We propose a method for the compensation and phase correction of the amplitude spectrum based on the generalized S transform. The compensation of the amplitude spectrum within a reliable frequency range of the seismic...We propose a method for the compensation and phase correction of the amplitude spectrum based on the generalized S transform. The compensation of the amplitude spectrum within a reliable frequency range of the seismic record is performed in the S domain to restore the amplitude spectrum of reflection. We use spectral simulation methods to fit the time-dependent amplitude spectrum and compensate for the amplitude attenuation owing to absorption. We use phase scanning to select the time-, space-, and frequencydependent phases correction based on the parsimony criterion and eliminate the residual phase effect of the wavelet in the S domain. The method does not directly calculate the Q value; thus, it can be applied to the case of variable Q. The comparison of the theory model and field data verify that the proposed method can recover the amplitude spectrum of the strata reflectivity, while eliminating the effect of the residual phase of the wavelet. Thus, the wavelet approaches the zero-phase wavelet and, the seismic resolution is improved.展开更多
The low-pass fi ltering eff ect of the Earth results in the absorption and attenuation of the high-frequency components of seismic signals by the stratum during propagation.Hence,seismic data have low resolution.Consi...The low-pass fi ltering eff ect of the Earth results in the absorption and attenuation of the high-frequency components of seismic signals by the stratum during propagation.Hence,seismic data have low resolution.Considering the limitations of traditional high-frequency compensation methods,this paper presents a new method based on adaptive generalized S transform.This method is based on the study of frequency spectrum attenuation law of seismic signals,and the Gauss window function of adaptive generalized S transform is used to fi t the attenuation trend of seismic signals to seek the optimal Gauss window function.The amplitude spectrum compensation function constructed using the optimal Gauss window function is used to modify the time-frequency spectrum of the adaptive generalized S transform of seismic signals and reconstruct seismic signals to compensate for high-frequency attenuation.Practical data processing results show that the method can compensate for the high-frequency components that are absorbed and attenuated by the stratum,thereby eff ectively improving the resolution and quality of seismic data.展开更多
This article studies on Cauchy’s function f （z） and its integral, （2πi）J[ f （z）] ≡ ■C f （t）dt/（t z） taken along a closed simple contour C, in regard to their comprehensive properties over the entire z =...This article studies on Cauchy’s function f （z） and its integral, （2πi）J[ f （z）] ≡ ■C f （t）dt/（t z） taken along a closed simple contour C, in regard to their comprehensive properties over the entire z = x ＋ iy plane consisted of the simply connected open domain D ＋ bounded by C and the open domain D outside C. （1） With f （z） assumed to be C n （n ∞-times continuously differentiable） z ∈ D ＋ and in a neighborhood of C, f （z） and its derivatives f （n） （z） are proved uniformly continuous in the closed domain D ＋ = [D ＋ ＋ C]. （2） Cauchy’s integral formulas and their derivatives z ∈ D ＋ （or z ∈ D ） are proved to converge uniformly in D ＋ （or in D = [D ＋C]）, respectively, thereby rendering the integral formulas valid over the entire z-plane. （3） The same claims （as for f （z） and J[ f （z）]） are shown extended to hold for the complement function F（z）, defined to be C n z ∈ D and about C. （4） The uniform convergence theorems for f （z） and F（z） shown for arbitrary contour C are adapted to find special domains in the upper or lower half z-planes and those inside and outside the unit circle ｜z｜ = 1 such that the four general- ized Hilbert-type integral transforms are proved. （5） Further, the singularity distribution of f （z） in D is elucidated by considering the direct problem exemplified with several typ- ical singularities prescribed in D . （6） A comparative study is made between generalized integral formulas and Plemelj’s formulas on their differing basic properties. （7） Physical sig- nificances of these formulas are illustrated with applicationsto nonlinear airfoil theory. （8） Finally, an unsolved inverse problem to determine all the singularities of Cauchy function f （z） in domain D , based on the continuous numerical value of f （z） z ∈ D ＋ = [D ＋ ＋ C], is presented for resolution as a conjecture.展开更多
