Quantifying Uncertainties in Seismic Tomography

Abstract

Reflection tomography allows the determination of a velocity model that fits the traveltime data associated with reflections of seismic waves in the subsurface. The resulting nonlinear optimization problem is solved classically by a Gauss-Newton method based on successive linearizations of the forward operator. A linearized a posteriori analysis is possible in a Bayesian framework. In this presentation, we compare the results obtained by such technique, the ones obtained by a linear programming based method and the others by a global optimization approach