PROFESSIONAL TECHNICAL AND SCIENTIFIC PAPERS

 

Evaluating destress blasting for rock fracture and rockburst prediction in deep level hardrock mining

 

 

T. Zvarivadza^I; C. Yi^I; S. Dineva^I; M. Onifade^II; M. Khandelwal^II; B. Genc^III

^IDivision of Mining and Geotechnical Engineering, Luleå University of Technology Sweden. ORCiD: T. Zvarivadza http://orcid.org/0000-0003-1014-0405 . C. Yi http://orcid.org/0000-0002-5872-5173 . S. Dineva http://orcid.org/0000-0001-9419-2207
^IIInstitute of Innovation, Science and Sustainability, Federation University Australia, Australia. ORCiD: M. Onifade http://orcid.org/0000-0001-9933-266X . M. Khandelwal http://orcid.org/0000-0003-0368-3188
^IIISchool of Mining Engineering, University of the Witwatersrand, South Africa. ORCiD: B. Genc http://orcid.org/0000-0002-3943-5103

Correspondence

 

 

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ABSTRACT

Deep level hardrock mining faces increasing challenges from elevated in situ stresses and associated rockbursts. This study aims to develop a systematic framework for evaluating destress blasting effectiveness, with a focus on deep Swedish hardrock mines. The methodology integrates a critical review and adaptation of global best practices: advanced numerical modelling (static and dynamic simulations), field-based rock fracture monitoring using ground penetrating radar and borehole imaging, and application of rockburst prediction criteria including the strain energy storage coefficient (F), brittle shear ratio, and burst potential index. Geostatistical simulations, machine learning models, and industrial internet of things-based, real-time monitoring are proposed to enhance predictive accuracy, model calibration, and operational adaptability. Key findings show that effective destress blasting evaluation requires multi-modal integration of numerical outputs, field observations, microseismic trends, and uncertainty quantification, accounting for site-specific geological variability and dynamic stress redistribution. The study advances the field by proposing a predictive, feedback-driven evaluation framework tailored for deep Swedish mining conditions, capable of improving proactive rockburst risk management. It offers both practical tools for mining practitioners to optimise stress management, reduce seismic hazards, and enhance excavation safety, and academic foundations for future refinement of destress blasting models, geotechnical monitoring strategies, and adaptive design protocols. This research contributes to safer, more efficient deep mining operations by bridging gaps between theoretical modelling, empirical monitoring, and real-time, data-driven blast design optimisation strategies.

Keywords: destress blasting evaluation, deep-level hardrock mining, numerical modelling, rockburst prediction criteria, seismicity, geostatistical simulation, real-time monitoring (IIoT)

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Introduction

Deep level hardrock mining presents significant geomechanical challenges, particularly due to the elevated stress conditions that lead to strain bursts, rockbursts, and structural failures (Li et al., 2017; Wagner, 2019; Malkowski, Niedbalski, 2020; Swan, Li, 2023; Cai, 2024; An, Mu, 2025). As mining operations progress to greater depths, the management of stress redistribution becomes increasingly critical to ensuring the safety of workers and the stability of underground excavations. Traditional support and stress management techniques often become insufficient to control extreme stress concentrations and associated rockburst hazards as mining progresses to ever greater depths. This reality necessitates the exploration of proactive rock engineering methods capable of modifying the in situ stress field before failure occurs. Among these methods, destress blasting has emerged as a particularly promising approach for mitigating dynamic failure risks by strategically redistributing stress ahead of excavation faces. Destress blasting is a proactive rock engineering technique, which has gained recognition as a viable method for mitigating the risks associated with mining-induced seismicity and dynamic rock failures (Tang, 2000; Sainoki et al., 2017; Vennes, Mitri, 2017; Drover et al., 2018; Drover, Villaescusa, 2019; Vennes et al., 2020; Mertuszka et al., 2022; Shnorhokian, Ahmed, 2024a; Shnorhokian, Ahmed, 2024b). The fundamental principle of destress blasting is to precondition the rockmass ahead of mining faces, reducing its stored elastic strain energy and altering the in situ stress field to minimise the likelihood of catastrophic rock failures. Despite its engineering value, the practical application of destress blasting in deep hardrock mines remains a complex engineering challenge. Variations in geological conditions, rockmass properties, and stress anisotropy necessitate site-specific optimisation of blast design parameters, including charge distribution, hole spacing, initiation sequences, and delay timing. Whereas field observations and empirical studies have demonstrated the benefits of destress blasting in alleviating rockbursts and seismic risks, comprehensive evaluations of its effectiveness require effective methodologies that integrate numerical modelling, field instrumentation, and microseismic monitoring.

This study focuses on evaluating the performance of destress blasting in deep hardrock mining environments, with an emphasis on Swedish mining operations. Although extensive research on destress blasting has been conducted in other mining regions such as Canada (Cullen, 1988; Labrie et al., 1997; O'Donnell, 1999; Tang, 2000; Sampson-Forsythe et al., 2002; Andrieux et al., 2003; Andrieux, Hadjigeorgiou, 2008; Sainoki et al., 2017; Vennes, Mitri, 2017; Yao et al., 2019; Drover , Villaescusa, 2019; Vennes et al., 2020, among others) and South Africa (Hill et al., 1966; Malan et al., 1997; Toper et al., 1997; Toper et al., 2000; Toper, 2002; Toper et al., 2003; Durrheim, 2010; Modisha, Zvarivadza, 2015; Sengani, Amponsah-Dacosta, 2018; Sengani, Zvarivadza, 2018a; Sengani, Zvarivadza, 2018b; Zvarivadza, Sengani, 2018; Sengani et al., 2019; Sengani, 2020a; Sengani, 2020b, among others), where several important regional differences must be recognised. Even though valuable knowledge has been gained from extensive studies conducted in South African gold mines and Canadian hardrock operations, the direct application of these strategies to the deep hardrock mining environment of Sweden remains underexplored. Differences in geological structure, rockmass competency, stress regimes, and seismic response necessitate careful regional adaptation of destress blasting principles. Despite global advancements, there remains a critical research gap, i.e., a lack of systematically validated methodologies for evaluating the effectiveness of destress blasting under the unique geological and operational conditions found in Swedish deep hardrock mines. Current numerical modelling and field monitoring techniques are not fully calibrated to the rockmass conditions typical of Sweden. Without adaptation and validation, existing methods risk misrepresenting destress blasting performance, compromising operational safety and efficiency. The study aims to address the research gap by investigating the most suitable methodologies for assessing the efficiency of destress blasting under deep underground mining conditions. Comprehensive evaluations of destress blasting must consider both site-specific geological conditions and evolving monitoring technologies to ensure that methods are appropriately adapted for local conditions.

