Rapid Microgravity Assessment of Subsurface Cavities Using Terrain-UnCorrelated Anomalies

In emergency engineering contexts, conventional gravity-processing workflows based on Bouguer anomaly computation are often impractical, as they require high-resolution digital elevation models and assumptions about terrain density, both of which introduce delays and additional uncertainty.

Following a localized collapse beneath track 2 at the EAV Pozzuoli railway station (Naples, Italy), a microgravity survey was conducted to support rapid subsurface characterization. Optical inspections indicate that the cavity extends approximately 4.5 m deep and 6 × 6 m, and affected both tracks, and caused visible settlement of the station platforms. The objectives of the gravity investigation are to assess (i) the spatial relationship between the detected cavity and the pedestrian underpass connecting the station building to platform 2, and (ii) the downstream path of wastewater.

In this study, we adopt a fast gravity data processing strategy to estimate the gravity component generated by lateral subsurface density contrasts (Florio et al., 2025). A linear regression between Free-Air Anomalies and elevation enables a parameter-free decomposition of the gravity field into a terrain-correlated component (TCA) and a terrain-uncorrelated component (TUCA). This approach enhances the detection of anomalous features such as cavities or mass deficits and allows for the independent estimation of average terrain density.

TUCA processing is rapid, requires minimal input data, and can be performed directly in the field, making it particularly suitable for preliminary evaluations in time-critical geotechnical settings. This paper presents the TUCA workflow and its application to the Pozzuoli railway station case study, including the survey design and key results.