Improving the capability of detecting joints and fractures in rock mass from roof bolt drilling data by using wavelet analysis

Wenpeng Liu, Samer S. Saab, Jamal Rostami, Asok Ray

    Research output: Contribution to journalArticle

    Abstract

    To optimise ground supporting and mitigate ground instability, a proper understanding of the ground conditions is critical. The concept of monitoring drilling parameters of a bolter for ground characterisation, which refers to identifying geological features included locations of joints and strengths of rock layers, has been studied in the past few decades. Several intelligent drilling units have been developed for joint detection but have limited capabilities. For instance, the existing systems fail to discriminate joints with the aperture of less than 3.175 mm and tend to generate false alarms. The objective of this research was to develop more efficient and sensitive detection programs for joint detection. To achieve this objective, a series of full-scale drilling tests with various simulated joint conditions have been conducted, and new detection programs have been proposed based on pattern recognition algorithms. Moreover, wavelet analysis has been applied to pre-process data to further promote detection programs.

    Original languageEnglish (US)
    Pages (from-to)97-112
    Number of pages16
    JournalInternational Journal of Oil, Gas and Coal Technology
    Volume20
    Issue number1
    DOIs
    StatePublished - Jan 1 2019

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    Wavelet analysis
    Bolts
    Roofs
    Drilling
    Rocks
    Pattern recognition
    Monitoring

    All Science Journal Classification (ASJC) codes

    • Energy(all)

    Cite this

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    abstract = "To optimise ground supporting and mitigate ground instability, a proper understanding of the ground conditions is critical. The concept of monitoring drilling parameters of a bolter for ground characterisation, which refers to identifying geological features included locations of joints and strengths of rock layers, has been studied in the past few decades. Several intelligent drilling units have been developed for joint detection but have limited capabilities. For instance, the existing systems fail to discriminate joints with the aperture of less than 3.175 mm and tend to generate false alarms. The objective of this research was to develop more efficient and sensitive detection programs for joint detection. To achieve this objective, a series of full-scale drilling tests with various simulated joint conditions have been conducted, and new detection programs have been proposed based on pattern recognition algorithms. Moreover, wavelet analysis has been applied to pre-process data to further promote detection programs.",
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    Improving the capability of detecting joints and fractures in rock mass from roof bolt drilling data by using wavelet analysis. / Liu, Wenpeng; Saab, Samer S.; Rostami, Jamal; Ray, Asok.

    In: International Journal of Oil, Gas and Coal Technology, Vol. 20, No. 1, 01.01.2019, p. 97-112.

    Research output: Contribution to journalArticle

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