Zhang et al., 2023 - Google Patents
Unsupervised seismic random noise attenuation by a recursive deep image priorZhang et al., 2023
- Document ID
- 12672546250463262781
- Author
- Zhang Y
- Wang B
- Publication year
- Publication venue
- Geophysics
External Links
Snippet
The presence of random noise in field data significantly reduces the precision of subsequent seismic processing steps. As a result, random noise suppression is essential to improve the quality of field data. Because most traditional algorithms characterize seismic data linearly …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Saad et al. | Deep denoising autoencoder for seismic random noise attenuation | |
Wang et al. | Seismic trace interpolation for irregularly spatial sampled data using convolutional autoencoder | |
Fang et al. | Seismic data interpolation based on U-net with texture loss | |
Yu et al. | Interpolation and denoising of high-dimensional seismic data by learning a tight frame | |
Liu et al. | Deep-seismic-prior-based reconstruction of seismic data using convolutional neural networks | |
Fablet et al. | Learning variational data assimilation models and solvers | |
Chen et al. | Double-sparsity dictionary for seismic noise attenuation | |
Liang et al. | Seismic data restoration via data-driven tight frame | |
Wu et al. | Parametric convolutional neural network-domain full-waveform inversion | |
Nazari Siahsar et al. | Data-driven multitask sparse dictionary learning for noise attenuation of 3D seismic data | |
Yuan et al. | Ground-roll attenuation using generative adversarial networks | |
Yu et al. | Monte Carlo data-driven tight frame for seismic data recovery | |
Wu et al. | White noise attenuation of seismic trace by integrating variational mode decomposition with convolutional neural network | |
Beck et al. | A fast iterative shrinkage-thresholding algorithm for linear inverse problems | |
Yang et al. | FWIGAN: Full‐waveform inversion via a physics‐informed generative adversarial network | |
Wang et al. | Learning from noisy data: An unsupervised random denoising method for seismic data using model-based deep learning | |
Zhang et al. | Adjoint-driven deep-learning seismic full-waveform inversion | |
Yang et al. | Denoising of distributed acoustic sensing data using supervised deep learning | |
Si et al. | Attenuation of random noise using denoising convolutional neural networks | |
Zhang et al. | Unsupervised seismic random noise attenuation by a recursive deep image prior | |
Liu et al. | Convolutional sparse coding for noise attenuation in seismic data | |
Torres et al. | Least-squares reverse time migration via deep learning-based updating operators | |
Zhang et al. | Complete and representative training of neural networks: A generalization study using double noise injection and natural images | |
Liu et al. | DL2: Dictionary learning regularized with deep learning prior for simultaneous denoising and interpolation | |
Dong et al. | Seismic data reconstruction based on a multicascade self-guided network |