Abstract
Traditional fault identification involves manual marking by geological interpreters, which is time consuming, inefficient, and prone to human error. To address these issues and increase the accuracy of fault identification, a deep-learning-based fault identification method is proposed that uses an attention mechanism to focus on target features. A convolutional block attention module (CBAM) is used in the decoding layer of the U-Net network, and a ResNet-50 residual block is used in the encoding layer. Consequently, a fault identification method based on convolutional neural networks is established and referred to as Res-CBAM-UNet. To enhance the generalization ability of the network model, data augmentation on synthetic seismic data and their corresponding fault labels was performed, and the model was trained using the newly generated training dataset as the input. Subsequently, the model was compared and analyzed with CBAM-UNet, ResNet34-UNet, and ResNet50-UNet networks and tested using the seismic data from actual working areas. Results reveal that the designed Res-CBAM-UNet network has good fault identification performance with high continuity of identified faults and computational efficiency.
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Acknowledgments
We express our heartfelt gratitude to the editors and reviewers for their valuable comments and suggestions.
This research was jointly supported by the Fundamental Research Funds for the Central Universities (No. 2022JCCXMT01), the National College Students’ Innovation and Entrepreneurship Training Program Automatic Recognition of Earthquake Faults Based on Convolutional Neural Networks (No. 20220236), and the Open Fund of State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources (No. SKLCRSM22DC02).
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Xinwei Wang is an undergraduate student at the China University of Mining and Technology (Beijing). His major is geophysics, and his primary research direction is artificial intelligence.
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Wang, XW., Shi, SZ., Yao, XJ. et al. Automatic identification of seismic faults via integrating Residual Network-50 residual blocks and convolutional block attention modules. Appl. Geophys. 20, 20–35 (2023). https://doi.org/10.1007/s11770-023-1014-2
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DOI: https://doi.org/10.1007/s11770-023-1014-2