Depth-to-Time Conversion Errors in Bayesian Seismic Wavelet Estimation

We formulate a Bayesian model for assessing the depth-to-time conversion error in seismic wavelet estimation and use this in combination with a Bayesian wavelet estimator. <br>By using a stationary Gaussian stochastic process as a prior for the depth-to-time conversion error, we obtain the posterior distribution for the wavelet as a mixture of multinormal distributions. The mixing distribution is sampled using Markov chain Monte Carlo methods.<br><br>The method is tested on a dataset from offshore Norway. For the dataset we compare the estimated wavelet with a wavelet obtained from a standard method and a wavelet obtained from Bayesian method where the depth-to-time conversion error is neglected. For the case investigated the proposed method result in a wavelet with which is more focused in frequency domain and has larger peak amplitude than the alternatives.<br>