A review of laser scanning for geological and geotechnical applications in underground mining

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment. Although there are several published articles on laser scanning, there is a need to review them in the context of underground mining applications. To this end, a holistic review of laser scanning is presented including progress in 3D scanning systems, data capture/processing techniques and primary applications in underground mines. Laser scanning technology has advanced significantly in terms of mobility and mapping, but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency, dynamics, and environmental influences such as dust and water. Studies suggest that laser scanning has matured over the years for change detection, clearance measurements and structure mapping applications. However, there is scope for improvements in lithology identification, surface parameter measurements, logistic tracking and autonomous navigation. Laser scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer, geodetic networking and processing capacity remain limiting factors. Nevertheless, laser scanners are becoming an integral part of mine automation thanks to their affordability, accuracy and mobility, which should support their widespread usage in years to come

Terrestrial LiDAR in tunnels under construction : A study of potential use for engineering geological and operational applications, and work-flow design for data acquisition and processing

This thesis provides an assessment of the application of terrestrial LiDAR for rock mass characterisation and support design in drill and blast tunnels. The study includes establishing an appropriate work-flow for data acquisition in an operational tunnelling environment. This is determined through a full-scale pilot study, where the excavation of the Løren tunnel in Oslo, Norway, is followed over several months. An efficient work-flow for data processing and analysis for tunnel data is also established, using the software PolyWorks. An analysis utilising LiDAR for mapping of geological structures and extracting their orientations is conducted. Because absolute georeferencing of the LiDAR data has not been obtained, this involves a reorientation of the dataset, where the tunnel axis is oriented to true north. The result of this analysis is compared to the geological field mapping conducted by the engineering geologist on site, and generally show a good agreement. Mapping of large-scale structural features is shown to be possible from the intensity returns of the LiDAR instrument. The discontinuity orientation analysis demonstrates suitability of employing LiDAR for extracting a high amount of orientation measurements to assist the engineering geologist in evaluating rock mass quality. A study using LiDAR data for quantifying the roughness of the rock surface for establishing a new and improved method for calculating shotcrete volume is also included in this thesis. This study proposes two different parameters for calculating a roughness factor, appropriate for representing the true rock surface on which to apply shotcrete. The parameters include the mean profile length and the surface area of the blast round, as extracted from LiDAR data. The roughness factor calculated from surface area yields systematically higher values than the factor determined from profile lengths. A reduction of the area roughness factor is therefore proposed, by analysing how much the surface area decreases after a layer of shotcrete is applied. From the results presented in this study the chosen parameters appear to reflect the surface roughness of a blast round in a way that is useful for determining necessary shotcrete volume. However, further studies are necessary to confirm this

PORTABLE MULTI-CAMERA SYSTEM: FROM FAST TUNNEL MAPPING TO SEMI-AUTOMATIC SPACE DECOMPOSITION AND CROSS-SECTION EXTRACTION

The paper outlines the first steps of a research project focused on the digitalization of underground tunnels for the mining industry. The aim is to solve the problem of rapidly, semi-automatically, efficiently, and reliably digitizing complex and meandering tunnels. A handheld multi-camera photogrammetric tool is used for the survey phase, which allows for the rapid acquisition of the image dataset needed to produce the 3D data. Moreover, since often, automatic, and fast acquisitions are not supported by easy-to-use tools to access and use the data at an operational level, a second aim of the research is to define a method able to arrange and organise the gathered data so that it would be easily accessible. The proposed approach is to compute the 3D skeleton of the surveyed environment by employing tools developed for the analysis of vascular networks in medical imagery. From the computed skeletonization of the underground tunnels, a method is proposed to automatically extrapolate valuable information such as cross-sections, decomposed portions of the tunnel, and the referenced images from the photogrammetric survey. The long-term research goal is to create an effective workflow, both at the hardware and software level, that can reduce computation times, process large amounts of data, and reduce dependency on high levels of experience

Unplanned dilution and ore-loss optimisation in underground mines via cooperative neuro-fuzzy network

The aim of study is to establish a proper unplanned dilution and ore-loss (UB: uneven break) management system. To achieve the goal, UB prediction and consultation systems were established using artificial neural network (ANN) and fuzzy expert system (FES). Attempts have been made to illuminate the UB mechanism by scrutinising the contributions of potential UB influence factors. Ultimately, the proposed UB prediction and consultation systems were unified as a cooperative neuro fuzzy system

