Seismic data augmentation for automatic fault picking using deep learning

[1]  Christopher J. Bartel.  Data-centric approach to improve machine learning models for inorganic materials , 2021, Patterns.

[2]  N. Pham,et al.  Channel facies and faults multisegmentation in seismic volumes , 2021, First International Meeting for Applied Geoscience & Energy Expanded Abstracts.

[3]  Y Li,et al.  Data augmentation and its application in distributed acoustic sensing data denoising , 2021, Geophysical Journal International.

[4]  Sergey Fomel,et al.  Seismic data interpolation using deep learning with generative adversarial networks , 2020, Geophysical Prospecting.

[5]  Yang Liu,et al.  Automatic seismic facies interpretation using supervised deep learning , 2020 .

[6]  Weichang Li,et al.  Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks , 2020 .

[7]  Yue Zheng,et al.  Ground-roll attenuation using generative adversarial networks , 2020 .

[8]  Nam Pham,et al.  Improving the resolution of migrated images by approximating the inverse Hessian using deep learning , 2020, GEOPHYSICS.

[9]  Jing Tian,et al.  An image augmentation approach using two-stage generative adversarial network for nuclei image segmentation , 2020, Biomed. Signal Process. Control..

[10]  S. Fomel,et al.  Building realistic structure models to train convolutional neural networks for seismic structural interpretation , 2019, GEOPHYSICS.

[11]  Jakub Nalepa,et al.  Data Augmentation for Brain-Tumor Segmentation: A Review , 2019, Front. Comput. Neurosci..

[12]  Ke Yan,et al.  Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks , 2019, Scientific Reports.

[13]  Xinfei Yan,et al.  Seismic impedance inversion based on cycle-consistent generative adversarial network , 2019, SEG Technical Program Expanded Abstracts 2019.

[14]  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.

[15]  Martin J. Blunt,et al.  Stochastic Seismic Waveform Inversion Using Generative Adversarial Networks as a Geological Prior , 2018, Mathematical Geosciences.

[16]  Sergey Fomel,et al.  Automatic fault interpretation with optimal surface voting , 2018, GEOPHYSICS.

[17]  Francesco Picetti,et al.  A generative-adversarial network For seismic-imaging applications , 2018, SEG technical program expanded abstracts.

[18]  Sergey Fomel,et al.  Convolutional neural networks for fault interpretation in seismic images , 2018, SEG Technical Program Expanded Abstracts 2018.

[19]  Tao Zhao,et al.  A fault-detection workflow using deep learning and image processing , 2018, SEG Technical Program Expanded Abstracts 2018.

[20]  Mason Phillips,et al.  Plane-wave Sobel attribute for discontinuity enhancement in seismic images , 2017 .

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

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

[23]  Xinming Wu.  Directional structure-tensor-based coherence to detect seismic faults and channels , 2017 .

[24]  Joe D. Kington,et al.  Semblance, coherence, and other discontinuity attributes , 2015 .

[25]  Kurt J. Marfurt,et al.  Seismic Attributes for Prospect Identification and Reservoir Characterization , 2007 .

[26]  Kurt J. Marfurt,et al.  Eigenstructure-based coherence computations as an aid to 3-D structural and stratigraphic mapping , 1999 .

[27]  R. Lynn Kirlin,et al.  3-D seismic attributes using a semblance‐based coherency algorithm , 1998 .

[28]  Ido Dagan,et al.  Similarity-Based Methods for Word Sense Disambiguation , 1997, ACL.

[29]  L. V. Kantorovich,et al.  Mathematical Methods of Organizing and Planning Production , 1960 .