Semi-automatic extraction of rock mass structural data from high resolution LIDAR point clouds

Abstract

In this paper a Matlab tool called DiAna (Discontinuity Analysis), for the 2D and 3D geo-structural analysis of rock mass discontinuities on high resolution laser scanning data is presented.

The proposed approach is able to semi-automatically retrieve some relevant rock mass parameters, namely orientation, number of sets, spacing/frequency (and derived RQD), persistence, block size and scale dependent roughness, by analyzing high resolution point clouds acquired from terrestrial or aerial laser scanners.

In addition, with a specific DiAna option called filterveg, we are able to remove vegetation or other disturbing objects from the point cloud, which is one of the main problems in LIDAR data processing.

Some examples of the proposed method have demonstrated its ability to investigate rock masses characterized by irregular block shapes, and suggest applications in the field of engineering geology and emergency management, when it is often advisable to minimize survey time in dangerous environments and, in the same time, it is necessary to gather all the required information as fast as possible.

Introduction

When we face engineering geology problems in rock, it is fundamental to reconstruct the 3D geometry and the structural setting of the rock masses, sometimes in inaccessible areas. An accurate description of the geometrical and mechanical properties of the material is specifically required, as the overall mechanical behavior of a rock mass depends on its structure, on the characteristics of discontinuities and on the properties of intact rock.

Traditional geomechanical surveys are performed in situ, either in one dimension (scanline method) or two dimensions (window method), and require direct access to the rock face for the collection of the relevant parameters [1].

ISRM [2] selected the following ten parameters for the quantitative description of discontinuities in rock masses: orientation, spacing, persistence, roughness, wall strength, aperture, filling, seepage, number of sets, and block size.

For practical and safety reasons, traditional geomechanical surveys are often carried out on limited sectors of the rock mass, and usually they do not provide data for a complete reconstruction of the full variability of a rock mass.

Nowadays, several techniques are available for retrieving high resolution 3D representations of land surface, such as digital photogrammetry [3], [4], laser scanning (terrestrial and aerial) [5], [6] and SAR interferometry [7].

In addition, the increased computational performance of personal computers allows us to process large amounts of data in a relatively short time.

The advantage of employing remote and high resolution surveying techniques for geomechanical purposes is based on the capability of performing both large scale [8], [9] and small scale [10], [11] analyses and to rapidly obtain information on inaccessible rock exposures.

Sometimes, the features of interest can be very large [12], and they could actually remain unnoticed if only a small scale field survey is performed. On the other side, the observation of small details, such as discontinuity planes or traces and surface roughness, is a key element for the geomechanical characterization of the rock mass.

In order to perform correct analyses from a statistical point of view, we need, therefore, to investigate a portion of the rock face as wide as possible.

The capability of capturing small details depends primarily on the resolution and on the accuracy of the survey method.

The main product of a long range laser scanning survey [13] is a high resolution point cloud, obtained by measuring with high accuracy (millimetric or centimetric) the distance of a mesh of points on the object, following a regular pattern with polar coordinates. The high acquisition rate (up to hundreds of thousands of points/s) allows to immediately obtain the detailed 3D shape of the object.

Laser scanning data can be processed by true coloring point clouds from high resolution optical digital images, or by triangulating points in order to create Digital Surface Models (DSM).

One of the main tasks when we have to interpret the acquired data is the vegetation removal [14].

Two main different levels of automation can be conceived, to extract the most relevant rock mass geomechanical characteristics, hidden in the point cloud.

• Manual: by inspecting the point cloud or the derived surface, fitting local planes, taking measurements, drawing polylines of interest, etc. [15]. This procedure has, however, a non-systematic character, is time-consuming and tends to neglect the smallest features. It is a subjective or biased analysis, as only those discontinuities, which appear to be important are investigated. The success of this approach depends on the quality of digital data and on the skill and experience of the geologist.

• Automatic/semi-automatic: by selecting a specific algorithm for the segmentation of the original data in clusters of points belonging to the same discontinuity. This can be defined as an objective or random analysis, since all detectable discontinuities within the surveyed area are sampled. Since raw data can contain up to tens of millions of points, the adopted algorithm should be optimized to make computational time acceptable. In the author's opinion, it is important to use both approaches, because they can complement each other.

In this paper we present a Matlab tool called DiAna (Discontinuity Analysis), for the 2D and 3D semi-automatic extraction of rock mass structural information from high resolution point clouds obtained from a terrestrial laser scanner.

In particular, six of the ten ISRM [2] parameters can be evaluated (orientation, number of sets, spacing/frequency, persistence, block size and scale dependent roughness) and a specific option for vegetation removal (filterveg) is implemented.

After a review of the state of the art for indirect rock face characterization, both 2D and 3D versions of the proposed tool are described, and a field application is presented to validate the semi-automatically obtained results with traditional field survey data.