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Spectral sparse Bayesian learning reflectivity inversion
Author(s) -
Yuan Sanyi,
Wang Shangxu
Publication year - 2013
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/1365-2478.12000
Subject(s) - reflectivity , inversion (geology) , bayesian probability , geology , series (stratigraphy) , computer science , bayesian inference , inverse problem , synthetic data , remote sensing , sparse matrix , algorithm , pattern recognition (psychology) , artificial intelligence , optics , mathematics , physics , seismology , paleontology , mathematical analysis , tectonics , quantum mechanics , gaussian
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.