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Abstract In order to study the mechanics, acoustic emission (AE) and electromagnetic emission (EME) response law of bursting liability coal at different loading rates, uniaxial compression tests were carried out on coal mass from Konggu Coal Mine. The corresponding relations among mechanical properties, AE and EME signals in the process of coal failure under loading were analyzed, and the energy evolution law of coal failure with bursting liability under loading rate was discussed. The results show that within a certain range of loading rate, the higher the loading rate, the higher the compressive strength and peak load of bursting liability coal, and the shorter the time for coal to reach the peak load. Under different loading rates, the mechanics, AE and EME signals of coal samples can be well corresponded. When the loading rate is low, the number of blocks destroyed of coal sample is large and the block size is relatively small, and the blocks are mainly scattered around the test platform. When the loading rate is high, the number of damaged blocks is relatively small and the block size is relatively large, and the blocks are far away from the test bench. When loading at a low rate, the internal cracks in coal can be fully developed and connected, and the energy release rate is relatively uniform in the process of loading and failure of coal sample. In the case of high loading rate, the energy release rate of coal sample in the loading process is much smaller than that in the moment of failure. Combining the above test results with the actual situation of the working face, it can be concluded that the total energy stored in the coal of fast mining increases and the threshold of impact decreases compared with that of slow mining. Therefore, under the disturbance of external dynamic load, rapid mining is more likely to induce rock burst.

Rock mass structure rupture is an important factor that seriously restricts the construction and safe operation of underground space engineering projects such as mines, subways and tunnels. Realizing the identification of rock mass fracture state is one of the hotspots and emphases of current research. In this study, some experiments of sandstone loading failure under different conditions were carried out, the acoustic emission Mel-frequency cepstral coefficient (MFCC) and its fluctuation difference during the whole loading process were extracted, the variation law of the coefficient and its fluctuation difference during the whole loading failure process was studied, and the correlation characteristics of coefficient No.1 (According to the calculation of acoustic emission Mel-frequency cepstral coefficients, it can be seen that a set of acoustic emission Mel-frequency cepstral coefficient includes 12, and coefficient No.1 refers to the first Mel-frequency cepstral coefficient) and its fluctuation difference with the fracture state of sandstone were analyzed. Based on this, a method to identify sandstone fracture state using the Mel-frequency cepstral of acoustic emission was proposed, and the identification criteria was constructed. The identification effect was finally verified. The results show that with loading increase, the coefficient No.1 increases as a whole, and the coefficient value and its discreteness increase significantly in the failure stage and show significant regular fluctuations. The fluctuation difference of the coefficient has the characteristics of periodic variation. The size of the fluctuation difference and its fluctuation can characterize the fracture of sandstone. The overall increase and sudden increase of the fluctuation difference can reflect the macroscopic fracture of sandstone in the unstable deformation and post-peak failure stage, and the sudden increase level of the fluctuation difference can reflect the fracture degree of sandstone. The acoustic emission Mel-frequency cepstral coefficient and its fluctuation difference show good response characteristics to sandstone fracture, which is less affected by different loading conditions, thus they have applicability in reflecting sandstone fracture. The coefficient No.1 and its fluctuation difference have a good correlation with the fracture state of sandstone. The correlation can be divided into three stages as: in the micro-fracture stage of sandstone, the coefficient No.1 and its fluctuation difference are intensively distributed; in the unstable deformation stage just prior to the peak load, the distribution range increases sharply, the overall value increases and the high abnormal value appears; in the post-peak failure stage, the distribution range further increases, the overall value is higher, and more high abnormal values appear. The identification method and criteria of sandstone fracture state were constructed by using the 75% site value and outliers of coefficient No.1 and the 75% site value and outliers of the fluctuation difference of coefficient No.1. The effect of the identification criteria was tested by the confusion matrix of the three-classification model. The accuracy and precision of identification are 90.43% and 94.45%, respectively, which indicate the identification effect is good. The results can provide a reference for the identification of the fracture state of other types of coal and rocks, and for the monitoring and early warning of coal rock instability.

