高斯束层析与全波形反演联合速度建模

基于叠前地震波场模拟的全波形反演（FWI）技术走向实用化充满挑战性。陆上地震资料的低信噪比、缺失低频、复杂近地表及三维数据的巨大内存需求和海量计算等因素都制约了FWI技术的应用推广。为此，研究并开发了高斯束层析速度建模技术，弥补了传统射线层析无法反演得到的中波数速度成分，为FWI提供了高精度宏观速度模型，有效避免了周波跳跃现象；利用基于相位匹配的互相关目标泛函取代L₂范数求解梯度，降低了反演对低频数据的依赖，有效解决了FWI海量计算难题；利用伪保守波动方程形成自伴随的正演算子，大幅度提升了基于伴随状态法的梯度计算能力，增强了反演对陆地数据的适应性，实现了陆地资料的FWI高分辨率速度建模能力。

A joint velocity model building method with Gaussian beam tomography and full waveform inversion for land seismic data

The full waveform inversion (FWI) based on prestack seismic wavefield fitting is facing a few challenges in the application,for instances,low signal-to-noise ratio (SNR) land seismic data,missing low frequency information,complicated near-surface,and huge memory needed for massive 3D seismic data processing.In terms of low-precision land seismic migrations,we propose in this paper a velocity model building method by Gaussian Beam tomography,which not only makes up medium wave-number components unachievable in conventional methods based on ray tracing tomography but also provides FWI with a high precise macroscopic velocity model that helps to avoid effectively cycle skips.Besides,in order to reduce the dependency of inversion on low frequencies,we replace the L2 norm with a cross-correlation function based on phase matching,which figures out validly the problem of intensive computing of FWI.Furthermore,with a self-adjoint forward operator from pseudo-conservative wave equations,the gradient computing capacity based on an adjoint-state method is enhanced dramatically,which improves the inversion adaptation and realizes a high-resolution velocity model building for land seismic data.