Towards full waveform ambient noise inversion. Issue 1 (9th October 2017) - Sager, Korbinian; Ermert, Laura; Boehm, Christian; Fichtner, Andreas
SUMMARY: In this work we investigate fundamentals of a method—referred to as full waveform ambient noise inversion—that improves the resolution of tomographic images by extracting waveform information from interstation correlation functions that cannot be used without knowing the distribution of noise sources. The fundamental idea is to drop the principle of Green function retrieval and to establish correlation functions as self-consistent observables in seismology. This involves the following steps: (1) We introduce an operator-based formulation of the forward problem of computing correlation functions. It is valid for arbitrary distributions of noise sources in both space and frequency, and for any type of medium, including 3-D elastic, heterogeneous and attenuating media. In addition, the formulation allows us to keep the derivations independent of time and frequency domain and it facilitates the application of adjoint techniques, which we use to derive efficient expressions to compute first and also second derivatives. The latter are essential for a resolution analysis that accounts for intra- and interparameter trade-offs. (2) In a forward modelling study we investigate the effect of noise sources and structure on different observables. Traveltimes are hardly affected by heterogeneous noise source distributions. On the other hand, the amplitude asymmetry of correlations is at least to first order insensitive to unmodelled Earth structure. Energy and waveform differences are sensitive to both structure and the distribution of noise sources. (3) We design and implement an appropriate inversion scheme, where the extraction of waveform information is successively increased. We demonstrate that full waveform ambient noise inversion has the potential to go beyond ambient noise tomography based on Green function retrieval and to refine noise source location, which is essential for a better understanding of noise generation. Inherent trade-offs between source and structure are quantified using Hessian-vector products.