Seismic reservoir characterization requires a transform of seismically derived properties such as P- and S-wave velocities, acoustic impedances, elastic impedances, or other seismic attributes into parameters describing lithology and reservoir conditions. A large number of different rock physics models have been developed to obtain this link. Their relevance is, however, constrained by the type of lithology, porosity range, textural complexity, saturation conditions, and the dynamics of the pore fluid. Because the number of rock physics parameters is often higher than the number of seismic parameters, this is known to be an underdetermined problem with nonunique solutions. We have studied the framework of inverse rock physics modeling which aims at direct quantitative prediction of lithology and reservoir quality from seismic parameters, but where nonuniqueness and data error propagation are also handled. The procedure is based on a numerical reformulation of rock physics models so that the seismic parameters are input and the reservoir quality data are output. The modeling procedure can be used to evaluate the validity of various rock physics models for a given data set. Furthermore, it provides the most robust data parameter combinations to use for either porosity, lithology, and pore fluid prediction, whenever a specific rock physics model has been selected for this cause.