Linearised versus Nonlinear Estimates of Uncertainty in Full Waveform Inversion

Abstract

Seismic full waveform inversion (FWI) is a powerful technique to generate high resolution images of the Earth's interior. However, significant uncertainty exists in all FWI solutions due to imperfect acquisition geometries, inherent noise in the data, and nonlinearity of the forward problem. Probabilistic Bayesian FWI addresses this non-uniqueness by estimating the entire family of possible model solutions described by the posterior probability density function (pdf). The posterior pdf can be estimated using nonlinear inversion methods to quantify full uncertainties. Alternatively, by linearising the physics relating parameters and observations around the maximum a posteriori solution, the posterior pdf is usually approximated by a Gaussian pdf. This is referred to as the linearised method. In this work, we apply both nonlinear and linearised methods to 2D acoustic Bayesian FWI problems. We use a variational inference algorithm for the nonlinear case, in which a transformed Gaussian is optimised to approximate the posterior pdf. The results can be compared with those from a linearised, locally-Gaussian based method. We also apply Stein variational gradient descent for comparison. The results show that while both the linearised and nonlinear methods recover the posterior mean models accurately, they exhibit significantly different posterior uncertainty structures, especially around layer interfaces, due to the linearisation of wave physics. Linearised uncertainty estimates are shown to be significantly less accurate: they provide far less accurate fits to observed waveform data, and yield biased estimates of inferred meta-properties such as volumes of geological bodies. This work therefore motivates the application of fully nonlinear inversion methods in Bayesian FWI if accurate uncertainty estimates over parameters, or inferred or interpreted meta-properties are important.