Deep Learning for Seismic Data Enhancement and Representation

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 n...

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Ngā kaituhi matua:	Wang, Shirui (Author), Hu, Wenyi (Author), Wu, Xuqing (Author), Chen, Jiefu (Author)
Kaituhi rangatōpū:	SpringerLink (Online service)
Hōputu:	Tāhiko īPukapuka
Reo:	Ingarihi
I whakaputaina:	Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Putanga:	1st ed. 2024.
Rangatū:	Advances in Oil and Gas Exploration & Production,
Ngā marau:	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.
Urunga tuihono:	https://doi.org/10.1007/978-3-031-75745-7
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Rārangi ihirangi:

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.

Deep Learning for Seismic Data Enhancement and Representation

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