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The time frequency-based techniques, short-time fourier transform (STFT) and continuous wavelet transform (CWT), have been used for understanding the frequency dependent scattering and enhancement of signal-to-noise ratio (SNR) in high scattering austenitic stainless steel with grain size in the range of 30 to 210 pm. STFT and CWT analysis indicated that the higher frequency content of back-wall echoes (BWEs) decreases rapidly with the propagation distance; however, those of backscatter decrease slowly. Furthermore, the frequency of the BWEs decreases dramatically with an increase in grain size, and the higher frequency content of the back-wall is always lower than the backscatter; this shows good promise for enhancement of SNR by time frequency analysis. A CWT-based methodology that was found to correlate SNR improvement with wavelet scales is proposed for processing of ultrasonic signals in austenitic stainless steel specimens with different grain sizes. The pseudo frequencies have been calculated for the wavelet scales. A comparative study has also been made to correlate peak frequencies at BWE locations obtained using STFT with pseudo frequencies calculated by the proposed methodology using CWT. The improvement in SNR with wavelet scales approach has been used for the detection of discontinuities whose signals are merged in high backscatter noise in the coarse grain austenitic stainless steel. [ABSTRACT FROM AUTHOR]

Abstract: This study researches the behaviour of spectral broadening index (SBI) obtained from spectra achived using short-time Fourier transform (STFT) analysis compared to that of SBI based on autoregressive (AR) modeling of clinical Doppler lower extremity vein signal. Doppler signals from 12 healthy subject with eight different physiologic situations were analysed. Sonograms obtained from Doppler signals were used to compare the applied methods in terms of their frequency resolution and their effectiveness for the determination of SBI. The AR based sonograms produced narrower spectra compared to STFT sonograms. Besides, the magnitude of the STFT based SBI was larger than that of the AR based SBI. Furthermore, standard deviations and coefficient of variations of STFT and AR based SBIs changed depending on each physiologic situation. The results of this research have also shown that despite the qualitative improvement in the individual frequency spectra, there was no quantitative advantage in using the AR approach over the STFT for the determination of SBI. Moreover there was also an additional computational complexity income connections with AR modeling. [Copyright &y& Elsevier]

Seismic signal analysis is the main step in data processing through the petroleum exploration via costly seismic investigations. Precision of target delineation by seismic data for exploratory drilling strongly depends on resolution of the seismic image. However, resolution of the seismic image is restricted by the band-limited nature of the seismic signal and inherent deficiencies in signal enhancement and random noise attenuation methods. Generally, it is required to increase resolution of the seismic data both in time and frequency domains. General solution to achieve this goal is employing time – frequency transformation (TFT) methods. The most applicable and conventional TFT method is the short time Fourier transform (STFT). Nevertheless, the STFT method does not provide sufficient resolution for further seismological interpretation on data. Thus, reassignment and simultaneous synchrosqueezing transform methods were introduced that increase resolution in time by reassignment of coefficients to their true position. However, they are still not appropriate for obtaining required resolution in seismic signal. The local maximum synchrosqueezing transform (LMSST) was introduced as an efficient method for signal analysis. The LMSST method yet was not used for analysis of seismic data and the procedure of its parameters optimization for applying on seismic data is unclear. Therefore, in the presented study, we propose a strategy for defining optimum parameters of the LMSST method and the procedure of its application on seismic data, where high resolution seismic image is required for hydrocarbon exploration. The proposed strategy was applied on two synthetic data examples, considering the quality factor expression and contamination by random noise and a field data example from a natural gas reservoir. Result of applying the proposed strategy on synthetic and field data examples and comparison of results with the leading-edge methods, revealed that this strategy could be considered as an alternative to the common signal enhancement methods. Additionally, the proposed method consists of the advantage of easy implementation and full reconstruction of the original signal. It was also proved that the proposed strategy is a robust method against added random noise compared to other competitive methods when applied on nonstationary seismic signal. [ABSTRACT FROM AUTHOR]