Coherent Noise Attenuation Research Articles

Coherent Noise Attenuation by Kurtosis-Guided Adaptive Dictionary Learning Based on Variational Sparse Representation

In seismic data denoising, coherent noise is usually challenging to remove because it has similar features with signal. Therefore, attenuating coherent noise is of great importance for seismic data. In order to obtain a good denoising result, it is necessary to fully consider the features of coherent noise and choose the appropriate denoising methods. Here, we propose a kurtosis-guided adaptive dictionary learning (KGADL) algorithm based on variational sparse representation model. First, the variational sparse representation is to construct initial dictionary that depends on the seismic data, so that it can accurately contain the features of the seismic data, so as to improve the accuracy of sparse representation. Then, we use the K-singular value decomposition (K-SVD) algorithm to update the dictionary and introduce kurtosis to measure each atom after updating. Atoms with high kurtosis values usually exhibit strong irregularities and are considered noise, while atoms with low kurtosis values have more feature of valid waves and are considered signal. The atoms with low kurtosis values characterizing valid signal are retained to get the special dictionary that adapts to the complexity of the input seismic data, and this dictionary is used to suppress the coherent noise in seismic data. Finally, the denoising performance of the proposed method is illustrated by examples of both synthetic and field seismic data.

Seismic Coherent Noise Removal of Source Array in the NSST Domain

The technique of the source array based on the vibroseis can provide the strong energy of a seismic wave field, which better meets the need for seismic exploration. The seismic coherent noise reduces the signal-to-noise ratio (SNR) of the source array seismic data and affects the seismic data processing. The traditional coherent noise removal methods often cause some damage to the effective signal while suppressing coherent noise or cannot suppress the interference wave effectively at all. Based on the multi-scale and multi-direction properties of the non-subsampled Shearlet transform (NSST) and its simple mathematical structure, the seismic coherent noise removal method of source array in NSST domain is proposed. The method is applied to both the synthetic seismic data and the filed seismic data. After processing with this method, the coherent noise of the seismic data is greatly removed and the effective signal information is greatly protected. The analysis of the results demonstrates the effectiveness and practicability of the proposed method on coherent noise attenuation.

Random and coherent noise attenuation for 2D land seismic reflection line acquired in Iraq

ABSTRACT Noises are common events in seismic reflection data that have very striking features in seismograms, affecting seismic data processing and interpretation. Noise attenuation is an essential phase in seismic processing data, usually resulting in enhancing the signal-to-noise ratio (SNR) and improving the subsurface seismic image. Groundroll presence is a major fashion of significant noise in land seismic surveys. It is a type of coherent noise present in seismograms that appears as linear events, in most cases overlapping the reflections and probably making it challenging to recognise. There are several domains used in noise attenuation techniques. The domain transformations are a complex algorithm used commonly during the processing of seismic data; Therefore, a large number of methods have been developed to attenuate these types of noise to preserve the frequency bandwidth and enhance the SNR of the seismic data. In the time-offset domain, the noise wave such as groundroll and random noises overlap over time; a different domain makes it easier to successfully isolate coherent, random noise and reflection events. We have used effective algorithms in different domains such as (shot, receiver, t-x, f-x, f-k, R-T, and offset class) to attenuate coherent and random noises present in the data. The results indicate that the different domains can reveal features and geological structures that have been masked by the noises present in current data. Because these filtering techniques encourage significant improvements in the final image quality in the 2D seismic section, possibly giving the interpreter an advantage, particularly in structural and stratigraphic interpretation.

