전기전자탐사에 적용되는 심층 학습 연구 동향 및 적용 사례 분석
Research Trends and Case Studies of Deep Learning Applications in Geo-electric and Electromagnetic Surveys
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전기전자탐사의 장비가 발전하면서, 탐사 시 측정할 수 있는 자료의 양이 증가하고 있을 뿐만 아니라 최근에는 광물 탐사 외에도 지하수 탐사나 오염 영역 탐사 등 매우 다양한 분야에서 적용되고 있다. 전기전자탐사 자료의 역산은, 계산 비용이 많이 들면서도 초기모델과 정규화 인자에 영향을 받는 한계가 있다. 이에 심층학습을 적용하여 자료를 처리하고 해석하는 기술에 대한 관심이 증가하여, 개선된 신경망을 이용한 심층 학습으로 역산을 구현하고자 하는 연구들이 수행되고 있다. 이 논문에서는 자료 처리에 적용될 수 있는 심층학습 방법들을 먼저 간략히 소개하고, 기존의 전기전자탐사 역산법을 간단히 설명하며 심층학습을 이용한 방법론을 분석하였다. 이후 전자탐사에 적용되는 기계학습 연구 동향과 그 사례들을 소개하고, 심층학습 적용의 장단점을 분석하였다.
Abstract
Technological innovations within the context of electrical and electromagnetic (EM) surveys have allowed for a rapid, efficient, and easier acquisition of a high quantity of data. Such innovations have been integral in mineral exploration and groundwater surveys. On the other hand, conventional inversion of electrical or EM survey data is computationally time-consuming and expensive. To circumvent the limitations of conventional inversion, the implementation of deep learning (DL) using improved neural networks has garnered substantial attention. In this study, we review various DL methods that can be used as substitutes for traditional inversion methods. Specifically, we investigate cases highlighting the successful implementation of DL to electrical or EM surveys and also comprehensively examine the advantages and disadvantages of such an application of DL.
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