Seismic interpretation is crucial in seismic exploration to identify geological structures in the field. However, interpretation is often challenging due to inherent low-resolution (LR) limitations, and acquiring high-quality data is expensive with no guaranteed fidelity. To address these challenges, we propose a novel supervised deep learning-based method to enhance seismic data resolution by simultaneously focusing on dominant wavelet frequency and the spatial domain. First, we employ a deep learning module to estimate the location of low-frequency wavelets as semantic information, providing a prior that allows the model to process these signals and precisely enhance the dominant frequency. Subsequently, we use a feature wrapper to integrate the LR data with the semantic priors. We use them as conditions for a diffusion model to generate high-frequency features, considered priors with higher-frequency wavelets. Finally, we input the priors and the LR data into a feature fusion module (FFM) to generate the final output, doubling the sampling points and traces to achieve high-resolution (HR) data with high-frequency wavelets. The models are trained separately using cross-entropy, Kullback-Leibler divergence, and Charbonnier loss. The priors we use and the design of the generative diffusion model ensure high fidelity, preventing false seismic events and over-smoothing. Experiments on field data demonstrate the superiority of our method compared to three other approaches, highlighting its potential as a powerful seismic super-resolution tool in practical applications.

For subsequent seismic data processing and interpretation, it is important to obtain high-quality distributed acoustic sensing (DAS) signals from down-hole DAS data containing various complex noises. Model-based denoising methods mainly treat this signal estimation issue as a maximum a posteriori (MAP) optimization problem, for its relatively transparent mathematical model and wide range of applications. However, the manually designed prior assumption in MAP cannot accurately describe the actual distribution of DAS data, so the optimization parameters for obtaining high-quality solutions are difficult to determine, making it unavailable in DAS signal estimation. To solve these problems, we propose to emulate the optimization process of MAP with neural networks and accomplish the signal estimation task in feature space via some customized optimization modules. Specifically, we first construct an optimization unit (OPTU) to simulate the optimization process. And then, in order to further obtain the signal distribution of DAS data, we design in each OPTU, a multiscale dense feature aggregation (MDFA) module with the idea of back-projection fusion. With the help of OPTU, the optimization estimation process would be implemented more finely and automatically, expanding the application of MAP for accurate DAS signal estimation. Experiments on both synthetic and field DAS data demonstrate that our method can successfully estimate the high-quality signals from DAS data corrupted by complex noises, with less energy loss.

Nowadays, one of the predominant deep learning approaches to improve the quality of DAS VSP seismic data is executed through supervised learning, which requires paired training set including data simulation with relevant parameters and solutions of elastic wave equations. However, differences between simulated data and field data in terms of signal regulations and noise distributions often leads to poor results. An alternative approach is self-supervised learning, such as the representative framework--Blind Spot Network (BSN), but unfortunately, the effective information in blind spots cannot be fully utilized. To solve this problem, this paper considers BSN as a basis and establishes a novel self-supervised network--blind spot visualization (BSV) to suppress random noise and improve the quality of DAS VSP data. In BSV, one branch is dedicated to first produce more denoised data with blind spots and then recover the valid information covered by the blind spots, assuming that the signal is partially data-dependent and the DAS noise is conditionally data-independent. The other branch is designed to generate a target for training without blind spots, so that the dual-branch network can accomplish a self-supervised task in the way of supervised learning. More than that, unlike BSN, we utilize a tailor-made blind spot mapper (BSM) to recover effective information in the blind spots. Results of field data testing prove BSV’s advantages in suppressing random noise and improving the quality of DAS VSP data, although test on synthetic data is nearly identical to supervised learning.