Optimising mobile laser scanning for underground mines

Despite several technological advancements, underground mines are still largely relied on visual inspections or discretely placed direct-contact measurement sensors for routine monitoring. Such approaches are manual and often yield inconclusive, unreliable and unscalable results besides exposing mine personnel to field hazards. Mobile laser scanning (MLS) promises an automated approach that can generate comprehensive information by accurately capturing large-scale 3D data. Currently, the application of MLS has relatively remained limited in mining due to challenges in the post-registration of scans and the unavailability of suitable processing algorithms to provide a fully automated mapping solution. Additionally, constraints such as the absence of a spatial positioning network and the deficiency of distinguishable features in underground mining spaces pose challenges in mobile mapping. This thesis aims to address these challenges in mine inspections by optimising different aspects of MLS: (1) collection of large-scale registered point cloud scans of underground environments, (2) geological mapping of structural discontinuities, and (3) inspection of structural support features. Firstly, a spatial positioning network was designed using novel three-dimensional unique identifiers (3DUID) tags and a 3D registration workflow (3DReG), to accurately obtain georeferenced and coregistered point cloud scans, enabling multi-temporal mapping. Secondly, two fully automated methods were developed for mapping structural discontinuities from point cloud scans – clustering on local point descriptors (CLPD) and amplitude and phase decomposition (APD). These methods were tested on both surface and underground rock mass for discontinuity characterisation and kinematic analysis of the failure types. The developed algorithms significantly outperformed existing approaches, including the conventional method of compass and tape measurements. Finally, different machine learning approaches were used to automate the recognition of structural support features, i.e. roof bolts from point clouds, in a computationally efficient manner. Roof bolts being mapped from a scanned point cloud provided an insight into their installation pattern, which underpinned the applicability of laser scanning to inspect roof supports rapidly. Overall, the outcomes of this study lead to reduced human involvement in field assessments of underground mines using MLS, demonstrating its potential for routine multi-temporal monitoring

Near-field blast vibration monitoring and analysis for prediction of blast damage in sublevel open stoping

The work presented in this thesis investigates near-field blast vibration monitoring, analysis, interpretation and blast damage prediction in sublevel open stoping geometries. As part of the investigation, seven stopes at two Australian sublevel open stoping mines were used as case studies. The seven stopes represented significant ranges in stope shapes, sizes, geotechnical concerns, extraction sequences, stress conditions, blasting geometries and rock mass properties.The blast damage investigations at the two mine sites had three main components. The first component was rock mass characterisation, which was performed using static intact rock testing results, discontinuity mapping, mining-induced static stress modelling and geophysical wave propagation approaches. The rock mass characterisation techniques identified localised and large-scale variations in rock mass properties and wave propagation behaviours in relation to specified monitoring orientations and mining areas. The other components of the blast damage investigations were blast vibration monitoring and analysis of production blasting in the seven stopes and stope performance assessments.The mine-based data collection period for the case studies lasted from January, 2006 to February, 2008. A key element of the data collection program was near-field blast vibration monitoring of production blasts within the seven study stopes. The instrumentation program consisted of 41 tri-axial accelerometers and geophone sondes, installed at distances from 4m to 16m from the stope perimeters. A total of 59 production firings were monitored over the course of the blast vibration monitoring program. The monitoring program resulted in a data set of over 5000 single-hole blast vibration waveforms, representing two different blasthole diameters (89mm and 102mm), six different explosive formulations and a wide range in charge weights, source to sensor distances, blasthole orientations and blasting geometries.The data collected in the blast vibration monitoring program were used to compare various near-field charge weight scaling relationships such as Scaled Distance and Holmberg-Persson prediction models. The results of these analyses identified that no single charge weight scaling model could dependably predict the measured near-field peak amplitudes for complex blasting geometries. Therefore, the general form of the charge weight scaling relationship was adopted in conjunction with nonlinear multivariable estimation techniques to analyse the data collected in the study stopes and to perform forward vibration predictions for the case studies.Observed variations in the recorded near-field waveforms identified that instantaneous peak amplitude such as peak particle velocity (PPV) did not accurately describe the characteristics of a large portion of the data. This was due to significant variations in frequency spectra, variable distributions of energy throughout the wave durations and coupling of wave types (e.g. P- and S-wave coupling). The wave properties that have been proposed to more accurately characterise complex nearfield vibrations are the total wave energy density (ED[subscript]W-tot), stored strain energy density (ED[subscript]W-SS) and the wave-induced mean normal dynamic strain (ε[subscript]W-MN). These wave properties consider the activity of the blast-induce wave at a point in the rock mass over the entire duration instead of the instantaneous amplitude.A new analytical approach has been proposed to predict blast-induced rock mass damage using rock mass characterisation data, blast vibration monitoring results and rock fracture criteria. The two-component approach separately predicts the extent of blast-induced damage through fresh fracturing of intact rock and the extent from discontinuity extension. Two separate damage criteria are proposed for the intact rock portion of the rock mass based on tensile and compressive fracture strain energy densities and compressive and tensile fracture strains. The single criterion for extension of existing discontinuities is based on the required fracture energy density to activate all macro-fractures in a unit volume of the rock mass.The proposed energy-based criteria for intact rock fracture and extension of discontinuities integrate strain rate effects in relation to material strength. The strainbased criterion for intact rock fracture integrates the existing mining-induced static strain magnitudes. These factors have not been explicitly considered in existing empirical or analytical blast damage prediction models. The proposed blast damage prediction approach has been applied to two stopes during the two mine site case studies

