3D acoustic least-squares reverse time migration using the energy norm

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. S261-S270 ◽  
Author(s):  
Daniel Rocha ◽  
Paul Sava ◽  
Antoine Guitton
Keyword(s):  
Least Squares ◽  
Convergence Rates ◽  
Energy Norm ◽  
Ocean Bottom ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Data Set ◽  
Image Domain ◽  
Time Migration ◽  
And Migration

We have developed a least-squares reverse time migration (LSRTM) method that uses an energy-based imaging condition to obtain faster convergence rates when compared with similar methods based on conventional imaging conditions. To achieve our goal, we also define a linearized modeling operator that is the proper adjoint of the energy migration operator. Our modeling and migration operators use spatial and temporal derivatives that attenuate imaging artifacts and deliver a better representation of the reflectivity and scattered wavefields. We applied the method to two Gulf of Mexico field data sets: a 2D towed-streamer benchmark data set and a 3D ocean-bottom node data set. We found LSRTM resolution improvement relative to RTM images, as well as the superior convergence rate obtained by the linearized modeling and migration operators based on the energy norm, coupled with inversion preconditioning using image-domain nonstationary matching filters.

scholarly journals Least-squares reverse time migration of marine data with frequency-selection encoding

Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. S233-S242 ◽  
Author(s):  
Wei Dai ◽  
Yunsong Huang ◽  
Gerard T. Schuster
Keyword(s):  
Least Squares ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Data Set ◽  
Frequency Selection ◽  
Time Migration ◽  
Order Of Magnitude ◽  
Marine Data ◽  
And Migration ◽  
Encoding Method

The phase-encoding technique can sometimes increase the efficiency of the least-squares reverse time migration (LSRTM) by more than one order of magnitude. However, traditional random encoding functions require all the encoded shots to share the same receiver locations, thus limiting the usage to seismic surveys with a fixed spread geometry. We implement a frequency-selection encoding strategy that accommodates data with a marine streamer geometry. The encoding functions are delta functions in the frequency domain, so that all the encoded shots have unique nonoverlapping frequency content, and the receivers can distinguish the wavefield from each shot with a unique frequency band. Because the encoding functions are orthogonal to each other, there will be no crosstalk between different shots during modeling and migration. With the frequency-selection encoding method, the computational efficiency of LSRTM is increased so that its cost is comparable to conventional RTM for the Marmousi2 model and a marine data set recorded in the Gulf of Mexico. With more iterations, the LSRTM image quality is further improved by suppressing migration artifacts, balancing reflector amplitudes, and enhancing the spatial resolution. We conclude that LSRTM with frequency-selection is an efficient migration method that can sometimes produce more focused images than conventional RTM.

Elastic least-squares reverse time migration in the image domain

2017 ◽  
Author(s):  
Bruno Pereira-Dias ◽  
André Bulcão ◽  
Djalma Soares Filho ◽  
Roberto Dias ◽  
Felipe Duarte ◽  
...  
Keyword(s):  
Least Squares ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Image Domain ◽  
Time Migration

Elastic least-squares reverse time migration using the energy norm

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. S237-S248 ◽  
Author(s):  
Daniel Rocha ◽  
Paul Sava
Keyword(s):  
Least Squares ◽  
Normal Incidence ◽  
Wave Mode ◽  
Energy Norm ◽  
Earth Model ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Mode Decomposition ◽  
High Convergence Rate

Incorporating anisotropy and elasticity into least-squares migration is an important step toward recovering accurate amplitudes in seismic imaging. An efficient way to extract reflectivity information from anisotropic elastic wavefields exploits properties of the energy norm. We derive linearized modeling and migration operators based on the energy norm to perform anisotropic least-squares reverse time migration (LSRTM) describing subsurface reflectivity and correctly predicting observed data without costly decomposition of wave modes. Imaging operators based on the energy norm have no polarity reversal at normal incidence and remove backscattering artifacts caused by sharp interfaces in the earth model, thus accelerating convergence and generating images of higher quality when compared with images produced by conventional methods. With synthetic and field data experiments, we find that our elastic LSRTM method generates high-quality images that predict the data for arbitrary anisotropy, without the complexity of wave-mode decomposition and with a high convergence rate.

