中文摘要：

全波形反演是一个高度非线性的优化问题,当地震数据中缺少低频成分而初始速度与真实速度相差较远时,反演容易陷入局部极小值。笔者提出一种新的目标函数,将模拟地震记录和观测记录的归一化互相关与最小二乘结合。互相关侧重相位匹配,具有更强的线性,能减弱“跳周”现象。通过设置权重因子,在反演前期利用互相关先恢复低波数的背景速度模型,再加入最小二乘约束恢复高波数的模型细节。数值模拟试验结果表明,基于该目标函数的全波形反演不依赖精确的初始模型和低频信息,向全局极小值迅速收敛,能有效改善反演的稳定性,并获得比基于常规目标函数的全波形反演更精确的结果。

英文摘要：

Full waveform inversion （FWI） is a highly non-linear optimization problem. When there is a lack of low-frequency components in the seismic data and the initial velocity is far from the true velocity, it is easily trapped into local minima. The authors propose a new objective function that combines the normalized cross-correla-tion of modeled and observed data with the least squares criterion. Cross-correlation emphasizes on phase matching and behaves in a more linear way, thus can mitigate the cycle-skipping issue. By setting a weighting factor, the au-thors use the cross-correlation norm to update the low-wave number components of the velocity model at the early stage of the inversion, while using the least squares criterion to update high-wave number details of the model. Nu-merical examples show that FWI based on the new objective function converges quickly toward global minima and does not rely on accurate initial velocity model as well as low-frequency information, it can improve the robustness of the inversion and generate more accurate inversion results than the conventional approach.