View in EDS HTML Full Text PDF Full Text

Post-Stack Seismic Inversion with Non-Convex Total Generalized Variation Regularization.

Saved in:
Bibliographic Details
Title: Post-Stack Seismic Inversion with Non-Convex Total Generalized Variation Regularization.
Authors: Zou, Jian1 (AUTHOR), Li, Lu1 (AUTHOR), Luo, Lan1 (AUTHOR), Gu, Jun1 (AUTHOR), Chen, Zhong1 (AUTHOR) chenzhong@yangtzeu.edu.cn
Source: Remote Sensing. Jun2026, Vol. 18 Issue 11, p1730. 22p.
Subjects: Acoustic impedance, Mathematical regularization, Optimization algorithms, Petrophysics, Regularization parameter, Electronic data processing, Seismic traveltime inversion
Abstract: Highlights: What are the main findings? Developed a non-convex TGV (NCTGV) model that integrates non-convex sparsity with higher-order structural preservation. Established global convexity conditions to ensure stable optimization and unique solutions via the ADMM algorithm. What are the implications of the main findings? Effectively eliminates staircase artifacts and blurring to produce sharper, high-resolution stratigraphic boundaries. Achieved superior quantitative accuracy and field-scale scalability for high-fidelity reservoir characterization. Post-stack seismic inversion can reconstruct high-resolution acoustic impedance (AI) models from band-limited and noisy seismic reflections, which is crucial for identifying underground structures and characteristics. Traditional regularization methods, including total variation (TV) and total generalized variation (TGV), are prone to oversmoothing and staircase artifacts, thereby limiting their effectiveness in complex geological environments. In this paper, we introduce a novel regularization method based on non-convex TGV (NCTGV), which integrates the classical TGV regularization into a convex non-convex framework. This integration enables the model to simultaneously promote sparsity and preserve higher-order structural continuity. The resulting seismic inversion model was effectively solved using the alternating direction method of multipliers (ADMM), with a provably convergent scheme adapted to the NCTGV structure. Numerical experiments demonstrated the improved performance of the proposed technique. Compared to existing regularization techniques such as TV, NCTV, and TGV, the NCTGV method achieved lower root-mean-square error (RMSE). It also obtained higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) scores, together with enhanced vertical resolution. Visual inspection confirmed that the NCTGV-inverted impedance models exhibited clearer stratigraphic boundaries and sharper geological features. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 194586951
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Post-Stack Seismic Inversion with Non-Convex Total Generalized Variation Regularization.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zou%2C+Jian%22">Zou, Jian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Lu%22">Li, Lu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Lan%22">Luo, Lan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gu%2C+Jun%22">Gu, Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Zhong%22">Chen, Zhong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chenzhong@yangtzeu.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 11, p1730. 22p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Acoustic+impedance%22">Acoustic impedance</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+regularization%22">Mathematical regularization</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Petrophysics%22">Petrophysics</searchLink><br /><searchLink fieldCode="DE" term="%22Regularization+parameter%22">Regularization parameter</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Seismic+traveltime+inversion%22">Seismic traveltime inversion</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? Developed a non-convex TGV (NCTGV) model that integrates non-convex sparsity with higher-order structural preservation. Established global convexity conditions to ensure stable optimization and unique solutions via the ADMM algorithm. What are the implications of the main findings? Effectively eliminates staircase artifacts and blurring to produce sharper, high-resolution stratigraphic boundaries. Achieved superior quantitative accuracy and field-scale scalability for high-fidelity reservoir characterization. Post-stack seismic inversion can reconstruct high-resolution acoustic impedance (AI) models from band-limited and noisy seismic reflections, which is crucial for identifying underground structures and characteristics. Traditional regularization methods, including total variation (TV) and total generalized variation (TGV), are prone to oversmoothing and staircase artifacts, thereby limiting their effectiveness in complex geological environments. In this paper, we introduce a novel regularization method based on non-convex TGV (NCTGV), which integrates the classical TGV regularization into a convex non-convex framework. This integration enables the model to simultaneously promote sparsity and preserve higher-order structural continuity. The resulting seismic inversion model was effectively solved using the alternating direction method of multipliers (ADMM), with a provably convergent scheme adapted to the NCTGV structure. Numerical experiments demonstrated the improved performance of the proposed technique. Compared to existing regularization techniques such as TV, NCTV, and TGV, the NCTGV method achieved lower root-mean-square error (RMSE). It also obtained higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) scores, together with enhanced vertical resolution. Visual inspection confirmed that the NCTGV-inverted impedance models exhibited clearer stratigraphic boundaries and sharper geological features. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194586951
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18111730
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 1730
    Subjects:
      – SubjectFull: Acoustic impedance
        Type: general
      – SubjectFull: Mathematical regularization
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Petrophysics
        Type: general
      – SubjectFull: Regularization parameter
        Type: general
      – SubjectFull: Electronic data processing
        Type: general
      – SubjectFull: Seismic traveltime inversion
        Type: general
    Titles:
      – TitleFull: Post-Stack Seismic Inversion with Non-Convex Total Generalized Variation Regularization.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zou, Jian
      – PersonEntity:
          Name:
            NameFull: Li, Lu
      – PersonEntity:
          Name:
            NameFull: Luo, Lan
      – PersonEntity:
          Name:
            NameFull: Gu, Jun
      – PersonEntity:
          Name:
            NameFull: Chen, Zhong
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 11
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1