Discrimination of Buried Plastic and Metal Objects in Subsurface Soil

The Electrical Conductivity Object Locator (ECOL) has been developed with the goal of detecting buried objects. Its specific capability to detect and characterize small-size plastic and metal objects buried at shallow depths is demonstrated. The technique can also detect larger objects at greater depths. The ECOL technique maps the soil subsurface conductivity and identifies variations in the conductivity between buried objects and their surroundings. The subsurface conductivity is mapped in two major steps: 1) Low-frequency (1 to 100 Hz) and low-amplitude (<200 μA) currents injected into the soil induce potential and magnetic fields in and around the subsurface soil. The potential and magnetic fields are measured using appropriate sensors placed above the soil surface. 2) Using the measured values as boundary conditions, a fast optimization algorithm, and an accurate matrix inversion routine, the subsurface conductivity is estimated. The ECOL technology has been verified extensively through computer simulation and field tests. Computer simulations were conducted using small and large plastic and metal objects buried at various depths between 10 and 50 meters; field-tests were conducted using small objects buried at shallow depths. They indicate that the ECOL technique is able to identify buried plastic discs (20 cm diameter; 5 cm thick) or metal discs (15 cm diameter, 7 cm thick) at shallow depths of about 15 cm. Note that during the field tests, the soil was cluttered with roots and pebbles. The moisture content of the soil did not affect the ability to locate buried objects. During the field tests, conventional sensors, such as reference “half-cells” were used in measuring electrical potentials. The subsurface conductivity was reconstructed using a set of finite elements and a simultaneous stochastic perturbation approximation algorithm, which were specifically developed for this technique. The codes were validated through extensive computer simulation of the experimental conditions.