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Xintong Dong
Researcher at Jilin University
Publications - 11
Citations - 207
Xintong Dong is an academic researcher from Jilin University. The author has contributed to research in topics: Noise reduction & Noise. The author has an hindex of 3, co-authored 11 publications receiving 23 citations.
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Journal ArticleDOI
Denoising the Optical Fiber Seismic Data by Using Convolutional Adversarial Network Based on Loss Balance
Xintong Dong,Yue Li +1 more
TL;DR: Experimental results have demonstrated that CADN can suppress most of the DAS noise and enhance the SNR of DAS seismic data; also, it can recover the effective signals completely, even the extremely weak effective signals reflected by deep layers.
Journal ArticleDOI
New Suppression Technology for Low-Frequency Noise in Desert Region: The Improved Robust Principal Component Analysis Based on Prediction of Neural Network
Xintong Dong,Tie Zhong,Yue Li +2 more
TL;DR: To approach real desert seismic data, a variety of seismic wavelets are used to simulate different types of seismic events, and then these synthetic seismic events and real desert low-frequency noise to construct training set are used.
Journal ArticleDOI
Generative Adversarial Network for Desert Seismic Data Denoising
TL;DR: This work adopts the strategy of generative adversarial network (GAN) to construct a GAN for denoising, which can greatly recover events and suppress random noise in synthetic and real desert seismic data.
Journal ArticleDOI
The Denoising of Desert Seismic Data Based on Cycle-GAN With Unpaired Data Training
TL;DR: The experiment shows that the Cycle-GAN with unpaired data training can effectively suppress desert seismic noise and retain the effective signal amplitude and the denoising result has less false seismic reflection.
Journal ArticleDOI
Seismic Random Noise Attenuation by Applying Multiscale Denoising Convolutional Neural Network
TL;DR: A novel multiscale DnCNN (MSDCNN) is developed as an attempt for random noise suppression, which can effectively suppress the random noise and accurately preserve reflection events, even under low SNR conditions.