A stochastic approach to seismic inversion using the ensemble Kalman filter (EnKF) is proposed. Seismic depth and time image data are used as the input for EnKF stochastic seismic inversion. The sonic log is used to estimate source wavelet and create initial models for the inversion, which provides an efficient integration of sonic log data and seismic data. We use both travel time and waveform...

Abstract Although the governing equations of many systems, when derived from first principles, may be viewed as known, it is often too expensive to numerically simulate all interactions they describe. Therefore, researchers seek simpler descriptions that describe complex phenomena without resolving interacting components. Stochastic differential (SDEs) arise naturally models in this context. Th...

In this paper we propose a new sampling-free approach to solve Bayesian model inversion problems that is an extension of the previously proposed spectral likelihood expansions (SLE) method. Our approach, called stochastic embedding (SSLE), uses recently presented (SSE) method for local expansion refinement approximate function at core problems. We show that, similar SLE, results in analytical e...