The seismic imaging methods currently in the development stage need to be tested for experimental validation under controlled conditions. Yet natural media are very complex, and moreover, the parameters along the measurement profile prove difficult to evaluate independently of the seismic method itself. To satisfy this need, the ultrasonic measurement laboratory (MUSC) presented in this research has been devised to experimentally model seismic field measurements by using reduced-scale models. This facility is composed of small-scale models of the underground, an optical table with two moving arms, a laser interferometer, and adapted piezoelectric transducers used as the seismic sources. The source system has been adapted to simulate the behavior of a point-surface seismic source. This is essential to reproduce the spatial energy distribution of a surface seismic source and supersedes the sources used in the past for other reduced-scale seismic experimental models. The comparisons of experimental data collected with MUSC and numerical data simulated by means of finite-element viscoelastic modeling indicate very good agreement of time arrivals and amplitudes for a range of propagation distances until the amplitude has decreased to the system noise level. These results demonstrate that the MUSC laboratory is a system with plenty of promise for validating seismic imaging methods through testing on a perfectly known propagation model prior to field application

Full waveform inversion is a powerful tool for quantitative seismic imaging from wide‐azimuth seismic data. The method is based on the minimization of the misfit between observed and simulated data. This amounts to the solution of a large‐scale nonlinear minimization problem. The inverse Hessian operator plays a crucial role in this reconstruction process. Accounting accurately for the effect of this operator within the minimization scheme should correct for illumination deficits, restore the amplitude of the subsurface parameters, and help to remove artefacts generated by energetic multiple reflections. Conventional minimization methods (nonlinear conjugate gradient, quasi‐Newton methods) only roughly approximate the effect of this operator. In this study, we are interested in the truncated Newton minimization method. These methods are based on the computation of the model update through a matrix‐free conjugate gradient solution of the Newton linear system. We present a feasible implementation of this method for the full waveform inversion problem, based on a second‐order adjoint state formulation for the computation of Hessian‐vector products. We compare this method with conventional methods within the context of 2D acoustic frequency full waveform inversion for the reconstruction of P‐wave velocity models. Two test cases are investigated. The first is the synthetic BP 2004 model, representative of the Gulf of Mexico geology with high velocity contrasts associated with the presence of salt structures. The second is a 2D real data‐set from the Valhall oil field in North sea. Although, from a computational cost point of view, the truncated Newton method appears to be more expensive than conventional optimization algorithms, the results emphasize its increased robustness. A better reconstruction of the P‐wave velocity model is provided when energetic multiple reflections make it difficult to interpret the seismic data. A better trade‐off between regularization and resolution is obtained when noise contamination of the data requires one to regularize the solution of the inverse problem.