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By R. J. Durrheim, Y. Yabe, M. S. D. Manzi, DSeis team, N. Z. Nkosi, S. B. Mngadi, Jr. H. Ogasawara

On 5 August 2014, the Orkney M5.5 earthquake, the largest in South African gold mining districts, occurred with an unusual strike-slip mechanism at great depth of 4.78 km below the surface. In a rare case, the ML 5.5 earthquake occurred within the area covered by the legacy 2D and 3D reflection seismic data acquired in the 1990s and 2000s for gold exploration and mine development. In addition, the earthquake ML 5.5 rupture plane was recorded and accurately delineated by the underground in-mine seismic network near the source region. The integration of the legacy 2D reflection seismic data and mine seismicity data allowed us to identify a near-vertical structure, striking northnorthwest-south southeast (NNW-SSE). The ICDP-DSeis team drilled three holes (Hole A, Hole B, and Hole C), and two holes (Hole B and Hole C), intersected the upper edges of the ML 5.5 rupture. These holes recovered metasediments, metabasalts, intrusive rocks including dolerite sills and lamprophyre dykes adjacent to the fault zone, and the fault gouge. Late Prof. Tullis Onstott and geomicrobiologists installed a packer in Hole A and successfully recovered saline water and detected gas (~10 MPa). Slip weakening and rupture propagation are significantly influenced by the existence of fault gouges and the production of wear material between two sliding rock surfaces. Using the fault gouge material recovered from Hole C, we conducted friction experiments under dry and room temperature conditions at high slip velocity of ~100 mm/s and normal stress of 2 MPa. The resulting steady-state frictional strength was ~0.66 over a slip weakening distance of ~9.1 m. The steady-state frictional strength was high, which may be caused by large gouge thickness, and the rate of wear generation. As a result, it is proposed that the Moab Khotsong ML 5.5 seismogenic zone is complex and could be controlled by three main processes: a) the complex structural architecture of the seismogenic zone (e.g., intersection of fault, lamprophyre dykes, and dolerite sills); b) the mechanical process induced by tectonic and/or mining related stresses; and c) the mechanical and chemical processes caused by the water and rock interaction.

Jan 12, 2025

By I. James, R. J. Durrheim, M. S. D. Manzi, O. Valeshin, J. Lepine, L. V. Socco, C. Colombero, A. Malehmir, M. K. Rapetsoa, M. Sihoyiya

The FUTURE project aims to enhance seismic surveying in South African mining by integrating advanced acquisition technologies. In this paper, we detail surface and underground seismic surveys at the South Deep gold mine as part of the project. Surface seismic acquisition used co-located broadband micro-electromechanical sensors, one and three component recorders connected to geophones, distributed acoustic sensing using a straight fibre optic cable, along with a 6 ton broadband seismic vibrator operating with 4–180 Hz linear sweeps. In addition, underground surveys employed a combination of straight, and helically wound fibre optic cables, cabled-based and nodal sensors, along with a 500 kg drophammer mounted on a skid steer bobcat. The surface and underground seismic arrays were time synchronised using a GPS-time transmitter to allow for accurate seismic data recording in a GPS-denied environment. Results show the efficacy of these techniques in acquiring high-resolution seismic data, revealing detailed subsurface geological structures and mineralisation down to 3500 m below ground surface. Surface data revealed clear seismic reflections corresponding to the Black Reef and Ventersdorp Contact Reef. An underground P-wave velocity model exhibits low velocity anomalies that are associated with fracturing, while a P-wave reflection stack section shows significant reflections and a degree of faulting below the tunnel floor that was not observed on the surface reflection seismic data. We highlight the potential of these technologies to enhance safety, efficiency, and sustainability in mining. This study sets a benchmark for future reflection seismic surveys, paving the way for safer, more efficient, deep mineral exploration and extraction practices.

