Although most mining companies utilise systems for slope monitoring, experience indicates that mining operations continue to be surprised by the occurrence of adverse geotechnical events. A comprehensive and robust performance monitoring system is an essential component of slope management in an open pit mining operation. The development of such a system requires considerable expertise to ensure the monitoring system is effective and reliable. Written by instrumentation experts and geotechnical practitioners, Guidelines for Slope Performance Monitoring is an initiative of the Large Open Pit (LOP) Project and the fifth book in the Guidelines for Open Pit Slope Design series. Its 10 chapters present the process of establishing and operating a slope monitoring system; the fundamentals of pit slope monitoring instrumentation and methods; monitoring system operation; data acquisition, management and analysis; and utilising and communicating monitoring results. The implications of increased automation of mining operations are also discussed, including the future requirements of performance monitoring. Guidelines for Slope Performance Monitoring summarises leading mine industry practice in monitoring system design, implementation, system management, data management and reporting, and provides guidance for engineers, geologists, technicians and others responsible for geotechnical risk management.

This empirical study investigates the mechanistic reasoning behind the occurrence of large magnitude fluid-injection-induced seismic events during hydraulic fracturing in the Kiskatinaw area. The data unveiled atypical non-parabolic spatio-temporal distributions of induced seismic event hypocenters—previously unreported in existing literature. These distributions were predominantly associated with larger magnitude events. Distinctly, our research delves into the concept of hydraulic diffusivity within fractured reservoirs, interpreting the observed patterns in these unique spatio-temporal hypocentral growth distributions. The study reveals that in unconventional fractured reservoirs of this region smaller seismic events are linked to active stages via low hydraulic diffusive and highly hydraulically connected, dispersed, fracture network, while larger magnitudes can associate with highly diffusive and concentrated fractured pathways with limited hydraulic connectivity. This was attributed to the contrasting storativity of connected fracture networks impacting fluid pressure propagation pace to hydraulically connected seismogenic faults. Furthermore, the data pointed to an inverse relationship between hydraulic diffusivity and the number of hydraulically connected structures to the active stage, leading to higher pressure build-ups and larger seismic event magnitudes at greater diffusivity levels. This understanding offers insights into the variances in seismic responses across stages and wells. Intriguingly, unlike standard hydraulic fracturing of unconventional reservoir models emphasizing on tensile fracture generation, our findings underscore the significant role of pre-existing natural fractures in inducing shear slip during fluid injection. The seismic energy release to hydraulic energy input ratio observed was considerably higher than in settings with more massive rocks, aligning with results reported for enhanced geothermal operations. Conclusively, fluid injection in certain fractured reservoirs of the KSSMA can lead to significant pressure buildup perturbations, causing larger seismic events, while in others, a multitude of smaller events prevails, highlighting the complex interplay of hydraulic diffusivity, fracture intensity, and connectivity in determining seismic responses.

Spalling and strainbursting are common mechanisms for tunnel instability in high-stress environments with massive to moderately jointed rock mass conditions. These mechanisms are complex and not adequately captured for ground support design using conventional analytical, empirical, and numerical approaches due to the inability to address: i) stress fracture localization and directional deformation, ii) variable energy release rates for gradual spalling and discrete strainbursting events under mining-induced loading paths, and iii) the localized deformation and energy demands imposed on the ground support from the spalling or strainbursting ground. Explicit numerical simulation of the spalling and strainbursting mechanisms with bonded block models has the potential to considerably improve the engineering design and ground support of deep mining and civil tunnels; however, very few studies have applied this approach. This article addresses the suitability and mechanistic validation of two-dimensional bonded block models using the distinct element method for simulating spalling and strainbursting at the mine tunnel scale. The critical importance of mechanical damping parameters is highlighted and shown with conceptual simulations, and a preferred damping mode is identified. A contact constitutive model with linear strain softening is introduced and tested, which increases control over energy release rates and damage evolution in the simulations. The models are validated to show they capture the correct rupture modes, depth of spalling/strainbursting, and kinetic energy release for a range of common rock types. The results provide a helpful engineering tool for excavation and ground support design for spalling and strainbursting behaviours.

