Multiscale reflection phase inversion with migration deconvolution

Chen, Yuqing; Feng, Zongcai; Fu, Lei; AlTheyab, Abdullah; Feng, Shihang; Schuster, Gerard T.

doi:10.1190/geo2018-0751.1

Cited by 18 publications

(14 citation statements)

References 39 publications

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“…Our future research will explore different ways to improve the inversion results. It is likely that using a reflection MPI method and applying an offset rolling strategy to the reflections will significantly improve the accuracy of the lowwavenumber velocity model, as shown by Chen et al (2019) using both synthetic data and field data. Another possible approach is to start from the envelope-based FWI objective function (Chi, Dong and Liu 2014), which may mitigate the problem of missing low-frequency components in the data.…”

Section: Discussion a N D C O N C L U S I O N Smentioning

confidence: 99%

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Multiscale phase inversion for 3D ocean‐bottom cable data

Feng

Schuster

2019

Geophysical Prospecting

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We invert three‐dimensional seismic data by a multiscale phase inversion scheme, a modified version of full waveform inversion, which applies higher order integrations to the input signal to produce low‐boost signals. These low‐boost signals are used as the input data for the early iterations, and lower order integrations are computed at the later iterations. The advantages of multiscale phase inversion are that it (1) is less dependent on the initial model compared to full waveform inversion, (2) is less sensitive to incorrectly modelled magnitudes and (3) employs a simple and natural frequency shaping filtering. For a layered model with a three‐dimensional velocity anomaly, results with synthetic data show that multiscale phase inversion can sometimes provide a noticeably more accurate velocity profile than full waveform inversion. Results with the Society of Exploration Geophysicists/European Association of Geoscientists and Engineers overthrust model shows that multiscale phase inversion more clearly resolves meandering channels in the depth slices. However, the data and model misfit functions achieve about the same values after 50 iterations. The results with three‐dimensional ocean‐bottom cable data show that, compared to the full waveform inversion tomogram, the three‐dimensional multiscale phase inversion tomogram provides a better match to the well log, and better flattens angle‐domain common image gathers. The problem is that the tomograms at the well log provide an incomplete low‐wavenumber estimate of the log's velocity profile. Therefore, a good low‐wavenumber estimate of the velocity model is still needed for an accurate multiscale phase inversion tomogram.

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Section: Discussion a N D C O N C L U S I O N Smentioning

confidence: 99%

“…5 The MPI mainly focused on explaining shallow reflections and did not emphasize the deep reflection arrivals. In this case, we should apply reflection MPI as suggested by Chen et al (2019).…”

Section: Volve 3d Ocean-bottom Cable Datamentioning

confidence: 99%

Multiscale phase inversion for 3D ocean‐bottom cable data

Feng

Schuster

2019

Geophysical Prospecting

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“…where g denotes the backpropagated Green's function for the acoustic wave-equation, and the dot operator means the derivative with respect to time. Equations (13) and (14) are nothing but to conduct two RTMs. As (12) represents the standard Fréchet derivative of the envelope with respect to the velocity variations, we will also compare the results of the proposed method with the envelope inversion results in the next numerical experiment section.…”

Section: Connective Functionmentioning

confidence: 99%

“…Kwon et al [12] have made a series of advances in the Laplace-Fourier domain to invert the data with low frequencies, therefore making their inversions more robust than conventional FWI. Recently, Yao et al [13] and Chen et al [14] revised the velocity models gradually from near-offset to faroffset reflections so that the objective functions can more likely reach the global minima and the corrected velocity models can approximate the true model. To focus on matching the kinematic information rather than the dynamics, Sun and Schuster [15] and Fu et al [16] developed a phase mismatch objective function that is largely insensitive to the amplitude mismatch between the predicted and observed arrivals.…”

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confidence: 99%

Skeletonized Wave-Equation Refraction Inversion With Autoencoded Waveforms

Han

Chen

Hanafy

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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We present a method that skeletonizes the first arriving seismic refractions by machine learning and inverts them for the subsurface velocity model. In this study, first arrivals can be compressed in a low-rank sense with their skeletal features extracted by a well-trained autoencoder. Empirical experiments suggest that the autoencoder's 1 × 1 or 2 × 1 latent vectors vary continuously with respect to the input seismic data. It is, therefore, reasonable to introduce a misfit functional measuring the discrepancies between the predicted and the observed data in a low-dimensional latent space. The benefit of this approach is that an elaborated autoencoding neural network not only refines intrinsic information hidden in the refractions but also improves the quality of inversion for a reliable background velocity model. Numerical tests on both synthetic and field data demonstrate the effectiveness of this method, especially in recovering the low-to-intermediate wavenumber parts of the subsurface velocity distribution. Comparisons are made with the other three relevant methods, the wave-equation travel-time (WT) inversion, the envelope inversion, and the full waveform inversion (FWI). As expected, the cycle skipping problem is alleviated due to the reduction of dimensions of data space. This method outperforms the envelope inversion in resolution, and it is no worse than WT. Moreover, there is no need for careful manual travel-time picking with this methodology. In general, this inversion framework provides an extendable strategy to compress any input data for reconstructing high-dimensional physical parameters.

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“…This is a problem because the SNR significantly diminishes with increasing offset, especially for reflections from deep targets. If their SNR can be enhanced, then the deep reflections can be used to enhance the accuracy of reflection full waveform inversion (Chen et al, 2019) for deep targets.…”

Section: Introductionmentioning

confidence: 99%

Noise reduction with reflection supervirtual interferometry

et al. 2020

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To image deeper portions of the earth, geophysicists must record reflection data with much greater source-receiver offsets. The problem with these data is that the signal-to-noise ratio (S/N) significantly diminishes with greater offset. In many cases, the poor S/N makes the far-offset reflections imperceptible on the shot records. To mitigate this problem, we have developed supervirtual reflection interferometry (SVI), which can be applied to far-offset reflections to significantly increase their S/N. The key idea is to select the common pair gathers where the phases of the correlated reflection arrivals differ from one another by no more than a quarter of a period so that the traces can be coherently stacked. The traces are correlated and summed together to create traces with virtual reflections, which in turn are convolved with one another and stacked to give the reflection traces with much stronger S/Ns. This is similar to refraction SVI except far-offset reflections are used instead of refractions. The theory is validated with synthetic tests where SVI is applied to far-offset reflection arrivals to significantly improve their S/N. Reflection SVI is also applied to a field data set where the reflections are too noisy to be clearly visible in the traces. After the implementation of reflection SVI, the normal moveout velocity can be accurately picked from the SVI-improved data, leading to a successful poststack migration for this data set.

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Multiscale reflection phase inversion with migration deconvolution

Cited by 18 publications

References 39 publications

Multiscale phase inversion for 3D ocean‐bottom cable data

Multiscale phase inversion for 3D ocean‐bottom cable data

Skeletonized Wave-Equation Refraction Inversion With Autoencoded Waveforms

Noise reduction with reflection supervirtual interferometry

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