Stochastic Seismic AVO Inversion

One of the main challenge problems in geophysics is getting reliable seismic inverse models while the uncertainty is assessed. Seismic inverse problems may be tackled in a probabilistic framework resulting in a set of equiprobable acoustic and elastic impedance models. Here we show a new geostatistical seismic AVO method from where density, Vp and Vs models are retrieved. With the resulting Earth models we also compute the correspondent synthetic pre-stack data and the zero-reflectivity R(0) and Gradient (G) models. We successfully applied this workflow to a 3D synthetic seismic dataset from where density, Vp and Vs models were known. The final best models achieved a global correlation between the original and the synthetic seismograms of about 0.80.