Intelligent Seismic Inversion

Theory, Methods and Applications
Erscheinungsjahr 2026
ISBN: 978-981-9264-05-6
Verlag: Springer

Buch, Englisch, 241 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Springer Geophysics

This book investigates in detail intelligent seismic inversion technology, a rapidly evolving interdisciplinary field at the intersection of geophysics, artificial intelligence, and applied mathematics. As the cornerstone of subsurface characterization for oil and gas exploration, mineral resource assessment, and geological hazard mitigation, conventional seismic inversion suffers from heavy manual dependency, limited generalization, and unreliable uncertainty quantification. Pursuing a systematic approach, the book establishes a complete fundamental framework for intelligent seismic inversion, presenting eight cutting-edge methodologies that address the most pressing challenges in the field. The book features numerous high-quality illustrations, algorithm flowcharts, and field application case studies that clarify complex theoretical concepts and practical implementation details. It uniquely emphasizes the integration of physical constraints with data-driven approaches to resolve the critical issue of physical inconsistency in pure AI methods. For readers, this book provides an authoritative, up-to-date overview of the entire field, bridging the gap between academic research and industrial practice. It equips readers with both theoretical knowledge and practical skills to solve real-world subsurface characterization problems. The book is intended for undergraduate and graduate students in geophysics and petroleum engineering, researchers exploring AI applications in earth sciences, and professional engineers working in energy exploration and geological engineering.

Zielgruppe

Research

Autoren/Hrsg.

Song, Cao

Lu, Wenkai

Wang, Yuqing

Wang, Qi

Ma, Qiming

Geng, Weiheng

Wang, Yalin

Liu, Chenliang

Fachgebiete

• Naturwissenschaften Physik Angewandte Physik Geophysik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Preface.- Fundamental of intelligence seismic inversion.- Closed loop seismic inversion.- Seismic inversion based on cycle.- Forward physics constrained intelligence seismic inversion.- Multi task constrained intelligence seismic inversion.- Seismic inversion based on domain adaptation.

Über Autor(innen)

Dr. Cao Song received the bachelor’s degree in automation from Changsha University of Science and Technology, Changsha, China, in 2018, the master’s degree in automation from the School of Automation, Central South University, Changsha, in 2021, the Ph.D. degree in automation from the Department of Automation, Tsinghua University, Beijing, China in 2025. He is currently a Lecturer with Central South University, Changsha, China. His research interests include pattern recognition, deep learning, and its application in seismic inversion. He has published 20 papers in geophysical related journals and conferences, including , , and . He has applied for 20 invention patents. He serves as a Youth Editorial Board Member of , and acts as a reviewer for various international journals, including , , , , , , and .

Pro. Wenkai Lu received the bachelor’s degree in automation control from Tsinghua University, Beijing, China, in 1991, and the Ph.D. degree in geophysics from the Petroleum University, Beijing, in 1996. He is currently a Full Professor with the Department of Automation, Tsinghua University. His research interests include signal processing, image processing, pattern recognition, machine learning, artificial intelligence and its application in seismic inversion.

Dr. Yuqing Wang received the bachelor’s degree in automation from Tsinghua University, Beijing, China, in 2017, the Ph.D. degree with the Department of Automation, Tsinghua University, Beijing, China in 2022. Her research interests include intelligence seismic inversion, pattern recognition, machine learning, and signal processing.

Mr. Qi Wang received the bachelor’s degree in automation from the National University of Defense Technology, Changsha, China, in 2011, the master’s degree with the Department of Automation, Tsinghua University, Beijing, China in 2022. His research interests include intelligence seismic inversion, pattern recognition, machine learning, and signal processing.

Dr. Qiming Ma received the B.S. degree from Tongji University, Shanghai, China, in 2018, and the master's degree in automation in 2022 from Tsinghua University, Beijing, China, where he is currently working toward the Doctoral degree with the Department of Automation. His research interests include intelligence seismic inversion, intelligent system testing and verification, image processing, and autonomous driving.

Prof. Weiheng Geng received his Bachelor and Ph.D degrees in geophysics from China University of Petroleum (Beijing), Beijing, China, in 2017 and 2023, respectively. Since Nov. 2025, Dr. Geng has been in the College of Artificial Intelligence, China University of Petroleum (Beijing) as an Associate Professor. His research interests include seismic forward modeling, and poststack and prestack seismic inversion.