Deep Learning for Seismic Data Enhancement and Representation
Description
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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Persons
Shirui Wang received his BS. Degree in Electronic Information Engineering at University of Electronic Science and Technology of China. He is currently a Ph.D. candidate at the department of Electrical and Computer Engineering, University of Houston. He has published 16 papers and participated in 2 patents. His research interests include machine learning and data science for seismic data processing and analysis.
Dr Wenyi Hu received his Ph.D. degree in electrical engineering from Duke University, Durham, NC, USA, in 2005. From 2005 to 2009, he was a Research Scientist at Schlumberger-Doll Research. He was with ExxonMobil Upstream Research Company from 2009 to 2013 working there as a Senior Research Specialist. Between 2013 and 2021, he was the Vice President of Research at Advanced Geophysical Technology Inc, where he conducted research on geophysical modeling, imaging, and inversion, signal processing, and machine learning. He joined Schlumberger as the Global ML/AI Scientist - Subsurface in 2021.
Dr Xuqing Wu received the Ph.D. degree in computer science from the University of Houston, Houston, TX, USA, in 2011. He is currently an Associate Professor of Computer Information Systems with the College of Technology, University of Houston. Prior to joining the University of Houston in 2015, he was a Data Scientist and Software Engineer of the Energy and IT industry. His research interests include scientific machine learning, probabilistic modeling, and subsurface sensing.
Dr. Jiefu Chen is an Associate Professor with the Department of Electrical and Computer Engineering, University of Houston. He received the Ph.D. degree in electrical engineering from Duke University in 2010. From 2011 to 2015, he was a Staff Scientist with Weatherford International. Dr. Chen has published more than 100 technical papers in computational electromagnetics, inverse problems, machine learning for scientific computing, oilfield data analytics, seismic data processing, subsurface wireless communication, and well logging. Dr. Chen is a Full Member of USNC-URSI Commission F, National Academies of Sciences, Engineering, and Medicine, and a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE). He has been serving as an associate editor for IEEE Journal on Multiscale and Multiphysics Computational Techniques since 2018, and for IEEE Transactions on Geoscience and Remote Sensing since 2020.Shirui Wang received his BS. Degree in Electronic Information Engineering at University of Electronic Science and Technology of China. He is currently a Ph.D. candidate at the department of Electrical and Computer Engineering, University of Houston. He has published 16 papers and participated in 2 patents. His research interests include machine learning and data science for seismic data processing and analysis.
Content
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.