分频分时振幅阈值去噪方法在渤海湾盆地的应用研究

具有双曲线特征的非线性外源噪声,会严重影响地震数据的最终成像质量,通常这种外源噪声与有效反射的物理特征差异不大且两者频带近乎重叠,因此常规去噪技术很难取得理想的压制效果。若直接剔除被外源噪声干扰的炮记录将会增加采集成本;反之会在一定程度上降低资料的信噪比,影响资料的后续处理。将分频分时振幅阈值去噪方法应用于渤海地震资料的外源噪声压制,在有效压制外源噪声的同时,能够很好地保护有效信号。同时,测试还表明该方法还可以较好地压制近偏移距多次波。

Wigner-Ville distribution and its application in seismic attenuation estimation

The attenuation of seismic signals is often characterized in the frequency domain using statistical measures of the power spectrum. However, the conventional Fourier transform-based power spectrum estimation methods suffer from time-frequency resolution problems. Wigner-Ville distribution, which is a member of Cohen class time-frequency distributions, possesses many appealing properties, such as time-frequency marginal distribution, time-frequency localization, etc. Therefore, Wigner-Ville distribution offers a new way for estimating the attenuation of seismic signals. This paper initially gives a brief introduction to Wigner-Ville distribution and the smoothed Wigner-Ville distribution that is effective in reducing the cross-term effect, and then presents a method for seismic attenuation estimation based on the instantaneous energy spectrum of the Wigner-Ville distribution. A real data example from central Tarim Basin in western China is presented to illustrate the effectiveness of the proposed method. The results show that the Wigner-Ville distribution-based seismic attenuation estimation method can effectively detect the difference between reef, shoal and lagoon facies by their attenuation properties, indicating that the estimated seismic attenuation can be used for reef and shoal carbonate reservoir characterization.

Application of sparse time-frequency decomposition to seismic data

The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time and frequency domains. To improve the resolution of the time-frequency decomposition results, we use the instantaneous frequency distribution function（IFDF） to express the seismic signal. When the instantaneous frequencies of the nonstationary signal satisfy the requirements of the uncertainty principle, the support of IFDF is just the support of the amplitude ridges in the signal obtained using the short-time Fourier transform. Based on this feature, we propose a new iteration algorithm to achieve the sparse time-frequency decomposition of the signal. The iteration algorithm uses the support of the amplitude ridges of the residual signal obtained with the short-time Fourier transform to update the time-frequency components of the signal. The summation of the updated time-frequency components in each iteration is the result of the sparse timefrequency decomposition. Numerical examples show that the proposed method improves the resolution of the time-frequency decomposition results and the accuracy of the analysis of the nonstationary signal. We also use the proposed method to attenuate the ground roll of field seismic data with good results.

Fractional S-transform-part 2:Application to reservoir prediction and fluid identification

The fractional S-transform （FRST） has good time-frequency focusing ability. The FRST can identify geological features by rotating the fractional Fourier transform frequency （FRFTfr） axis. Different seismic signals have different optimal fractional parameters which is not conducive to multichannel seismic data processing. Thus, we first decompose the common-frequency sections by the FRST and then we analyze the low-frequency shadow. Second, the combination of the FRST and blind-source separation is used to obtain the independent spectra of the various geological features. The seismic data interpretation improves without requiring to estimating the optimal fractional parameters. The top and bottom of a limestone reservoir can be clearly recognized on the common-frequency section, thus enhancing the vertical resolution of the analysis of the low-frequency shadows compared with traditional ST. Simulations suggest that the proposed method separates the independent frequency information in the time-fractional-frequency domain. We used field seismic and well data to verify the proposed method.

A quantitative analysis method for GPR signals based on optimal biorthogonal wavelet

Due to the disturbances arising from the coherence of reflected waves and from echo noise,problems such as limitations,instability and poor accuracy exist with the current quantitative analysis methods.According to the intrinsic features of GPR signals and wavelet time–frequency analysis,an optimal wavelet basis named GPR3.3 wavelet is constructed via an improved biorthogonal wavelet construction method to quantitatively analyse the GPR signal.A new quantitative analysis method based on the biorthogonal wavelet(the QAGBW method)is proposed and applied in the analysis of analogue and measured signals.The results show that compared with the Bayesian frequency-domain blind deconvolution and with existing wavelet bases,the QAGBW method based on optimal wavelet can limit the disturbance from factors such as the coherence of reflected waves and echo noise,improve the quantitative analytical precision of the GPR signal,and match the minimum thickness for quantitative analysis with the vertical resolution of GPR detection.

