Least-squares reverse time migration using controlled-order multiple reflections

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. S347-S357 ◽  
Author(s):  
Yike Liu ◽  
Xuejian Liu ◽  
Are Osen ◽  
Yu Shao ◽  
Hao Hu ◽  
...  
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Multiple Reflections ◽  
Numerical Tests ◽  
Elimination Algorithm ◽  
Reflection Data ◽  
Time Migration ◽  
Least Squares Migration

Reverse time migration (RTM) using multiples generates inherent crosstalk artifacts due to the interference among multiples of different orders. We have developed a method to remove such crosstalk. This approach first separates the recorded seismic data into primary reflections and multiples using the surface-related multiples elimination algorithm and then isolates the multiples into different orders. We can take any specified, say the [Formula: see text]th, order of multiples data as the incident wave and the next higher order multiples data, ([Formula: see text])th order, as the corresponding primary reflection data for imaging. We have applied the least-squares migration scheme to these two successive orders of multiples. Our method is denoted as least-squares RTM using controlled-order multiples (LSRTM-CM). Our numerical tests demonstrated that LSRTM-CM can significantly improve imaging quality compared with straightforward seismic imaging using multiples without multiples separation.

Least-squares migration with primary- and multiple-guided weighting matrices

Geophysics ◽  
2019 ◽  
Vol 84 (3) ◽  
pp. S171-S185 ◽  
Author(s):  
Chuang Li ◽  
Jianping Huang ◽  
Zhenchun Li ◽  
Han Yu ◽  
Rongrong Wang
Keyword(s):  
Free Surface ◽  
Least Squares ◽  
Seismic Data ◽  
Synthetic Data ◽  
Velocity Model ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Multiple Reflections ◽  
Time Migration ◽  
Least Squares Migration

Least-squares migration (LSM) of seismic data is supposed to produce images of subsurface structures with better quality than standard migration if we have an accurate migration velocity model. However, LSM suffers from data mismatch problems and migration artifacts when noise pollutes the recorded profiles. This study has developed a reweighted least-squares reverse time migration (RWLSRTM) method to overcome the problems caused by such noise. We first verify that spiky noise and free-surface multiples lead to the mismatch problems and should be eliminated from the data residual. The primary- and multiple-guided weighting matrices are then derived for RWLSRTM to reduce the noise in the data residual. The weighting matrices impose constraints on the data residual such that spiky noise and free-surface multiple reflections are reduced whereas primary reflections are preserved. The weights for spiky noise and multiple reflections are controlled by a dynamic threshold parameter decreasing with iterations for better results. Finally, we use an iteratively reweighted least-squares algorithm to minimize the weighted data residual. We conduct numerical tests using the synthetic data and compared the results of this method with the results of standard LSRTM. The results suggest that RWLSRTM is more robust than standard LSRTM when the seismic data contain spiky noise and multiple reflections. Moreover, our method not only suppresses the migration artifacts, but it also accelerates the convergence.

Plane-wave least-squares reverse time migration with a preconditioned stochastic conjugate gradient method

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. S33-S46 ◽  
Author(s):  
Chuang Li ◽  
Jianping Huang ◽  
Zhenchun Li ◽  
Rongrong Wang
Keyword(s):  
Plane Wave ◽  
Least Squares ◽  
Conjugate Gradient ◽  
Sampling Method ◽  
Singular Spectrum Analysis ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Numerical Tests ◽  
Time Migration

This study derives a preconditioned stochastic conjugate gradient (CG) method that combines stochastic optimization with singular spectrum analysis (SSA) denoising to improve the efficiency and image quality of plane-wave least-squares reverse time migration (PLSRTM). This method reduces the computational costs of PLSRTM by applying a controlled group-sampling method to a sufficiently large number of plane-wave sections and accelerates the convergence using a hybrid of stochastic descent (SD) iteration and CG iteration. However, the group sampling also produces aliasing artifacts in the migration results. We use SSA denoising as a preconditioner to remove the artifacts. Moreover, we implement the preconditioning on the take-off angle-domain common-image gathers (CIGs) for better results. We conduct numerical tests using the Marmousi model and Sigsbee2A salt model and compare the results of this method with those of the SD method and the CG method. The results demonstrate that our method efficiently eliminates the artifacts and produces high-quality images and CIGs.

