The improvement and application of preconditional conjugate gradient deconvolutions
Youming Li · Progress in geophysics · 2006
The preconditional conjugate gradient deconvolution is a blind deconvolution which is combined with the precondition conjugate gradient(PCG) based on Krylov subspace to decrease the computation,improves the speed,and the transition matrix is not necessary anymore be positive and symmetric.It is used to improve the vertical resolution of seismic data by compressing the length of wavelet,and estimate reflectivity series.But there are still some problems,so the improvement is raised as following: First,when amplitudes of raw data are the first several maximum,let amplitudes of high-frequency data equalize those of raw data.Second,when amplitude spectra of raw data are larger than a specific number,let amplitude spectra of high-frequency data equalize those of raw data.From the application of a model and real data,we can see that the result of this method is good.