Adaptive multiple subtraction based on multi-traces convolutional signal blind separation
Li Zhong · Chinese Journal of Geophysics · 2012
This paper represents the adaptive multiple subtraction problem as a blind signal separation problem using multi-traces convolutional signal blind separation model.By expressing the difference between the predicted and true multiples using a 2D convolutional kernel,we propose an adaptive multiple subtraction method based on the multi-traces convolutional signal blind separation technique,which adopts maximization of the non-Gaussianity of the recovered primaries as the objective function.To solve the above non-linear optimization problem,we transfer it to an iterative linear one,which is realized by the iterative least squares algorithm.Taking advantage of the multi-traces convolutional signal blind separation model,the proposed method is applicable to the situation that there are differences in the time-space domain between the predicted and true multiples.Through the processing of the simple model data,the Pluto data and the real seismic data,the validity of the proposed method is demonstrated.