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Cycle-Consistent Generalized S-Transform Network for Seismic Time–Frequency Analysis
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AI
DOI：10.1109/TGRS.2024.3387097.png)
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Abstract
En 中文
S-transform (ST) and its generalized versions are commonly used for seismic data processing and interpretation. Nevertheless, these transforms have several unavoidable drawbacks, including parameter selection and the Heisenberg uncertainty principle's restriction. To overcome these drawbacks, we suggest a cycle-consistent generalized ST network (CGSTN), inspired by sparse-based transforms. The CGSTN contains four main modules, i.e., two generators and two discriminators. The forward generator is trained to convert a seismic trace into a 2-D high-resolution time-frequency (TF) spectrum, while the inverse generator is trained to reconstruct the seismic trace based on the generated TF spectrum provided by the forward generator. Similarly, one discriminator is trained to distinguish whether the TF spectrum generated by the forward generator is a true TF spectrum or not, while the other is utilized to distinguish the seismic trace generated by the inverse generator. After model training, we apply the well-trained CGSTN to a 3-D field data volume acquired in the Ordos Basin, Northwest China. The results show that the CGSTN can obtain the TF spectrum with higher resolution than the contrastive methods, benefiting further reservoir delineation.
Keywords:
Cycle-consistent generative adversarial network
deep learning
generalized S-transform (GST)
reservoir delineation
Journal
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8.6
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2.1W
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10.7W