Multichannel Statistical Broadband Wavelet Deconvolution for Improving Resolution of Seismic Signals

Many popular deconvolution methods based on Robinson's convolutional model have played an important role in improving the temporal resolution of seismic data. However, the outcomes of applying these deconvolution methods to real land seismic data are not always desirable due to the effect of noise in the deconvolution process. Although the noise in the seismogram can be minimized during the recording process, the effect of residual noise on deconvolution operators can result in poor deconvolution output. To address the shortcomings of conventional deconvolution methods, we developed a new deconvolution method based on a multichannel statistical principle. In the proposed method, we have extended the surface-consistent convolutional model to include a noise component, thus including the noise effect on deconvolution operators in the deconvolution process. According to the proposed multichannel statistical strategy, we first calculated the autocorrelation of the seismogram, in which the lateral variation effect on the wavelet is considered because of inhomogeneities in the vicinity of sources and receivers. Then, we adopted a local fitting technique to approximate the autocorrelation of the seismic wavelet. To obtain the seismic data with a broad bandwidth and low-noise level, we used the integral-Ricker wavelet as the desired output wavelet. Tests on synthetic data and real land seismic data demonstrate the effectiveness of the proposed method in increasing the resolution of seismic signals.