Abstract

Numerous measurement and characterization methods have been developed over the past decades to quantify the roughness of rock joints. The characterization methods traditionally consider a 2D linear trace and are often limited to the laboratory scale. In this research, 3D point clouds of joint surfaces obtained with Terrestrial Laser Scanning (TLS) have been analyzed to obtain 3D representations of joint roughness. While TLS enables remote acquisition of large scale in-situ joint surfaces, the resulting data include inherent range noise which generally results in significant overestimation of joint roughness. To denoise the TLS data, two wavelet transforms are applied in combination with different thresholds. The denoising procedure is successfully tested by comparing TLS data obtained on a 20×30 cm joint sample at a range of 10 m, to reference data for the same joint surface obtained using the Advanced TOpometric Sensor (ATOS).

1 Introduction

Rock joint roughness can be measured and parameterized according to several different methods. In-situ measurements are traditionally made using a contour gauge or a simple profilograph with a straight edge. Results are generally sparse and limited to 2D profiles measurements. Therefore, many existing roughness parameters are based on 2D profiles including the Joint Roughness Coefficient (JRC), maximum asperity angle (Patton 1966) and different statistical parameters, e.g. roughness profile index or average angle (Papaliangas 1995).

Remote sensing has recently been applied to joint characterization, with measurement techniques including total station, photogrammetry, the Advanced TOpometric Sensor (ATOS) and Terrestrial Laser Scanning (TLS), as described in (e.g. Feng 2003, Haneberg 2007, Grasselli et al. 2002, Khoshelham et al. 2011). Remote sensing techniques can provide 3D digital surface data, allowing new roughness parameterization approaches such as fractals (e.g. Fardin et al. 2004) or the angular threshold method (Grasselli 2001).

In our research the efficacy of TLS as a means for in-situ characterization of joint surfaces is investigated. TLS enables fast, accurate and detailed acquisition of distant, inaccessible, large scale surfaces. However, the data resolution and accuracy are limited by laser spot size and range noise, respectively. Laser point density (resolution) determines the smallest observable roughness scale, and data accuracy influences roughness amplitude. Sturzenegger & Stead (2009) extracted first-order roughness from TLS data, however the question remains whether reliable estimation of second-order roughness is also possible. Therefore, the objective of this research is to define a method, which can efficiently remove the TLS range noise while preserving important details of the surface roughness.

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