Some research gaps in the literature

Destress blasting has been studied in various underground mining contexts, with notable contributions focusing on its theoretical basis, practical implementation, and field performance assessments. Early studies by Roux et al. (1957), Cook et al. (1966), and Blake (1972) introduced the concept of preconditioning brittle rockmasses to mitigate rockburst hazards. Subsequent investigations expanded on these ideas, developing numerical models and field validation techniques to refine destress blasting strategies (Miao et al., 2022; Tang, Mitri, 2001; Andrieux, Hadjigeorgiou, 2008). These studies highlighted key design considerations, including the need for appropriate explosive energy levels, borehole configurations, and initiation sequences tailored to specific geological settings. One of the primary research gaps in existing literature is the challenge of accurately predicting and quantifying the effectiveness of destress blasting. Traditional evaluation methods have relied on empirical observations and qualitative assessments, which, in as much as informative, lack the predictive capability needed for optimising blast designs in variable rockmasses. More recent studies have employed numerical modelling approaches, such as finite element and discrete element methods, to simulate stress redistribution and fracture propagation resulting from destress blasting (Miao et al., 2022). These models, however, often require extensive calibration and may not fully capture the complex interactions between pre-existing geological discontinuities and blast-induced stress waves. Another limitation in current research is the lack of standardised criteria for assessing destress blasting performance. Various metrics have been proposed, including stress dissipation factors, elastic modulus reductions, and seismic event reductions (Sainoki et al., 2017). The relative effectiveness of these criteria remains an open question, particularly in the context of different mining environments. Despite microseismic monitoring having been used to track stress changes following destress blasting, there is a need for more refined methodologies that integrate multiple data sources, including geophysical surveys, borehole imaging, and numerical simulations to provide a comprehensive evaluation framework.

Scope, objectives, and contributions of the study

This study systematically evaluates the effectiveness of destress blasting in deep hardrock mining, with a specific focus on Swedish underground mines. It consolidates existing research on numerical modelling, evaluation techniques, and seismic monitoring to understand how destress blasting influences stress redistribution, rockmass response, and seismic hazard mitigation. The study critically reviews numerical simulations, comparing static and dynamic approaches, and assesses key design parameters such as blast energy distribution and borehole spacing. It also examines geophysical monitoring techniques, including ground penetrating radar (GPR) and borehole periscopes, as well as seismic monitoring and rockburst prediction criteria, such as the strain energy storage coefficient (F), brittle shear ratio (BSR), and burst potential index (BPI). The study also proffers some innovative destress blasting evaluation methods incorporating geostatistics, machine learning, and IloT real-time monitoring. The research highlights optimisation strategies and decision-support frameworks that improve destress blasting applications, drawing crucial perspectives from global best practices. The study provides a structured framework for evaluating destress blasting efficiency by integrating theoretical models, empirical assessments, and monitoring techniques. Unlike previous studies that focus solely on numerical simulations or qualitative observations, this research offers a holistic approach to stress relief assessment. It also addresses a critical knowledge gap by exploring how destress blasting can be adapted to the unique geological and operational conditions of Swedish hardrock mines. The findings of the study contribute to improved safety, enhanced operational decision-making, and the refinement of destress blasting strategies in deep underground mining.

Several approaches can be used to evaluate the efficiency of a particular chosen destress blasting design. Most of the approaches are based on the ability of the destress blast design to reduce rockburst potential (burst proneness) of the rockmass or on reducing the deformation potential of the exposed rockmass after excavation. Each approach utilises key parameters associated with rock bursting which can be assessed before and after the implementation of a chosen destress blasting design. This paper dwells on those key evaluation techniques, which are suitable for informing destress blasting practice in deep underground hardrock mines of Sweden. It is of significant importance to use at least two different approaches to assess the performance of a destress blast design to consolidate confidence in the performance results. Through its coverage, the study contributes to the scientific advancement of stress management strategies in deep mining and offers practical guidance for safer, more efficient underground operations in Sweden.

 

Numerical modelling

Calibrated numerical models can be used to assess the efficiency of an adopted destress blast design. Several researchers have evaluated destress blast designs through numerical modelling with a view to select the most appropriate design for a given set of mining conditions (Sui et al., 2025; Shnorhokian, Ahmed, 2024a; Hashemi et al., 2023; Fulawka et al., 2022a; Hashemi, Katsabanis, 2021; Baranowski et al., 2019; Drover et al., 2018; Sainoki et al., 2017; Andrieux, Hadjigeorgiou, 2008; Saharan, 2004; Toper et al., 2003; Toper, 2003; Borg, 1988). The discussion on numerical modelling and its application in destress blasting can be lengthy and extensive. Miao et al. (2022) provide a detailed review on the application of numerical modelling in destress blasting design. They note that two main modelling approaches, static and dynamic, are commonly employed - the static method modifies rockmass properties to simulate destress blasting's impact, while dynamic modelling focuses on the dynamic fracture process without predetermined damage. Miao et al. (2022) compare different destress blasting numerical methods, including continuum-based, discrete-based, and coupled methods. Other key issues they explore in detail include the fracture mechanism, the assessment of destress blasting designs efficiency, factors influencing destress blasting efficiency, and highlighting the challenges and difficulties associated with numerical modelling of destress blasting.

To maintain some modicum of brevity of this paper, and yet cover key issues relevant to the successful use of numerical modelling as a destress blasting design tool, this discussion zooms into and shines the spotlight on the work by Sainoki et al. (2017). Sainoki et al. (2017) warn that traditional ways of modelling, which assume that the damage zone induced by the destress blast is spread over the whole drift face, may result in overly optimistic destress blast results. This is a result of the models predicting lower than actual resultant stresses around the destressed excavation due to oversimplification of the prevailing conditions in the rockmass. To mitigate against this, Sainoki et al. evaluated the damage of each hole individually and obtained results more reflective of prevailing ground conditions after preconditioning as shown in Figure 1. Assessing the major principal stress (MPa) legend for each case in Figure 1, it can be noted that the stress drops from a maximum of 226 MPa (before destressing) to 186 MPa and 218 MPa for the traditional modelling case and the individual damage zones modelling case, respectively. This shows that the traditional modelling approach potentially underestimates the peak major principal stress after destress blasting by about 32 MPa in this scenario.

In the destress blast evaluation model, Sainoki et al. (2017) adopted the concept of rock fragmentation factor (α) as introduced by Blake (1972) as per Equation 1. The concept is based on the observation that effective destress blasting leads to the widespread extension of cracks existing in the rockmass and generation of more cracks, leading to significant stress release ahead of the mining drift face. When this happens, the stiffness of the rockmass is significantly reduced, resulting in a much lower elastic modulus compared to the elastic modulus before destressing. As expected, the elastic modulus of the rockmass remains about the same when α is 1; that is when there is little to no rock fragmentation before destressing. In rock mechanics terms, the softening of the rock due to action of stress can be explained using the Poissons effect. The changes in Poissons ratio before and after destress blasting, can also be used to evaluate the efficiency of a destress blast design using Equation 2, as suggested by Tang and Mitri (2001) as per Equation 2. When α is 1 (relatively unfragmented rockmass), the Poissons ratio remains almost the same. When α is 0 (significantly fragmented rockmass) the Poissons ratio almost doubles, signifying enormous softening of the rockmass due to destress blasting.

Sainoki et al. (2017) also use the concept of stress dissipation factor (β) developed by Tang and Mitri (2001) to assess destress blasting efficiency (Equation 3). When β is 1, the stress after destress blasting is reduced significantly, showing that the high stress in the immediate vicinity of the mining drift face has been dissipated further into the rockmass, reducing the strain burst risk at the mining face. β = 0 indicates a scenario where the stress in the immediate vicinity of the mining face remains the same, that is when no destress blasting is employed.

