Access the full text.
Sign up today, get DeepDyve free for 14 days.
S. Mousavi, Weiqiang Zhu, W. Ellsworth, G. Beroza (2019)
Unsupervised Clustering of Seismic Signals Using Deep Convolutional AutoencodersIEEE Geoscience and Remote Sensing Letters, 16
Weiqiang Zhu, S. Mousavi, G. Beroza (2018)
Seismic Signal Denoising and Decomposition Using Deep Neural NetworksIEEE Transactions on Geoscience and Remote Sensing, 57
Seungjun Nah, Tae Kim, Kyoung Lee (2016)
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
P. Harris, R. White (1997)
Improving the performance of f–x prediction filtering at low signal‐to‐noise ratiosGeophysical Prospecting, 45
(2016)
SSD: single shot multibox detector lecture notes in computer science
Xintong Dong, Yue Li, Baojun Yang (2019)
Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statisticGeophysical Journal International
Hongying Lu, B. Cheng, Zhongmin Shen, Tianji Xu (2013)
Gas and water reservoir differentiation by time-frequency analysis: a case study in southwest ChinaActa Geodaetica et Geophysica, 48
Yue Li, Linlin Li, Chao Zhang (2019)
Desert seismic signal denoising by 2D compact variational mode decompositionJournal of Geophysics and Engineering
(2016)
Seismic random noise attenuation using directional total variation in the shearlet domain
S. Mousavi, C. Langston (2017)
Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake dataGeophysics, 82
H. Kaur, Sergey Fomel, N. Pham (2019)
Ground Roll Attenuation Using Generative Adversarial Network81st EAGE Conference and Exhibition 2019
Xintong Dong, Yue Li (2021)
Denoising the Optical Fiber Seismic Data by Using Convolutional Adversarial Network Based on Loss BalanceIEEE Transactions on Geoscience and Remote Sensing, 59
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie (2016)
Feature Pyramid Networks for Object Detection2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
S. Mousavi, C. Langston (2016)
Hybrid Seismic Denoising Using Higher-Order Statistics and Improved Wavelet Block ThresholdingBulletin of the Seismological Society of America, 106
Yuxing Zhao, Yue Li, Xintong Dong, Baojun Yang (2019)
Low-Frequency Noise Suppression Method Based on Improved DnCNN in Desert Seismic DataIEEE Geoscience and Remote Sensing Letters, 16
Hongyuan Yang, Y. Long, Jun Lin, Fengjiao Zhang, Zubin Chen (2017)
A seismic interpolation and denoising method with curvelet transform matching filterActa Geophysica, 65
C. Langston, S. Mousavi (2019)
Separating Signal from Noise and from Other Signal Using Nonlinear Thresholding and Scale‐Time Windowing of Continuous Wavelet TransformsSeparating Signal from Noise and from Other SignalBulletin of the Seismological Society of America, 109
S. Mousavi, C. Langston, S. Horton (2016)
Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transformGeophysics, 81
M. Bekara, M. Baan (2009)
Random and coherent noise attenuation by empirical mode decompositionSeg Technical Program Expanded Abstracts
Feng Wang, Shengchang Chen (2019)
Residual Learning of Deep Convolutional Neural Network for Seismic Random Noise AttenuationIEEE Geoscience and Remote Sensing Letters, 16
Mo Li, Yue Li, N. Wu, Yanan Tian, Teng Wang (2020)
Desert seismic random noise reduction framework based on improved PSO–SVMActa Geodaetica et Geophysica, 55
Jing Zhang, D. Tao (2019)
FAMED-Net: A Fast and Accurate Multi-Scale End-to-End Dehazing NetworkIEEE Transactions on Image Processing, 29
H. Kaur, Sergey Fomel, N. Pham (2020)
Seismic ground‐roll noise attenuation using deep learningGeophysical Prospecting, 68
Sergey Ioffe, Christian Szegedy (2015)
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftArXiv, abs/1502.03167
M Bekara (2009)
V89Geophysics, 74
Yijun Yuan, Xu Si, Y. Zheng (2020)
Ground-roll attenuation using generative adversarial networksGeophysics, 85
S. Cao, Xiang-tao Chen (2005)
The second-generation wavelet transform and its application in denoising of seismic dataApplied Geophysics, 2
Xiaofu Sun, Yue Li (2020)
Application of adaptive iterative low-rank algorithm based on transform domain in desert seismic signal analysisActa Geodaetica et Geophysica, 55
Yi Chang, Luxin Yan, Li Liu, Houzhang Fang, Sheng Zhong (2019)
Infrared Aerothermal Nonuniform Correction via Deep Multiscale Residual NetworkIEEE Geoscience and Remote Sensing Letters, 16
C. Zhang, Y. Li, H. Lin, B. Yang, N. Wu (2015)
Adaptive Threshold Based Shearlet Transform Noise Attenuation Method for Surface Microseismic Data
Xinrui Jiang, Nannan Wang, J. Xin, Xi Yang, Yi Yu, Xinbo Gao (2020)
Image super-resolution via multi-view information fusion networksNeurocomputing, 402
Shibai Yin, Yibin Wang, Herbert Yang (2019)
A Novel Residual Dense Pyramid Network for Image DehazingEntropy, 21
Seismic exploration is an essential way for stratigraphic information acquisition and resource exploitation. However, the unique near surface conditions of the desert region in northwest China pose a special problem. On the one hand, compared with Gaussian noise, desert noise concentrates in the low-frequency band, which is seriously overlapped with those of the effective signals; On the other hand, its nonstationary, nonlinear, and non-Gaussian characteristics seriously affect the accuracy of weak signal recovery and increase the difficulty of noise suppression. In the collected field data, effective signals are often submerged by the intense low-frequency noise. Considering that traditional denoising methods have some limitations on this kind of noise, a new multi-scale dense connection feature fusion (MS-DCFF) denoising convolution neural network is presented in this paper. This denoising network can adaptively learn the potential features of effective signals through multi-scale feature fusion techniques and increase the degree of information exchange by utilizing the dense connections between different blocks. Moreover, we construct relatively complete training dataset containing an effective signal dataset and a noise dataset for desert low-frequency noise suppression, thereby boosting the feasibility of the network for desert noise suppression. Both simulation and field data prove that the superior performance of the proposed MS-DCFF in the aspect of low-frequency noise (mainly includes random noise and surface waves) suppression and effective signal recovery, compared with band-pass filtering, f-x filtering, and DnCNNs.
"Acta Geodaetica et Geophysica" – Springer Journals
Published: Apr 21, 2021
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.