Influence des ondes réfléchies sur l'inversion de formes d'onde : vers une meilleure compréhension des ondes réfléchies et leur utilisation dans l'inversion de formes d'onde (2016)

Full waveform inversion (FWI) has attracted worldwide interest for its capacity to estimate the physical properties of the subsurface in details. It is often formulated as a least-squares data-fitting procedure and routinely solved by linearized optimization methods. However, FWI is well known to suffer from cycle skipping problem making the final estimations strongly depend on the user-defined initial models. Reflection waveform inversion (RWI) is recently proposed to mitigate such cycle skipping problem by assuming a scale separation between the background velocity and high-wavenumber reflectivity. It explicitly considers reflected waves such that large-wavelength variations of deep zones can be extracted at the early stage of inversion. Yet, the large-wavelength information of the near surface carried by transmitted waves is neglected.In this thesis, the sensitivity of FWI and RWI to subsurface wavenumbers is revisited in the frame of diffraction tomography and orthogonal decompositions. Based on this analysis, I propose a new method, namely joint full waveform inversion (JFWI), which combines the transmission-oriented FWI and RWI in a unified formulation for a joint sensitivity to low wavenumbers from wide-angle arrivals and short-spread reflections. High-wavenumber components are naturally attenuated during the computation of model updates. To meet the scale separation assumption, I also use a subsurface parameterization based on compressional velocity and acoustic impedance. The temporal complexity of this approach is twice of FWI and the memory requirement is the same.An integrated workflow is then proposed to build the subsurface velocity and impedance models in an alternate way by JFWI and waveform inversion of the reflection data, respectively. In the synthetic example, JFWI is applied to a streamer seismic data set computed in the synthetic Valhall model, the large-wavelength characteristics of which are missing in the initial 1D model. While FWI converges to a local minimum, JFWI succeeds in building a reliable velocity macromodel. Compared with RWI, the involvement of diving waves in JFWI improves the reconstruction of shallow velocities, which translates into an improved imaging at greater depths. The smooth velocity model built by JFWI can be subsequently taken as the initial model for conventional FWI to inject high-wavenumber content without obvious cycle skipping problems.The main promises and limitations of the approach are also reviewed in the real-data application on the 2D OBC profile cross-cutting gas cloud.Several initial models and offset-driven strategies are tested with the aim to manage cycle skipping while building subsurface models with sufficient resolution. JFWI can produce an acceptable velocity model provided that the cycle skipping problem is mitigated and sufficient low-wavenumber content is recovered at the early stage of inversion. Improved scattering-angle illumination provided by 3D acquisitions would allow me to start from cruder initial models.