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Seismic Facies Segmentation via a Segformer-Based Specific Encoder-Decoder-Hypercolumns Scheme
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DOI：10.1109/TGRS.2023.3244037.png)
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摘要
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Seismic facies classification plays an important role in oil and gas reservoir interpretation. In the past few years, convolution neural network (CNN)-based models have been widely used in supervised seismic facies classification. However, to improve some inherent limitations of CNNs, it is significant to explore alternative architectures, such as the Transformer with the self-attention mechanism. In this study, based on the Segformer, a cutting-edge semantic segmentation Transformer, we propose a U-shaped model of joint the Segformer and the Hypercolumn representation for seismic facies classification, named U-Segformer-Hyper. As the emerging lightweight variant of the Transformer for seismic facies segmentation, the proposed U-Segformer-Hyper consists of a specific encoder-decoder-hypercolumns scheme, which can extract various features in different layers and fuse the output features of different layers with different scales. In the application of the public F3 seismic data, compared with the CNN Benchmark model, the U-Segformer-Hyper model has fewer parameters, fewer floating-point operations per second (FLOPS), and higher accuracy of classification in both the section-based and patch-based training modes. Moreover, the proposed U-Segformer-Hyper is open source, which benefits to further explore alternative deep models in seismic interpretation.
Keyword:
Decoding
Image segmentation
Oil insulation
Transformers
Circuit faults
Training
Feature extraction
Hypercolumn
Segformer
seismic facies segmentation
self-attention mechanism
Transformer
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
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