Rockbursts have become a significant hazard in underground mining, underscoring the need for a robust early warning model to ensure safety management. This study presents a novel approach for rockburst prediction, integrating the Mann-Kendall trend test (MKT) and multi-indices fusion to enable real-time and quantitative assessment of rockburst hazards. The methodology employed in this study involves the development of a comprehensive precursory index library for rockbursts. The MKT is then applied to analyze the real-time trend of each index, with adherence to rockburst characterization laws serving as the warning criterion. By employing a confusion matrix, the warning effectiveness of each index is assessed, enabling index preference determination. Ultimately, the integrated rockburst hazard index Q is derived through data fusion. The results demonstrate that the proposed model achieves a warning effectiveness of 0.563 for Q, surpassing the performance of any individual index. Moreover, the model's adaptability and scalability are enhanced through periodic updates driven by actual field monitoring data, making it suitable for complex underground working environments. By providing an efficient and accurate basis for decision-making, the proposed model holds great potential for the prevention and control of rockbursts. It offers a valuable tool for enhancing safety measures in underground mining operations.

Rock fracturing is often accompanied by electromagnetic phenomenon. As a vector field, in addition to the intensity that is widely concerned, the generated electromagnetic field also has obvious directionality. To this end, a set of electromagnetic antennas capable of simultaneous three-axis measurement is used to monitor the electromagnetic vector field generated from rock fracturing based on Brazilian tests. The signal amplitude on each axis can represent the magnitude of actual magnetic flux density component on the three axes. The intensity and directional characteristics of electromagnetic signals received at different positions are studied using vector synthesis. The directionality of electromagnetic radiation measured using a three-axis electromagnetic antenna shows that the direction of the magnetic flux intensity generated by rock fracturing tends to be parallel to the crack surface, and the measured signal intensity is greater in a direction closer to the crack surface.

The characterization of micro-surface mechanical and electrical properties of the natural rock materials remains inadequate, and their macroscopic performance can be better comprehended by investigating the surface properties. With this purpose, the present research focuses on characterizing the micro-surface morphology, Derjaguin-Muller-Toporov (DMT) modulus, adhesion, and potential of granite, shale, and limestone by employing the atomic force microscope (AFM) as a pioneer attempt. The results show that the micro-surface morphology of the rock fluctuates within hundreds of nanometers, among which the granite micro-surface is comparatively the smoothest, followed by limestone. The morphology of the shale is the roughest, indicating that the regional difference of shale micro-surface is dominant. The distribution of the adhesion on rock micro-surface is uneven; the average adhesion of eight measuring areas for shale is 23.93 nN, accounting for three times of granite and limestone, while the surface DMT modulus of shale is relatively lower than granite and limestone. It is inferred from the obtained results that higher surface adhesion is helpful to the gas adsorption of shale, and the lower surface DMT (elastic) modulus is useful to the formation of fractures and pores. Thus, these two are the micromechanical basis of shale gas adsorption. Additionally, introducing a method to reduce the surface adhesion will benefit the exploration of unconventional resources such as shale gas. The micro-surface of the three types of rocks all shows electricity, with average potential ranging from tens of millivolts to hundreds of millivolts. Besides, the micro-surface potential of the rocks are heterogeneous, and both positive and negative points can be found. The existence and uneven distribution of micro-surface potential provide a robust physical basis for the electromagnetic radiation generated by rock fracture under loading. This study offers a new method for revealing the adsorption characteristics of unconventional gas reservoir rocks and the electromagnetic radiation mechanism of the rock fracture.

