Measuring and analyzing the tunnel deformation through convergence measurements and cross-correlation with geological mapping is essential for understanding and controlling tunnel stability, worker safety and predicting failure zones. In order to achieve this, a new device, the so-called GroundProbe GML is tested and analyzed in this study. Currently, the deformation in the Kristineberg mine is measured through conventional techniques, such as the tape extensometer, that are labor intensive and time-consuming. On top of that, the geological mapping is done by visual inspection by experienced geologists. There is no system in place that correlates the geological setting to the deformation of the tunnels. This research aims to explore the possibility to quantify tunnel wall convergence and correlate the movement to geological circumstances. An accuracy analysis is performed, to identify the accuracy of the Light Detection and Ranging (LiDAR) technology in comparison to conventional techniques. This analysis is conducted in both a controlled (above-ground) and an uncontrolled (underground) environment and focuses on the use of different targets, i.e. prisms or reflectors, and the difference between continuous and periodic measurements. The fresh rock surfaces are also mapped by means of geological LiDAR data as well as Red-Green-Blue (RGB) image analysis and this is correlated with tunnel wall convergence. This is done to increase the understanding of the impact of the geological setting on the tunnel wall convergence.
The accuracy presented by GroundProbe of _3 mm for periodic measurements is difficult to achieve in an underground environment with mining activity. Especially the deformation of measurement points that are at a more oblique angle to the scanner prove to be difficult to assess. The geological LiDAR data can be used to distinguish different rock types and geological features with limited subjectivity. The transition zones from one rock type to the other become clearly visible from the geological LiDAR data. Additionally, geological features such as joints can be identified when combining LiDAR data with RGB image analysis. The primary promotor of tunnel wall convergence is the hanging wall that pushes down on the foot wall in combination with non-uniform waste rock areas, that are for example split into two sections by a joint. In a tunneling environment where mining activities are conducted, such as the application of shotcrete, scaling, blasting and leveling of the floor, periodic measurements with a LiDAR set-up on a tripod are difficult. One of the main limitations is the absence of stable reference points that the LiDAR scanner uses to determine its XYZ-position. The oblique angle can also be a limiting factor, measurement points closer to the LiDAR scanner provide more reliable results. For continuous measurements however, this proves to be less relevant because the repositioning error can be omitted. The LiDAR scanning technology has the potential to be used as geological mapping tool based on the varying reflectivities of different rock types. By combining LiDAR technology with RGB image analysis, a more thorough understanding of the geological setting can be obtained. By relating this understanding to the quantification of the tunnel wall convergence, this technique can be used to correlate the geology to the movement of the rock mass.

Site-specific calibration of laser induced breakdown spectroscopy (LIBS) analysis can be achieved by presenting known geological sample data to update algorithms, therefore improving analysis of unknown samples post-calibration. The goal of this project is to optimise drill core analysis by LIBS for samples from Kittilä gold mine. In total 227 samples have been produced and analysed with a range of analytical techniques to build a comprehensive, site-specific reference library. The data from these analyses has been input into the reference library and utilised by algorithms for spectral peak identification during LIBS analysis of bulk drill core material. The semi-quantitative and quantitative data of 40 Kittilä samples has been used as a comparison to ICP-AES data used by the mine. The Kittilä powders were used to produce 39 pellets, each representative of a section of drill core of which the ICP-AES and TXRF data was known. The pellets were scanned by LIBS and the data then compared to the known data. LIBS showed strong correlations (>0.75) with ICP-AES data for As, Cr, Cu, Fe, Mn and S, poor correlations were seen for Pb and Co, however these can be explained by issues related to ICP-AES. LIBS pellet data proved strongly competitive as 5/9 elements displayed stronger correlations between LIBS and ICP-AES than TXRF. Subsequently, the successful scanning of drill core boxes directly has proven that calibration can increase the speed with which the bulk material can be effectively analysed.