Accelerating seismic scattered noise attenuation in offset-vector tile domain: Application of deep learning

Recent years have witnessed many practical applications of supervised deep learning in seismic processing. However, a weak generalization behavior prevents widespread implementation on large-scale prestack data sets for coherent noise attenuation. This is particularly true when addressing strong near-surface scattered noise in land seismic data. To alleviate this problem, we have combined deep learning with an offset-vector tile (OVT) partitioning method to suppress strong scattered noise. With the OVT partitioning method, seismic data are spatially uniformly sampled, offering a favorable foundation for network learning. Specifically, the reflection probability distribution is more stationary than the noise distribution, making it easier for the network to learn the reflections. Accordingly, we use the direct signal learning strategy rather than the commonly used residual learning strategy to train the network. To construct high-quality training labels, we adopt the 3D continuous wavelet transform (3D CWT), which can exploit the 3D spatial correlation in OVT gathers. General use of these labels can produce results similar to 3D CWT but is highly efficient. To further improve denoising performance, we propose a training sample construction approach that leverages middle-offset OVT volumes with varying azimuths in light of midoffset relatively high signal-to-noise ratio characteristics. The field data experiment demonstrates that our proposed method also has an excellent generalization ability. Despite only using six middle-offset gathers for training, the trained network is able to effectively process 1260 OVTs in a timely manner.

Separation of seismic multiple reflection-refraction based on morphological component analysis with high-resolution linear Radon transform

Investigating coherent noise attenuation is a continuing concern within seismic signal processing. As a common type of linear coherent noise, the multiple reflection-refraction (MRR) occurs in seismic records in which low-velocity strata overlie high-velocity strata, such as deserts, the Loess Plateau, etc. Due to its high velocity and strong energy, MRR seriously distorts the reflections and affects the interpretation. MRR has linear morphological characteristics on the shot gathers. Thus, a linear Radon transform with a surgical mute is usually applied to suppress MRR. However, significant numbers of shallow reflections are removed unintentionally in the [Formula: see text] domain when reflections overlap MRR. Therefore, a robust method that reduces the leakage of reflection energy is required. We have developed a novel method to attenuate MRR by examining the morphological difference between MRR and useful signals in the [Formula: see text] domain. MRRs are oblique linear events on the shot records, whereas the useful signals are quasi-hyperbolic events under the assumption of horizontal layers. After the high-resolution linear Radon transform, MRRs are ideally mapped into point features, whereas the useful signals are aligned with narrow curve bands in the ellipse. To better separate them in the [Formula: see text] domain, we use the morphological component analysis (MCA) theory and select the 2D stationary wavelet transform and the shearlet transform as sparse representation subdictionaries of point features and curved features, respectively. After MCA separation, we apply the inverse Radon transform to the separated MRRs and subtract them from the original seismic data. Subtraction can better preserve the amplitude of reflections. We use synthetic data and field data to illustrate the effectiveness of our method and demonstrate that making full use of the preceding morphological differences can improve the results of MRR attenuation.

Adaptive ground-roll attenuation using local nonlinear filtering

Source-generated coherent noise suppression is a long-standing issue in land seismic data processing. Traditional methods—such as f–k filtering, τ-p transform, and median filtering—often fail to remove coherent noise in irregular data, especially in the case that the noise highly overlaps with the signal. To address this issue, we developed a method based on local nonlinear filtering (LNF) to attenuate coherent noise in seismic data. In the proposed method, a novel similarity coefficient algorithm is presented to determine the optimal velocity of coherent noise. An initial model of coherent noise is constructed using a mixed median–mean filter and is then refined by the proposed weighting correction algorithm. After modeling the noise, coherent noise in seismic data can be then suppressed by adaptively subtracting the estimated noise from the raw seismic data. To verify the effectiveness of the proposed method, we applied it to regular and irregular synthetic data and a 3D field dataset containing strong ground roll. The results demonstrate that the new method successfully attenuates the ground roll and obtains better results in ground-roll attenuation and the preservation of useful signals compared with the three commonly used methods. Synthetic and real data examples indicate that the method is applicable to the attenuation of coherent noise in irregular data.

