Spatial Resolution in Bayesian Inversion of Gravimetric Data

CO_2 capture and storage of CO_2 in geological structures are important parts in reaching the targets of the International Energy Agency on cutting CO_2 emissions related to energy. Quantitative description of the development of an underground gas plume is important both to ensure public safety and to gain general support for underground storage. Methods for a quantitative prediction of the distributions of the subsurface CO_2 are developed, using statistical models as a key tool for integrating data within physical models. In this presentation Bayesian inversion of gravimetric data is described and through an example with synthetic data we illustrate the power of the method. We quantify what we learn from the data in terms of best estimate and what remains undetermined by providing the uncertainty. This knowledge is crucial for integrating gravimetric data in a joint inversion with seismic data. The results show that the gravimetric data carry information about low-frequent components in the density change and that
gravimetric inversion alone is not sufficient for inferring local details. However, the data can verify properties on a larger scale, such as average density change.