MÉTIVIER , Ludovic

A method for combining the asymptotic operator designed by Beylkin (Born migration operator) for the solution of linearized inverse problems with full waveform inversion is presented. This operator is used to modify the standard L2 norm that measures the distance between synthetic and observed data. The modified misfit function measures the...

Full Waveform Inversion (FWI) methods use generally gradient based method, such as the nonlinear conjugate gradient method or more recently the l-BFGS quasi-Newton method. Several authors have already investigated the possibility of accounting more accurately for the inverse Hessian operator in the minimization scheme through Gauss-Newton or...

Multi-parameter full waveform inversion is a challenging problem, mainly because of the existence of trade-offs between subsurface parameters. Mitigating these trade-offs requires to account accurately for the inverse Hessian operator during the minimization process. In this study, we investigate the efficiency of the truncated Newton...

Full Waveform Inversion (FWI) is a promising seismic imaging method. It aims at computing quantitative estimates of the subsurface parameters (bulk wave velocity, shear wave velocity, rock density) from local measurements of the seismic wavefield. Based on a particular wave propagation engine for wavefield estimation, it consists in minimizing...

At the exploration scale, the simulation of seismic wave propagation in the subsurface requires using absorbing boundary conditions. For the last twenty years, the method of choice has been the Perfectly Matched Layer (PML) method. PML are easy to implement and remarkably accurate. However, in certain situations such as the simulation of...

International audience

Seismic wave modeling requires using adapted boundary conditions to simulate infinite or semi-infinite media. Because of its efficiency, the Perfectly Matched Layers (PML) method has rapidly become the standard for acoustic and elastic propagation. However, PML are not adapted to anisotropic media for which the method becomes amplifying....

Full Waveform Inversion (FWI) is a powerful method for reconstructing subsurface parameters from local measurements of the seismic wavefield. This method consists in minimizing a distance between predicted and recorded data. The predicted data is computed as the solution of a wave propagation problem. Conventional numerical methods for the...

International audience

Full waveform inversion (FWI) is a powerful method for reconstructing subsurface parameters from local measurements of the seismic wavefield. This method consists in minimizing the distance between predicted and recorded data. The predicted data are computed as the solution of a wave-propagation problem. Conventional numerical methods for the...

Full Waveform Inversion (FWI) is a promising seismic imaging method. It aims at computing quantitative estimates of the subsurface parameters (bulk wave velocity, shear wave velocity, rock density) from local measurements of the seismic wavefield. Based on a particular wave propagation engine for wavefield estimation, it consists in minimizing...

Full Waveform Inversion (FWI) methods use generally gradient based method, such as the nonlinear conjugate gradient method or more recently the l-BFGS quasi-Newton method. Several authors have already investigated the possibility of accounting more accurately for the inverse Hessian operator in the minimization scheme through Gauss-Newton or...

Full Waveform Inversion (FWI) applications classically rely on efficient first-order optimization schemes, as the steepest descent or the nonlinear conjugate gradient optimization. However, second-order information provided by the Hessian matrix is proven to give a useful help in the scaling of the FWI problem and in the speed-up of the...

Full Waveform Inversion (FWI) applications classically rely on efficient first-order optimization schemes, as the steepest descent or the nonlinear conjugate gradient optimization. However, second-order information provided by the Hessian matrix is proven to give a useful help in the scaling of the FWI problem and in the speed-up of the...