Visualizing rock mass displacement and strain data for interpretation of TARP levels as a safety measure

Crucial information about safety, design and operation from ground movement data can be utilized in underground mines. Communicating the information and exploring new findings can be a challenge, however, in a decade where there is an ever-increasing volume and velocity of data due to advancements in mining technologies. This challenge escalates for geotechnical data as they are multidimensional, depending on the space and the rate at which they take place. To tackle this challenge, geotechnical engineers should focus on using simple yet compelling visualization of rock mass data in 3D. Competent visualizations of ground conditions as a trigger action and response plan (TARP) could ease the communication of safety concerns and the exploration of rock mass displacement and strain surrounding an opening at an underground mine. Therefore, this study focuses on using scientific visualization methods by rendering geotechnical data over the surface of a 3D mesh model to display TARP levels. The study was conducted at the East Decline of the San Xavier (SX) Underground Mine, a laboratory mine operated by the University of Arizona. The project aims to produce visualizations that optimize the use of geotechnical data in near-real time. Introduction Spatiotemporal analysis of ground movement around an opening at an underground mine is important for geotechnical assessment, monitoring hazards and risks, and ground support. However, there are some challenges in analyzing spatiotemporal data in an underground environment. The rock mass is a heterogeneous body with macro and micro structures, changing geology and changing strengths. This nature of rock mass becomes even more variable as mining projects are not bound to a small area over the course of a few months, but are bound over a large orebody and host rock during the whole life of the mine. Therefore, analytical methods and predictive simulations of rock mass movement can fall short without sufficient data collection of the current state of the rock mass. Nevertheless, the collected numerical data can be hard to analyze and to drive information. So, with adequate data on rock mass movement, visualizations can be created in 2D and 3D, for data analysis or scientific representation. The data could also be used to calibrate and improve already existing analytical methods and simulations.For the purposes given above, this article showcases a demonstration of a simple yet effective 3D visualization of rock displacement at the SX laboratory mine’s East Decline. The SX laboratory mine. The SX Mine is situated about 48 km (30 miles) south of Tucson, AZ, in the northern Sonoran Desert at an elevation of 1,085 m (3,560 ft) above sea level. The area has an average annual temperature of 65 °F. It receives approximately 32 cm of rainfall each year (Sternberg et al., 1988). The SX mine was originally opened in 1875 to exploit a pyro-metasomatic orebody. Between 1880 and 1918, it produced up to 45 t/d (50 stpd) of lead, zinc and silver but was forced to close at the end of World War I due to a decline in market value. The mine was later reopened by the Eagle Picher Mining Co. from 1943 to 1952, during which time production ranged from 36 to 72 kt/a (40,000 to 80,000 stpy).However, in 1952, the mine was closed again due to falling market values for lead and zinc (Wilson, 1960). After intermittent operations by various companies, the mine was donated to the University of Arizona in 1975 for instructional and research purposes (Sternberg et al., 1988). The University of Arizona’s School of Mining Engineering and Mineral Resources is currently developing a new decline, known as the East Decline. This decline features a round-arched profile with dimensions of 5 m (16 ft) by 5 m (16 ft). Development began in 2020, and approximately 150 m (492 ft) of advancement has been completed so far. The East Decline starts with shallow cover, beginning at about 3 m (10 ft) at the portal and reaching a current maximum depth of around 20 m (65 ft). The development has been carried out using blasting and a rock bolting machine. Plans for the site include using it as a testing ground for equipment, machinery and research. Data collection of displacement is being conducted with multipoint borehole extensometers (MPBXs) and a remote, automated data acquisition system. The MPBXs provide displacement data in millimeters and display the axial movement of the rock mass, parallel to an MPBX. For a comprehensive and sufficient analysis and assessment of ground movement, a total of six MPBXs are used on the ribs (walls) and back (roof) of the opening. In the opening, two MPBXs are installed on the left rib, two on the right rib and two on the roof, with a distance of 2.5 m (8 ft) between each pair of MPBXs. Figure 1: Profile view of the opening with three out of six MPBXs installed around it. Figure 1 shows the profile of the opening where three MPBXs are installed at the East Decline. Background 'To achieve a successful visualization, it is crucial to understand the goals of the visualization and to understand what factors need to be taken into account. This understanding also encompasses whether a visualization is necessary and what type of visualization could be used. Over time, statisticians, mathematicians and data analysts have brought forward practices that best utilize and create visualizations of data and scientific phenomena. However, other unique challenges occur when data are visualized in 3D. This type of high-dimensional visualization shares a common challenge: