Detection of underground tunnels using electrical resistivity and refraction seismic tomography methods

Authors

IHU

Abstract

Geophysical techniques remain the only ways to remotely and non-destructively sense the earth’s near subsurface and as such have the most promising prospect for rapid and accurate detection of underground tunnels. Today, electrical resistivity and seismic refraction geophysical methods have been greatly developed to identify structures and underground cavities. In this study, the ability of these methods to detect tunnels has been investigated using a case study. Seismic methods are sensitive to velocity and density changes of the rock, while the electrical response is dependent upon the electrical resistivity of the rock. In this paper, we present a case study using electrical resistivity tomography (ERT) data and refraction seismic tomography (SRT) data of a tunnel site. Also, to evaluate the capabilities of resistivity method as the main method to detect buried structures in this paper, geo-electrical abnormalities of a rectangular block through various simulations are examined. Electrical resistivity methods utilize direct currents or low frequency alternating currents to investigate the electrical properties of the subsurface. In the resistivity method, the source is artificially-generated electric current introduced into the ground using electrodes. The potential differences are measured at the surface and the pattern of potential differences provides information on the distribution of subsurface electrical resistivity. In near-surface refraction tomography, the travel times of seismic energy recorded at the surface by multiple source-receiver combinations are used to generate an optimized model of the distribution of seismic velocities in the subsurface. In ERT, the forward problem uses the finite-element method to compute the electric potential response of the earth due to a given input electric current. The inverse algorithm iteratively finds the best distribution of subsurface resistivity that best fits the observed data. The purpose of this research is to determine the location of underground tunnel using inverse modeling of ERT and SRT data, and evaluate the capabilities of resistivity method to determine the location of underground tunnels using geo-electrical abnormalities of a rectangular block through various simulations. Geophysical field surveys were performed at a site with a known tunnel. The tunnel is a 1 mx1.6 m concrete lined tunnel about 80m long. The data presented here was collected where the tunnel is at a depth of about 6m. Based on prior knowledge of the tunnel location the surveys are approximately perpendicular to the tunnel and were purposely centered on the approximate location of the tunnel in order maximize the geophysical sampling in the vicinity of the tunnel. The seismic refraction survey was performed using 96 geophones. A geophone spacing of 0.5m was used for a total spread length of 47.5m. The electrode layout consisted of 50 electrodes in a 1m dipole-dipole configuration for a total 49m spread length. In this study, we chose to use the dipole-dipole configuration due to its good lateral resolution. In this research, to determine the exact location of the tunnel, data obtained from ERT and SRT survey were inverted using Res2Dinv software and Rayfract software, respectively. The results of both methods show abnormalities in the tunnel under test site. The tunnel shows up in the electrical imaging as regions of high resistivity since both the concrete and air of the tunnel are higher resistivity than the conductive weathered rock. In practice, the resistive abnormally of the tunnel gets smoothed out and is larger than the actual tunnel. Therefore, in the ERT, the tunnel coincides with one of the high resistivity anomalies, but a second, shallower resistive abnormally of unknown provenance appears just to the east. Based on the results of the seismic survey, the velocity tomographic image is inadequate for tunnel detection as the smoothing inherent to the tomographic calculations results in only slight changes in velocity near the tunnel location. Instead, the ray coverage density mapping associated with ray tracing displays small regions of low coverage associated with the tunnel. In this instance the use of both methods would suggest that this second ERT abnormality is not a tunnel and illustrates how the use of both seismic refraction and ERT can be used to increase the reliability of detecting tunnels. Finally, with the simulation of resistivity data obtained from a rectangular block, the effects of various parameters such as depth of the tunnel, overburden conductivity, thickness of overburden on identifying underground tunnel, wasere investigated; this is done to clarify the status and ability of this method in detecting underground cavities. To approximate simulated data to the fact, five percent extra noise was added to the data. To this end geological models, which can be a target in the ground like an underground tunnel, were produced in Res2Dmod software. These synthetic models were provided with reverse modeling using Res2Dinv software. The results of simulation and modeling show that the electrical resistivity method is most widely geophysical method used for detecting underground tunnels.
 

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References

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Journal of the Earth and Space Physics
Volume 42, Issue 3
December 2016
Pages 587-606

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