Hyperspectral change detection is critical for analyzing the temporal evolution of the feature components in multi-temporal hyperspectral images. However, existing methods often fall short of fully exploiting the spatio-temporal-spectral correlations within these images, thereby limiting their accuracy and robustness. This paper introduces a novel hyperspectral change detection method, termed dual collaborative sparse unmixing via variable splitting augmented Lagrangian and total variation (DCLSUnSAL-TV). By integrating dual collaborative sparsity and total variation regularizers, this method capitalizes on the local similarity of changes in the feature components, leveraging the low-rank property of hyperspectral difference images (HSDIs) and their inherent spatial-spectral correlations. A customized abundance-wise-truncation and ensemble strategy is designed to obtain the change map by aggregating the subpixel-level changes with respect to each endmember. Comprehensive comparison and ablation experiments demonstrate the effectiveness of the proposed method in improving the accuracy of change detection. The source code is available at https://github.com/2alsbz/DCLSUnSAL_TV.

Seismic wavelet interference limits the vertical resolution of seismic data, making it challenging to accurately characterize subsurface geological structures. Seismic reflectivity estimation is a key process for improving the vertical resolution of seismic data. Deep learning-based seismic reflectivity estimation methods typically rely on labels generated from well data, which can be costly to obtain. Moreover, seismic reflectivity estimation is an ill-posed problem, meaning that while the estimated seismic reflectivity may fit the observed data, it can still differ significantly from the true reflectivity, particularly concerning thin layers. To address these challenges, we propose an unsupervised deep learning deconvolution framework guided by a physical convolution model and super-resolution mathematical theory. The network is trained using a combination of reconstruction loss, position prior loss, and sparse loss. Specifically, reconstruction loss establishes a closed-loop connection between the low-resolution seismic data and the estimated seismic reflectivity using the Robinson convolution model, eliminating the need for labels in training. Position prior loss estimates the seismic reflectivity position based on the super-resolution mathematical theory, which is particularly effective for thin layers, improving the interpretability and accuracy of the seismic reflectivity estimation. Sparse loss, based on the$L1$norm, enforces sparsity in the seismic reflectivity estimation, enhancing its stability. Both synthetic and field data examples demonstrate that the proposed method outperforms conventional sparse-spike deconvolution method, providing better thin-layer seismic reflectivity estimates and improved lateral continuity.

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.

The comprehensive requirements of effectively suppressing various noises, preserving signal amplitudes, recovering weak signals, balancing the effects between local and global, and reducing computational costs have always been the crucial task of data processing of downhole DAS. Traditional methods, single-mechanism convolutional neural network models, and training methods with a single patch size, due to their simplistic structures, limited data processing capabilities, and a large number of network layers, often struggle to meet the aforementioned comprehensive requirements. In this paper, employing a progressive training method, we propose a novel non-local selective attention multi-scale residual network (NSKMRNet). At its core lies an iterative multi-scale residual block (MRB): multiple resolution convolution streams allow for the transfer of contextual information from the low-resolution stream to consolidate high-resolution features; parallel non-local contextual processing utilizes a distillation mechanism for extracting useful information, and weighted attention handling between adjacent scale streams for merging information. The feature maps selected from a randomly chosen training iteration clearly reflect the noise suppression process and the importance of iterative process. In experiments conducted on both synthetic and field DAS data, our method achieved an SNR (Signal-to-Noise Ratio) improvement of around 30 dB, surpassing other methods. It maximally preserves signal amplitudes, resulting in minimal signal residuals in the amplified field records. Moreover, in terms of FLOPs (Floating Point Operations) and training time, our method is within an acceptable range but significantly lower than more complex networks. The proposed network holds promise as a potentially valuable tool in meeting the comprehensive requirements for processing of downhole DAS data.

Distributed acoustic sensing (DAS) is a novel and revolutionary technology that is widely used in the field of seismic exploration. However, the problem of low signal-to-noise ratio (SNR) has always been a serious challenge affecting its processing and interpretation. At present, deep learning-based algorithms show remarkable potential in DAS vertical seismic profile (VSP) data denoising, but the recovery of deep-layer weak signals still needs to be further improved. To solve the above problems, we propose a dual attention denoising network (DADN) combining spatial attention (SA) and channel attention (CA) double attention block (DAB) to improve the recovery effect of deep-layer weak signals. The DADN consists of multiple DABs and utilizes encoder–decoder structure to extract signal features at multiple scales. At each scale, DADN uses a DAB to reassign the weights of feature maps on each channel and region to focus on useful information, which is beneficial for accurately extracting signal features and recovering deep-layer weak signals. The information flow undergoes downsampling and upsampling operations to finally achieve an accurate estimation of the DAS VSP signals. In addition, gradient-weighted class activation mapping (Grad-CAM) is introduced to visually interpret the signal features learned by the network. The visualization results show that the network does accurately distinguish between signals and noise. After training on the constructed semisynthetic DAS VSP dataset, DADN demonstrated competitive performance in both noise suppression and weak signals retention.

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.

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.

In recent years, supervised deep learning-based denoising methods have been popularized and developed rapidly in the field of seismic data processing. Supervised training, however, is limited by the quality and quantity of the paired training data (noisy clean or noisy noise). Data labeling is a time-consuming and expensive work. Compared with raw seismic data, only a small amount of seismic data have been correctly labeled, which, to some extent, limits the long-term development of supervised deep learning-based methods in the field of seismic data denoising. In this letter, we propose an improved denoising framework based on an unsupervised deep learning-based denoising method Noise2Noise, which only needs unprocessed raw seismic data to train the denoising model. Moreover, unlike Noise2Noise, the proposed method does not need to repeatedly collect seismic data to obtain a training pair with similar signal, which is more convenient and effective. Specifically, we propose a block random sampler that can generate training pairs using raw seismic data, which satisfies the training assumption of Noise2Noise that the training pair has a similar signal. In addition, our method has no requirements for the network structure and noise distribution prior and is flexible. Both synthetic seismic data and field seismic data denoising results show that our method can effectively suppress the random noise, and the denoising performance is equivalent to that of supervised deep learning-based denoising methods. In addition, our method may provide a certain reference for geophysical-related research.

