Microseismic Events Research Articles

Study of seismic activity in the Haiyuan-Liupanshan region using automated microseismic detection techniques

Using continuous waveform data from 27 fixed seismic stations in Ningxia spanning nearly a decade from 2013 to September 2022, the automatic detection matching method (PALM) was employed to capture 34,373 seismic events across the region, which is twelve times more than manual cataloging during the same period. The events were used as a template to search for smaller seismic events in the Haiyuan-Liupanshan region. After location, a total of 14,741 microseismic events were detected. Based on this expanded dataset, we calculated b value and analysis the depth profiles of seismic. The findings indicate that the distribution of microseismicity has characteristics of zonation, clustering and interruption. Seismic activity aligns with active faults and closely correlates with tectonic zones. The areas with seismic gaps and low b-value zones indicate a higher stress level on the north side of the middle section of the Haiyuan Fault, which was also the epicenter of the magnitude 4.9 Pingchuan earthquake on December 31, 2023. The seismic profiles show that the regional earthquakes mainly occur at a depth of more than 30 km. The depth distribution of these earthquakes reflects the inclination of the fault and thus corresponds exactly to the actual conditions.

Unconventional Fracture Networks Simulation and Shale Gas Production Prediction by Integration of Petrophysics, Geomechanics and Fracture Characterization

The proficient application of multistage fracturing methods enhances the status of the Duvernay shale formation as a highly esteemed shale reservoir on a global scale. Nevertheless, the challenge is in accurately characterizing unconventional fracture behavior and predicting shale productivity due to the complex distributions of natural fractures, pre-existing faults, and reservoir heterogeneity. The present study puts forth a Geo-Engineering approach to comprehensively investigate the Duvernay shale reservoir in the vicinity of Crooked Lake. To begin with, on the basis of the experimental results and well-logging interpretations, a high-quality petrophysical and geomechanical model is constructed. Subsequently, the establishment of an unconventional fracture model (UFM) takes into account the heterogeneity of the reservoir and the interactions between hydraulic fractures and pre-existing natural fractures/faults and is further validated by 18,040 microseismic events. Finally, the analysis of well productivity is conducted by numerical simulations, revealing that the agreement between the simulated and observed production magnitudes exceeds 89%. This paper will guide the efficient development of increasingly important unconventional shale resources.

Compound disaster characteristics of rock burst and coal spontaneous combustion in island mining face: A case study

The island mining face in coal mines often encounters stress concentration and significant air leakage, elevating the risk of rock burst and spontaneous combustion of residual coal in the goaf. This study centers on the investigation of the 9305 island mining face within a mine situated in Shandong Province, China. Leveraging the features of coal spontaneous combustion and data from microseismic and gas monitoring throughout the mining process, a novel method for calculating safe mining speeds under compound disasters is introduced. The safe mining speed of the 9305 island mining face is stratified, and a prevention and control technology integrating long distance directional drilling for impact-spontaneous combustion compound disasters is proposed. Findings suggest that to mitigate spontaneous combustion of residual coal, considering the seam's spontaneous combustion tendency, the safe mining speed should exceed 3.7 m per day. Accounting for the distribution of microseismic events during coal seam extraction, the safe mining speed is maintained below 4.8 m per day in the unprotected mining area and capped at 8 m per day in the protected zone. Through a comprehensive analysis considering coal seam spontaneous combustion tendency, impact tendency, and time to coal seam oxidation to critical temperature, the safe mining speed for the island mining face ranges from 3.7 m per day to 4.8 m per day in the unprotected area and from 5.14 m per day to 8 m per day in the protected area. The proposed long distance directional drilling 'one hole, multiple purposes' scheme enables an integrated approach encompassing pressure relief before coal seam extraction, water injection during mining, and grouting post-mining. This method effectively prevents and controls the compound disasters of rock burst and coal spontaneous combustion, presenting innovative technical solutions for the safe extraction of coal resources.