LDPC based time-frequency double differential space-time coding for multi-antenna OFDM systems

Differential space-time coding was proposed recently in the literature for multi-antenna systems, where neither the transmitter nor the receiver knows the fading coefficients. Among existing schemes, double differential space-time (DDST) coding is of special interest because it is applicable to continuous fast time-varying channels. However, it is less effective in fre- quency-selective fading channels. This paper’s authors derived a novel time-frequency double differential space-time (TF-DDST) coding scheme for multi-antenna orthogonal frequency division multiplexing (OFDM) systems in a time-varying fre- quency-selective fading environment, where double differential space-time coding is introduced into both time domain and fre- quency domain. Our proposed TF-DDST-OFDM system has a low-complexity non-coherent decoding scheme and is robust for time- and frequency-selective Rayleigh fading. In this paper, we also propose the use of state-of-the-art low-density parity-check (LDPC) code in serial concatenation with our TF-DDST scheme as a channel code. Simulations revealed that the LDPC based TF-DDST OFDM system has low decoding complexity and relatively better performance.

Blind Source Separation based on Time-Frequency Morphological Characteristics for Rigid Acoustic Scattering by Underwater Objects

Separation of the components of rigid acoustic scattering by underwater objects is essential in obtaining the structural characteristics of such objects. To overcome the problem of rigid structures appearing to have the same spectral structure in the time domain, time-frequency Blind Source Separation （BSS） can be used in combination with image morphology to separate the rigid scattering components of different objects. Based on a highlight model, the separation of the rigid scattering structure of objects with time-frequency distribution is deduced. Using a morphological filter, different characteristics in a Wigner-Ville Distribution （WVD） observed for single auto term and cross terms can be simplified to remove any cross-term interference. By selecting time and frequency points of the auto terms signal, the accuracy of BSS can be improved. A simulation experimental has been used to analyze the feasibility of the new method, with changing the pulse width of the transmitted signal, the relative amplitude and the time delay parameter. And simulation results show that the new method can not only separate rigid scattering components, but can also separate the components when elastic scattering and rigid scattering exist at the same time. Experimental results confirm that the new method can be used in separating the rigid scattering structure of underwater objects.

Estimation of structural modal parameters by fourier transform with an optimal window

An adaptive Fourier Transform （FT） with an optimal window has been proposed for the time-frequency analysis of nonstationary time series. The method allows for a good estimation of both frequency and amplitude of the spectrum and can be easily applied to the general case of time-varying signals. The evaluation of the proposed approach has been performed on measured time-varying signals from a suspension bridge model and a steel frame model whose data have the typical non-stationary characteristics. The numerical results show that the proposed approach can overcome some of the difficulties encountered in the classic Fourier transform technique and can achieve higher computation accuracy.

Time-frequency characteristics of blasting vibration signals measured in milliseconds

In order to study the time-frequency characteristics of blasting vibration signals, measured in milliseconds, we carried out site blasting vibration tests at an open pit of the Jinduicheng Mine. Based on recorded field data and applying a combination of RSPWVD and wavelet, we analyzed the time-fre- quency characteristics of recorded field data, summarized the time-frequency characteristics of blasting vibration signals in different frequency bands and present detailed information of blasting vibration sig- nals in milliseconds of high time-frequency resolutions. Because RSPWVD can be seen as of definite physical significance to signal energy distribution in time and frequency domains, we studied the energy distribution of blasting vibration signals for various milliseconds intervals from a perspective of energy distribution. The results indicate that the effect of milliseconds intervals on time-frequency characteris- tics of blasting vibration signals is significant; the length of delay time directly affects the energy distri- bution of blasting vibration signals as well as the duration of energy in ffeauencv bands.

lp norm inverse spectral decomposition and its multi-sparsity fusion interpretation

Spectral decomposition has been widely used in the detection and identifi cation of underground anomalous features(such as faults,river channels,and karst caves).However,the conventional spectral decomposition method is restrained by the window function,and hence,it mostly has low time–frequency focusing and resolution,thereby hampering the fi ne interpretation of seismic targets.To solve this problem,we investigated the sparse inverse spectral decomposition constrained by the lp norm(0<p≤1).Using a numerical model,we demonstrated the higher time–frequency resolution of this method and its capability for improving the seismic interpretation for thin layers.Moreover,given the actual underground geology that can be often complex,we further propose a p-norm constrained inverse spectral attribute interpretation method based on multiresolution time–frequency feature fusion.By comprehensively analyzing the time–frequency spectrum results constrained by the diff erent p-norms,we can obtain more refined interpretation results than those obtained by the traditional strategy,which incorporates a single norm constraint.Finally,the proposed strategy was applied to the processing and interpretation of actual three-dimensional seismic data for a study area covering about 230 km^(2) in western China.The results reveal that the surface water system in this area is characterized by stepwise convergence from a higher position in the north(a buried hill)toward the south and by the development of faults.We thus demonstrated that the proposed method has huge application potential in seismic interpretation.

