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Full waveform inversion (FWI) is an iterative identification process thatserves to minimize the misfit of model-based simulated and experimentallymeasured wave field data, with the goal of identifying a field of parametersfor a given physical object. The inverse optimization process of FWI is basedon forward and backward solutions of the (elastic or acoustic) eave equation.In a previous paper [1], we explored opportunities of using the finite cellmethod (FCM) as the wave field solver to incorporate highly complex geometricmodels. Furthermore, we demonstrated that the identification of the model'sdensity outperforms that of the velocity -- particularly in cases where unknownvoids characterized by homogeneous Neumann boundary conditions need to bedetected. The paper at hand extends this previous study: The isogeometricfinite cell analysis (IGA-FCM) -- a combination of isogeometric analysis (IGA)and FCM -- is applied for the wave field solver, with the advantage that thepolynomial degree and subsequently also the sampling frequency of the wavefield can be increased quite easily. Since the inversion efficiency stronglydepends on the accuracy of the forward and backward wave field solution and ofthe gradient of the functional, consistent and lumped mass matrixdiscretization are compared. The resolution of the grid describing the unknownmaterial density is the decouple from the knot span grid. Finally, we proposean adaptive multi-resolution algorithm that refines the material grid onlylocally using an image processing-based refinement indicator. The developedinversion framework allows fast and memory-efficient wave simulation and objectidentification. While we study the general behavior of the proposed approach on2D benchmark problems, a final 3D problem shows that it can also be used toidentify voids in geometrically complex spatial structures.

Isogeometric Multi-Resolution Full Waveform Inversion based on the Finite Cell Method