LAUSR

We analyze the mathematical requirements for conventional reverse time migration (RTM) and summarize their rationale. The known information provided by current acquisition system is inadequate for the second-order acoustic wave equations. Therefore, we introduce a dual-sensor seismic acquisition system into the coupled first-order acoustic wave equations. We propose a new dual-sensor reverse time migration called dual-sensor RTM, which includes two input variables, the pressure and vertical particle velocity data. We focus on the performance of dual-sensor RTM in estimating reflection coefficients compared with conventional RTM. Synthetic examples are used for the study of estimating coefficients of reflectors with both dual-sensor RTM and conventional RTM. The results indicate that dual-sensor RTM with two inputs calculates amplitude information more accurately and images structural positions of complex substructures, such as the Marmousi model, more clearly than that of conventional RTM. This shows that the dual-sensor RTM has better accuracy in backpropagation and carries more information in the directivity because of particle velocity injection. Through a simple point-shape model, we demonstrate that dual-sensor RTM decreases the effect of multi-pathing of propagating waves, which is helpful for focusing the energy. In addition, compared to conventional RTM, dual-sensor RTM does not cause extra memory costs. Dual-sensor RTM is, therefore, promising for the computation of multi-component seismic data.