Accurate identification and extraction of harmonic signals typically contained in geophysical/geodetic observables have been pursued since the advent of digital signal processing technology. The harmonic analytic techniques are mainly categorized as signal recognition and restoration process. For the former, various numerical methodologies have been devised, many of which have roots in the conventional Fourier harmonic analysis (such as the power spectral analysis) (Slepian & Pollak, 1961) and others are non-Fourier-based spectral methods (such as the maximum-entropy spectrum) (e.g., Carlin & Louis, 2008;Thomson, 1982). Although these approaches have many useful applications, the analysis of quasi-periodic signals (here, we mean signals with time-varying complex amplitude/frequency) is not well presented by these methods. For instance, those spectral analysis methods are not sensitive enough for identifying harmonics in damped cosines\sines, and poor spectral resolution restricted the hybrid signals detected. The AR-z spectrum proposed by Chao (2015, 2018) is a useful signal recognition method (extended from the autoregressive (AR) method of Chao and Gilbert (1980)),