Search Results (184)

With the increasing depth of mining operations, accurate identification and assessment of rock mass instability risks are crucial for ensuring mine safety. This study proposes an integrated framework combining the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), fuzzy comprehensive evaluation (FCE) and kernel density estimation (KDE) for the identification and dynamic assessment of high-risk zones in deep mining. Using microseismic monitoring data from a lead–zinc mine in Northwest China (January–June 2023), the HDBSCAN algorithm adaptively identified 86 high-density clusters from 11,638 events. The weights of five evaluation indicators (moment magnitude, apparent stress, stress drop, peak ground acceleration, and ringing count) were determined objectively using the Euclidean distance method. FCE was then applied to classify cluster risk levels, revealing that 70.9% of the clusters were rated as high-risk (Level IV). KDE further illustrated the spatiotemporal migration of high-risk zones, showing a systematic shift from northeast to southwest along stopes and roadways, driven by mining unloading and geological structures. The integrated HDBSCAN-FCE-KDE framework demonstrates strong applicability and reliability in identifying and predicting rock mass instability risks, providing a scientific basis for proactive risk management in deep mining environments. Full article

Since September 2021, numerous seismic events with spectral peaks below 1 Hz occurred on the island of Vulcano, Italy, 131 years after its last eruption. The local monitoring network recorded microseismicity mostly in the form of months-long swarms, concurrent with anomalous values of other geophysical and geochemical parameters. By applying a machine learning technique (Self-Organizing Maps, SOMs), we obtained an inventory of ~6600 seismic signals, identifying and separating exogenous signals (anthropic noise) from distinct families of events. These families were located below La Fossa Crater (where the last eruption of the volcano happened) from the surface to a depth of 2.2 km b.s.l. Based on the seismic signature and source location of these events, we hypothesize unsealed/sealed processes through a network of shallow fractures favored by fluid pressure. After the return to background values of geochemical and geophysical parameters in 2023, a resumption of microseismicity occurred between May and June 2024. A test application of the SOM to the new data confirmed the non-destructive source of the new recorded signals, which shared families, location, and depths with our previous inventory. This test showed that SOM can be an effective tool for supporting real-time monitoring and warning of future unrest at Vulcano. Full article

Rock bursts frequently occur in the fault group area in China, seriously restricting the safe and efficient production of coal mines. Based on field investigation, physical experiments, and numerical simulation, this study investigates the rupture types and spatial evolution of microseismic events during the excavation of working face through fault group areas in the TB Coal Mine, where the hard roof asymmetric is cut by faults. It reveals the cooperative instability mechanism of faults and hard roof, as well as the mechanisms of rock burst. Targeted rock burst prevention measures are proposed, including “roof blasting to cut off dynamic and static load transfer” and “coal blasting to reduce abutment stress”. The results demonstrate the following: (1) during mining in fault group areas, the synchronous activation of faults induces shear-type and high-energy microseismic events and the subsequent movement of hard roof, which has been cut by faults, forms asymmetric parallelograms and symmetric inverted trapezoids, and induces tensile-type and high-energy microseismic events. The synchronous activation of faults and the breaking of the hard roof are identified as the primary reason for high-energy microseismic events. (2) As the fault dip angle approaches 90º, the compressive strength of the fault-segmented hard roof strata decreases. Under synchronous activation of faults, roof failure concentrates in the central, right, and left sections for fault combinations with dip angles of 70° + 70°, 90° + 70°, and 110° + 70°, respectively. (3) Numerical simulations reveal two rock burst mechanisms in faults—hard roof systems: a forward “high dynamic stress and high static stress” type and a rear “low dynamic stress and high static stress “ type, which is consistent with in situ monitoring data. (4) For the three stages in which the 502 working face approaches, passes through, and mines away from the fault group area, a stress relief scheme combining roof blasting and coal blasting is proposed. Compared with the 501 working face, during the mining of the 502 working face, the total microseismic frequency and energy decreased by 71.9% and 87.9%, respectively, and the effectiveness of these measures is verified. Full article

As climate-driven disasters intensify globally, this study investigates how environmental volatility influences the pro-environmental initiatives of micro-entrepreneurs in Chile. While Chile possesses world-class seismic resilience, the 2020–2025 period marked a dramatic shift toward hydro-climatological extremes, including mega-fires and catastrophic flooding. Integrating construal level theory, protection motivation theory, and the concept of focusing events, this research examines the psychological and structural drivers of business adaptation. Results indicate that residing in disaster-prone regions is insufficient to trigger proactivity; instead, a stark distinction exists between abstract geographic proximity and the behavior triggered by personal exposure. Furthermore, mediation analysis provides mixed support for the role of business profit; while profit loss negatively mediated equipment efficiency and recycling, the magnitude was marginal. This coping gap suggests that resource-constrained actors favor low-cost survivalist tactics over systemic shifts due to depleted organizational slack. Ultimately, the study highlights that disasters are powerful but inefficient teachers; without addressing technical and financial barriers to mitigation, global supply chains remain fragile despite localized disaster experiences. Full article

