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http://dx.doi.org/10.7582/GGE.2022.25.1.26

Development of Data Analysis and Interpretation Methods for a Hybrid-type Unmanned Aircraft Electromagnetic System  

Kim, Young Su (Dept. of Earth Resources and Environmental Engineering, Hanyang University)
Kang, Hyeonwoo (Dept. of Earth Resources and Environmental Engineering, Hanyang University)
Bang, Minkyu (Dept. of Earth Resources and Environmental Engineering, Hanyang University)
Seol, Soon Jee (Dept. of Earth Resources and Environmental Engineering, Hanyang University)
Kim, Bona (Korea Institute of Geoscience and Mineral Resources (KIGAM))
Publication Information
Geophysics and Geophysical Exploration / v.25, no.1, 2022 , pp. 26-37 More about this Journal
Abstract
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.
Keywords
unmanned aircraft EM; rotation correction; RNN; noise; apparent resistivity;
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Times Cited By KSCI : 4  (Citation Analysis)
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