The demand for critical minerals continues to increase as the world shifts towards green energy solutions. This trend has compelled the mining industry to mine deeper as near-surface mineral deposits become depleted, exposing projects to higher stress environments in more massive, brittle rock. Under high stresses, massive rock fails via stress fracturing, resulting in a progressive, non-violent manner as spalling or suddenly and violently as strainbursting. These phenomena ultimately lead to bulking around the excavation that increases susceptibility to damage or failure of safety-critical rock support systems, creating risks for workers and costly interruptions in production. Deformation-based support design (DBSD) is a recent and innovative approach for designing support in highly stressed brittle rock, offering several advantages to address these challenges. However, the limited availability of purpose-focused field-based data and systematic procedures for effectively determining DBSD critical parameters often hinders its optimization, particularly in deep caving operations. This paper presents a systematic method and recommendations to improve design justification and optimization of the DBSD approach for the PT Freeport Indonesia (PTFI) deep mill level zone (DMLZ) panel cave mine. The study introduces a systematic process that integrates stress fracturing and bulking monitoring techniques, leveraging monitoring data to calibrate the critical parameters of the DBSD. A site-specific predictive function is proposed for estimating the depth of stress fracturing and the corresponding displacement based on the transient nature of the bulking factor across the extraction level footprint. Implementing this procedure into PTFI's DBSD tool effectively improves the forecasting of preventative support maintenance in areas experiencing stress fracturing, thereby promoting the integrity and reliability of the excavation.

Support systems are vital for worker safety and to minimize production delays due to collapse incidents or the need for rehabilitation. The purchase of support elements and their installation also represent a significant cost for the operational budget of any mine. However, current standard practices rely on empirical methods developed several decades ago or on simplified kinematic models of the rock mass and its interaction with the support system based on wedge analysis and key-block theory. Experience shows that these lack the robustness required for today's mining depths and safety needs. The advent of faster computer processors and new numerical modelling tools allows us to move away from these over-simplified methodologies and embrace new approaches that can more accurately simulate the failure mechanisms encountered and interactions between the rock mass and the support elements. A new methodology using the 3D distinct-element modelling software PFC3D, combined with DFN simulations, was developed to evaluate support system strategies at the Raglan Mine. The joint strength properties were calibrated against overbreak data from lidar scans. The study demonstrated the effectiveness of the Rigid Block Modelling-DFN approach in simulating complex failure mechanisms in a gravitational stress environment. It provided valuable insights into the trade-offs between using PM12 and rebar #7 for supporting the backs of drifts, as well as determining the optimal timing for installing secondary long supports at intersections.

In cave mines, wet inrushes occur when there is an uncontrolled inflow of fine, wet material from drawpoints. Currently, uncertainty exists regarding the spatial-temporal pattern and severity of inrush incidents. This uncertainty arises from the limited understanding of wet inrush mechanisms within the complex conditions of a cave mine. In this study, the existing gaps in knowledge around the spatial and temporal patterns of inrush incidents were addressed using machine learning techniques. A random forest (RF) model was employed to analyse the inrush database collected at the Deep Ore Zone mine over several years. The conceptual understanding of inrush mechanisms and triggers, along with historical evidence, was employed to establish an initial set of key inrush variables to be used in the RF model. The developed RF model demonstrated promising performance with an accuracy of 85%. The feature importance results indicated that previous inrush history, fragment size, draw rate (short term and long term), differential draw index (short term and long term) and history of inrush at neighbouring drawpoints had the highest impact on inrush susceptibility. The insights gained provide an improved assessment of inrush susceptibility, thereby improving the strategies employed to mitigate inrush risk.

