Date of Award
Spring 5-8-2025
Degree Type
Dissertation/Thesis
Degree Name
Masters in Computer Science
Department
Computer Science
Committee Chair/First Advisor
Da Hu
Second Advisor
Bobin Deng
Third Advisor
Chen Zhao
Abstract
Ground Penetrating Radar (GPR) is increasingly recognized as a reliable, non-invasive method for forensic investigations, particularly for detecting buried human remains. However, its effectiveness depends heavily on understanding GPR signal characteristics, which vary significantly across different subsurface conditions. This study uses simulation to systematically explore these signal characteristics by employing the gprMax software and the realistic AustinMan human body model buried under diverse media. Synthetic GPR datasets were generated by systematically varying parameters such as soil type and antenna frequency. Sequential signal processing techniques, including Time-Zero Correction and Eigenimage Processing, were applied to enhance data interpretation. Additionally, multiple B-scan datasets were compiled to create depth-wise C-scan visualizations, enabling clearer identification and estimation of the depth and orientation of the simulated human remains. The results demonstrate the potential of simulation-based GPR studies to overcome limitations related to data scarcity and interpretation complexity in forensic applications.
Comments
This research was supported by the Office of Research Interdisciplinary Initiatives Seed Grants program at Kennesaw State University (KSU).