Generative Adversarial Network for Desert Seismic Data Denoising

Wang, Hongzhou; Li, Yue; Dong, Xintong

doi:10.1109/tgrs.2020.3030692

Cited by 42 publications

(9 citation statements)

References 35 publications

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“…Among them, MFFCNN [6] designed a new CNN layer architecture with different kernel sizes in noise feature extraction, which has been verified by the ASPP architecture proposed by Chen et al [45]. Generative adversarial network (GAN) [25] is another way to train denoising models, with Wang et al [46] proposing a GAN-based denoising model to solve the poor continuity of events problem. However, this method needs pairwise noisy&clean data for the supervised training process, which is impossible to obtain from field seismic data.…”

Section: B Image Style Transfer Methodsmentioning

confidence: 93%

Disentangling Noise Patterns From Seismic Images: Noise Reduction and Style Transfer

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Seismic interpretation is a fundamental approach for obtaining static and dynamic information about subsurface reservoirs, such as geological faults/salt bodies and associated fluid types and distribution. Due to the exponential growth in seismic data volume and considerable uncertainty in manual interpretation, deep learning (DL) algorithms have been introduced to assist seismic interpretation. Our investigation of the trained neural networks suggests that they underperform on seismic data with different noise characteristics. One of the main issues is that the noise patterns of seismic data are highly inconsistent due to many factors, including geological features, sampling parameters and human intervention. To address this problem, we propose a noise pattern transfer (NPT) framework to transfer or remove seismic noise style between datasets by treating noise patterns as styles of image, which can also improve the generality of automatic seismic interpretation algorithms. Extensive experiments on three synthetic datasets and two field seismic datasets demonstrate the promising performance of our proposed NPT approach. Pairs of clean and stylised seismic data are generated by extending the use of the neural style transfer algorithm beyond the artistic domain. We then demonstrate how our method achieves superior noise pattern transferability between datasets and denoising performance on field datasets. Associated improvements in accuracy and generalisation of the neural network-based fault recognition tasks successfully demonstrate the practicality of our NPT approach. The source code is made publicly available online at https://github.com/Magnomic/nptcode.

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Section: B Image Style Transfer Methodsmentioning

confidence: 93%

Disentangling Noise Patterns From Seismic Images: Noise Reduction and Style Transfer

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…However, the scattered light signal with weak energy is extremely susceptible to background noise, which negatively affects the quality of the acquired seismic data (Binder et al, 2020). In addition, the in-well acquisition environment also brings new challenges to data processing, and some disturbances are not present in conventional seismic surveys, such as time-varying optical noise and coupling noise (Wang et al, 2021). The seismic data collected in the field is mixed with a wide variety of noise due to the underground geological conditions, collection conditions and environmental factors.…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al (2021) proposed an improved ResNet to achieve seismic random noise attenuation. Wang et al (2022)) are applied to seismic noise attenuation tasks (Creswell et al, 2017;Wang et al, 2021), and some successful applications on ground record processing have been achieved. Furthermore, transfer learning was introduced into the training of denoising networks to enhance the generalization of the model to process the real records (Li et al, 2022;Sun et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, some denoising models based on unsupervised learning or self-supervised learning have been proposed to address the lack of paired data in seismic signal processing (Wang et al, 2022;Yang et al, 2021;Liu et al, 2021;Qiu et al, 2022). Meanwhile, deep learning-based algorithms have also achieved good results in denoising DAS records (Zhao et al, 2022;Wang et al, 2021). In general, these denoising networks aim to establish a non-linear high-dimensional mapping relationship between noisy records and desired signals.…”

Section: Introductionmentioning

confidence: 99%

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Multi-scale interactive network in the application of DAS seismic data processing

Wang

Lin²,

Shao³

et al. 2023

Front. Earth Sci.

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Distributed acoustic sensing (DAS) is regarded as a novel acquisition technology for seismic data. Compared with conventional electrical geophones, DAS has a series of obvious advantages including low-cost, high spatial resolution, good coverage, and strong resistance to the harsh environment. Noise attenuation is an essential step in seismic data processing. However, there are two main difficulties faced by the denoising task of DAS seismic data. On the one hand, some background noise in DAS seismic data, such as optical low-frequency noise, horizontal noise, and fading noise, is unique and not presented in the conventional seismic data; on the other hand, the signal-to-noise ratio (SNR) of DAS seismic data is relatively low. Recently, a convolutional neural network (CNN) has shown superior denoising performance compared to the traditional method. To follow this promising trend, we propose a multi-scale interactive convolutional neural network (MSI-Net) and apply it to denoise the challenging DAS seismic data. Different from most of the existing CNN architecture used in seismic data denoising, the MSI-Net considers both coarse-scale and fine-scale features by improving the inherent serial convolution to multi-scale parallel convolution, which is beneficial to recover detailed information. Moreover, we utilize some connections to achieve the information interaction between different scales, which promotes the flow of information and enables the network to extract more informative multi-scale features from the DAS seismic data. Moreover, both synthetic and real examples demonstrate that the proposed MSI-Net can effectively attenuate a variety of unique DAS background noise and also completely recover the weak signals. Compared with conventional CNN architecture, MSI-Net exhibits better performance in global SNR and local details.

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“…In order to train, GANs use an unsupervised learning technique that is widely applicable to both unsupervised and semi-supervised learning. Compared to other neural network models, GANs can produce seismic data that is clearer and more realistic (Wang et al, 2020). By incorporating cyclic consistency loss, CycleGAN (Zhu et al, 2017), a GAN variant, more effectively realizes the network adversarial learning process.…”

Section: Introductionmentioning

confidence: 99%

Seismic random noise suppression using improved CycleGAN

et al. 2023

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Random noise adversely affects the signal-to-noise ratio of complex seismic signals in complex surface conditions and media. The primary challenges related to processing seismic data have always been reducing the random noise and increasing the signal-to-noise ratio. In this study, we propose an improved cycle-consistent generative adversarial network (CycleGAN) seismic random noise suppression method. First, the generator replaces the original cycle-consistent generative adversarial network generator network structure with the Unet structure combined with the Resnet structure in order to increase the diversity of seismic data feature extraction and decrease the loss of seismic data details. Second, in order to improve the network’s stability, the feature extraction effect, the event texture preservation effect, and the signal-to-noise ratio, the Least Square GAN (LSGAN) square difference loss is used in place of the conventional generative adversarial network cross-entropy loss. The feasibility of the proposed method was confirmed using model and real seismic data, both of which demonstrated that the improved cycle-consistent generative adversarial network method effectively suppressed random noise in seismic data. In addition, the denoising effect was superior to both the widely used FX deconvolution denoising method and original cycle-consistent generative adversarial network denoising method.

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Generative Adversarial Network for Desert Seismic Data Denoising

Cited by 42 publications

References 35 publications

Disentangling Noise Patterns From Seismic Images: Noise Reduction and Style Transfer

Disentangling Noise Patterns From Seismic Images: Noise Reduction and Style Transfer

Multi-scale interactive network in the application of DAS seismic data processing

Seismic random noise suppression using improved CycleGAN

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