As conventional reserves of oil and gas resources continue to decrease, the target of seismic exploration is progressively shifting to the unconventional field. Fine-amplitude structure can form rich oil and gas resources under favorable conditions, which is an important unconventional reservoir. Surface seismic exploration is capable of revealing a large range of stratum structure, but its resolution is limited, and it is unable to detect fine-amplitude structures of small closure. Distributed acoustic sensing (DAS) vertical seismic profile (VSP) technology is a new, high-precision method for oil and gas resource detection, but it can only collect stratum information from a small area in the vicinity of the well. To explore the stratum structure with finer profile information, expand the identification range, and take full advantages of these two exploration methods, this letter proposes the stratum correlation technology to calculate correlation coefficients between DAS VSP data and surface seismic data at the same interface. By analyzing the shape and variation of the correlation coefficient curves, it is possible to locate and extend the information on fine-amplitude structure with a closure of less than 12 m. Stratum correlation processing can improve the accuracy of surface seismic data, clarify the existence and location of fine-amplitude structures, and extend the lateral extent of borehole DAS fine profiles.

Distributed acoustic sensing (DAS), an emerging technology for signal acquisition, has been progressively applied to collect vertical seismic profile (VSP) data. Unfortunately, the obtained DAS VSP data are usually contaminated by various complex noise, which poses a major obstacle to subsequent processing; therefore, suppressing the noise in the DAS VSP data is a critical step. With the development of neural networks, deep learning is widely used for seismic data denoising. Supervised learning-based denoising methods, however, require massive amounts of training datasets with labels. The lack of labeled datasets limit the performance of supervised learning methods. The recently proposed unsupervised learning-based denoising methods reduce the reliance on labeled data, but they are not suitable for processing seismic data containing multiple types of complex noise. In this study, we propose a semi-supervised denoising network (SSDN) that contains both supervised and unsupervised paths. The supervised path is trained using a synthetic dataset to extract rich signal features. Unsupervised path exploits the distribution difference between signal and noise, using field dataset to extract realistic and accurate signal features. The backbone network consists of a three-layer pyramid structure and incorporates a multiscale fusion strategy to improve network performance. The idea of semi-supervised learning reduces the reliance on labeled data, takes full advantage of the distribution characteristic of the field data, and benefits the generalization ability of the denoising model. Experimental results on one synthetic data and four field DAS VSP data demonstrate that the proposed method obtains competitive performance in intense noise suppression and effective signal recovery.

Distributed acoustic sensing (DAS) technology has been applied in vertical seismic profiling to provide high-precision seismic records for oil and gas exploration in recent years. However, these records are often contaminated by various noises due to the limitations of acquisition instrument and borehole environment, resulting in an unclear reflection of the geologic structure. The object of this paper is to remove the severe noise contamination through signal decomposition, time-frequency description and feature classification. Firstly, we design an improved decomposition method to avoid signal distortion and reduce frequency aliasing during the decomposition process. A flexible sifting iterative stop condition is applied to sift through unevenly distributed noises in the DAS records. Secondly, we construct a high-dimensional attribute space using time and frequency factors to represent the confused decomposed components. This ascending-dimensional feature mapping facilitates the description of differences between seismic signal and noise. Finally, we establish a two-level ensemble framework based on tree-structure learners to complete the classification tasks in the high-dimensional feature space. Experiments have proved that this method effectively recovers seismic signal waves while accurately suppressing the complex noises. The improved decomposition method overcomes signal distortion and reduces frequency aliasing, providing clearer information for feature extraction. The tree-structure ensemble model exhibits high accuracy and strong generalization, ensuring the low-attenuation recovery of effective signals. Furthermore, this research reveals the mechanism by which noise interferes with signals and identifies the dominant frequency of random noise.

