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

1 Scopus citations

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

All Science Journal Classification (ASJC) codes

  • Energy(all)

Fingerprint Dive into the research topics of 'Improving the capability of detecting joints and fractures in rock mass from roof bolt drilling data by using wavelet analysis'. Together they form a unique fingerprint.

  • Cite this