SeisGAN: Improving Seismic Image Resolution and Reducing Random Noise Using a Generative Adversarial Network

Lin, Lei; Zhong, Zhi; Cai, Chuyang; Li, Chenglong; Zhang, Heng

doi:10.1007/s11004-023-10103-8

Cited by 7 publications

(1 citation statement)

References 67 publications

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“…Perceptual loss has been applied in seismic image denoising tasks, where SeisGAN [29] and MSRD-GAN [30] both adopted a supervised approach, extracting perceptual features through a pre-trained VGG network [31] and combining pixel-level loss, perceptual loss, and adversarial loss to compute the generator loss according to Equation (3).…”

Section: Discriminator Network Structurementioning

confidence: 99%

Time–Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks

Wei,

Sun,

Zong

2024

Applied Sciences

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Existing deep learning-based seismic signal denoising methods primarily operate in the time domain. Those methods are ineffective when noise overlaps with the seismic signal in the time domain. Time–frequency domain-based deep learning methods are relatively rare and usually employ single loss function, resulting in suboptimal performance on low SNR signals and potential damage to P wave. This paper proposes a method based on generative adversarial networks (GANs). Compared to convolutional neural networks, the discriminator in GANs helps retain more true signal details by judging denoising performance. Additionally, an attention mechanism is introduced to fully extract signal features, and a perceptual loss is employed to evaluate the difference between the denoised result and the target’s high-level features. Experimental results show that this method can effectively improve SNR and ensure that the denoised result is close to the true signal. Furthermore, by comparing DeepDenoiser and ARDU, it is proven that the proposed method achieves better denoising performance, especially for low SNR signals, while causing less damage to the seismic signals.

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