Jan 12, 2025

By S. Plaatjie, L. Linzer, M. S. D. Manzi, M. Sihoyiya

Fiber-Optic Sensing and UAV-Platform Techniques for Innovative Mineral Exploration (FUTURE) is a joint European Union and South African consortium project funded under ERA-NET Cofund ERA-MIN3. The project aims to develop cost-effective and novel seismic methods to image geological structures ahead of the mine face, ultimately leading to improved decision-making and safer mining practices. In this study we present results from numerical simulations conducted at South Deep Gold Mine for active in-mine seismic surveys. The study uses a finite differencing code known as WAVE3D, which models models the propagation of a seismic wave generated by stress pertubations that are applied to a defined grid. The numerical simulation model included two major ore bodies (Black Reef and Ventersdorp Contact Reef) and mine tunnels. The model was constrained using laboratory physical property measurements from underground borehole samples such as seismic velocities (e.g., compressional and shear waves) and bulk densities. Two case studies were created, one modelled the active shots and receivers on surface, and the other modelled the shots at depth (in a mine tunnel at ~4000 m depth) with receivers on surface. In both cases, the objective was to investigate the wavefield propagation through the rockmass between the mine tunnel and surface. The simulated wavefield produced by the source located along the mine tunnel was successfully recorded on surface. The surface-recorded wavefield exhibits clear one-way P-wave arrival times at approximately 450 ms to 500 ms, which can be used to calculate the surface-tunnel P-wave refraction tomogram. The results from the study were used to constrain the design, and for the acquisition and processing of the real surface and in-mine seismic surveys acquired under the FUTURE project.

Jan 12, 2025

By P. Lenegan, R. Masethe, R. I. L. Ferreira

Underground mining in high rock-stress environments can induce unanticipated dynamic rockmass deformation, posing a serious risk to the workforce. Data collected by seismic networks are imperative for the design-as-you-mine process. Often an inappropriate assessment of the seismic hazard is done when unnecessary data are included in analytical techniques, particularly short-term time frames. Depending on the methodology or application of data analysis and interpretation, some of the information may not be relevant and even detract from the objective. By using the spatial event clusters associated with active workplaces to define the shape and size of the polygons for data selection for subsequent analysis, a problematic subjective component can be eliminated. The mining of tabular reefs in South Africa, be they gold- or platinum group metals, is very extensive, and underground workplaces are often widespread. Production stoppages are very costly, especially for marginal mines, so there is just cause not to evacuate an entire operation’s workforce following the occurrence of a large mining-induced seismic event. A rationale based on the damaging peak particle velocities (PPV) of the past seismicity can exclude the unaffected underground personnel and allow a quick orderly withdrawal where required. Micro-deformation may precede rockbursting, which can occur on-shift close to the workface. A mine-wide array of geophones suitable for the accurate location and analysis of seismicity may not be able to detect these higher-frequency emissions. Inexpensive but fit-for-purpose accelerometers, providing real-time, continuous measurement of acoustic rockmass deformation near the workplace, integrated through underground and surface communications networks, are shown to record ‘rock-talk’, clearly distinguishable from the instrument background noise, which can be in the order of hours leading up to an imminent damaging seismic event.

Jan 12, 2025

By S. Durapraj, R. T. Masethe, U. Maqina, A. C. Adoko

This study investigates seismicity and rockburst control within the Merensky Reef and UG2 orebody of an intermediate-depth platinum mine located in South Africa's Bushveld Complex. Utilising a mine-wide seismic monitoring system, over 1,900 seismic events were recorded between April 2023 and May 2024, revealing complex seismic patterns driven by mining-induced stress changes. The largest event, with a magnitude of ML 2.0, underscores the seismic risks associated with mining at increasing depths. Key findings include the spatial and temporal variability of seismic events, with high seismic activity observed near pillars and geological structures. A significant portion of events correlated with pillar and foundation failures. Moment tensor analysis identified dominant failure modes, such as stress-driven fracture closures and dynamic pillar failures. To mitigate risks, the study emphasises advanced support system redesign and rockburst risk assessment using the rockburst damage potential (RDP) method. The research highlights the importance of monitoring, geotechnical compliance, and adaptive designs in enhancing safety and reducing seismic hazards in underground mining operations. The study applied the RDP framework, classifying damage across R1 to R5 scales, where most instances fell into low-risk categories (R1 and R3), indicating minor to moderate damage. However, data limitations and the tabular stoping method's complexity led to inconclusive results, highlighting the need for more robust data and customised risk assessment tools. Despite these challenges, the RDP approach provided valuable insights into rockburst mechanisms and the effectiveness of existing support systems.

