[논문]Generative Adversarial Network for Desert Seismic Data Denoising

Wang, Hongzhou; Li, Yue; Dong, Xintong

doi:10.1109/tgrs.2020.3030692

Generative Adversarial Network for Desert Seismic Data Denoising

IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society, v.59 no.8, 2021년, pp.7062 - 7075

Wang, Hongzhou (Jilin University, College of Communication Engineering, Changchun, China) , Li, Yue (Jilin University, College of Communication Engineering, Changchun, China) , Dong, Xintong (Jilin University, College of Communication Engineering, Changchun, China)

Abstract ▼ AI-Helper

Seismic exploration is a kind of exploration method for oil and gas resources. However, the disturbance of numerous random noise will decrease the quality and signal-to-noise ratio (SNR) of real seismic records, which brings difficulties to the following works of processing and interpretation. The seismic records of desert region pose a particular problem because of the strong energy noise and the spectrum overlapping between effective signals and random noise. Recent research works demonstrate that a convolutional neural network (CNN) can increase the SNR of seismic records. The optimization of denoising methods based on CNN is principally driven by the loss functions that largely focus on minimizing the mean-squared reconstruction error between denoising records and theoretical pure records. The denoising results estimated by the CNN model are often lacking the perfection of the signal structure. Therefore, when processing seismic records with low SNR, the denoising results often have a lack of effective signal in some traces, which leads to the poor continuity of events. In order to solve this problem, we adopt the strategy of generative adversarial network (GAN) to construct a GAN for denoising. It is divided into two parts: the generator (the denoising network based on CNN) is used to remove noise, while the discriminator is used to guide the generator to restore the structure information of effective signals. The generator and discriminator enhance the performance of each other through adversarial training, and the generator after adversarial training can greatly recover events and suppress random noise in synthetic and real desert seismic data.

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Generative Adversarial Network for Desert Seismic Data Denoising

Abstract ▼ AI-Helper

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