Conventional deconvolution methods based on Robinson’s convolutional model have been playing an important role in improving the temporal resolution of seismic data for years. However, the application of these methods to real data is not always desirable due to some assumptions on the seismic wavelet and the seismogram, especially the noise-free assumption. In order to address the shortcomings of conventional deconvolution methods, the noise-free assumptio, we develop a multi-trace statistical broadband wavelet deconvolution based on the surface-consistent deconvolution method. In our proposed method, we maintained the standard assumptions that the source wavelet is minimum phase and the reflectivity is statistically white. However, we extended the Robinson’s convolutional model to include the noise component and use a Ricker-like wavelet which is the integral of the Ricker wavelet to be the desired output wavelet. Synthetic and real data examples are provided to show the effectiveness of the proposed deconvolution method.


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