ABSTRACT: A crucial aspect of rock engineering design for deep mining projects is comprehending the in-situ stress field. This is particularly true for panel cave mining, where the excavation and support design of the extraction level tunnels and pillars must account for an initial stress increase related to their development and subsequently to the abutment stress resulting from undercutting and cave initiation. Thus, stress measurement should extend beyond establishing the pre-mining stress state to continuous monitoring of the evolving stress field, spatially and temporally, throughout operations. To do so accurately poses challenges; a lack of repeatability or permanency hinders existing methods. This paper introduces an innovative technique for stress measurement, merging borehole-distributed sensing and geophysics to address these challenges in deep mining. The technique aims to provide comprehensive, non-destructive measurements of stress magnitudes and orientations over large rock volumes, offering repeatable testing and monitoring opportunities. Feasibility and validation testing results showcase the potential benefits of this approach, emphasizing its role in enhancing in-situ stress understanding and management for optimized and safe deep mining and caving operations. 1. INTRODUCTION Engineering analyses for excavation stability and support design hinge on establishing specific boundary conditions. Among these, the in-situ stress state is paramount for rock engineering design of deep mining projects. The in-situ stress is a tensor quantity whose measurement poses a formidable challenge, often fraught with difficulty and reliability concerns. It is common for stress measurements to reveal conflicting stress interpretations, introducing considerable uncertainty and risk to deep mining projects. This inherent complexity frequently results in suboptimal design performance and costly errors. Various methods for stress measurement are in use, each presenting notable limitations and challenges. These are typically categorized into two methodologies, as outlined by Amadei and Stephansson (1997). The first involves measuring strain responses, such as strain relief from overcoring, allowing for the inversion of the in-situ stress field (Sjöberg et al., 2003). However, these measurements encounter reliability issues related to conformability, requiring meticulous preparation and a complete attachment of the measurement probe to the rock, and from being a point measurement, neglecting the inherent heterogeneity and anisotropy of rock and its effect on the strain response of the larger rock mass volume. The second type of methodology involves measuring the pressure necessary to initiate and/or maintain an open fracture of a specific orientation, often induced through hydraulic fracturing (Haimson & Cornet, 2003). Despite offering insights into larger rock volumes, the latter methods are inherently destructive, generating new fractures that prevent repeat validation testing over time. Additionally, the nature of these measurements and the effort required often limits them to a single point in time, restricting the ability to monitor stress changes over extended periods, a consideration particularly relevant in the dynamic environments of deep mining and panel caving.

Inrush hazards involve a sudden inflow of material into an underground mine excavation. In a cave-mining setting, this typically involves high amounts of fine-grained material and water flowing from an extraction-level drawpoint. Operations encountering these hazards have developed tactical and strategic risk management plans to address the risk posed to mine safety and production. These plans have improved over time in meeting safety requirements owing to the utilization of evolving automated systems. However, managing the impact of inrushes on production rates has remained an area of improvement, particularly in mature caves with high quantities of fine material and stored water. In such conditions, understanding the impact of draw strategies on inrush susceptibility is particularly important to guide a risk-informed draw optimization strategy, with the goal of minimizing spill frequency while maintaining target production rates. In this study, the impact of draw strategies on inrush susceptibility was assessed through the statistical analysis of draw and spill history at PT Freeport Indonesia's Deep Ore Zone mine. Two draw-related variables were used for quantification of draw strategy: Draw Rate and Differential Draw Index. Differential Draw Index is a new parameter developed in this study to quantify draw non-uniformity, with the aim of capturing the impact of differential draw rates on inrush susceptibility between neighboring drawpoints. The results from this study show that Draw Rate and Differential Draw Index should be considered concurrently when assessing the impact of draw strategy on inrush hazards. An up to 8-fold increase in spill probability was observed when both Draw Rate and Differential Draw Index were in their upper ranges, compared to when either of these variables was in its lower ranges. The results also showed that if a drawpoint had a long-term history of isolated draw, it was more susceptible to inrush under short-term non-uniform draw.

ABSTRACT: The long-term stability of engineered slopes is becoming a critical focus point as the number of open pit mines anticipated to close in the coming years is increasing, and governments, regulators, and society are collectively placing more emphasis on sustainable management of mineral resources and land use (IISD, 2021). However, there are currently few guidelines on assessing long-term slope stability. Of central importance is the recognition that rock mass properties are not constants, and therefore, open pit slopes that are presently stable may not remain so in the future. Conventional engineering analyses generally assume the strength of a rock mass to be constant and, in doing so, fail to explain the temporal nature of rock slope behavior seen in monitoring data. Data shows that pit slope movements are intermittent, correlating with benching and seasonal precipitation patterns. These initiate episodic damaging events that, in the closure context, control strength degradation and impact long-term slope performance through progressive failure (Eberhardt et al. 2004). This talk will summarize the author's research over the last 20 years into progressive failure, its advancement of our mechanistic understanding of deep-seated rock slope failure, and recent results and guidance in applying it to open pit slope stability assessments and mitigation efforts to aid mine closure designs. Empirical data will be presented to show the evidence for progressive failure, with a focus placed on transient pore pressures driven by seasonal precipitation. Upon closure, changes to the slope geometry (i.e., benching) cease, but seasonal precipitation continues. Progressive failure posits that transient pore pressures in response to infiltration or groundwater recharge act to locally decrease effective stresses, promoting slip along non-persistent discontinuities, which in turn may cause the slip of adjacent fractures and/or the failure of intact rock bridges. Such repeated fluctuations in pore pressures and effective stresses thus are a key driver of progressive failure and can be equated to fatigue, where the rock slope experiences a slow weakening through repeated load cycles. Results from mine closure analyses will be presented, demonstrating how slope displacement monitoring and modeled groundwater fluctuations can be used to calibrate numerical models and establish the degree of criticality present in a slope. The modeling of seasonal variations further enables reference to be made to time in calculations that are otherwise limited to stress-strain behavior. This provides a means to assess displacement rate thresholds at which behavior change may occur for a given failure mode, which can be used to establish and constrain early warning alarm thresholds and trigger action response plans (TARPs). Examples will also be provided incorporating allowances for the development of a pit lake post-closure and for long-term stability improvement through engineered buttress designs.