Accurate picking of microseismic P-wave arrival times is essential for the localization and monitoring of mining-induced seismic events. Conventional Short-Term Average/Long-Term Average (STA/LTA) detectors, while computationally efficient, are highly susceptible to noise interference. Conversely, deep learning approaches exhibit superior noise robustness but often involve substantial computational redundancy and compromised real-time performance. To address these limitations, we propose a novel two-stage picking framework that integrates STA/LTA with a lightweight U-Net, enabling rapid preliminary detection followed by fine-grained refinement. In the first stage, STA/LTA rapidly scans continuous waveforms to identify candidate windows potentially containing P-wave arrivals. In the second stage, a lightweight U-Net performs sample-level regression within each candidate window to refine arrival-time estimates with high precision. This coarse-to-fine paradigm effectively balances computational efficiency and picking accuracy. Experimental validation on 500 Hz microseismic data acquired from a coal mine in Gansu Province demonstrates that the proposed method achieves a hit rate of 63.21% within a tolerance window of ±0.01 s. This represents performance improvements of 25.42% and 40.47% over convolutional neural network (CNN) and STA/LTA methods, respectively, while reducing the mean absolute error to 0.0130 s. Furthermore, the model exhibits consistent performance on independent test sets, confirming its generalization capability and noise robustness. By combining the computational efficiency of STA/LTA with the representational power of deep learning, the proposed approach demonstrates significant potential for real-time industrial deployment. Full article

Microseismic identification and localization of signals from single-component distributed optical fiber acoustic sensors (DAS) in vertical wells are limited by low signal-to-noise ratio and lack of directional information, making effective signal identification and accurate localization difficult. Improving the detection rate and accuracy of such data events is helpful for analyzing the effect of fracturing. To address this, this paper proposes a method for automatically picking and locating microseismic events based on dual fitting modeling and waveform inversion. First, empirical mode decomposition (EMD) is used to adaptively decompose and reconstruct the original DAS signal to filter out approximately 80% of high-frequency noise (noise above 200 Hz). Second, the classic short-time average/long-time average energy ratio algorithm is used to pick all “event points.” Finally, DBSCAN density clustering and RANSAC robust fitting are combined to perform secondary screening and fitting modeling of the “event points” to obtain the continuous event arrival time distribution along the well section direction, and the spatial location of the seismic source is inverted based on the fitting results. Tested with experimental data from Well XX, the automatic detection rate reached 96%, and the accuracy of machine detection compared with manual judgment reached 95%. Full article

The early warning of rock mass failure in deep hard-rock mines presents a significant challenge for mine safety management. Microseismic monitoring data offer a novel analytical approach to address this issue. This study investigates the evolutionary patterns of rock mass failure in mining areas through the analysis of spatiotemporal energy data from microseismic events. Initially, key spatiotemporal energy parameters are extracted to identify microseismic events associated with localized damage and their periodic characteristics. Subsequently, a spatiotemporal fractal dimension analysis method is established to achieve fractal interpretation of the data by integrating field cloud maps. Finally, an early warning model centered on temporal energy is constructed, which delineates warning zones through a comprehensive evaluation of fractal dimensions, thereby providing decision-making support for mine safety. Full article