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.

Desert seismic data are often characterized by low signal-to-noise ratio (SNR) due to the fickle surface conditions and desert random noise with nonstationarity, nonlinearity, spatial directivity, and low-frequency characteristics. This low SNR is likely to affect the following inversion and interpretation. Therefore, robust noise attenuation is crucial to improve the SNR of desert seismic data. We propose a novel method alternating direction method of multipliers-based denoising convolutional neural network (ADMM-CNN) by combining low-rank decomposition with feed-forward denoising convolutional neural network (DnCNN). DnCNN is a deep-learning-based method for noise removal, which can make good noise attenuation performance through training. However, there is no feature extraction procedure before model learning in its structure, so DnCNN cannot make full use of the prior information of signals. Combining low-rank decomposition just addresses this issue. Compared with DnCNN, our method has two main novelties. First, we use the synthetic seismic data that resemble the characteristics of field desert seismic data and desert noise to train the ADMM-CNN. Second, we use ADMM to decompose the data into three layers (low-rank, sparse, and perturbation) which are used as the inputs of three channels neural network. Through this decomposition, the neural network can capture more features and prior information of desert seismic data. Both synthetic field data tests demonstrate the robuster performance of ADMM-CNN compared to traditional methods and DnCNN, also that the ADMM-CNN method suppresses the desert noise more thoroughly and greatly improves SNR of desert seismic data at the same time.

Distributed acoustic sensing (DAS) technology is a rapidly evolving fiber-optic sensing technology that has been gradually applied to vertical seismic profile (VSP) data. The DAS VSP data are often contaminated by multiple interference waves, such as random noise, coupled noise, horizontal noise, and the existing methods can only map it from noisy data to effective reflections. However, interference waves are also a relative concept, and many interference waves still have certain application values. Therefore, this article proposes an innovative algorithm called attribute-guided target data separation network (Att-TDSN), which can not only complete the conventional signal-noise separation task but also achieve noise-noise separation task. Specifically, we first propose a flexible and efficient training set, namely, multidimensional weak label training set (Mul-WLTS), which introduces attribute features as the weak labels and specifies the dimension of weak labels according to the number of target data types (effective reflections and several common interference waves). Then, we use the weak labels to guide our network to map training data to specified data types, assisting the network to focus its attention on each kind of target data. Finally, the network parameters are trained by a training mode called “one-way matching and two-way constraint.” “One-way matching” makes data separation result unique, and “two-way constraint” can improve the algorithm’s amplitude preserving ability. Experiments on synthetic and field DAS VSP data show that our network can accurately separate target data. Moreover, for the traditional denoising task, Att-TDSN also has a better denoising performance than existing methods.

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.

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.

Existing denoising algorithms often need to meet some premise assumptions and applicable conditions, such as the signal-to-noise ratio (SNR) cannot be too low, and the noise needs to obey a specific distribution (such as Gaussian distribution) and to satisfy some properties (such as stationarity). For the desert noise that shares the same frequency band with the effective signal and has complex characteristics (nonlinear, nonstationary, and non-Gaussian), it is difficult to find a universally applicable method. In response to this problem, a multiscale geometric analysis (MGA) convolutional neural network (CNN) is proposed in this article. One of the most important features of the CNN is that it can extract data-rich intrinsic information from the training set without relying on a priori assumption. By introducing the CNN into the MGA, a new kind of denoising method can be created, which can achieve good results even under a low SNR. This article takes the non-subsampled contourlet transform as an example to create a denoising network named NC-CNN for high-efficiency and intelligent denoising of desert seismic data. The processing results of synthetic seismic records and field seismic records prove that NC-CNN can effectively suppress the low-frequency noise (random noise and surface wave), and the effective signal almost has no energy loss. In addition, the reconstruction ability of the missing signals is also an advantage of this method.

High-quality seismic data are the basis for stratigraphic imaging and interpretation, but the existence of random noise can greatly affect the quality of seismic data. At present, most understanding and processing of random noise still stay at the level of Gaussian white noise. With the reduction of resource, the acquired seismic data have lower signal-to-noise ratio and more complex noise natures. In particular, the random noise in the desert area has the characteristics of low frequency, non-Gaussian, nonstationary, high energy, and serious aliasing between effective signal and random noise in the frequency domain, which has brought great difficulties to the recovery of seismic events by conventional denoising methods. To solve this problem, an improved feed-forward denoising convolution neural network (DnCNN) is proposed to suppress random noise in desert seismic data. DnCNN has the characteristics of automatic feature extraction and blind denoising. According to the characteristics of desert noise, we modify the original DnCNN from the aspects of patch size, convolution kernel size, network depth, and training set to make it suitable for low-frequency and non-Gaussian desert noise suppression. Both simulation and practical experiments prove that the improved DnCNN has obvious advantages in terms of desert noise and surface wave suppression as well as effective signal amplitude preservation. In addition, the improved DnCNN, in contrast to existing methods, has considerable potential to benefit from large data sets. Therefore, we believe that it can open a new direction in the area of seismic data processing.