Subsurface Lithic Detection via Archaeoacoustic Stress-Testing (AST): A Theoretical Framework

This paper introduces Archaeoacoustic Stress-Testing (AST), a secondary analytical framework for high-resolution LiDAR datasets (25cm DHMV II). While conventional LiDAR successfully maps surface morphology, it frequently fails to differentiate between natural geological features, medieval organic structures, and Roman lithic foundations. This limitation often results in "false positive" excavations. By treating 3D-extruded meshes as collision geometry within a simulated acoustic environment, AST facilitates material differentiation based on density-derived absorption coefficients (alpha). Validation conducted at the Gallo-Roman Villa of Jette demonstrates that AST can bypass "optical silence" in traditional aerial surveys to identify both documented foundations and previously unrecorded peripheral anomalies. This methodology establishes a non-invasive, predictive heuristic that mitigates the risk of excavating post-Roman agricultural modifications by verifying the "stone skeletons" of the Roman landscape prior to physical intervention.