Practical implementation of several different destress blasting designs is obviously expensive, hence the need to simulate several different destress blasting designs using numerical models to select a few suitable designs to physically assess. The field tests of the few suitable designs allow practitioners to choose the best destress design for a particular set of geotechnical ground conditions. Numerical modelling in three dimensions facilitates the identification of underground areas prone to rockbursts and, therefore, aids design of underground constructions (Kaiser, Cai, 2012). It cannot be overemphasised that numerical modelling to prescribe a suitable destress blasting design for a deep hardrock mining operation should follow the standard modelling stages (procedure) posited by Starfield and Cundall (1988).

Although Sainoki et al. (2017) introduced the fragmentation factor (α) and stress dissipation factor (β) to improve evaluation metrics, studies rarely integrate these parameters into real-time, adaptive modelling frameworks. The present study identifies this as a critical gap: linking numerical outputs (e.g., changes in α and β) dynamically to field-monitored seismic responses would enable feedback-driven optimisation of destress blast designs, something not yet systematically achieved. Cross-validation of numerical predictions with independent fracture network imaging data, such as GPR or borehole periscope observations is critical for a successful destress blasting strategy. This study supports multi-modal integration, combining numerical simulations with direct fracture monitoring and seismic monitoring trends, to holistically evaluate and refine destress blasting performance.

 

Rock fracture monitoring ahead of the excavation face

To gauge the effectiveness of an adopted, destress blast design, evaluation of the rock fracturing ahead of the mining face after the blast can be done. Effective destress blasting is expected to create or extend a network of fractures, which connect the destress blast holes sockets. This is critical in assessing the most optimum number of destress blast holes to use with the production blast holes. The network of fractures provides a path for stress relief from the immediate vicinity of the blasting face into the rockmass. The intensity of fracturing ahead of the mining face due to destress blasting can be assessed using ground penetrating radar, borehole periscope, and mining face drill core discing observations.

Ground penetrating radar

In the study of application of destress blasting to manage seismic hazard, Sengani (2020b) noted that the application of the ground penetration radar technique successfully identified seismic and non-seismic hazards, which could not be identified through conventional and numerical evaluations in hardrock mining. Fractures in the rockmass and geological structures causing breaks in the rockmass (discontinuities) can be picked using the GPR technique. The technique uses the geophysics approach of rapidly reflecting electromagnetic waves at high resolution in a scan target, providing vital information about the distribution of rockmass discontinuities. Sengani (2020b) provides an account of the theoretical background of the GPR and its application in different fields including archaeology, construction, mining, exploration of gem tourmaline pockets and vugs, identification of voids and cavities, and assessing the depth of a water table. Sengani (2020b) notes that, although GPR has been extensively used in mining and construction (Shi et al., 2018; Liu et al., 2018; Hao et al., 2016; Fedorova et al., 2016; Moradipour et al., 2015; Ralston, Strange, 2015; Lualdi, Zanzi, 2004; Jha et al., 2004; Toper et al., 1999; Ralston et al., 2001; Ralston, Hainsworth, 1999; Hainsworth et al., 1999; Murray et al., 1996; Noon, 1996; Chufo, Johnson, 1993; Mowrey et al., 1995; Daniels, 1980; Ellerbruch, Belsher, 1978), there are few studies, which cover particular application of GPR in rock mechanics (Sengani et al., 2019; Sengani, Zvarivadza, 2018a; Sengani, Amponsah-Dacosta, 2018; Sengani, Zvarivadza, 2017; Toper, 2003; Grodner, 2001). Figure 2 illustrates the application of GPR in a mining tunnel.

The GPR equipment is shown in Figure 3.

 

 

The intensity of fracturing ahead of the mining face can be assessed using colour coding, as presented in Table 1. Dark red colour indicates a fracture frequency of more than 20 fractures per metre and dark blue colour represents a fracture frequency of less than 5 fractures per metre.

 

 

Example GPR scans for destress blasting using four destress blast holes and five destress blast holes on the mining face for deep level hardrock mining in South Africa are presented in Figure 4 and Figure 5, respectively. Using Table 1 to interpret the results, it can be seen that more intensive fracturing ahead of the mining face is achieved when using five destress blast holes compared to four destress blast holes.

 

 

 

 

It then becomes critically important to choose the optimum number of destress blast holes or blast hole spacing to achieve optimum destress blasting results. It should be noted that using more holes than necessary leads to unnecessary costs and waste of time in drilling the additional holes.

Understanding both the advantages and disadvantages of using GPR allows for a balanced assessment of its suitability in specific underground mining contexts. Consideration of geological conditions, expertise in data interpretation, and the specific goals of the mining operation are crucial in determining the effectiveness of GPR for assessing rock fracturing ahead of the mining face. The advantages and disadvantages of GPR are presented in Table 2.

A close look at some practical applications and case studies demonstrating GPR effectiveness

GPR has been increasingly applied in underground mining environments to assess rockmass fracturing, evaluate the effectiveness of destress blasting, and detect hazardous geological structures that conventional methods often miss. Several practical examples demonstrate the reliability and operational value of GPR for these purposes. One notable application was documented in great technical detail at Mponeng Mine (in South Africa), one of the deepest hardrock gold mines in the world, by Thebethe, (2018). Van Schoor et al. (2022) report on the successful application of GPR in South African deep platinum and gold mines to detect and characterise fracture intensities ahead of mining faces. Momayez et al. (1996) present the application of GPR in Canadian mines to enhance safety, reduce costs, and improve productivity. They note that, in the Kidd Creek base metal mine, GPR was used to monitor the stability of a sill pillar, detect sulphide pockets, assess rockfill quality, and identify voids or fractures in filled areas. The technology proved effective in mapping geological structures and evaluating the integrity of underground supports, such as culvert systems (Momayez et al.,1996). These case studies, among many others, collectively validate GPR as a practical, non-destructive, and real-time method, which can be used for evaluating destress blasting effectiveness through rockmass fracturing characterisation and guiding proactive ground control strategies. Although challenges such as signal attenuation in conductive environments remain, advances in GPR antenna technology and data processing have significantly improved its penetration depth and resolution, making it an essential tool in modern deep mining operations.

Borehole periscope

The GPR scan results can be verified using the technique of borehole camera. Rock fracturing ahead of the mining face can also be evaluated using a borehole periscope (camera). The technique can be used to verify the results of the GPR scans. This is a distinctly valuable alternative ,which can be used to detect some of the errors, which may occur due to a malfunctioning GPR. The reverse also applies in verifying borehole camera results using the GPR technique. The borehole camera can detect the fracture frequency from the collar of a borehole all the way up to the end of the borehole. Integrating borehole cameras with GPR has emerged as a powerful method to improve subsurface interpretation in deep underground hardrock mining. In spite of the fact that GPR provides broad detection of geological structures, it is prone to false anomalies due to environmental interference (Dang et al., 2018). Borehole cameras offer high-resolution visual imaging that verifies and refines GPR interpretations by directly observing fractures and discontinuities (Zou et al., 2021; Yuan et al., 2023). This combination addresses the calibration challenges of GPR, enhancing the certainty of features like fault zones and supporting more accurate rockmass modelling (Kulich, Bleibinhaus, 2020). Borehole camera data also enable advanced image processing and automated analysis, further improving reliability (Zou et al., 2021). This integrated approach strengthens subsurface characterisation, optimises mining support design, and enhances operational safety by cross-validating anomalies detected by GPR (Yuan et al., 2023). Together, the methods overcome the individual limitations of each technology, offering a reliable framework for managing geological risks in deep mining environments (Dang et al., 2018; Zou et al., 2021; Yuan et al., 2023; Kulich, Bleibinhaus, 2020).

