احمدرضا امینی - موسسه ژئوفیزیک دانشگاه تهران
محمدعلی ریاحی - موسسه ژئوفیزیک دانشگاه تهران

A Bayesian inference for wavelet estimation from seismic and well data is studied. The forward model is based on the convolution model where the reflectivity is calculated from the well logs. The estimated wavelet is given as a probability density function such that uncertainty in the wavelet is considered in the problem. Seismic noise and possible mistie between the seismic and well time axis are included in the model. The wavelet estimation is obtained by Markov Chain Monte Carlo (MCMC) simulation and the gibbs sampler as one of its algorithms.