Seismic dip estimation based on the twodimensional Hilbert transform and its application in random noise attenuation

In seismic data processing, random noise seriously affects the seismic data quality and subsequently the interpretation. This study aims to increase the signal-to-noise ratio by suppressing random noise and improve the accuracy of seismic data interpretation without losing useful information. Hence, we propose a structure-oriented polynomial fitting filter. At the core of structure-oriented filtering is the characterization of the structural trend and the realization of nonstationary filtering. First, we analyze the relation of the frequency response between two-dimensional（2D） derivatives and the 2D Hilbert transform. Then, we derive the noniterative seismic local dip operator using the 2D Hilbert transform to obtain the structural trend. Second, we select polynomial fitting as the nonstationary filtering method and expand the application range of the nonstationary polynomial fitting. Finally, we apply variableamplitude polynomial fitting along the direction of the dip to improve the adaptive structureoriented filtering. Model and field seismic data show that the proposed method suppresses the seismic noise while protecting structural information.

An iterative curvelet thresholding algorithm for seismic random noise attenuation

In this paper,we explore the use of iterative curvelet thresholding for seismic random noise attenuation.A new method for combining the curvelet transform with iterative thresholding to suppress random noise is demonstrated and the issue is described as a linear inverse optimal problem using the L1 norm.Random noise suppression in seismic data is transformed into an L1 norm optimization problem based on the curvelet sparsity transform. Compared to the conventional methods such as median filter algorithm,FX deconvolution, and wavelet thresholding,the results of synthetic and field data processing show that the iterative curvelet thresholding proposed in this paper can sufficiently improve signal to noise radio（SNR） and give higher signal fidelity at the same time.Furthermore,to make better use of the curvelet transform such as multiple scales and multiple directions,we control the curvelet direction of the result after iterative curvelet thresholding to further improve the SNR.

Random noise attenuation by f–x spatial projection-based complex empirical mode decomposition predictive filtering

The frequency–space（f–x） empirical mode decomposition（EMD） denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function（IMF） results in signal damage and limited denoising. Second, decomposing the real and imaginary parts of complex data may lead to inconsistent decomposition numbers. Thus, we propose a new method named f–x spatial projection-based complex empirical mode decomposition（CEMD） prediction filtering. The proposed approach directly decomposes complex seismic data into a series of complex IMFs（CIMFs） using the spatial projection-based CEMD algorithm and then applies f–x predictive filtering to the stationary CIMFs to improve the signal-to-noise ratio. Synthetic and real data examples were used to demonstrate the performance of the new method in random noise attenuation and seismic signal preservation.

An adaptive noise attenuation method for edge and amplitude preservation

Noise intensity distributed in seismic data varies with different frequencies or frequency bands; thus, noise attenuation on the full-frequency band affects the dynamic properties of the seismic reflection signal and the subsequent seismic data interpretation, reservoir description, hydrocarbon detection, etc. Hence, we propose an adaptive noise attenuation method for edge and amplitude preservation, wherein the wavelet packet transform is used to decompose the full-band seismic signal into multiband data and then process these data using nonlinear anisotropic dip-oriented edge-preserving fi ltering. In the fi ltering, the calculated diffusion tensor from the structure tensor can be exploited to establish the direction of smoothing. In addition, the fault confidence measure and discontinuity operator can be used to preserve the structural and stratigraphic discontinuities and edges, and the decorrelation criteria can be used to establish the number of iterations. These parameters can minimize the intervention and subjectivity of the interpreter, and simplify the application of the proposed method. We applied the proposed method to synthetic and real 3D marine seismic data. We found that the proposed method could be used to attenuate noise in seismic data while preserving the effective discontinuity information and amplitude characteristics in seismic refl ection waves, providing high-quality data for interpretation and analysis such as high-resolution processing, attribute analysis, and inversion.

