Geophysics and Geophysical Exploration (지구물리와물리탐사)

Korean Society of Earth and Exploration Geophysicists (한국지구물리·물리탐사학회)
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Development of Data Analysis and Interpretation Methods for a Hybrid-type Unmanned Aircraft Electromagnetic System

하이브리드형 무인 항공 전자탐사시스템 자료의 분석 및 해석기술 개발

  • 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))
  • 김영수 (한양대학교 자원환경공학과) ;
  • 강현우 (한양대학교 자원환경공학과) ;
  • 방민규 (한양대학교 자원환경공학과) ;
  • 설순지 (한양대학교 자원환경공학과) ;
  • 김보나 (한국지질자원연구원)
  • Received : 2022.01.13
  • Accepted : 2022.02.25
  • Published : 2022.02.28
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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.

최근의 정보기술발달에 힘입어 소형 무인 비행체를 활용한 각종 물리탐사 방법들이 제안되고 그 해석방법들에 대한 연구가 소개되고 있다. 이 연구에서는 한국지질자원연구원에서 개발 중인 송수신 분리형 무인 항공 전자탐사 장비를 소개하고 획득한 자료의 타당성 검증을 위해 수행된 시험자료를 분석하여 해석하는 방법을 제안하는 연구를 수행하였다. 특히, 수신기가 드론에 매달린 채로 탐사가 수행되기 때문에 발생되는 흔들림 성분의 영향을 고찰하고 회전변환을 이용하여 보정하였다. 한편, 비행체에 의한 탐사는 송수신기 간의 거리, 고도 등 여러 탐사 변수들이 실시간으로 변하게 되고 획득한 자료는 지상 탐사보다 더 많은 잡음을 포함하게 되어 전통적인 해석방법으로의 해석에 많은 어려움이 따른다. 따라서, 이 연구에서는 획득한 전자탐사자료를 이용하여 빠르게 겉보기 비저항을 예측할 수 있는 순환 인공 신경망 모델을 구축하였으며, 현장자료의 분석을 통해 얻어진 잡음들을 수치모델링을 통해 생성한 학습자료에 포함시켜 잡음이 포함된 자료의 예측성능을 향상시켰다. 학습된 순환 신경망 모델을 시험탐사 현장자료에 적용시킨 결과 지상탐사 및 전기비저항 탐사 결과와 유사한 겉보기 비저항을 예측함을 확인하였다.

Keywords

Acknowledgement

이 연구는 한국지질자원연구원의 주요사업인 "국내 바나듐(V) 등 에너지 저장광물 정밀탐사기술 개발 및 부존량 예측(21-3211)" 과제(GP2020-007)의 일환으로 수행되었습니다. 또한, 이 연구는 산업통상자원부(MOTIE)와 한국에너지기술평가원(KETEP)의 지원을 받아 수행한 연구 과제입니다(No. 20194010201920).

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