In reflection seismology, seismic wavelet estimation is of great significance for high resolution reflectivity inversion. The method for wavelet estimation can be classified into two categories. One is deterministic and the other is statistic. For the latter, a conventional method uses a spectrum fitting method to estimate the seismic wavelet. The commonly used methods are correlation-based method, the log-spectrum-averaging method and spectrum-shaping method. All of these methods assume that the reflection coefficient sequence (RCS) is white and the source wavelets are zero-phase which may not be valid under certain conditions. In this paper, we propose a new approach to obtain the seismic wavelet based on deep neural network (DNN). We compare the wavelet obtained by our method with the wavelet obtained by widely used spectrum modeling method. Then, the obtained wavelet is applied to perform the inversion of the RCS using the HPP algorithm. Compared with the conventional method, DNN can achieve a more accurate wavelet even if source wavelet is not zero-phase. The resolution of reflectivity inversion is significantly enhanced by using the obtained wavelet.


Article metrics loading...

Loading full text...

Full text loading...


  1. Symes, W. W.
    [2009] The seismic reflection inverse problem. Inverse Problems, 25, 1–39.
    [Google Scholar]
  2. Rosa, A. L. R. and Ulrych, T. J.
    [1991] Processing via spectral modeling Geophysics56, 1244–51.
    [Google Scholar]
  3. Alpaydin, E.
    , [2009] Introduction to machine learning: MIT Press.
  4. Adler, J., and O.Öktem
    , [2017], Solving ill-posed inverse problems using iterative deep neural networks. Inverse Problems, 33, no. 12, https:// doi.org/10.1088/1361-6420/aa9581.
    [Google Scholar]
  5. Hoff, Peter D.
    [2017] Lasso, fractional norm and structured sparse estimation using a Hadamard product parametrization. Computational Statistics & Data Analysis, 115, 186–198.
    [Google Scholar]
  6. Baan, M. V. D. and Pham, D. T.
    [2008] Robust wavelet estimation and blind deconvolution of noisy surface seismics. Geophysics, 73,V37–V46.
    [Google Scholar]
  7. Otis, R. M. and Smith, R.B.
    [1997] Homomorphic Deconvolution by Log Spectral Averaging. Geophysics, 42, 1146–57.
    [Google Scholar]
  8. Baan, M. V. D.
    [2008] Time-varying wavelet estimation and deconvolution by kurtosis maximization. Geophysics, 73, V11–V18.
    [Google Scholar]

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error