Seismic Facies Segmentation via A Segformer-based Specific Encoder-Decoder-Hypercolumns Scheme

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 signific...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 61; p. 1
Main Authors: Wang, Zhiguo, Wang, Qiannan, Yang, Yang, Liu, Naihao, Chen, Yumin, Gao, Jinghuai
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Circuit faults
Classification
Coders
Convolution
Decoding
Feature extraction
Floating point arithmetic
Hypercolumn
Image segmentation
Neural networks
Oil insulation
Oil reservoirs
Segformer
Seismic data
Seismic facies segmentation
Seismic surveys
Self-attention mechanism
Semantic segmentation
Training
Transformer
Transformers
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary: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, 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.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3244037