Self-Potential Method: Theoretical Modeling and Applications in Geosciences

The book focuses on advances in Self-Potential geophysical data modeling, interpretation techniques, and applications in exploration, contamination, paleochannels, archaeology, glaciology, geomorphology, subsurface mapping, and hydrocarbon exploration. It provides state-of-art information and extensive modeling and field investigations from around the world, making it valuable for students, research workers, scientists, and practitioners in Applied Geophysics, Near Surface Geophysics, Potential field, Electrical and Electromagnetic methods, Mathematical Modeling Techniques in Earth Sciences, and Environmental Sciences.

Format: Hardback
Length: 314 pages
Publication date: 22 August 2021
Publisher: Springer Nature Switzerland AG

The book delves into the intricate realm of advancements in self-potential geophysical data modeling, exploring a wide range of interpretation techniques, innovative ideas, and an integrated approach to delineate subsurface structures associated with exploration, contamination, buried paleochannels, archaeological investigations, glaciology, geomorphology, subsurface mapping, and hydrocarbon exploration. With a primary focus on providing state-of-the-art information regarding research breakthroughs and new developments in these fields, coupled with extensive modeling and field investigations from around the globe, it serves as an invaluable resource for students, research workers, scientists, faculty members in applied geophysics, near-surface geophysics, potential field, electrical and electromagnetic methods, mathematical modeling techniques in earth sciences, as well as environmental and other practical problems associated with earth sciences.

The book is organized into five chapters, each dedicated to a specific aspect of self-potential data modeling. Chapter 1 provides an overview of the theoretical foundations and principles of self-potential data modeling, including the development of the electric dipole model and its application to subsurface structures. Chapter 2 explores various modeling techniques, such as finite difference, finite element, and Monte Carlo methods, and their application to different geological settings. Chapter 3 discusses the interpretation of self-potential data, including the use of inversion techniques, statistical analysis, and visualization methods to extract information about subsurface structures and properties. Chapter 4 explores the application of self-potential data modeling in exploration, including mineral and hydrocarbon exploration, groundwater exploration, and archaeological investigations. Chapter 5 presents an integrated study of self-potential data modeling, combining different techniques and approaches to address complex geological problems. Throughout the book, numerous case studies and examples are provided to illustrate the practical application of self-potential data modeling in various geological settings. These case studies cover a wide range of applications, including mineral exploration, groundwater resources, archaeological investigations, and hydrocarbon exploration. The book is written in a clear and concise manner, making it accessible to students, researchers, and practitioners in the field of geophysics. It includes detailed explanations of complex concepts, accompanied by numerous figures, tables, and equations to enhance understanding and visualization. Additionally, the book provides references to relevant literature and research papers, allowing readers to further explore the topics discussed in greater detail. Overall, the book serves as a comprehensive and up-to-date resource for anyone interested in advancing their knowledge and understanding of self-potential data modeling and its applications in geosciences.

Weight: 658g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030793326
Edition number: 1st ed. 2021