Jan 12, 2025

By Wedam Nyaaba, Edward Wellman, Angelina Anani, Eduardo Cordova, Sefiu O, Cluber Rojas, Adewuyi

This study aims to investigate the optimal method for ore pass construction in incompetent rock masses. We evaluated the conventional and raise boring methods based on safety, efficiency, excavation control and ground support for ore pass construction. A stability analysis using analytical Q-raise (QR method) and kinematic analysis methods for ore pass construction with a raise borer before and after grout injection of the rock mass was performed. As a case study, an ore pass (diameter of 3 m, depth of 100 m) within an incompetent rock mass was considered to gain further insight. The rock mass was characterized according to the classification methods Q Barton, rock quality designation (RQD), rock mass rating (RMR) and geological strength index (GSI). The grout intensity number (GIN) method of grout injection was used. The safety factor of less than 1.075 obtained before injection was lower than the acceptance criteria. However, grout injection before raise borer excavation resulted in a safety factor greater than 1.5. Using a raise borer without pre-grouting indicated that the maximum unsupported diameter (MUSD) of the ore pass was less than the required 3 m and equal to or larger than 3 m post-grouting.

Dec 1, 2024

By Firat Atalay

Estimating the spatial distribution of ore grade is one of the most critical steps to proceed with an investment decision on a deposit. Kriging is the most widely used method to estimate the ore grade, but alternative techniques are being developed. Machine learning algorithms can be used as alternative methods to classical kriging. In this paper, the iron (Fe) grade of a deposit is estimated with the XGBoost algorithm, and the results are compared with kriging estimation results. For the estimations, samples collected from drillholes were used. Due to the different natures of the estimation methods, different steps were taken to perform the estimations. The results show that XGBoost produced higher-range estimates, which is a desired result in ore grade estimation, and the minimum and maximum of the estimates were lower and higher than the kriging estimates, respectively. However, like kriging, the estimation results were smoother than composites, and the variance of the XGBoost estimates was lower than the variance of composites. This means that even though estimation with XGBoost mitigates the smoothing effect, the estimation results suffer from smoothing like kriging.

Dec 1, 2024

By A. Doroudgar, H. Noferesti

Unstable rock failure or rock bursting is one of the most dangerous problems in deep hard rock mines. This type of failure, that occurs uncontrollably, is associated with a significant release of strain energy from the surrounding rock. In this study, the numerical simulation of unstable rock failure was carried out using the three-dimensional explicit Lagranian finite-volume finite difference method to identify the onset and location of unstable failure events in the model. On this basis, a numerical simulation of mine pillars with width-to-height ratios of 0.5, 2 and 3 was performed. A criterion was developed, based on the calculation of the elastic strain energy, to determine the prone areas for the initiation of rockburst in the models. The results of this study showed that, in softer loading systems, due to large amounts of stored elastic strain energy, an unstable equilibrium is established, which leads to the rapid transfer of strain energy to the rock in the form of violent failure. It was also found that the unstable failure starts at the corners and free surface of the model. The new criterion developed in this study may help predict the occurrence of rockbursts in slender pillars and the design of pillars to control human and financial losses caused by this phenomenon.