Coherent noise attenuation for passive seismic data based on iterative two-dimensional model shrinkage

Coherent noise attenuation for passive seismic data based on iterative two-dimensional model shrinkage

Seismic noise attenuation via convolution neural network with learningnoise prior

The presence of noise degrades the quality of seismic data and makes the subsequent processing tasks and interpretation more challenging. Therefore, seismic noise attenuation is a key step in the processing of seismic data. We propose a novel convolutional neural network (CNN) framework with learning noise prior. Unlike conventional CNN-based seismic denoising methods, this new network is composed of a noise extractor and a denoiser. The noise extractor extracts noise from the original data to provide a high-precision noise prior to the denoising process. The denoiser uses the noise prior for denoising of the seismic data. This method is superior to the presently used networks in terms of the denoising effect. Additionally, the proposed network can be applied for random noise suppression as well as coherent noise attenuation. Synthetic and field tests illustrated the superiority of the proposed approach over the traditional denoising methods in suppressing noise and improving the signal-to-noise ratio (SNR) of seismic data.

Attenuation of the Multiple Reflection-Refraction in 2-D Common-Shot Gather via Random-Derangement-Based FX Cadzow Filter

The attenuation of coherent noise plays a crucial role in reflection seismology but still poses some technical challenges. The multiple reflection-refraction (MRR) is one of the main coherent noises in land seismic surveys. The Cadzow filter can effectively attenuate incoherent noise. But it struggles in attenuating coherent noise. After keeping reflection events relatively horizontal, some researchers randomly rearrange the trace orders of the input data to realign coherent noise in an incoherent position so that the Cadzow filter can treat the realigned coherent noise as incoherent noise. Accordingly, a random-derangement-based FX Cadzow filter is proposed to attenuate MRRs in 2-D common-shot gathers based on the linear feature of MRR events and randomization of limited trace orders of the input data. In practice, after linear-move-out (LMO) with an estimated dip, the input data are divided into several small windows to obtain a group of relatively horizontal MRR events. Then, these windows are randomized and filtered one by one. Due to the limited trace orders of each window, there are many possible rearrangements after randomization. Some rearrangements may lead to undesirable filtering performance. To obtain the rearrangements that lead to good filtering performance, each window's trace orders are randomly deranged by resampling from the input orders with uniform distribution. A 2-D synthetic data experiment shows the influence of different rearrangements on the filtering performance. Both 2-D synthetic data and 2-D field data demonstrate that the proposed method outperforms the 2-D FK filter to attenuate both aliased and nonaliased MRRs.

Karhunen Loeve Transform with adaptive dictionary learning for coherent and random noise attenuation in seismic data

Seismic reflection data are used by geologists to identify the best site for oil and gas explorations. The raw seismic image cannot be directly used for explorations because the noises may hinder the primary reflections and result in misinterpretations. In the proposed research work, the denoising method with adaptive dictionary learning is attempted to attenuate the coherent and random noises which affect the seismic reflections. In this method, Karhunen Loeve Transform (KLT) is combined with K-means Singular Value Decomposition (KSVD) to improve the Signal to Noise Ratio (SNR) of the seismic data. The combination of fixed transform using KLT and learning-based dictionary using KSVD remove the redundant data while retaining the necessary data for further interpretations. KLT is applied on the whole seismic image to decorrelate the coefficients and retain the primary reflections. The horizontal events in the seismic image are preserved as they represent the eigen images with large energy. The KSVD is applied to the KLT resultant data, which denoises the patch of data while simultaneously updating the dictionary. The noise is eliminated using local sparsity of the image where mutually overlapping small image blocks are learned to yield the self-adaptive redundant dictionary. It is then used to obtain the sparse representation of the image blocks by eliminating noise. The algorithm is tested for both synthetic and field seismic images, and the results indicate a better reduction of random and coherent noise with the acceptable execution time compared to other denoising algorithms used. The KLT combined with KSVD method decorrelate the seismic data from noise by preserving the primary reflections and discarding the redundant noise.