Distributed acoustic sensing (DAS) has been gradually applied to vertical seismic profiling (VSP), where the generated DAS VSP seismic data contains types of complex noise. Therefore, data denoising plays an important role in collecting high-quality geological information. Generative adversarial network(GAN) has been widely used in seismic exploration data denoising these years, but problems such as insufficient optimization objectives, poor signal retention continuity, and insufficient accuracy still remains when processing DAS VSP data. To address these problems, this paper proposes DuGAN, a deep learning network for multi-scale feature extraction and global information discrimination, to better meet the requirements of high-precision in DAS VSP data denoising. Our method takes GAN as the basic architecture and chooses the multi-scale codec network U-net to explore the potential correlation of DAS data at different scales and a more robust feature representation of DAS signals. In addition, DuGAN is more inclined to emphasize the global role of discriminator so that the entire network ensures the integrity of effective signal structure from a global perspective. Also, for more accurate recovery of the DAS reflected signal, we adjust the loss function in adversarial training and tilt the target optimized space towards the discriminator. Experiments on synthetic and field DAS seismic data show that DuGAN has better denoising performance-not only the noise-covered signal can be recovered, but also the overall effective events are better preserved.

In recent years, distributed acoustic sensing (DAS) has been widely used for vertical seismic profile (VSP) data acquisition. Compared with conventional geophones, the data collected by DAS usually contains more noise. Therefore, denoising is an essential step in DAS VSP data processing. Benefiting from the development of neural networks, learning-based methods are widely used for seismic data denoising. However, supervised learning-based denoising methods are often limited in practical applications due to the scarcity of labeled datasets. Although recently proposed self-supervised learning-based methods, such as blind spot network (BSN), alleviate the reliance on ground-truth data, the harsh conditions that noise should satisfy zero-mean and statistical independence are also difficult to meet. In this study, we propose an improved BSN for spatially correlated noise suppression in DAS VSP data. Specifically, we observe that the spatial correlation of DAS noise decreases as the distance between noise pixels increases, and that the rate of decrease is direction-dependent. Based on this observation, we improve the pixel-shuffle down-sampling (PD) strategy to sample and recombine the DAS VSP data to increase the distance between adjacent pixels, thereby weakening the spatial correlation of noise to meet the application conditions of BSN. In addition, we also propose a signal detail enhancement strategy to compensate for the effect of PD strategy on signal detail recovery. Qualitative and quantitative analysis on the denoising results of one synthetic and three field DAS VSP data show that the proposed method can effectively remove noise with certain spatial correlation, showing competitive performance.

Distributed fiber-optical acoustic sensing (DAS) is a new and booming technology in seismic exploration. DAS technology has been gradually applied to the exploration of vertical seismic profile (VSP) due to its strong resistance to high temperature and pressure, high sensitivity, high precision (trace interval can be accurate to about 1 m), and so on. However, real DAS-VSP data are always contaminated by both random and coherent noises, which greatly affects the quality of DAS-VSP data. In order to suppress the background noise and increase the signal-to-noise ratio (SNR), a convolutional neural network (CNN) based on leaky rectifier linear unit (ReLU) and forward modeling is proposed and named L-FM-CNN. In terms of network architecture, Leaky ReLU is adopted as the activation function of CNN, which can enhance the recovery ability of trained CNN denoising model to the weak effective signals. As for the training data set, we construct a high-authenticity theoretical pure seismic data set for DAS-VSP data through the complexity of forward models and the diversification of physical parameters. In addition, we propose a new mean square error (MSE) loss function combined with an energy ratio matrix (ERM). The ERM can adjust the SNR between the signal patch and noise patch during the network training and thus increase the robustness of trained CNN denoising model for the DAS-VSP data with different SNRs, especially the DAS-VSP data with extremely low SNR. Both synthetic and real experiments prove the effectiveness of the proposed L-FM-CNN.

Noise reduction is an essential step in seismic exploration. Formally, the random broadband noise in the desert seismic is characterized as nonlinear, nonstationary, and non-Gaussian, and the energy is concentrated mainly in the low-frequency range. Moreover, the reflected signals generally cover the same spectral region as a strong random broadband noise. In this letter, a method combining unsupervised feature learning and the time–frequency transform (TFT) technique is proposed to reduce random broadband noise in desert seismic data. First, as a TFT technique, the variational mode decomposition (VMD) is carried out to decompose the multicomponent desert seismic signal into an ensemble of band-limited modes. Then, we apply an unsupervised feature learning method on each decomposed mode for detecting desert seismic events. Finally, the inverse VMD transform is conducted to obtain the final denoised result. This method is tested on both synthetic and field desert seismic data, demonstrating its preferable performance in reducing random broadband noise and preserving reflected signals.

