seismic waves are non-stationary due to its propagation through the earth. time-frequency transformsare suitable tools for analyzing non-stationary seismic signals. spectral decomposition can reveal thenon-stationary characteristics which cannot be easily observed in the time or frequency representationalone. various types of spectral decomposition methods have been introduced by some researche...

a r t i c l e i n f o a b s t r a c t Keywords: Time–frequency analysis Windowed Fourier transform Wavelet transform Synchrosqueezing Time–frequency representations (TFRs) of signals, such as the windowed Fourier transform (WFT), wavelet transform (WT) and their synchrosqueezed versions (SWFT, SWT), provide powerful analysis tools. Here we present a thorough review of these TFRs, summarizing al...

Time–frequency analysis is central to signal processing, with standard adaptation to higher dimensions for imaging applications, and beyond. However, although the theory, methods, and algorithms for stationary signals are well developed, mathematical analysis of non-stationary signals is almost nonexistent. For a real-valued signal defined on the time–domain R, a classical approach to compute i...

Many signals from the physical world can be modeled accurately as a superposition of amplitude-and frequency-modulated waves. This includes audio signals (speech, music), medical data (ECG) aswell as temporal series (temperature or electric consumption). This thesis deals with the analysis of suchsignals, called multicomponent because they contain several modes. The techniques i...

A highly localized data-association measure, termed intrinsic synchrosqueezing transform (ISC), is proposed for the analysis of coupled nonlinear and non-stationary multivariate signals. This is achieved based on a combination of noise-assisted multivariate empirical mode decomposition and short-time Fourier transform-based univariate and multivariate synchrosqueezing transforms. It is shown th...