f Integration of Information from Diverce Sources

From a probabilistic point-of-view solving inverse problems can be seen as a way of combining states of information in form of probability density functions. Typically, the states of information are provided by a set of observed data and some a priori information obtained independently of the data. The solution to the inverse problem is then the combined state of information quantified by the a posteriori probability density function. Within this probabilistic framework we will discuss methods for combining information from diverse sources. Specifically we will discuss methods for combining information from pre-stack seismic waveform data, a priori geological structural information and information about the relation between rock physics parameters (such as permeability, and oil saturation). One approach is to solve such an inverse problem sequentially: Initially an elastic inversion of the seismic data is performed followed by a transformation of elastic properties to rock physics parameters. Another approach is to directly solve the inverse problem parametrised with rock physics model parameters. We will discuss the benefits and challenges combining these sources of information sequentially and directly using the probabilistic formulation of inverse problems.