Deep Learning for Seismic Data Enhancement and Representation
暫譯: 深度學習在地震數據增強與表徵中的應用
Wang, Shirui, Hu, Wenyi, Wu, Xuqing
- 出版商: Springer
- 出版日期: 2025-02-01
- 售價: $7,220
- 貴賓價: 9.5 折 $6,859
- 語言: 英文
- 頁數: 120
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3031757440
- ISBN-13: 9783031757440
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相關分類:
DeepLearning
海外代購書籍(需單獨結帳)
相關主題
商品描述
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.
商品描述(中文翻譯)
地震成像是地下探測的關鍵組成部分，依賴於高品質的地震數據獲取系統以及有效的地震處理演算法。地震數據的質量涉及多種因素，如獲取設計、環境限制、取樣解析度和噪聲。本書的重點在於通過利用深度學習強大的特徵工程能力，研究高效的地震數據表示和信號增強解決方案。
本書深入探討地震數據表示和增強問題，涵蓋從地震獲取設計到隨後的質量改善和壓縮技術。考慮到獲取適合的標記訓練數據集以解決地震數據處理問題的挑戰，我們專注於探索消除標籤需求的深度學習方法。我們將新穎的深度學習技術與傳統的地震數據處理方法相結合，並構建針對地震數據處理量身定制的網絡和框架。本書的編輯和作者來自學術界和業界，擁有地震數據處理和成像的實踐經驗。
作者簡介
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.
作者簡介(中文翻譯)
王士瑞於中國電子科技大學獲得電子信息工程學士學位。目前他是休士頓大學電機與計算機工程系的博士候選人。他已發表16篇論文並參與2項專利。他的研究興趣包括用於地震數據處理和分析的機器學習和數據科學。
胡文毅博士於2005年在美國北卡羅來納州杜克大學獲得電機工程博士學位。從2005年到2009年，他在Schlumberger-Doll Research擔任研究科學家。2009年至2013年，他在埃克森美孚上游研究公司擔任高級研究專家。2013年至2021年間，他是先進地球物理技術公司的研究副總裁，進行地球物理建模、成像和反演、信號處理及機器學習的研究。2021年，他加入Schlumberger擔任全球機器學習/人工智慧科學家 - 地下部門。
吳旭青博士於2011年在美國休士頓大學獲得計算機科學博士學位。目前他是休士頓大學技術學院計算機信息系統的副教授。在2015年加入休士頓大學之前，他曾是能源和IT行業的數據科學家和軟體工程師。他的研究興趣包括科學機器學習、概率建模和地下感測。
陳介福博士是休士頓大學電機與計算機工程系的副教授。他於2010年在杜克大學獲得電機工程博士學位。2011年至2015年，他在Weatherford International擔任科學家。陳博士在計算電磁學、反問題、科學計算的機器學習、油田數據分析、地震數據處理、地下無線通信和井下測井等領域發表了100多篇技術論文。陳博士是美國國家科學、工程和醫學院USNC-URSI F委員會的正式成員，以及電氣和電子工程師學會（IEEE）的高級會員。自2018年以來，他擔任IEEE多尺度與多物理計算技術期刊的副編輯，自2020年以來擔任IEEE地球科學與遙感期刊的副編輯。