A guide to least-squares reverse time migration for subsalt imaging: Challenges and solutions

2017 ◽  
Vol 5 (3) ◽  
pp. SN1-SN11 ◽  
Author(s):  
Chong Zeng ◽  
Shuqian Dong ◽  
Bin Wang
Keyword(s):  
Least Squares ◽  
Confidence Level ◽  
Input Data ◽  
Convergence Rates ◽  
Depth Image ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Area Of Interest ◽  
Time Migration ◽  
Subsalt Imaging

Least-squares reverse time migration (LSRTM) overcomes the shortcomings of conventional migration algorithms by iteratively fitting the demigrated synthetic data and the input data to refine the initial depth image toward true reflectivity. It gradually enhances the effective signals and removes the migration artifacts such as swing noise during conventional migration. When imaging the subsalt area with complex structures, many practical issues have to be considered to ensure the convergence of the inversion. We tackle those practical issues such as an unknown source wavelet, inaccurate migration velocity, and slow convergence to make LSRTM applicable to subsalt imaging in geologic complex areas such as the Gulf of Mexico. Dynamic warping is used to realign the modeled and input data to compensate for minor velocity errors in the subsalt sediments. A windowed crosscorrelation-based confidence level is used to control the quality of the residual computation. The confidence level is further used as an inverse weighting to precondition the data residual so that the convergence rates in shallow and deep images are automatically balanced. It also helps suppress the strong artifacts related to the salt boundary. The efficiency of the LSRTM is improved so that interpretable images in the area of interest can be obtained in only a few iterations. After removing the artifacts near the salt body using LSRTM, the image better represents the true geology than the outcome of conventional RTM; thus, it facilitates the interpretation. Synthetic and field data examples examine and demonstrate the effectiveness of the adaptive strategies.

Multiparameter least-squares reverse time migration for acoustic—elastic coupling media based on ocean bottom cable data

2019 ◽  
Vol 16 (3) ◽  
pp. 327-337
Author(s):  
Ying-Ming Qu ◽  
Chong-Peng Huang ◽  
Chang Liu ◽  
Chang Zhou ◽  
Zhen-Chun Li ◽  
...  
Keyword(s):  
Least Squares ◽  
Ocean Bottom ◽  
Elastic Coupling ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration

scholarly journals Plane-wave least-squares reverse-time migration

Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. S165-S177 ◽  
Author(s):  
Wei Dai ◽  
Gerard T. Schuster
Keyword(s):  
Phase Shift ◽  
Plane Wave ◽  
Least Squares ◽  
Plane Waves ◽  
Migration Velocity ◽  
Stable Convergence ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Data Set ◽  
Time Migration

A plane-wave least-squares reverse-time migration (LSRTM) is formulated with a new parameterization, where the migration image of each shot gather is updated separately and an ensemble of prestack images is produced along with common image gathers. The merits of plane-wave prestack LSRTM are the following: (1) plane-wave prestack LSRTM can sometimes offer stable convergence even when the migration velocity has bulk errors of up to 5%; (2) to significantly reduce computation cost, linear phase-shift encoding is applied to hundreds of shot gathers to produce dozens of plane waves. Unlike phase-shift encoding with random time shifts applied to each shot gather, plane-wave encoding can be effectively applied to data with a marine streamer geometry. (3) Plane-wave prestack LSRTM can provide higher-quality images than standard reverse-time migration. Numerical tests on the Marmousi2 model and a marine field data set are performed to illustrate the benefits of plane-wave LSRTM. Empirical results show that LSRTM in the plane-wave domain, compared to standard reverse-time migration, produces images efficiently with fewer artifacts and better spatial resolution. Moreover, the prestack image ensemble accommodates more unknowns to makes it more robust than conventional least-squares migration in the presence of migration velocity errors.