Mechanism of rockburst induced by the microseismic event in the floor strata of high tectonic stress zones: A case study

With the increase of mining scope, rockburst occurs frequently, but its generation mechanism has not been understood comprehensively. Based on a rockburst in the coal pillar area of high tectonic stress zones (HTSZs), this study analyzed the distribution characteristics of large-energy microseismic (MS) events by using data statistics. The mechanical cause of the MS event that induced the rockburst was revealed by means of seismic moment tensor inversion. On this basis, by using numerical simulation, this study explored the distribution characteristics of static load in rockburst area and the effect of dynamic load in the floor, and then proposed the rockburst mechanism. The results show that under the squeezing action, the floor strata in HTSZs implode and transmit energy outward in the form of stress waves. This causes the cumulative damage and stress of the coal body in the fast track of coal pillar area increase in a short time. Since the coal in this area has already been in the critical stress state, small stress changes may lead to coal failure and rockburst. In this case of rockburst, the high static load of coal is the main force source, and the dynamic load plays a role in increasing coal body damage and inducing rockburst. Combined with seismic moment tensor inversion and numerical simulation, this paper proposes a rockburst research scheme, which makes the simulation of dynamic load more reasonable. The results provide the theoretical basis for rockburst control under similar conditions.

CHIM-Net: A Combined Hierarchical Information Model for Predicting Time, Space and Intensity of Mining Microseismic Events

CHIM-Net: A Combined Hierarchical Information Model for Predicting Time, Space and Intensity of Mining Microseismic Events

Unsupervised clustering of mining-induced microseismicity provides insights into source mechanisms

Microseismic source mechanisms in underground mines can provide information about the rock mass response to mining. Conventional approaches to such studies rely upon moment tensor solutions that are susceptible to modeling assumptions and need reliable information about source locations and high-resolution velocity models. We propose the application of unsupervised clustering to group microseismic events into different classes directly from the waveform data such that the events in a specific class have similar source mechanisms. Our method has three main steps, first using spectral decomposition to separate the source terms from the path-receiver contributions in the observed amplitude spectra of events occurring in spatially dense clusters. Second, reducing the number of features from the source spectra using independent component analysis (ICA). Third, applying a Gaussian mixture model (GMM) to the reduced feature matrix to obtain event clusters. To test our method, we generate synthetic waveform data using the receiver network and the recorded microseismic event locations in an underground potash mine in Saskatchewan. Results show the ability of our method to separate events into different classes corresponding to differences in source mechanisms. Application to field data recorded in the mine during February 2021 successfully discriminates between blasts and microseismic events. The data recorded between 1 March and 30 June 2021 that contain microseismic events only are divided into two dominant classes. Using known moment tensors (MT) of some of these events for labeling, we interpret one of the two classes as having dominant double-couple mechanisms as compared to the other which most likely corresponds to the linear dipole-tensile mechanisms. Our method, combined with some expert knowledge such as MT of some larger magnitude events, can offer an assessment of source types of large microseismic populations as often encountered in induced seismicity.

Theoretical study on the critical index of rock burst stress monitoring in coal seam drilling

Rock burst disasters severely restrict the safe and efficient mining of coal. The fundamental cause of their occurrence is the concentration of stress within the coal mass. Stress monitoring in coal seam drilling is widely used as an effective method for rock burst monitoring. However, how to scientifically and reasonably set the critical values of early warning indicators that match the conditions of each mine has always been a key issue restricting the accurate prediction of rock burst by the drilling stress method. This paper adopts a method combining theoretical analysis and field practice to conduct research on the critical values of drilling stress early warning indicators. Based on perturbation response instability theory, a mechanical model for the occurrence of impact ground pressure has been established. Based on the instability theory of disturbance response, a mechanical model for the occurrence of impact ground pressure has been established, leading to the derivation of the expression for the near-field critical stress of impact ground pressure events. The theoretical formula for the critical value of drilling stress early warning indicators was obtained based on the difference between the critical stress of rock burst occurrence and the actual stress in the roadway. This formula includes the mechanical parameters of the coal mass and its propensity for rock burst, roadway support stress, mining depth, stress concentration coefficient, and the initial installation pressure of the stress gauge. They can be determined by the geological and mining technical conditions of each mine. This theoretical formula breaks the uniformity of the critical values for stress warning indicators in various mine drill holes, allowing each mine to scientifically determine its critical value based on its own conditions. This theoretical method has been applied to a high-stress mine in Shanxi, China, and the critical values of drilling stress early warning indicators were obtained. When the monitored stress exceeded the critical value, dynamic phenomena of anchor rod and cable fractures occurred in the roadway roof. The distribution of microseismic events also shifted towards the warning area, and the microseismic monitoring indicators reached the warning values. This confirmed the engineering feasibility of the critical values for drilling stress early warning indicators determined by the theoretical method.