For imaging and interpretation, high-quality seismic data are necessary. However, noise, which is strong in field desert seismic data, inevitably diminishes the quality of the data and reduces the signal-to-noise ratio. Moreover, the effective signals and noise in field desert seismic data are mostly distributed in the low-frequency band, which leads to severe spectral aliasing. Recently, some deep learning methods have improved the quality of desert seismic data in certain aspects. However, due to limitations of their networks and the serious spectral aliasing of desert seismic data, the denoising results usually show some false seismic reflections. To solve the above problems, we introduce Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (U-GAT-IT) to the denoising of desert seismic data in a semisupervised manner. U-GAT-IT is an unsupervised attentional generative adversarial network (GAN) combined with an attention module guided by the class activation map (CAM). The attention module guided by the CAM can guide the model to better distinguish between noise and effective signals. The experiment shows that the U-GAT-IT can effectively suppress desert seismic noise. Also, the denoising result has fewer false seismic reflections.

Since seismic data will be interfered with by a host of complicated noise during the acquisition process, the quality of the acquired seismic data is usually poor. The overlap of signals and noise makes it difficult to extract effective signals from desert seismic records. Therefore, the suppression of seismic noise and the retention of seismic signals are key issues in seismic signal processing. In order to improve the quality of the data obtained, we propose an unsupervised relative attributes-based generative adversarial network (RAGAN), which includes a generator, a discriminator, and an attribute match-aware discriminator. By encoding the data of different attributes in seismic records, the denoising task can be regarded as the conversion process of the data corresponding to the attributes. The relative attributes obtained by the difference between the target attribute and the original attribute are used to control the attributes of the data generated by the generator, so as to achieve the purpose of noise suppression. Experimental results of both synthetic and field seismic records show that the proposed method performs better than part of conventional methods.

Distributed acoustic sensing (DAS) is a new downhole vertical seismic profile (VSP) acquisition technology, which has many advantages of low cost, sensitive signal capture capability and high spatial-temporal resolution. It can provide dense wavefield information for subsequent processing. Although DAS has obvious advantages over geophones, some weakness may limit its application. The main challenge is that DAS data are polluted by various types of noise, including optical abnormal noise, random background noise, fading noise, and so on. The noise brings great difficulties to the interpretation of seismic data. In order to suppress the noise and recover the buried weak effective signals, we design a new sparse parallel-subnet network (SPSNet) in this paper. It includes a parallel-subnet feature extraction module with sparse mechanism, simultaneously extracting global and local dual features. In this way, we can extract as much detailed information as possible from DAS seismic data. Then the following enhancement module fuses these features for signals complement. Another outstanding advantage is its high efficiency owing to the parallel structure. Compared with the complex networks with the same noise suppression effect, SPSNet has higher work efficiency. We generate a large number of geologic structure models with different parameters to optimize SPSNet. The denoising results show that the proposed method can effectively suppress a variety of noise in DAS seismic data. And the deep layer signals with weak energy are also well recovered.

Distributed acoustic sensing (DAS) is widely recognized as a new technology to replace conventional geophones for the acquisition of seismic data. However, the collected data often contain a lot of coupled noise due to cable slapping and ringing along the borehole casing, which brings great difficulties to the interpretation of seismic data. The existing conventional coupled noise reduction methods often need to estimate the parameters of each coupled noise (such as amplitude, noise period, attenuation coefficient, etc.), which takes a lot of time and cannot meet the requirements for large-data-volume DAS seismic data processing. In addition, some deep learning-based denoising methods lack detailed analysis on coupled noise and have problems in the construction of training sets, resulting in insufficient generalization ability of the denoising model. To solve these problems, we propose a coupled noise reduction method based on the convolutional neural network (CNN). The proposed method does not need to estimate the parameters of coupled noise, and the denoising process is more convenient and efficient. In addition, through the analysis of DAS seismic data, we also construct a training set for coupled noise reduction using real data and synthetic data. The denoising results of both synthetic data and field data show that the proposed method can effectively reduce the coupled noise in DAS seismic data, and the effective signal has almost no energy loss. After processing, the signal affected by coupled noise becomes clear and continuous, providing high-quality data support for subsequent interpretation.

