Modeling data error during deconvolution

Modeling data error during deconvolution

Jon Claerbout and Antoine Guitton

Abstract:

Our current decons take the data sacrosanct and find the best noncausal wavelet to deconvolve it with. We propose allowing the data to include an explicit noise that does not fit the convolutional model. We write regressions to define this noise, and develop an expression for the gradient needed to fit the regressions.