3D reverse time migration using a wavefield domain dynamic approach

Reverse time migration (RTM) is achieved by a forward and reverse time propagation of source and receiver wavefields respectively, followed by an imaging condition. The quality of the image from any RTM implementation depends directly on the method’s ability to separate the true reflection data from the backscattered correlation noise between the source and receiver wavefields. In this paper we demonstrate the application of an inverse scattering imaging condition that significantly reduces this backscattered correlation noise and results in a much higher quality subsurface image than is achieved by typical RTM imaging conditions.

Reverse-time migration (RTM) using the crosscorrelation imaging condition requires that the forward-propagated source wavefield and the backward-propagated receiver wavefield be accessible within the imaging domain at the same time step. There are two categories of methods to balance the computer memory requirement and the computational complexity of RTM: checkpointing methods and source-wavefield reconstruction methods. We have developed a new source-wavefield reconstruction method to improve the balance between the computer memory requirement and the computational complexity of RTM. During the forward simulation of the source wavefield, we stored boundary wavefields only at one or two layers of spatial grid points and reconstructed the back-propagated source wavefield at the same time step as that of the back-propagated receiver wavefield, using a high-order wave-equation extrapolation scheme. One conventional RTM method uses boundary wavefields stored at multiple layers of spatial grid points and a high-order finite-difference (FD) scheme to reconstruct the back-propagated source wavefield. For an FD scheme with the eighth or sixteenth order of accuracy in space, our new method used only 37.5% of the computer memory required by this conventional method to store the boundary wavefields. This reduction of computer memory usage is significant because storing the boundary wavefields consumes most of the computer memory required for 3D migration using reconstructed source wavefields. Moreover, our method maintained the spatial order of accuracy of the FD scheme for the entire imaging domain, whereas some conventional methods reduce the spatial-order accuracy of the FD scheme near the boundaries to back-propagate the source wavefield to decrease the computer memory requirement. We validated our method using synthetic seismic data. Our method produced 2D and 3D migration images of complex subsurface structures as accurate as those yielded using an RTM method without reducing the spatial order of accuracy near the boundaries.

Reverse time migration (RTM) is a technique used to obtain high-resolution images of underground reflectors; however, this method is computationally intensive when dealing with large amounts of seismic data. Multi-source RTM can significantly reduce the computational cost by processing multiple shots simultaneously. However, multi-source-based methods frequently result in crosstalk artifacts in the migrated images, causing serious interference in the imaging signals. Plane-wave migration, as a mainstream multi-source method, can yield migrated images with plane waves in different angles by implementing phase encoding of the source and receiver wavefields; however, this method frequently requires a trade-off between computational efficiency and imaging quality. We propose a method based on deep learning for removing crosstalk artifacts and enhancing the image quality of plane-wave migration images. We designed a convolutional neural network that accepts an input of seven plane-wave images at different angles and outputs a clear and enhanced image. We built over 500 1024×256 velocity models, and employed each of them using plane-wave migration to produce raw images at 0°, ±10°, ±20°, and ±30° as input of the network. Labels are high-resolution images computed from the corresponding reflectivity models by convolving with a Ricker wavelet. Random sub-images with a size of 512×128 were used for training the network. Numerical examples demonstrated the effectiveness of the trained network in crosstalk removal and imaging enhancement. The proposed method is superior to both the conventional RTM and plane-wave RTM (PWRTM) in imaging resolution. Moreover, the proposed method requires only seven migrations, significantly improving the computational efficiency. In the numerical examples, the processing time required by our method was approximately 1.6% and 10% of that required by RTM and PWRTM, respectively.

Reverse-time migration (RTM) in 2.5D offers an alternative to improve resolution and amplitude when imaging 2D seismic data. Wave propagation in 2.5D assumes translational invariance of the velocity model. Under this assumption, we implement a finite-difference (FD) modeling algorithm in the mixed time-space/wavenumber domain to simulate the velocity and pressure fields for acoustic wave propagation and apply it in RTM. The 2.5D FD algorithm is truly parallel, allowing an efficient implementation in clusters. Storage and computing time requirements are strongly reduced compared to a full 3D FD simulation of the wave propagation. This feature makes 2.5D RTM much more efficient than 3D RTM, while achieving improved modeling of 3D geometrical spreading and phase properties of the seismic waveform in comparison to 2D RTM. Together with an imaging condition that compensates for uneven illumination and/or the obliquity factor, this allows recover of amplitudes proportional to the earth’s reflectivity. Numerical experiments using synthetic data demonstrate the better resolution and improved amplitude recovery of 2.5D RTM relative to 2D RTM.