Close-distance multi-seam mining frequently induces secondary surface deformation and subsidence. Extracting a lower coal seam beneath an existing goaf repeatedly disturbs the overburden, often leading to roof collapse and the expansion of vertical water-conducting fractures that connect the working face to aquifers. Furthermore, the overlying goaf increases the risk of water inrush into active lower workings. This study investigates the mechanisms of strata reactivation and fracturing within an overlying goaf during lower seam extraction at a mine in Northwest China. Using theoretical analysis, numerical simulation, and microseismic monitoring, the research examines the secondary fracture mechanisms of the goaf roof and the resulting water-inrush potential. Research Findings: Strata Instability: Analysis of the key sandstone strata indicates that subsidence (W) of the key rock blocks satisfies 3.17 < W₁ = 4.61 m < 18 m for the lower seam and 3.17 m < W₂ = 5.31 m < 69.6 m for the 3-1# seam. These values confirm that key rock blocks in the basic roof undergo “reactivated” instability following fracture during lower seam mining. Pressure Relief and Fluid Dynamics: Mining-induced fracture initiation and propagation trigger strata reactivation. As the distance to the center of the goaf decreases, the subsidence of the overburden increases, ultimately resulting in a “trapezoidal” bending deformation pattern. Due to secondary activation, the roof subsidence 30 m above the 221 coal seam increased from 1.89 m to 5.475 m. The layers of high-strength, medium-grained sandstone and siltstone overlying the 317 coal seam and beneath the 221 goaf serve as high-strength material for the overlying rock formations. This suppresses the development of the caving zone and fracture zone, leading to subsidence failing to reach the sum of the heights of the two coal seams (6.8 m) and only reaching a value of 5.475 m. During extraction, the stress field undergoes a distinct evolution: it transitions from an initial “regular triangular” pressure-relief zone into a tripartite “weak–strong–strong” distribution. Furthermore, fluid discharge in the overlapping zone between the 317 working face and the 221 goaf increased sequentially, displaying an “alternating” pattern of peak vector variations as the face advanced. Microseismic Activity: Monitoring within the 300–500 m range identified frequent low-energy events and high-magnitude events (10⁴ J, 10⁵ J). These findings demonstrate that secondary excavation directly impacts the aquifer, creating a significant water-inrush hazard for the active working face. Full article

Frequent rockbursts in staggered roadways beneath residual coal pillars pose a critical challenge for the slice mining of ultra-thick coal seams. Taking the LW250101-2 of Huating Coal Mine as a case study, this paper systematically reveals the stress evolution laws and rockburst mechanism induced by irregular residual pillars by integrating microseismic (MS) monitoring, moment tensor inversion, and numerical simulation. First, source mechanism inversion analysis elucidated that compressive-shear failure of coal pillars was the dominant rupture mode in five of the eight recorded rockburst events. Second, numerical simulations demonstrate that the width of the left wing and the thickness of the right wing of the “L-shaped” coal pillar structure are the key geometric factors controlling rockburst risk; larger dimensions correlate with more intense stress concentration and higher-energy MS events. Moreover, the stress deflection effect of “L-shaped” coal pillars causes the haulage gateway of the LW250101-2 to remain in a state of stress accumulation, increasing its susceptibility to rockburst. Finally, a synergistic prevention system consisting of deep-hole roof blasting, large-charge coal blasting, and ultra-deep large-diameter boreholes was implemented. Field monitoring confirms that these measures dissipated high-stress concentrations, reduced rockburst frequency to zero and ensured safe mining. Full article

Deep learning algorithms are pivotal in the identification and classification of microseismic signals in mines subjected to impact pressure. However, conventional machine learning techniques often struggle to balance interpretability, computational efficiency, and accuracy. To address these challenges, this paper presents a hybrid feature selection and Transformer-based model for microseismic signal classification. The proposed model employs a hybrid feature selection method for data preprocessing, followed by an enhanced Transformer for signal classification. The study first outlines the underlying principles of the method, then extracts key seismic features—such as zero-crossing rate, maximum amplitude, and dominant frequency—from various microseismic signal types. These features undergo importance and correlation analyses to facilitate dimensionality reduction. Finally, a Transformer-based classification framework is developed and compared against several traditional deep learning models. The results reveal significant differences in the waveforms and spectra of different microseismic signal types. The selected feature parameters exhibit high representativeness and stability. The proposed model achieves an accuracy of 90.86%, outperforming traditional deep learning approaches such as CNN (85.2%) and LSTM (83.7%) by a considerable margin. This approach provides a reliable and efficient solution for the rapid identification of microseismic events in rockburst-prone mines. Full article

Mining-induced seismicity presents significant challenges to the safety and operational continuity of underground mines, particularly in deep and highly stressed environments. This study proposes a methodological framework for seismic risk evaluation inspired by predictive-maintenance principles and applied to a high-resolution microseismic catalog from a Chilean underground mine. Using a combination of data filtering and correlation analyses, we identify the seismic parameters that control the most variability in the dataset: moment magnitude, frequency corner, and both dynamic and static stresses. Based on this, we perform a Principal Component Analysis (PCA), which clearly demonstrates the physical interconnection between the selected parameters, thereby helping to better characterize the seismic events and the mining environment. Using these results, a PCA-based risk map is constructed, enabling the delineation of zones with different levels of seismic risk. Additionally, a temporal tracking of potentially hazardous seismicity is included. The proposed methodology demonstrates that microseismic behavior can be effectively represented in a reduced-dimension space, offering a promising foundation for predictive and data-driven risk-assessment tools capable of supporting real-time decision-making in underground mining operations. Full article