An exact adjoint operation pair in time extrapolation and its application in least-squares reverse-time migration

Geophysics ◽  
2009 ◽  
Vol 74 (5) ◽  
pp. H27-H33 ◽  
Author(s):  
Jun Ji
Keyword(s):  
Least Squares ◽  
Adjoint Operator ◽  
Synthetic Data ◽  
Forward Modeling ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Product Test ◽  
Least Squares Migration ◽  
Time Extrapolation

To reduce the migration artifacts arising from incomplete data or inaccurate operators instead of migrating data with the adjoint of the forward-modeling operator, a least-squares migration often is considered. Least-squares migration requires a forward-modeling operator and its adjoint. In a derivation of the mathematically correct adjoint operator to a given forward-time-extrapolation modeling operator, the exact adjoint of the derived operator is obtained by formulating an explicit matrix equation for the forward operation and transposing it. The programs that implement the exact adjoint operator pair are verified by the dot-product test. The derived exact adjoint operator turns out to differ from the conventional reverse-time-migration (RTM) operator, an implementation of wavefield extrapolation backward in time. Examples with synthetic data show that migration using the exact adjoint operator gives similar results for a conventional RTM operator and that least-squares RTM is quite successful in reducing most migration artifacts. The least-squares solution using the exact adjoint pair produces a model that fits the data better than one using a conventional RTM operator pair.

scholarly journals Imaging Complex Subsurface Structures for Geothermal Exploration at Pirouette Mountain and Eleven-Mile Canyon in Nevada

2021 ◽  
Vol 9 ◽  
Author(s):  
Yunsong Huang ◽  
Miao Zhang ◽  
Kai Gao ◽  
Andrew Sabin ◽  
Lianjie Huang
Keyword(s):  
High Resolution ◽  
Least Squares ◽  
Seismic Data ◽  
Seismic Wave Propagation ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Geothermal Exploration ◽  
Subsurface Structures ◽  
Time Migration ◽  
Ground Roll

Accurate imaging of subsurface complex structures with faults is crucial for geothermal exploration because faults are generally the primary conduit of hydrothermal flow. It is very challenging to image geothermal exploration areas because of complex geologic structures with various faults and noisy surface seismic data with strong and coherent ground-roll noise. In addition, fracture zones and most geologic formations behave as anisotropic media for seismic-wave propagation. Properly suppressing ground-roll noise and accounting for subsurface anisotropic properties are essential for high-resolution imaging of subsurface structures and faults for geothermal exploration. We develop a novel wavenumber-adaptive bandpass filter to suppress the ground-roll noise without affecting useful seismic signals. This filter adaptively exploits both characteristics of the lower frequency and the smaller velocity of the ground-roll noise than those of the signals. Consequently, this filter can effectively differentiate the ground-roll noise from the signal. We use our novel filter to attenuate the ground-roll noise in seismic data along five survey lines acquired by the U.S. Navy Geothermal Program Office at Pirouette Mountain and Eleven-Mile Canyon in Nevada, United States. We then apply our novel anisotropic least-squares reverse-time migration algorithm to the resulting data for imaging subsurface structures at the Pirouette Mountain and Eleven-Mile Canyon geothermal exploration areas. The migration method employs an efficient implicit wavefield-separation scheme to reduce image artifacts and improve the image quality. Our results demonstrate that our wavenumber-adaptive bandpass filtering method successfully suppresses the strong and coherent ground-roll noise in the land seismic data, and our anisotropic least-squares reverse-time migration produces high-resolution subsurface images of Pirouette Mountain and Eleven-Mile Canyon, facilitating accurate fault interpretation for geothermal exploration.