Copper is a critical metal for electrification, and is witnessing a step-change in demand associated with the transition away from fossil fuels to clean energy and electric transportation systems. By 2030, a copper supply gap of ten million metric tons per year is expected, equivalent to the global copper supply required to meet the Paris Agreement targets. This essay discusses the challenges ahead as we seek to close this anticipated supply gap, with a particular focus on the need for new underground mining methods to access deeper copper deposits. The shift to targeting deep underground deposits is pushing the mining industry beyond its experience base, creating a need for novel engineering approaches to mitigate new geological hazards, while also managing new economic risk factors. Success in these endeavours is critical to the advancement of the clean energy transition.

ABSTRACT: In a highly stressed environment, massive rock masses fail through stress fracturing, resulting in rock slabs or spalls that can detach from the excavation perimeter either progressively in a non-violent manner as spalling, or suddenly and violently in the form of strainbursting. Both phenomena ultimately lead to rock mass bulking near the excavation boundary that reduces support system capacity, creating safety risks for workers and costly interruptions in production. An innovative and recent approach to designing support in highly stressed brittle rock, known as deformation-based support design (DBSD), offers advantages in addressing the challenges associated with brittle failures around underground excavations. However, the limited availability of purpose-focused field data and systematic procedures for effectively utilizing monitoring techniques to determine DBSD's critical parameters often hinder its optimization, particularly in deep caving operations. This paper presents a systematic method and recommendations to improve design justification and optimization of the DBSD approach for the PT Freeport Indonesia (PTFI) Deep Mill Level Zone (DMLZ) panel cave mine. The study introduces a systematic process that integrates stress fracturing and bulking monitoring techniques, leveraging monitoring data to calibrate the critical parameters of the DBSD. A site-specific predictive function is proposed for estimating the depth of stress fracturing and the corresponding displacement based on the transient nature of the bulking factor across the extraction level footprint. Implementing this procedure into PTFI's DBSD tool effectively improves the forecasting of preventative support maintenance in areas with high demand, thereby promoting the integrity and reliability of the excavation. 1. INTRODUCTION Experiences gained from past and ongoing deep mining and caving operations suggest that brittle failure around underground excavations have been a significant challenge and contributes to hazards in deep underground operations. This phenomenon poses a safety risk for workers and can result in costly interruptions to production. Brittle failure occurs through stress fracturing. Fractures begin to form when induced stresses exceed the crack initiation strength of the rock (Martin, 1997; Kaiser et al., 2000). These fractures will propagate parallel to the maximum compressive stress and open perpendicular to the direction of minimum confinement. This opening mode distinguishes these fractures as extensional fractures, and differentiates them from shear fractures that develop under higher confinements and involve a shearing displacement mode. Thus, near the excavation boundary where confining stresses are low, the failure process is characterized by the formation of extensional fractures growing parallel to the excavation boundary. This, in turn, results in the creation of a set of rock slabs or spalls that can detach from the perimeter of the excavation, known as spalling. As the deviatoric stresses increase relative to the strength of the rock, it progresses deeper into the rock mass. However, as the spalling moves deeper into the rock mass away from the excavation, the higher confining stresses encountered start to suppress and limit the progression of extensional fracturing, resulting in the transition toward the formation of shear fracturing.