GPR data noise attenuation on the curvelet transform

Signal extraction is critical in GRP data processing and noise attenuation. When the target depth is shallow, its refl ection echo signal will overlap with the background noise, affecting the detection of arrival time and localization of the target. Thus, we propose a noise attenuation method based on the curvelet transform. First, the original signal is transformed into the curvelet domain, and then the curvelet coefficients of the background noise are extracted according to the distribution features that differ from the effective signal. In the curvelet domain, the coarse-scale curvelet atom is isotropic. Hence, a two-dimensional directional filter is designed to estimate the high-energy background noise in the coarsescale domain, and then, attenuate the background noise and highlight the effective signal. In this process, we also use a subscale threshold value of the curvelet domain to fi lter out random noise. Finally, we compare the proposed method with the average elimination and 2D continuous wavelet transform methods. The results show that the proposed method not only removes the background noise but also eliminates the coherent interference and random noise. The numerical simulation and the real data application suggest and verify the feasibility and effectiveness of the proposed method.

Improved random noise attenuation using f-x empirical mode decomposition and local similarity

Conventional f-x empirical mode decomposition（EMD） is an effective random noise attenuation method for use with seismic profiles mainly containing horizontal events.However,when a seismic event is not horizontal,the use of f-x EMD is harmful to most useful signals.Based on the framework of f-x EMD,this study proposes an improved denoising approach that retrieves lost useful signals by detecting effective signal points in a noise section using local similarity and then designing a weighting operator for retrieving signals.Compared with conventional f-x EMD,f-x predictive filtering,and f-x empirical mode decomposition predictive filtering,the new approach can preserve more useful signals and obtain a relatively cleaner denoised image.Synthetic and field data examples are shown as test performances of the proposed approach,thereby verifying the effectiveness of this method.

Seismic noise attenuation using nonstationary polynomial fitting

We propose a novel method for seismic noise attenuation by applying nonstationary polynomial fitting （NPF）, which can estimate coherent components with amplitude variation along the event. The NPF with time-varying coefficients can adaptively estimate the coherent components. The smoothness of the polynomial coefficients is controlled by shaping regularization. The signal is coherent along the offset axis in a common midpoint （CMP） gather after normal moveout （NMO）. We use NPF to estimate the effective signal and thereby to attenuate the random noise. For radial events-like noise such as ground roll, we first employ a radial trace （RT） transform to transform the data to the time-velocity domain. Then the NPF is used to estimate coherent noise in the RT domain. Finally, the coherent noise is adaptively subtracted from the noisy dataset. The proposed method can effectively estimate coherent noise with amplitude variations along the event and there is no need to propose that noise amplitude is constant. Results of synthetic and field data examples show that, compared with conventional methods such as stationary polynomial fitting and low cut filters, the proposed method can effectively suppress seismic noise and preserve the signals.

Inversion-based data-driven time-space domain random noise attenuation method

Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter （PEF）, is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data＇s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance.

Nonstationary signal inversion based on shaping regularization for random noise attenuation

Prediction filtering is one of the most commonly used random noise attenuation methods in the industry;however,it has two drawbacks.First,it assumes that the seismic signals are piecewise stationary and linear.However,the seismic signal exhibits nonstationary due to the complexity of the underground structure.Second,the method predicts noise from seismic data by convolving with a prediction error filter(PEF),which applies inconsistent noise models before and after denoising.Therefore,the assumptions and model inconsistencies weaken conventional prediction filtering's performance in noise attenuation and signal preservation.In this paper,we propose a nonstationary signal inversion based on shaping regularization for random noise attenuation.The main idea of the method is to use the nonstationary prediction operator(NPO)to describe the complex structure and obtain seismic signals using nonstationary signal inversion instead of convolution.Different from the convolutional predicting filtering,the proposed method uses NPO as the regularization constraint to directly invert the eff ective signal from the noisy seismic data.The NPO varies in time and space,enabling the inversion system to describe complex(nonstationary and nonlinear)underground geological structures in detail.Processing synthetic and field data results demonstrate that the method eff ectively suppresses random noise and preserves seismic refl ection signals for nonstationary seismic data.