A novel approach for mapping induced fractures and associated lineaments has been introduced based on microseismic data, and thus their contribution towards identification, and prediction of the risk zones which may lead to a collapse or rockburst in a coal mine. By taking Tashan Coal Mine (TCM) in Datong coalfield of China as an example, the real-time microseismic data was acquired for a period of 7 months. The microseismic data was processed using standard processing schemes followed by source location and parameters estimation. The proposed approach utilizes microseismic events, trace them to map the induced fractures, and based on their network to interpret associated lineaments. Based on the configuration, geometry, and network, thousands of fractures were identified which vary in length. Regional lineaments have been interpreted based on the orientation of these fractures. The fractures and lineaments distribution were further correlated with computed microseismic energy distribution. The spatial distribution of microseismic energy helped in identification of a geological discontinuity (fault) that clearly separates the study area into northern and southern parts. The reactivation of this fault may pose significant threats to mass stability, life, and property. The northern half is characterized by more and short length fractures while southern by prolonged and fewer fractures. The upper northern half is dominated by high energy (≥50, 000 J), while the lower part is characterized by variable energy pattern (22,000 to 40, 000 J). It is inferred from the energy-lineaments correlation figure, the northern half describes relatively stronger rock type in comparison to the southern part suggesting different rock deformational behavior. The fractures and lineaments converging towards a single point define risk zones where future collapse or rockburst can be expected. This identification of risk zones provides a strong basis for the real-time prediction of future catastrophic hazards in the coal mines. This study may benefit global underground engineering researchers for hazards prediction and establishing of the early warning systems.

The stress and gas pressure in deep coal seams are very high, and instability and failure rapidly and intensely occur. It is important to study the infrared precursor characteristics of gas-bearing coal instability and failure. In this paper, a self-developed stress-gas coupling failure infrared experimental system was used to analyse the infrared radiation temperature (IRT) and infrared thermal image precursor characteristics of gas-free coal and gas-bearing coal. The changes in the areas of the infrared temperature anomalous precursor regions and the effect of the gas on the infrared precursors were examined. The results show that high-temperature anomalous precursors arise mainly when the gas-free coal fails under loading, whereas the gas-bearing coal has high-temperature and low-temperature anomalous precursors. The area of the high-temperature anomalous precursor is approximately 30%–40% under gas-bearing coal unstable failure, which is lower than the 60%–70% of the gas-free coal. The area of the low-temperature abnormal precursor is approximately 3%–6%, which is higher than the 1%–2% of the gas-free coal. With increasing gas pressure, the area of the high-temperature anomalous precursor gradually decreases, and the area of the low-temperature anomalous precursor gradually increases. The high- and low-temperature anomalous precursors of gas-bearing coal are mainly caused by gas desorption, volume expansion, and thermal friction. The presence of gas inhibits the increase in IRT on the coal surface and increases the difficulty of infrared radiation (IR) monitoring and early warning for gas-bearing coal.

In order to accurately predict the gas concentration, find out the gas abnormal emission in advance, and take effective measures to reduce the gas concentration in time, this paper analyzes multivariate monitoring data and proposes a new dynamic combined prediction method of gas concentration. Spearman’s rank correlation coefficient is applied for the dynamic optimization of prediction indicators. The time series and spatial topology features of the optimized indicators are extracted and input into the combined prediction model of gas concentration based on indicators dynamic optimization and Bi-LSTMs (Bi-directional Long Short-term Memory), which can predict the gas concentration for the next 30 min. The results show that the other gas concentration, temperature, and humidity indicators are strongly correlated with the gas concentration to be predicted, and Spearman’s rank correlation coefficient is up to 0.92 at most. The average R2 of predicted value and real value is 0.965, and the average prediction efficiency R for gas abnormal or normal emission is 79.9%. Compared with the other models, the proposed dynamic optimized indicators combined model is more accurate, and the missing alarm of gas abnormal emission is significantly alleviated, which greatly improves the early alarming accuracy. It can assist the safety monitoring personnel in decision making and has certain significance to improve the safety production efficiency of coal mines.