Deconvolution denoising in the f-x domain has some defects when facing situations like complicated geology structure, coherent noise of steep dip angles, and uneven spatial sampling. To solve these problems, a new fil...Deconvolution denoising in the f-x domain has some defects when facing situations like complicated geology structure, coherent noise of steep dip angles, and uneven spatial sampling. To solve these problems, a new filtering method is proposed, which uses the generalized S transform which has good time-frequency concentration criterion to transform seismic data from the time-space to time-frequency-space domain （t-f-x）. Then in the t-f-x domain apply Empirical Mode Decomposition （EMD） on each frequency slice and clear the Intrinsic Mode Functions （IMFs） that noise dominates to suppress coherent and random noise. The model study shows that the high frequency component in the first IMF represents mainly noise, so clearing the first IMF can suppress noise. The EMD filtering method in the t-f-x domain after generalized S transform is equivalent to self-adaptive f-k filtering that depends on position, frequency, and truncation characteristics of high wave numbers. This filtering method takes local data time-frequency characteristic into consideration and is easy to perform. Compared with AR predictive filtering, the component that this method filters is highly localized and contains relatively fewer low wave numbers and the filter result does not show over-smoothing effects. Real data processing proves that the EMD filtering method in the t-f-x domain after generalized S transform can effectively suppress random and coherent noise of steep dips.展开更多
The recognition of human movements based on radar m-D(micro-Doppler) signatures attracts great interest in the field of radar research on automatic target recognition. Because there are multiple frequency components o...The recognition of human movements based on radar m-D(micro-Doppler) signatures attracts great interest in the field of radar research on automatic target recognition. Because there are multiple frequency components overlapping seriously in the radar echoes from walking humans, it is a very difficult work to recognize walking humans based on radar echoes. In this paper, a recognition method of walking humans based on radar m-D signatures is proposed. In this method, the m-D spectrum is generated by generalized S transform first,and then the entropy segmentation is used to segment the interesting region from the original spectrum. Next,the m-D features are extracted from the m-D region. Lastly, the support vector machine is used to recognize different walking human targets. The simulation experiments considering two factors of height and velocity are also conducted to test the performance of this proposed method.展开更多
基金supported by the National Natural Science Foundation of China(No.41204091)New Teachers’ Fund for Doctor Stations,the Ministry of Education(No.20105122120001)Science and Technology Support Program from Science and Technology Department of Sichuan Province(No.2011GZ0244)
文摘We propose a method for the compensation and phase correction of the amplitude spectrum based on the generalized S transform. The compensation of the amplitude spectrum within a reliable frequency range of the seismic record is performed in the S domain to restore the amplitude spectrum of reflection. We use spectral simulation methods to fit the time-dependent amplitude spectrum and compensate for the amplitude attenuation owing to absorption. We use phase scanning to select the time-, space-, and frequencydependent phases correction based on the parsimony criterion and eliminate the residual phase effect of the wavelet in the S domain. The method does not directly calculate the Q value; thus, it can be applied to the case of variable Q. The comparison of the theory model and field data verify that the proposed method can recover the amplitude spectrum of the strata reflectivity, while eliminating the effect of the residual phase of the wavelet. Thus, the wavelet approaches the zero-phase wavelet and, the seismic resolution is improved.
基金This research is supported by the National Science and Technology Major Project of China(No.2011ZX05024-001-03)the Natural Science Basic Research Plan in Shaanxi Province of China(No.2021JQ-588)Innovation Fund for graduate students of Xi’an Shiyou University(No.YCS17111017).
文摘The low-pass fi ltering eff ect of the Earth results in the absorption and attenuation of the high-frequency components of seismic signals by the stratum during propagation.Hence,seismic data have low resolution.Considering the limitations of traditional high-frequency compensation methods,this paper presents a new method based on adaptive generalized S transform.This method is based on the study of frequency spectrum attenuation law of seismic signals,and the Gauss window function of adaptive generalized S transform is used to fi t the attenuation trend of seismic signals to seek the optimal Gauss window function.The amplitude spectrum compensation function constructed using the optimal Gauss window function is used to modify the time-frequency spectrum of the adaptive generalized S transform of seismic signals and reconstruct seismic signals to compensate for high-frequency attenuation.Practical data processing results show that the method can compensate for the high-frequency components that are absorbed and attenuated by the stratum,thereby eff ectively improving the resolution and quality of seismic data.
