Economic and Environmental Geology (자원환경지질)

The Korean Society of Economic and Environmental Geology (대한자원환경지질학회)
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Evaluating the Effectiveness of an Artificial Intelligence Model for Classification of Basic Volcanic Rocks Based on Polarized Microscope Image

편광현미경 이미지 기반 염기성 화산암 분류를 위한 인공지능 모델의 효용성 평가

  • Sim, Ho (Department of Earth System Sciences, Yonsei University) ;
  • Jung, Wonwoo (Department of Earth System Sciences, Yonsei University) ;
  • Hong, Seongsik (Department of Earth System Sciences, Yonsei University) ;
  • Seo, Jaewon (Department of Earth System Sciences, Yonsei University) ;
  • Park, Changyun (Department of Geology, Kyungpook National University) ;
  • Song, Yungoo (Department of Earth System Sciences, Yonsei University)
  • 심호 (연세대학교 지구시스템과학과) ;
  • 정원우 (연세대학교 지구시스템과학과) ;
  • 홍성식 (연세대학교 지구시스템과학과) ;
  • 서재원 (연세대학교 지구시스템과학과) ;
  • 박창윤 (경북대학교 지질학과) ;
  • 송윤구 (연세대학교 지구시스템과학과)
  • Received : 2022.06.19
  • Accepted : 2022.06.28
  • Published : 2022.06.28
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Abstract

In order to minimize the human and time consumption required for rock classification, research on rock classification using artificial intelligence (AI) has recently developed. In this study, basic volcanic rocks were subdivided by using polarizing microscope thin section images. A convolutional neural network (CNN) model based on Tensorflow and Keras libraries was self-producted for rock classification. A total of 720 images of olivine basalt, basaltic andesite, olivine tholeiite, trachytic olivine basalt reference specimens were mounted with open nicol, cross nicol, and adding gypsum plates, and trained at the training : test = 7 : 3 ratio. As a result of machine learning, the classification accuracy was over 80-90%. When we confirmed the classification accuracy of each AI model, it is expected that the rock classification method of this model will not be much different from the rock classification process of a geologist. Furthermore, if not only this model but also models that subdivide more diverse rock types are produced and integrated, the AI model that satisfies both the speed of data classification and the accessibility of non-experts can be developed, thereby providing a new framework for basic petrology research.

암석 분류에 필요한 인적, 시간적 소모를 최소화하기 위해 최근 인공지능을 활용한 암석 분류 연구가 대두되었다. 이에 본 연구에서는 편광현미경 박편 이미지를 활용하여 염기성 화산암을 세분류하고자 하였다. 분류에 사용된 인공지능 모델은 Tensorflow, Keras 라이브러리를 기반으로 합성곱 신경망 모델을 자체 제작하였다. Olivine basalt, basaltic andesite, olivine tholeiite, trachytic olivine basalt 기준시료 박편을 개방 니콜, 직교 니콜, 그리고 gypsum plate를 장착하고 촬영한 이미지 총 720장을 인공지능 모델에 training : test = 7 : 3 비율로 학습시켰다. 학습결과, 80~90%이상의 분류 정확도를 보였다. 각각의 인공지능 모델의 분류 정확도를 확인하였을 때, 본 모델의 암석분류 방식이 지질학자의 암석 분류 프로세스와 크게 다르지 않을 것으로 예상된다. 나아가 본 모델 뿐 아니라 보다 다양한 암석종을 세분시키는 모델을 제작하여 통합한다면, 데이터 분류의 신속성과 비전문가의 접근성 모두를 만족시키는 인공지능 모델을 개발할 수 있으며, 이를 통해 암석학 기초연구의 새로운 틀을 마련할 수 있을 것으로 생각된다.

Keywords

Acknowledgement

이 연구는 한국연구재단의 광화유체 진화의 미시적 해석: 마그마성-열수 광화작용의 새로운 이해 사업의 (NRF-2018R1D1A1B07051418)의 일환으로 수행되었음을 밝힙니다.

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