Sengani and Zvarivadza (2018a) present the application of borehole periscope in the assessment of rock fracturing ahead of deep level hardrock mining faces destressed using destress blasting. Figure 6 illustrates borehole periscope observations of rock fracturing ahead of a mining pillar face destressed using destress blasting. The interpretation of the fracture frequency relative to rockburst potential of the mining face can be done using the criteria suggested by Sengani and Zvarivadza (2017) in Table 3.

 

 

 

 

As expected, the fracturing is high in the immediate vicinity of the pillar face and decreases towards the pillar core. Table 4 gives the advantages and disadvantages of the borehole camera method.

Drill core observations

The degree of fracturing ahead of the excavation face can also be observed by collecting drill core from the excavation face before and after destress blasting and analysing it, as per Figure 7.

 

 

The advantages and disadvantages of the drill core observations method are presented in Table 5.

Key insights on evaluating destress blasting performance using rock fracture monitoring

The evaluation of rock fracturing ahead of the excavation face is a critical aspect in the assessment of destress blast designs within underground mining operations. Various techniques are employed to understand the effectiveness of these designs, each offering unique perspectives into the condition of the rockmass. The discussion revolves around three primary methods: GPR, borehole periscope, and drill core observations.

GPR emerges as a non-destructive testing method, allowing for the assessment of rock fracturing without causing any damage to the rockmass. Its real-time imaging capabilities and high resolution provide detailed evaluation of rock fractures and structures, facilitating accurate characterisation. One of its key advantages lies in its versatility, making it applicable in various geological settings and rock types. Studies show that GPR can be used accurately to identify rock fracturing and geological discontinuities in a rockmass, thereby serving as a reliable tool to assess destress blast design effectiveness for deep underground mining. Limitations come to light in conditions characterised by high conductivity or attenuation, hindering its depth penetration capabilities. Challenges in resolution variability based on frequency and geological conditions, also pose complexities in data interpretation. The accuracy of GPR is influenced by surface roughness, affecting the interaction of radar signals with fracture surfaces.

The borehole periscope serves as a valuable alternative, providing direct visual access to the interior of the borehole for real-time observation and assessment of rock conditions. Its detailed inspection capabilities offer high-resolution imaging, enabling a focused examination of potential fracture zones and geological anomalies. Despite its advantages, limitations arise from its dependence on borehole accessibility. In situations where boreholes are sparse or difficult to access, the coverage and applicability of this method may be constrained. The risk of equipment damage or loss also poses challenges, impacting reliability and lifespan.

Drill core observations involve direct sampling of in situ rock from the mining excavation face. This method offers high-quality geological information, allowing for a detailed analysis of rock types, structures, and fracture patterns. Quantitative assessments of fractures, including orientation, density, and size distribution, provide valuable knowledge. Integration with geophysical data enhances the accuracy of rock fracturing assessments. Restrictions come from the disruption caused by the drilling process, potentially altering fracture patterns and stress conditions. The process is also relatively costly and time-intensive, with delays in obtaining real-time data. The spatial coverage of drill core samples is limited, making it challenging to extrapolate findings to broader areas of the mining face.

Each method, despite providing valuable information, comes with its own set of advantages and disadvantages. A critical consideration is the specific context of the underground mining operation. For instance, GPR is lauded for its non-destructive nature and versatility, yet its limitations in certain geological conditions must be acknowledged. Borehole periscope, with its direct visual access, proves advantageous in targeted analysis but faces challenges in accessibility and equipment fragility. Drill core observations offer high-quality geological information but are constrained by their cost, time intensity, and limited spatial coverage.The choice of technique is not a one-size-fits-all decision but depends on the goals, geological conditions, and safety objectives of the mining operation. Combining these methods can offer a more comprehensive understanding of the rockmass, mitigating the limitations of individual techniques. It is crucial to recognise that accurate interpretation of data from these methods requires expertise, and challenges may arise in distinguishing fractures from other geological structures, especially in complex conditions.

The significance of considering environmental factors, such as water content in the rockmass, cannot be overstated. The performance of these methods, particularly GPR, may be influenced by factors like water saturation, impacting their effectiveness. The issue of electromagnetic interference is also raised, where external sources like power lines can compromise the quality and reliability of data obtained. Such considerations highlight the need for a holistic approach that incorporates not only the technical aspects of these methods but also the external conditions that may affect their performance.It can be noted that the evaluation of rock fracturing ahead of the mining face is a multifaceted task that requires a careful balance between various techniques. The advantages of non-destructive testing, real-time imaging, and high-resolution analysis must be weighed against limitations in depth penetration, resolution variability, and interpretational challenges. The application of these methods should align with the specific goals of the mining operation and the geological context, ensuring a comprehensive and reliable assessment of rockmass conditions.To further assist readers in appreciating the relative strengths and limitations of different rock fracture monitoring methods, a detailed side-by-side technical comparison of ground penetrating radar, borehole periscope inspections, and drill core observations is presented in Table 6.

This structured comparison enables a clearer understanding of how each method complements the others and informs the choice of evaluation techniques in practical deep mining environments.

The comparison clearly shows that no single method is universally superior; rather, each offers distinct technical advantages under different operational conditions. An integrated monitoring approach, taking the advantages of the rapid survey capabilities of GPR, the detailed visualisation offered by borehole periscopes, and the geological richness captured in drill cores, is recommended for achieving a robust, high-confidence assessment of rock fracture networks following destress blasting interventions.

 

Use of rockburst prediction criteria

Calculation of the energy balance in terms of the strain energy stored in the rockmass and the energy released by the rockmass enables rockburst control practitioners to determine the propensity of the rockmass to burst. In view of destress blasting, the energy balance can be determined before and after destress blasting to evaluate the efficiency of the destress blast design.

From the work of his research team conducted over many years, together with observations from several other expert researchers, Mitri (2022) provided three key rockburst criteria, which can be used to assess the efficiency of destress blasting in deep underground hardrock mining, which are F, BSR, and BPI. Each criterion provides valuable perspectives into the potential for rockmass failure and aids in optimising destress blasting strategies for enhanced safety and efficiency. The technical details and motivation behind the use of F, BSR, and BPI in evaluating destress blasting design performance revolve around their capabilities to quantify energy storage, assess brittleness, and provide a comprehensive index of rockmass properties. These criteria, when integrated and monitored over time, enable practitioners to make informed decisions to prevent or mitigate the risk of rockbursts in underground mining operations through the design of efficient destress blasts. The three are discussed as follows.

Strain energy storage coefficient (F)

The principle of F is premised on the observations of strain energy (area under a stress-strain curve of a rock sample) changes when a rock sample is uniaxially compressed to 80% of its maximum uniaxial compressive strength and the load is released gradually to zero. Since loading the specimen to 0.8UCS makes transition from elastic behaviour to plastic behaviour, permanent damage is experienced by the specimen, leading to strain energy being spent on plastic deformation (represented as W_sp (area OAC) in Figure 8). The total strain energy generated in the specimen when it is uniaxially compressed to 0.8UCS is represented as W_tot (area OAB in Figure 8). The strain energy stored in the specimen (W_st) is the difference between Wtot and W_sp. From the work of Kidybinski (1981) and Wang et al. (1998), F is defined as the ratio of Wst to W_sp and Equations 4 to 7 are used in the formulation of the ratio using Figure 8.