Seismic random noise suppression using an adaptive nonlocal means algorithm

Nonlocal means filtering is a noise attenuation method based on redundancies in image information. It is also a nonlocal denoising method that uses the self-similarity of an image, assuming that the valid structures of the image have a certain degree of repeatability that the random noise lacks. In this paper, we use nonlocal means filtering in seismic random noise suppression. To overcome the problems caused by expensive computational costs and improper filter parameters, this paper proposes a block-wise implementation of the nonlocal means method with adaptive filter parameter estimation. Tests with synthetic data and real 2D post-stack seismic data demonstrate that the proposed algorithm better preserves valid seismic information and has a higher accuracy when compared with traditional seismic denoising methods （e.g., f-x deconvolution）, which is important for subsequent seismic processing and interpretation.

基于SYSNOISE的环境敏感点声屏障设计仿真研究

声屏障是降低交通噪声等流动性声源的一种有效而经济的方法。通过分析声屏障的降噪原理,研究和探讨不同声源状况的声屏障插入损失计算模型以及城市主干道声屏障的声学设计方法,选择噪音超标的校园作为研究对象,根据得到的插入损失计算模型对所选校园设计出相应的声屏障,并对声屏障进行验证,结果表明所设计的声屏障具有良好的隔声降噪效果。

Low-frequency swell noise suppression based on U-Net

Low-frequency band-shaped swell noise with strong amplitude is common in marine seismic data.The conventional high-pass fi ltering algorithm widely used to suppress swell noise often results in serious damage of effective information.This paper introduces the residual learning strategy of denoising convolutional neural network(DnCNN)into a U-shaped convolutional neural network(U-Net)to develop a new U-Net with more generalization,which can eliminate low-frequency swell noise with high precision.The results of both model date tests and real data processing show that the new U-Net is capable of effi cient learning and high-precision noise removal,and can avoid the overfi tting problem which is very common in conventional neural network methods.This new U-Net can also be generalized to some extent and can eff ectively preserve low-frequency eff ective information.Compared with the conventional high-pass fi ltering method commonly used in the industry,the new U-Net can eliminate low-frequency swell noise with higher precision while eff ectively preserving low-frequency eff ective information,which is of great signifi cance for subsequent processing such as amplitude-preserving imaging and full waveform inversion.

Calculation Model for the Propagation of Audible Noise from High Voltage Transmission Lines

Audible noise from high voltage transmission lines’ corona discharge has become one of the decisive factors affecting design of high voltage transmission lines, thus it is very important to study the spatial propagation characteristics of audible noise for its accurate pre- diction. A calculation model for the propagation of audible noise is presented in this paper, which is based on the basic equation of the sound wave and can involve the influences of the atmosphere absorption and ground effects. The effects of different ground impedances and the atmospheric attenuation on the distribution of sound pressure level are discussed in this paper. The results show that the atmospheric absorp- tion may increase the attenuation of the audible noise, and the ground surface affects both the amplitude and phase of the sound. The spatial distribution fluctuates considering the ground effects. The atmospheric attenuation and the ground effect are closely related to the frequency of the noise. In the frequency range of the audible noise, the influence of atmospheric attenuation on the spatial propagation characteristics is more obvious in high frequency while ground has significant influences in low frequency.

Temporal and Spatial Images of Ambient Noise Intensity in the Chinese Mainland

Successive waveforms of the vertical component recorded by 888 broadband seismic stations in the China Seismography Network from January,2010 to June,2011 are used to investigate the temporal and spatial distribution of ambient noise intensity,and the images of ambient noise intensity at the period of 10 s in the Chinese Mainland are obtained. The temporal variation of ambient noise intensity shows some seasonal and periodic characteristics. The maximum ambient noise intensity occurred from January,2011 to March,2011. The spatial distribution images of ambient noise intensity show obvious zoning features,which doesnt correlate with surface geology,suggesting that the noise field is stronger than the site factors. The strength in southeastern coastal areas reaches its maximum and generally decreases toward to inland areas,and arrives at the minimum in the Qinghai-Tibetan Plateau. The zonal intensity distribution is probably correlated with ocean tides from the Philippine Ocean and the Pacific Ocean. It also shows that the influence from the Indian Ocean seems small. However, the ambient noise intensity increases to a certain degree in the Xinjiang area,indicating that the main source of ambient noise in the western area of the Chinese Mainland is not derived from the East and South China Sea,but rather from the deep interior of the Eurasian continent. The ambient noise intensity obtained in this study can supply reference for seismology research based on ambient noise correlation. Moreover,it can supply basic data for attenuation research based on ambient noise, and thus help achieve the object of retrieving the attenuation of Rayleigh waves from ambient noise.

