Stage level, volume and time-frequency information content of Lake Tana using stochastic and wavelet analysis methods

2012 ◽  
Vol 27 (10) ◽  
pp. 1475-1483 ◽  
Author(s):  
Y. Chebud ◽  
A. Melesse
Keyword(s):  
Wavelet Analysis ◽  
Information Content ◽  
Lake Tana ◽  
Time Frequency ◽  
Frequency Information ◽  
Analysis Methods

scholarly journals Stage level, volume, and time-frequency information content of Lake Tana using stochastic and wavelet analysis methods

2010 ◽  
Vol 7 (4) ◽  
pp. 5525-5546
Author(s):  
Y. Chebud ◽  
A. Melesse
Keyword(s):  
Monte Carlo ◽  
Wavelet Analysis ◽  
Information Content ◽  
Lake Level ◽  
Stochastic Simulations ◽  
Stochastic Methods ◽  
Multiple Time Scales ◽  
Lake Tana ◽  
Time Frequency ◽  
Analysis Methods

Abstract. Lake Tana is the largest fresh water body situated in the north western highlands of Ethiopia. It serves for local transport, electric power generation, fishing, ecological restoration, recreational purposes, and dry season irrigation supply. Evidence show, the lake has dried at least once at about 15 000–17 000 BP (before present) due to a combination of high evaporation and low precipitation events. Past attempts to observe historical fluctuation of Lake Tana based on simplistic water balance approach of inflow, out-flow and storage have failed to capture well known events of drawdown and rise of the lake that have happened in the last 44 years. This study is aimed at simulating the lake level, specifically extreme events of the lake variation using stochastic approaches. Fourty-four years of daily, monthly and mean annual lake level data has showed a Gaussian variation with goodness of fit at 0.01 significant levels of the Konglomorov-Simrnov test. Three stochastic methods were employed, namely perturbations approach, Monte-Carlo methods and wavelet analysis, and the results were compared with the stage level measurements. The stochastic simulations predicted the lake stage level of the 1972, 1984 and 2002/2003 historical droughts 99% of the time. The information content (frequency) of fluctuation of Lake Tana for various periods was resolved using Wigner's Time-Frequency Decomposition method. The wavelet analysis agreed with the perturbations and Monte Carlo simulations resolving the time (1970s, 1980s and 2000s) in which low frequency and high spectral power fluctuation has occurred. In summary, the Monte-Carlo and perturbations methods have shown their superiority for risk analysis over deterministic methods while wavelet analysis has met reconstructing stage level historical record at multiple time scales. A further study is recommended on dynamic forecasting of the Lake Tana stage level using a combined approach of the perturbation and wavelet analysis methods.

scholarly journals Measuring time-frequency information content using the Renyi entropies

2001 ◽  
Vol 47 (4) ◽  
pp. 1391-1409 ◽  
Author(s):  
R.G. Baraniuk ◽  
P. Flandrin ◽  
A.J.E.M. Janssen ◽  
O.J.J. Michel
Keyword(s):  
Information Content ◽  
Measuring Time ◽  
Time Frequency ◽  
Frequency Information ◽  
Rényi Entropies ◽  
Renyi Entropies

scholarly journals Wavelet analysis of molecular dynamics: Efficient extraction of time-frequency information in ultrafast optical processes

2013 ◽  
Vol 139 (22) ◽  
pp. 224103 ◽  
Author(s):  
Javier Prior ◽  
Enrique Castro ◽  
Alex W. Chin ◽  
Javier Almeida ◽  
Susana F. Huelga ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Wavelet Analysis ◽  
Time Frequency ◽  
Frequency Information ◽  
Ultrafast Optical ◽  
Efficient Extraction

Identifying Reservoir Fluids by Wavelet Transform of Well Logs

2006 ◽  
Vol 9 (05) ◽  
pp. 574-581 ◽  
Author(s):  
Wenzheng Yue ◽  
Guo Tao ◽  
Zhengwu Liu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Data Interpretation ◽  
Well Logging ◽  
Oil Field ◽  
New Method ◽  
Well Logs ◽  
Time Frequency ◽  
Frequency Information ◽  
Reservoir Fluid

