01276nam--2200385---450-9900006551502033160-582-27969-00065515USA010065515(ALEPH)000065515USA01006551520011001d1995----km-y0itay0103----baeng||||||||001yyBoundary-field equation methods for a class of nonlinear problemsGabriel N. Gatica and George C. HsiaoHarlowLongman1995178 p.24 cmPitman research notes in mathematics series3312001Pitman research notes in mathematics series331001-------2001Problem al contornoSoluzioni numericheMetodo degli elementi finiti515.355GATICA,Gabriel N.41497HSIAO,George C.313294ITsalbcISBD990000655150203316515.355 GAT11659 ING515.355BKTECPATTY9020011001USA01115520020403USA011715PATRY9020040406USA011645Boundary-field equation methods for a class of nonlinear problems959862UNISA04071nam 22007575 450 991091869640332120241219115236.09783031757457303175745910.1007/978-3-031-75745-7(CKB)37054911900041(MiAaPQ)EBC31851876(Au-PeEL)EBL31851876(DE-He213)978-3-031-75745-7(OCoLC)1484076315(EXLCZ)993705491190004120241219d2024 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierDeep Learning for Seismic Data Enhancement and Representation /by Shirui Wang, Wenyi Hu, Xuqing Wu, Jiefu Chen1st ed. 2024.Cham :Springer Nature Switzerland :Imprint: Springer,2024.1 online resource (164 pages)Advances in Oil and Gas Exploration & Production,2509-37389783031757440 3031757440 Chapter 1: Introduction -- Chapter 2: Full Waveform Inversion With Low-Frequency Extrapolation -- 3: Deep Learning For Seismic Deblending -- Chapter 4: Blind-Trace Network For Self-Supervised Seismic Data Interpolation -- Chapter 5: Self-Supervised Learning For Anti-Aliased Seismic Data Interpolation Using Dip Information -- Chapter 6:Deep Learning For Seismic Data Compression -- Chapter 7: Conclusion.Seismic imaging is a key component of subsurface exploration, and it depends on a high-quality seismic data acquisition system with effective seismic processing algorithms. Seismic data quality concerns various factors such as acquisition design, environmental constraints, sampling resolution, and noises. The focus of this book is to investigate efficient seismic data representation and signal enhancement solutions by leveraging the powerful feature engineering capability of deep learning. The book delves into seismic data representation and enhancement issues, ranging from seismic acquisition design to subsequent quality improvement and compression technologies. Given the challenges of obtaining suitable labeled training datasets for seismic data processing problems, we concentrate on exploring deep learning approaches that eliminate the need for labels. We combined novel deep learning techniques with conventional seismic data processing methods, and construct networks and frameworks tailored for seismic data processing. The editors and authors of this book come from both academia and industry with hands-on experiences in seismic data processing and imaging.Advances in Oil and Gas Exploration & Production,2509-3738GeophysicsData miningElectrical engineeringImage processingDigital techniquesComputer visionArtificial intelligenceData processingGeophysicsData Mining and Knowledge DiscoveryElectrical and Electronic EngineeringComputer Imaging, Vision, Pattern Recognition and GraphicsData ScienceGeophysics.Data mining.Electrical engineering.Image processingDigital techniques.Computer vision.Artificial intelligenceData processing.Geophysics.Data Mining and Knowledge Discovery.Electrical and Electronic Engineering.Computer Imaging, Vision, Pattern Recognition and Graphics.Data Science.550Wang Shirui635487Hu Wenyi1781186Wu Xuqing1781187Chen Jiefu855618MiAaPQMiAaPQMiAaPQBOOK9910918696403321Deep Learning for Seismic Data Enhancement and Representation4305950UNINA