Distributed acoustic sensing (DAS) is a new exploration technology widely used to acquire vertical seismic profiles (VSPs). DAS can achieve low-cost and high-density observations, but the signal-to-noise ratio (SNR) of the VSP data collected by DAS is low compared with traditional electrical geophones. Moreover, DAS VSP data cover many types of noise, including random noise, fading noise, checkerboard noise, and long-period noise. These noises bring many difficulties to the imaging and interpretation of DAS VSP data. To solve this problem, we proposed a multi-stage denoising network (MSDN) to denoise DAS VSP data. MSDN is a progressive denoising network consisting of four stages. MSDN can recover the signal details better than a single-stage denoising network, which is beneficial when processing deep reflection signals. In addition, MSDN combines residual structure and an attention mechanism. The residual structure can prevent the degradation of the deep neural network, while the attention mechanism can make the network focus on effective signals, making network learning more accurate and efficient. Both synthetic data and field data denoising results showed that MSDN could effectively remove various complex noises and restore signals covered by noise. Compared with other denoising methods, our method has improved signal amplitude preservation ability and noise suppression ability.

With the goal of obtaining high-quality downhole distributed acoustic sensing (DAS) data from multiple types of complex noise, denoising plays an important role. However, conventional denoising methods cannot achieve a satisfactory effect toward multiple types of complex noise. Recently, convolutional neural networks (CNNs) exhibit dramatic improvements over conventional methods. The existing CNN-based methods typically operate through incorporation with conventional methods for adaptive threshold, additional multiscale idea, or attentional mechanism for more features extraction. In these cases, serious signal loss or artificial events are usually generated. Maybe, the recovered events are not continuous with some breakpoints and derangement. To resolve these problems in current networks, we propose a novel iterative parallel-attention guided multibranch residual network (PA-MRNet) with the collective goals of suppressing multiple types of complex noise and recovering buried weak signals. The core of our method is an attentional multibranch residual block (AMRB) containing: parallel multiresolution convolution streams for multiresolution features extraction, proposed parallel attention block (PAB) design for interested features capture, and novel parallel-attention guided fusion block for features fusion. In a word, our method can learn an enriched set of features to suppress multiple types of complex noise and simultaneously recover weak signals continuously with less energy loss. Experiments on synthetic and field DAS data demonstrate the better performance of the proposed iterative PA-MRNet.

Distributed acoustic sensing (DAS), increasingly mature technology for signal acquisition, has been gradually applied in the field of environmental monitoring and seismic exploration. However, the usually strong noise in DAS data and the huge amplitude contrasts among direct waves, reflected waves, and converted waves significantly complicate subsequent data processing and interpretation, which would further limit the wide application and rapid development of DAS in the seismic exploration field. Taking the high-accuracy processing requirements of DAS data into consideration, we propose a multiscale progressive fusion attention network (MPFAN) with pyramidal structure trained by small samples, which explores the collaborative representation of complementary information between DAS noise and its multiscale versions in a multiscale direction to realize the fine modeling of DAS noise and then achieve the suppression of DAS noise. At each scale, MPFAN uses a recurrent calculation to explore the potential correlation of complex DAS noise and learn the global texture, and reassigns the weights of feature maps on each channel through an attention mechanism to focus on detailed information. At each hierarchy, the information flow converges from the bottom to the top layer of the network to finally achieve an accurate estimation of DAS noise. Experiments show that, after training on a forward DAS dataset, which is composed of synthetic noise-free DAS signals and some DAS noises from both synthetic and field data, MPFAN performs better in high degree than some classical methods—not only more noises are obviously suppressed but also more reflected signals are better preserved.