reducing dimensionality for effective display. In 2004, Dos Santos and Brodlie (2004) proposed a unified approach for filtering both multidimensional and multivariate data, enabling the extraction of data based on constraints related to their position or value within an n-dimensional window, as well as the selection of dimensions for display. Kehrer and Hauser (2013) highlighted the significance of visualization and visual analysis in the exploration, analysis and presentation of scientific data, particularly in disciplines where data and model scenarios are increasingly complex. Scientific data often exhibit multifaceted characteristics, being spatiotemporal, multivariate, multimodal, multirun or stemming from simulations. This complexity introduces both opportunities and challenges for visualization research, particularly when dealing with data of varying dimensionality. Visualization and interaction techniques are frequently combined with numerical methods to address these challenges. Data, science and information visualization. Visualizations are crucial for data analysis and information extraction, whether they use simple 2D plots that represent the relation between two variables or numerical methods in mathematical models. There are crucial aspects to consider when analyzing data to ensure goals are fulfilled, whether they are to ensure safety at an underground mine or to have a deeper understanding of rock behavior. As Tukey (1990) stated, for data analysis to be truly beneficial for an engineer or a data analyst, the visualizations should enforce attention by impactful visualizations, give an easy flow of attention between different variables, and showcase phenomena and events instead of numbers themselves. It is important to consider Tukey’s considerations as they can be applied to underground geomechanics visualizations ensuring safety and further understanding of the rock mass. For instance, a visualization focusing on safety should enforce a strong and immediate impact for higher situational awareness of TARP levels, such as in this study. Using these methods of scientific visualization will bring attention to the behavior of rock and save time. Use of shader programs and computational rendering. With advancements in computational graphics and the computing power of personal computers, visualizations can be used in various ways that were not possible before, including 3D visualizations that consist of higher dimensions and higher variables.Another advancement that uses the graphics processing unit (GPU) is shader programs. Shaders are small scripts that run parallel on a GPU to render 3D and 2D scenes and images. Shaders render 3D objects by using the information of a 3D mesh, including normal, tangents and vertices. Even though there are other methods and programming languages dedicated to developing visualizations, shaders can be accessed through different applications and software, such as game development engines and 3D modeling software, which makes them suitable for use in this case study. Analysis and visualizations of quantitative rock mass data Some real-world physical and mathematical problems cannot be solved using analytical methods and do not have exact solutions. Therefore, these problems need approximate solutions or answers that clarify the uncertainty. The behavior of rock mass, especially displacement, and strain around an opening are some of these physical phenomena for which exact solutions are challenging to find. This unpredictability and the difficulty in quantifying movement and its analytical relation to the space on the rock mass is hard to calculate. To examine and understand the movement of a rock mass body around an opening, various numerical methods can be used, such as finite element models, in which rock mass characteristics are used. These numerical methods are beneficial for the preparation and planning of a mine cycle and developing support systems. However, due to the unpredictable nature of rock mass and lithology, the models using these methods need to be calibrated often and might not represent the current state of rock mass behavior. Another way to examine and understand the movement of rock mass is to use discrete and continuous data, and/or 3D data obtained in desired frequencies. Photogrammetry, lidar and laser scanning technologies that construct 3D point clouds give accurate data on movement surrounding an opening with the precise location of the movement, as seen in Patwardhan and Karim (2024). However, 3D data need to be acquired in person or by scanning machinery, such as a drone, and the process is not yet fully automated. Processing and managing 3D data are also cumbersome and require high computation powers due to the large quantities of data points.Even though these challenges exist, a systematic approach to using 3D scanning technology for monitoring displacement and strain in underground mines is still lacking. Utilizing quantitative displacement and strain data obtained from reliable sensors can be more efficient. In addition, the correct and strategic placement of sensors around an opening gives data that can be interpreted using numerical methods. However, one of the major challenges of monitoring rock behavior using sensor technologies comes from the large gap in the industry to utilize visualization of data. There are many use cases and research studies in geomechanics that employ visualizations either through mathematical models or 3D point cloud data, yet visualization of monitoring data collected from data acquisition systems is lacking in the literature. The lack of visualization in the literature poses