Summary The wavelet-transform (WT) method has been applied to logs to extract reservoir-fluid information. In addition to the time (depth)/frequency analysis generally performed by the wavelet method, we also have performed energy spectral analysis for time/frequency-domain signals by the WT method. We have further developed a new method to identify reservoir fluid by setting up a correlation between the energy spectra and reservoir fluid. We have processed 42 models from an oil field in China using this method and have subsequently applied these rules to interpret reservoir layers. It is found that identifications by use of this method are in very good agreement with the results of well tests. Introduction An important log-analysis application is determining reservoir-fluid properties. It is common practice to calculate the water and oil saturations of reservoir formations by use of electrical logs. With the development of well-logging technology, a number of methods have been developed for reservoir-fluid typing with well logs (Hou 2002; Geng et al. 1983; Dahlberg and Ference 1984). A recent report has also described reservoir-fluid typing by the T2 differential spectrum from nuclear-magnetic-resonance (NMR) logs (Coates et al. 2001). However, because of the interference from vugs, fractures, clay content, and mud-filtrate invasion, the reservoir-fluid information contained in well logs is often concealed. The reliability of these log interpretations is thus limited in many cases. Therefore, it is desirable to find a more reliable and consistent way of reservoir-fluid typing with well logs. In this paper, we present a new method using the WT for fluid typing with well logs. The WT technique was developed with the localization idea from Gabor's short-time Fourier analysis and has been expanded further. Wavelets provide the ability to perform local analysis (i.e., analyze a small portion of a larger signal) (Daubechies 1992).This localized analysis represents the next logical step: a windowing technique with variable-sized regions. Wavelet analysis allows the use of long time intervals, where more-precise low-frequency information is wanted, and shorter intervals, where high-frequency information is needed. Wavelet analysis is capable of revealing aspects of data that other signal-analysis techniques miss: aspects such as trends, breakdown points, discontinuities in higher derivatives, and self-similarity. In well-logging-data processing, wavelet analysis has been used to identify formation boundaries, estimate reservoir parameters, and increase vertical resolution (Lu and Horne 2000; Panda et al. 1996; Jiao et al. 1999; Barchiesi and Gharbi 1999). For data interpretation, however, the identification of hydrocarbon-bearing zones by wavelet analysis is still under investigation. In this study, we have developed a technique of wavelet-energy-spectrum analysis (WESA) to identify reservoir-fluid types. We have applied this technique to field-data interpretation and have achieved very good results.

Wavelet analysis based on time–frequency information discriminate chronic ankle instability

2010 ◽  
Vol 25 (3) ◽  
pp. 256-264 ◽  
Author(s):  
A. Martínez-Ramírez ◽  
P. Lecumberri ◽  
M. Gómez ◽  
M. Izquierdo
Keyword(s):  
Wavelet Analysis ◽  
Ankle Instability ◽  
Chronic Ankle Instability ◽  
Time Frequency ◽  
Frequency Information

LFM Signal Detection Method Based on Fractional Fourier Transform

2014 ◽  
Vol 989-994 ◽  
pp. 4001-4004 ◽  
Author(s):  
Yan Jun Wu ◽  
Gang Fu ◽  
Yu Ming Zhu
Keyword(s):  
Fourier Transform ◽  
Signal Detection ◽  
Frequency Analysis ◽  
Experimental Analysis ◽  
Fractional Fourier Transform ◽  
Detection Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Frequency Information ◽  
Strong Impulse

As a generalization of Fourier transform, the fractional Fourier Transform (FRFT) contains simultaneity the time-frequency information of the signal, and it is considered a new tool for time-frequency analysis. This paper discusses some steps of FRFT in signal detection based on the decomposition of FRFT. With the help of the property that a LFM signal can produce a strong impulse in the FRFT domain, the signal can be detected conveniently. Experimental analysis shows that the proposed method is effective in detecting LFM signals.

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