Shirui Wang (11 results)

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    • Language: English

      Published by Springer, 2025

      3031757475 / 9783031757471

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      Taschenbuch. Condition: Neu. Deep Learning for Seismic Data Enhancement and Representation | Shirui Wang (u. a.) | Taschenbuch | Advances in Oil and Gas Exploration & Production | xvi | Englisch | 2025 | Springer | EAN 9783031757471 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

    • Language: English

      Published by Springer, 2025

      3031757475 / 9783031757471

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    • Language: English

      Published by Springer, 2025

      3031757475 / 9783031757471

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    • Language: English

      Published by Springer, 2024

      3031757440 / 9783031757440

      • Hardcover
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    • Language: English

      Published by Springer Verlag GmbH, 2024

      3031757440 / 9783031757440

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    • Language: English

      Published by Springer, Berlin, Springer Nature Switzerland, Springer, 2024

      3031757440 / 9783031757440

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      Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 152 pp. Englisch.

    • Language: English

      Published by Springer, Berlin, Springer, 2025

      3031757475 / 9783031757471

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      Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 152 pp. Englisch.

    • Language: English

      Published by Springer, Palgrave Macmillan Dez 2025, 2025

      3031757475 / 9783031757471

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      Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 168 pp. Englisch.

    • Language: English

      Published by Springer, Palgrave Macmillan Dez 2024, 2024

      3031757440 / 9783031757440

      • Hardcover
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      Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000

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      Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 168 pp. Englisch.

    • Language: English

      Published by Springer, 2025

      3031757475 / 9783031757471

      • Softcover
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    • Language: English

      Published by Springer, 2025

      3031757475 / 9783031757471

      • Softcover
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