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.

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.

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.

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.

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.

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.

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.

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.

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.

Seismic data analysis based on spatial subsets

There are some limitations when we apply conventional methods to analyze the massive amounts of seismic data acquired with high-density spatial sampling since processors usually obtain the properties of raw data from common shot gathers or other datasets located at certain points or along lines. We propose a novel method in this paper to observe seismic data on time slices from spatial subsets. The composition of a spatial subset and the unique character of orthogonal or oblique subsets are described and pre-stack subsets are shown by 3D visualization. In seismic data processing, spatial subsets can be used for the following aspects： （1） to check the trace distribution uniformity and regularity; （2） to observe the main features of ground-roll and linear noise; （3） to find abnormal traces from slices of datasets; and （4） to QC the results of pre-stack noise attenuation. The field data application shows that seismic data analysis in spatial subsets is an effective method that may lead to a better discrimination among various wavefields and help us obtain more information.

Attenuation of seismic migration smile artifacts with deep learning

Attenuation of migration artifacts on Kirchhoff migrated seismic data can be challenging due to the relatively low amplitude of migration artifacts compared to reflections as well as the overlap in the kinematics of reflection and migration smiles.Several‘conventional’filtering methods exist and recently deep learning based workflows have been proposed.A deep learning workflow can be a simple and fast alternative to existing methods.In case of supervised training of a deep neural network using training data made by physics-based modelling or actual migrations is expensive and lacks diversity in terms of noise,amplitude,frequency content and wavelet.This can result in poor generalization beyond the training data without re-training and transfer learning.In this paper we demonstrate successful applications of migration smile separation using a conventional U-net architecture.The novelty in our approach is that we do not use synthetic data created from physics-based modelling,but instead use only synthetic data build form basic geometric shapes.Our domain of application is the migrated common offset domain,or simply the stack of the pre-stack migrated data,where reflections resemble local geology and migration smiles are upward convex hyperbolic patterns.Both patterns were randomly perturbed in many ways while maintaining their intrinsic features.This approach is inspired by the common practice of data augmentation in deep learning for machine vision applications.Since many of the standard data augmentation techniques lack a geophysical motivation,we have instead perturbed our synthetic training data in ways to make more sense for a signal processing perspective or given our‘domain knowledge’of the problem at hand.We did not have to retrain the network to produce good results on the field dataset.The large variety and diversity in examples enabled the trained neural network to show encouraging results on synthetic and field datasets that were not used in training.

Theoretical Calculation and Analysis of Muffler Based on Multilayer Sound Absorbing Material

The multilayer impedance composite sound absorption structure of the new muffler is proposed by combining the microporous plate structure with the resonant sound absorption structure of the porous material.Firstly,the acoustic impedance and acoustic absorption coefficient of the new muffler structure are calculated by acoustic electric analogy method,and then the noise attenuation is calculated.When the new muffler structure parameters change,the relationship among the noise frequency,the sound absorption coefficient and the noise attenuation is calculated by using MATLAB.Finally,the calculated results are compared with the experimental data to verify the correctness of the theoretical calculation.The variation of resonance peak,resonance frequency and attenuation band width of each structural parameter is analyzed by the relation curve.The conclusion shows that it is feasible to use multilayer sound absorbing materials as the body structure of the new muffler.And the influence relationship between the change of various parameters of the sound absorption structure with the sound absorption coefficient and noise attenuation is obtained.

Seismic Data Processing and Interpretation on the Loess Plateau, Part1: Seismic Data Processing

Branching river channels and the coexistence of valleys, ridges, hiils, and slopes＇as the result of long-term weathering and erosion form the unique loess topography. The Changqing Geophysical Company, working in these complex conditions, has established a suite of technologies for high-fidelity processing and fine interpretation of seismic data. This article introduces the processes involved in the data processing and interpretation and illustrates the results.

Experiments on excitation and data processing of low-frequency vibroseis in permafrost area of the tibetan plateau

Few seismic exploration work was carried out in Tibetan Plateau due to the characteristics of alpine hypoxia and harsh environmental protection needs.Complex near surface geological conditions,especially the signal shielding and static correction of permafrost make the quality of seismic data is not ideal,the signal to noise ratio(SNR)is low,and deep target horizon imaging is difficult.These data cannot provide high quality information for oil and gas geological survey and structural sedimentary research in the area.To solve the issue of seismic exploration in Tibetan Plateau,this test used low frequency vibroseis wide-line and high-density acquisition scheme.In view of the actual situation of the study area,the terrain,the source and the diff erent observation system were simulated,and the processing technique was adopted to improve the quality of seismic data.Low-frequency components with a minimum of 1.5Hz of vibroseis ensure the deep geological target imaging quality in the area,the seismic profi le wave group is clear,and the SNR is relatively high,which can meet the needs of oil and gas exploration.Seismic data can provide the support for the development of oil and gas survey in the Tibet plateau.

Noise control and utility:From regulatory network to spatial patterning

Stochasticity(or noise) at cellular and molecular levels has been observed extensively as a universal feature for living systems. However, how living systems deal with noise while performing desirable biological functions remains a major mystery. Regulatory network configurations, such as their topology and timescale, are shown to be critical in attenuating noise, and noise is also found to facilitate cell fate decision. Here we review major recent findings on noise attenuation through regulatory control, the benefit of noise via noise-induced cellular plasticity during developmental patterning and summarize key principles underlying noise control.