Nov 27, 2024

By M. Mokgohloa, T. R. Stacey

Boring of tunnels and shafts in hard rock under high stress conditions is becoming more common as mines and tunnels are developed at greater depths. Under the high stress conditions, fracturing of the often-brittle rock occurs in the walls, backs, and ahead of advancing faces of excavations. Fracturing can have a significant impact on boring activities: sidewall spalling, which affects machine gripper capacity, and fractures can also develop in the rock ahead of the face, leading to blocky rock conditions, which may have a significant effect on machine excavation. Fracturing may develop dynamically, leading to rock bursts manifesting in the form of strain bursts, which can be hazardous and destructive. In brittle rock, fracturing is commonly extensional in nature. The focus of this paper is on fracturing in the face of the excavation. Examples of such fracturing behaviour are described briefly in the paper. Numerical analyses were carried out to predict the initiation of extension fractures and their orientations ahead of excavation surfaces, and the resulting formation of rock slabs/plates, and the stability of these plates. The results should be beneficial for the evaluation of the conditions prior to, and during boring operations.

Nov 27, 2024

By D. F. Malan, U. Du Preez, P. S. Heyns

South Africa has two main platinum reef deposits, namely the Merensky reef and the UG2 reef. These reefs are currently mined using traditional drilling and blasting methods. Mechanized cutting could potentially enable continuous mining, which would offer significant advantages. This would require thorough understanding of the cutability of the rock. To explore this, a linear cutting machine was developed to conduct laboratory scale cutting tests. This work describes the development and commissioning of the cutting machine using sandstone, as well as subsequent characterization tests on UG2 reef samples. UG2 reef has large variability in strength on a millimeter scale. This introduces uncertainty in the test results due to added variance from one cut to the next for the same UG2 reef sample. Another problem is the variability in rock properties of the UG2 reef, when testing samples from different mines. The cutting tests led to fine fragmentation, which is known to be a significant problem for cleaning operations in underground stopes and warrants further research. A finite element method simulation of the rock cutting was conducted using ANSYS LS-DYNA and the continuous surface cap model to simulate rock cutting in the UG2 reef. It was found that it is possible to use LS-DYNA with the continuous surface cap model to model rock cutting of UG2 reef and get acceptable results, but the user must calibrate the model parameters using the experimental results. Therefore, the model is only fit for one set of cutting parameters and further work is required to generalize results.

Nov 27, 2024

By J. Hadjigeorgiou, G. Knox

Since their introduction in 2010, paddled energy-absorbing rockbolts have been widely used in seismically active hard rock mines. This paper provides new data and reviews previous work to quantify the performance of paddled energy-absorbing rockbolts under controlled laboratory conditions. Of significance is the realization that the typical split location in impact tests, at the centre between two paddle anchor points can at best provide an upper limit value. This inherent variability in performance under different testing configurations should be acknowledged and taken into consideration in the design of ground support in seismic conditions. This paper discusses the reduction in capacity of paddled rockbolts as a function of loading angle from a maximum value during axial tests (0° loading angle) to the lowest value during pure shear (loading angle of 90°). A significant reduction in displacement capacity is observed as the loading angle changes between axial (0°) and 40°. Beyond a 40° loading angle up to shear (90°) loading the reduction in displacement capacity is less significant. Due to the variances in the capacity observed through the variance of the testing configuration: loading mechanism, direction of loading, location / presence of a discontinuity, it must be recognized that results from laboratory-based testing are not independent of the testing configuration. This should be acknowledged when extrapolating anticipated performance in the field.

Oct 9, 2024

By G. de Tomi, G. C. Teodoro, E. Rodovalho, T. M. El Hajj, J. A. S. Tenório

The selection of an underground mining method stems from a multivariate analysis that considers geomechanical, geological, economic, and operational parameters. Even after identifying the most compatible method, there is no assurance that the mining company will achieve the best performance. The geological complexity of some deposits requires adaptations of methods described in the literature to obtain more selectivity and reduce mining waste. There are some studies on geometry of underground structures, but a methodology that describes an adaptation of a room-and-pillar mining method for ore bodies with down-dip varying from 20° to 25° is novel. The present work aimed to reduce dilution by adapting the traditional room-and-pillar mining method (TRP) to inclined ore bodies. This new method is entitled short-hole room-and-pillar (SHRP). The equations that measure the dilution are defined according to the geometry of stopes and openings. The results comprise comparative analyses of the operational and planned dilutions to measure the performance of the SHRP method. The average operating dilution of the SHRP method was more than five times lower than the planned dilution according to the TRP method. Low operational dilution indicates high selectivity of the method and its potential to reduce underground mining tailings.

