IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i14p4105-d590163.html
   My bibliography  Save this article

An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration

Author

Listed:
  • Shaoyong Liu

    (Department of Applied Geophysics, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Wenting Zhu

    (Department of Applied Geophysics, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Zhe Yan

    (Department of Applied Geophysics, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

  • Peng Xu

    (Wave Phenomena and Intellectual Inversion Imaging Research Group (WPI), School of Ocean and Earth Science, Tongji University, Shanghai 200092, China)

  • Huazhong Wang

    (Wave Phenomena and Intellectual Inversion Imaging Research Group (WPI), School of Ocean and Earth Science, Tongji University, Shanghai 200092, China)

Abstract

The estimation of the subsurface acoustic impedance (AI) model is an important step of seismic data processing for oil and gas exploration. The full waveform inversion (FWI) is a powerful way to invert the subsurface parameters with surface acquired seismic data. Nevertheless, the strong nonlinear relationship between the seismic data and the subsurface model will cause nonconvergence and unstable problems in practice. To divide the nonlinear inversion into some more linear steps, a 2D AI inversion imaging method is proposed to estimate the broadband AI model based on a broadband reflectivity. Firstly, a novel scheme based on Gaussian beam migration (GBM) is proposed to produce the point spread function (PSF) and conventional image of the subsurface. Then, the broadband reflectivity can be obtained by implementing deconvolution on the image with respect to the calculated PSF. Assuming that the low-wavenumber part of the AI model can be deduced by the background velocity, we implemented the AI inversion imaging scheme by merging the obtained broadband reflectivity as the high-wavenumber part of the AI model and produced a broadband AI result. The developed broadband migration based on GBM as the computational hotspot of the proposed 2D AI inversion imaging includes only two GBM and one Gaussian beam demigraton (Born modeling) processes. Hence, the developed broadband GBM is more efficient than the broadband imaging using the least-squares migrations (LSMs) that require multiple iterations (every iteration includes one Born modeling and one migration process) to minimize the objective function of data residuals. Numerical examples of both synthetic data and field data have demonstrated the validity and application potential of the proposed method.

Suggested Citation

  • Shaoyong Liu & Wenting Zhu & Zhe Yan & Peng Xu & Huazhong Wang, 2021. "An Effective Acoustic Impedance Imaging Based on a Broadband Gaussian Beam Migration," Energies, MDPI, vol. 14(14), pages 1-12, July.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4105-:d:590163
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/14/4105/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/14/4105/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhen Yang & Jun Lu, 2020. "Second-Order Approximation of the Seismic Reflection Coefficient in Thin Interbeds," Energies, MDPI, vol. 13(6), pages 1-19, March.
    2. Hao Wu & Yingpin Chen & Shu Li & Zhenming Peng, 2019. "Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving," Energies, MDPI, vol. 12(14), pages 1-15, July.
    3. Shulin Pan & Ke Yan & Haiqiang Lan & José Badal & Ziyu Qin, 2020. "A Sparse Spike Deconvolution Algorithm Based on a Recurrent Neural Network and the Iterative Shrinkage-Thresholding Algorithm," Energies, MDPI, vol. 13(12), pages 1-13, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhe Yan & Zheng Zhang & Shaoyong Liu, 2021. "Improving Performance of Seismic Fault Detection by Fine-Tuning the Convolutional Neural Network Pre-Trained with Synthetic Samples," Energies, MDPI, vol. 14(12), pages 1-13, June.
    2. Zhe Yan & Yonglong Yang & Shaoyong Liu, 2020. "True Amplitude Angle Gathers from Reverse Time Migration by Wavefield Decomposition at Excitation Amplitude Time," Energies, MDPI, vol. 13(23), pages 1-16, November.
    3. Xuegong Zhao & Hao Wu & Xinyan Li & Zhenming Peng & Yalin Li, 2020. "Seismic Reflection Coefficient Inversion Using Basis Pursuit Denoising in the Joint Time-Frequency Domain," Energies, MDPI, vol. 13(19), pages 1-15, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4105-:d:590163. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.