Application of Seismic Attributes and Unsupervised Machine Learning Methods for Identification of Hidden Faults in Basement and Carbonate Rocks - A Thesis

Aug 2, 2026

Thesis research focuses on enhancing fault identification in basement and carbonate rock settings through the application of seismic attributes combined with unsupervised machine learning. The work develops a novel geometric seismic attribute for improved fault visualization, then applies multiattribute analysis to strengthen interpretation of areas relevant to subsurface processes. Results evaluate seismic-attribute patterns and machine-learning outputs, supported by structured methodology, discussion of performance, and conclusions outlining how hidden faults can be recognized from seismic data.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages97

Reading Time4 h 4 min

Acknowledgements

List of Figures

List of Tables

Abstract

Chapter 1. Application of a Novel Geometric Seismic Attribute for Enhancing Fault Visualization in Areas of Potential Carbon Capture and Storage

Introduction

Dataset

Methodology

Seismic Attributes

Machine Learning Methods

Results

Multiattribute Analysis

Machine Learning

Discussion

Conclusions

References

UNIVERSITY OF OKLAHOMA GRADUATE COLLEGE APPLICATION OF SEISMIC ATTRIBUTES AND UNSUPERVISED MACHINE LEARNING METHODS FOR IDENTIFICATION OF HIDDEN FAULTS IN BASEMENT AND CARBONATE ROCKS A THESIS SUBMITTED TO THE GRADUATE FACULTY in partial fulfillment of the requirements for the Degree of MASTER OF SCIENCE By DIANA KATERINE SALAZAR FLOREZ Norman, Oklahoma APPLICATION OF SEISMIC ATTRIBUTES AND UNSUPERVISED MACHINE LEARNING METHODS FOR IDENTIFICATION OF HIDDEN FAULTS IN BASEMENT AND CARBONATE ROCKS A THESIS APPROVED FOR THE SCHOOL OF GEOSCIENCES BY THE COMMITTEE CONSISTING OF Dr. Heather Bedle, Chair Dr. Brett M. Carpenter Dr. Jacob Walter © Copyright by DIANA K SALAZAR FLOREZ 2023 All Rights Reserved. To mom and dad ACKNOWLEDGEMENTS I would like to start by expressing my deepest gratitude to my advisor Dr. Heather Bedle. She trusted me when I was an undergrad student and since then she continued to believe in me, support me, understand me when the moments were tough, and provide me with many opportunities as a graduate student, both professionally and personally. I will always be grateful to her because my biggest and most important achievements were thanks to the outcomes of our work together. To Dr. Carpenter and Dr. Walter, thank you so much for serving on my committee. I could not be more grateful to Dr. C for the uncountable meetings and his willingness to help; his guidance and advice were crucial during the final and most critical stage of this work. To the School of Geosciences at the University of Oklahoma, the Mewbourne College of Earth and Energy, the Colombian Student Association, the Graduate College, and the Society of Exploration Geophysicist, for providing me with scholarships and awards that were of great help for my academic and professional development. To my closest friends Pamela Blanco, Miguel Romero, Ronnie and Karen Martinez, and Alayna Wong. Thanks for being my family here, for cheering me up, helping me take breaks, and especially for reminding me of the life outside my studies. Thanks to Gloria and Mitch Burrus for all the evenings, OU games, and Thanksgiving spent together. To my friends and colleagues from the School of Geosciences and AASPI, Raymond Ng, Mario Ballinas, Ganiyat Shodunke, Laura Ortiz, David Lubo, Emily Jackson, Grace Barber, Marcus Maas, Alex Vera, Karelia La Marca, Bobby Buist, and many others. Thanks for your advice, support, and many other moments of happiness shared. Lastly, and most importantly, my very loved ones. My parents, who no matter the adversities did everything they could to take me where I am, a very proud first-generation child who is now getting her master’s. My sister, who has been chasing her dreams for years and has fought many battles is more than an inspiration to me, but a role model. And my husband, mon amour, who was able to understand me more than anyone and was there every single time. TABLE OF CONTENT ACKNOWLEDGEMENTS ................................................................................................................................... V LIST OF FIGURES .............................................................................................................................................. IX LIST OF TABLES ............................................................................................................................................. XIV ABSTRACT ...................................................................................................................................................... XV CHAPTER 1. APPLICATION OF A NOVEL GEOMETRIC SEISMIC ATTRIBUTE FOR ENHANCING FAULT VISUALIZATION IN AREAS OF POTENTIAL CARBON CAPTURE AND STORAGE............................................................................................................................................................ 1 ABSTRACT .................................................

FAQs

What is the thesis focused on?

The thesis focuses on identifying hidden faults in basement and carbonate rocks by combining seismic attributes with unsupervised machine learning methods.

How does the research improve fault visualization?

It uses a novel geometric seismic attribute and multiattribute analysis to enhance fault visualization in geologic areas of interest.

What types of methods are included besides seismic attributes?

The research includes unsupervised machine learning methods, alongside the seismic attributes workflow, to support interpretation of fault-related features.

Aug 2, 2026

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