Adaptive Kalman filtering deconvolution via dyadic wavelet transform

Abstract

A new approach of adaptive Kalman filtering deconvolution (AKFD) is developed basing on dyadic wavelet transform. The technique discards the assumption of stationarity for signals in predictive deconvolution, and overcomes improving resolution at the price of decreasing signal-to-noise (SNR) obviously. The technique can well compress the reflection waveforms, but the noises are not variable in substance. So it has a better ability of resistance noise. Suppression false reflections in dyadic wavelet transform domain is better than by applying AKFD in time domain. In addition the technique also has the characteristic of adaptive Kalman filter in every band for a signal respectively, it enhances the adaptation of Kalman filtering, so the resolution is obvious higher than that one in time domain. A great deal of numerical models and real seismic data indicate that the technique has obvious effect. At the same time, the technique also overcomes the drawback of increasing the low- frequency component of AKFD in time domain. A great deal of numerical models and real seismic data indicate that the technique has obvious effect. The approach not only suits for seismic data, but also can be used for reference to another similar signal processing.