【摘要】 全波形反演(FWI)算法对低频信息和初始模型的依赖比较严重,容易发生周波跳跃现象而陷入局部极小点。为减少周波跳跃现象对全波形反演的影响,提出了波场相位相关时移全波形反演算法,在反演之前对模拟数据进行处理,提高观测数据和模拟数据的匹配程度;同时为减少初始模型对FWI的影响,利用Curvelet变换的多尺度特性,在反演的不同阶段选择不同尺度的数据参与反演,从而改善全波形反演因初始模型误差较大而出现周波跳跃的问题。利用Marmousi模型对波场相位相关时移全波形反演算法进行了测试,结果表明,该反演算法以及利用Curvelet变换多尺度数据参与反演可以明显改善FWI对低频信息和初始模型的依赖,得到较好的反演结果。更多还原

【Abstract】 Full waveform inversion(FWI)is sensitive to low frequency information and initial model and is easy to fall into local minima because of cycle-skipping issue.To reduce the effect of cycle-skipping,we propose wavefield phase correlation shifting FWI method.By processing the simulated data before inversion,we improve the matching degree between observed data and simulated data.We also exploit the multi-scale characteristics of Curvelet transform to reduce the effect of initial model by selecting observed data of different scales information during inversion.Test of frequency-domain FWI based on Marmousi model shows that the inversion result has a better stability,and both of the wavefield phase correlation shifting FWI and the multi-scale Curvelet transform FWI can reduce the dependence of low frequency information and initial model.更多还原