Thesis Abstract

Abstract
This thesis presents an in-depth investigation into the application of machine learning techniques in seismic data processing for subsurface imaging. The study focuses on the utilization of advanced machine learning algorithms to enhance the processing and interpretation of seismic data for improved subsurface imaging in geophysics. The research aims to address the challenges and limitations associated with traditional seismic data processing methods by leveraging the capabilities of machine learning models. The introduction sets the stage by highlighting the importance of subsurface imaging in geophysics and the role of seismic data processing in achieving accurate and detailed subsurface images. The background of the study provides a comprehensive overview of seismic data acquisition, processing, and interpretation, emphasizing the need for more efficient and accurate processing techniques. The problem statement identifies the existing challenges in seismic data processing and the potential benefits of integrating machine learning into the workflow. The objectives of the study are outlined to guide the research process, focusing on the development and implementation of machine learning algorithms for seismic data processing. The limitations of the study are acknowledged to provide a clear understanding of the scope and constraints of the research. The scope of the study defines the boundaries and extent of the research, specifying the target applications and datasets for experimentation. The significance of the study is highlighted in terms of its potential contributions to the field of geophysics, particularly in improving subsurface imaging accuracy and efficiency. The structure of the thesis is outlined to provide a roadmap of the organization and flow of the research content. Definitions of key terms are provided to ensure clarity and understanding of the terminology used throughout the thesis. The literature review chapter presents a comprehensive analysis of existing studies and methodologies related to machine learning in seismic data processing and subsurface imaging. The research methodology chapter details the experimental setup, data collection, preprocessing techniques, and machine learning algorithms used in the study. The discussion of findings chapter presents the results of the experiments, analysis of the outcomes, and comparison with existing methods. In conclusion, the study demonstrates the effectiveness of machine learning in improving seismic data processing for subsurface imaging applications. The findings highlight the potential of machine learning algorithms to enhance the accuracy, efficiency, and reliability of subsurface imaging techniques in geophysics. The thesis contributes to the advancement of geophysical research by introducing innovative approaches to seismic data processing and interpretation.

Thesis Overview