Comparisons of five least-squares adaptive matched filtering methods in multiple suppression

Abstract

Abstract In this paper, we comprehensively compare the application effects of five least-squares adaptive matched filtering methods (the single-channel, the multichannel, the equipoise multichannel, the pseudo multichannel and the equipoise pseudo multichannel) for multiple suppression in three representative datasets with different degrees of orthogonality. By introducing an error function, we can quantitatively analyse the influence of the five methods for multiple suppression in terms of the filter length, the normalized regularization factor, the number of matched channels, the iteration number, the amplitude ratio and noise immunity. In addition, we provide the corresponding optimal parameters or their selection principles. The comparison results show that: (i) the dependence on orthogonality is not the same for these five methods; only the equipoise multichannel and the equipoise pseudo multichannel methods can effectively reduce the dependence on orthogonality; (ii) the single-channel method is relatively balanced in all aspects; (iii) the pseudo multichannel and the equipoise pseudo multichannel methods have a stronger shaping ability but generate larger errors; (iv) the multichannel method requires a higher degree of orthogonality and (v) the optimal parameters derived from the three datasets will be better reference values for complex models or field data for the multiple suppression.