Antileakage least-squares spectral analysis for seismic data regularization and random noise attenuation

By Ebrahim Ghaderpour, Wenyuan Liao, Michael P. Lamoureux. 2018

46 citations recorded by the source corpus.

Spatial transformation of irregularly sampled data series to regularly sampled data series is a challenging problem in many areas such as seismology. The classical Fourier analysis is limited to regularly sampled data series. On the other hand, the least-squares spectral analysis (LSSA) can analyze irregularly sampled data series. Although the LSSA method takes into account the correlation among the sinusoidal basis functions of irregularly spaced series, it still suffers from the problem of spectral leakage: energy leaks from one spectral peak into another. We propose an iterative method called antileakage LSSA to attenuate the spectral leakage and consequently regularize irregular data series. In this method, we first search for a spectral peak with the highest energy, and then we remove (suppress) it from the original data series. In the next step, we search for a new peak with the highest energy in the residual data series and remove the new and the old components simultaneously from the original data...

Subfields: Computer Vision and Pattern Recognition, Geophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/97e8535d7d0408681fc05e46d5326f1ab9cf6050