随着勘探目标日益复杂，多分量地震资料准确成像已成为地震勘探亟待解决的前沿课题。弹性波逆时偏移剖面存在分辨率低、信噪比低、振幅失衡等问题。弹性波最小二乘逆时偏移能解决此问题，但其偏移剖面（参数扰动）与弹性波逆时偏移剖面（PP、PS、SP和SS）不同，无法直接与地下介质的PP、PS、SP和SS反射系数建立联系，难以用于后续的资料处理及解释，影响油气识别的精度。本项目拟将反演理论引入弹性波逆时偏移，利用GANs深度学习技术探究高效智能的弹性波场分解与合成方法，研究以弹性波逆时偏移剖面为反演目标的弹性波逆时偏移和反偏移的理论机制，构建新的弹性波逆时偏移和反偏移算子，推导相应的梯度敏感核方程，形成一套有效的多分量资料偏移新方法。本研究能获得高品质的弹性波逆时偏移剖面，对弹性波偏移成像理论的完善有重要科学意义，也对当前复杂的勘探任务有重要的实用价值。

With the increasing complexity of exploration targets, how to obtain accurate migration images of multi-component seismic data in complex medium has become a frontier subject urgently to be solved for seismic exploration. Elastic reverse time migration (RTM) images have low spatial resolution, low signal-to-noise ratio (SNR), and unbalanced amplitudes. Elastic least squares reverse time migration (LSRTM) can resolve this problem. However its migration images (parameter perturbations) are different from them of elastic RTM (PP, PS, SP and SS). There is no direct relationship between the images and the PP, PS, SP, and SS reflection coefficient information of subsurface medium, which makes them difficult to be applied to the subsequent seismic data processing and interpretation, and affects the accuracy of oil and gas identification. In this research, we will intend to develop a practical RTM imaging method for multi-component seismic data by introducing least-squares inversion into elastic RTM. We explore an efficient and intelligent elastic wavefield decomposition and composition methods using the deep learning technique of GANs. In addition, we construct new elastic reverse time migration and demigration operators by studying the theoretical mechanism of elastic reverse time migration and demigration, which considers the conventional elastic reverse time migration images as the inversion object. Then we derive the corresponding gradient sensitivity equation. This research can provide an effective migration method for accurate imaging of multi-component seismic data. This new migration method can produce high-quality elastic reverse time migration images. It not only has important scientific significance for improving the elastic migration imaging theory, but also has important practical value for the current complex exploration task.