Frequency domain full waveform elastic inversion of marine seismic data from the Alba field using a Bayesian trans-dimensional algorithm

We present an algorithm to recover the Bayesian posterior model probability density function of subsurface elastic parameters, as required by the full pressure field recorded at an ocean bottom cable due to an impulsive seismic source. Both the data noise and source wavelet are estimated by our algorithm, resulting in robust estimates of subsurface velocity and density. In contrast to purely gradient based approaches, our method avoids model regularization entirely and produces an ensemble of models that can be visualized and queried to provide meaningful information about the sensitivity of the data to the subsurface, and the level of resolution of model parameters. Our algorithm is trans-dimensional and performsmodel selection, sampling over a wide range of model parametrizations. We follow a frequency domain approach and derive the corresponding likelihood in the frequency domain. We present first a synthetic example of a reservoir at 2 km depth with minimal acoustic impedance contrast, which is difficult to study with conventional seismic amplitude versus offset changes. Finally, we apply our methodology to survey data collected over the Alba field in the North Sea, an area which is known to show very little lateral heterogeneity but nevertheless presents challenges for conventional post migration seismic amplitude versus offset analysis.