Thesis Abstract

Abstract
The rapid advancements in technology have opened up new possibilities for enhancing the analysis of seismic data for subsurface characterization. This thesis explores the application of machine learning algorithms in seismic data analysis to improve the accuracy and efficiency of subsurface characterization. The primary objective of this study is to investigate the effectiveness of machine learning techniques in processing seismic data and extracting valuable insights for subsurface characterization. The thesis begins with an introduction that highlights the importance of subsurface characterization in various industries such as oil and gas exploration, geothermal energy production, and environmental monitoring. The background of the study provides an overview of traditional seismic data analysis methods and their limitations, leading to the need for more advanced techniques such as machine learning. The problem statement identifies the challenges faced in subsurface characterization using conventional methods and emphasizes the potential benefits of integrating machine learning into the process. The objectives of the study include evaluating different machine learning algorithms for seismic data analysis, optimizing the parameters for improved accuracy, and assessing the performance of the models in subsurface characterization tasks. The limitations of the study are acknowledged, including the availability of high-quality seismic data, computational resources, and potential biases in the machine learning models. The scope of the study outlines the specific focus areas and the types of seismic data that will be analyzed using machine learning techniques. The significance of the study lies in its potential to revolutionize the field of subsurface characterization by providing more accurate and reliable results through the automation and optimization of data processing tasks. The structure of the thesis is outlined to guide the reader through the different chapters and sections, providing a clear roadmap of the research process. A detailed literature review in Chapter Two examines existing research on machine learning applications in seismic data analysis and subsurface characterization. The review covers various machine learning algorithms, data preprocessing techniques, feature selection methods, and model evaluation metrics relevant to the study. Chapter Three describes the research methodology, including data collection, preprocessing, feature engineering, model selection, training, and evaluation. The chapter also discusses the experimental setup, performance metrics, and validation procedures used to assess the effectiveness of the machine learning models in subsurface characterization tasks. Chapter Four presents a comprehensive discussion of the findings obtained from the experiments, including the performance comparison of different machine learning algorithms, the impact of parameter tuning on model accuracy, and the insights gained from the analysis of seismic data using machine learning techniques. Finally, Chapter Five provides a summary of the key findings, conclusions drawn from the study, implications for future research, and recommendations for implementing machine learning in seismic data analysis for subsurface characterization. The thesis concludes by highlighting the potential of machine learning to transform the field of subsurface characterization and pave the way for more efficient and accurate data analysis techniques.

Thesis Overview