Road sinkhole detection with 2D ambient noise tomography

Abstract

Seismic methods are often used for detection of precollapsed sinkholes (voids) under roadways for remediation to minimize risks to the safety of the traveling public. Although active-source seismic methods can provide accurate subsurface profiles, they require closing the traffic flow for hours during testing and may potentially cause sinkhole collapse due to ground perturbation by source excitation. To address these issues, we have developed a new 2D ambient noise tomography (2D ANT) method for imaging voids under roadways. Instead of using the approximated Green’s function, whose required assumption of energy balance at both sides of each receiver pair is rarely satisfied, the crosscorrelation function of traffic noise recordings is inverted directly to obtain velocity structures. To adopt the concepts of seismic interferometry and derive the model structural kernel, passing-by vehicles are assumed to be moving sources along the receiver array. The source power-spectrum density is determined via the reverse time imaging approach to approximate the source distribution. The 2D ANT method is first demonstrated on a realistic synthetic model with the accurate recovery of the model variable layers and a buried void. To demonstrate its effectiveness on real-world problems, we successfully apply it to field data for assessment of a repaired sinkhole under U.S. Route 441, Florida, USA. The field experimental result finds that the method is capable of resolving the subsurface S-wave velocity ([Formula: see text]) structure and detecting a low-velocity anomaly. The inverted [Formula: see text] profile from 2D ANT generally agrees with that of 2D active-source full-waveform inversion, including the [Formula: see text] value and depth of the anomaly. To our best knowledge, this is the first study to directly invert the waveform crosscorrelation of traffic noise recordings to extract material property at the engineering meter scale (<30 m depth).