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

Authors: Wenpeng Liu; Samer S. Saab Jr.; Jamal Rostami; Asok Ray

Addresses: Earth Mechanics Institute, Department of Mining Engineering, Colorado School of Mines, Golden, CO, 80401, USA ' Department of Electrical Engineering, The Pennsylvania State University, State College, PA, 16802, USA ' Earth Mechanics Institute, Department of Mining Engineering, Colorado School of Mines, Golden, CO, 80401, USA ' Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, State College, PA, 16802, USA

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. [Received: July 15, 2017; Accepted: November 18, 2017]

Keywords: wavelet analysis; cumulative sum algorithm; CUSUM algorithm; roof bolter; drilling parameters; joint detection; ground support optimisation.

DOI: 10.1504/IJOGCT.2019.096508

International Journal of Oil, Gas and Coal Technology, 2019 Vol.20 No.1, pp.97 - 112

Received: 15 Jul 2017
Accepted: 18 Nov 2017
Published online: 05 Dec 2018 *

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