Improving multiple removal using least-squares dip filters and independent component analysis

Abstract

The suppression of multiple events is a crucial task in seismic data processing, and the adaptive subtraction of the predicted multiples is recognized as one of the main challenges for the success of the surface-related multiple elimination technique. The traditional least-squares matching approach can affect the primary events because the estimated multiples tend to adapt to the primaries under the minimum energy condition. We investigate two filtering techniques for improving the multiple removal results. In the first proposed method, we combine the advantages of the least-squares and pattern dip-based subtraction methods. Doing so, we exploit the separation of primaries and multiples in the dip domain, and then we apply the least-squares adaptive subtraction in each dip band before recomposing the data to obtain the final subtraction result. As a result of the dip decomposition, the primary-multiple interferences are reduced, allowing for a more reliable least-squares filtering. In the second method, we propose to replace the multiple subtraction step by a separation step using independent component analysis (ICA) methods. We employ the ICA method after least-squares adaptive filtering. Because of the non-Gaussian distributions of the involved signals, primaries and multiples can be separated by computing the optimal rotation between these two signals. We apply the ICA method in local 2D time-space windows to better compensate the space and time variant character of the data. Two-dimensional synthetic and field data examples demonstrate that the multiple subtraction results of both methods are indeed improved with respect to the classical least-squares method.