Feasibility of Identifying Shale Sweet Spots by Downhole Microseismic Imaging

Several studies suggest that shale sweet spots are likely associated with a low Poisson’s ratio in the shale layer. Compared with conventional geophysical techniques with active seismic data, it is straightforward and cost-effective to delineate the distribution of 3D Poisson’s ratios using microseismic data. In this study, an alternating method is proposed to determine microseismic event locations, 3D P-wave velocity, and Poisson’s ratio models with data recorded from downhole monitoring arrays. The method combines the improved 3D traveltime tomography, which inverts P and S arrivals for 3D P-wave velocity and Poisson’s ratio structures simultaneously, and a 3D grid search approach for event locations in an iterative fashion. The traveltime tomography directly inverts the Poisson’s ratio structure instead of calculating the Poisson’s ratios from P- and S-wave velocities (i.e., Vp and Vs) that are inverted by conventional traveltime tomography separately. The synthetic results and analysis suggest that the proposed method recovers the true Poisson’s ratio model reasonably. Additionally, we apply the method to a field dataset, which indicates that it may help delineate the reservoir structure and identify potential shale sweet spots.

Machine Learning-Driven Quantification of CO2 Plume Dynamics at Illinois Basin Decatur Project Sites Using Microseismic Data

This study utilizes machine learning to quantify CO2 plume extents by analyzing microseismic data from the Illinois Basin Decatur Project (IBDP). Leveraging a unique dataset of well logs, microseismic records, and CO2 injection metrics, this work aims to predict the temporal evolution of subsurface CO2 saturation plumes. The findings illustrate that machine learning can predict plume dynamics, revealing vertical clustering of microseismic events over distinct time periods within certain proximities to the injection well, consistent with an invasion percolation model. The buoyant CO2 plume partially trapped within sandstone intervals periodically breaches localized barriers or baffles, which act as leaky seals and impede vertical migration until buoyancy overcomes gravity and capillary forces, leading to breakthroughs along vertical zones of weakness. Between different unsupervised clustering techniques, K-Means and DBSCAN were applied and analyzed in detail, where K-means outperformed DBSCAN in this specific study by indicating the combination of the highest Silhouette Score and the lowest Davies–Bouldin Index. The predictive capability of machine learning models in quantifying CO2 saturation plume extension is significant for real-time monitoring and management of CO2 sequestration sites. The models exhibit high accuracy, validated against physical models and injection data from the IBDP, reinforcing the viability of CO2 geological sequestration as a climate change mitigation strategy and enhancing advanced tools for safe management of these operations.

A Real-Time Inverted Velocity Model for Fault Detection in Deep-Buried Hard Rock Tunnels Based on a Microseismic Monitoring System

Microseismic monitoring is an effective and widely used technology for dynamic fault disaster early warning and prevention in deep-buried hard rock tunnels. However, the insufficient understanding of the distribution of native faults poses a major challenge to yielding precise early warnings of disasters using an MS (Microseismic Monitoring System). Velocity field inversion is a reliable means to reflect fault information, and there is an urgent need to establish a real-time velocity field inversion method during tunnel excavation. In this paper, a method based on an MS is proposed to achieve the inversion of the velocity field in the monitoring area using microseismic event and excavation blasting data. The velocity field inversion method integrates the reflected wave ray-tracing method based on PSO (Particle Swarm Optimization) theory and FWI (Full-Waveform Inversion) theory. The accuracy of the proposed velocity inversion method was verified by various classic numerical simulation cases. In numerical simulations, the robustness of our method is evident in its ability to identify anomalous structural surfaces and velocity discontinuities ahead of the tunnel face.

