主成分分析波场重构反演与全波形反演联合速度重构

全波形反演倚重低频成分，但地震资料中往往缺乏低频信息。为确保全波形反演在缺少低频信息时能稳定收敛，本文联合波场重构反演和全波形反演，利用波场重构反演在优化过程中拥有较大自由度的优势模拟低频部分，并以波场重构反演结果作为较高频部分的初始模型，进行全波形反演。实际应用过程中，低频部分的波场重构反演使用主成分分析法，通过降维缩短计算耗时；高频部分使用基于Curvelet变换的稀疏全波形反演和主成分分析，使得全波形反演在缺少低频成分时也能高效地收敛。二维Marmousi模型试算结果表明，本文方法在缺少低频信息条件下可得到高效稳定的全波形反演结果。

A joint velocity reconstruction method: principal component analysis based wavefield-reconstruction inversion combined with full waveform inversion

Full waveform inversion (FWI) depends on low-frequency information which is always absent in seismic data.To ensure that FWI can steadily converge in the condition of lack of low-frequency information,we use a joint method of wavefield-reconstruction inversion (WRI) combined with FWI.Since WRI has more freedom in the optimization to find a solution,we utilize WRI to inverse the low-frequency part and then perform FWI for the high-frequency part.During the inversion of low-frequency part,we use principal component analysis (PCA) to decrease the data volume by reducing the number of shots.As for the high-frequency part,we use PCA as well as curvelet transformation based FWI to update model.All these approaches ensure the convergence when lack of low-frequency data.Examples based on 2D Marmousi model illustrate that with the help of the proposed method,we can efficiently achieve a FWI robust result even when there is no low-frequency data.