Isbn: 9783031757440 - deep learning for seismic data enhancement and representation (advances in oil and gas exploration & production) (5 resultados)

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  • Idioma: Inglés

    Editorial: Springer, 2024

    3031757440 / 9783031757440

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    Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand

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    Condición: new. Questo è un articolo print on demand.

  • Idioma: Inglés

    Editorial: Springer Verlag GmbH, 2024

    3031757440 / 9783031757440

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    Librería: moluna, Greven, Alemaniamoluna

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    Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

  • Idioma: Inglés

    Editorial: Springer, Berlin, Springer Nature Switzerland, Springer, 2024

    3031757440 / 9783031757440

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    Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.

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    Buch. Condición: 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.…

  • Idioma: Inglés

    Editorial: Palgrave Macmillan, 2024

    3031757440 / 9783031757440

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    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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    EUR 192,68

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    Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…

  • Idioma: Inglés

    Editorial: Springer, Palgrave Macmillan Dez 2024, 2024

    3031757440 / 9783031757440

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    Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000

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    EUR 181,89

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    Buch. Condición: 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.…