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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]

A thorough excavation of the deep ultra-thick mines is highly challenging in rock engineering, which mainly depends on the rheological properties of coal-rock mass, high-stress concentrations, and complex geological conditions. Under heterogeneous settings, a detailed understanding of microseismic (MS) spatio-temporal response to rock mechanical behavior is essential for the efficient and safe-yield of deep ultra-thick mines. This paper utilizes real-time passive seismological data of 36 months in an ultra-thick coal mine through LLTCC mining to quantify the coal-rock mass mechanical behavior. In the post-processing of geophysical data, Power Spectral Density (PSD) was performed and the MS source parameters were computed containing over 18,000 events. The events were classified into different energy-levels to evaluate the increased-pressure rock mass. The Gaussian results reveal that the distribution of MS events coincides with the increased rock pressure. The high energy events (≥ 50,000 J) accounted for 57.93% of the microseismic-induced earthquakes. Moreover, the cloud energy density maps were obtained to identify the zones of high-stress concentration and enhanced seismicity. It is inferred that the overburden pressure and intense fracturing developed the high-stress concentration zones during excavation. Furthermore, the temporal parameters computed from MS data show that sharp-rise and sharp-drop variation can be regarded as early warning indicators for increased rock pressure. High deviation in energy and frequency also evidenced the increased rock pressure and micro-fractures to macro-fracture development. Based on these precursory parameters, a comprehensive early warning method was proposed. Besides, 3D visualization of the fracturing process and identification of new faults assisted in the accurate evaluation of the ultra-thick mine. Several risk zones were identified based on the seismic energy and fractures correlation. Finally, with the help of a deep learning LSTM approach, a prominent peak in time-series data is predicted that defines the increased rock pressure and further characterizes the coal-rock mechanical response. Compared with the existing studies, our integrated geophysical and mine engineering workflow can provide accurate insights to depict the rock mass mechanical behavior of the deep mines as an aid for disaster prediction and early warning. Highlights: A detailed understanding of microseismic Spatio-temporal response to rock mechanical behavior is presented. Field recorded microseismicity is quantitatively correlated with regions of increased-rock pressure and stress concentration. Temporal trends in the microseismic source parameters evidenced increased-rock pressure and a comprehensive early warning model is proposed. A novel microseismological approach is presented to model the mining-induced fracturing process and correlated with seismic energy to identify risk zones. Machine learning based LSTM approach pioneered and resulted in the prediction of rock mass response on time-series data. [ABSTRACT FROM AUTHOR]

• Determination of new engineering parameter as prediction indicator. • Regional minefield geological response and deformation mechanism are revealed. • A robust and cost-effective workflow for dynamic disaster identification. • Integration of traditional and new rockburst prediction indicators. Due to seismic load, complex geological and geo-mechanical conditions of rock mass in Steeply Inclined and Extremely Thick Coal Seam (SIETCS) mines, the prediction and prevention of dynamic disasters remain quite challenging. Traditionally, physical models and numerical simulations are widely practiced for disaster prediction at shallow depths which becomes difficult in SIETCS mines. The drilling tests and above models are generally time-consuming and uneconomic providing miniature coverage. Whilst, rockburst driven disasters require detailed regional geological understanding and mechanical behavior of rock mass. For this purpose, a non-invasive and cost-effective geophysical workflow covering regional-scale geology is introduced for accurate identification and prediction of rockburst in a SIETCS mine of northwestern China. Passive seismological in-situ observations were made for a period of twenty-four months to compute the seismic source parameters, map fracturing process, identify regional lineaments and calculate important rockburst prediction indicators. The spatiotemporal distribution of induced-microseismicity, three-states stress theory, and mapped fractures helped in the identification of three rockbursts, several high-energy tremors, and the prediction of two new rockburst-prone regions. The mapped fractures through the novel 'Seismo-Frac model' showed that for the simultaneous excavation in B3 + 6 and B2 + 1 coal seams subjected to repetitive episodes of seismic deformation, the shear-displacement accumulation, prying effects of rock pillar and roof coupled with dynamic stress are the primary mode of deformation. The mapped fractures and lineaments are unevenly distributed in the minefield with a maximum density of the fractures concentrated in the B3 + 6 coal seam and rock pillar. Furthermore, through conditional probability analysis, the prediction efficiency of source parameter averages (E and N), seismological parameters (b-value and Δb), and maximum potential magnitude (M m) was compared. A sharp-increase in source parameter averages while a sharp-decrease in the seismological parameters proved to be effective rockburst prediction indicators. This study also introduces decreasing occurrence frequency of fractures and lineaments as the new prediction indicators. This method, while a panacea for imaging the entire fracturing phenomenon, provides insights with widespread implications for academic researchers and industry practitioners to mitigate dynamic disasters in global underground engineering excavation. [ABSTRACT FROM AUTHOR]

• The outburst mechanism is described by the Solid-Fluid transition of gas-bearing coal. • A unified model with the solid-fluid transition is established to describe the outburst initiation and development. • FEMLIP is adopted to solve the large deformation during the outburst process. As coal and gas outburst is a frequent disaster and its mechanism still unclear, consistent numerical modeling is indispensable to enable further investigations owing to difficulties in field observation. As gas-bearing coal with outburst proneness is considered a granular material, the outburst phenomenon is described by a solid-fluid transition that is developed in gas-bearing coal. Inter particle meso-contacts become bondless while the gas-bearing coal is sufficiently degraded. The solid-fluid transition occurs in case of the vanishing second-order work ( d 2 w) when the gas pressure threshold (p t) is satisfied. A unified model for describing the consistent process of outburst is established herein. Finite elements method with Lagrangian integration points (FEMLIP) is used to model solid-fluid transition behavior of gas-bearing coal. The numerical results present the entire process of the outburst and satisfies a four-stage description. The unified model for outburst is beneficial for the study of the outburst mechanism. [ABSTRACT FROM AUTHOR]

Coal samples with different joints morphology were subjected to uniaxial compression experiments, cracks evolution was recorded by Nikon D5300 and acoustic emission (AE) energy signals were collected by AEwin Test for Express-8.0. During loading process, coal samples deformed elastically with no obvious cracks changes, then they expanded gradually along the trace of the original cracks, accompanied by the formation of secondary cracks, and eventually produced a large-scale fracture. It was more interesting that the failure mode of samples were all shear shape, whatever the original cracks morphology was. With cracks and damage evolution, AE energy radiated regularly. At the early loading stage, micro damage and small scale fracture events only induced a few AE events with less energy, while large scale fracture leaded to a number of AE events with more energy at the later stage. Based on the multifractal theory, the multifractal spectrum could explain AE energy signals frequency responses and the causes of AE events with load. Multifractal spectrum width (Aa), could reflect the differences between the large and small AE energy signals. And another parameter (Af) could reflect the relationship between the frequency of the least and greatest signals in the AE energy time series. This research is helpful for us to understand cracks evolution and AE energy signals causes. [ABSTRACT FROM AUTHOR]