The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications

By Gaurav Singh Thakur, Eugene Brevdo, Neven S. Fuckar, Hau‐Tieng Wu. Signal Processing, 2011

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We analyze the stability properties of the Synchrosqueezing transform, a time-frequency signal analysis method that can identify and extract oscillatory components with time-varying frequency and amplitude. We show that Synchrosqueezing is robust to bounded perturbations of the signal and to Gaussian white noise. These results justify its applicability to noisy or nonuniformly sampled data that is ubiquitous in engineering and the natural sciences. We also describe a practical implementation of Synchrosqueezing and provide guidance on tuning its main parameters. As a case study in the geosciences, we examine characteristics of a key paleoclimate change in the last 2.5 million years, where Synchrosqueezing provides significantly improved insights. Highlights? We study the stability of the Synchrosqueezing transform for spectral analysis. ? Synchrosqueezing is shown to be robust to bounded errors and Gaussian white noise. ? We describe a numerical implementation and compare it with other techniques. ? We apply Synchrosqueezing to proxies describing the evolution of the global climate.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2666e344b0a2b2aed0495f2c0adcd4056e9815c0

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Citations in the corpus

Time-Frequency Reassignment and Synchrosqueezing: An Overview (2013, background): “Another simple yet efficient approach related to the resolution of (12) was developed in [24] and consists of a lo-”
New method for modal identification of super high‐rise building structures using discretized synchrosqueezed wavelet and Hilbert transforms (2017, methodology): “The discretized form of Tf(ω, b), e T~f , is computed for a fixed k using the algorithm proposed by Thakur et al.([29])”
Applications of the synchrosqueezing transform in seismic time-frequency analysis (2014, methodology): “Both approaches aim to decompose seismic data into a sum of oscillatory signals with smoothly varying frequencies and smoothly varying amplitudes (Fomel, 2013; Thakur et al., 2013), which is the principle of the decomposition using the CWT (Daubechies et al.”