 

 

The criterion for judging rockburst intensity based on F is given in Table 7.

 

 

F serves as an indicator of the energy that can be released during a rockburst. Higher values of F imply a greater potential for energy release, signalling a higher risk of rockmass failure. Monitoring changes in F before and after destress blasting helps evaluate the effectiveness of the blasting in reducing strain energy storage. A decrease in F indicates successful energy dissipation, contributing to rockburst prevention.

Despite the fact that F provides critically valuable information into the propensity of a rockmass to release stored strain energy during failure, it is important to recognise that the determination of F is sensitive to sampling variability and the inherent differences between laboratory testing conditions and actual in situ field conditions. Sampling variability arises due to the natural heterogeneity of rockmasses. Laboratory specimens are often extracted as relatively small, intact core samples that may not fully represent the large-scale variability found underground. Factors such as mineral composition, grain size distribution, microfracturing, alteration zones, and anisotropy within the rockmass may not be adequately captured in the limited volume of laboratory samples. Laboratory-derived F values might either underestimate or overestimate the energy storage potential of the broader rockmass as a result, depending on whether the sampled cores are stronger or weaker than the average in situ conditions. Laboratory tests typically impose idealised loading conditions, such as uniform uniaxial compression at controlled strain rates, that differ significantly from the complex, multi-axial and time-dependent stress regimes experienced in situ. Stress states are influenced by excavation-induced stress concentrations, seismic wave propagation, and long-term creep behaviour in the field, which are not easily replicated in laboratory environments. Laboratory tests are performed on relatively dry samples, whereas in situ rockmasses may be saturated or partially saturated, further altering their mechanical response and energy storage behaviour.

The transition from laboratory to field conditions therefore introduces uncertainty into the direct application of F values. It is recommended to use a statistically significant number of samples for laboratory testing to mitigate this, capturing a broad spectrum of geological variability. Laboratory-derived F values should ideally be calibrated against observed field behaviour, such as the incidence and severity of rockbursts, microseismic energy release patterns, and measured deformation responses. Integrating F with in situ stress measurements, seismic monitoring, and numerical modelling in high-risk areas enhances the robustness of destress blasting design and rockburst risk assessments. Acknowledging these limitations ensures that the interpretation of F is contextually appropriate, and that risk management strategies based on F are both technically defensible and operationally effective in complex deep mining environments.

The advantages and disadvantages of using F for rockburst prediction, and destress blasting design and evaluation are given in Table 8.

Brittle shear ratio (BSR)

Adopting the work of Castro et al. (1997) on rockmass damage initiation, Mitri (2022) explains the application of BSR in hardrock mining to evaluate rockburst potential. The key aspects of the criterion are summarised as follows:

Where:

σ₁ is the major principal stress.

σ₃ is the minor principal stress.

UCS is the uniaxial compressive strength.

The rockburst propensity using the BSR criterion is assessed using potential for strain bursting as shown in Table 9.

 

 

BSR values less than 0.35 suggest no to minor rockmass damage, with no potential for strain bursting. As BSR values increase, the potential for strain bursting escalates, moving from minor to major damage categories. Before destress blasting, BSR can be calculated based on stress conditions and rock properties. This pre-blasting assessment sets a baseline for evaluating the effectiveness of destress blasting in altering the stress state and reducing the potential for brittle failure. After destress blasting, changes in BSR can be monitored. A decrease in BSR indicates a reduction in the potential for brittle failure, suggesting successful stress redistribution and a lowered risk of strain bursting. BSR values guide the optimisation of destress blasting parameters. Adjustments can be made to the blasting design to achieve the desired reduction in BSR, aligning with safety objectives and minimising the risk of rockbursts.

BSR has proven to be an effective indicator for assessing rockburst potential in hardrock mining environments, yet it is critical to acknowledge that its reliability is strongly context-dependent. The BSR calculation fundamentally relies on accurate measurements of the major and minor principal stresses (σ₁ and σ₃) and the uniaxial compressive strength (UCS) of the rockmass. In deep level mining, obtaining precise in situ stress measurements presents considerable challenges due to complex geological structures, variable rockmass properties, and operational constraints. In situ stress fields in deep hardrock mines are often highly heterogeneous in practice, influenced by factors such as lithological layering, faulting, historical mining-induced stress redistributions, and natural anisotropies. Techniques commonly used to measure in situ stresses, such as overcoring, hydraulic fracturing, and borehole slotter methods, are each subject to inherent limitations, measurement errors, and assumptions that may not fully capture the true stress state, especially at great depth (Ljunggren et al., 2003; Gaines et al., 2012; Lin et al., 2018; Li et al., 2024; Li et al., 2025). These uncertainties directly propagate into BSR calculations, affecting the accuracy of the predicted rockburst proneness.

BSR values are also sensitive not only to the absolute magnitudes of principal stresses, but also to the stress ratio between σ₁ and σ₃. Localised stress concentrations, stress shadow effects from nearby excavations, and time-dependent stress changes can all skew the stress measurements, thereby impacting the calculated BSR. Caution must be exercised when interpreting BSR in isolation, particularly in geologically complex or seismically active areas. To enhance the robustness of BSR-based assessments, it is recommended that BSR be used alongside multiple independent data sources. These may include microseismic monitoring data, numerical modelling outputs (e.g., elastic or elastoplastic stress models), mapped fracture orientations, and observational records of rockmass behaviour (e.g., spalling, slabbing, or strainbursting). Sensitivity analyses, considering plausible variations in stress magnitude and orientation, are also vital to quantify the impact of uncertainty on BSR predictions. It can be noted that BSR remains a valuable and practical tool for evaluating brittle failure potential in underground mines, but a clear understanding of its context dependence and sensitivity to in situ stress uncertainties is essential. Integrating BSR assessments with other empirical, observational, and numerical techniques provides a more comprehensive and justifiable approach to managing rockburst hazards in deep mining environments.

Several studies shed more light on the concept of BSR, coverage of which is significant to destress blasting studies. These studies include the following: Rojas-Perez et al., 2024; Shnorhokian, Ahmed, 2024a; Kabwe et al., 2023; Zhang et al., 2023b; Shnorhokian, Mitri, 2022; Vennes, Mitri, 2022; Heidarzadeh et al., 2021; Heidarzadeh et al., 2020; Vennes et al., 2020; Sainoki et al., 2019; Vennes, 2019; Zhou et al., 2018; Shnorhokian et al., 2018; Vennes, Mitri, 2017; Vennes, Mitri, 2016; Sainoki et al., 2016; Shnorhokian et al., 2015; Shnorhokian et al., 2014. The advantages and disadvantages of using BSR for rockburst prediction and destress blasting design and evaluation are presented in Table 10.

