Directional Total Variation Regularized High-Resolution Prestack AVA Inversion

Guangtan Huang, Xiaohong Chen, Shan Qu, Min Bai, Yangkang Chen

Research output: Contribution to journalArticleScientificpeer-review


Prestack seismic inversion has emerged as a powerful technique for reconstructing parameters attribute to the subsurface properties and building the geophysical parameter models. However, the inversion algorithms always suffer from spatial blur and low resolution. Total variation (TV) regularization preserves the spatial variation boundary of data by highlighting the sparsity of the first-order difference, which is regarded as an important technical means for image restoration. However, when the data do not change along the spatial grid direction, TV regularization is prone to a staircase effect. In this article, a directional TV (DTV) method is proposed to conduct the prestack amplitude variation with offset/angle (AVO/AVA) inversion. The method consists of three essential steps: estimating the seismic slope attribute from the seismic data, introducing seismic slope attribute to the TV regularization to establish the objective function, and optimizing the objective function by the split-Bregman algorithm. Finally, the conventional and proposed methods are applied to the synthetic and the real seismic data. The comparison of different methods demonstrates that the proposed method is applicable to reveal the detailed subsurface models, alleviate the staircase effect or artifact substantially, and further upgrade the quality of prestack inversion results.

Original languageEnglish
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Publication statusPublished - 2021


  • Data models
  • Digital TV
  • Directional total variation (DTV)
  • high resolution
  • Mathematical model
  • Media
  • prestack amplitude variation with angle (AVA) inversion
  • Rocks
  • seismic slope.
  • Tools
  • Transforms


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