Novel Statistical Approach to Blind Recovery of Earth Signal and Source Wavelet using Independent Component Analysis
نویسنده
چکیده
Abstract: This paper provides a new statistical approach to blind recovery of both earth signal and source wavelet given only the seismic traces using independent component analysis (ICA) by explicitly exploiting the sparsity of both the reflectivity sequence and the mixing matrix. Our proposed blind seismic deconvolution algorithm consists of three steps. Firstly, a transformation method that maps the seismic trace convolution model into multiple inputs multiple output (MIMO) instantaneous ICA model using zero padding matrices has been proposed. As a result the nonzero elements of the sparse mixing matrix contain the source wavelet. Secondly, whitening the observed seismic trace by incorporating the zero padding matrixes is conducted as a pre-processing step to exploit the sparsity of the mixing matrix. Finally, a novel logistic function that matches the sparsity of reflectivity sequence distribution has been proposed and fitted into the information maximization algorithm to obtain the demixing matrix. Experimental simulations have been accomplished to verify the proposed algorithm performance over conventional ICA algorithms such as Fast ICA and JADE algorithm. The mean square error (MSE) of estimated wavelet and estimated reflectivity sequence shows the improvement of proposed algorithm
منابع مشابه
Novel statistical approach to Blind Recovery of Earth Signals and Source Wavelet
Seismic signals are a convolution of a source wavelet and the response at each earth layer boundary. Despite attempts to record the outgoing source wavelet at the surface, often little is known of the source wavelet at depth, and there is no a priori knowledge of the earth response or the noise. Current practice makes the basic assumptions that the source wavelet is known, and the noise can be ...
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