Transformer assisted dual U-net for seismic fault detection

Wang, Zhiwei; You, Jiachun; Liu, Wei; Wang, Xingjian

doi:10.3389/feart.2023.1047626

Cited by 10 publications

(1 citation statement)

References 29 publications

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“…The model, trained on synthetic datasets, demonstrates superior performance on the Netherlands F3 dataset [35] compared to the complete 3D Unet model. The Dual Unet with Transformer model proposed by Wang et al [36] combines the traditional U-Net with the Transformer U-Net, and they found that the binary cross-entropy loss (BCE) performed best after comparing six different loss functions. Finally, FaultSSL [37] is a semi-supervised fault recognition framework.…”

Section: Related Workmentioning

confidence: 99%

Improving Seismic Fault Recognition with Self-Supervised Pre-Training: A Study of 3D Transformer-Based with Multi-Scale Decoding and Fusion

Zhang,

Chen,

2024

Remote Sensing

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Seismic fault interpretation holds great significance in the fields of geophysics and geology. However, conventional methods of seismic fault recognition encounter various issues. For example, models trained on synthetic data often exhibit inadequate generalization when applied to field seismic data, and supervised learning is heavily dependent on the quantity and quality of annotated data, being susceptible to the subjectivity of interpreters. To address these challenges, we propose applying self-supervised pre-training methods to seismic fault recognition, exploring the transfer of 3D Transformer-based backbone networks and different pre-training methods on fault recognition tasks, thereby enabling the model to learn more powerful feature representations from extensive unlabeled datasets. Additionally, we propose an innovative pre-training strategy for the entire segmentation network based on the characteristics of seismic data and introduce a multi-scale decoding and fusion module that significantly improves recognition accuracy. Specifically, during the pre-training stage, we compare various self-supervision methods, like MAE, SimMIM, SimCLR, and a joint self-supervised learning approach. We adopt multi-scale decoding step-by-step fitting expansion targets during the fine-tuning stage. Ultimately merging features to refine fault edges, the model displays superior adaptability when handling narrow, elongated, and unevenly distributed fault annotations. Experiments demonstrate that our proposed method achieves state-of-the-art performance on Thebe, the currently largest publicly annotated dataset in this field.

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Section: Related Workmentioning

confidence: 99%

Improving Seismic Fault Recognition with Self-Supervised Pre-Training: A Study of 3D Transformer-Based with Multi-Scale Decoding and Fusion

Zhang,

Chen,

2024

Remote Sensing

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Machine learning for subsurface geological feature identification from seismic data: Methods, datasets, challenges, and opportunities

Lin,

Zhong,

et al. 2024

Earth-Science Reviews

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ResACEUnet: An Improved Transformer Unet Model for 3D Seismic Fault Detection

Zu,

Zhao,

et al. 2024

Journal of Geophysical Research: Machine Learning and Computati

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Detecting fault constitutes a pivotal aspect of seismic interpretation, significantly influencing the outcomes of petroleum and gas exploration. As artificial intelligence advances, convolutional neural network (CNN) has proven effective in detecting faults in seismic interpretation. Nevertheless, the receptive field of a convolutional layer within CNN is inherently limited, focusing on extracting local features, which lead to the detection of fewer and discontinuous fault features. In this study, integrating the local feature extraction capabilities of CNN with the global feature extraction prowess of transformer, we proposed a U‐shaped hybrid architecture model named ResACEUnet (Attention‐Convolution Unet with Efficient block) to detect fault of three‐dimensional (3D) seismic data. In ResACEUnet, we introduced a module called ACE block, which integrates convolution and attention mechanisms. This module enabled the model to simultaneously extract local features and model global contextual information, capturing more accurate fault features. In addition, we utilized a joint loss function named BCEDice loss, which composed of BCE (binary cross‐entropy) loss and dice loss to tackle the challenge of imbalanced positive and negative samples. The model was trained on a synthetic data set, with a range of data augmentation techniques were employed to bolster its generalization capabilities and robustness. We implemented our proposed method on the offshore F3 seismic data from the Netherlands and seismic data from Kerry3D and Parihaka in New Zealand. Compared to conventional popular models such as Unet, ResUnet, and SwinUnetR, ResACEUnet demonstrated superior capabilities in capturing more features and identifying fault with higher accuracy and continuity.

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Transformer assisted dual U-net for seismic fault detection

Cited by 10 publications

References 29 publications

Improving Seismic Fault Recognition with Self-Supervised Pre-Training: A Study of 3D Transformer-Based with Multi-Scale Decoding and Fusion

Improving Seismic Fault Recognition with Self-Supervised Pre-Training: A Study of 3D Transformer-Based with Multi-Scale Decoding and Fusion

Machine learning for subsurface geological feature identification from seismic data: Methods, datasets, challenges, and opportunities

ResACEUnet: An Improved Transformer Unet Model for 3D Seismic Fault Detection

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