Plane-wave domain least-squares reverse time migration with free-surface multiples

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. S477-S487 ◽  
Author(s):  
Xuejian Liu ◽  
Yike Liu
Keyword(s):  
Free Surface ◽  
Plane Wave ◽  
Least Squares ◽  
Computational Time ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Multiple Reflections ◽  
Time Migration ◽  
Computationally Intensive ◽  
Dynamic Plane

Free-surface multiples have been used in the reverse time migration (RTM) procedure to provide additional subsurface illumination. However, imaging multiple reflections with conventional RTM operators generates many crosstalk artifacts. Least-squares RTM (LSRTM) can be used to iteratively suppress crosstalk artifacts of multiples; however, the method is computationally intensive. By applying the linear Radon transformation to hundreds of shots of acquired data to produce dozens of plane-wave gathers, we have developed plane-wave domain LSRTM with free-surface multiples, which could efficiently provide images from the multiples with the crosstalk artifacts effectively suppressed. The proposed method has high computational efficiency when the dynamic plane-wave encoding scheme is used, in which only one or two plane-wave gathers of multiples are migrated at each iteration. We apply the method to numerical Pluto1.5 data and find that the proposed method can reduce most crosstalk artifacts and enhance spatial resolution, using even less computational time relative to RTM with multiples. Furthermore, joint imaging of primaries and multiples by plane-wave LSRTM can provide broader illumination and higher fold for the subsurface when compared with the imaging process with primaries only.

scholarly journals Q-least-squares reverse time migration with viscoacoustic deblurring filters

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. S425-S438 ◽  
Author(s):  
Yuqing Chen ◽  
Gaurav Dutta ◽  
Wei Dai ◽  
Gerard T. Schuster
Keyword(s):  
Convergence Rate ◽  
Least Squares ◽  
Field Data ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Acoustic Data ◽  
Numerical Tests ◽  
Time Migration ◽  
Preconditioning Method ◽  
Number Of Iterations

Viscoacoustic least-squares reverse time migration, also denoted as Q-LSRTM, linearly inverts for the subsurface reflectivity model from lossy data. Compared with conventional migration methods, it can compensate for the amplitude loss in the migrated images due to strong subsurface attenuation and can produce reflectors that are accurately positioned in depth. However, the adjoint [Formula: see text] propagators used for backward propagating the residual data are also attenuative. Thus, the inverted images from [Formula: see text]-LSRTM with a small number of iterations are often observed to have lower resolution when compared with the benchmark acoustic LSRTM images from acoustic data. To increase the resolution and accelerate the convergence of [Formula: see text]-LSRTM, we used viscoacoustic deblurring filters as a preconditioner for [Formula: see text]-LSRTM. These filters can be estimated by matching a simulated migration image to its reference reflectivity model. Numerical tests on synthetic and field data demonstrate that [Formula: see text]-LSRTM combined with viscoacoustic deblurring filters can produce images with higher resolution and more balanced amplitudes than images from acoustic RTM, acoustic LSRTM, and [Formula: see text]-LSRTM when there is strong attenuation in the background medium. Our preconditioning method is also shown to improve the convergence rate of [Formula: see text]-LSRTM by more than 30% in some cases and significantly compensate for the lossy artifacts in RTM images.

Elastic least-squares reverse time migration with hybrid l1/l2 misfit function

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. S271-S291 ◽  
Author(s):  
Bingluo Gu ◽  
Zhenchun Li ◽  
Peng Yang ◽  
Wencai Xu ◽  
Jianguang Han
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Synthetic Data ◽  
Adjoint Equations ◽  
P Wave ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
S Wave ◽  
Time Migration ◽  
Wave Image