Reliable 3D imaging is a required tool for developing models of complex geologic structures. Reverse time migration (RTM), as the most powerful depth imaging method, has become the preferred imaging tool because of its ability to handle complex velocity models including steeply dipping interfaces and large velocity contrasts. Finite-difference methods are among the most popular numerical approaches used for RTM. However, these methods often encounter a serious issue of numerical dispersion, which is typically suppressed by reducing the grid interval of the propagation model, resulting in large computation and memory requirements. In addition, even with small grid spacing, numerical anisotropy may degrade images or, worse, provide images that appear to be focused but position events incorrectly. Recently, stereo-operators have been developed to approximate the partial differential operator in space. These operators have been used to develop several weak-dispersion and efficient stereo-modeling methods that have been found to be superior to conventional algorithms in suppressing numerical dispersion and numerical anisotropy. We generalized one stereo-modeling method, fourth-order nearly analytic central difference (NACD), from 2D to 3D and applied it to 3D RTM. The RTM results for the 3D SEG/EAGE phase A classic data set 1 and the SEG Advanced Modeling project model demonstrated that, even when using a large grid size, the NACD method can handle very complex velocity models and produced better images than can be obtained using the fourth-order and eighth-order Lax-Wendroff correction (LWC) schemes. We also applied 3D NACD and fourth-order LWC to a field data set and illustrated significant improvements in terms of structure imaging, horizon/layer continuity and positioning. We also investigated numerical dispersion and found that not only does the NACD method have superior dispersion characteristics but also that the angular variation of dispersion is significantly less than for LWC.

Heuristic resolution for multiple inheritance in object-oriented expert system building tools

Summary Reverse-Time Migration (RTM) is routinely used for depth velocity model building and imaging in deepwater exploration around the world. In this study, we employed analytical redatuming to improve the efficiency of true-amplitude RTM for deepwater Earth model. This method analytically redatums both the source and receiver wavefields from surface to water bottom and injects the redatumed wavefield into the computational domain while preserving true-amplitude in the migrated common-shot image. By using analytic redatuming in deepwater area, the total runtime, memory and disk space requirements are significantly reduced. In addition, the image quality is improved by overcoming the singularity of point-source injection in the finite difference method, accurate positioning of source and receivers, and the reduction of numerical dispersion noise. The underlying theories developed in this paper can be extended to some other important applications such as Full-Waveform Inversion (FWI), true-amplitude layer-stripping RTM/FWI.

The quality of seismic images obtained by reverse time migration (RTM) strongly depends on the imaging condition. We propose a new imaging condition that is motivated by stationary phase analysis of the classical crosscorrelation imaging condition. Its implementation requires the Poynting vector of the source and receiver wavefields at the imaging point. An obliquity correction is added to compensate for the reflector dip effect on amplitudes of RTM. Numerical experiments show that using an imaging condition with obliquity compensation improves reverse time migration by reducing backscattering artifacts and improving illumination compensation.

Summary Intrinsic attenuation in seismic wave propagation leads to amplitude loss and phase dispersion in seismic data. The attenuation effects are a common cause for inaccurate image location and dimmed energy in reverse time migration (RTM). Conventional viscoacoustic RTM (Q-RTM) methods implement attenuation compensation by reversing the sign of the amplitude loss term and keeping the dispersion term unchanged for both forward and backward wavefield extrapolation, which require decoupled viscoacoustic wave equation and give rise to numerical instability issue. To address these problems, we have developed a stable and effective attenuation compensated viscoacousitc RTM method. The proposed method uses an attenuated source and receiver wavefield energy to normalize the crosscorrelation imaging condition to compensate the attenuation effect. Due to the use of attenuated wavefield in the new imaging condition, the instability issue is avoided. Numerical tests prove the validation and effectiveness of the proposed method.

