Elastic least-squares reverse time migration

Feng, Zongcai; Schuster, Gerard T.

doi:10.1190/segam2016-13863861.1

Cited by 15 publications

(17 citation statements)

References 41 publications

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“…Our multiparameter deblurring filter is tested for its e↵ectiveness with the elastic migration and linearized inversion methods, where the migration images are those for the P-and S-reflectivity sections (Duan et al, 2016;Feng and Schuster, 2017;Guo and Alkhalifah, 2017;Ren et al, 2017). The results show that this filter not only balances the amplitude and increases the resolution, but also reduces the crosstalk artifacts in the elastic migration images.…”

Section: Introductionmentioning

confidence: 99%

“…This is because we calculate the elastic RTM image with the PP data using the adjoint state method and the PP di↵raction patterns of P-and S-wave velocity are opposite in polarity (Tarantola, 1986;Feng and Schuster, 2017). This polarity mismatch is corrected by iteratively fitting the data using conventional LSRTM, as shown in the LSRTM S image in Figure 15.…”

Section: Gulf Of Mexico Datamentioning

confidence: 99%

“…As shown in Figures 17c and 17d, we generate the predicted common shot gathers (CSGs) using the RTM P and S images with filtering individually. The predicted CSGs are computed using linearized elastic forward modeling (Feng and Schuster, 2017). The observed common shot gather (CSG) is shown in Figure 17a.…”

Section: Gulf Of Mexico Datamentioning

confidence: 99%

“…With a complex di↵raction pattern for the S-wave velocity (Feng and Schuster, 2017), the S image is more sensitive to these errors and is therefore not well-focused by either the multiparameter deblurring filter or the least-squares migration (LSM). However, we can see that the multiparameter debluring filter still improves the amplitude balancing of the P and S images similar to conventional LSM.…”

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confidence: 99%

“…If the density is taken into account, it is better to invert for the reflecitity model of impedance m = ( I p /I p , I s /I s ) T , where I p = ⇢V p and I s = ⇢V s are the background P-and S-wave impedance models and I p and I s are the accociated perturbations (Tarantola, 1986;Feng and Schuster, 2017). The proposed multiparameter deblurring filter can also be applied to elastic full waveform inversion (FWI) (Tarantola, 1986;Mora, 1987;Sears et al, 2010), where the deblurring filter needs to be updated every few iterations.…”

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confidence: 99%

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Multiparameter deblurring filter and its application to elastic migration and inversion

2018

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Di↵erent types of model parameters, such as the P-and S-wave velocities, can be coupled to one another in multiparameter seismic inversion. This coupling e↵ect is not taken into account by the conventional approximation to the Hessian inverse for a single type of parameter, and so can lead to a slow rate of convergence by an iterative inversion method. To solve this problem, we now present a multiparameter deblurring filter that approximates the Hessian inverse. This filter takes into account the coupling between di↵erent parameters by using stationary local filters to approximate the submatrices of the Hessian inverse for the same and di↵erent types of parameters. The filters are calculated by matching the reference multiparameter migration images to their reference reflectivity models. Numerical tests with elastic migration and inversion show that the multiparameter deblurring filter not only reduces the footprint noise, balances the amplitude and increases the resolution of the 1

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Section: Introductionmentioning

confidence: 99%

Section: Gulf Of Mexico Datamentioning

confidence: 99%

Section: Gulf Of Mexico Datamentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Multiparameter deblurring filter and its application to elastic migration and inversion

2018

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Target‐level waveform inversion: a prospective application of the convolution‐type representation for the acoustic wavefield

Costa

Medeiros

et al. 2018

Geophysical Prospecting

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Nowadays, full‐waveform inversion, based on fitting the measured surface data with modelled data, has become the preferred approach to recover detailed physical parameters from the subsurface. However, its application is computationally expensive for large inversion domains. Furthermore, when the subsurface has a complex geological setting, the inversion process requires an appropriate pre‐conditioning scheme to retrieve the medium parameters for the desired target area in a reliable manner. One way of dealing with both aspects is by waveform inversion schemes in a target‐oriented fashion. Therefore, we propose a prospective application of the convolution‐type representation for the acoustic wavefield in the frequency–space domain formulated as a target‐oriented waveform inversion method. Our approach aims at matching the observed and modelled upgoing wavefields at a target depth level in the subsurface, where the seismic wavefields, generated by sources distributed above this level, are available. The forward modelling is performed by combining the convolution‐type representation for the acoustic wavefield with solving the two‐way acoustic wave‐equation in the frequency–space domain for the target area. We evaluate the effectiveness of our inversion method by comparing it with the full‐domain full‐waveform inversion process through some numerical examples using synthetic data from a horizontal well acquisition geometry, where the sources are located at the surface and the receivers are located along a horizontal well at the target level. Our proposed inversion method requires less computational effort and, for this particular acquisition, it has proven to provide more accurate estimates of the target zone below a complex overburden compared to both full‐domain full‐waveform inversion process and local full‐waveform inversion after applying interferometry by multidimensional deconvolution to get local‐impulse responses.

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Frequency‐domain double‐plane‐wave least‐squares reverse time migration

Zhao

Sen

2019

Geophysical Prospecting

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Least‐squares reverse time migration is often formulated as an iterative updating process, where estimating the gradient of the misfit function is necessary. Traditional time domain shot‐profile least‐squares reverse time migration is computationally expensive because computing the gradient involves solving the two‐way wave equation several times in every iteration. To reduce the computational cost of least‐squares reverse time migration, we propose a double‐plane‐wave least‐squares reverse time migration method based on a misfit function for frequency‐domain double‐plane‐wave data. In double‐plane‐wave least‐squares reverse time migration, the gradient is computed by multiplying frequency‐domain plane‐wave Green's functions with the corresponding double‐plane‐wave data residual. Because the number of plane‐wave Green's functions used for migration is relatively small, they can be pre‐computed and stored in a computer's discs or memory. We can use the pre‐computed plane‐wave Green's functions to obtain the gradient without solving the two‐way wave equation in each iteration. Therefore, the migration efficiency is significantly improved. In addition, we study the effects of using sparse frequency sampling and sparse plane‐wave sampling on the proposed method. We can achieve images with correct reflector amplitudes and reasonable resolution using relatively sparse frequency sampling and plane‐wave sampling, which are larger than that determined by the Nyquist theorem. The well‐known wrap‐around artefacts and linear artefacts generated due to under‐sampling frequency and plane wave can be suppressed during iterations in cases where the sampling rates are not excessively large. Moreover, implementing the proposed method with sparse frequency sampling and sparse plane‐wave sampling further improves the computational efficiency. We test the proposed double‐plane‐wave least‐squares reverse time migration on synthetic models to show the practicality of the method.

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Elastic least-squares reverse time migration

Cited by 15 publications

References 41 publications

Multiparameter deblurring filter and its application to elastic migration and inversion

Multiparameter deblurring filter and its application to elastic migration and inversion

Target‐level waveform inversion: a prospective application of the convolution‐type representation for the acoustic wavefield

Frequency‐domain double‐plane‐wave least‐squares reverse time migration

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