Application of artificial intelligence in coal mine ultra-deep roadway engineering—a review

The deep integration of computer field and coal mining field is the only way to coal mine intellectualization. A variety of artificial intelligence tools have been applied in open-pit and shallow coal mines. However, with the geometric increase of coal demand, the contradiction between supply and demand is becoming more and more serious, and the exploitation of resources from shallow layer (> 600 m) has become an inevitable trend. Well then, as a new engineering scene, the harsh conditions of “three high and one disturbance” seriously threaten the safety of personnel. The superposition of complex mining environment makes the number of input factors increase sharply, which leads to the application of artificial intelligence in deep coal mine roadway engineering. The guidance is not mature, the construction of various databases is missing, and there are still some problems in universality and applicability. To this end, this paper starts with the introduction of the operating characteristics of various artificial intelligence tools, and conducts a comprehensive study of the relevant high-level articles published in top journals. It systematically sorts out the research progress that has successfully solved the five directions of rock mechanics strength, surrounding rock stability, rock-burst, roof fall risks and micro-seismic events. While objectively evaluating the comprehensive performance of different tools, it also expounds its own views on key research and results. Literature review shows that whether as a development tool or a comparative model, the application of ANN in the field of coal mining is more than 98%, and it performs extremely well in the direction of surrounding rock stability and roof fall risk, with an accuracy rate of more than 90%. As the most mature direction of AI tools application, rock mechanical strength has experienced the development process from “SVM → ANN → DL → XGBoost → RF”. The dataset is from small samples (< 100) to big samples (> 1000), and the R2 of tree-based models can be stabilized at more than 95%. The research on rock-burst prediction mainly focuses on field factors and micro-seismic monitoring data. Whether it is a small sample or a large-scale data model, the accuracy of BN remains above 85%. The prediction and evaluation of micro-seismic events is a new direction in recent years. The image processing and application of CNN is extremely important. The signal recognition and classification accounts for more than 90%, and the research potential of source location needs to be further explored. In general, the nature of the rock itself is the first choice for almost all influencing factors. At the same time, the update iteration of monitoring methods (micro-seismic, ground sound, separation, deformation, etc.) expands the development of the database, making it possible to obtain the data due to threat to life and cost of equipment, which is very difficult to obtain before. In the process of parameter selection at the input end, the method of combining lithology conditions, geological environment and monitoring data will gradually become the first choice for research. Finally, in the follow-up work collation and on-the-spot investigation, it mainly focuses on the existing problems of deep coal mines, explores the application potential of artificial intelligence in deep coal mine roadway engineering, puts forward the possible research focus and challenging problems in the future, and gives its own opinions.

LGF SeismoLocator: A Deep Learning Model for Precision Microseismic Event Localization in Coal Mines

LGF SeismoLocator: A Deep Learning Model for Precision Microseismic Event Localization in Coal Mines

Seismic anisotropy of granitic rocks from a fracture stimulation well at Utah FORGE using ultrasonic measurements

For effective fracture stimulation in Enhanced Geothermal Systems (EGS), characterization of subsurface rock elastic properties is essential. Because seismic anisotropy significantly influences hydraulic fracturing, locating and characterizing microseismic events, and forecasting stress trajectories, our research undertakes laboratory experiments to study seismic anisotropy parameters of core samples from the Utah FORGE EGS site. We evaluated two rock samples from well 58–32, at two depths of 2074.16 m (6,805 feet) and 2268.01 m (7,441 feet), respectively. We analyze the mineralogy and classify the shallower rock as syenogranite, and the deeper rock as orthogneiss and monzonite. We cut cylindrical plugs from these two core samples, imaged them by computed tomography (CT) X-ray imaging, and measured their seismic anisotropy using ultrasonic waves at 1 MHz and under varying confining pressures (5–50 MPa) in ambient temperature conditions. With the assumption of the transversely isotropic (TI) medium, we measured three P-wave velocities and two S-wave velocities along different propagation directions to compute the Thomsen anisotropy parameters. Our results showed that the measured seismic velocities of each rock sample increase with increasing effective pressures, a behavior likely caused by closure of microcracks identified in our CT images. We also find that the measured anisotropies are higher at lower effective pressures. The maximum measured anisotropies for P-waves and S-waves are ∼23% and ∼20%, respectively. Therefore, we expect in the field operation that anisotropy should increase with increasing pore pressure caused by injection, which could be valuable in future EGS reservoir management and monitoring.

