Global inversion for anisotropy during full-waveform inversion

Abstract:

Full-waveform inversion (FWI) is a powerful tool for quantitative estimation of high-resolution high-fidelity models of subsurface seismic parameters, typically P-wave velocity. The solution to FWI’s posed nonlinear inverse problem is obtained via an iterative series of linearized local updates to a start model, assuming this model lies within the basin of attraction to the global minimum. Thanks to many successful published applications to three-dimensional (3D) field datasets, its advance has been rapid and driven in large-part by the oil and gas industry. The consideration of seismic anisotropy during FWI is of vital importance, as it holds influence over both the kinematics and dynamics of seismic waveforms. If not appropriately taken into account then inadequacies in the anisotropy model are likely to manifest as significant error in the recovered velocity model. Conventionally, anisotropic FWI employs either an a priori anisotropy model, held fixed during FWI, or it uses a multi-parameter local inversion scheme to recover the anisotropy as part of the FWI; both of these methods can be problematic. Constructing an anisotropy model prior to FWI often involves intensive (and hence expensive) iterative procedures, such as travel-time tomography or moveout velocity analysis. On the other hand, introducing multiple parameters to FWI itself increases the complexity of what is already an underdetermined inverse problem. We propose that global rather than local FWI can be used to recover the long-wavelength acoustic anisotropy model, and that this can then be followed by more-conventional local FWI to recover the detailed model. We validate this approach using a full 3D field dataset, demonstrating that it avoids problems associated to crosstalk that can bedevil local inversion schemes, and reconciles well with in situ borehole measurements. Although our approach includes a global inversion for anisotropy, it is nonetheless affordable and practical for 3D field data.