Spectral sparse Bayesian learning reflectivity inversion
Yuan, Sanyi; Wang, Shangxu*
A spectral sparse Bayesian learning reflectivity inversion method, combining spectral reflectivity inversion with sparse Bayesian learning, is presented in this paper. The method retrieves a sparse reflectivity series by sequentially adding, deleting or re-estimating hyper-parameters, without pre-setting the number of non-zero reflectivity spikes. The spikes with the largest amplitude are usually the first to be resolved. The method is tested on a series of data sets, including synthetic data, physical modelling data and field data sets. The results show that the method can identify thin beds below tuning thickness and highlight stratigraphic boundaries. Moreover, the reflectivity series, which is inverted trace-by-trace, preserves the lateral continuity of layers.