Archaeoacoustic Stress-Testing (AST): A Proposed Theoretical Framework for Stone Identification in the Civitas Nerviorum

This paper introduces a framework for Subsurface Acoustic Characterization (SAC) as a secondary analytical layer for high-resolution LiDAR datasets. While LiDAR effectively maps surface morphology, it cannot differentiate between man- made lithic foundations and natural geological anomalies. By applying seismic refraction principles to identified topographic features, this methodology allows for material differentiation based on acoustic impedance (Z). This paper proposes a theoretical model applied to the Gallo-Roman Villa of Jette. I argue that AST can overcome visual data limitations in traditional aerial surveys to identify both documented foundations and previously unrecorded peripheral anomalies. This methodology aims to establish a non-invasive, predictive technique that reduces the risk of excavating post-Roman agricultural modifications by characterizing the probable structural density of the Roman landscape prior to physical intervention.