The presence of a high concentration of Cl− in saltwater will erode the structure and facilities, reducing the stability and service life of the underground oil storage cavern. In order to reduce the risk of seawater intrusion, this paper studies the risk and prevention technology of seawater intrusion based on a case study of a coastal and large-span underground oil storage cavern. A refined three-dimensional hydrogeological model that comprehensively considers permeability coefficient partitions, faults, and fractured zones are constructed. The seepage fields and seawater intrusion risks of the reservoir site in its natural state, during construction, and during operation are examined, respectively. The study quantifies the water inflow and optimizes the seawater intrusion prevention technology. The results indicate that there is no risk of seawater incursion into the cavern under natural conditions. The water inflows after excavating the top, middle, and bottom sections of the main cavern are predicted to be 6797 m3/day, 6895 m3/day, and 6767 m3/day, respectively. During the excavation period, the water supply from the water curtain system is lower than the water inflow of the cavern, providing the maximum water curtain injection of 6039 m3/day. The water level in the reservoir area decreased obviously in the excavation period, but the water flow direction is from the cavern to the sea. Additionally, the concentration of Cl− in the cavern area is less than 7 mol/m3; hereby, there are no seawater intrusion risks. When only the horizontal water curtain system is deployed, seawater intrusion occurs after 18 years of cavern operation. The concentration of Cl− in the southeast of the cavern group exceeds 50 mol/m3 in 50 years, reaching moderate corrosion and serious seawater intrusion. In addition to the horizontal curtain above the cavern, a vertical water curtain system could be added on the southeast side, with a borehole spacing of 10 m and extending to 30 m below the cavern group. This scheme can effectively reduce seawater intrusion risk and extend the service life of the cavern. The findings of this research can be applied as guidelines for underground oil storage caverns in coastal areas to tackle seawater intrusion problems.

Acoustic emission (AE) monitoring is an effective tool to quantify the dynamic damage that may cause heavy casualties and huge property losses in rock engineering. Instead of traditional failure evaluation methods, in this paper, the coal failure mechanism is evaluated in a complicated geological environment under uniaxial compression tests by employing machine learning (ML) and automatic speech recognition (ASR). Taking advantage of the ASR technology, the Mel-frequency cepstrum coefficients (MFCC) were extracted as sample features, while ML was used to paradigm the artificial intelligent evaluation of the failure probability of coal (AIEFPC). Additionally, the five-fold cross-validation method was used to assess the AIEFPC predictive effect incorporating cumulative hits number, cumulative ring count, and amplitude as sample features. The influence of category weight on the prediction effect of AIEFPC on a different category of sample sets has been discussed and analyzed. The results show that AIEFPC has the potential to use the MFCC of the 40 ms AE segment at any time to predict the dangerous state of the coal sample with a prediction accuracy of >85%. The probability value of the hazardous samples is computed through AIEFPC that further helped in evaluating the reliability of the prediction results. It is inferred from the obtained results that a larger category weight value of the hazardous samples can improve the prediction accuracy of AIEFPC than the safe sample. This research provides a new way of effectively predicting the coal failure probability before the damage and failure that can be applied to worldwide case-studies.

In this study, we established a dynamic ejection coal burst model for a coalmine roadway subject to stress, and held that the stress concentration zone at the roadway side is the direct energy source of this ejection. The formation and development of such burst undergoes three stages: (1) instability and propagation of the cracks in the stress concentration zone, (2) emerging of a layered energy storage structure in the zone, and (3) ejection of coal mass or coal burst due to instability. Moreover, we figured out the initial strength of periodic cracks is parallel to the maximal dominant stress direction in the stress concentration zone and derived from the damage strain energy within the finite area of the zone based on the Griffith energy theory. In addition, we analyzed the formation process of the layered energy storage structure in the zone, simplified it as a simply supported restraint sheet, and calculated the minimum critical load and the internally accumulated elastic energy at the instable state. Furthermore, we established a criterion for occurrence of the coal burst based on the variational principle, and analyzed the coal mass ejection due to instability and coal burst induced by different intensity disturbances. At last, with the stratum conditions of Junde Coalmine as the model prototype, we numerically simulated the load displacement distribution of the stress concentration zone ahead of the working face disturbed by the main roof-fracture-induced dynamic load during the mining process as well as their varying characteristics, and qualitatively verified the above model. Keywords: Coal and rock burst, Evolution model, Dynamic ejection, Stress concentration, Layered energy storage structure

