Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Application of deep learning approaches in igneous rock hyperspectral imaging

    77520.pdf (490.5Kb)
    Access Status
    Open access
    Authors
    Sinaice, Brian
    Kawamura, Youhei
    Kim, Jaewon
    Okada, Natsuo
    Kitahara, Itaru
    Jang, Hyong Doo
    Date
    2019
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Sinaice, B.B. and Kawamura, Y. and Kim, J. and Okada, N. and Kitahara, I. and Jang, H.D. 2019. Application of deep learning approaches in igneous rock hyperspectral imaging. In: 28th International Symposium on Mine Planning & Equipment Selection, 2nd Dec 2019, Perth, Australia.
    Source Title
    Proceedings of the 28th International Symposium on Mine Planning and Equipment Selection - MPES2019
    Source Conference
    28th International Symposium on Mine Planning & Equipment Selection
    Additional URLs
    10.1007/978-3-030-33954-8_29
    Faculty
    Faculty of Science and Engineering
    School
    WASM: Minerals, Energy and Chemical Engineering
    URI
    http://hdl.handle.net/20.500.11937/77319
    Collection
    • Curtin Research Publications
    Abstract

    Hyperspectral imaging has been applied in remote sensing amongst other disciplines, success in these has triggered its extensive use. Hence, it comes as no surprise that we took advantage of this technology by conducting a study aimed at the spectral analysis of several igneous rocks, and to deduce the spectral signatures of each rock unit using neural networks. Through visual observations and comparisons of these spectral signatures, parameters such as band curvature (shape), tilt (position) and strength were used for lithological discrimination. Even with this said, there often exists similarities in rocks, which are rather difficult to differentiate by means of visual or graphical analysis. However, with numerous technologies making new waves in today’s era and artificial intelligence (AI) being at the forefront of these developments, it was best fitting to employ deep learning, often referred to as a subset of AI; to train/learn from these hyperspectral signatures with a goal aimed at classifying these rocks. Deep learning has networks such as the convolution neural network (CNN), which has algorithms that excel in feature representation from visual imagery; taking into account that the more data is fed into the training process and later used as a database for further training, the higher the future prediction accuracy. Gathered outcomes from the CNN show exceptionally high prediction accuracy capabilities of 96%; suggesting viable field and laboratory usage of these systems as a unit for mining and rock engineering applications.

    Related items

    Showing items related by title, author, creator and subject.

    • Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods
      Ma, Junjie; Li, Tianbin; Shirani Faradonbeh, Roohollah ; Sharifzadeh, Mostafa; Wang, Jianfeng; Huang, Yuyang; Materials, Chunchi; Peng, Feng; Zhang, Hang (2024)
      The degree of rock mass discontinuity is crucial for evaluating surrounding rock quality, yet its accurate and rapid measurement at construction sites remains challenging. This study utilizes fractal dimension to characterize ...
    • Molecular and isotope chronostratigraphy of tertiary source rocks and crude oils
      Eiserbeck, Christiane (2011)
      The exploration and production of petroleum from the subsurface is an important sector of industry to maintain the standards of our modern life. The availability of these natural resources has diminished in the past decades ...
    • Improved RMR rock mass classification using artificial intelligence algorithms
      Gholami, R.; Rasouli, Vamegh; Alimoradi, A. (2012)
      Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.