An efficient step-length formula for correlative least-squares reverse time migration

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. S221-S238 ◽  
Author(s):  
Youshan Liu ◽  
Jiwen Teng ◽  
Tao Xu ◽  
Zhiming Bai ◽  
Haiqiang Lan ◽  
...  
Keyword(s):  
Objective Function ◽  
Least Squares ◽  
Step Length ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Step Size ◽  
Time Migration ◽  
Seismic Records ◽  
Complex Models ◽  
Analytical Step

In correlative least-squares reverse time migration (CLSRTM), the estimation of the optimal step size is usually determined by fitting a parabola and finding its minimum; it involves at least two times extra reading of all seismic records, which significantly lowers the efficiency of the algorithm. To improve the efficiency of the CLSRTM algorithm, we have deduced an analytical step-length (ASL) formula based on the linear property of the demigration operator. Numerical examples performed with the data synthetized by the Marmousi and Sigsbee2A models were used to test its validity. In complex models with imperfect migration velocity, such as the Sigabee2A model, our formula makes the value of the objective function converges to a much smaller minimum. Additional numerical tests performed with the data either acquired irregularly or contaminated by different noise levels verify the robustness of the ASL formula. Compared with the commonly used parabolic search method, the ASL formula is much more efficient because it is free from an extra estimation of the value of the objective function.

Prestack correlative least-squares reverse time migration

Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. S159-S172 ◽  
Author(s):  
Xuejian Liu ◽  
Yike Liu ◽  
Huiyi Lu ◽  
Hao Hu ◽  
Majid Khan
Keyword(s):  
Objective Function ◽  
Least Squares ◽  
Step Length ◽  
Inversion Algorithm ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Initial Image ◽  
Data Set ◽  
Time Migration ◽  
Gradient Descent Algorithm

In the correlative least-squares reverse time migration (CLSRTM) scheme, a stacked image is updated using a gradient-based inversion algorithm. However, CLSRTM experiences the incoherent stacking of different shots during each iteration due to the use of an imperfect velocity, which leads to image smearing. To reduce the sensitivity to velocity errors, we have developed prestack correlative least-squares reverse time migration (PCLSRTM), in which a gradient descent algorithm using a newly defined initial image and an efficiently defined analytical step length is developed to separately seek the optimal image for each shot gather before the final stacking. Furthermore, a weighted objective function is also designed for PCLSRTM, so that the data-domain gradient can avoid a strong truncation effect. Numerical experiments on a three-layer model as well as a marine synthetic and a field data set reveal the merits of PCLSRTM. In the presence of velocity errors, PCLSRTM shows better convergence and provides higher quality images as compared with CLSRTM. With the newly defined initial image, PCLSRTM can effectively handle observed data with unbalanced amplitudes. By using a weighted objective function, PCLSRTM can provide an image with enhanced resolution and balanced amplitude while avoiding many imaging artifacts.

Comparison between Born and Kirchhoff operators for least-squares reverse time migration and the constraint of the propagation of the background wavefield

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R725-R739 ◽  
Author(s):  
Kai Yang ◽  
Jianfeng Zhang
Keyword(s):  
Least Squares ◽  
Born Approximation ◽  
Computational Cost ◽  
Stopping Time ◽  
Step Length ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Recording Time ◽  
Time Migration ◽  
Computational Strategy

The Born approximation and the Kirchhoff approximation are two frameworks that are extensively used in solving seismic migration/inversion problems. Both approximations assume a linear relationship between the primary reflected/scattered data to the corresponding physical model. However, different approximations result in different behaviors. For least-squares reverse time migration (LSRTM), most of the algorithms are constructed based on Born approximation. We have constructed a pair of Kirchhoff modeling and migration operators based on the Born modeling operator and the connection between the perturbation model and the reflectivity model, and then we compared the different performances between Born and Kirchhoff operators for LSRTM. Numerical examples on Marmousi model and SEAM 2D salt model indicate that LSRTM with Kirchhoff operators is a better alternative to that with Born operators for imaging complex structures. To reduce the computational cost, we also investigate a strategy by restricting the propagation of the background wavefield to a stopping time rather than the maximum recording time. And this stopping time can be chosen as half of the maximum recording time. This computational strategy can be used in LSRTM procedures of predicting the primary reflected data, calculating the step length, and computing the gradient. Theoretical analyses and numerical experiments are given to justify this computational strategy for LSRTM.

Source-independent elastic least-squares reverse time migration

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. S1-S16 ◽  
Author(s):  
Jinwei Fang ◽  
Hui Zhou ◽  
Hanming Chen ◽  
Ning Wang ◽  
Yufeng Wang ◽  
...  
Keyword(s):  
Objective Function ◽  
Least Squares ◽  
Noisy Data ◽  
Simulated Data ◽  
High Accuracy ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Numerical Examples ◽  
Time Migration ◽  
Seismic Images

Elastic least-squares reverse time migration (LSRTM) has been developed recently for its high accuracy imaging ability. The theory is based on minimizing the misfit between the observed and simulated data by an iterative algorithm to refine seismic images toward the true reflectivity. We have developed a new elastic LSRTM with the same modeling equations for source and receiver wavefield extrapolations, except for their source terms. The LSRTM has a natural advantage to solve the source and receiver wavefields using the same modeling system; thus, it is easy to implement LSRTM. In practice, it is difficult to obtain an accurate source wavelet, so a convolution-based objective function is used in our source-independent elastic LSRTM. Such an objective function can relax the requirement of an accurate wavelet, and improve the robustness of the inverse problem in the presence of noise. The numerical examples indicate that our method has the ability to recover the reflectivity models with an incorrect source wavelet from noisy data.

