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    Practical Models To Distinguish Between Seismic Events And Blast Signals

    Access Status
    Fulltext not available
    Authors
    Shirani Faradonbeh, Roohollah
    Ghiffari Ryoza, Muhammad
    Jang, Hyongdoo
    Topal, Erkan
    Date
    2023
    Type
    Conference Paper
    Metadata
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    Citation
    Shirani Faradonbeh, R. and Ghiffari Ryoza, M. and Jang, H. and Topal, E. 2023. Practical Models To Distinguish Between Seismic Events And Blast Signals. In: 5th International Underground Excavations Symposium and Exhibition, 5th June 2023, Istanbul, Turkey.
    Source Conference
    5th International Underground Excavations Symposium and Exhibition
    Additional URLs
    https://uyak.org.tr/uyak2023-bildiriler-kitabi.pdf
    Faculty
    Faculty of Science and Engineering
    School
    WASM: Minerals, Energy and Chemical Engineering
    Collection
    • Curtin Research Publications
    Abstract

    The seismic events are contaminated with the blast/noise signals in microseismic monitoring of the underground excavations, negatively affecting the interpretation and detection of high-stress zones. This study proposes explicit and comprehensible classifiers by hybridizing the principal component analysis (PCA) with genetic programming (GP) and classification and regression tree (CART) algorithms. Six discriminant parameters representing the spectrum and source characteristics of the signals were used as input variables. PCA reduced the problem's dimensionality to two components, which were then fed into GP and CART algorithms as the new input variables. A systematic hyperparameter tuning procedure was employed to find the optimum values of the controlling parameters of the algorithms. The hybrid PCA-GP and PCA-CART classifiers provided practical mathematical equations and tree structures, respectively, capable of distinguishing between the signal types with high accuracy. However, the PCA-GP model outperformed the PCA-CART model based on the performance indices.

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