Sep 9, 2024

By A Seneviratne, Y Li, N Udugampola, X Ai, B Li

LoRa (Long Range) is a low-power, long-range wireless communication technology that has gained significant research interest for its potential use in multi-hop linear wireless sensor networks (LWSNs) for underground mines. These networks deploy LoRa-enabled sensor nodes in underground mines, together with LoRa repeaters placed along mine tunnels to relay sensor data to a LoRa gateway, which connects to a backend network for application-level processing. Since the packets generated by sensor nodes farthest from the gateway have to hop through many repeaters, their probability of successfully reaching the gateway is significantly lower due to the higher chance of packet collisions compared to sensor nodes closer to the gateway, resulting in a lack of network fairness for the sensor nodes. In this paper, we first present an algorithm to improve the fairness of a LoRa LWSN by grouping neighbouring nodes and adjusting the number of packet re-transmissions and transmission intervals for each group. Then, we demonstrate that the fairness of a simulated LoRa LWSN can be improved by approximately 500 per cent by applying our algorithm. We verify our algorithm with network simulations under different network traffic levels and grouping strategies, obtaining fairness improvements in each scenario. Our algorithm and results will pave the way towards developing highly fair LWSNs for underground mining applications.

Sep 1, 2024

By Y Tang, G Si

The analysis of rock fragment sizes plays a pivotal role in various stages of geotechnical and mining engineering. Traditional case-based methods often falter in addressing the variability in rock fragment images due to fluctuating geological conditions and imaging environments. To counter this challenge, AI-based models necessitate training on expansive and diverse data sets, encompassing a wide range of conditions and fragment types. However, the creation of such data sets is often hampered by financial and environmental constraints. This paper introduces a novel approach for rock fragment recognition, which leverages the power of AI for data augmentation, automated annotation and prediction. The methodology integrates three key components: Style-Based Generator Architecture for Generative Adversarial Networks (StyleGAN) for data augmentation, the Segment Anything Model (SAM) for automated annotation, and the multiscale version of you only look once (YOLO-MS) real-time instance segmentation model. The proposed method begins by training a new StyleGAN model using the original data set. It then delves into exploring the latent vectors of different rock fragment images to generate specific ‘style representations’. These latent vectors form the foundational elements of each image in a high-dimensional space, encapsulating critical features such as size, shape, texture, and colour variations. The next step involves conducting quality checks on the generated images to ensure their realism and suitability as training data. Subsequently, the augmented image set is annotated using modified SAM module, followed by training the YOLO-MS real-time instance segmentation model. The performance of this model, trained on the augmented data set, is compared with a model trained on the original data set, particularly in terms of generalisation abilities in new scenarios. The results exhibit a significant enhancement in accuracy, robustness, and generalisability of the model trained on the augmented data set, underscoring the potential of StyleGAN as a powerful tool for data augmentation in geotechnical engineering image analysis.

Sep 1, 2024

By C Zhang, R Liang, I Canbulat, S Saydam, B Li

The integration of digital twins in mining represents a significant advancement towards enhancing operational efficiency and decision-making. Central to this development is the role of visualisation, a crucial element in mining digitalisation. This paper aims to develop a data-driven visualisation solution for the construction of mining digital twins, highlighting how this innovation revolutionises the representation of physical mining environments. It spotlights the shortcomings of conventional visualisation methods, particularly their inability to accurately depict complex underground environments and their predominantly historical orientation. To address these challenges, this paper proposes a novel, data-driven approach. This approach extensively utilises CAD files, encompassing data from geological surveys to intricate mine plans, thereby significantly elevating the precision of digital twins. It supports real-time updates and enables flexible interaction with a variety of data types. Moreover, it introduces a comprehensive data diagram alongside the adaptation of a 3D tile index. This strategy ensures the efficient rendering of extensive mining environments, allowing for a more dynamic and interactive digital twin model. In conclusion, the implementation of data-driven visualisation in the construction of mining digital twins marks a pivotal advancement in the industry’s digital transformation. It promises a more detailed, accurate, and dynamic depiction of mining operations.

