Abstract
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.