Species-Related Difference to Noise Reduction Between Trees in Urban Forest: The Abidar Forest Park (Case Study)

Sound pollution is one of the most important urban problems which endangers mental and physical health of the residents.This study was aimed to assess the influence of different tree species,including Fraxinus rotundifolia,Robinia pseudoacacia,Platanus orientalis,Platycladus orientalis,and Pinus eldarica,in reducing noise pollution in the Abidar Forest Park.A further objective was to identify the contaminated areas of Sanandaj city and to propose suitable noise absorbent tree species in consistent conditions.For each tree stands the noise measurements were performed during intervals at frequencies of 250,500 and 1000 Hz,besides an open area with the same topography.With regards to the second purpose,a total of 50 stations with residential,commercial,residentialcommercial,and green space applications were selected across the city.Equivalent Continuous Sound Pressure Level(Leq)was determined in five replicates for 30 min.The measurements were performed under stable weather conditions and low wind velocity at 17:00(traffic peak)in summer and fall.All of the Leq values were above the threshold noise level.The highest noise reduction was recorded in summer(i.e.,green season);Platanus and Platycladus species demonstrated the highest and lowest noise absorption(31.43 dB and 22.28 dB,respectively).Furthermore,a meaningful difference was observed between Leq values of commercial,residential,commercial-residential,and green space urban applications,and the central parts of the city showed noticeable noise pollution.Taken together,due to being exposed to higher than the acceptable threshold noise level,the residents of Sanandaj will be endangered to health problems in the near future;thus consideration should be given to the noise pollution sources.

Noise attenuation of frequency-modulated multi-rotor using sound field reproduction

Multi-rotor aircraft has great potential in urban traffic and military use and its noise problem has attracted more attention recently.Multi-rotor aircrafts are typically controlled by changing the rotation speeds of the rotors.To reduce the noise of multiple frequency-modulated rotors,a global noise attenuation method is proposed in this study.First,the fast prediction method is used to estimate the global noise of the multirotor with different configurations online.Meanwhile,the sound field reproduction method is used to obtain the control signal of the loudspeaker array to achieve global noise attenuation.Then,the influence of array arrangement on noise reduction is analyzed in the acoustic modal domain,which reveals that different optimization models are needed to minimize the noise power or/and the noise pressure in some directions when the scale of the array is limited.Next,to improve the real-time performance of the system,the online calculation of the optimal control signal is transformed into the offline design of the optimal filter,which satisfies the target frequency-domain characteristics.Finally,the experimental results of the noise of a model quadrotor in the anechoic chamber were consistent with the predicted results.The simulation results of noise attenuation for the quadrotor show that the method proposed reduced the global noise power by about 13 dB.Moreover,the noise region radiated from the quadrotor to the ground with the boundary of 40 dB was reduced to 8.4%of that before control.