significant challenges for data visualization in underground mines. Puppala et al. (2018) provide an overview of techniques for geotechnical visualizations in 2D and 3D and how they are used to enhance understanding of rock or soil conditions and hazardous conditions. Their overview includes a 3D visualization of a hydraulic fill dam (Caballero et al., 2022). They summarize the importance of using visualizations to understand stability and performance. Providakis et al. (2019) visualized ground settlement of the surface above an underground tunnel using analytical methods of settlement with building information modeling (BIM) models of the surrounding buildings on the surface, near the tunnel. Even though analytical methods are used instead of monitoring data, simple yet clear visualizations are considered an impactful decision-making tool. A similar case study was conducted by Ninićet al. (2024) where soil settlement above an underground tunnel was analyzed using a BIM framework. Kim et al. (2020) used geostatistical spatial interpolation techniques to visualize and estimate the distribution of soil properties over a surface using borehole data sets. Their study focuses on technical visualization techniques over a soil surface area and discusses the predictions from these visualizations to the observed values. Studies such as those by Kim et al. (2020) can be great applications for other rock mass data sets for the underground mining industry. Similar to this study, Lyu and Wang (2024) visualized subsurface geology in 3D with borehole data sets using a method called BCS. Civelekler and Pekkan (2022) developed 3D visualizations of soil properties by applying them in a geographic information system (GIS), which they believe can provide preliminary information for scientists and engineers. Wang et al. (2020) developed a geotechnical visualization tool for large-scale simulations for mobile devices instead of desktops and laptops. The above studies provide strong foundational work and methodologies to create scientific visualizations and simulations with impactful visualizations. However, they do not incorporate rock mass data such as movement and strain. Sekiya et al. (2022) tackled this gap in geotechnical visualization by creating a 3D visualization of tunnel opening deformation by acquiring data from microelectromechanical (MEMS) accelerometers. Similarly, Liang et al. (2024) developed a data-driven 3D model integrated with real-time data visualization. The data sets are visualized over the corresponding spatial area of the underground mine as simplistic glyphs or symbols, where the underground space is represented with computer-aided design (CAD) files. Their visualizations also include geological structures in corresponding space at the mine. Similarly, Seymour et al. (2016) visualized seismic data in 3D. The seismic data are represented around the 3D model of the mine according to its georeferenced space, in a 3D scene. Keeping in mind the methods used for the examination of rock mass, and the literature, this article aims to provide insight into how to develop a scientific visualization of continuous rock mass displacement, considering its relations to space on the rock mass over time. This visualization method for spatiotemporal data for underground mines uses quantitative data obtained by MPBX instrumentation. The methods for developing the visualization include using 3D meshes to represent the East Decline, colors rendered over the 3D mesh to represent the state of displacement, and desired timelapse animations to illustrate near-real-time representations. The goal of developing scientific visualizations of rock mass data for underground geomechanics is to provide: A visual display of the data, representing the state of the rock mass for safety and geotechnical analysis. A 3D visualization of the opening to represent the position of an incoming datum, and its relations with other data. A visual display of the rock movement phenomena over time. Figure 2: Complex and realistic 3D mesh of the East Decline generated using point cloud data. Methodology The methodology consists of three major components: (1) generating, editing and implementing 3D meshes, (2) developing an algorithm to differentiate the areas of an underground opening and (3) developing an algorithm to interpolate displacement data over two points across an area on the 3D mesh of the underground opening. Cloud Compare, a point cloud editing and analysis software, and Blender, a 3D graphics software, were used to create and edit 3D meshes in the first step. The developed meshes then were used as the base to render the visualization algorithms. Unity, a real-time 3D development platform, was used to develop the final 3D scene as it provided an environment to conduct the second and third steps. The first step required the manual acquisition and manual editing of point clouds or the creation of a simplistic representative 3D model. The second and third steps were automated with the script after completion of the first step and importation into Unity. The second and third steps were conducted by shader programs: specifically, HLSL (High-level Shader Language) used in Unity. Figure 3: Representative and simple 3D mesh generated in Blender according to profile shape and dimensions. Step 1: Model preparation for Unity. This step involves 3D model generation and editing. The edits include UV unwrapping and scaling of the model. Mesh generation can be conducted in two ways, depending on geotechnical engineers’ preference for precision and time consumption. One way of generating 3D models is by using point cloud data from