Sep 1, 2024

By J Leem, S Mehrishal, J Kim, A Fereshtenejad, J Song

Rock mass discontinuity mapping plays a crucial role in developing a reliable Discrete Fracture Network (DFN), providing insights into the distribution of geometrical and mechanical properties of discontinuities. This enables an accurate assessment of geotechnical infrastructure and mining excavations. Data obtained through conventional mapping approaches are substantially limited by factors such as accessibility and subjectivity. Moreover, these conventional methods may pose inherent risks due to manual techniques, exposing practitioners to potential dangers. Recent digital developments in remote surveying techniques offer opportunities for safer and more reliable rock mass discontinuity surveys. These technologies allow extensive three-dimensional surveying and the production of high-resolution 3D surface models, applicable at any time. This paper presents a workflow for digital discontinuity mapping employing drone technology and photogrammetry. Additionally, the capabilities and challenges of applying artificial intelligence (AI) algorithms in the rock mass discontinuity characterisation process are discussed.

Sep 1, 2024

By B P. Banerjee, D Patra, S Raval

Roof bolts are an essential component of roof support systems in underground mines. They are used to provide structural support to the roof of the mine and prevent it from collapsing. Therefore, it is imperative to perform regular assessments of these roof bolts to avert any hazards. Manual investigations of roof bolts are done by surveyors in underground mines, which is extremely timeconsuming and challenging due to the low-light conditions in underground mines accompanied by stringent mine access rules. To this end, automated recognition of roof bolts in 3D point cloud data obtained through laser scanning in underground mines serves as a potential solution to aid roof bolt monitoring. With the continuous advancements in laser scanning-based 3D data acquisition systems, the feasibility of making such surface estimations from 3D point cloud data has increased severalfold.

Sep 1, 2024

By C Xu, B Li

Multi-sensor fusion visual perception technology significantly enhances visual perception capabilities in complex and harsh environments by combining visual data from different sensors. This technology is especially useful for scenarios where traditional single visible light sensors, such as standard cameras, struggle due to poor lighting, adverse visual conditions, or other visual obstructions. In environments with low light, smoke, or dust, the performance of conventional visible light sensors degrades, limiting their application. By integrating data from various sensors like radar, infrared thermal imaging, and others, multi-sensor fusion technology provides a more comprehensive and complementary visual solution. This fusion not only enhances the robustness of visual systems but also greatly expands their potential applications in fields like visual surveillance. In recent years, deep learning methods have become the mainstream technology for addressing multi-sensor fusion issues, demonstrating significant advantages in adaptive feature selection compared to traditional machine learning methods. However, deep learning-based multi-sensor fusion algorithms still face several challenges. The design of network structures is often redundant, lacking effective screening of useful components within multimodal information. Mainstream fusion algorithms focus excessively on improving display effects without adequately considering the needs of downstream applications and tasks. Existing fusion perception algorithms are generally designed for open visual scenes and lack targeted design for complex visual degradation factors present in tunnel environments of mines. Current deep learning methods for designing multi-sensor fusion networks heavily rely on manual experience to create fusion modules. This reliance increases the complexity of network design and can lead to redundancy. Redundant network modules are difficult to identify in end-to-end learning tasks at this stage, significantly slowing down network inference and potentially interfering with the output of fusion perception. Additionally, training these fusion modules typically requires large amounts of well-annotated multimodal data, further increasing implementation difficulty and cost. For instance, the fusion network design proposed by Li and Wu (2018) focuses on extracting multimodal fusion features, while Zhao et al (2020) introduced a feature decomposition mechanism in the feature fusion and extraction modules. Researchers like Zhang and Ma (2021), Xu et al (2020), and Liu et al (2017) have attempted to improve network structures through dense cascades and the introduction of residual connections. Although these methods have improved the handling of multimodal information to some extent, they still face limitations in distinguishing between useful and redundant information, with redundancy persisting in network designs. Ideally, the design of fusion networks should be more intelligent, guiding network structure design heuristically based on the importance of different modal information for the current task. This would reduce information redundancy and network weights, requiring the development of new algorithms or frameworks capable of automatically identifying and optimising fusion strategies, thereby reducing dependence on manual experience while improving fusion efficiency and network performance. Current deep learning designs for multi-sensor fusion algorithms often overlook the

