• Title/Summary/Keyword: 암석 이미지

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Development of deep learning-based rock classifier for elementary, middle and high school education (초중고 교육을 위한 딥러닝 기반 암석 분류기 개발)

  • Park, Jina;Yong, Hwan-Seung
    • Journal of Software Assessment and Valuation
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    • v.15 no.1
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    • pp.63-70
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    • 2019
  • These days, as Interest in Image recognition with deep learning is increasing, there has been a lot of research in image recognition using deep learning. In this study, we propose a system for classifying rocks through rock images of 18 types of rock(6 types of igneous, 6 types of metamorphic, 6 types of sedimentary rock) which are addressed in the high school curriculum, using CNN model based on Tensorflow, deep learning open source framework. As a result, we developed a classifier to distinguish rocks by learning the images of rocks and confirmed the classification performance of rock classifier. Finally, through the mobile application implemented, students can use the application as a learning tool in classroom or on-site experience.

Evaluation Method of Rock Characteristics using X-ray CT images (X-ray CT 이미지를 이용한 암석의 특성 평가 방안)

  • Kim, Kwang Yeom;Yun, Tae Sup
    • Tunnel and Underground Space
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    • v.29 no.6
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    • pp.542-557
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    • 2019
  • The behavior of rock mass is influenced by its microscopic feature of internal structure generating from forming and metamorphic process. This study investigated a new methodology for characterization of rock based on the X-ray CT (computed tomography) images reflecting the spatial distribution characteristics of internal constituent materials. The X-ray image based analysis is capable of quantification of heterogeneity and anisotropy of rock fabric, size distribution and shape parameter analysis of rock mineral grains, fluid flow simulation based on pore geometry image and roughness evaluation of unexposed joint surface which are hardly acquired by conventional rock testing methods.

Evaluation of Pore Size Distribution of Berea Sandstone using X-ray Computed Tomography (X-ray CT를 이용한 베레아 사암의 공극크기분포 산정)

  • Kim, Kwang Yeom;Kim, Kyeongmin
    • The Journal of Engineering Geology
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    • v.24 no.3
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    • pp.353-362
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    • 2014
  • Pore structures in porous rock play an important role in hydraulic & mechanical behaviour of rock. Porosity, size distribution and orientation of pores represent the characteristics of pore structures of porous rock. While effective porosity can be measured easily by conventional experiment, pore size distribution is hard to be quantified due to the lack of corresponding experiment. We assessed pore size distribution of Berea sandstone using X-ray CT image based analysis combined with associated images processing, i.e., image filtering, binarization and skeletonization subsequently followed by the assessment of local thickness and star chord length. The aim of this study is to propose a new and effective way to evaluate pore structures of porous rock using X-ray CT based analysis for pore size distribution.

Changes of Effective Elastic Moduli due to Crack Growth in Rock (암석내의 균열전파에 따른 유효탄성계수의 변화)

  • 신종진;전석원
    • Tunnel and Underground Space
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    • v.10 no.3
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    • pp.301-308
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    • 2000
  • Non-linear behavior of rock under compression can be predicted by a crack model. Crack growth in rock renders rock anisotropic. The degree of anisotropy is explained in terms of elastic moduli as a function of load level. In this study, we calculate the changes of elastic moduli due to crack growth numerically by using a crack model and compare these values with experimental results obtained from the measurement of ultrasonic wave velocities. Image processing technique is used to obtain the initial crack information needed for the numerical calculation of elastic moduli.

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Changes of Effective Elastic Moduli due to Crack Growth in Rock (암석내의 균열전파에 따른 유효탄성계수의 변화)

  • 신종진;전석원
    • Proceedings of the Korean Society for Rock Mechanics Conference
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    • 2000.09a
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    • pp.47-55
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    • 2000
  • Non-linear behavior of rock under compression can be predicted by a crack model. Crack growth in rock renders rock anisotropic. The degree of anisotropy is explained in terms of elastic moduli as a function of load level. In this study, we calculate the changes of elastic moduli due to crack growth numerically by using a crack model and compare these values with experimental results obtained from the measurement of ultrasonic wave velocities. Image processing technique is used to obtain the initial crack information needed for the numerical calculation of elastic moduli.

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A Feasibility Study on Application of a Deep Convolutional Neural Network for Automatic Rock Type Classification (자동 암종 분류를 위한 딥러닝 영상처리 기법의 적용성 검토 연구)

  • Pham, Chuyen;Shin, Hyu-Soung
    • Tunnel and Underground Space
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    • v.30 no.5
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    • pp.462-472
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    • 2020
  • Rock classification is fundamental discipline of exploring geological and geotechnical features in a site, which, however, may not be easy works because of high diversity of rock shape and color according to its origin, geological history and so on. With the great success of convolutional neural networks (CNN) in many different image-based classification tasks, there has been increasing interest in taking advantage of CNN to classify geological material. In this study, a feasibility of the deep CNN is investigated for automatically and accurately identifying rock types, focusing on the condition of various shapes and colors even in the same rock type. It can be further developed to a mobile application for assisting geologist in classifying rocks in fieldwork. The structure of CNN model used in this study is based on a deep residual neural network (ResNet), which is an ultra-deep CNN using in object detection and classification. The proposed CNN was trained on 10 typical rock types with an overall accuracy of 84% on the test set. The result demonstrates that the proposed approach is not only able to classify rock type using images, but also represents an improvement as taking highly diverse rock image dataset as input.

