Seismic trace interpolation for irregularly spatial sampled data using convolutional autoencoder

[1]  Lynn Burroughs,et al.  Rank-Reduction-Based Trace Interpolation , 2010 .

[2]  Mauricio D. Sacchi,et al.  Interpolation and extrapolation using a high-resolution discrete Fourier transform , 1998, IEEE Trans. Signal Process..

[3]  Mauricio D. Sacchi,et al.  Beyond alias hierarchical scale curvelet interpolation of regularly and irregularly sampled seismic data , 2010 .

[4]  Mauricio D. Sacchi,et al.  Parallel matrix factorization algorithm and its application to 5D seismic reconstruction and denoising , 2015 .

[5]  Yu Geng,et al.  Dreamlet-based interpolation using POCS method , 2014 .

[6]  Joshua Ronen,et al.  Wave‐equation trace interpolation , 1987 .

[7]  Felix J. Herrmann,et al.  Seismic Data Reconstruction with Generative Adversarial Networks , 2018, 80th EAGE Conference and Exhibition 2018.

[8]  SchmidhuberJürgen.  Deep learning in neural networks , 2015 .

[9]  Baishali Roy,et al.  Time-lapse reservoir property change estimation from seismic using machine learning , 2017 .

[10]  Jian Sun,et al.  Convolutional neural networks at constrained time cost , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Guangmin Hu,et al.  Unsupervised seismic facies analysis via deep convolutional autoencoders , 2018 .

[12]  Jianwei Ma,et al.  Three-dimensional irregular seismic data reconstruction via low-rank matrix completion , 2013 .

[13]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[14]  David M. Cole,et al.  A comparison of popular neural network facies-classification schemes , 2017 .

[15]  Yang Liu,et al.  Seislet transform and seislet frame , 2010 .

[16]  Sergey Fomel,et al.  FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation , 2019, GEOPHYSICS.

[17]  Ning Zhang,et al.  Deep-learning-based seismic data interpolation: A preliminary result , 2019, GEOPHYSICS.

[18]  Daniel Trad,et al.  Interpolation and multiple attenuation with migration operators , 2003 .

[19]  Sadegh Karimpouli,et al.  A new approach to improve neural networks' algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (SCMNN) , 2010 .

[20]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[21]  Jiwei Liu,et al.  Seismic Waveform Classification and First-Break Picking Using Convolution Neural Networks , 2018, IEEE Geoscience and Remote Sensing Letters.

[22]  Mauricio D. Sacchi,et al.  Minimum weighted norm interpolation of seismic records , 2004 .

[23]  Xueqing Zhou,et al.  Inversion of the permeability of a tight gas reservoir with the combination of a deep Boltzmann kernel extreme learning machine and nuclear magnetic resonance logging transverse relaxation time spectrum data , 2017 .

[24]  Jürgen Schmidhuber,et al.  Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction , 2011, ICANN.

[25]  Benfeng Wang,et al.  Simultaneous seismic data interpolation and denoising with a new adaptive method based on dreamlet transform , 2015 .

[26]  Xishuang Dong,et al.  A scalable deep learning platform for identifying geologic features from seismic attributes , 2017 .

[27]  Felix J. Herrmann,et al.  Non-parametric seismic data recovery with curvelet frames , 2008 .

[28]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[29]  Anne H. Schistad Solberg,et al.  Convolutional neural networks for automated seismic interpretation , 2018, The Leading Edge.

[30]  Peng Zhang,et al.  Seismic data interpolation using generalised velocity‐dependent seislet transform , 2017 .

[31]  Haibin Di,et al.  Developing a seismic texture analysis neural network for machine-aided seismic pattern recognition and classification , 2019, Geophysical Journal International.

[32]  Amir Adler,et al.  Deep-learning tomography , 2018 .

[33]  S. Spitz.  Seismic trace interpolation in the F-X domain , 1991 .

[34]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[35]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

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

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

[38]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[39]  Yu Geng,et al.  Preliminary study on Dreamlet based compressive sensing data recovery , 2013 .

[40]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[41]  Graham W. Taylor,et al.  Adaptive deconvolutional networks for mid and high level feature learning , 2011, 2011 International Conference on Computer Vision.

[42]  Jianwei Ma,et al.  Intelligent interpolation by Monte Carlo machine learning , 2018 .

[43]  Jianwei Ma,et al.  What can machine learning do for seismic data processing? An interpolation application , 2017 .

[44]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[46]  Yoshua Bengio,et al.  What regularized auto-encoders learn from the data-generating distribution , 2012, J. Mach. Learn. Res..

[47]  Xiaohong Chen,et al.  An Improved Weighted Projection Onto Convex Sets Method for Seismic Data Interpolation and Denoising , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[48]  Hassan Mansour,et al.  Reconstruction of Seismic Wavefields via Low-Rank Matrix Factorization in the Hierarchical-Separable Matrix Representation , 2013 .

[49]  Benfeng Wang,et al.  An Efficient POCS Interpolation Method in the Frequency-Space Domain , 2016, IEEE Geoscience and Remote Sensing Letters.

[50]  Tomaso Poggio,et al.  Automated fault detection without seismic processing , 2017 .