Surface expressions of discontinuities, and the estimation of their 3-D orientations using combined LiDAR and optical imaging

The importance of the collection and analysis of data on discontinuities cannot be overemphasized. Problems which include sampling difficulties, risks, limited access to rock faces and exposures, and the delay in data collection has led to a high need for data collection tools and analysis techniques that can overcome these problems. Discontinuities manifest themselves as either traces or as facets. Traces are linear features that intersect with both the discontinuity and the rock cut. Facets are the actual discontinuity surfaces that are exposed in the rock cut. Facets can be natural or induced. Identifying a facet as either natural or induced can sometimes be very difficult and can affect analytical results. The orientation of facets can be estimated from LiDAR point cloud. The orientation of traces can be estimated from optical imaging methods. LiDAR scanning alone cannot measure traces, neither can optical imaging methods measure facets. This is complicated by the fact that both facets and \u27traces\u27 are often present in the same rock cut, making the selection of an appropriate methodology or tool very difficult if not impossible. The set of traces in a rock mass usually belong to a set of facets of the same rock mass. These set of traces and facets can be combined either by the use of stereonets or by the equation of the angle between two lines. This research has provided a simple method by which the orientation of facets can be estimated from LiDAR point cloud. It has also provided a simple method by which the orientation of traces could be estimated from 2-D images. Additionally, this research has provided a reasonable way by which professionals could differentiate between traces, natural, and induced facets. Finally, this research has provided a methodology by which traces from optical images can be combined to facets from point cloud data --Abstract, page iii

DEVELOPMENT OF A METHODOLOGY FOR THE EVALUTATION OF UAV-BASED PHOTOGRAMMETRY: IMPLEMENTATION AT AN UNDERGROUND MINE

Autonomous systems in underground mining are increasingly being implemented as tools to collect data in inaccessible areas and improve the safety of mine personnel. There are many areas in the underground mining environment that cannot be accessed by personnel due to the high potential for ground fall and insufficient ground support. By combining unmanned aerial vehicles (UAVs) with technologies such as photogrammetry and LiDAR (Light Detection and Ranging) scanners, 3D point clouds can be created for inaccessible sites. A 3D digital point cloud can provide valuable geotechnical information such as the ability to measure discontinuities, inspect rock conditions, generate accurate volume estimates, and obtain a georeferenced geometry of the inaccessible opening. There are many challenges to operating UAVs and collecting high-quality imagery in underground environments including poor lighting and visibility, dust, water, confined spaces, air turbulence, and a lack of GPS coverage for navigation and stability. Due to the difficult flying conditions and GPS-denied environment, several companies are developing UAVs with LiDARbased simultaneous localization and mapping (SLAM) to enhance the obstacle detection and avoidance capabilities of the platforms and minimize the potential for a collision. The objective of this research was to develop a methodology that can be used to evaluate UAV-based imaging tools designed to fly in underground environments. A series of demonstrations was designed to test the functionalities of available UAVs and to identify the most effective platforms for collecting UAV-based photogrammetric imagery in an underground mine. Each of the four participating teams was challenged to fly their UAV-based systems (Hovermap, Elios, M2, Ranger/Batonomous) in underground drifts and long-hole stopes while capturing high-quality imagery that could be used to create a 3D digital photogrammetric model of the opening. The demonstrations were held at Barrick Gold Corporation’s Golden Sunlight Mine (GSM) in Whitehall, MT. The systems were evaluated based upon the performance of the collision avoidance (or recovery) system in the underground environment and the quality and accuracy of the data provided. By successfully completing the underground flights and demonstrating well-developed SLAM-based collision avoidance, the Hovermap system proved to be the most reliable, robust, and easily controllable system. The Elios system, relying on collision recovery rather than avoidance, is an affordable alternative for flights in difficult environments. The imagery collected by each system was used to generate photogrammetric point clouds using three software packages: Agisoft PhotoScan, Bentley ContextCapture, and Pix4Dmapper. The point clouds were qualitatively compared based on completeness and detail and quantitatively evaluated for accuracy by comparing the geometry of the point cloud to LiDAR scans of the stopes. Based on the results of the qualitative comparison, the point clouds considered in the accuracy evaluation were built using the photogrammetry software Bentley ContextCapture. When the photogrammetric point clouds were compared with the LiDAR point clouds (assumed to be an accurate baseline reference), the mean error values ranged between 0.47 and 2.86 feet. Despite the different conditions and locations in which the imagery was collected for each model, the observed error varies by less than one order of magnitude. Improvements in the coverage and overlap of the imagery as well as in the method used for georeferencing could further increase the accuracy of the photogrammetric point clouds