文摘This article studies on Cauchy’s function f （z） and its integral, （2πi）J[ f （z）] ≡ ■C f （t）dt/（t z） taken along a closed simple contour C, in regard to their comprehensive properties over the entire z = x ＋ iy plane consisted of the simply connected open domain D ＋ bounded by C and the open domain D outside C. （1） With f （z） assumed to be C n （n ∞-times continuously differentiable） z ∈ D ＋ and in a neighborhood of C, f （z） and its derivatives f （n） （z） are proved uniformly continuous in the closed domain D ＋ = [D ＋ ＋ C]. （2） Cauchy’s integral formulas and their derivatives z ∈ D ＋ （or z ∈ D ） are proved to converge uniformly in D ＋ （or in D = [D ＋C]）, respectively, thereby rendering the integral formulas valid over the entire z-plane. （3） The same claims （as for f （z） and J[ f （z）]） are shown extended to hold for the complement function F（z）, defined to be C n z ∈ D and about C. （4） The uniform convergence theorems for f （z） and F（z） shown for arbitrary contour C are adapted to find special domains in the upper or lower half z-planes and those inside and outside the unit circle ｜z｜ = 1 such that the four general- ized Hilbert-type integral transforms are proved. （5） Further, the singularity distribution of f （z） in D is elucidated by considering the direct problem exemplified with several typ- ical singularities prescribed in D . （6） A comparative study is made between generalized integral formulas and Plemelj’s formulas on their differing basic properties. （7） Physical sig- nificances of these formulas are illustrated with applicationsto nonlinear airfoil theory. （8） Finally, an unsolved inverse problem to determine all the singularities of Cauchy function f （z） in domain D , based on the continuous numerical value of f （z） z ∈ D ＋ = [D ＋ ＋ C], is presented for resolution as a conjecture.
基金sponsored by the National Natural Science Foundation of China (Grant No. 41174114)the National Natural Science Foundation of China and China Petroleum & Chemical Corporation Co-funded Project (No. 40839905)
文摘Deconvolution denoising in the f-x domain has some defects when facing situations like complicated geology structure, coherent noise of steep dip angles, and uneven spatial sampling. To solve these problems, a new filtering method is proposed, which uses the generalized S transform which has good time-frequency concentration criterion to transform seismic data from the time-space to time-frequency-space domain （t-f-x）. Then in the t-f-x domain apply Empirical Mode Decomposition （EMD） on each frequency slice and clear the Intrinsic Mode Functions （IMFs） that noise dominates to suppress coherent and random noise. The model study shows that the high frequency component in the first IMF represents mainly noise, so clearing the first IMF can suppress noise. The EMD filtering method in the t-f-x domain after generalized S transform is equivalent to self-adaptive f-k filtering that depends on position, frequency, and truncation characteristics of high wave numbers. This filtering method takes local data time-frequency characteristic into consideration and is easy to perform. Compared with AR predictive filtering, the component that this method filters is highly localized and contains relatively fewer low wave numbers and the filter result does not show over-smoothing effects. Real data processing proves that the EMD filtering method in the t-f-x domain after generalized S transform can effectively suppress random and coherent noise of steep dips.
基金supported by National Natural Science Foundation of China(Grant Nos.611711226120131861471019)
文摘The recognition of human movements based on radar m-D(micro-Doppler) signatures attracts great interest in the field of radar research on automatic target recognition. Because there are multiple frequency components overlapping seriously in the radar echoes from walking humans, it is a very difficult work to recognize walking humans based on radar echoes. In this paper, a recognition method of walking humans based on radar m-D signatures is proposed. In this method, the m-D spectrum is generated by generalized S transform first,and then the entropy segmentation is used to segment the interesting region from the original spectrum. Next,the m-D features are extracted from the m-D region. Lastly, the support vector machine is used to recognize different walking human targets. The simulation experiments considering two factors of height and velocity are also conducted to test the performance of this proposed method.