One of the key challenges in assessing, managing and mitigating induced-seismicity hazards related to hydraulic fracturing and fluid injection activities is understanding how geological and operational features influence the likelihood and severity of an event. Geological features point to the pre-existing conditions that affect a well’s susceptibility to generating induced seismicity. In contrast, operational features are controllable and can be engineered to mitigate and minimize potential hazards. In recent years, with increased data availability and the rapid development of machine learning techniques, the application of these statistical tools has been proposed to investigate induced seismicity. However, this raises the question of the performance and interpretability of these methods, which requires thorough investigation. This paper presents the results of a detailed study utilizing data for the Montney region of northeastern British Columbia that investigates the robustness of several machine learning algorithms in predicting induced seismicity likelihood and severity and compares the importance of geological and operational features on the triggering and maximum magnitude of these events. The analyses include seismic monitoring, regional geology and well completions data, and the novel use of geophysical well log data to provide a more comprehensive database of geological features.

Stress-induced brittle fracturing near an excavation boundary results in a volume increase, known as bulking. Excessive bulking places added demand on the rock support, which, if not detected and addressed through preventative support maintenance (i.e., proactively added reinforcement), can cause the support to fail, leading to a safety hazard and costly production delays for underground mining operations. For caving mines, these project risks are exacerbated during cave establishment due to the large abutment stress from undercutting that redistributes and concentrates stresses near excavations critical for production. This paper reports the findings from research conducted to develop and improve geotechnical monitoring practices to support preventative support maintenance in deep mining operations. This research uses a unique geotechnical monitoring database collected for the Deep Mill Level Zone panel cave mine. The data was collected across a large footprint during the mine's ramp-up period and represents an initial step toward best practices for data collection at cave mines operating in high-stress environments. Borehole camera surveys supplemented by multi-point borehole extensometers have been used to determine the depth of stress fracturing in pillar walls as a function of the distance away from the undercut. Convergence measurements and LiDAR scanning are used to characterize the corresponding rock mass bulking. The results show that the interpretation of monitoring data can be used to identify the long-term depth of stress fracturing and bulking trends in response to undercut advances. These show that direct measures of stress-induced fracturing damage provide an early indication of excavations vulnerable to bulking and that LiDAR scanning is an effective method for capturing the onset of bulking and anticipating local areas likely to experience greater deformation demand as bulking progresses. Proactive and strategic geotechnical monitoring based on the long-term depth of stress-induced fracturing trends is proposed to assist with preventative support maintenance practices.

Discrete fracture network (DFN) models allow discontinuity data to be stochastically quantified and used to represent a jointed and faulted rock mass, providing a means to assess potential failure modes for a planned tunnel and the corresponding excavation methods and support design.Required inputs are generally obtained from borehole data to obtain representative values at the tunnel depth, but with limited opportunities for ground truthing and validation.Results are presented from a validation exercise comparing DFN results from discontinuity data sampled across two different spatial scales, first from a deep geotechnical borehole followed by a co-located deep shaft.The results indicate that both under-and over-sampling of different discontinuity sets occurs due to orientation bias and trace visibility.The corrections and workflow developed demonstrate the utility of continuous data collection and updating of DFN analyses as projects transition from investigation and design to construction.

Digitalization in rock engineering has resulted in significant technological advancements and the increasing use of machine learning techniques. As rock engineering transitions into becoming more data-driven, machine learning can help rock engineers improve the efficiency and utilization of large data sets in the design process. While machine learning is a powerful tool, the success of machine learning algorithms is intrinsically related to the quality and quantity of data available. It is commonly accepted that machine learning algorithms that are trained on poor quality data will result in poor and inaccurate (i.e. highly subjective) results. To limit the human factors that result from using data that represent qualitative assessments rather than objective measurements of physical properties, it is imperative to improve the data analysis and preparation/labelling process. Data preparation is especially important when applying machine learning to rock engineering problems due to the inductive and empirical nature of the design process as a result of the inherent variability of geological materials. Despite data preparation accounting for more than half of the machine learning process, there is limited research on data preparation for machine learning in rock engineering. This paper aims to fill this gap by providing a set of guidelines on the necessary data preparation steps for applying machine learning to rock engineering problems, thereby helping rock engineers improve the performance of their machine learning models.