Saad et al., 2023 - Google Patents
Unsupervised deep learning for 3D interpolation of highly incomplete dataSaad et al., 2023
- Document ID
- 12289623571079504630
- Author
- Saad O
- Fomel S
- Abma R
- Chen Y
- Publication year
- Publication venue
- Geophysics
External Links
Snippet
We propose to denoise and reconstruct the 3D seismic data simultaneously using an unsupervised deep learning (DL) framework, which does not require any prior information about the seismic data and is free of labels. We use an iterative process to reconstruct the …
- 238000004422 calculation algorithm 0 abstract description 63
Classifications
-
- 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
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
- G01V1/364—Seismic filtering
-
- 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
- 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
- G01V1/282—Application of seismic models, synthetic seismograms
-
- 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
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
-
- 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
- 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
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/30—Noise handling
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/003—Seismic data acquisition in general, e.g. survey design
- G01V1/005—Seismic data acquisition in general, e.g. survey design with exploration systems emitting special signals, e.g. frequency swept signals, pulse sequences or slip sweep arrangements
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V99/00—Subject matter not provided for in other groups of this subclass
- G01V99/005—Geomodels or geomodelling, not related to particular measurements
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Saad et al. | Unsupervised deep learning for 3D interpolation of highly incomplete data | |
Yu et al. | Deep learning for denoising | |
Fang et al. | Seismic data interpolation based on U-net with texture loss | |
Zhang et al. | Can learning from natural image denoising be used for seismic data interpolation? | |
Yu et al. | Interpolation and denoising of high-dimensional seismic data by learning a tight frame | |
Wang et al. | Deep-learning-based seismic data interpolation: A preliminary result | |
Chen et al. | Double-sparsity dictionary for seismic noise attenuation | |
Beckouche et al. | Simultaneous dictionary learning and denoising for seismic data | |
Liang et al. | Seismic data restoration via data-driven tight frame | |
Wu et al. | White noise attenuation of seismic trace by integrating variational mode decomposition with convolutional neural network | |
Zu et al. | A periodically varying code for improving deblending of simultaneous sources in marine acquisition | |
Zu et al. | Hybrid-sparsity constrained dictionary learning for iterative deblending of extremely noisy simultaneous-source data | |
Jiang et al. | A convolutional autoencoder method for simultaneous seismic data reconstruction and denoising | |
Oropeza et al. | Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis | |
Kreimer et al. | A tensor higher-order singular value decomposition for prestack seismic data noise reduction and interpolation | |
Trad | Five-dimensional interpolation: Recovering from acquisition constraints | |
Gao et al. | Five-dimensional seismic reconstruction using parallel square matrix factorization | |
Larsen Greiner et al. | Unsupervised deep learning with higher-order total-variation regularization for multidimensional seismic data reconstruction | |
Liu et al. | OC-seislet: Seislet transform construction with differential offset continuation | |
Cheng et al. | Computational efficient multidimensional singular spectrum analysis for prestack seismic data reconstruction | |
Lari et al. | Adaptive singular spectrum analysis for seismic denoising and interpolation | |
Wei et al. | Reconstruction of irregular missing seismic data using conditional generative adversarial networks | |
Nakayama et al. | Machine-learning-based data recovery and its contribution to seismic acquisition: Simultaneous application of deblending, trace reconstruction, and low-frequency extrapolation | |
Liu et al. | Convolutional sparse coding for noise attenuation in seismic data | |
Wang et al. | Seismic multiple suppression based on a deep neural network method for marine data |