Random-noise suppression in seismic data: What can deep learning do?

[1]  Wei Wang,et al.  Preconditioning point-source/point-receiver high-density 3D seismic data for lacustrine shale characterization in a loess mountain area , 2017 .

[2]  P. E. Harris,et al.  Improving the performance of f-x prediction filtering at low signal-to-noise ratios , 1997 .

[3]  Yang Liu,et al.  A 2D multistage median filter to reduce random seismic noise , 2006 .

[4]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[5]  J. Bednar,et al.  Applications of median filtering to deconvolution, pulse estimation, and statistical editing of seismic data , 1983 .

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

[7]  I. F. Jones,et al.  SIGNAL‐TO‐NOISE RATIO ENHANCEMENT IN MULTICHANNEL SEISMIC DATA VIA THE KARHUNEN‐LOÉVE TRANSFORM* , 1987 .

[8]  Greg Beresford,et al.  Some analyses of 2-D median f-k filters , 1995 .

[9]  Yang Liu,et al.  A 1D time-varying median filter for seismic random, spike-like noise elimination , 2009 .

[10]  M. Baan,et al.  Local singular value decomposition for signal enhancement of seismic data , 2007 .

[11]  Brendan J. Meade,et al.  Enabling large‐scale viscoelastic calculations via neural network acceleration , 2017, 1701.08884.

[12]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[13]  Yunjin Chen,et al.  Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.