Burst potential index (BPI)

The concept of energy balance can be used to evaluate the burst potential of the rockmass as a result of stress redistribution when destress blasting is implemented. This entails manipulation of the energy balance equations to ascertain the amount of energy available to induce rockbursting. Several studies on rockburst prediction based on energy balance evaluation have been done and are crucial to destress blasting design research (Watson et al., 2025; Gao et al., 2024; Zhang et al., 2023b; Luo, Gong, 2023; Askaripour et al., 2022; Li et al., 2022; Papadopoulos, Benardos, 2021; Gao et al., 2020; Khademian, Ozbay, 2019; Zhao et al., 2019; Khademian, Ugur, 2018; Zhou et al., 2018; Hamdi et al., 2017; Mazaira, Konicek, 2015; Qiu et al., 2014; Sirait et al., 2013; Lafont et al., 2013; Konicek et al., 2013; Al Heib et al., 2013; Wattimena et al., 2012; Brady, Brown, 2006; Tang, 2000; Tang, Mitri, 2001; Tajdus et al., 1997; Mitri et al., 1993; Minney, Naismith, 1993; Hedley, 1992; Boldstad, 1990; Salamon, 1984, 1983, 1974,1970; Walsh, 1977; McMahon, 1988; Budavari, 1983; Ivanov et al., 1980; Cook et al., 1966, 1978). The rockburst potential is influenced by excavation geometry and stress changes. Studies by Ortlepp (1983) show that the rockmass behaves elastically in rockburst scenarios. Mitri et al. (1999) note that, due to this elastic behaviour, the energy changes in the rockmass as a result of rock bursting can be modelled using elastic constitutive laws. The use of elastic laws to evaluate the energy available to induce rock bursting is conservative, since the energy used in fracturing the rock is not accounted for, therefore overestimating the energy available to induce rock bursting (Mitri et al., 1999). For practical purposes, Mitri et al. (1999) provide the following formulation (Equation 9), which can be used to evaluate the burst potential of a rockmass due to stress and excavation geometry changes.

Where:

ESR is energy stored in the rock as determined through numerical modelling.

e_c is the critical energy capacity of the rock.

σ_c is uniaxial compressive strength.

Strain bursting is highly likely to occur when the BPI is greater than 100%.

In as much as BPI offers a powerful quantitative method for assessing the propensity of rockmass failure based on the energy balance approach, it is important to recognise that its accuracy is inherently dependent on the reliability of the numerical modelling inputs used to estimate the energy stored in the rockmass (ESR). Uncertainties in critical input parameters, including in situ stress magnitudes and orientations, mechanical properties such as Young's modulus, Poissons ratio, uniaxial compressive strength (UCS), and rockmass anisotropy, can significantly influence the predicted energy accumulation and dissipation within the rockmass. Slight variations in measured UCS or elastic modulus due to sample heterogeneity, scale effects, or laboratory testing variability, as an example, can lead to notable differences in modelled stress fields and therefore the computed ESR values. Geological complexities such as unaccounted for joint sets, faults, or heterogeneous lithological transitions, may not be fully captured in the numerical model, introducing further uncertainties into the energy distribution predictions. Boundary conditions, assumed failure criteria (e.g., Mohr-Coulomb versus Hoek-Brown), and simplifications in material behaviour (linear elasticity versus strain-softening) within the model can introduce systematic biases. These factors can result in either underestimation or overestimation of BPI values, potentially affecting the assessment of burst proneness and leading to either excessive conservatism or overlooked hazards. Recognising these limitations, it is recommended that BPI assessments be supported by careful sensitivity analyses, wherein the impacts of key parameter variations are systematically evaluated. Probabilistic modelling approaches and back-analysis of historical seismic events, where feasible, can also help in refining the input parameters and reducing uncertainty. BPI results, ultimately, should not be interpreted in isolation but rather integrated with complementary observational data, such as microseismic monitoring trends, rock fracture mapping, and field-measured stress changes, to provide a balanced and robust evaluation of rockburst risk following destress blasting. Acknowledging and appropriately managing input uncertainties is critical for ensuring that BPI-based assessments remain a reliable and practical tool for rockburst hazard management in deep level mining operations.

BPI provides a holistic assessment of the rockmass, considering multiple mechanical properties. This comprehensive approach enhances the predictive capability of rockburst potential. Evaluating changes in BPI before and after destress blasting aids in optimising blasting parameters. A decrease in BPI signifies positive modifications in the rockmass properties, indicating successful destress blasting. The advantages and disadvantages of using BPI for rockburst prediction and destress blasting design and evaluation are presented in Table 11.

Key insights on evaluating destress blasting performance using rock burst prediction criteria

Rockburst prediction criteria can be handy in the design of efficient destress blasting. Some of the key rockburst prediction criteria applicable to hardrock mining, as presented by Mitri (2022), are F, BSR, and BPI. The energy balance method is pivotal in the assessment of the burst potential of underground mining excavations. It involves assessing the strain energy stored in the rockmass against the energy released, aiding in the determination of rockmass propensity to burst. The three criteria presented by Mitri (2022) are derived from this method, offering a holistic approach to evaluating destress blasting efficiency. Selected practical examples from deep mining operations are integrated in this discussion to further illustrate the applicability of these criteria in evaluating destress blasting performance.

F is a crucial indicator representing the ratio of strain energy stored to that spent on plastic deformation during uniaxial compression. High F values suggest a greater potential for energy release, signifying a higher risk of rockmass failure. Monitoring F changes post-destress blasting aids in assessing its effectiveness. The advantages include quantitative energy assessment, sensitivity to rockmass changes, and serving as an early warning system. Challenges arise from its complexity of calculation, dependency on sample quality, and limited spatial coverage.

In the Sanshandao gold mine case study conducted by Cai (2016), F was applied to evaluate five rock types, resulting in the classification of four rock types as having strong rockburst potential and one as medium. The findings aligned with other indices (e.g., brittleness coefficient, burst energy coefficient), confirming the high rockburst risk of the mine and, showing that F serves as a predictive tool, enabling proactive rockburst prevention in deep mining operations (Cai, 2016). Targeted destress blasting can successfully reduce F values in rockburst prone zones, and subsequent operational records can confirm this by showing a marked decline in burst events to validate F as a practical tool for pre- and post-blasting evaluation.

Rockburst potential is assessed by BSR based on the ratio of major to minor principal stress over the uniaxial compressive strength. It categorises potential for strain bursting from 'no' to 'major.' BSR proves sensitive to stress conditions, allowing real-time monitoring and dynamic optimisation of blasting parameters. Its challenges involve dependency on UCS, limited spatial coverage, and potential oversimplification of rockmass behaviour. Vennes et al. (2020) employed the BSR technique (creating fractured panels to dissipate stress in ore pillars, reducing local stiffness and stress concentrations) to evaluate the effectiveness of large-scale destress blasting for their case study of the Copper Cliff mine. Numerical modelling, validated by field stress measurements, showed that destress blasting lowered BSR values in targeted zones. In one stope, they noted that BSR decreased from 0.93 to 0.87 after blasting, reducing the volume of high-risk ore (BSR > 0.7) from 15% to 7.7% (Vennes et al., 2020). The practical application of BSR lies in guiding blast design and mining sequences. Mining practitioners can optimise destress blasting parameters (e.g., explosive energy, panel geometry) by monitoring BSR changes to ensure stress relief and safer extraction. The study by Vennes et al. (2020) confirmed that BSR, combined with other indices like the BPI, provides a reliable framework for rockburst risk management in deep, high-stress mining environments.

