In the depths of a modern open-pit mine, a laser scanner performs a silent, rapid dance, capturing the complex slopes of the rock face. Within moments, it gathers millions of data points, creating a digital twin so precise it can detect shifts smaller than a pea.
Imagine a 100-meter-high rock face at an open-pit mine. Now, imagine being able to monitor that entire surface for minute, potentially dangerous shifts from the safety of an office, using a technology that can reliably detect movements on the level of centimeters. This is not a vision of the future; it is the reality offered by Terrestrial Laser Scanning (TLS) today. The monitoring of highwall slopes is a critical task for ensuring safe mining operations, as any uncontrolled rockslide can have severe impacts on both safety and economics 1 . Through advanced laser technology and sophisticated data processing, engineers can now derive exceptionally accurate surface models of mine walls, transforming how the industry manages risk and operational efficiency.
At its core, Terrestrial Laser Scanning is a contactless measuring technology that uses laser beams to rapidly capture the shape and dimensions of physical objects. In mining, it replaces or supplements older techniques like total stations or radar. The scanner works by emitting laser pulses and measuring the time it takes for each pulse to return after hitting a surface. Each measurement becomes a single point in a 3D coordinate system, and together, millions of these points form a "point cloud"—a rich, digital representation of the entire scanned environment 7 .
After the point cloud is acquired, the data undergoes pre-processing to reduce noise and remove outliers. The next stage is often mesh generation, where algorithms connect the points into a continuous surface of triangles. Research has explored different algorithms for this, such as Delaunay 2.5D XY Plane and Delaunay 2.5D Best Fitting Plane, which project points onto a plane before triangulating them 1 . The quality of this mesh directly impacts the final model's accuracy.
Laser scanner captures millions of 3D points to create a point cloud of the mine wall surface.
Noise reduction and outlier removal to clean the raw point cloud data.
Algorithms connect points into a continuous surface using triangulation methods.
Conversion to NURBS surfaces for smooth representation of complex geometries.
To truly understand the capabilities of this technology, let's examine a key experiment detailed in a 2018 research paper published in the International Journal of Coal Science and Technology 1 .
Researchers conducted experiments at two locations: the Coc Sau open-pit mine in Vietnam and the experimental mine of AGH University of Science and Technology in Poland. They used two different terrestrial LiDAR scanners (a Leica C10 and a GeoMax Zoom300) to capture 3D point cloud data of the pit walls 1 .
The core of the experiment was a direct comparison of three different mesh generation algorithms:
After generating the meshes, the researchers optimized them and then generated corresponding NURBS surfaces. The accuracy of each resulting surface model was assessed by calculating the average distance between the model and the original point cloud data 1 .
The findings were revealing. The results showed that the average distance between the surface model and the point cloud was within a remarkably tight range of 5.6 to 5.8 millimeters, with a standard deviation of 6.2 to 6.8 mm, depending on the algorithm used 1 . This level of precision confirms that TLS is more than capable of detecting the centimeter-level displacements critical for slope stability monitoring.
Furthermore, the study yielded two critical insights. First, it confirmed that the quality of the final surface depends heavily on both the quality of the input data and the specific algorithm used to generate the mesh 1 . Second, it found that the accuracy of the NURBS surfaces was 4 to 5 times lower than that of the optimized meshes they were derived from. This indicates that while NURBS offer smooth surfaces, the meshing stage is where the highest geometric fidelity is preserved 1 .
| Algorithm Used | Average Distance to Point Cloud | Standard Deviation |
|---|---|---|
| Delaunay 2.5D XY Plane | ~5.6 - 5.8 mm | ~6.2 - 6.8 mm |
| Delaunay 2.5D Best Fitting Plane | ~5.6 - 5.8 mm | ~6.2 - 6.8 mm |
| Mesh from Points | ~5.6 - 5.8 mm | ~6.2 - 6.8 mm |
| Note: Accuracy varied based on the specific algorithm and input data quality. The values represent the range of results observed across the tested algorithms 1 . | ||
While the aforementioned experiment focused on processing algorithms, other rigorous research has shed light on how the very act of scanning influences results. A controlled experiment conducted in 2024 assessed the TLS's performance in detecting surface anomalies—like bulges in a retaining wall—with respect to scanning distance and angle of incidence 5 .
The researchers found that TLS is capable of detecting surface anomalies with a tolerance of within a few millimeters, even when the reference and follow-up scans are taken from different positions. This high level of accuracy was maintained for distances between 2 and 30 meters and angles of incidence between 90° (perpendicular) and 30° 5 . This is crucial for real-world mining applications, where it is not always possible to scan from an ideal, head-on position.
| Scanning Parameter | Effective Range for Millimetric Tolerance | Significance |
|---|---|---|
| Distance | 2 - 30 meters | Allows for safe scanning from a distance in unstable areas. |
| Angle of Incidence | 90° - 30° | Provides flexibility in scanner placement around complex pit geometries. |
| Method | Principle | Pros and Cons |
|---|---|---|
| C2C (Cloud-to-Cloud) | Distance from a point to its nearest point in the other cloud. | Pro: Simple and fast. Con: Highly sensitive to noise and roughness 5 . |
| C2M (Cloud-to-Mesh) | Shortest distance from a point to a surface mesh. | Pro: More robust than C2C. Con: Provides only Euclidean distance, not in a specific direction 5 . |
| M3C2 | Distance between local surfaces in the reference and deformed scans. | Pro: Highly accurate, accounts for surface orientation. Con: Requires longer calculation time and input parameters 5 . |
Creating accurate mine wall models requires a suite of hardware and software solutions, each playing a vital role in the workflow.
The primary data collection tool. Modern scanners like the Leica C10 or GeoMax Zoom300 can capture millions of points per second with millimeter-level accuracy 1 . For faster, more mobile data capture in underground mines, handheld mobile LiDAR systems using SLAM technology are also now employed 3 .
These are the mathematical engines that convert discrete points into a continuous surface. The choice between algorithms like Delaunay 2.5D and Mesh from Points can impact the final model's accuracy, especially in complex geological settings 1 .
Accuracy is meaningless without verification. This involves using independent checkpoints surveyed with higher-accuracy methods like total stations and statistical measures like Root Mean Square Error (RMSE) to quantify how well the model fits reality 4 .
The transition from academic research to practical application is where this technology truly shines. Mining companies worldwide are leveraging TLS to achieve new levels of efficiency and safety.
At Barberton Mines in South Africa, the adoption of mobile LiDAR scanners cut the time required for underground mapping by half. The scanners enabled daily progress mapping, accurate stockpile volume measurements, and safer inspections of transport shafts for rock movement—all with minimal disruption to production 3 .
Furthermore, the integration of LiDAR with drone technology allows for the rapid and safe scanning of vast open-pit areas. Drones equipped with LiDAR can map hundreds of hectares in a fraction of the time required by traditional ground teams, eliminating the need for personnel to access dangerous highwalls or unstable slopes 7 9 . This data is crucial for calculating stockpile volumes, monitoring slope stability over time, and planning future excavation.
The ability to derive highly accurate surface models from terrestrial laser scanning represents a paradigm shift in mine management. What was once a manual, hazardous, and time-consuming process is now a rapid, precise, and contactless operation. Research has firmly established that this technology can provide millimeter-level accuracy, giving engineers the data they need to prevent catastrophic failures, optimize operations, and save lives.
The future of mining lies in digitalization and automation, and the precise digital replicas created by laser scanning are the foundational layer upon which this smarter, safer industry is being built.