lidar scans, laser scans or photogrammetry scans of openings at the East Decline. After acquiring the point cloud data, they are transformed into a 3D mesh made up of low triangulations in Cloud Compare using built-in tools. As the point cloud data are too large, the 3D mesh is generated with a low triangulation count, which results in a smoother and faster rendering scene without losing accuracy when using interpolation over the surface of the mesh. The 3D mesh is then exported as a .ply file from Cloud Compare and imported into Blender for scaling and isometric embedding, which is also called UV unwrapping, done with the unwrapping tool. This method of generating 3D meshes results in more realistic and spatially accurate 3D models (Fig. 2). However, due to the complexity of the 3D mesh, UV unwrapping can be challenging. The acquisition of point cloud data can be lengthy and requires personnel or machinery, convoluting the process to update and develop 3D models as advancements are made. Nevertheless, georeferenced point cloud data can ensure the rendered visualizations are accurately representative of the decline. Another way the 3D mesh is generated is by creating simplistic, representative 3D solid models of the openings at the East Decline that match real-world scale. This method was done directly in Blender by using the modeling tool to manually create a solid object with a horseshoe arc shape and the dimensions of height of 5 m (16 ft) and width of 5 m (16 ft) (Fig. 3). A representative model could also be created by using the .dxf or .shp files that were developed during the planning stages. The 3D model was then scaled and UV unwrapped. This process compared to generating a 3D mesh from point cloud data requires more hands-on work in Blender. Also, the model is a representation and might not show the precise locations of the movement changes in the decline as advancement takes place, and inaccuracies should be kept in mind when visualizations are created for more advanced numerical methods in geotechnical analysis. However, as this process does not require 3D point cloud data, scanning of the decline is not required, which can ensure worker safety and save time in the long run as simplistic 3D models can be used whenever needed. Therefore, an optimal methodological approach will be to apply both ways, where mesh generation in the short term uses representative models and in the long term uses updated complex models from scanning data. After generating the 3D mesh, the triangulations need to be represented in a 2D space to define each triangle in each vertex of the 3D mesh UV coordinate. This process is similar to “mapping” 3D objects into 2D spaces, such as generating an accurate and reliable 2D map of the Earth, which is a sphere-like 3D object. The UV coordinate system is a 2D coordinate system similar to a cartesian coordinate system. It is used to translate each vertex of the triangulations that are in 3D space onto a 2D space through isometric embedding. The isometric embedding is a semi-automated process using the unwrapping function of the unwrapping tool in Blender. Even though this method does not require an isometric embedding algorithm to be developed, the 3D mesh needs to be laid out onto the UV coordinate system through the manual efforts of a miner. This process is crucial as it will determine the scale of the 2D representation of the 3D mesh. which in turn affects the distance used in the interpolation of displacement data. It also determines the horizontal and the vertical space, as U represents the X axis (horizontal space) and V represents the Y axis (vertical space). This study unwraps the 3D mesh into five major segments that consist of the face, floor, back, left rib and right rib of the underground opening (Fig. 4). Figure 4: The UV maps of (a) the complex mesh of the decline and (b) the representative mesh of the decline. Step 2: Differentiation of the spaces on a 3D mesh. This step in the methodology can be done in various ways with various complexities using HLSL. For this study, the decline at the SX was differentiated into five segments: the left rib, right rib, back, floor and face. Each segment will use its own interpolation to represent the displacement change over that area. For instance, the interpolation of displacement on the left rib will be represented independently, then the right rib and the back. Understanding displacement relative to all spaces around an opening is crucial and might require more complex interpolation methods such as kriging. However, due to various challenges and complexities, interpolation will be based on three of the segments: the left rib, right rib and back. For the interpolation to happen between the desired areas independently, the 3D mesh data are used in the shader code that uses a basic weighted distribution algorithm to differentiate the spaces on the 3D mesh. The back of the 3D mesh is defined as positive Y, the right rib as negative X and the left rib as positive Y, according to the world space coordinates in Unity. After defining the ribs and roof and to which axis they correspond, the interpolation algorithm is used. Step 3: Interpolation of displacement data over the 3D mesh. A basic linear interpolation is used for rendering the data with its corresponding color. A representation of the linear interpolation is displayed on a 2D plane in Fig. 5. The mentioned desired points of the 3D mesh are where the MPBXs are implemented at the decline from which the displacement data would be coming. The interpolation between the lengths is rendered by assigning the color value to each displacement datum. Figure 