To address the severe geological hazards (e.g., high ground stress and rock burst) that threaten safety and efficiency in ultra-deep mining, this study develops a comprehensive microseismic monitoring system tailored for the Sishanling Iron Mine—a typical ultra-large-scale, ultra-deep mine with an extraction depth exceeding 1500 m. The system integrates high-sensitivity sensors, real-time data transmission, and intelligent processing algorithms. A scientifically designed sensor deployment plan achieves full-coverage of key mining areas, while a multi-level data processing framework encompassing signal acquisition, event detection, location inversion, and magnitude calculation enhances result accuracy. Applied in actual operations, the system effectively captures microseismic events with magnitudes from −2.14 to −1.96, achieving optimal planar and spatial positioning errors of 6.75 m and 9.66 m, respectively. It provides real-time early warning for hazards like rock burst, thereby mitigating risks and ensuring operational continuity. This work offers a practical reference for constructing microseismic systems in similar “double super” mines and enriches the theoretical and technical framework for safety monitoring in deep mining. Full article

Microseismic source localization is essential for the early warning of disasters in deep rock mass engineering. Traditional time difference methods require a dense sensor network, which is often impractical in large-scale scenarios with low-density sensor placement. Three-component microseismic sensors offer a promising alternative by utilizing multi-axis sensing, but their application depends on accurate sensor attitude estimation—a challenge due to installation deviations, integration errors, magnetic interference, and ambiguity in P-wave polarization direction. This study proposes an attitude calculation and source localization method based on P-wave polarization analysis. For attitude estimation, a unit vector from the sensor to the event is used as a reference; the P-wave polarization direction is extracted via covariance matrix analysis, and a novel “direction–vector–rotation–matrix cross-optimization” method resolves polarization–vector ambiguity. Multi-event data fusion enhances stability and robustness. For source localization, a “1 three-component + 1 single-component” sensor scheme is introduced, combining distance, azimuth, and distance difference constraints to achieve accurate positioning while substantially reducing hardware and energy costs. Field validation at the Yebatan Hydropower Station shows an average reference vector conversion error of 7.72° and an average localization deviation of 10.72 m compared with a conventional high-precision method, meeting engineering early-warning requirements. The proposed approach provides a cost-effective, efficient technical solution for large-scale microseismic monitoring with low sensor density, supporting sustainable infrastructure development through improved disaster risk management. Full article

Microseismic monitoring is extensively applied in hydraulic fracturing and mineral extraction, with accurate event localization being a critical component. Recently, deep learning approaches have shown promise for microseismic event localization; however, most of these supervised methods depend on large, labeled datasets, which are costly and challenging to acquire. To mitigate this issue, we propose a semi-supervised approach based on a residual convolutional autoencoder (RCAE) for automated microseismic localization, designed to leverage limited labeled data effectively and improve source localization accuracy even with small sample sizes. Our method employs pre-training by masking and reconstructing unlabeled seismic records, while integrating residual connections within the encoder to enhance feature extraction from seismic signals. This enables high localization accuracy with minimal labeled data, resulting in significant cost savings. Experimental results indicate that our method surpasses purely supervised approaches on both a 2D salt dome model and a 3D homogeneous half-space model, validating its effectiveness in microseismic localization. Further comparisons with baseline models highlight the method’s advantages, providing an innovative solution for improving cost-efficiency in practical applications. Full article

To enhance production efficiency, shale gas development often employs tighter well spacing and aggressive fracturing strategies. However, these approaches can result in well interference, where overlapping fracture networks between adjacent wells adversely affect gas production. This study introduces a comprehensive evaluation method for assessing fracture interference, with a specific focus on the role of Repeatedly Stimulated Volume (RSV). By integrating fracture network analysis with fracturing fluid migration modeling, we propose a combined static and dynamic risk assessment framework. The results demonstrate that RSV is a critical indicator of fracture interference—larger RSV values signify greater fracture overlap and intensified fluid migration between wells. Key engineering parameters influencing RSV are identified, including well spacing, fluid volume, and fracture design. Supported by real-time monitoring techniques such as microseismic events and pressure data, our dynamic assessment approach enables proactive management of interference risks. This work offers practical insights for optimizing shale gas development, allowing for improved production efficiency while mitigating interference-related drawbacks. Full article