Sep 1, 2024

By S Padekar, C Tapia, D Likhachev, S Harlikar, D Sapkale

In the quest to access valuable critical minerals, humankind is expanding the frontier into extreme environments like space and deep-sea mining. These ambitious enterprises involve considerable costs and lengthy development cycles to ensure the robustness of mining systems in the face of the harshest conditions and unforeseen challenges. Unfortunately, obtaining information from these unexplored territories is complicated. Data is often scarce, incomplete or impossible to obtain until equipment is deployed, leaving engineers facing many uncertainties throughout the design, testing, production and preparation of such missions. Despite these challenges, these extreme environments operate under consistent physical and chemical principles that can be faithfully digitally replicated. However, unexplored areas and dynamic systems like weather and geology continue to carry uncertainties. While eliminating uncertainties remains elusive, Digital Twins offers a means of robust risk management tool. These virtual replicas can excel in simulating extreme conditions and unexpected events, helping optimise and validate mining system designs tailored for such hostile environments. This study takes inspiration from the Mars Science Laboratory (MSL) Curiosity Rover’s wheel damage rate to develop a Multiphysics Modelling and Virtual Motion Simulation (MMVMS) system. Using advanced Generative Design, this MMVMS system refines the MSL’s design, simulates component and system performance, and assesses the risk of premature damage while operating on Mars. Additionally, MMVMS integrates cutting-edge CAD design technologies from aerospace and manufacturing to realistically recreate the Martian environment and a detailed representation of MSL, allowing mobility verification and validation simulation to test different components in terms of materials, design, weight, power and, therefore, cost. The research highlights the precision of MMVMS in replicating extreme conditions, simulating and evaluating internal and external variables influencing the entire system (Rover) in real-time, and predicting movements, failures, and premature damage. This sophisticated Digital Twin technology, showcased in this research, holds promise not only for space missions but also to be adapted for terrestrial and deep-sea mining operations.

Sep 1, 2024

By T Y. Ko, Y S. Kang, S J. Park, J H. Hwang

The lunar subsurface, particularly the lava tubes formed by ancient volcanic activity, presents a promising environment for establishing safe and sustainable human habitats on the Moon. Tunnel Boring Machines (TBMs), widely used for excavating tunnels on Earth, offer a potential solution for developing these subsurface spaces. However, the unique lunar conditions, including extreme temperatures, high vacuum, radiation, and the extraordinary strength and abrasiveness of lunar rocks, necessitate significant adaptations to existing TBM technology. This study investigates the feasibility of utilising TBMs for lunar subsurface exploration and base construction, addressing the challenges posed by the harsh lunar environment. We review previous conceptual designs for lunar TBMs, highlighting their strengths and limitations in addressing these challenges. Additionally, we explore innovative excavation methods, such as microwave-assisted rock weakening and undercutting techniques, to enhance the efficiency and effectiveness of lunar TBM operations. By evaluating the geological characteristics of the lunar crust and analysing the potential of lava tubes as launching points for TBM operations, this study provides a comprehensive assessment of the technical requirements and operational considerations for lunar TBM implementation. The findings of this research contribute to the growing body of knowledge on lunar base development and pave the way for future human exploration and utilisation of the Moon’s subsurface resources.

Sep 1, 2024