Distributed acoustic sensing (DAS) is a novel technology, which has the advantages of full well coverage, high sampling density, and strong tolerance to harsh environments. However, compared with conventional geophones, the signal-to-noise ratio (SNR) of vertical seismic profile (VSP) data obtained using DAS is low, and there are many types of noise (such as random noise, coupled noise, fading noise, background abnormal interference, horizontal noise, and checkerboard noise). These noises bring great difficulties to the interpretation of seismic data. Existing DAS VSP data denoising methods generally can only suppress one type of noise. Faced with DAS VSP data with many types of noise, the denoising process is extremely complicated. To solve the above problems, we propose a DAS VSP data denoiser based on the convolutional neural network (CNN), which can suppress a variety of common noise at one time, and the denoising process is more convenient and efficient. In addition, since there is currently no publicly available training set for DAS VSP data, we also use field data and synthetic data to construct a training set for the denoiser. The denoising results show that the proposed method can effectively suppress a variety of common noise in DAS VSP data and the effective signal has almost no energy attenuation. Both the shallow layer signal affected by strong noise and the deep layer signal with weak energy are well recovered.

Seismic prospecting is a common method used in oil and gas resource exploration. However, due to the limitations of current collection techniques, seismic records acquired in the field are typically contaminated by severe incoherent noise, which has negative implications for the subsequent processing and interpretation procedures. In addition, numerous traditional denoising algorithms have been applied in order to mitigate this problem, but further improvements are required, especially for the seismic data with spectral overlapping between effective signals and background noise. In recent years, feedforward denoising convolutional neural networks (DnCNNs) have been applied to suppress the complex random noise, and a series of essential insights have been gained. Nonetheless, conventional denoising networks always extract data features depending on single-scale information, resulting in impaired performance when coping with seismic records with a low signal-to-noise ratio (SNR). For solving this problem, a novel multiscale DnCNN (MSDCNN) is developed as an attempt for random noise suppression. Unlike conventional DnCNN, MSDCNN has a hierarchical structure capable of extracting features at different scales and capturing informative and discriminatory features through effective information integration. Meanwhile, the cross-scale feature interaction also increases the processing accuracy when confronted with weak reflection events. Experimental results derived from both synthetic and field data indicate that the proposed network can effectively suppress the random noise and accurately preserve reflection events, even under low SNR conditions.

At present, most of the seismic data denoising methods based on deep learning attempt to establish a synthetic seismic data set as the network training set to train network parameters. However, the synthetic data set cannot completely reflect the structural characteristics of the field seismic data, resulting in some false seismic reflections in field denoised results. For this reason, this article proposes an attribute-based denoising algorithm for seismic data called attribute-based double constraint denoising network (Att-DCDN). This method applies encoder-decoder and attribute classifier to constitute the generative adversarial network (GAN) and attenuates seismic noise by controlling with/without target attributes (noise attribute and signal attribute). Compared with the noise-free field seismic data, attribute vectors of the field data are easier to obtain. Therefore, our training set includes not only the synthetic seismic data but also the field seismic data, so as to reduce accuracy requirement of the synthetic noise-free data. In addition, we propose a double-constraint training way to reduce the losses of effective reflections during the denoising process. Specifically, we consider both noise attenuation and signal retention, i.e., reconstruction loss and residual loss are introduced to constrain recovery of effective reflections, and attribute classification loss and adversarial loss are applied to constrain the attenuation of seismic noise. Both the experimental results of synthetic and field seismic records show that our algorithm can effectively suppress the seismic noise and recover the effective reflections almost completely, even the weak signal areas that are seriously polluted by the seismic noise.