BPI evaluates burst potential by considering energy storage, excavation geometry, and rock properties. It provides a comprehensive assessment, incorporating multiple mechanical properties. Decreases in BPI post-destress blasting indicate positive modifications in rockmass properties. Advantages include a holistic evaluation and consideration of excavation geometry, while challenges include complexity in numerical modelling, potential conservatism, and difficulties in field implementation. One effective application of the BPI is demonstrated in the study by Xu et al. (2022), which evaluates rockburst tendencies in the context of sill pillar recovery strategies using finite element modelling. They reveal that the assessment of tangential stress, alongside the BPI, provides more clarity into the stability of stope accesses during recovery operations, thereby facilitating safe mining practices. Verma et al. (2024) propose an energy-based strain burst criterion that integrates stress conditions and material characteristics. They introduce a 'Burst Envelope' concept, which aids in understanding localised failure mechanisms, and develop a scalar burst index that quantifies burst potential. This approach underscores the significance of material properties and stress conditions in predicting rockburst events. Numerical simulations based on mining-induced stress redistribution can predict BPI values exceeding 100% in mining areas. Controlled destress blasts can be strategically deployed together with monitoring BPI values to decrease the BPI values below critical threshold. This intervention holds great potential to directly mitigate the risk of large-scale violent failures in high-extraction stopes and can demonstrate the operational value of BPI in guiding destress strategies.

The three criteria offer a comprehensive toolkit for assessing rockburst potential. F, BSR, and BPI, even alongside their advantages, share common challenges such as sensitivity to sample quality, limited spatial coverage, and the need for expertise in interpretation. It can be noted that a holistic approach, combining multiple criteria for a more robust assessment, is of significant importance. The practical application of these criteria in real-world mining scenarios is notable. Each criterion provides evaluation, yet the interplay between them and their integration into broader risk management strategies is crucial. Practical challenges, such as the complexity of calculations and limitations in field implementation, need to be addressed for effective use. The practical examples highlighted in this study collectively underline that rockburst prediction criteria such as F, BSR, and BPI are not just theoretical tools; they have proven, tangible application in real mining scenarios. Integrating these indices into routine mining practice, alongside numerical modelling and seismic monitoring, provides a powerful, validated methodology for optimising destress blasting and improving underground mine safety.

The extensive list of studies cited highlights the depth of research in this field. It also points to potential gaps, such as the limited integration of microseismic data and challenges in representing dynamic rockmass changes. Future research could focus on refining these criteria and addressing the evolving needs of mining operations. The rockburst prediction criteria offer a nuanced approach to destress blasting design. Recognising their challenges, their potential in enhancing safety and efficiency in underground mining operations is evident. A collaborative effort in further research and practical implementation is crucial for refining these criteria and ensuring their effectiveness in diverse geological conditions.

 

Seismic monitoring

Mining-induced seismic events can be monitored using seismic monitoring systems, enabling mining personnel to forecast rockburst hazards and effectively manage them (Durrheim, 2025; Rapetsoa et al., 2025; Wu et al., 2024; Zhang et al., 2023a; Li et al., 2023; Fulawka et al., 2022a; Fulawka et al., 2022b; Zhao et al., 2022; Feng et al., 2022; Wang et al., 2022; Wang et al., 2021; Zhang et al., 2021; Vennes et al., 2020; Rahimi et al., 2020, Ma et al., 2020; Dahnér, Dineva, 2020; Simser, 2019; De Santis et al., 2019; Wesseloo, 2018; Ma et al., 2018; Konicek, Waclawik, 2018; Brown, Hudyma, 2018, He et al., 2017; Sainoki et al., 2017; Sengani, Zvarivadza, 2017; Zhang et al., 2017; Glazer, 2016; Mendecki, Malovichko, 2010; Hudyma, Potvin, 2010; Durrheim, 2010; Hudyma, Brummer, 2007; Hudyma, Potvin, 2004; Blake, Hedley, 2003; Beck, Brady, 2002; Potvin, Hudyma, 2001; Duplancic, Brady, 1999; Mendecki, 1996; Hasegawa et al., 1989, among many others). Figure 9 and Figure 10 illustrate the distribution of micro seismic events before and after implementing destress blasting respectively for a deep underground hardrock mine.

 

 

 

 

It is evident from field observations that destress blasting is accompanied by significantly reduced seismic events at a local scale of the mine. A properly functional seismic monitoring system is a practical and handy tool to observe destress blasting efficiency. Noting the significant value of seismic monitoring, Simser (2019) argues that: A modern deep hardrock mine arguably should not operate without a seismic monitoring system. Simser's (2019) assertion is fundamentally accurate, still, it is important to provide a balanced discussion by acknowledging practical considerations such as costs, system limitations, and data resolution challenges. Seismic monitoring systems are invaluable for detecting stress redistribution, locating seismic sources, and tracking precursors to dynamic failures, thereby enabling proactive rockburst management. The installation, operation, and maintenance of high-quality seismic arrays, however, entail significant financial investment. Costs include not only the initial purchase of geophones, accelerometers, and associated hardware, but also the ongoing requirements for data processing infrastructure, specialist staff, calibration, and periodic system upgrades to maintain sensitivity and reliability. The spatial resolution and sensitivity of seismic monitoring systems are also inherently limited by the number and placement of sensors. Insufficient sensor density can result in large uncertainties in event localisation, magnitude estimation, and focal mechanism analysis, particularly in complex geological environments where wave propagation is heterogeneous. On top of that, distinguishing between mining-induced seismicity and background microseismic noise often demands sophisticated signal processing techniques and expert interpretation, increasing operational complexity. Technological advances such as micro electro mechanical system (MEMS) based accelerometers and machine learning enhanced data interpretation are improving capabilities, but challenges persist, especially for detecting low-energy microseismic events critical for early warning. It can be noted from this that, although seismic monitoring is imperative for deep level mines, a well refined perspective recognises that its deployment must be optimised relative to mine-specific risk profiles, operational budgets, and geotechnical conditions. A tiered approach is recommended: critical areas of the mine may warrant dense, high-resolution monitoring, while less active zones could be covered with lower-density arrays, ensuring effective, risk-prioritised seismic hazard management without excessive financial burden. Amid the clear value of seismic monitoring for managing rockburst risk and evaluating destress blasting effectiveness, it is equally important to critically consider the limitations of seismic systems, their complementary role alongside other monitoring techniques, and practical evidence of their integrated application in deep mining operations.

 

Limitations, complementary value, and practical evidence of seismic monitoring

Seismic monitoring plays a critical role in managing rockburst risks and evaluating the effectiveness of destress blasting in deep level hardrock mining. Seismic systems provide vital real-time feedback into stress redistribution and dynamic failure mechanisms; there are, however, practical limitations that must be acknowledged when implementing these systems. Spatial resolution and event detection thresholds pose significant challenges. The ability of a seismic system to accurately locate and characterise events is heavily dependent on the number, sensitivity, and strategic placement of sensors. Sparse networks can result in large location uncertainties, misinterpretation of source mechanisms, and failure to detect small, but important, microseismic precursors (Mendecki, 1996; Hudyma, Potvin, 2004; Peng et al., 2020; Barthwal, Van der Baan, 2020). Hidayat et al. (2021) discuss the importance of hypocenter relocation to minimise uncertainties in microseismic activity identification, which is crucial for ensuring the safety of mining operations under complex geological conditions. High-frequency, low-energy microseismic events, which often precede larger rockbursts, may go undetected if sensor sensitivity or density is insufficient. Environmental and operational noise in an active mine, caused by drilling, blasting, equipment operation, and ventilation, can mask true seismic signals. Discriminating between mining-induced seismicity and background noise demands sophisticated signal processing and experienced interpretation, increasing operational complexity (Wesseloo, 2018).