Full-waveform automatic location of small seismic events in an underground mine using synthetic strain Greens tensors

A reliable method to locate microseismic evets is an important objective for routine mine seismic monitoring. We developed an algorithm that can automatically and reliably locate an event from a single uniaxial trace. Signals created by microseismic events are recorded as seismograms that consist of direct P- and S-waves as well as the waves reflected off the underground excavations. We take advantage of the extra complexity these reflections introduce as they make the signal more unique. In our investigation we cross-correlated unaltered synthetic seismograms against trial waveforms created from a linear combination of appropriate waveforms from a library of numerically calculated strain Green's tensors. We use a single uniaxial synthetic seismogram to invert for the source location, six moment tensor components, and frequency. The resulting cost functions are constructed as maximal correlations at each node in the mine grid through the true source x-planes. This function is maximized by differential evolution to simultaneously yield the source parameters. Sensitivity tests such as shifting the mine plans and altering the background velocities of the media are investigated to test the robustness of the method. We contaminate the data with 40% white noise to test the resolution against simulated real recorded seismograms. The method is demonstrated using synthetic data calculated with a realistic underground 3D velocity model.

Location method of ill-conditioned microseismic source and its engineering application

Microseismic event location is one of the core parameters in microseismic monitoring, and the accuracy of localization will directly affect the effectiveness of engineering applications. However, limited by spatial factors, the geometry of the sensor installation will be close to linear, which makes the localization equation suffer from the pathological problem, and the localization accuracy is greatly reduced. To address this problem, the reasons for the pathological problem are analyzed from the perspective of the objective function residuals and coefficient matrix. The pathological problem is caused by the combined effect of the poorer sensor array and data errors, and its residual isosurface shows a conical distribution, and as the residual value decreases, the apex of the isosurface gradually extends to the far side, and the localization results do not converge. For this reason, an improved regularized Newton downhill localization algorithm is proposed. In this method, firstly, the Newtonian downhill method is improved so that the magnitudes of the seismic source parameters are the same, and the condition number of the coefficient matrix is reduced; then, the L-curve method is used to calculate the regularization factor for the pathological equations, and the coefficient matrix is improved; finally, the pathological equations are regularized, and the seismic source coordinates are obtained by the improved Newtonian downhill method. The results of engineering applications show that compared with the traditional algorithm based on automatic of P-arrival picking, the number of effective microseismic events calculated by the proposed localization algorithm is increased by 194.7 %, and the localization accuracy is substantially improved. The proposed algorithm reduces the problem of low accuracy of S-arrival picking and allows localization using only P-wave arrival. The method reduces the quality requirements of the data and significantly improves the utilization of microseismic events and positioning accuracy.

Seismicity associated with hydraulic fracturing in Changning shale gas field, China: Constraints from source mechanisms, stress field and fluid overpressure thresholds

Source properties and stress fields are critical to understand fundamental mechanisms for fluid-induced earthquakes. In this study, we identify the focal mechanism solutions (FMSs) of 360 earthquakes with local magnitude ML ≥ 1.5 in the Changning shale gas field from January 2016 to May 2017 by fitting three-component waveforms. We then constrain the directions of the maximum horizontal stress (σHmax) for four dense earthquake clusters using the stress tensor inversion method. The stress drops of 121 earthquakes with ML ≥ 1.5 are calculated using the spectral ratio method. We examine the spatiotemporal heterogeneity of stress field, and discuss the cause of non-double-couple (non-DC) components in seismicity clusters. Following the Mohr-Coulomb criterion, we estimate the fluid overpressure thresholds from FMS for different seismic clusters, providing insights into potential physical mechanisms for induced seismicity. The FMS results indicate that shallow reverse earthquakes, with steep dip angles, characterize most events. The source mechanisms of earthquakes with ML ≥1.5 are dominated by DC components (> 70%), but several earthquakes with ML > 3.0 and the microseismic events nearby during injection period display significant non-DC components (> 30%). Stress inversion results reveal that the σHmax direction ranges from 120° to 128°. Stress drops of earthquakes range between 0.10 and 64.49 MPa, with high values occurring on reverse faults situated at a greater distance from the shale layer, accompanied by a moderate rotation (≤ 25°) in the trend of σHmax. The seismic clusters close to the shale layer exhibit low fluid overpressure thresholds, prone to being triggered by high pore-pressure fluid. The integrated results suggest that the diffusion of high pore pressures is likely to be the primary factor for observed earthquakes. The present results are expected to offer valuable insights into the origin of anomalous seismicity near the shale gas sites.