An Iterative Focal Denoising Strategy for Passive Seismic Data

Passive seismic source imaging can extract geophysical information from underground noise and has been widely utilized in geophysical research. Compared with conventional active seismic exploration, it is low-cost and eco-friendly; however, the application of passive seismic data is limited by coherent noise in the virtual-shot gathers. An approach involving direct denoising in the virtual-shot gathers has not previously been discussed; therefore, we present an iterative denoising strategy for passive seismic data. The reflection-preserving characteristic of focal transformation is adopted in the virtual-shot gathers to eliminate the coherent noise, and L1-norm sparse inversion is utilized to obtain a more accurate solution during focal transformation. A key aspect of this strategy is clean focal operator building at high noise levels. We apply local similarity as the criterion for extracting the majority of reflection energy for the focal operator. Because of strong coherent noise, a clean focal operator cannot be obtained in one iteration. We therefore obtain both denoised passive seismic data and a clean focal operator by denoising using a cleaner focal operator and operator building using updated denoising results. The presented approach can overcome the limits of coherent noise in virtual-shot gathers, which is significant for subsequent data processing and wider application. Synthetic examples achieve excellent performance in coherent noise attenuation and reflection energy reconstruction, especially in far-offset sections.

Multimeasurement aliased-noise attenuation for sparse land seismic acquisition using compressed sensing

Land data are contaminated by energetic surface waves that have low apparent velocities relative to the seismic signal. The conventional land acquisition approach to noise attenuation is to deploy arrays of hard-wired geophones whose combined response acts as a spatial filter. Besides the shortcomings of a fixed array response that is sensitive to perturbations, using a large amount of equipment limits the field efficiency of array-based acquisition. The advent of point-receiver acquisition technology addresses some of these limitations by enabling measurement of each single geophone along a receiver line and removing the slowest energetic noise by velocity-based filtering. However, according to the classic sampling theorem, the proper sampling of noise requires sampling the shortest wavelength twice. Because surface waves have slow propagation velocities, it is not feasible to sample the noise properly and a compromise is made between quality and efficiency. We have developed two different methods to improve the efficiency of point-receiver technology by allowing for wider receiver spacing without compromising the coherent noise attenuation capabilities. First, we combine compressed sensing-based methods such as the matching pursuit approach with the gradient measurement inferred from a novel 5C sensor to dealias and attenuate source-generated noise. Second, we have developed a method to design optimized acquisition geometries by using the spectral characteristics of the surface waves, where the objective is to place a limited number of receivers at the most favorable locations to help attenuate the coherent noise. We determine the effectiveness of our methods through synthetic and field data examples in which we find that spatially aliased coherent noise can be reconstructed to a denser grid and attenuated.

Swell-noise attenuation: A deep learning approach

Noise attenuation for ordinary images using machine learning technology has achieved great success in the computer vision field. However, directly applying these models to seismic data would not be effective since the evaluation criteria from the geophysical domain require a high-quality visualized image and the ability to maintain original seismic signals from the contaminated wavelets. This paper introduces an approach equipped with a specially designed deep learning model that can effectively attenuate swell noise with different intensities and characteristics from shot gathers with a relatively simple workflow applicable to marine seismic data sets. Three significant benefits are introduced from the proposed deep learning model. First, our deep learning model doesn't need to consume a pure swell-noise model. Instead, a contaminated swell-noise model derived from field data sets (which may contain other noises or primary signals) can be used for training. Second, inspired by the conventional algorithm for coherent noise attenuation, our neural network model is designed to learn and detect the swell noise rather than inferring the attenuated seismic data. Third, several comparisons (signal-to-noise ratio, mean squared error, and intensities of residual swell noises) indicate that the deep learning approach has the capability to remove swell noise without harming the primary signals. The proposed deep learning-based approach can be considered as an alternative approach that combines and takes advantage of both the conventional and data-driven method to better serve swell-noise attenuation. The comparable results also indicate that the deep learning method has strong potential to solve other coherent noise-attenuation tasks for seismic data.