We have developed the theory and synthetic tests of elastic least-squares reverse time migration (ELSRTM). In this method, a least-squares reverse time migration algorithm is used to image multicomponent seismic data based on the first-order elastic velocity-stress wave equation, in which the linearized elastic modeling equations are used for forward modeling and its adjoint equations are derived based on the adjoint-state method for back propagating the data residuals. Also, we have developed another ELSRTM scheme based on the wavefield separation technique, in which the P-wave image is obtained using P-wave forward and adjoint wavefields and the S-wave image is obtained using P-wave forward and S-wave adjoint wavefields. In this way, the crosstalk artifacts can be minimized to a significant extent. In general, seismic data inevitably contain noise. We apply the hybrid [Formula: see text] misfit function to the ELSRTM algorithm to improve the robustness of our ELSRTM to noise. Numerical tests on synthetic data reveal that our ELSRTM, when compared with elastic reverse time migration, can produce images with higher spatial resolution, more-balanced amplitudes, and fewer artifacts. Moreover, the hybrid [Formula: see text] misfit function makes the ELSRTM more robust than the [Formula: see text] misfit function in the presence of noise.

Least-squares reverse time migration with sparse regularization in the 2D wavelet domain

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. S313-S325
Author(s):  
Feipeng Li ◽  
Jinghuai Gao ◽  
Zhaoqi Gao ◽  
Xiudi Jiang ◽  
Wenbo Sun
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
Least Squares ◽  
Conjugate Gradient ◽  
Seismic Data ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Sparse Constraint ◽  
Sparse Regularization ◽  
Time Migration

The inadequate sampling of seismic data in the spatial dimension results in migration artifacts. Conventional least-squares reverse time migration (LSRTM) could improve the image quality. However, even LSRTM will not work in some inadequately sampling situations. To mitigate the impact of migration artifacts, we have developed a new LSRTM method with a sparse regularization, which takes advantage of the effective sparse representation of the subsurface reflectivity model in the 2D undecimated wavelet transform (UWT) domain. Different from other sparse regularizations, a sparseness constraint in the 2D UWT domain is applied on the angle slices of the image. To efficiently solve the least-squares inversion problem, we employ an inversion scheme using the conjugate gradient method that uses a soft threshold method to achieve sparse constraint in updating the conjugate gradient direction. Compared with the sparse constraint based on the discrete wavelet transform (DWT), the threshold in this method is angle-dependent and is determined according to the energy distribution of each angle slice. To avoid overregularization that can lead to instability and increase the number of iterations, we also apply an exponential threshold strategy. Numerical tests on synthetic datasets demonstrate that our method is capable of improving the image quality by enhancing the resolution and suppressing migration artifacts caused by inadequately sampled seismic data. The method can converge more rapidly than conventional LSRTM. Because this method performs sparse regularization on several slopes, it achieves better performance on enhancing complex structures with discontinuities such as the steeply dipping faults compared to DWT-based regularization.

Least-squares migration of multisource data with a deblurring filter

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. R135-R146 ◽  
Author(s):  
Wei Dai ◽  
Xin Wang ◽  
Gerard T. Schuster
Keyword(s):  
Wave Equation ◽  
Least Squares ◽  
Computational Efficiency ◽  
Computational Cost ◽  
Processing Technique ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Migration Method ◽  
Least Squares Migration

Least-squares migration (LSM) has been shown to be able to produce high-quality migration images, but its computational cost is considered to be too high for practical imaging. We have developed a multisource least-squares migration algorithm (MLSM) to increase the computational efficiency by using the blended sources processing technique. To expedite convergence, a multisource deblurring filter is used as a preconditioner to reduce the data residual. This MLSM algorithm is applicable with Kirchhoff migration, wave-equation migration, or reverse time migration, and the gain in computational efficiency depends on the choice of migration method. Numerical results with Kirchhoff LSM on the 2D SEG/EAGE salt model show that an accurate image is obtained by migrating a supergather of 320 phase-encoded shots. When the encoding functions are the same for every iteration, the input/output cost of MLSM is reduced by 320 times. Empirical results show that the crosstalk noise introduced by blended sources is more effectively reduced when the encoding functions are changed at every iteration. The analysis of signal-to-noise ratio (S/N) suggests that not too many iterations are needed to enhance the S/N to an acceptable level. Therefore, when implemented with wave-equation migration or reverse time migration methods, the MLSM algorithm can be more efficient than the conventional migration method.

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