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Deep learning for seismic data enhancement and representation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning for seismic data enhancement and representation/ by Shirui Wang ... [et al.].
其他作者:	Wang, Shirui.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xvi, 152 p. :ill. (chiefly color), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Seismic tomography - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-031-75745-7
ISBN:	9783031757457

Deep learning for seismic data enhancement and representation
Deep learning for seismic data enhancement and representation[electronic resource] /by Shirui Wang ... [et al.]. - Cham :Springer Nature Switzerland :2024. - xvi, 152 p. :ill. (chiefly color), digital ;24 cm. - Advances in oil and gas exploration & production,2509-3738. - Advances in oil and gas exploration & production..

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.

ISBN: 9783031757457

Standard No.: 10.1007/978-3-031-75745-7doiSubjects--Topical Terms:

3754162
Seismic tomography
--Data processing.

LC Class. No.: QE538.5

Dewey Class. No.: 551.1

Deep learning for seismic data enhancement and representation
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505 0 $a 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.
520 $a 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.
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