Using Data‐Regrouping Methods to Attenuate Shot‐to‐Shot Coherent Interference Noise in Marine Seismic Data

AbstractsUnwanted interference noise often causes problems during seismic reflection data acquisition and processing. In this paper we present two novel data‐regrouping methods that enable improved shot‐to‐shot coherent interference noise attenuation in marine seismic data. The two approaches are based on line mixing and/or shot skipping: (1) for a 3‐D marine seismic reflection data acquisition which is typically carried out by shooting closely spaced parallel lines, we propose to mix the shot gathers from two adjacent sail lines to break up shot‐to‐shot coherent noise. This approach takes advantage of the fact that adjacent sail lines are usually acquired with a significant time interval, resulting in different types of noise; (2) for a 2‐D/3‐D acquisition, we propose an algorithm to skip and reorder the original shots to decrease the continuity of the shot‐to‐shot coherent noise. This approach could also supply more aperture data for the first or last shot from both sides, which is beneficial for the coherent noise removal at the edge area. After regrouping the original data, the coherent noise can then be further randomized or isolated by sorting and transforming to other domains, typically the common p‐shot domain where a random noise attenuation tool could be applied to identify and attenuate such noise. Field data examples confirm the effectiveness of the methods.

Robust Estimation of Multiple Local Dips via Multidirectional Component Analysis

We propose a novel method for robust estimation of multiple local dips in seismic data via signal decomposition. The decomposition is achieved by regularizing each separated component to have a single dip. The single-dip regularization is built based on the fact that piecewise constant dips indicate that local gradient vectors lie on the same directions. The method is formulated as an optimization problem, which is solved in the framework of a block coordinate descent method. The proposed method is robust to random noise and can be used to estimate multiple dips accurately. As the proposed method has clear physical meaning, it is easy to be applied for other tasks, such as random and coherent noise attenuation, signal separation, and so on. We test the feasibility of our method in estimating local dips with synthetic and field data and compare with the structural tensor method and the plane-wave destruction method. Results demonstrated that the proposed method can estimate multiple dips and is more robust to noise than the other two methods. Applications for random noise attenuation, coherent signals separation, and unconformity detection are also tested.

Three-parameter Radon transform in layered transversely isotropic media

Radon transform is a powerful tool with many applications in different stages of seismic data processing, because of its capability to focus seismic events in the transform domain. Three-parameter Radon transform can optimally focus and separate different seismic events, if its basis functions accurately match the events. In anisotropic media, the conventional hyperbolic or shifted hyperbolic basis functions lose their accuracy and cannot preserve data fidelity, especially at large offsets. To address this issue, we propose an accurate traveltime approximation for transversely isotropic media with vertical symmetry axis, and derive two versions of Radon basis functions, timevariant and time-invariant. A time-variant basis function can be used in time domain Radon transform algorithms while a time-invariant version can be used in, generally more efficient, frequency domain algorithms. Comparing the time-variant and time-invariant Radon transform by the proposed basis functions, the time-invariant version can better focus different seismic events; it is also more accurate, especially in presence of vertical heterogeneity. However, the proposed time-invariant basis functions are suitable for a specific type of layered anisotropic media, known as factorized media. We test the proposed methods and illustrate successful applications of them for trace interpolation and coherent noise attenuation.

Simultaneous Coherent and Random Noise Attenuation by Morphological Filtering With Dual-Directional Structuring Element

Seismic data are highly corrupted by noise or unwanted energies arising from different kinds of sources. In general, seismic noise can be divided into two categories, namely, coherent noise and random noise, and is treated with essentially different methods. Traditional methods often utilize the differences in frequency, wavenumber, or amplitude to separate signal and noise. However, the application of traditional methods is limited if the above-mentioned differences are too small to distinguish. For this reason, we have proposed a novel morphology-based technique to simultaneously attenuate random noise and coherent noise, i.e., to extract the useful signal. In this technique, we first flatten the signal by normal move out correction or other alternative approaches. For the extraction of the flatten reflections, we propose dual-directional mathematical morphological filtering, which can detect morphological information of the seismic waveforms from two orthogonal directions and then separate signal and other unwanted energy utilizing their difference in morphological scales. Application of the proposed technique on synthetic and field data examples demonstrates a successful performance.

