Seismic wavelet estimation

Based on the convolutional model, a seismic trace is the convolution of seismic source wavelet and reflection coefficient series of the earth. Seismic source wavelet estimation is one of the most important stages in processing and interpretation of seismic data. Accurate estimation of wavelet increases the efficiency of the deconvolution and temporal resolution of seismic data. On the other hand, the most important stage of seismic data interpretation is the inversion of seismic data to seismic impedance. The quality of inversion depends on the correlation of synthetic and real seismic traces in the well position. With increased accuracy in estimating source wavelet, the correlation increases. Different methods have been introduced for estimating seismic source wavelet, such as homomorphic deconvolution, least squares method, autoregressive method and Hopfield neural network method. In this paper, we used frequency behavior of reflection coefficient series and seismic source wavelet, and then attenuated the effect of reflection coefficient series of the earth from seismic trace and estimated the seismic source wavelet. The amplitude spectrum of reflection coefficients series behaves as a signal with high frequency content, whereas the amplitude spectrum of seismic source wavelet behaves as a signal with low frequency