Thesis Abstract

Abstract
The utilization of machine learning techniques in geophysics has gained significant attention in recent years as a means to enhance the interpretation of seismic data for subsurface imaging. This thesis focuses on the application of machine learning algorithms to improve the accuracy and efficiency of interpreting seismic data for subsurface imaging purposes. The research aims to investigate the potential benefits of integrating machine learning into traditional seismic data interpretation workflows and to assess the impact of these techniques on subsurface imaging outcomes. The study begins with a comprehensive review of the existing literature on machine learning applications in geophysics, highlighting the various algorithms and methodologies that have been utilized for seismic data interpretation. This literature review provides a foundation for understanding the current state of the field and identifies gaps in knowledge that this research seeks to address. The methodology chapter outlines the research design and approach adopted for this study, including data collection methods, preprocessing techniques, feature selection, and the implementation of machine learning algorithms for seismic data interpretation. The research methodology aims to demonstrate the effectiveness of machine learning in enhancing the interpretation of seismic data and improving subsurface imaging results. The findings chapter presents the results of the study, including the performance metrics of the machine learning algorithms in comparison to traditional interpretation methods. The analysis of the findings highlights the strengths and limitations of using machine learning for subsurface imaging and provides insights into the potential areas for further research and development. The discussion chapter critically evaluates the implications of the study findings and discusses the practical implications of integrating machine learning into seismic data interpretation workflows. The chapter also explores the challenges and future directions of applying machine learning techniques in geophysics for subsurface imaging. In conclusion, this thesis demonstrates the potential of machine learning to enhance the interpretation of seismic data for subsurface imaging applications. By leveraging advanced algorithms and computational techniques, geophysicists can improve the accuracy, efficiency, and reliability of subsurface imaging results. The research contributes to the growing body of knowledge on machine learning applications in geophysics and provides valuable insights for future research and development in this field.

Thesis Overview