Financial costs present another constraint. Seismic monitoring systems require substantial investment for high-quality sensors, cabling, data acquisition systems, and ongoing maintenance. Additional costs are associated with data analysis expertise, either internally trained or outsourced (Durrheim, 2010). Mines must balance the cost of the system against the potential risk reduction benefit it provides. Another limitation lies in data interpretation complexity. Even with real-time monitoring, understanding the implications of seismic trends requires integration with geological models, mining sequences, and stress analysis. Standalone seismic data, without correlation to other monitoring data, may lead to incomplete or misleading conclusions regarding rockmass stability. Despite these limitations, seismic monitoring complements other rock engineering techniques effectively. When combined with numerical modelling, seismic monitoring validates model predictions of stress redistribution, allowing dynamic updating of rockburst risk maps (Ma et al., 2020). Ground penetrating radar and borehole periscope inspections provide direct imaging of fracture development, which can be correlated with microseismic event clustering zones, enhancing fracture network interpretation. Drill core analysis, in a similar approach, provides physical verification of fracture patterns predicted by seismicity patterns, strengthening confidence in rockmass behaviour models.

Practical evidence of the value of integrated seismic monitoring exists. Simser (2019) reports on Canadian hardrock mines that implemented comprehensive seismic monitoring systems integrated with numerical modelling to manage rockburst risks during deep mining. Seismic monitoring remains one of the best tools for estimating rockburst risk, asserts Simser (2019) from his practical observations from the Canadian hardrock mines. For effective large-scale destress blasting campaigns, seismic data can reveal spatial migration of seismicity away from production areas. Seismic moment tensor analysis correlation with fracture orientations observations through borehole logging can provide strong validation of the combined monitoring approach. In the deep gold mines of South Africa, seismic systems have been used in conjunction with visual geological mapping and borehole imaging to predict and manage strainbursts in high-risk panels, notably improving safety performance (Durrheim, 2010; Sengani, Zvarivadza, 2017; Sengani, 2020a). Seismic monitoring systems have practical limitations related to resolution, noise sensitivity, cost, and interpretation complexity, but they are indispensable for effective rockburst risk management. Their power lies not in standalone deployment but in strategic integration with other monitoring and modelling techniques, providing a holistic, dynamic understanding of the evolving rockmass response to mining activities. Future advancements in real-time data fusion, machine learning event classification, and integration with energy-based rockburst prediction models are expected to further enhance the role of seismic monitoring in modern deep level mining.

 

Innovative destress blasting evaluation methods incorporating geostatistics, machine learning, and IIoT real-time monitoring

Employing geostatistical methods like kriging or conditional simulation could strongly quantify spatial uncertainty and enhance the reliability of blast performance predictions. Geostatistics could help optimise the placement and configuration of destress blast holes by precisely modelling rockmass heterogeneity and identifying areas with higher probabilities of rockburst occurrences. Zvarivadza (2023) covers key concepts on the application of geostatistical methods in the evaluation of destress blasting efficiency.

Incorporation of machine learning techniques could significantly advance the predictive capabilities of the destress blasting evaluation framework. Given the complex, multivariate nature of the data involved, ranging from rock mechanics parameters, seismic data, fracture network imaging, and geomechanical modelling outputs, machine learning approaches such as random forests, gradient boosting, or neural networks could efficiently handle non-linear interactions and provide predictive perspectives with higher accuracy. Machine learning models could also dynamically adapt to incoming data, continually refining destress blasting strategies based on historical outcomes, thereby promoting proactive management of rockburst risks.

Integrating Industrial IoT based real-time monitoring systems, could further enhance data quality and operational responsiveness (Zvarivadza et al., 2024). Deploying sensor networks that continuously capture seismic, deformation, and environmental data in real-time could markedly improve the temporal resolution and accuracy of the evaluation. Such real-time data streams, combined with advanced analytics and cloud-based visualisation platforms, would allow immediate detection and response to stress changes and rockmass behaviour. This integration could substantially elevate the operational reliability of destress blasting, transitioning from retrospective assessments to real-time, data-driven decision-making frameworks. Table 12 presents some innovative destress blasting evaluation methods incorporating geostatistics, machine learning, and IIoT real-time monitoring.

The integration of geostatistical methods, machine learning models, and real-time IIoT monitoring frameworks significantly broadens the traditional destress blasting evaluation approach, offering a multi-dimensional, data-driven, and adaptive strategy that enhances both scientific rigour and operational resilience in deep underground hardrock mining environments.

 

Conclusions

Practically reliable evaluation of destress blasting is vitally important for developing a reliable destress blasting strategy. This study advances the evaluation of destress blasting by moving beyond traditional descriptive summaries and providing a structured, integrated framework that critically addresses key gaps in current practice. Previous research has highlighted the importance of destress blasting in mitigating seismic hazards in deep hardrock mining. Our work systematically tailors global best practices to the unique geological and operational challenges of deep Swedish mines. The study proposes refinements better suited for Sweden's deep hardrock mining environments by critically reviewing numerical modelling approaches and highlighting their limitations, such as oversimplification of damage zones and the need for anisotropic, time-dependent models. The integration of field-based fracture monitoring methods (GPR, borehole periscope imaging, and drill core observations) with predictive numerical simulations provides a notable, multi-modal evaluation framework. This study also reflects on the combined application of rockburst prediction criteria (F, BSR, BPI) as essential quantitative tools for assessing stress relief effectiveness pre- and post-destress blasting, offering a more robust and risk-informed evaluation process. It is also important to note that this work considers the incorporation of emerging technologies, such as geostatistical simulations to characterise spatial variability, machine learning to predict blast outcomes dynamically, and Industrial IoT-based real-time monitoring to move evaluation from retrospective assessments to predictive, adaptive control.

The new insights gained include:

> Emphasising the critical role of uncertainty quantification in blast performance prediction.

> Highlighting the importance of integrating low-magnitude seismic responses into adaptive destress blasting optimisation.

> Proposing an operational pathway for real-time feedback loops linking monitoring data to model recalibration during ongoing mining.

The proposed framework practically enables mining practitioners to design destress blasting strategies that are site-specific, dynamically adaptable, and scientifically grounded. This can improve operational safety, reduce unplanned seismic disruptions, and optimise drilling and blasting costs for the mining industry. From an academic perspective, the study contributes a refined evaluation methodology that future researchers can extend, particularly by combining energy-based rockburst prediction models with real-time data streams. Future research should focus on validating the proposed framework through field trials in deep Swedish mines, refining machine learning predictive models with larger datasets, and developing standardised protocols for real-time blast effectiveness evaluation based on integrated multi-sensor data. Our study lays the groundwork for a more rigorous, predictive, and operationally impactful approach to destress blasting evaluation in deep hardrock mining environments. It bridges the gap between theoretical destress blasting models and practical deep mining realities, offering a predictive, adaptable framework that strengthens both scientific understanding and field-based application in high-stress mining environments.

 

Acknowledgements

The authors gratefully acknowledge the financial support from the Strategic Innovation Programme for the Swedish Mining and Metal Producing Industry (STRIM), which is a joint investment from VINNOVA (The Swedish Governmental Agency for Innovation Systems), the Swedish Energy Agency, and Formas, with an additional in-kind contribution from Zinkgruvan Mining AB, LKAB, and Boliden (Ref. No.: 2020-04459).

 

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Correspondence:
B. Genc
Email: bekir.genc@wits.ac.za

Received: 6 Mar. 2025
Revised: 21 Apr. 2025
Accepted: 2 May 2025
Published: June 2025