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Song, H., Gao, Y., Chen, W., Xue, Y., Zhang, H. and Zhang, X. (2020) Seismic Random Noise Suppression Using Deep Convolutional Autoencoder Neural Network. Journal of Applied Geophysics, 178, Article ID: 104071.
https://doi.org/10.1016/j.jappgeo.2020.104071

has been cited by the following article:

• TITLE: Research on the Identification Method of Fluvial Sandstone Reservoirs Based on U-Net Networks

  AUTHORS: Fumei Wang, Weiwei Yu, Feifei Wu, Yan Li, Qing Yang, Xuanguo Yuan

  KEYWORDS: Lithological Oil and Gas Reservoirs, Fluvial Facies, Convolutional Neural Networks, U-Net

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.16 No.1, January 15, 2026

  ABSTRACT: With the deepening of oil and gas exploration, the exploration targets have gradually shifted from structural oil and gas reservoirs to lithological oil and gas reservoirs. The fluvial sandstone reservoir is an important type of lithological oil and gas reservoir. The fluvial sandstone presents characteristics in seismic data, such as the uneven thickness that is difficult to distinguish, the poor lateral continuity, and the strong subjectivity in tracking and interpretation, which pose great challenges to seismic interpretation work. To solve this problem, this study proposes a method for identifying channel sand bodies based on convolutional neural networks using the image semantic segmentation idea. This method mainly adopts the U-Net network structure, which can effectively combine low-resolution information and high-resolution information. The actual application effect shows that compared with traditional manual interpretation, this method has higher efficiency and accuracy.