A random layer-stripping method for seismic reflectivity inversion

Hondori, Ehsan Jamali; Mikada, Hitoshi; Goto, Tada-nori; Takekawa, Junichi

doi:10.1071/eg13013

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…However, the results of these recursion schemes are sensitive to noise in the data or inaccuracies in the generated reflectivity function (Berteussen and Ursin, 1983). Furthermore, the recursion formulae are often applied trace-by-trace, and thus they do not allow spatial regularization of the impedance map (see, e.g., Jamali Hondori et al, 2013). Wen et al (2014) use the basis pursuit decomposition with the contourlet transform to force lateral continuity of the estimated reflectivity section.…”

Section: Introductionmentioning

confidence: 98%

“…Then, the generated reflectivity is input into one of the standard recursion formulas to obtain AI (Berteussen and Ursin, 1983;Oldenburg et al, 1983;Walker and Ulrych, 1983;Jamali Hondori et al, 2013). However, the results of these recursion schemes are sensitive to noise in the data or inaccuracies in the generated reflectivity function (Berteussen and Ursin, 1983).…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear multichannel impedance inversion by total-variation regularization

Gholami

2015

GEOPHYSICS

136

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The analysis of acoustic impedance (AI) allows for the mapping of seismic reflection data to lithology, and hence it plays an important role in the interpretation of poststack seismic data. The AI is obtainable from the inversion of the earth reflectivity series. Efficient deconvolution methods have been developed for recovering the reflectivity series from band-limited poststack data, which are multiple free, zero offset, and migrated. However, calculation of the AI from the reflectivity, when considering the spatial correlation of the impedance parameters, demands the solution of a constrained nonlinear inverse problem. Two efficient algorithms are proposed for solving the nonlinear impedance problem in multichannel form with the total-variation (TV) constraint to recover impedance maps with blocky structures. The first uses the continuous earth model for reflectivity, which allows linearizing the problem in the logarithm domain. The second uses the discrete (layered) earth model for reflectivity. In this case, a nonlinear problem is solved in the original domain. The main properties of the algorithms, which are built based on the split-Bregman technique and the discrete cosine transform are as follows: (1) They are multichannel inversion procedures, so they respect the spatial and temporal correlation of the data, (2) they impose the TV constraint on the parameters to generate blocky structures, (3) they are fast such that a large 3D impedance model with a blocky structure can be recovered easily on a personal computer, and (4) the computational complexities of both algorithms are the same. Numerical tests using simulated 2D data obtained from a benchmark Marmousi model and also 2D and 3D field data confirmed that the proposed algorithm generates more accurate with higher resolution impedance models compared with the conventional methods.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Nonlinear multichannel impedance inversion by total-variation regularization

Gholami

2015

GEOPHYSICS

136

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“…A pre-stack time migrated (PSTM) section is used to develop a reflectivity section through sparse spike inversion 2) . In a trace by trace process, the best set of spikes which their convolution with known seismic wavelet reproduces the input trace with minimum error are extracted.…”

Section: (1) Fwi Initial Model From Well Interpolationmentioning

confidence: 99%

Developing Initial Model for Seismic Full Waveform Inversion Using Conventional Data Processing Tools

Hondori¹,

Mikada²,

Asakawa³

et al. 2016

Recent Advances in Exploration Geophysics

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We believe Full waveform inversion (FWI) is capable to be used as a part of seismic data processing routines. In order to check the possibility of using FWI in conventional data processing sequences, we evaluated the effect of different initial models on inversion results. We developed new initial models for full waveform inversion using horizon-guided well interpolation and compared it against initial velocities converted from stacking velocities, with and without dip move-out (DMO) correction. Acoustic full waveform inversion results from Marmousi2 model showed that when the subsurface structure has strong dips, interval velocities which are converted from stacking velocity without dip corrections fail to initialize FWI properly. However, applying dip move-out correction on the seismic data will relax the dip complexities in the velocity analysis stage and a good initial model for FWI could be developed. Alternatively, horizon-guided well interpolation uses velocities derived from well logs and makes an interpolated velocity model along the picked horizon by a constrained post-stack inversion. This makes an initial velocity model for full waveform inversion which ensures the convergence to the correct solution.

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“…Assuming an available depth section of seismic data, which could be the result of depth conversion of time migrated section, we define a separate inverse problem which extracts the reflection coefficients of the depth section 1) . Based on the convolutional model, any seismic trace is composed of source wavelet and a reflectivity series of spikes whose amplitudes are reflection coefficients.…”

Section: Reflectivity Inversionmentioning

confidence: 99%

“…In this paper we suggest a reflectivity inversion process which is applied on the seismic depth section prior to FWI in order to provide a long-wavelength starting velocity model. This method first extracts the reflection coefficients from depth section by using a global optimization algorithm 1) then an acoustic impedance section is built from the extracted reflection coefficients. Finally, by using a known density model a P wave velocity model is calculated which can be used as initial model for full waveform inversion.…”

Section: Introductionmentioning

confidence: 99%

A Reflectivity Guided Elastic Full Waveform Inversion

Hondori¹,

Mikada²,

Asakawa³

2014

Recent Advances in Exploration Geophysics

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Full waveform inversion (FWI) produces subsurface images by minimizing the misfit between observed data and calculated model using iterative local optimization algorithms like conjugate gradient method. This approach requires a starting model which should appear in the neighborhood of the global solution of the FWI problem to ensure that the modeled waveforms are less than half a period away from the recorded data. Usually, reflection traveltime tomography is used to create a long-wavelength background velocity model for starting FWI iterations. In this paper we suggest an alternative method to develop the starting model for FWI by using a reflectivity inversion technique. A depth section of migrated data is used to extract the reflection coefficients and impedance section, then the impedance section is converted to velocity model by considering a known density model. The reflectivity inversion can detect subsurface geological structures very well and on the other hand, an approximate known density model is a fair assumption for FWI and does not dramatically affect the long-wavelength model. We applied our method on a part of Marmousi2 model in order to develop P and S wave velocity models via elastic full waveform inversion in the frequency domain.

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A random layer-stripping method for seismic reflectivity inversion

Cited by 12 publications

References 13 publications

Nonlinear multichannel impedance inversion by total-variation regularization

Nonlinear multichannel impedance inversion by total-variation regularization

Developing Initial Model for Seismic Full Waveform Inversion Using Conventional Data Processing Tools

A Reflectivity Guided Elastic Full Waveform Inversion

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