Attention mechanism-based deep denoiser for desert seismic random noise suppression

[1]  S. Yuan,et al.  Double-scale supervised inversion with a data-driven forward model for low-frequency impedance recovery , 2021, GEOPHYSICS.

[2]  Andrew I. Wilterson,et al.  The attention schema theory in a neural network agent: Controlling visuospatial attention using a descriptive model of attention , 2021, Proceedings of the National Academy of Sciences.

[3]  Yangkang Chen,et al.  A fully unsupervised and highly generalized deep learning approach for random noise suppression , 2021, Geophysical Prospecting.

[4]  Hua Zhang,et al.  Seismic random noise suppression using deep convolutional autoencoder neural network , 2020 .

[5]  Qiang Zhao,et al.  Robust dictionary learning for erratic noise-corrupted seismic data reconstruction , 2020, Acta Geophysica.

[6]  Yue Li,et al.  Denoising of desert seismic signal based on synchrosqueezing transform and Adaboost algorithm , 2020, Acta Geophysica.

[7]  Yue Li,et al.  Desert seismic noise suppression based on multimodal residual convolutional neural network , 2020, Acta Geophysica.

[8]  Chao Zhang,et al.  Desert seismic random noise reduction based on LDA effective signal detection , 2019, Acta Geophysica.

[9]  Yue Li,et al.  Noise reduction for desert seismic data using spectral kurtosis adaptive bandpass filter , 2018, Acta Geophysica.

[10]  Yangkang Chen,et al.  Random noise attenuation using local signal-and-noise orthogonalization , 2015 .

[11]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[12]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[13]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[14]  Qiankun Feng,et al.  Intelligent random noise modeling by the improved variational autoencoding method and its application to data augmentation , 2021 .