Application of Machine Learning in Seismic Data Analysis for Subsurface Imaging

Thesis Abstract

Abstract
The application of machine learning techniques in geophysics has gained increasing attention in recent years due to its potential to enhance the analysis and interpretation of seismic data for subsurface imaging. This thesis focuses on investigating the efficacy of utilizing machine learning algorithms in seismic data analysis to improve the accuracy and efficiency of subsurface imaging. The research aims to address the limitations of traditional seismic interpretation methods and explore the capabilities of machine learning in processing large volumes of seismic data to extract meaningful geological information. Chapter 1 provides an introduction to the research topic, highlighting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The literature review in Chapter 2 presents a comprehensive analysis of existing studies on machine learning applications in geophysics, seismic data analysis, and subsurface imaging. The review covers various machine learning algorithms, data preprocessing techniques, feature extraction methods, and model evaluation approaches relevant to the research topic. Chapter 3 details the research methodology, including data collection, preprocessing, feature selection, model development, training, testing, and validation processes. The chapter also discusses the selection criteria for machine learning algorithms, parameter tuning strategies, and performance evaluation metrics used in the study. Chapter 4 presents a detailed discussion of the research findings, including the comparative analysis of machine learning models, evaluation of model performance, interpretation of geological features, and insights gained from the analysis of seismic data. The conclusion and summary in Chapter 5 provide a comprehensive overview of the research findings, key contributions, implications for the field of geophysics, and recommendations for future research. The study demonstrates the potential of machine learning in enhancing seismic data analysis for subsurface imaging, offering valuable insights into the geological structure and properties of the subsurface. The research findings contribute to advancing the application of machine learning techniques in geophysics and provide a foundation for further exploration in this field. Overall, this thesis contributes to the growing body of knowledge on the application of machine learning in geophysics and underscores the importance of leveraging advanced data analytics tools to improve the accuracy and efficiency of subsurface imaging in the field of seismic exploration.