Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms for seismic data interpretation in the field of geophysics. The seismic data interpretation process is crucial in understanding subsurface structures and identifying potential hydrocarbon reservoirs. Traditional manual interpretation methods are time-consuming and often subjective, leading to potential inaccuracies in the final results. With the advancements in machine learning techniques, there is a growing interest in leveraging these algorithms to automate and improve the seismic data interpretation process. Chapter 1 of the thesis provides an introduction to the research topic, presents the background of the study, defines the problem statement, outlines the objectives, discusses the limitations and scope of the study, highlights the significance of the research, and presents the structure of the thesis along with the definition of key terms. Chapter 2 comprises a comprehensive literature review that explores existing studies related to seismic data interpretation, machine learning algorithms, and their applications in geophysics. This chapter aims to provide a theoretical foundation for the research and identify gaps in the current literature that this study seeks to address. Chapter 3 focuses on the research methodology employed in this study. The chapter discusses the data collection process, the selection of machine learning algorithms, the preprocessing techniques used, the model training and evaluation methods, as well as the validation procedures implemented. Chapter 4 presents an elaborate discussion of the findings obtained through the application of machine learning algorithms for seismic data interpretation. The chapter evaluates the performance of the models, compares the results with traditional interpretation methods, and discusses the implications of the findings on the field of geophysics. In Chapter 5, the thesis concludes with a summary of the key findings, a discussion of the contributions to the field of geophysics, and recommendations for future research. The chapter also highlights the practical implications of using machine learning algorithms for seismic data interpretation and discusses potential challenges and areas for further exploration. Overall, this thesis contributes to the growing body of research on the application of machine learning algorithms in geophysics, specifically focusing on seismic data interpretation. The findings of this study have the potential to enhance the efficiency, accuracy, and reliability of subsurface imaging processes, ultimately benefiting the exploration and production activities in the oil and gas industry.

Thesis Overview