5: Basic linear interpolation between two colors rendered onto a 2D plane mesh in Unity. There are two color schemes used to represent data: (1) the traditional traffic-light color scheme to represent the displacement as TARP levels using red, yellow and green, and (2) a color-blind-friendly palette that consists of dark blue, green and yellow. The red indicates an increase in cumulative displacement whereas green represents a nonchanging cumulative displacement. The same principle applies with the color-blind-friendly color scheme where the maximum thresholds of the TARP levels are represented as navy blue, and the minimum thresholds are in yellow. Results Following the three steps, a successful visualization was achieved. The data from the MPBXs were imported into Unity, where each of the MPBXs was assigned a color variable. The color variables change the red, green, blue, alpha (RGBA) values on the mesh according to the incoming data with set thresholds. The imported data were assigned a TARP level according to each threshold that was set, to represent the risk conditions at the decline. The visualization was rendered on the 3D models, between where the MPBXs were placed, therefore resulting in a smooth interpolation on each side and top of the 3D model, where the sides represent the ribs, and the top represents the roof of the decline. In Fig. 6, on both the complex and representative model, the interpolation of TARP levels according to displacement data can be seen on the roof and left rib. The roof successfully displays an interpolation between the data that are coming from the two MPBXs that were installed on the roof. The displacement from each MPBX is in the same threshold, resulting in a smooth singular color without transitions, representing the risk hazard of that area. The left rib also successfully displays the interpolation between the data coming from the two MPBXs that were installed on the left rib. The displacement from one MPBX shows higher values, while the other shows lower values, resulting in two thresholds of TARP level. This transition between TARP levels can be seen in the visualization as the interpolation smoothly changes from one RGBA value to another. Nevertheless, improvements are needed for the complex model of the decline. The area between the roof and the left rib does not have a smooth transition and contains a segment of gray color representing the absence of data. This is due to the complexity of the triangulated mesh, causing challenges when unwrapping it onto a 2D UV map. Further work is currently being conducted to improve the final rendering of the visualization on the complex model. Conclusions Scientific visualization in 3D is capable of giving crucial information about the safety of ground conditions and of aiding geotechnical analysis processes. However, most visualization in the geotechnical domain comes from preexisting mathematical simulations and analytical processes, with minimal representation of the current state of the rock mass by using monitoring data. Therefore, this article focused on developing a basic 3D visualization of rock mass movement following the profile of an opening at the East Decline at the SX Mine. Figure 6: The complex and representative model rendering the interpolation of TARP level between two MPBXs, for the roof and left rib separately. The goal of the visualizations was to convey the direct status of risks surrounding ground movement using TARP levels. Even though other methods for TARP-level visualizations are used in the industry, an area-focused interpolated visualization that represents movement over a surface has not been applied. As seen in the results, the interpolation of displacement data and representing it according to TARP levels gives insight into the hazard levels of the ribs and roof separately. This aids the assessment of ground movement and gains further information. Future work will consist of improving the visualization rendered on the complex 3D model of the decline. Other interpolation techniques such as kriging will be considered, to gain even further understanding of the ground movement where the ribs and the roof will be relative to each other. n References Caballero, S. R., Bheemasetti, T. V., Puppala, A. J., and Chakraborty, S. (2022). Geotechnical Visualization and Three-Dimensional Geostatistics Modeling of Highly Variable Soils of a Hydraulic Fill Dam. Journal of Geotechnical and Geoenvironmental Engineering, 148(11), 05022006. https://doi.org/10.1061/ (ASCE)GT.1943-5606.0002872.CiVelekler, E., and Pekkan, E. (2022). 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Tunnelling and Underground Space Technology, 126, 104535. https://doi.org/10.1016/j. tust.2022.104535.Sternberg, B., Ryan, T., McGill, J. and Breitrick, M. (1988). The San Xavier Geophysics and Tunnel Detection Test Site. Laboratory for Subsurface Imaging (LASI). University of Arizona, 156 p.Tukey, J. W. (1990). Data-Based Graphics: Visual Display in the Decades to Come. Statistical Science, 5(3). https://doi.org/10.1214/ss/1177012101.Wang, R.-X., Wang, R., Fu, P., and Zhang, J.-M. (2020). Portable interactive visualization of large-scale simulations in geotechnical engineering using Unity3D. Advances in Engineering Software, 148, 102838. https://doi.org/10.1016/j.advengsoft.2020.102838.Wilson, C.A. (1960). Ore Controls of the San Xavier Mine, MS Thesis, University of Arizona.

Visualizing rock mass displacement and strain data for interpretation of TARP levels as a safety measure

Authored by: Nilufer K.B. Akbulut, Angelina Anani and Sefiu O. Adewuyi

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