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;Jung, Wonwoo;Hong, Seongsik;Seo, Jaewon;Park, Changyun;Song, Yungoo
    • Economic and Environmental Geology
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    • v.55 no.3
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    • pp.309-316
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    • 2022
  • 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.

A study on the asperity degradation of rock joint surfaces using rock-like material specimens (유사 암석 시편을 사용한 암석 절리면 돌출부 손상 연구)

  • Hong, Eun-Soo;Kwon, Tae-Hyuk;Cho, Gye-Chun
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.11 no.3
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    • pp.303-314
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    • 2009
  • Image analyses for sheared joint specimens are performed to study asperity degradation characteristics with respect to the roughness mobilization of rock joints. Four different types of joint specimens, which are made of high-strength gypsum materials, are prepared by replicating the three-dimensional roughness of rock joints. About twenty jointed rock shear tests are performed at various normal stress levels. The characteristic and scale of asperity degradation on the sheared joint specimens are analyzed using the digital image analysis technique. The results show that the asperity degradation characteristic mainly depends on the normal stress level and can be defined by asperity failure and wear. The asperity degradation develops significantly around the peak shear displacement and the average amount of degraded asperities remains constant with further displacement because of new degradation of small scale asperities. The shear strength results using high-strength gypsum materials can not fully represent physical properties of each mineral particles of asperities on the natural rock joint surface. However the results of this quantitative estimation for the relationship between the peak shear displacement and the asperity degradation suggest that the characterization of asperity degradation provides an important insight into mechanical characteristics and shear models of rock joints.

Image Processing in Deciphering the Letter Written in Rocks by Experiment of Sample Texts (영상신호처리에 의한 금석문 음각문자 판독 - 샘플시료를 이용한 실험을 통하여)

  • Hwang, Jae-Ho
    • Proceedings of the KIEE Conference
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    • 2003.11c
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    • pp.765-768
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    • 2003
  • 금속이나 암석에 음각(陰角)으로 각인된 문자나 그림들은 날씨나 주변 빛 환경에 따라 시각으로 입력되는 정보에 큰 차이를 보인다. 이를 이미지검출장치를 통해 읽어드려 디지털 이미지 신호로 만들고자 할 때는 더욱 그 정도가 심하여 대상체가 위치하는 빛 환경이나 검출기 특성에 각별한 신경을 써야한다. 자연광이나 전구 그리고 기후나 날씨에 의해 조성되는 빛 환경은 조도(照度), 조사각도(照射角度), 그림자 및 대상체 표면 상태 등이 중요한 결정 인자들이다. 빛 환경이 디지털 이미지 질(質)에 끼치는 영향을 최소화하기 위한 실험실 차원의 빛환경조정실을 구축하였다. 외부 유입 광선을 모두 차단하고 지향성이 있는 조명에 의해서만 대상체에 빛이 조사되도록 하고 디지털 카메라로 대상체의 이미지를 담았다. 음각 문자를 새긴 샘플석문(石文)을 제작하고 실험실 안의 정량화된 빛환경 하에서 석문의 이미지를 취득하였다. 전처리 과정을 통해 노이즈를 제거하고 이미지의 질을 향상시켰다. 처리된 이미지를 분석하여 문자영역과 바탕영역의 신호패턴을 추출한 다음 룩업 테이블, 조도 레벨 슬라이징, 중첩의 원리 및 Morphology 등의 기법을 알고리즘화하여 2진 형태의 음각문자를 판독 및 복원하는데 성공하였다.

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Development of Triaxial Cells Operable with In Situ X-ray CT for Hydro-Mechanical Laboratory Testing of Rocks (원위치 X-ray CT 촬영이 가능한 암석의 수리-역학 실험용 삼축셀 개발)

  • Zhuang, Li;Yeom, Sun;Shin, Hyu-Soung
    • Journal of the Korean Geotechnical Society
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    • v.36 no.9
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    • pp.45-55
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    • 2020
  • X-ray computed tomography (CT) is very useful for the quantitative evaluation of internal structures, particularly defects in rock samples, such as pores and fractures. In situ CT allows 3D imaging of a sample subjected to various external treatments such as loading and therefore enables observation of changes that occur during the loading process. We reviewed state-of-the-art of in situ CT applications for geomaterials. Two triaxial cells made using relatively low density but high strength materials were developed aimed at in situ CT scanning during hydro-mechanical laboratory testing of rocks. Preliminary results for in situ CT imaging of granite and sandstone samples with diameters ranging from 25 mm to 50 mm show a resolution range of 34~105 ㎛ per pixel pitch, indicating the feasibility of in situ CT observations for internal structural changes in rocks at the micrometer scale. Potassium iodide solution was found to improve the image contrast, and can be used as an injection fluid for hydro-mechanical testing combined with in situ CT scanning.