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020 _a9783031757457
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024 7 _a10.1007/978-3-031-75745-7
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050 4 _aQC801-809
072 7 _aPHVG
_2bicssc
072 7 _aSCI032000
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100 1 _aWang, Shirui.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_989120
245 1 0 _aDeep Learning for Seismic Data Enhancement and Representation
_h[electronic resource] /
_cby Shirui Wang, Wenyi Hu, Xuqing Wu, Jiefu Chen.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXVI, 152 p. 107 illus., 80 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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490 1 _aAdvances in Oil and Gas Exploration & Production,
_x2509-3738
505 0 _aChapter 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.
520 _aSeismic 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.
650 0 _aGeophysics.
_969226
650 0 _aData mining.
_96941
650 0 _aElectrical engineering.
_965586
650 0 _aImage processing
_xDigital techniques.
_964228
650 0 _aComputer vision.
650 0 _aArtificial intelligence
_xData processing.
650 1 4 _aGeophysics.
_969226
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aElectrical and Electronic Engineering.
_965588
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_965550
650 2 4 _aData Science.
700 1 _aHu, Wenyi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_989121
700 1 _aWu, Xuqing.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_989122
700 1 _aChen, Jiefu.
_eauthor.
_0(orcid)0000-0001-9940-7043
_1https://orcid.org/0000-0001-9940-7043
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_989123
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031757440
776 0 8 _iPrinted edition:
_z9783031757464
776 0 8 _iPrinted edition:
_z9783031757471
830 0 _aAdvances in Oil and Gas Exploration & Production,
_x2509-3738
_969230
856 4 0 _uhttps://doi.org/10.1007/978-3-031-75745-7
912 _aZDB-2-EES
912 _aZDB-2-SXEE
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