Application of Machine Learning in Seismic Data Interpretation for Subsurface Imaging

Thesis Abstract

Abstract
The application of machine learning techniques in geophysics has gained significant attention in recent years due to its potential to enhance subsurface imaging processes. This thesis investigates the utilization of machine learning algorithms for seismic data interpretation to improve the accuracy and efficiency of subsurface imaging. The study focuses on addressing the challenges faced in traditional seismic interpretation methods and aims to demonstrate the effectiveness of machine learning in enhancing subsurface imaging capabilities. The introduction provides a comprehensive overview of the research problem, highlighting the importance of accurate subsurface imaging in various geophysical applications. The background of the study discusses the evolution of seismic data interpretation techniques and the limitations of conventional methods. The problem statement identifies the key challenges in subsurface imaging and emphasizes the need for advanced technologies such as machine learning to overcome these challenges. The objectives of the study are outlined to guide the research process, with a specific focus on evaluating the performance of machine learning algorithms in seismic data interpretation. The limitations of the study are acknowledged, including data availability constraints and computational limitations. The scope of the study defines the boundaries within which the research will be conducted, outlining the specific focus areas and methodologies to be employed. The significance of the study lies in its potential to revolutionize subsurface imaging practices by leveraging machine learning capabilities. By integrating advanced algorithms into seismic data interpretation processes, this research aims to improve the accuracy, speed, and reliability of subsurface imaging results. The structure of the thesis provides a roadmap for the organization of the research work, outlining the chapters and key components of the thesis. The literature review synthesizes existing research on machine learning applications in geophysics and seismic data interpretation. It highlights the advancements made in the field and identifies gaps that this study aims to address. The research methodology chapter details the experimental setup, data collection procedures, and analytical techniques used to evaluate the performance of machine learning algorithms in seismic data interpretation. The discussion of findings chapter presents the results of the research, including the comparative analysis of traditional methods and machine learning-based approaches. The findings demonstrate the effectiveness of machine learning algorithms in improving the accuracy and efficiency of subsurface imaging. The conclusion and summary chapter provide a comprehensive overview of the research outcomes, discussing the implications of the findings and suggesting directions for future research in this field. In conclusion, this thesis contributes to the advancement of geophysical research by demonstrating the potential of machine learning in enhancing subsurface imaging for various applications. The findings of this study offer valuable insights into the benefits of integrating advanced technologies into traditional seismic interpretation processes, paving the way for more accurate and efficient subsurface imaging practices.