Bayesian joint AVO and RMO inversion

Residual moveout (RMO) can have a severe impact on the seismic amplitude with offset (AVO) analysis. It is therefore common practice to quality control and correct the processed seismic data for RMO before AVO analysis. However, a complicating factor is that AVO effect and tuning may result in up- or down-dipping events that are easily mistaken for events with RMO, e.g., AVO Class 2p response. Flattening these events will lead to wrong AVO estimation. We present a new Bayesian joint RMO and AVO inversion to estimate RMO time shifts and AVO intercept and gradient. The joint inversion corrects the seismic data based on RMO and AVO prior models, rather than explicitly assuming that the data should be flattened. The prior models will typically guide towards flat gathers, but will also allow for up and down dipping events when these are possible within the prior model. The method is illustrated on synthetic and real seismic data. For cases where flat events are correct, the results are similar to sequential methods with RMO correction before AVO analysis, but in situations where dipping seismic reflectors may be misinterpreted as events with RMO, the joint inversion provides better results by evaluating RMO and AVO simultaneously. The inversion results include the posterior covariance matrix which represents uncertainties for the AVO intercept and gradient, the RMO time shifts, and the correlations between these at all samples. The RMO time shift uncertainty varies within seismic gathers and depends on how clear and well determined the seismic events are. The uncertainty of the RMO is lower for clear and continuous events, but increases in zones with weaker and noisy events. The uncertainty of the RMO time shifts has a low impact on the uncertainty of the AVO intercept, but increases the uncertainty of the AVO gradient significantly.