Characterization of Micro-seismic Activity in Northern Cyprus Using Complexity and Corner Frequency Methods

Cyprus is an island country located in the eastern Mediterranean, to the south of Türkiye and the western of Syria and Lebanon, and is a popular tourist destination. Due to being surrounded by seas on all four sides, meticulous planning of rescue, assistance, and evacuation plans is necessary in the face of disasters such as earthquakes and tsunamis. Tectonically, the southern part of the island is controlled by the Cyprus Arc, while the northern part is dominated by the Kyrenia Range. The demand for raw materials for construction and industry is met through controlled quarry blasting operations carried out by open-pit quarry companies in the districts of Kyrenia and Nicosia. As a result, both natural and artificial seismic events occur in the region, and these quakes are documented in seismic catalogs by seismology centers. However, due to the low energy content of micro-seismic events and the inadequacy of seismic stations on the island, the source types of these seismic events can be misidentified in the catalogs. In this context, the study focuses on 122 seismic events with magnitudes between 0.9≤Ml≤2.7 that occurred in Northern Cyprus during the January 2018 - December 2021 period (4 years). The seismic events recorded by the station LFK, operated by Boğaziçi University Kandilli Observatory and Earthquake Research Institute Regional Earthquake-Tsunami Monitoring Center (KOERI-RETMC), were classified using Linear and Quadratic Discriminant Functions based on complexity and corner frequency methods. According to the results obtained, 10 of the 122 seismic events were identified as natural, and 96 were determined to be artificial, resulting in a general success rate of 86.89%. However, classification results for 16 seismic events were inconclusive with the methods used. As a result, more detailed secondary analyses should be conducted to accurately determine the source types of micro-seismic events, and the seismic catalogs should be updated accordingly.

Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring.

A deep-seated landslide could release numerous microseismic signals from creep-slip movement, which includes a rock-soil slip from the slope surface and a rock-soil shear rupture in the subsurface. Machine learning can effectively enhance the classification of microseismic signals in landslide seismic monitoring and interpret the mechanical processes of landslide motion. In this paper, eight sets of triaxial seismic sensors were deployed inside the deep-seated landslide, Jiuxianping, China, and a large number of microseismic signals related to the slope movement were obtained through 1-year-long continuous monitoring. All the data were passed through the seismic event identification mode, the ratio of the long-time average and short-time average. We selected 11 days of data, manually classified 4131 data into eight categories, and created a microseismic event database. Classical machine learning algorithms and ensemble learning algorithms were tested in this paper. In order to evaluate the seismic event classification performance of each algorithmic model, we evaluated the proposed algorithms through the dimensions of the accuracy, precision, and recall of each model. The validation results demonstrated that the best performing decision tree algorithm among the classical machine learning algorithms had an accuracy of 88.75%, while the ensemble algorithms, including random forest, Gradient Boosting Trees, Extreme Gradient Boosting, and Light Gradient Boosting Machine, had an accuracy range from 93.5% to 94.2% and also achieved better results in the combined evaluation of the precision, recall, and F1 score. The specific classification tests for each microseismic event category showed the same results. The results suggested that the ensemble learning algorithms show better results compared to the classical machine learning algorithms.

Assessment of Microseismic Events via Moment Tensor Inversion and Stress Evolution to Understand the Rupture of a Hard–Thick Rock Stratum

Assessment of Microseismic Events via Moment Tensor Inversion and Stress Evolution to Understand the Rupture of a Hard–Thick Rock Stratum

Study on the early warning of cracking and water inrush risk of coal mine roof and floor

Microseismic monitoring has proven to be an effective approach for detecting and preempting water inrush incidents within mining operations. However, challenges persist, particularly in terms of relying on a singular early warning index and the complexities involved in quantification. In response to these obstacles, a dedicated investigation was undertaken against the backdrop of mining activities at the 11,023 working face of Paner Coal Mine. Primarily, a novel methodology for categorizing the roof and floor into distinct zones was established based on the vertical distribution of microseismic events. Furthermore, this study delves into the dynamic evolution of key source parameters, such as microseismic energy, apparent stress, and apparent volume, amidst mining disturbances, enabling a comprehensive evaluation of the risk associated with roof and floor cracking, as well as potential water inrush incidents. A groundbreaking approach to early warning was proposed, operating on three pivotal dimensions: the depth of fractures, the intensity of fractures, and the likelihood of water inrush. Through rigorous validation during mining operations at the 11,023 working face, the efficacy was substantiated. Ultimately, the achievements offer invaluable insights and practical guidance for the advancement and implementation of water inrush early warning systems in coal mining contexts.