Acoustic scattering mechanism and noise attenuation of circumferentially non-uniform liner with spectral-wave guide method

In this paper,a model is established with application of the spectral-wave guide method,which has higher accuracy and can serve as a rapid calculation tool for sound transmission calculations.Based on this calculation model,some numerical results of circumferentially non-uniform lined annular/circular ducts are carried out,and some physical mechanisms can be discovered.The numerical results show that periodical impedance distributions along the circumferential direction will lead to discontinuous scattered modes with regular spacing;and mirror-symmetric structure liner will converge the energy of opposite modes.Relying on this mechanism,the potential of acoustic scattering can be further developed by suppressing lower or enhancing higher order modes with expressly designed segmented liner configurations.In particular,the intrinsic mechanism of mode redistribution brought about by the non-uniform liner can be subtly utilized to attenuate broadband noise.The present work indeed shows that circumferentially non-uniform liner is conducive to the reduction of the practical broadband sound source.Furthermore,the effects of nonuniform flow are considered in the model,then distinction of noise attenuation and scattered modes energy in different flows is shown.A possible mechanism is proposed that refraction effects in complex flows lead to the distinction.These works show that the current model has profound potential and availability for the research and designs of circumferentially non-uniform liner.

基于谐波分解恢复弱势信号的高分辨率处理技术

地震资料高分辨率处理是预测薄储集层的有效手段,高分辨率处理的主要目的是有效恢复地震高、低频信息和拓宽频带,同时保持资料的信噪比和保真度。基于谐波分解恢复弱势信号的高分辨率处理技术,以压缩小波变换为基础,根据信号谐波分量对地震高、低频弱势信号进行恢复。首先,基于有效频带内的地震信号,采用压缩小波变换将其分解为各基频信号,然后计算各基频信号的高次谐波与低次谐波,将计算出的高次谐波与低次谐波加入小波变换系数中,最后进行小波逆变换,即可恢复高、低频弱势信号。在实现过程中,仅仅在有效频带内估算基频信号,能较好地保持信号的信噪比。地震信号的小波变换系数与地层反射系数具有一致性,因此该技术具有较高的保真度,可以较好保持相对振幅关系。实际应用表明,在保持资料信噪比的同时,该技术能大幅拓宽地震资料的频带,处理后的地震剖面断点更清楚,分辨能力改善明显,能较好地识别6 000 m以深约40 m的薄储集层。

松辽盆地西南部砂岩型铀矿地震资料处理技术研究

文章针对松辽盆地西南部砂岩型铀矿地震资料噪声严重,干扰波能量比较强,资料频带窄、主频低、以及地震记录单炮之间、深浅层和远近道之间能量差异比较大的特点,在地震资料处理的叠前去噪、振幅补偿以及提高分辨率等关键环节开展了研究。在去噪环节,采用了叠前多域组合分步去噪方法,针对性地去除面波、线性噪声、强能量干扰以及次生干扰等,增强了目的层有效反射信号强度,提高地震资料信噪比。在振幅补偿中采用地表一致性振幅补偿和几何扩散补偿方法,可以明显消除远近道、浅层与深层之间以及道间能量差异。在提高分辨率环节通过地表一致性反褶积和预测反褶积方法,可消除地震波传播过程中子波变化引起的波形、频谱等特征的差异,压缩子波,缩小子波旁瓣,拓宽有效信号频带,提高地震资料分辨率。最后建立了一套地震资料的处理流程,并建立了相应环节的质量监控措施。通过与前人处理结果对比,此次建立的地震资料处理方法可以明显提高地震资料的品质。

无线紫外光MIMO通信系统信道容量估算方法

无线紫外光通信系统易受大气散射等因素的影响,导致通信信道容量估算准确率较低。为此,提出一种无线紫外光多入多出(Multiple-Input Multiple-Output, MIMO)通信系统信道容量估算方法。根据信道特征,构建无线紫外光MIMO通信系统的信道容量估计模型,获取无线紫外光的单次散射传输特性,利用符号补码式的方法改进空时编码,设定高级检测器中符号的接收能耗,使每个符号保持在标准功率数值范围内,完成通信系统信道检测式编码。根据不同发射角和接收角计算无线紫外光通信的路径损耗比,通过泊松随机分布获得最终通信系统信道容量的估算结果。实验结果表明,所提方法的传输速率可高达8 Mbps,误码率始终低于10-3bit/s,估算准确率在90%以上,估计时间低于4 ms,估算效果好,可为通信行业发展提供理论依据。