Towards broadband multicomponent seismology and practical iterated inversion

地震图像提供了地球表面下最好的景象；但是，尽管有80年的历史，它们仍然远未达到最佳状态。今天，地震计算技术正在从结合物理理论和实际经验的标准方法(SM)过渡到更牢固地植根于数学物理的非常现代的全波形反演(FWI)。然而，地震数据中低频成分的不足、震源波形本身的未知、物理知识的不完全理解以及所需的极端计算工作都阻碍了这种转变。因此，SM是主要的方法，而FWI很少在专门的研究实验室之外进行尝试。SM使用复杂的数据处理序列来创建地下的反射率图像。然后，结合井信息，反演过程将反射率图像转换为大地属性，如阻抗(密度乘以速度)。FWI是一个基本的迭代过程，它通过最小化实际地震数据和预测地震数据之间的差异来收敛于阻抗模型。FWI从不创建反射率图像，也不使用良好的控制，而SM不预测合成数据，也不迭代。我们将创建一类新的多分量数据地震反演方法，将SM最健壮的特征与FWI最有希望的概念相结合。在SM中，我们将保留大部分数据处理步骤、反射率图像的创建以及与井控的匹配。后者便于源波形估计，并提供所需的低频信息。在FWI中，我们将结合迭代、数据建模(预测)和数据残差成像的概念。提出的方法，我们称之为IMMI(迭代建模、偏移和反演)，将产生与井中测量结果相匹配的地下属性估计，并预测记录的地震数据中的大多数特征。这样的估计应该比SM目前实现的估计可靠得多。这将对资源勘探和地下环境研究产生重大好处。

Seismic images provide the best possible views of the earth below its surface; but, despite an 80 year history, they are still far from optimal. Today, seismic computational techniques are transitioning from a standard methodology (SM), which incorporates an evolved blend of physical theory and practical experience, to the very modern full-waveform inversion (FWI) that is much more firmly rooted in mathematical physics. However, this transition is hindered by insufficient low-frequency content in seismic data, by the inherently unknown seismic source waveform, by incompletely understood physics, and by the extreme computational effort required. As a consequence, SM is the dominant approach while FWI is rarely attempted outside of dedicated research labs. SM uses a sophisticated data processing sequence to create a reflectivity image of the subsurface. Then, incorporating well information, an inversion process converts the reflectivity image to earth properties such as impedance (density times velocity). FWI is a fundamentally iterative process that converges on an impedance model by minimizing the difference between real and predicted seismic data. FWI never creates a reflectivity image and does not use well control while SM does not predict synthetic data and is not iterated. We will create a new class of seismic inversion methods for multicomponent data that combines the most robust features of SM with the most promising concepts from FWI. From SM, we will retain most of the data processing steps, the creation of a reflectivity image, and the matching to well control. The latter facilitates the source waveform estimation and provides the needed low frequency information. From FWI, we will incorporate the concepts of iteration, data modelling (prediction), and imaging of the data residual. The proposed approach, which we call IMMI (Iterated Modelling, Migration, and Inversion), will produce estimates of subsurface properties that both match measurements in wells and also predict most features in the recorded seismic data. Such estimates should be much more reliable than those presently achieved by SM. This will have significant benefits to resource exploration and to subsurface environmental studies.