Ground-penetrating radar (GPR) enables noninvasive imaging in structural mapping applications such as unmarked grave detection. However, burial signatures can be cryptic and require expert analysis. This study investigates attribute enhancement and machine learning to automate identification of variable-signature graves at Green Hill Cemetery in Frankfort, Kentucky. Nine complementary seismic attributes were computed from the GPR envelope reflectivity volume to boost discontinuities and patterns indicative of shafts. Coherent energy, pseudofrequency, and similarity transforms showed optimal visualization enhancement. These volumes were input into unsupervised k-means and self-organizing map (SOM) machine learning models to cluster potential burial sites. Both methods accurately characterized strong anomaly reflections associated with apparent grave boundaries. However, limitations emerged in classifying subtler signatures that are likely linked to deteriorated or deeper interments. SOM clustering provided finer segmentation between noise and targets. Collectively, attributes amplified burial edges for easier recognition, while machine learning clustering expedited identification of most vault structures and unmarked sites. However, edge case discrimination remains a challenge. Results suggest that hybrid human-machine learning analysis can enhance efficiency over purely manual interpretation. With further method refinement, automated attribute and machine learning workflows show strong potential for accelerating GPR-based cemetery and archaeological mapping.

You do not have access to this content, please speak to your institutional administrator if you feel you should have access.