Least-squares reverse time migration of multiples

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. S11-S21 ◽  
Author(s):  
Dongliang Zhang ◽  
Gerard T. Schuster
Keyword(s):  
Free Surface ◽  
Least Squares ◽  
Synthetic Data ◽  
Time History ◽  
Ocean Bottom ◽  
Virtual Source ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Sea Bed

The theory of least-squares reverse time migration of multiples (RTMM) is presented. In this method, least squares migration (LSM) is used to image free-surface multiples where the recorded traces are used as the time histories of the virtual sources at the hydrophones and the surface-related multiples are the observed data. For a single source, the entire free-surface becomes an extended virtual source where the downgoing free-surface multiples more fully illuminate the subsurface compared to the primaries. Since each recorded trace is treated as the time history of a virtual source, knowledge of the source wavelet is not required and the ringy time series for each source is automatically deconvolved. If the multiples can be perfectly separated from the primaries, numerical tests on synthetic data for the Sigsbee2B and Marmousi2 models show that least-squares reverse time migration of multiples (LSRTMM) can significantly improve the image quality compared to RTMM or standard reverse time migration (RTM) of primaries. However, if there is imperfect separation and the multiples are strongly interfering with the primaries then LSRTMM images show no significant advantage over the primary migration images. In some cases, they can be of worse quality. Applying LSRTMM to Gulf of Mexico data shows higher signal-to-noise imaging of the salt bottom and top compared to standard RTM images. This is likely attributed to the fact that the target body is just below the sea bed so that the deep water multiples do not have strong interference with the primaries. Migrating a sparsely sampled version of the Marmousi2 ocean bottom seismic data shows that LSM of primaries and LSRTMM provides significantly better imaging than standard RTM. A potential liability of LSRTMM is that multiples require several round trips between the reflector and the free surface, so that high frequencies in the multiples suffer greater attenuation compared to the primary reflections. This can lead to lower resolution in the migration image compared to that computed from primaries. Another liability is that the multiple migration image is more down-dip limited than the standard primaries migration image. Finally, if the surface-related multiple elimination method is imperfect and there are strong multiples interfering with the primaries, then the resulting LSRTMM image can be significantly degraded. We conclude that LSRTMM can be a useful complement, not a replacement, for RTM of primary reflections.

Improved subsalt images with least-squares reverse time migration

2017 ◽  
Vol 5 (3) ◽  
pp. SN25-SN32 ◽  
Author(s):  
Ping Wang ◽  
Shouting Huang ◽  
Ming Wang
Keyword(s):  
Least Squares ◽  
Field Data ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Lateral Velocity ◽  
Image Domain ◽  
Time Migration ◽  
Iterative Inversion ◽  
Velocity Variations ◽  
Subsalt Imaging

Complex overburdens often distort reservoir images in terms of structural positioning, stratigraphic resolution, and amplitude fidelity. One prime example of a complex overburden is in the deepwater Gulf of Mexico, where thick and irregular layers of remobilized (i.e., allochthonous) salt are situated above prospective reservoir intervals. The highly variant salt layers create large lateral velocity variations that distort wave propagation and the illumination of deeper reservoir targets. In subsalt imaging, tools such as reflection tomography, full-waveform inversion, and detailed salt interpretation are needed to derive a high-resolution velocity model that captures the lateral velocity variations. Once a velocity field is obtained, reverse time migration (RTM) can be applied to restore structural positioning of events below and around the salt. However, RTM by nature is unable to fully recover the reflectivity for desired amplitudes and resolution. This shortcoming is well-recognized by the imaging community, and it has propelled the emergence of least-squares RTM (LSRTM) in recent years. We have investigated how current LSRTM methods perform on subsalt images. First, we compared the formulation of data-domain versus image-domain least-squares migration, as well as methods using single-iteration approximation versus iterative inversion. Then, we examined the resulting subsalt images of several LSRTM methods applied on the synthetic and field data. Among our tests, we found that image-domain single-iteration LSRTM methods, including an extension of an approximate inverse Hessian method in the curvelet domain, not only compensated for amplitude loss due to poor illumination caused by complex salt bodies, but it also produced subsalt images with fewer migration artifacts in the field data. In contrast, an iterative inversion method showed its potential for broadening the bandwidth in the subsalt, but it was less effective in reducing migration artifacts and noise. Based on our understanding, we evaluated the current state of LSRTM for subsalt imaging.

Vector-acoustic reverse time migration of Volve ocean-bottom cable data set without up/down decomposed wavefields

Geophysics ◽  
2015 ◽  
Vol 80 (4) ◽  
pp. S137-S150 ◽  
Author(s):  
Matteo Ravasi ◽  
Ivan Vasconcelos ◽  
Andrew Curtis ◽  
Alexander Kritski
Keyword(s):  
Ocean Bottom ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Data Set ◽  
Time Migration
Sign in / Sign up

Export Citation Format

Share Document