Least-squares reverse time migration with an angle-dependent weighting factor

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. S299-S310 ◽  
Author(s):  
Kai Yang ◽  
Jianfeng Zhang
Keyword(s):  
Least Squares ◽  
Step Length ◽  
Weighting Factor ◽  
Deep Part ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Pass Filter ◽  
Time Migration ◽  
L2 Norm ◽  
The Right

Least-squares reverse time migration (LSRTM) produces higher quality images than conventional RTM. However, directly using the standard gradient formula, the inverted images suffer from low-wavenumber noise. Using a simple high-pass filter on the gradient can alleviate the effect of the low-wavenumber noise. But, owing to the illumination issue, the amplitudes are not balanced and in the deep part they are often weak. These two issues can be mitigated by the iterative approach, but it needs more iterations. We introduced an angle-dependent weighting factor to weight the gradient of LSRTM to suppress the low-wavenumber noise and also to emphasize the gradient in the deep part. An optimal step length for the L2-norm objective function is also presented to scale the gradient to the right order. Two numerical examples performed with the data synthesized on the Sigsbee2A and Marmousi models indicate that when using this weighted gradient combined with the preconditioned [Formula: see text]-BFGS algorithm with the optimal step length, only a few iterations can achieve satisfying results.

scholarly journals A GPU implementation of least-squares reverse time migration

2021 ◽  
Vol 1719 (1) ◽  
pp. 012030
Author(s):  
Phudit Sombutsirinun ◽  
Chaiwoot Boonyasiriwat
Keyword(s):  
Least Squares ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Gpu Implementation

From acoustic to elastic inverse extended Born modeling: a first insight in the marine environment

Geophysics ◽  
2021 ◽  
pp. 1-73
Author(s):  
Milad Farshad ◽  
Hervé Chauris
Keyword(s):  
Least Squares ◽  
Marine Environment ◽  
Computational Cost ◽  
Physical Parameters ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
S Wave ◽  
Time Migration ◽  
Physical Density ◽  
Acoustic Approximation

Elastic least-squares reverse time migration is the state-of-the-art linear imaging technique to retrieve high-resolution quantitative subsurface images. A successful application requires many migration/modeling cycles. To accelerate the convergence rate, various pseudoinverse Born operators have been proposed, providing quantitative results within a single iteration, while having roughly the same computational cost as reverse time migration. However, these are based on the acoustic approximation, leading to possible inaccurate amplitude predictions as well as the ignorance of S-wave effects. To solve this problem, we extend the pseudoinverse Born operator from acoustic to elastic media to account for the elastic amplitudes of PP reflections and provide an estimate of physical density, P- and S-wave impedance models. We restrict the extension to marine environment, with the recording of pressure waves at the receiver positions. Firstly, we replace the acoustic Green's functions by their elastic version, without modifying the structure of the original pseudoinverse Born operator. We then apply a Radon transform to the results of the first step to calculate the angle-dependent response. Finally, we simultaneously invert for the physical parameters using a weighted least-squares method. Through numerical experiments, we first illustrate the consequences of acoustic approximation on elastic data, leading to inaccurate parameter inversion as well as to artificial reflector inclusion. Then we demonstrate that our method can simultaneously invert for elastic parameters in the presence of complex uncorrelated structures, inaccurate background models, and Gaussian noisy data.

Q least-squares reverse time migration based on the first-order viscoacoustic quasidifferential equations

Geophysics ◽  
2021 ◽  
pp. 1-65
Author(s):  
Yingming Qu ◽  
Yixin Wang ◽  
Zhenchun Li ◽  
Chang Liu
Keyword(s):  
Wave Equation ◽  
Least Squares ◽  
Surface Topography ◽  
Density Perturbation ◽  
Second Order ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
First Order ◽  
Time Migration ◽  
Grid Scheme

Seismic wave attenuation caused by subsurface viscoelasticity reduces the quality of migration and the reliability of interpretation. A variety of Q-compensated migration methods have been developed based on the second-order viscoacoustic quasidifferential equations. However, these second-order wave-equation-based methods are difficult to handle with density perturbation and surface topography. In addition, the staggered grid scheme, which has an advantage over the collocated grid scheme because of its reduced numerical dispersion and enhanced stability, works in first-order wave-equation-based methods. We have developed a Q least-squares reverse time migration method based on the first-order viscoacoustic quasidifferential equations by deriving Q-compensated forward-propagated operators, Q-compensated adjoint operators, and Q-attenuated Born modeling operators. Besides, our method using curvilinear grids is available even when the attenuating medium has surface topography and can conduct Q-compensated migration with density perturbation. The results of numerical tests on two synthetic and a field data sets indicate that our method improves the imaging quality with iterations and produces better imaging results with clearer structures, higher signal-to-noise ratio, higher resolution, and more balanced amplitude by correcting the energy loss and phase distortion caused by Q attenuation. It also suppresses the scattering and diffracted noise caused by the surface topography.

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