In this letter, we have applied a new filtering method for the simultaneous noise reduction and enhancement of seismic signals using a fractal conservation law, which is simply a partial differential equation (PDE) modified by a nonlocal fractional antidiffusive term of lower order. By using an integral formula, which is based on Taylor-Poisson's formula and Fubini's theorem for an antidiffusive term, and then taking the fast Fourier transform of the PDE, the filter in the frequency domain can be derived. The study showed that this filter eliminates the high frequencies, amplifies the medium frequencies, and preserves the low frequencies. Thus, some features of the signal, such as relative maxima or minima, can be preserved or even amplified. Usually, these features are flattened by other denoising methods. The properties of this novel method are tested on both synthetic seismic data and real common shot point seismic records. Additionally, we compared this method with time-frequency peak filtering (TFPF), which has a good performance under low signal-to-noise ratio. The experimental results illustrate the superior performance of our method versus TFPF in the recovery of seismic events by removing random noise and enhancing valid signal.

Time-frequency peak filtering (TFPF) is a method commonly used for seismic random noise attenuation due to its excellent practical application. However, a conventional TFPF often produces significant deviations at the peak or valley of the signal where the linearity is poor. Here, we propose a novel hyperbolic-trace TFPF (HT-TFPF) approach to reduce these deviations and recover the effective signal more completely. In this method, a hyperbolic trace with a certain curvature is ascertained by fitting the reflection event to scan the seismic record. Data sequences are extracted from the seismic record along these hyperbolic traces, and their linearity is greatly improved. Then, they are taken as a new input for TFPF. HT-TFPF can preserve the signal amplitude while achieving an excellent performance of noise suppression with a long unbias window length. Tests on both synthetic records and common shot point data indicate that the HT-TFPF method can attenuate more random noise and recover events more clearly and continuously than the conventional TFPF.

The time–frequency peak filtering (TFPF) algorithm is an effective method for seismic random noise attenuation. The conventional TFPF filters seismic data only along the channel direction, ignoring the spatial characteristics of the reflection events, which results in the loss of directional information. In order to improve the filtering performance of TFPF, we adopt a Radon transform to implement spatiotemporal 2-D TFPF, which is doing TFPF in the Radon domain. Since this method takes the spatial correlation of the reflection events into account, it could extract the reflection events better than the conventional TFPF. As the conventional Radon transform may produce a smearing phenomenon, we apply an improved least squares high-resolution Radon transform to assist TFPF in this letter. Thus, the events can be highly focused to energy points in different positions of the Radon domain. Then we identify the signal parts and process them by TFPF with short window length (WL). The other parts are considered as noise, and we process them by TFPF with long WL. By virtue of this new method, we can preserve the valid signal better and suppress the random noise more effectively. Through experiments on the synthetic seismic records and the field seismic data, the new method possesses a superior performance in random noise attenuation and seismic event preservation compared with the conventional TFPF and Radon TFPF methods.

Time-frequency peak filtering (TFPF) is effective in suppressing random noise in seismic records. However, the signal may be attenuated at the same time, especially for the high-frequency signal. In this letter, we propose a curvature-varying hyperbolic trace TFPF to reduce the error. We sample the seismic record along the time-distance curve of the event. For the event area, the sampled signal in each sampling trace is approximately linear (low frequency) due to the correlation of the signal along the time-distance curve, which reduces the bias of the seismic wave estimation brought by TFPF. However, the curvatures of seismic events are various, so adopting traces with a single curvature to sample the whole seismic record is not reasonable. As the events with different curvatures are located in different areas in Radon domain, the events with almost the same curvature can be separated out by an inverse Radon transform of only part of the Radon data. The optimal hyperbolic sampling traces are chosen for each separated subrecord, and we filter the data along the traces by TFPF. The denoised signal is reconstructed by summing all of the processed subrecords. Compared with TFPF, our method gets a good performance in both seismic reflection event preservation and noise attenuation.

