Seismic Adaptive Multiple Subtraction Using a Structure-oriented Matched Filter

Abstract

Multiple removal is a crucial step in seismic data processing prior to velocity model building and imaging. After the prediction, adaptive multiple subtraction is employed to suppress multiples (considered noise) in seismic data, thereby highlighting primaries (considered signal). In practice, conventional adaptive subtraction methods fit the predicted and recorded multiples in the least-squares sense using a sliding window, formulating a localized adaptive matched filter. Subsequently, the filter is applied to the prediction to remove multiples from the recorded data. However, such a strategy runs the risk of over attenuating the useful primaries under the minimization energy constraint. To avoid damage to valuable signals, we propose a novel approach that replaces the conventional matched filter with a structure-oriented version. From the predicted multiples, we extract the structural information to be used in the derivation of the adaptive matched filter. The proposed structure-oriented matched filter emphasizes the structures of predicted multiples which helps to better preserve primaries during the subtraction. Synthetic and field data examples demonstrate the efficacy of the proposed structure-oriented adaptive subtraction approach, highlighting its superior performance in multiple removal and primary preservation compared to conventional methods on 2D regularly sampled data.