An integrated approach to span design in open stope mining

In order to develop an appropriate mine design, a thorough understanding of the rock mass conditions and its potential response to mining is required. Rock mass characterisation is a key component in developing models of the rock mass and its engineering behaviour, and relies on disparate data collected by exploration geologists, mine geologists, rock mechanics engineers and technicians, in a variety of formats. Optimal rock mass model development requires the effective integration of all data sources, which currently requires considerable effort in collecting, managing, collating, validating and analysing this data.The importance of understanding the spatial variability of rock mass conditions has been highlighted as a major issue. The traditional approach of using simplistic models of “average” rock mass conditions can lead to sub-optimal designs, which may result in unplanned additional costs or economic implications of dilution and ore loss. The design of stope and pillars should be optimised for the prevailing rock mass conditions in the various regions of the mine.Some of the existing design tools used for open stope design have shown poor reliability in their performance predictions. Though some may have been originally developed to assist in initial stope size selection (i.e. pre-feasibility and feasibility levels), they are potentially being inappropriately relied upon for detailed design. Consideration of large scale structures on stability and their influence on local rock mass conditions are also important aspects of open stope design that are commonly over-looked. There is a need to select design methodologies that are optimised for the stage of project development. It is also important to emphasise the iterative, evolutionary and interdisciplinary nature of open stope design.This thesis proposes a framework that attempts to integrate different rock mass characterisation models, numerical modelling and stope performance data to assist in improving the overall excavation design process. The key philosophy behind design optimisation is the continual reduction in uncertainty in collected data, analysis and design methods used with a view to improving the overall reliability of the design. A stope span design optimisation approach is proposed which attempts to ensure that the appropriate methodologies in data collection, data analysis, rock mass model formulation and stope design are utilised at relevant project stages in order to minimise uncertainty and maximise design reliability. The design optimisation approach recognises that the appropriateness of a particular design methodology is highly dependant on the availability of an appropriate rock mass model, which is in turn dependant on the availability of quality rock mass data. With respect to the design of spans in open stope mining, the key aims of the proposed integrated approach are to; • Assess the suitability of data for analysis • If data is unsuitable, assess the most appropriate data collection strategy • Assess the most appropriate approach to rock mass modelling • Assess the most appropriate design methodologies • Assess the reliability of the design criteria and quantify the potential economic impact of the design on the projectOptimisation of the design process also requires integration of state-of-the-art techniques in data collection, analysis, modelling and engineering analysis and design at the appropriate stage of project development. During development of this thesis a number of improvements have been proposed in key areas in the rock engineering design process which can be incorporated into the integrated approach, including; • A rock mass data model has been developed that assists in facilitating the ongoing rock mass characterisation process. The data model is capable of integrating rock mass data from various sources, which promotes sharing of data and avoids duplication of data collection efforts. The data model is able to query rock mass data, define relationships between data types, apply bias corrections, and perform basic analysis for use in subsequent detailed analysis and rock mass modelling. • An implicit based approach to spatial rock mass and deterministic discontinuity modelling can be employed to improve understanding of the spatial variability of rock mass parameters, inter-relationships between rock mass characteristics on their role in design. For example, understanding the influence of large-scale structures on rock mass characteristics and excavation performance. • Improved scale independent geometrical assessments of stope performance have been proposed that maximise the use of stope performance data. • An integrated back analysis framework has been presented that is able to account for structural complexity, scale and features that cannot be directly incorporated into linear elastic numerical modelling codes. • With regard to linear elastic back analyses, an number of improvements have been proposed, as well as a suggested method to assess appropriateness of continuum models based on discontinuity intensity and critical span