Mathematical morphological filtering for linear noise attenuation of seismic data

Linear coherent noise attenuation is a troublesome problem in a variety of seismic exploration areas. Traditional methods often use the differences in frequency, wavenumber, or amplitude to separate the useful signal and coherent noise. However, the application of traditional methods is limited or even invalid when the aforementioned differences between useful signal and coherent noise are too small to be distinguished. For this reason, we have managed to develop a new algorithm from the differences in the shape of seismic waves, and thus, introduce mathematical morphological filtering (MMF) into coherent noise attenuation. The morphological operation is calculated in the trace direction of a rotating coordinate system. This rotating coordinate system is along the direction of the trajectory of coherent noise to make the energy of the coherent noise distributed along the horizontal direction. The MMF approach is more effective than mean and median filters in rejecting abnormal values and causes fewer artifacts compared with [Formula: see text]-[Formula: see text] filtering. Our technique requires that coherent noise can be picked successfully. Application of our technique on synthetic and field seismic data demonstrates its successful performance.

Signal extraction using randomized-order multichannel singular spectrum analysis

Multichannel singular spectrum analysis (MSSA) is an effective algorithm for random noise attenuation; however, it cannot be used to suppress coherent noise. This limitation results from the fact that the conventional MSSA method cannot distinguish between useful signals and coherent noise in the singular spectrum. We have developed a randomization operator to disperse the energy of the coherent noise in the time-space domain. Furthermore, we have developed a novel algorithm for the extraction of useful signals, i.e., for simultaneous random and coherent noise attenuation, by introducing a randomization operator into the conventional MSSA algorithm. In this method, which we call randomized-order MSSA, the traces along the trajectory of each signal component are randomly rearranged. Two ways to extract the trajectories of different signal components are investigated. The first is based on picking the extrema of the upper envelopes, a method that is also constrained by local and global gradients. The second is based on dip scanning in local processing windows, also known as the Radon method. The proposed algorithm can be applied in 2D and 3D data sets to extract different coherent signal components or to attenuate ground roll and multiples. Different synthetic and field data examples demonstrate the successful performance of the proposed method.

Coherent and incoherent seismic noise attenuation using parabolic radon transform and its application in environmental geophysics

The Radon transform is a mathematical tool that has been widely used in seismic data processing and image analysis. Three types of Radon transforms have been used as noise attenuation techniques in seismic data processing: the slant-stack or τ-p transform; the hyperbolic Radon transform; and the parabolic Radon transform. Ground-roll is a main type of strong noises in environmental geophysics. Attenuation of these types of noises is vital for improving the signal-to-noise ratio of seismic data. In the time–offset (t–x) domain, the ground-roll noise and other noisy waves just like; random noises, direct waves and airwaves overlap each other by the terms of time, which makes it difficult to attenuate coherent and incoherent noises in environmental geophysics. Nevertheless, significant different features shown in the time–slowness (τ–p) domain make it possible to separate coherent and incoherent noise and reflection waves effectively. A new method is proposed to separate these waves, using parabolic Radon transform (PRT). Amplitude and phase information is preserved during the proposed transformation. The reversibility and curvature of PRT provide a foundation for ground-roll noise suppression in the time-slowness domain. Synthetic datasets field and data examples have been implemented to illustrate coherent and incoherent noises attenuation can be achieved with a very little distortion of the effective signals. When compared with the linear radon ground-roll attenuation method, the field example results show the superiority of our method in suppressing the ground-roll noise and preserving the amplitude and phase information of effective waves.