SaltFormer: A hybrid CNN-Transformer network for automatic salt dome detection

IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Geosciences Pub Date : 2024-11-19 DOI:10.1016/j.cageo.2024.105772
Yang Li , Suping Peng , Dengke He
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Abstract

Salt dome interpretation of seismic data is a crucial task in the exploration and development of oil and gas. Conventional techniques, such as multi-attribute analysis, are laborious, time-consuming, and susceptible to subjective biases in their results. To achieve a more automated and precise identification of salt dome, we developed a hybrid network for salt dome detection. In order to optimally exploit both local and global features, a hierarchical Vision Transformer is employed as an encoder for feature extraction. Concurrently, the concurrent spatial and channel squeeze & excitation attention module is utilized to improve detection accuracy in the decoder. Furthermore, we leveraged the complementarity of information between multiple tasks to enhance the model’s generalization performance. Using the competition data from the Kaggle platform provided by TGS-NOPEC Geophysics Company, automatic segmentation of salt domes was completed with a detection accuracy of 85.20%. A series of experiments were conducted using state-of-the-art models and the SaltFormer model, which was found to have higher detection accuracy compared to other networks. Finally, the test conducted with seismic field data from the Netherlands offshore F3 block in the North Sea demonstrate that the novel method is highly effective in detecting salt domes in seismic data.
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SaltFormer:用于自动检测盐穹的混合 CNN-Transformer 网络
地震数据的盐穹解译是勘探和开发油气的一项重要任务。多属性分析等传统技术费力、费时,而且结果容易出现主观偏差。为了实现更加自动化和精确的盐穹顶识别,我们开发了一种用于盐穹顶检测的混合网络。为了充分利用局部和全局特征,我们采用了分层视觉变换器作为特征提取的编码器。同时,利用并发空间和信道挤压& 激励注意模块来提高解码器的检测精度。此外,我们还利用多个任务之间的信息互补性来提高模型的泛化性能。利用 TGS-NOPEC 地球物理公司提供的 Kaggle 平台竞赛数据,完成了盐穹顶的自动分割,检测准确率达到 85.20%。使用最先进的模型和 SaltFormer 模型进行了一系列实验,发现与其他网络相比,SaltFormer 的检测准确率更高。最后,利用荷兰北海近海 F3 区块的地震现场数据进行的测试表明,这种新方法在检测地震数据中的盐穹顶方面非常有效。
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来源期刊
Computers & Geosciences
Computers & Geosciences 地学-地球科学综合
CiteScore
9.30
自引率
6.80%
发文量
164
审稿时长
3.4 months
期刊介绍: Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.
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