Multichannel antileakage least-squares spectral analysis for seismic data regularization beyond aliasing

Abstract

The antileakage least-squares spectral analysis is a new method of regularizing irregularly spaced data series. This method mitigates the spectral leakages in the least-squares spectrum caused by non-orthogonality of the sinusoidal basis functions on irregularly spaced series, and it is robust when data series are wide-sense stationary. An appropriate windowing technique can be applied to adapt this method to non-stationary data series. When data series present mild aliasing, this method can effectively regularize the data series; however, additional information or assumption is needed when the data series is coarsely sampled. In this paper, we show how to incorporate the spatial gradients of the data series into the method to regularize data series presenting severe aliasing and show its robust performance on synthetic and marine seismic data examples.