Application of Machine Learning Techniques in Seismic Data Analysis for Improved Subsurface Imaging

Thesis Abstract

Abstract
Seismic data analysis plays a crucial role in understanding subsurface structures for various geophysical applications. With the advancements in machine learning techniques, there is a growing interest in utilizing these methods to enhance the interpretation of seismic data for improved subsurface imaging. This thesis focuses on exploring the application of machine learning algorithms in seismic data analysis to address the challenges associated with traditional interpretation methods and to achieve higher accuracy and efficiency in subsurface imaging. The introductory chapter provides an overview of the research background, problem statement, objectives, limitations, scope, significance of the study, structure of the thesis, and definition of key terms. The literature review in Chapter Two critically evaluates existing studies on the application of machine learning in seismic data analysis, highlighting the strengths and limitations of different approaches. Ten key themes emerge from the literature, including feature extraction, classification algorithms, deep learning models, and data augmentation techniques. Chapter Three outlines the research methodology adopted in this study, which includes data collection, preprocessing, feature extraction, model selection, training, and evaluation. The methodology also covers the use of synthetic data generation and transfer learning techniques to enhance the performance of machine learning models in seismic data analysis. The chapter provides a detailed description of the experimental setup and evaluation metrics used to assess the effectiveness of the proposed approach. In Chapter Four, the findings of the study are presented and discussed in detail. The results demonstrate the effectiveness of machine learning techniques in improving subsurface imaging accuracy and efficiency compared to traditional methods. The discussion covers the impact of feature selection, model complexity, and data augmentation on the performance of machine learning models in seismic data analysis. Furthermore, the chapter explores the practical implications of the findings and potential areas for further research in this field. Finally, Chapter Five summarizes the key findings of the study, discusses the implications for geophysical research and industry applications, and provides recommendations for future work. The conclusion highlights the significance of integrating machine learning techniques into seismic data analysis for enhanced subsurface imaging capabilities. Overall, this thesis contributes to the advancement of geophysical research by showcasing the potential of machine learning in improving the accuracy and efficiency of subsurface imaging techniques.