The Fourier transform remains one of the most popular spectral methods in time-series analysis, so much so that the word “spectrum” is virtually equivalent to “Fourier spectrum” (Huang et al., 2001). This method assumes that a time series extends from positive to negative infinity (stationarity) and consists of a linear superposition of sinusoids (linearity). However, geophysical signals are ne...

Spectral analysis is an important step in seismic data processing and interpretation. The frequency contents of seismic traces vary with time due to the fact that the earth is non-stationary medium. Methods were available to improve the temporal and spectral resolution, such as windowed Fourier transform, wavelet transform, S-transform and Matching Pursuit Decomposition, etc. This paper describ...

The Fourier transform remains one of the most popular spectral methods in time-series analysis, so much so that the word “spectrum” is virtually equivalent to “Fourier spectrum” (Huang et al., 2001). This method assumes that a time series extends from positive to negative infinity (stationarity) and consists of a linear superposition of sinusoids (linearity). However, geophysical signals are ne...

One of the main heritage tools used in scientific and engineering data spectrum analysis is the Fourier Integral Transform and its high performance digital equivalent the Fast Fourier Transform (FFT). Both carry strong a-priori assumptions about the source data, such as linearity, of being stationary, and of satisfjmg the Dirichlet conditions. A recent development at the National Aeronautics an...

Pipeline networks are one of the most important transportation for gas, oil and water. Leakage in pipelines results extensive financial loss. To avoid this situation, an algorithm based on Empirical Mode Decomposition method (EMD) Hilbert-Huang Transform (HHT) is presented research. The objectives research to detect leakage by using EMD locate location leak HHT method. focuses Galvanized Iron (...

The Hilbert–Huang transform (HHT) is an empirically based data-analysis method. Its basis of expansion is adaptive, so that it can produce physically meaningful representations of data from nonlinear and non-stationary processes. The advantage of being adaptive has a price: the difficulty of laying a firm theoretical foundation. This chapter is an introduction to the basic method, which is foll...

Transient evoked otoacoustic emissions (TEOAEs) have been analyzed for objective assessment of hearing function and monitoring of the influence of noise exposure and ototoxic drugs. This paper presents a novel application of the Hilbert–Huang transform (HHT) for detection and time-frequency mapping of TEOAEs. Since the HHT does not distinguish between signal and noise, it is combined with ensem...

The Hilbert-Huang transform (HHT) method, which is designed to analyze nonstationary and nonlinear time-dependent data, is attracting lots of attention. The HHT first applies the empirical mode decomposition (EMD) to decompose data into intrinsic mode functions (IMF). The Hilbert transform then is applied to the IMFs to reveal its instantaneous frequency spectrum. However, because the EMD lacks...

The Hilbert-Huang transform (HHT) is a novel, adaptive approach to time series analysis. It does not impose a basis set on the data or otherwise make assumptions about the data form, and so the time–frequency decomposition is not limited by spreading due to uncertainty. Because of the high resolution of the time–frequency, we investigate the possibility of the application of the HHT to the sear...