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 network, deep learning is widely used for seismic data denoising. However, supervised learning-based denoising methods require massive amounts of training dataset with labels. The lack of labeled dataset limits 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 that contains both supervised and unsupervised paths. Supervised path is trained using synthetic dataset to extract rich signal feature. Unsupervised path exploits the distribution difference between signal and noise, using field dataset to extract realistic and accurate signal feature. The backbone network consists of a three-layer pyramid structure and incorporates multi-scale 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.

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 application. However, the manually designed prior assumption in MAP cannot accurately describe the actual distribution of DAS data, so that the optimization parameters for obtaining high-quality solutions are difficult to determine, making it unavailable in DAS signal estimation. To solve this 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 multi-scale 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.

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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.

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 multistage 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.