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EN
Vertical seismic profling (VSP) can provide more abundant seismic wavefeld information and better seismic data with high resolution and high quality for the complex underground geological structures compared with surface seismic data. Reverse time migration (RTM) method possesses signifcant advantages for the accurate identifcation of complex geological structures, and it’s considered to be the most accurate imaging method at present. Therefore, we develop a variable density acoustic RTM method which is applicable for VSP data to enhance the recognition capability of complex geological structures, and we also discuss diferent aspects of this proposed imaging method. Firstly, to efectively improve the modeling precision of seismic wavefelds, the wavefeld extrapolation of our VSP RTM method is realized by using an optimal staggered-grid fnite diference (SFD) method to solve the variable density acoustic wave equation, because this optimal SFD method uses the least square (LS) method to optimize the objective function established by the time–space domain dispersion relation to estimate its diference coefcients. In other words, the time–space domain LS-based SFD method has higher numerical simulation accuracy for seismic modeling. Secondly, to efectively reduce the boundary refections and storage requirements of our VSP RTM method, we adopt the PML absorbing boundary and the efective boundary storage strategy in the process of wavefeld extrapolation. Finally, to strengthen the quality and precision of VSP RTM results, the depth imaging profle of a shot is calculated by the normalized cross-correlation imaging condition of sources which can efectively eliminate the source efects on RTM results, and Laplace fltering is applied to eliminate the imaging noises in fnal RTM results efectively. The imaging results of diferent models show the efectiveness of our RTM method for VSP data, and it can more accurately identify the complex underground geological structures compared with the RTM method for conventional surface seismic data.
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