• Geophysics and Geophysical Exploration
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• v.21 no.3
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• pp.150-161
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• 2018

Recently, unmanned aircraft EM (electromagnetic) survey based on ICT (Information and Communication Technology) has been widely utilized because of the efficiency in regional survey. We performed the theoretical study on the unmanned airship EM system developed by KIGAM (Korea Institute of Geoscience and Mineral resources) as part of the practical application of unmanned aircraft EM survey. Since this system has different configurations of transmitting and receiving loops compared to the conventional aircraft EM systems, a new technique is required for the appropriate interpretation of measured responses. Therefore, we proposed a method to calculate the EM field for the arbitrary shaped transmitter and verified its validity through the comparison with analytic solution for circular loop. In addition, to simulate the magnetic responses by three-dimensionally (3D) distributed anomalies, we have adapted our algorithm to 3D frequency-domain EM modeling algorithm based on the edge-FEM (finite element method). Though the analysis on magnetic field responses from a subsurface anomaly, it was found that the response decreases as the depth of the anomaly increases or the flight altitude increases. Also, it was confirmed that the response became smaller as the resistivity of the anomaly increases. However, a nonlinear trend of the out-of-phase component is shown depending on the depth of the anomaly and the used frequency, that makes it difficult to apply simple analysis based on the mapping of the magnitude of the responses and can cause the non-uniqueness problem in calculating the apparent resistivity. Thus, it is a prerequisite to analyze the appropriate frequency band and flight altitude considering the purpose of the survey and the site conditions when conducting a survey using the unmanned aircraft EM system.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.46-51
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• 2013

In this paper, the optimum placement and shape of UHF antenna on the unmanned aerial vehicle (UAV) are analyzed by using the electromagnetic (EM) simulation on the various locations. The FEKO was used for the EM-simulation. In order to reduce the complexity of simulation and minimize the runtime and memory usage, the composite aircraft structure is simplified as the PEC model excluding the radome structure. The simulation was performed on the wing and ventral fin of UAV, and the antenna shape used the monopole, dipole, and bent monopole antennas. When the monopole antenna is mounted under the wing, two antennas need to be mounted under the right and left wings, and those antennas have to be switched as the direction of UAV wing to the line of sight (LOS) data-link (DL) ground antenna. In the case of mounting under the ventral fin, one antenna can be used regardless of the direction of UAV wing to the LOS DL ground antenna. Also, the antenna gain is improved by the blockage reduction. The antenna gain is further improved by using the bent monopole antenna. The optimum solution of UHF antenna placement and shape on UAV is to mount the bent monopole antenna under the ventral fin.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.26-37
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• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.