基于Sigmod数据规则化的2-Wasserstein全波形反演方法OA北大核心CSTPCD

As an effective method to address the cycle skip problem in traditional full waveform inversion(FWI),2-Wasserstein distance-based FWI(OT-FWI)typically requires appropriate data regularization.Therefore,the Sigmoid function is introduced for data regularization in OT-FWI to form the Sigmoid-based OT-FWI method.The Sigmoid function constrains the data within a spe-cific range by mapping the portions below zero into the values close to zero.Compared to common data regularization methods like affine scaling and exponential normalization,the Sigmoid function can better utilize the information of phases below zero in seismic data to further enhance inversion accuracy.A test using a 1D Ricker wavelet demonstrates that the Sigmoid regularization-based method can effectively enhance the convexity of the objective function and increase low-frequency information in the conjugate sources.Testing with synthetic model data and seismic data acquired in an exploration area in Eastern China shows that this ap-proach,compared to conventional data regularization methods,can further improve the accuracy of model inversion.