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Time Frequency Analysis Applications in Geophysics
(CRC Press, 2002-01-15)
In this chapter, we overview a number of applications of time-frequency representations in seismic data processing, from the analysis of seismic sequences to efficient attribute extraction to 3-D attributes for volumetric data.
Multiple Window Time Varying Spectrum Estimation
We overview a new non-parametric method for estimating the time-varying spectrum of a non-stationary random process. Our method extends Thomson's powerful multiple window spectrum estimation scheme to the time-frequency ...
Nonlinear Wigner-Ville Spectrum Estimation using Wavelet Soft Thresholding
The large variance of the Wigner-Ville distribution makes smoothing essential for producing readable estimates of the time-varying power spectrum of noise corrupted signals. Since linear smoothing trades reduced variance ...
A Simple Scheme for Adapting Time-Frequency Representations
Signal-dependent time-frequency representations, by adapting their functional form to fit the signal being analyzed, offer many performance advantages over conventional representations. In this paper, we propose a simple, ...
Optimal Phase Kernels for Time-Frequency Analysis
We consider the design of kernels for time-frequency distributions through the phase, rather than amplitude, response. While phase kernels do not attenuate troublesome cross-components, they can translate them in the ...
A Canonical Covariance Based Method for Generalized Joint Signal Representations
Generalized joint signal representations extend the scope of joint time-frequency representations to a richer class of nonstationary signals. Cohen's marginal-based generalized approach is canonical from a distributional ...
A Simple Covariance Based Characterization of Joint Signal Representations of Arbitrary Variables
Joint signal representations of arbitrary variables extend the scope of joint time-frequency representations, and provide a useful description for a wide variety of nonstationary signal characteristics. Cohen's marginal-based ...
Signal-Dependent Time-Frequency Analysis using a Radially Gaussian Kernel
Time-frequency distributions are two-dimensional functions that indicate the time-varying frequency content of one-dimensional signals. Each bilinear time-frequency distribution corresponds to a kernel function that ...
An Adaptive Optimal-Kernel Time-Frequency Representation
Time-frequency representations with fixed windows or kernels figure prominently in many applications, but perform well only for limited classes of signals. Representations with signal- dependent kernels can overcome this ...
Shear Madness: New Orthonormal Bases and Frames Using Chirp Functions
The proportional-bandwidth and constant-bandwidth time-frequency signal decompositions of the wavelet, Gabor, and Wilson orthonormal bases have attracted substantial interest for representing nonstationary signals. However, ...