It is vital to understand the electromagnetic radiation’s time-frequency characteristics in the process of coal and rock failure with different joint angles in order to reveal the generation mechanism of the electromagnetic radiation (EMR) and improve the accuracy of EMR early warning. We studied the time-frequency characteristics of EMR signals of coal samples with different joint angles. The study finds that, (1) with the increase of joint angle, the failure time and peak load of samples decrease first and increase later, and the postpeak failure time decreases gradually. The EMR counts’ peak value showed a slow rise, a sharp rise, and a slow rise in the three intervals of α = 0° to 45°, 45° to 60°, and 60° to 90°, respectively. The accumulated EMR counts showed a steady upward trend. The duration of the EMR waveform, the dominant frequency of the EMR, and the peak number of the frequency spectrum of coal samples are on the rise. (2) As the joint angle increases, the samples’ failure mode changes from the stage fracture dominated by tension cracks to the rapid fracture with the coexistence of shear and tension cracks and finally to the burst fracture which produces a large number of fragments. This is also the main reason for the difference of the EMR generation mechanism and the signal of samples with different joint angles. (3) According to the experimental results, we established the modified formulas for calculating the EMR threshold value and deviation of coal and rock with joints under different stress environments and revealed that the longer the EMR waveform duration, the higher the dominant frequency, and the more the number of spectrum peaks, the greater the burst risk of coal and rock.

This work extends research on the mechanism of electromagnetic radiation (EMR) induced by coal or rock fractures to the category of microscopic dynamic experimental research. A custom-made three-point bending test system and atomic force microscope (AFM) were integrated to obtain the microdynamic loading test system. The notched coal samples were prepared specially. The dynamic propagation of new microcracks in coal samples was measured, and the propagation velocity was calculated. The morphology and electro-mechanical characteristics of new microcracks were tested. More importantly, the causes of the changes in the electro-mechanical characteristics before and after fracture were analyzed, and the effects of these changes on the EMR were discussed. The results showed that the average propagation velocities during the same time interval are 9.5 μm/s, 12.1 μm/s, and 16.2 μm/s. The elastic modulus of the material at the microcrack edge is generally smaller than that of the material in other locations, while the adhesion and deformation are larger. Moreover, the closer the material is to the microcrack, the higher its surface potential. The electrons generated at the microcrack edge and emitted into the atmosphere, which made the greater potentials of the microcrack edge. Many electrons with different velocities and directions migrate in similar parallel-plate capacitors, which are formed by the relative microscale surface of the coal microcrack tip and have different field strengths, resulting in EMR with complex frequencies and different intensities. This study provides a micro-dynamic experimental basis for research on the electromagnetic radiation mechanism.

Aiming at problem of low efficacy of early warning of rock burst in coal mine, a multisystem and multiparameter integrated early warning method based on genetic algorithm (GA) is proposed. In this method, firstly, the temporal-spatial-intensity information of energy incubation process of rock burst is deeply mined, and the multidimensional precursory characteristic parameter system of rock burst is constructed. Secondly, the genetic algorithm is used to train the historical monitoring data to obtain the optimal critical value and fitness value of each precursory characteristic parameter, and then the early warning index WC of each monitoring system is calculated. Finally, the integrated rock burst early warning index IC is obtained by synthesizing the early warning index WC of each system. The value of IC corresponds to the specific rock burst risk level of the mine. This method is applied to Wudong coal mine in Xinjiang, China. Based on the actual situation of the mine, a multidimensional precursory characteristic parameter system of rock burst is constructed, which includes energy deviation (DE), frequency ratio (Fr), frequency deviation (DF), degree of dispersion (DS), and total high value of energy deviation (DH). After analyzing the rock burst danger status and risk level in the monitoring area, the early warning capability of this method is found to reach 0.896. Combining with the specific prevention and control measures corresponding to different rock burst risk levels, it can provide effective guidance for the field work.