Vector-based excitation amplitude imaging condition for elastic RTM

Five alternative algorithms were evaluated to circumvent the excessive storage requirement imposed by saving source wavefield snapshots used for the crosscorrelation image condition in 2D prestack elastic reverse time migration. We compared the algorithms on the basis of their ability, either to accurately reconstruct (not save) the source wavefield or to use an alternate image condition so that neither saving nor reconstruction of full wavefields was involved. The comparisons were facilitated by using the same (velocity-stress) extrapolator in all the algorithms, and running them all on the same hardware. We assumed that there was enough memory in a node to do an extrapolation, and that all input data were stored on disk rather than residing in random-access memory. This should provide a fair and balanced comparison. Reconstruction of the source wavefield from boundary and/or initial values reduced the required storage to a very small fraction of that needed to store source wavefield snapshots for conventional crosscorrelation, at the cost of adding an additional source extrapolation. Reverse time checkpointing avoided recursive forward recomputation. Two nonreconstructive imaging conditions do not require full snapshot storage or an additional extrapolation. Time-binning the imaging criteria removed the need for image time searching or sorting. Numerical examples using elastic data from the Marmousi2 model showed that the quality of the elastic prestack PP and PS images produced by the cost-optimized alternative algorithms were (virtually) identical to the higher cost images produced by traditional crosscorrelation.

Advances in computational capabilities as well as ongoing improvements in storage strategies have made reverse time migration (RTM) a feasible method for capturing images of complex structures. However, large storage requirements still restrict RTM applications, especially in anisotropic media. Utilising a first-order quasi-P-wave equation in vertically transversely isotropic (VTI) media, we investigate anisotropy and deduce an RTM equation for a staggered-grid high-order finite difference (FD) scheme incorporating a perfectly matched layer (PML) boundary in this study. We also develop an improved source wavefield storage strategy via a PML boundary method for VTI medium RTM using graphic processing unit (GPU) accelerated computation. Our proposed method significantly reduces the total volume of data storage required for conventional RTM while increasing calculation time by just a small amount. Checkpoints can be set based on GPU memory size, leading to the generation of high precision and high efficiency subsurface images. We carried out a series of numerical tests on simple anisotropic media and complex Hess 2D VTI models to verify the effectiveness of our proposed method.

The thermal profile of data centers plays a significant role in affecting the cooling cost and power budget of the system. While several dynamic and static approaches have been proposed so far, these have failed on considering the whole picture. This paper proposes a combined static and dynamic approach that shows the benefits of the efficient scheduling strategies on leading to thermal-efficient floorplans. The devised methodology comes out with a placement of processors and task scheduling for a heterogeneous system, where the main thermal metrics (maximum temperature and thermal gradient) have been optimized.

Reverse time migration: A prospect of seismic imaging methodology

ContextClimate and land-use changes affect species ranges and movements. However, these changes are usually overlooked in connectivity studies, and this could have adverse consequences in the definition of effective management measures.ObjectivesWe evaluated two ways to incorporate landscape dynamics: (i) by analyzing connectivity as a fluctuating phenomenon (i.e., time-varying connectivity); and (ii) by analyzing species movements from past to current ranges (i.e., spatio-temporal connectivity). We also compared these dynamic approaches with traditional static connectivity methods.MethodsWe compared the overall connectivity values and the prioritization of critical habitat patches according to dynamic and static approaches using habitat availability metrics (Probability of Connectivity and Equivalent Connected Area). This comparative research was conducted for species associated with broadleaf forests of the different ecoregions of the Iberian Peninsula. We considered species habitat preferences during movement and a wide range of dispersal abilities to assess functional connectivity.ResultsStatic approaches generated varying overall connectivity values and priority patches depending on the time snapshot considered and different from those generated by dynamic approaches. The two dynamic connectivity approaches resulted in very similar priority conservation patches, indicating their potential to guide enduring conservation measures that enhance connectivity between contemporary habitat patches at multiple time snapshots but also species range shifts in time.ConclusionsConnectivity is affected by landscape changes, and only dynamic approaches can overcome the issues associated with these changes and provide valuable information to guide improved and enduring measures in changing landscapes.