Seismic field data usually suffer from low-resolution (LR) image quality. In recent years, high-resolution (HR) seismic data acquisitions are either done in high-density seismic surveys or by capturing high- and low-frequency bands in broadband seismic surveys. However, both methods increase the financial, computational, and memory overhead required for oil and gas exploration. Moreover, to improve the resolution of already acquired seismic field data in a certain area, another survey would be carried out in the same area with more advanced equipment. This is a time-consuming high-cost approach. In our previous work, we showcased the application of convolutional neural networks (CNNs) for image super-resolution (SR) to increase the resolution of seismic images. While our application of SR for seismic images is optimized for better human perception, automatic seismic interpretation tasks may not benefit as much, such as facies classification considered in this work. We address this by proposing a novel loss function that incorporates facies information when training the SR model. The proposed loss function is applied to multiple SR models and results show that this facies-guided seismic image SR approach produces improved facies classification results while mitigating major sacrifices on the objective and subjective image quality of the reconstructed SR image.

Original languageEnglish
Article number4503213
JournalIEEE Transactions on Geoscience and Remote Sensing
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 1980-2012 IEEE.


  • Convolutional neural networks (CNNs)
  • deep learning (DL)
  • facies classification
  • image super-resolution (SR)
  • loss function
  • seismic images

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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