Coal and rock dynamic disasters have been the main concern in underground engineering because these seriously threaten the safety of miners and industrial production. Aiming to improve the EMR and AE monitoring technology, the refined nonlinear characteristics of EMR and AE during coal splitting failure are studied using Hilbert-H and multifractal theory, and valuable information pertaining to coal fracture law contained in EMR and AE waveform was revealed. The results show that the EMR and AE of coal splitting failure are related to the process of coal crack propagation. They possess the same initiation time and frequency band, however, the signal duration of EMR is comparatively longer than AE, and the main frequency of AE is higher than EMR. The EMR of coal splitting failure has the same excitation source as AE; nonetheless, the excited forms display different behavior. In terms of signal duration, the distribution of EMR signal is relatively uniform, the proportion of large-signal is less, the amount of information is more than that of AE, and the multifractal characteristics are more complicated. During the coal splitting failure, AE is mainly generated in the process of wall vibration caused by crack propagation, while the generation of EMR includes piezoelectric effect, charge separation, free charge vibration, charge neutralization and other processes, making EMR more complicated than AE and has a relatively low frequency. The research provides an effective method for studying nonlinear refinement characteristics of coal EMR and AE, and can provide an important basis for the study of the mechanism of EMR generation.

To investigate the bedding influence on coal mechanical behaviour in underground environments such as coal or rock burst, simulations of dynamic SHPB tests of pre-stressed coal specimens with different bedding angles were carried out using a particle flow code 2-dimensional (PFC2D). Three impact velocities of 4, 8 and 12 m/s were selected to study dynamic behaviours of coal containing bedding planes under different dynamic loads. The simulation results showed that the existence of bedding planes leads to the degradation of the mechanical properties and their weakening effect significantly depends on the angle θ between the bedding planes and load direction. With θ increaseing from 0° to 90°, the strength first decreased and subsequently increased and specimens became most vulnerable when θ was 30° or 45°. Five failure modes were observed in the specimens in the context of macro-cracks. Furthermore, energy characteristics combined with ultimate failure patterns revealed that maximum accumulated energy and failure intensity have a positive relation with the strength of specimen. When bedding planes were parallel or perpendicular to loading direction, specimens absorbed more energy and experienced more violent failure with increased number of cracks. In contrast, bedding planes with θ of 30° or 45° reduced the specimens’ ability of storing strain energy to the lowest with fewer cracks observed after failure. Keywords: Static–dynamic coupled loads, SHPB, Coal bedding angle, Strain energy, PFC2D

Coal burst is the violent failure of overstressed coal, and it is often accompanied by sound, coal ejection and seismic events. It is subsequently recognized as a serious safety risk of Australia after double fatalities coal burst happened at Austar Coal Mine. Considering the increasing trend of coal burst severity and frequency with mining depth, it is an urgent task to develop the coal burst risk assessment methods for Australia underground coal mines. Coal burst propensity index method is a widely used method of burst risk evaluation of coal as it is summed up from the coal burst research and practice of many countries. This paper presents the experimental and theoretical research of coal burst propensity index method for coal burst risk assessment in Australia. The definition of four indexes including elastic strain energy index (WET), bursting energy index (KE), dynamic failure time (DT) and uniaxial compression strength (RC) is introduced in the first part. Then, the standard laboratory test process and test parameter of coal burst propensity index is presented. DT test is conducted with 0.3 mm/min displacement control loading rate while other test is with 0.5 mm/min. Besides, modified data processing and risk classification method of test are proposed. Differentiate analysis of stress-strain curve is adopted in the data processing of DT and KE index. A four level risk classification form of burst risk is recommended for Australian underground coal mines. Finally, two likely improvement methods of WET test, including volumetric strain indicator method and theoretical calculation method, are discussed. Keywords: Coal burst, Coal burst propensity, Risk evaluation, Underground mining

