Angle-Weighted Reverse Time Migration With Wavefield Decomposition Based on the Optical Flow Vector

Reverse time migration (RTM) is based on the two-way wave equation, so its imaging results obtained by conventional zero-lag cross-correlation imaging conditions contain a lot of low-wavenumber noises. So far, the wavefield decomposition method based on the Poynting vector has been developed to suppress these noises; however, this method also has some problems, such as unstable calculation of the Poynting vector, low accuracy of wavefield decomposition, and poor effect of large-angle migration artifacts suppression. This article introduces the optical flow vector method to RTM to realize high-precision wavefield decomposition for both the source and receiver wavefields and obtains four directions of wavefields: up-, down-, left-, and right-going. Then, the cross-correlation imaging sections of one-way propagation components of forward- and back-propagated wavefields are optimized and stacked. On this basis, the reflection angle of each imaging point is calculated based on the optical flow vector, and an attenuation factor related to the reflection angle is introduced as the weight to generate the optimal stack images. The tests of theoretical model and field marine seismic data illustrate that compared with the conventional RTM with wavefield decomposition based on the Poynting vector, the angle-weighted RTM with wavefield decomposition based on the optical flow vector proposed in this article can achieve wavefield decomposition for both the source and receiver wavefields and calculate the reflection angle of each imaging point more accurately and stably. Moreover, the proposed method adopts angle weighting processing, which can further eliminate large-angle migration artifacts and effectively improve the imaging accuracy of RTM.

VSP Imaging Using Free-Surface Multiples With Wavefield Decomposition: Synthetic and Field Data Examples

Vertical seismic profiling (VSP) is an effective technique to provide high-resolution seismic images of the reservoir area. However, the quality of the images is limited by the poor illumination of primary reflection wave. In conventional VSP imaging, only the upgoing primaries are used. Adding free-surface–related multiples into the imaging process can significantly improve the coverage of the illuminated area. Conventional migration methods using multiples need the complex process of multiple prediction. Data-to-data migration (DDM) is an effective imaging technique for multiples in which the recorded data is migrated directly. To improve the imaging quality of DDM in VSP imaging, we propose separating the wavefield into downgoing and upgoing components using Hilbert transform when reverse-time migration (RTM) is implemented in DDM, and the inverse-scattering imaging condition is further applied to the decomposed wavefields. The proposed method eliminates low-frequency noises and false images generated from the conventional cross-correlation imaging condition, and further enhance the illumination in the VSP imaging. Synthetic examples and application to a walkaway field data demonstrate that it can attenuate the noise and improve the imaging resolution effectively. By using DDM with inverse scattering imaging condition and wavefield decomposition based on Hilbert transform, VSP imaging using free-surface–related multiples becomes a practical complement for conventional VSP imaging.

Vector elastic deconvolution migration with dual wavefield decomposition

Elastic-wave imaging using multi-component data can provide more useful subsurface information than acoustic-wave imaging, but is usually algorithmically challenging. We develop a vector elastic deconvolution migration method for high-resolution imaging of subsurface structures in isotropic and anisotropic elastic media. Our new method employs a vector deconvolution imaging condition based on dual wavefield decomposition, including an explicit directional wavefield separation using the Hilbert transform, and a P/S vector wavefield decomposition using the low-rank decomposition method. Using three elastic models, we numerically demonstrate that our new method produces notably higher-resolution and more amplitude-balanced elastic images compared with a cross-correlation-based vector elastic reverse-time migration method.

A high efficiency wavefield decomposition method based on Hilbert transform

Wavefield decomposition can be used to extract effective information in reverse time migration (RTM) and full waveform inversion (FWI). The wavefield decomposition methods based on the Hilbert transform (HTWD) and the Poynting vector (PVWD) are the most commonly used. The HTWD needs to save the wavefields at all time steps or introduce additional numerical simulation, which increases the computational cost. The PVWD cannot handle multi-wave arrivals, and its performance is poor in complex situations. We propose an efficient wavefield decomposition method based on the Hilbert transform (EHTWD). The EHTWD constructs two wavefields to replace the original wavefield and the wavefield after Hilbert transform. The first wavefield is obtained by using the dispersion relation to modify the frequency components. The other wavefield is obtained by time difference approximation. Therefore, there is a 90° phase change between the two wavefields. In EHTWD, we only need two wavefields at different moments, which avoids the additional numerical simulation. The EHTWD is also suitable for wavefield decomposition in arbitrary directions. Compared with HTWD, the computational complexity can be greatly reduced with the decrease of the number of imaging time slices. The numerical examples of wavefield decomposition demonstrate that the proposed method can realize wavefield decomposition in any direction. The examples of imaging decomposition and real data also show that the EHTWD suppresses the imaging noise effectively.