Abstract: The thick bed hydrocarbon reservoirs are primarily interested because of the more prolific production. When the amount of petroleum in the thick bed starts decreasing, the thin bed reservoirs are the alternative targets to maintain the reserves. The conventional interpretation of seismic data cannot delineate the thin bed having thickness less than the vertical seismic resolution. Therefore, spectral decomposition and instantaneous seismic attributes were used to delineate the thin bed in this study. Short Window Discrete Fourier Transform (SWDFT) spectral decomposition and instantaneous frequency attributes were used to reveal the thin bed reservoir, while Continuous Wavelet Transform (CWT) spectral decomposition and envelope (instantaneous amplitude) attributes were used to indicate hydrocarbon bearing zone. The study area is located in the Pohokura Field, Taranaki Basin, New Zealand. The thin bed target is the uppermost part of Mangahewa Formation, the most productive in the gas-condensate production in the Pohokura Field. According to the time-frequency analysis, SWDFT spectral decomposition can reveal the thin bed using a 72 Hz SWDFT isofrequency section and map, and that is confirmed by the instantaneous frequency attribute. The envelope attribute showing the high anomaly indicates the hydrocarbon accumulation area at the thin bed target. Moreover, the CWT spectral decomposition shows the low-frequency shadow zone and abnormal seismic attenuation in the higher isofrequencies below the thin bed confirms that the thin bed can be a prospective hydrocarbon zone.
Abstract: This paper introduces a new instantaneous frequency
computation approach -Counting Instantaneous Frequency for a
general class of signals called simple waves. The classsimple wave
contains a wide range of continuous signals for which the concept
instantaneous frequency has a perfect physical sense. The concept of
-Counting Instantaneous Frequency also applies to all the discrete data.
For all the simple wave signals and the discrete data, -Counting
instantaneous frequency can be computed directly without signal
decomposition process. The intrinsic mode functions obtained through
empirical mode decomposition belongs to simple wave. So
-Counting instantaneous frequency can be used together with
empirical mode decomposition.
Abstract: In this paper, a new adaptive Fourier decomposition
(AFD) based time-frequency speech analysis approach is proposed.
Given the fact that the fundamental frequency of speech signals often
undergo fluctuation, the classical short-time Fourier transform (STFT)
based spectrogram analysis suffers from the difficulty of window size
selection. AFD is a newly developed signal decomposition theory. It is
designed to deal with time-varying non-stationary signals. Its
outstanding characteristic is to provide instantaneous frequency for
each decomposed component, so the time-frequency analysis becomes
easier. Experiments are conducted based on the sample sentence in
TIMIT Acoustic-Phonetic Continuous Speech Corpus. The results
show that the AFD based time-frequency distribution outperforms the
STFT based one.
Abstract: There have been different approaches to compute the
analytic instantaneous frequency with a variety of background reasoning
and applicability in practice, as well as restrictions. This paper presents an adaptive Fourier decomposition and (α-counting) based
instantaneous frequency computation approach. The adaptive Fourier
decomposition is a recently proposed new signal decomposition
approach. The instantaneous frequency can be computed through the so called mono-components decomposed by it. Due to the fast energy
convergency, the highest frequency of the signal will be discarded by the adaptive Fourier decomposition, which represents the noise of
the signal in most of the situation. A new instantaneous frequency
definition for a large class of so-called simple waves is also proposed
in this paper. Simple wave contains a wide range of signals for which
the concept instantaneous frequency has a perfect physical sense.
The α-counting instantaneous frequency can be used to compute the highest frequency for a signal. Combination of these two approaches one can obtain the IFs of the whole signal. An experiment is demonstrated the computation procedure with promising results.
Abstract: In this paper, a new approach for target recognition based on the Empirical mode decomposition (EMD) algorithm of Huang etal. [11] and the energy tracking operator of Teager [13]-[14] is introduced. The conjunction of these two methods is called Teager-Huang analysis. This approach is well suited for nonstationary signals analysis. The impulse response (IR) of target is first band pass filtered into subsignals (components) called Intrinsic mode functions (IMFs) with well defined Instantaneous frequency (IF) and Instantaneous amplitude (IA). Each IMF is a zero-mean AM-FM component. In second step, the energy of each IMF is tracked using the Teager energy operator (TEO). IF and IA, useful to describe the time-varying characteristics of the signal, are estimated using the Energy separation algorithm (ESA) algorithm of Maragos et al .[16]-[17]. In third step, a set of features such as skewness and kurtosis are extracted from the IF, IA and IMF energy functions. The Teager-Huang analysis is tested on set of synthetic IRs of Sonar targets with different physical characteristics (density, velocity, shape,? ). PCA is first applied to features to discriminate between manufactured and natural targets. The manufactured patterns are classified into spheres and cylinders. One hundred percent of correct recognition is achieved with twenty three echoes where sixteen IRs, used for training, are free noise and seven IRs, used for testing phase, are corrupted with white Gaussian noise.
Abstract: The empirical mode decomposition (EMD) represents any time series into a finite set of basis functions. The bases are termed as intrinsic mode functions (IMFs) which are mutually orthogonal containing minimum amount of cross-information. The EMD successively extracts the IMFs with the highest local frequencies in a recursive way, which yields effectively a set low-pass filters based entirely on the properties exhibited by the data. In this paper, EMD is applied to explore the properties of the multi-year air temperature and to observe its effects on climate change under global warming. This method decomposes the original time-series into intrinsic time scale. It is capable of analyzing nonlinear, non-stationary climatic time series that cause problems to many linear statistical methods and their users. The analysis results show that the mode of EMD presents seasonal variability. The most of the IMFs have normal distribution and the energy density distribution of the IMFs satisfies Chi-square distribution. The IMFs are more effective in isolating physical processes of various time-scales and also statistically significant. The analysis results also show that the EMD method provides a good job to find many characteristics on inter annual climate. The results suggest that climate fluctuations of every single element such as temperature are the results of variations in the global atmospheric circulation.
Abstract: In this paper, we propose a novel time-frequency distribution (TFD) for the analysis of multi-component signals. In particular, we use synthetic as well as real-life speech signals to prove the superiority of the proposed TFD in comparison to some existing ones. In the comparison, we consider the cross-terms suppression and the high energy concentration of the signal around its instantaneous frequency (IF).