Abstract：Elastic parameters,which reflect reservoir features,can be extracted from inverted elastic impedance with elastic impedance decomposition.However conventional decomposition methods based on the least squares principle are implemented by inversing the elastic impedance decomposition matrix.It causes unstable results in the presence of noise due to large number of matrix conditions.We propose a stable elastic impedance inversion approach in this paper.According to the Bayesian theory,the regularization term for the elastic impedance decomposition is added by introducing the prior distribution of the elastic parameters,which can stabilize the process of the elastic impedance decomposition.Since elastic parameter extract process is band-limited and these parameters are not independent,a multi-variable Gauss distribution is used to describe statistical feature of the nature logarithm of the elastic parameters.At last,a flow of elastic impedance inversion is created by combining with a poststack inversion based on low frequency soft constraint.Model and real data tests reveal that this flow can own the great stability and the high accuracy.
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