Frequency‐domain double‐plane‐wave least‐squares reverse time migration. (14th June 2019) - Zhao, Zeyu; Sen, Mrinal K.
ABSTRACT: Least‐squares reverse time migration is often formulated as an iterative updating process, where estimating the gradient of the misfit function is necessary. Traditional time domain shot‐profile least‐squares reverse time migration is computationally expensive because computing the gradient involves solving the two‐way wave equation several times in every iteration. To reduce the computational cost of least‐squares reverse time migration, we propose a double‐plane‐wave least‐squares reverse time migration method based on a misfit function for frequency‐domain double‐plane‐wave data. In double‐plane‐wave least‐squares reverse time migration, the gradient is computed by multiplying frequency‐domain plane‐wave Green's functions with the corresponding double‐plane‐wave data residual. Because the number of plane‐wave Green's functions used for migration is relatively small, they can be pre‐computed and stored in a computer's discs or memory. We can use the pre‐computed plane‐wave Green's functions to obtain the gradient without solving the two‐way wave equation in each iteration. Therefore, the migration efficiency is significantly improved. In addition, we study the effects of using sparse frequency sampling and sparse plane‐wave sampling on the proposed method. We can achieve images with correct reflector amplitudes and reasonable resolution using relatively sparse frequency sampling and plane‐wave sampling, which are larger than that determined by the Nyquist theorem. The well‐known wrap‐around artefacts and linear artefacts generated due to under‐sampling frequency and plane wave can be suppressed during iterations in cases where the sampling rates are not excessively large. Moreover, implementing the proposed method with sparse frequency sampling and sparse plane‐wave sampling further improves the computational efficiency. We test the proposed double‐plane‐wave least‐squares reverse time migration on synthetic models to show the practicality of the method.