Simulation of Ground Penetrating Radar for the Detection of Buried Human Remains

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.