299Test/; https://doaj.org/toc/2151-1535Test |
| DOI: | 10.1109/JSTARS.2023.3308754 |
| الوصول الحر: | https://doaj.org/article/be9182a5608040ae85cf4987ef955664Test |
| رقم الانضمام: | edsdoj.be9182a5608040ae85cf4987ef955664 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 21511535 |
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| DOI: | 10.1109/JSTARS.2023.3308754 |