Applications of Machine Learning in Seismic Data Analysis for Subsurface Characterization

Thesis Abstract

Abstract
The utilization of machine learning techniques in the field of geophysics has gained significant attention in recent years due to its potential to enhance the analysis and interpretation of seismic data for subsurface characterization. This thesis explores the applications of machine learning algorithms in seismic data analysis to improve the understanding of subsurface structures and properties. The study aims to investigate the effectiveness of various machine learning models in processing seismic data and to assess their capability in accurately characterizing subsurface features. Chapter 1 provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the foundation for the subsequent chapters by outlining the research context and the importance of applying machine learning in seismic data analysis. In Chapter 2, a comprehensive literature review is presented, covering ten key aspects related to the application of machine learning in geophysical studies. The review examines existing research works, methodologies, and findings in the field, highlighting the strengths and limitations of different machine learning approaches in seismic data analysis for subsurface characterization. Chapter 3 focuses on the research methodology employed in this study. It includes detailed descriptions of the data collection process, selection of machine learning algorithms, parameter tuning, and model validation techniques. The chapter also discusses the evaluation criteria used to assess the performance of the machine learning models in analyzing seismic data and characterizing subsurface features. Chapter 4 presents an elaborate discussion of the findings obtained from the application of machine learning algorithms to seismic data analysis. The chapter analyzes the results, compares different models, and discusses the implications of the findings on subsurface characterization. It also addresses any challenges encountered during the analysis and proposes potential solutions for future research. In Chapter 5, the conclusion and summary of the thesis are provided. The chapter discusses the key findings, implications, and contributions of the study to the field of geophysics. It also highlights the strengths and limitations of the research, suggests areas for further investigation, and offers recommendations for the practical implementation of machine learning in seismic data analysis for subsurface characterization. Overall, this thesis aims to contribute to the advancement of geophysical studies by demonstrating the effectiveness of machine learning techniques in enhancing the analysis and interpretation of seismic data for subsurface characterization. Through a systematic investigation and evaluation of machine learning models, this research provides valuable insights into the potential applications of artificial intelligence in geophysics, paving the way for more accurate and efficient subsurface characterization methods.