Application of Machine Learning Techniques in Seismic Data Interpretation for Subsurface Characterization

Thesis Abstract

Abstract
Advancements in machine learning techniques have revolutionized the field of geophysics, particularly in the interpretation of seismic data for subsurface characterization. This thesis investigates the application of machine learning algorithms to enhance the interpretation of seismic data for subsurface characterization purposes. The research aims to address the limitations of traditional seismic interpretation methods by leveraging the power of machine learning to improve the accuracy and efficiency of subsurface characterization. The introduction provides a comprehensive overview of the research topic, highlighting the significance of applying machine learning techniques in geophysics and the potential benefits it offers for subsurface characterization. The background of the study delves into the existing literature on seismic data interpretation and machine learning applications in geophysics, setting the foundation for the research. The problem statement identifies the challenges faced in traditional seismic interpretation methods, such as subjective interpretation, time-consuming processes, and limited accuracy. The objectives of the study outline the specific goals and outcomes the research aims to achieve, including the development of machine learning models for seismic data interpretation and the evaluation of their performance. The limitations of the study are acknowledged, recognizing potential constraints in data availability, computational resources, and model generalization. The scope of the study defines the boundaries within which the research will be conducted, focusing on specific regions, data types, and machine learning algorithms. The significance of the study highlights the potential impact of integrating machine learning techniques into seismic data interpretation practices, including improved subsurface imaging, enhanced reservoir characterization, and reduced interpretation time. The structure of the thesis provides an overview of the organization of the research, outlining the chapters and their respective contents. The literature review chapter synthesizes existing research on seismic data interpretation and machine learning applications in geophysics, providing a comprehensive overview of the current state-of-the-art techniques and methodologies. The research methodology chapter details the approach, data sources, machine learning algorithms, and evaluation metrics used in the study. The discussion of findings chapter presents the results of applying machine learning techniques to seismic data interpretation, analyzing the performance of the developed models and comparing them to traditional interpretation methods. The conclusions drawn from the research findings are summarized in the final chapter, highlighting the contributions, implications, and future directions of the study. Overall, this thesis contributes to the growing body of knowledge on the application of machine learning techniques in geophysics, specifically for subsurface characterization through seismic data interpretation. By leveraging the capabilities of machine learning, this research aims to enhance the accuracy, efficiency, and reliability of subsurface characterization practices, ultimately advancing the field of geophysics and benefiting various industries reliant on subsurface imaging and interpretation.