Time-frequency peak filtering (TFPF) has been widely applied to suppress the random noise in seismic data in recent years. Conventional TFPF adopts a pseudo-Wigner-Ville distribution to ensure the approximate linearity of the signal. However, a short window length (WL) cannot effectively attenuate the random noise and a long WL can hardly recover the subtle structures of seismic events. In this letter, we discuss the different correlation integral values of signal and noise in the radial-trace domain for identifying the noise and signal segments. Then, a longer WL according to the noise intensity is used to remove the random noise and a shorter WL according to the frequency characteristics of the signal is used to preserve the details of the signal. The experiment results on both the synthetic model and the field seismic data show that this method can effectively remove noise from seismic record and maintain the amplitude of the valid signal.

In this letter, we research a spatiotemporal-domain-based time-frequency peak filtering (TFPF) method in order to remove the large error (bias) of the conventional TFPF in intermediate-frequency seismic record discrimination. An intermediate-frequency seismic signal is one whose dominant frequency is about 40 Hz or higher. To find a balance between noise attenuation and reflected signal preservation, the radial-trace TPFT (RT-TFPF) takes both the unbiased condition and the adjacent seismic traces' correlation into consideration, and uses the RT transform to obtain a reduced-frequency input to decrease the TFPF error. Therefore, this method can discriminate some reflection events weakened by the conventional TFPF algorithm. First, we decrease the intermediate frequencies along RTs nearly aligned with the reflection event. Then, we obtain a less biased TFPF estimation with suitable window length τ. Finally, we recover the original intermediate frequency with the inverse RT transform. With the RT-TFPF, we can enhance reflection events without sacrificing frequency components while attenuating as much random noise as possible. Experiments on both synthetic model and field data demonstrate that the RT-TFPF performs well both in random noise attenuation and intermediate-frequency preservation, in addition to presenting advantages over the conventional TFPF.

Time-frequency peak filtering (TFPF) is a new and effective tool for random-noise attenuation in the time-frequency domain. The conventional TFPF processes each channel of the seismic record independently with a fixed window length (WL). However, different frequency signals have different optimal WLs. Obviously, a fixed WL cannot effectively attenuate random noise for all frequency components at the same time. Depending on the geometry of the reflection, we can assume that the moveout of the reflected event is locally linear. With this in mind and taking the spatial correlation of the reflection events between adjacent channels in different layers into account, we propose a novel approach which is to do the TFPF along the local direction of the reflection event instead of along the channel. This method is called the local parallel radial-trace TFPF. It not only has the advantage of TFPF in denoising but also reduces the sensitivity of WL, making the filtering more flexible and effective. Both the synthetic model and seismic data have proved its better performance in noise attenuation and effective component preservation.

The time-frequency peak filtering (TFPF) is an effective tool in random-noise attenuation and has been applied to seismic record denoising in recent years. The window length (WL) of the time-frequency distribution (TFD) is the key to the conventional TFPF technology. A fixed WL is not optimal for both the low- and high-frequency components at the same time; an adaptive WL results in serious distortion of the reflected waveform. In this letter, we discuss a modified TFPF along the radial-trace direction and prove its advantage in TFD window selection. Experiments on both synthetic models and field data show that the radial-trace TFPF result is no longer much influenced by the WL as the conventional TFPF. Furthermore, it can provide better performance in both random-noise attenuation and reflected signal preservation with a fixed WL.

A main target of seismic data processing is to remove the surface waves and improve the quality of seismic records. Here, we propose a Co-Core Trace (CCT) transform filtering based on the Trace transform from image processing and apply it to seismic surface wave attenuation. The CCT transform is designed according to the distribution and propagation of the surface waves. In the CCT transform domain, the energies of surface waves are significantly enhanced and could be filtered out with a relative threshold, while the reflection events are saved due to the conspicuous disparity. Experiments on both synthetic model and field data demonstrate that the proposed algorithm performs well both in surface wave attenuation and reflected signal preservation, besides presenting advantages over some conventional methods.