Analysis of the Feasibility of UAS-Based EMI Sensing for Underground Utilities Detection and Mapping
This paper investigates the feasibility of using a linear current sensing (LCS) technique integrated on an unmanned aerial system (UAS) for detecting and mapping underground infrastructure rapidly and cost-effectively. The LCS technique is based on data from a wide band of electromagnetic induction frequencies (50 kHz to 2 MHz) using a vector magnetic field gradiometer. This technique takes advantage of a slowly decaying secondary magnetic field in order to achieve greater standoff detection distance ($$\frac{1}{R^2}$$ vs. $$\frac{1}{R^6}$$ for compact metallic targets during EMI sensing, where R is the distance from a target to the sensor). These secondary magnetic fields are produced by the excite current on long conductors, allowing detection at a distance of 10 meters or more. The system operates between tens of kHz to a few MHz and uses either an active EMI source or existing EM fields to excite this linear current on a long metallic subsurface target. Once excited, these linear currents produce a secondary magnetic field that is detected with an above ground triaxial magnetic field gradiometer. By moving and tracking its geolocation, the system outputs rich datasets sufficient to support high-fidelity forward and inverse EMI models for estimating the depth and orientation of deep underground long linear metallic infrastructure. The system’s hardware and its integration to a UAS system are outlined, along with the formulation of LCS theory, and numerical and experimental data are presented. The results illustrate that the LCS technique offers large standoff detection, is adaptable to UAS, and could be used effectively for detecting deep underground infrastructure such as wires and pipes.
- Research Organization:
- White River Technologies, Inc., Lebanon, NH (United States)
- Sponsoring Organization:
- USDOE Advanced Research Projects Agency - Energy (ARPA-E)
- Grant/Contract Number:
- AR0001334
- OSTI ID:
- 1981183
- Journal Information:
- Remote Sensing, Vol. 14, Issue 16; ISSN 2072-4292
- Publisher:
- MDPICopyright Statement
- Country of Publication:
- United States
- Language:
- English
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