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

Thesis Abstract

Abstract
The utilization of machine learning techniques in geophysics has gained significant attention in recent years due to their ability to enhance the efficiency and accuracy of seismic data interpretation for subsurface imaging. This thesis investigates the application of machine learning algorithms in seismic data processing and analysis to improve the understanding of subsurface structures. The study aims to develop a framework that integrates machine learning models with traditional seismic interpretation methods to enhance the quality and reliability of subsurface imaging. The introductory chapter provides an overview of the research background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter two presents a comprehensive literature review on the applications of machine learning in geophysics, seismic data interpretation, and subsurface imaging. The review highlights the advancements in machine learning algorithms and their potential benefits in improving the interpretation of seismic data for subsurface characterization. Chapter three outlines the research methodology, including data collection, preprocessing, feature selection, model development, and evaluation. The methodology section also discusses the selection of machine learning algorithms, parameter tuning, and validation techniques employed in the study. Additionally, the chapter covers the implementation of the proposed framework and the experimental setup for evaluating its performance. Chapter four presents a detailed discussion of the findings obtained from the application of machine learning techniques in seismic data interpretation. The results showcase the effectiveness of the developed framework in enhancing subsurface imaging accuracy and efficiency compared to traditional interpretation methods. The chapter also includes a comparative analysis of different machine learning algorithms to identify the most suitable approach for subsurface imaging tasks. Finally, chapter five provides a comprehensive conclusion and summary of the thesis, highlighting the key findings, contributions, and implications of the study. The conclusion also discusses the practical applications of the developed framework in geophysical exploration and its potential for future research directions. Overall, this thesis contributes to the advancement of geophysical research by demonstrating the utility of machine learning techniques in improving seismic data interpretation for subsurface imaging. Keywords Machine Learning, Seismic Data Interpretation, Subsurface Imaging, Geophysics, Data Processing, Feature Selection, Model Development, Evaluation, Framework Integration.