BROSSIER , Romain

The knowledge of Earth intern structures at different scales is of major interest for economy, humans, environment and science. Several methods have been developed for Earth imaging using seismic wave information. The full waveform inversion attempts to build quantitative high-resolution images of the subsurface physical parameters using the...

Quantitative imaging of the elastic properties of the subsurface at depth is essential for civil engineering applications and oil- and gas-reservoir characterization. A realistic synthetic example provides for an assessment of the potential and limits of 2D elastic full-waveform inversion FWI of wide-aperture seismic data for recovering...

Elastic full-waveform inversion is an ill-posed data-fitting procedure that is sensitive to noise, inaccuracies of the starting model, definition of multiparameter classes, and inaccurate modeling of wavefield amplitudes. We have investigated the performance of different minimization functionals as the least-squares norm [script l]2, the...

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) 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) 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...

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 powerful seismic imaging method, based on the iterative minimization of the distance between simulated and recorded wavefields. The inverse Hessian operator related to this misfit function plays an important role in the reconstruction scheme. As conventional methods use direct approximations of this operator,...

Full Waveform Inversion is a powerful tool for quantitative estimation of subsurface parameters (P-wave, S-wave velocities, density, attenuation, anisotropy parameters). This methods has been applied successfully to 2D acoustic and elastic reconstructions, as well as to 3D acoustic reconstructions. Most of the applications of Full Waveform...

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) is becoming an efficient tool to derive high resolution quantitative models of the subsurface parameters. The method relies on the minimization, through an iterative procedure, of the residual between recorded data and synthetic data computed by solving the two-way wave equation in a subsurface model. The growth of...

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...

International audience

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...

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 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) 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...

In the context of full waveform inversion (FWI), second-order optimization methods, which take into account more precisely the effect of the Hessian such as the quasi-Newton l-BFGS method, have shown superior convergence properties than first-order methods. When using source encoding techniques, the regeneration of the random variables to...

In the context of full waveform inversion (FWI), second-order optimization methods, which take into account more precisely the effect of the Hessian such as the quasi-Newton l-BFGS method, have shown superior convergence properties than first-order methods. When using source encoding techniques, the regeneration of the random variables to...

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...

When applied to wave propagation modeling in anisotropic media, Perfectly Matched Layers (PML) exhibit instabilities. Incoming waves are amplified instead of being absorbed. Overcoming this difficulty is crucial as in many seismic imaging applications, accounting accurately for the subsurface anisotropy is mandatory. In this study, we present...