Towards broadband multicomponent seismology and practical iterated inversion

地震图像提供了地球表面以下的最佳视图;但是，尽管有80年的历史，它们仍然远远不是最佳的。今天，地震计算技术正在从标准方法（SM）过渡到非常现代的全波形反演（FWI），标准方法（SM）融合了物理理论和实践经验，FWI更牢固地植根于数学物理。然而，这种过渡受到地震数据中低频含量不足、固有未知的震源波形、不完全理解的物理学以及所需的极端计算工作的阻碍。因此，SM是主要的方法，而FWI很少在专门的研究实验室之外尝试。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诸如阻抗（密度乘以速度）的地球属性。FWI是一个基本的迭代过程，它通过最小化真实的和预测的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，也不使用井控，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的地震反演方法，结合了最强大的功能SM与FWI的最有前途的概念。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。从FWI，我们将纳入迭代，数据建模（预测）和数据残差成像的概念。所提出的方法，我们称之为IMMI（迭代建模，迁移和反演），将产生地下属性的估计，既匹配威尔斯中的测量，也预测记录的地震数据中的大多数功能。这种估计数应比SM目前所作的估计数可靠得多。这将对资源勘探和地下环境研究产生重大效益。

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.