New Dynamic Stochastic Source Encoding Combined With a Minmax-Concave Total Variation Regularization Strategy for Full Waveform Inversion

摘要

To address problems, such as the computationally intensive inversion requirements, low inversion efficiency, and inadequate inversion accuracy caused by multiparameter crosstalk in a synchronous inversion, a new dynamic stochastic source encoding strategy combined with minmax-concave total variation (MCTV) regularization model constraints was proposed. This strategy avoids crosstalk noise between shots caused by the algorithm and greatly improves the inversion efficiency without affecting the inversion accuracy. By comparing a “cross”-shaped model with the multiparameter inversion results, we found that the MCTV regularization strategy boasts the best inversion effect. We further showed that dynamic stochastic source encoding can increase the inversion efficiency threefold by applying the 1994 British Petroleum (BP) migration international standard topography model and establishing a function to evaluate the most efficient inversion strategy from among seven options. Compared with the traditional stochastic source encoding strategies, dynamic stochastic source encoding was shown to better suppress crosstalk noise. The proposed strategy also presented a higher acceleration ratio; additionally, combining this strategy with MCTV regularization model constraints provided the clearest reconstructed image with the highest inversion precision and obtained the best evaluation score among the considered inversion strategies, albeit with a slight reduction in the total elapsed time-acceleration ratio.