Rate-embedded differential space-time-frequency coding scheme

In multiple-input-multiple-output orthogonal-frequency-division-multiplexing （MIMO-OFDM） system, a rate-embedded differential space-time-frequency （DSTF） coding scheme was proposed. Both the conventional space-time codes and coding techniques in frequency domain were employed to build high rate and low rate space-time-frequency message matrices. Then both types of message matrices were differentially transmitted alternately in the frequency domain. Consequently, the total transmission rate could be improved greatly. At receiver, a simple decision feedback differential detector （SDF-DD） was adopted to further enhance the total error performance with approximate DD complexity. Simulation results verified that the proposed scheme can implement high rate and high reliability differential transmission. Compared with the conventional DSTF coding schemes, the proposed scheme achieves higher spectral efficiency and much better error performance.

The Simulation and Characteristic Study of Wind Velocity for Long-Span Structures

The new technique that combines wave superposition with the fast Fourier transformation was introduced to simulate the nodal three-dimension relevant wind velocity time series of spatial structures. The wind velocity field where the spatial structure is located is assumed to be homogeneous. The wind’s power spectral density is divided into frequency spectral function and coherency function and the spectral functions are transformed as the superposition coefficients. The wavelet analysis has excellent localized characters in both time and frequency domains, which not only makes wind velocity time series analysis more accurate, but also can focus on any detail of the objective signal series. The discrete wavelet transformation was adopted to decompose and reconstruct the discrete wind velocity time series. The stability of wavelet analysis for the wind velocity time series was also proved.

Study of seismic spectrum decomposition based on CEEMD

Empirical mode decomposition( EMD) is a powerful tool of time-frequency analysis. EMD decomposes a signal into a series of sub-signals,called Intrinsic mode functions( IMFs). Each IMF contains different frequency components which can deal with the nonlinear and non-stationary of signal. Complete ensemble empirical mode decomposition( CEEMD) is an improved algorithm,which can provide an accurate reconstruction of the original signal and better spectral separation of the modes. The authors studied the decomposition result of a synthetic signal obtained from EMD and CEEMD. The result shows that the CEEMD has suitability in spectrum decomposition time-frequency analysis. Compared with traditional methods,a higher time-frequency resolution is obtained through verifying the method on both synthetic and real data.

The modulation recognition based on decision-making mechanism and neural network integrated classifier

A neural network integrated classifier(NNIC) designed with a new modulation recognition algorithm based on the decision-making tree is proposed in this paper.Firstly,instantaneous parameters are extracted in the time domain by the coordinated rotation digital computer(CORDIC) algorithm based on the extended convergence domain and feature parameters of frequency spectrum and power spectrum are extracted by the time-frequency analysis method.All pattern identification parameters are calculated under the I/Q orthogonal two-channel structure,and constructed into the feature vector set.Next,the classifier is designed according to the modulation pattern and recognition performance of the feature parameter set,the optimum threshold is selected for each feature parameter based on the decision-making mechanism in a single classifier,multi-source information fusion and modulation recognition are realized based on feature parameter judge process in the NNIC.Simulation results show NNIC is competent for all modulation recognitions,8 kinds of digital modulated signals are effectively identified,which shows the recognition rate and anti-interference capability at low SNR are improved greatly,the overall recognition rate can reach 100%when SNR is12dB.

A novel spectrum borrowing scheme in integrated 3G/WLAN networks

In the heterogeneous wireless networks of the next generation, a large number of different radio access technologies will be integrated into a common network. This paper considers optimizing the utilization of spectrum resource in heterogeneous environment consisting two different networks： wireless local area network （WLAN） and time division-synchronous code division multiple access （TD-SCDMA） network. An optimal joint spectrum borrowing scheme maximizing overall network revenue is proposed with quality of service （QoS） constraints over both the WLAN and the TD-SCDMA cellular networks. Simulation results illustrate that system revenue earned in the proposed joint spectrum borrowing scheme is significantly larger than the case when individual networks are optimized independently.

Sparse time-frequency representation of nonlinear and nonstationary data Dedicated to Professor Shi Zhong-Ci on the Occasion of his 80th Birthday

Adaptive data analysis provides an important tool in extracting hidden physical information from multiscale data that arise from various applications. In this paper, we review two data-driven time-frequency analysis methods that we introduced recently to study trend and instantaneous frequency of nonlinear and nonstationary data. These methods are inspired by the empirical mode decomposition method （EMD） and the recently developed compressed （compressive） sensing theory. The main idea is to look for the sparsest representation of multiscale data within the largest possible dictionary consisting of intrinsic mode functions of the form {a（t） cos（0（t））}, where a is assumed to be less oscillatory than cos（θ（t）） and θ ＇≥ 0. This problem can be formulated as a nonlinear ι0 optimization problem. We have proposed two methods to solve this nonlinear optimization problem. The first one is based on nonlinear basis pursuit and the second one is based on nonlinear matching pursuit. Convergence analysis has been carried out for the nonlinear matching pursuit method. Some numerical experiments are given to demonstrate the effectiveness of the proposed methods.