04239nam a22006375i 4500 978-3-031-75745-7 DE-He213 20251030114625.0 cr nn 008mamaa 241219s2024 sz | s |||| 0|eng d 9783031757457 978-3-031-75745-7 10.1007/978-3-031-75745-7 doi QC801-809 PHVG bicssc SCI032000 bisacsh PHVG thema Wang, Shirui. author. aut http://id.loc.gov/vocabulary/relators/aut 89120 Deep Learning for Seismic Data Enhancement and Representation [electronic resource] / by Shirui Wang, Wenyi Hu, Xuqing Wu, Jiefu Chen. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XVI, 152 p. 107 illus., 80 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Advances in Oil and Gas Exploration & Production, 2509-3738 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. Geophysics. 69226 Data mining. 6941 Electrical engineering. 65586 Image processing Digital techniques. 64228 Computer vision. Artificial intelligence Data processing. Geophysics. 69226 Data Mining and Knowledge Discovery. Electrical and Electronic Engineering. 65588 Computer Imaging, Vision, Pattern Recognition and Graphics. 65550 Data Science. Hu, Wenyi. author. aut http://id.loc.gov/vocabulary/relators/aut 89121 Wu, Xuqing. author. aut http://id.loc.gov/vocabulary/relators/aut 89122 Chen, Jiefu. author. (orcid)0000-0001-9940-7043 https://orcid.org/0000-0001-9940-7043 aut http://id.loc.gov/vocabulary/relators/aut 89123 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031757440 Printed edition: 9783031757464 Printed edition: 9783031757471 Advances in Oil and Gas Exploration & Production, 2509-3738 69230 https://doi.org/10.1007/978-3-031-75745-7 ZDB-2-EES ZDB-2-SXEE 65818 65818