Rockburst is a serious hazard in underground engineering, and accurate prediction of rockburst risk is challenging. To construct an intelligent prediction model of rockburst risk with interpretability and high accuracy, three binary scorecards predicting different risk levels of rockburst were constructed using ChiMerge, evidence weight theory, and the logistic regression algorithm. An intelligent rockburst prediction model based on scorecard methodology (IRPSC) was obtained by integrating the three scorecards. The effects of hazard sample category weights on the missed alarm rate, false alarm rate, and accuracy of the IRPSC were analyzed. Results show that the accuracy, false alarm rate, and missed alarm rate of the IRPSC for rockburst prediction in riverside hydropower stations are 75%, 12.5%, and 12.5%, respectively. Setting higher hazard sample category weights can reduce the missed alarm rate of IRPSC, but it will lead to a higher false alarm rate. The IRPSC can adaptively adjust the threshold and weight value of the indicator and convert the abstract machine learning model into a tabular form, which overcomes the commonly black box problems of machine learning model, as well as is of great significance to the application of machine learning in rockburst risk prediction.

Based on 3D modelling of typical tunnels in mines, the airflow distribution in a three-center arch-section tunnel is investigated and the influence of air velocity and cross section on airflow distribution in tunnels is studied. The average velocity points were analyzed quantitatively. The results show that the airflow pattern is similar for the three-center arch section under different ventilation velocities and cross sectional areas. The shape of the tunnel cross section and wall are the critical factors influencing the airflow pattern. The average velocity points are mainly close to the tunnel wall. Characteristic equations are developed to describe the average velocity distribution, and provide a theoretical basis for accurately measuring the average velocity in mine tunnels. Keywords: Mine tunnel, Turbulence, Airflow distribution, Three-center arch, Average velocity

In order to improve the monitoring and prediction of coal and gas outburst, this paper proposes a new method for dynamic regional prediction of coal and gas outburst using microseismic (MS) monitoring. The theoretical basis of this method is presented. An index evaluation system was established and applied, based on which field tests were carried out in a coal mine. The results show that seismic monitoring with frequency and energy indexes can obtain good results for mining disturbance intensity monitoring and geological structure detection; the regional stress distribution detected by seismic wave tomography is consistent with the theoretical stress field, making its use of great significance for optimizing coal and gas outburst drilling parameters and improving overall tunneling efficiency. This approach overcomes the limitations of traditional methods in the temporal and spatial dimensions and realizes dynamic and continuous monitoring of coal and gas outburst-prone areas.

Early warning of a potential rockburst risk and its area of occurrence helps to take effective and targeted measures to mitigate rockburst hazards. This study investigates the microseismic (MS) spatial-temporal precursory characteristic parameters in a typical steeply inclined and extremely thick coal seam (SIETCS) with high rockburst risk and proposes three spatial/temporal quantification parameters and a spatial-temporal early warning method. Analysis results of temporal parameters show that the sharp-rise-sharp-drop variation in total daily energy and event count can be regarded as a precursor for high energy tremor. The appearance of peak values of both energy deviation (≥20) and event count deviation (≥1) can be regarded as precursors that indicate imminent rockburst danger. A laboratory acoustic emission (AE) experiment reveals that precursor characteristics obtained from the study can be feasibly used to warn the rockburst risk. The spatial evolution laws of spatial parameters show that the high energy density index of MS (EDIM), velocity, velocity anomaly regions correlate well with stress concentration and rockburst risk areas. The field application verifies that the temporal-spatial early warning method can identify the potential rockburst risk in a temporal sequence and rockburst risk areas during the temporal early warning period.

