Adaptive multiple separation based on information maximization

Abstract

We are interested in the problem of separating primary and multiple seismic signals based on their statistical properties. We present a novel adaptive subtraction method based on an information maximization principle. Compared with previous methods, our proposed method has the theoretical advantages of utilizing higher-order statistics of the data and incorporating the filtering nature of the adaptive subtraction problem into our algorithm formulation. Furthermore, our proposed method provides a flexible framework for simultaneously subtracting more than one set of multiple predictions from the input data. We use simulations to show that our proposed adaptive subtraction method outperforms the popular least-square adaptive subtraction and the independent component analysis methods both quantitatively, as measured by the mean-squared error, and qualitatively, as evaluated by the visual quality of the image reconstruction.