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Record Nr. |
UNINA9910918696403321 |
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Autore |
Wang Shirui |
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Titolo |
Deep Learning for Seismic Data Enhancement and Representation / / by Shirui Wang, Wenyi Hu, Xuqing Wu, Jiefu Chen |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (164 pages) |
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Collana |
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Advances in Oil and Gas Exploration & Production, , 2509-3738 |
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Altri autori (Persone) |
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HuWenyi |
WuXuqing |
ChenJiefu |
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Disciplina |
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Soggetti |
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Geophysics |
Data mining |
Electrical engineering |
Image processing - Digital techniques |
Computer vision |
Artificial intelligence - Data processing |
Data Mining and Knowledge Discovery |
Electrical and Electronic Engineering |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Data Science |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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. |
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