The steeply inclined and extremely thick coal seams (SIETCS) under the condition of gob filling frequently suffer from the occurrence of rockbursts. Figuring out the mechanisms of rockbursts under this condition for taking targeted measures to mitigate rockburst hazards in SIETCS is of great significance. Using the typical SIETCS with an average dip angle of 87° at Wudong Coal Mine (WCM) as a case study, a mechanical model and elastic deformation energy (EDE) function of a “steeply inclined suspended roof structure” was developed, and the influence factors were analyzed by theoretical analysis. Simultaneously, the rockburst risk assessment was carried out based on the theory of a rockburst start-up. The pressure relief measures are optimized by comparing the pressure relief effects of three kinds of destress blasting schemes. The results indicate that the damage characteristics of rockburst are mainly floor heave, the sidewall’s inward deformation and roof subsidence. The damage degree of headentry on the roof side is more severe than that of tailentry, and the resultant impacts showed the directionality from the roof side to the coal side. The steeply inclined and suspended roof breakage is one of the main causes for the occurrence of rockbursts. The EDE of the roof increases with an increasing dip angle of the coal seam from 0° to 72.6° and then decreases as the dip angle increases. Furthermore, that increase is accompanied by the decrease of the lateral pressure coefficient and the supporting force coefficient. The EDE stored in the roof is sufficient to cause roof breakage and induce rockburst after the complete roof exceeds a certain length. The mechanism of rockburst in SIETCS for fully mechanized top-coal caving mining under gob filling conditions was proposed, i.e., “high compressive stress concentration plus breakage of the suspended roof-induced stress” rockburst, and this is further verified by ground destruction, microseismic (MS) monitoring and numerical modeling. The results also indicate that alternate deep and shallow hole-blasting modes are more suitable for pressure relief in SIETCS.

Coal bursts are a serious dynamic hazard for underground coalmines, and they attract the extensive interest of studies from mining and geotechnical researchers worldwide. More recently, coal-burst incidents were reported in some Australian coalmines as a result of inadequate geological assessment of coal-burst hazards. The coal-burst process is closely associated with the accumulation of elastic energy and the rapid dissipation of kinetic energy. This paper introduces the essential geological conditions for energy accumulation, and the likely precursors for rapid energy dissipation leading to coal burst, which can be used by Australian coalmines to determine their coal-burst risk accordingly. Different energy forms and their transformations during the coal-burst process are introduced in detail in this paper. The dominant geological factors resulting in the accumulation of massive energy are analyzed, and the likely precursors associated with the instant release of elastic energy are discussed.

Mineral resources are increasing important for sustainable development in modern society. As the progressive depletion of shallow mineral deposits, deep or ultra-deep mineral extraction is an inevitable choice for mineral and energy security in various countries in the future. The trend of occurrences of geohazards including rock burst, cascading ground failure, uncontrollable underground space squeezing, coal and gas outburst, and fires, will be expected to excessively increasing as deepening the mine depth. Mining safety science and engineering challenges are drawing more and more attention over last decade and beyond. In August 2023, the 6th International Symposium on Mine Safety Science and Engineering was held in Harbin to promote innovative development of mining safety, and foster international collaborations among scholars in the field of mining safety. It served as a platform for the exchange of the most recent advancements in mining safety scientific theories, technologies, and equipment by bringing together global talent. Over 400 attendees representing 9 countries, including Australia, Russia, United State, Kazakhstan, and Canada, engaged in academic discussions and knowledge sharing on new theories, technologies, equipment, and methods in mining safety science and engineering. The latest research results are of great significance in enhancing the practices of preventing mine disaster and ensuring the safety of mining operations. [ABSTRACT FROM AUTHOR]