語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Deep learning for seismic data enhancement and representation
紀錄類型:
書目-語言資料,印刷品 : 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.;
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:
1481105
Seismic tomography
--Data processing.
LC Class. No.: QE538.5
Dewey Class. No.: 551.1
Deep learning for seismic data enhancement and representation
LDR
:02670nam a2200337 a 4500
001
1153622
003
DE-He213
005
20241219115236.0
006
m d
007
cr nn 008maaau
008
250619s2024 sz s 0 eng d
020
$a
9783031757457
$q
(electronic bk.)
020
$a
9783031757440
$q
(paper)
024
7
$a
10.1007/978-3-031-75745-7
$2
doi
035
$a
978-3-031-75745-7
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QE538.5
072
7
$a
PHVG
$2
bicssc
072
7
$a
SCI032000
$2
bisacsh
072
7
$a
PHVG
$2
thema
082
0 4
$a
551.1
$2
23
090
$a
QE538.5
$b
.D311 2024
245
0 0
$a
Deep learning for seismic data enhancement and representation
$h
[electronic resource] /
$c
by Shirui Wang ... [et al.].
260
$a
Cham :
$c
2024.
$b
Springer Nature Switzerland :
$b
Imprint: Springer,
300
$a
xvi, 152 p. :
$b
ill. (chiefly color), digital ;
$c
24 cm.
490
1
$a
Advances in oil and gas exploration & production,
$x
2509-3738
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.
650
0
$a
Seismic tomography
$x
Data processing.
$3
1481105
650
0
$a
Deep learning (Machine learning)
$3
1381171
650
1 4
$a
Geophysics.
$3
686174
650
2 4
$a
Data Mining and Knowledge Discovery.
$3
677765
650
2 4
$a
Electrical and Electronic Engineering.
$3
1388937
650
2 4
$a
Computer Imaging, Vision, Pattern Recognition and Graphics.
$3
671334
650
2 4
$a
Data Science.
$3
1174436
700
1
$a
Wang, Shirui.
$3
1481104
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
830
0
$a
Advances in oil and gas exploration & production.
$3
1141207
856
4 0
$u
https://doi.org/10.1007/978-3-031-75745-7
950
$a
Earth and Environmental Science (SpringerNature-11646)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入