Zeyu Zhao

Double plane‐wave reverse‐time migration

We develop a new time-domain reverse-time migration method called double plane-wave reverse-time migration that uses plane-wave transformed gathers. Original shot gathers with appropriate data acquisition geometry are double slant stacked into the double plane-wave domain with minimal slant stacking artefacts. The range of plane-wave components needed for migration can be determined by estimating the maximum time dips present in shot gathers. This reduces the total number of input traces for migration and increases migration efficiency. Unlike the pre-stack shot-profile reverse-time migration where the number of forward propagations is proportional to the number of shots, the number of forward propagations needed for the proposed method remains constant and is relatively small even for large seismic datasets. Therefore, the proposed method can improve the efficiency of the migration and be suitable for migrating large datasets. Double plane-wave reverse-time migration can be performed for selected plane-wave components to obtain subsurface interfaces with different dips, which makes the migration method target oriented. This feature also makes the method a useful tool for migration velocity analysis. For example, we are able to promptly obtain trial images with nearly horizontal interfaces and adjust velocity models according to common image gathers. Seismic signal coming from steeply dipping interfaces can be included into the migration to build images with more detailed structures and higher spatial resolution as better velocity models become available. Illumination compensation imaging conditions for the proposed method are also introduced to obtain images with balanced amplitudes.

Double Plane Wave Least Squares Reverse Time Migration with Approximate Diagonal Hessian

We propose a least squares reverse time migration (RTM) strategy using double plane wave (DPW) data. We derive a DPW Born modelling operator that predicts the DPW data with the reflectivity models. The adjoint of the proposed modelling operator is recognized as the frequency domain DPW RTM operator. Both the modelling and the RTM operator utilize plane wave Green’s function. The DPW Born modelling operator together with its adjoint leads to an efficient DPW least squares RTM. To improve the convergence rate and obtain images with balanced amplitude, we derive a pre-conditioner, which is the diagonal matrix of the approximate Hessian matrix for the misfit function of the DPW data. We show that same plane wave Green’s functions can be used for the modelling operator, the RTM operator and the pre-conditioner throughout the iterative updating process. As a result, wavefield propagations are not required for the DPW least squares RTM during model updating processes, which significantly improves the efficiency of the least squares RTM.