Abstract:We extend the Huber norm to the least-squares reverse time migration (LSRTM) algorithm to promote its robustness.Then,considering a large amount of calculation of LSRTM,we introduce the stochastic optimization phase encoding ideas to multi-source LSRTM based on Huber norm.This can effectively improve the calculation efficiency and eliminate the noise.Finally,theoretical data modeled by conventional finite-difference method verifies the effectiveness and suitability of the proposed algorithm for complex models.
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