Multi-source judgment method for risk of water inrush and rock burst composite disaster

Abstract

The invention relates to the technical field of coal mine safety, in particular to a water inrush and rock burst composite disaster risk multi-source judgment method which comprises the steps that four fields of original data of a target underground area are collected in real time, and the four fields comprise a stress field, a vibration field, an energy field and a seepage field; extracting multivariate feature indexes from the four-field original data to obtain four-field multivariate indexes; constructing a core composite index based on the four-field multi-element indexes, quantifying the coupling strength of water inrush and rock burst, and judging a disaster dominant type; and calculating a comprehensive risk index based on the four-field multi-element indexes and the disaster dominant type, and judging the risk level of the water inrush and rock burst composite disaster according to the comprehensive risk index. According to the invention, accurate identification and graded prevention and control of composite disasters are realized, and the accuracy and timeliness of early warning of mine disasters are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine safety, and in particular to a multi-source determination method for the hazard of combined water inrush and rock burst disasters. Background Art

[0002] As mining operations extend deeper, coal mining beneath thick aquifers faces the severe challenge of coupled water inrush and rockburst hazards. Collaborative prevention and control is a key path to safe resource development. To achieve efficient disaster early warning, existing research has proposed field-specific monitoring technologies (such as stress sensing networks and hydrological monitoring systems) that attempt to predict risks by capturing anomalies in a single physical field. Integrating dynamic correlation analysis of stress and seepage fields, based on multi-field coupling mechanism theory, may improve the accuracy of complex disaster identification. After mining-induced stress concentration induces rock fracture, high-pressure water surges along the fractures and reversely exacerbates surrounding rock instability, forming a "water-rock linkage disaster chain." Quantifying the dynamic feedback loop between water hazard precursors and rockburst responses is crucial for assessing complex disaster risk. However, existing methods rely on static thresholds to segment disaster data, resulting in the loss of cross-field coupling information and the inability to construct water-rock stress synergy criteria. Therefore, it is urgent to develop complex disaster risk assessment methods that integrate multi-source dynamic information to overcome the bottleneck of inaccurate prevention and control measures and support safe mining strategies beneath thick aquifers. Summary of the Invention

[0003] The purpose of the present invention is to address the problem of early warning failure caused by isolated analysis of multiple physical fields in the monitoring of mine impact and water inrush composite disasters, and to provide a multi-source judgment method for the danger of water inrush and impact ground pressure composite disasters. Based on the four-field coupling monitoring method of stress field, energy field, vibration field and seepage field, the disaster judgment threshold is dynamically optimized and a multi-level progressive early warning mechanism is established to achieve accurate identification and graded prevention and control of composite disasters, significantly improving the accuracy and timeliness of mine disaster early warnings.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A multi-source determination method for the risk of combined water inrush and rock burst disasters, comprising:

[0006] Real-time acquisition of four-field raw data of the target downhole area, including stress field, vibration field, energy field, and seepage field;

[0007] Extracting multivariate characteristic indicators from the four fields of original data to obtain four multivariate indicators;

[0008] Based on the four multi-factor indicators, a core composite index is constructed to quantify the coupling intensity between water inrush and rock burst, and to determine the dominant type of disaster.

[0009] Calculate a comprehensive risk index based on the four-field multi-index and the disaster dominant type, and determine the water inrush and rock burst composite disaster risk level according to the comprehensive risk index.

[0010] Optionally, the data of the stress field is obtained by monitoring dual-source CT and three-way stress, the data of the vibration field is obtained by monitoring microseismic space-time activity, the data of the energy field is obtained by monitoring microseismic energy, and the data of the seepage field is obtained by monitoring hydrological information.

[0011] Optionally, the multi-index of the stress field includes wave speed anomaly coefficient, stress concentration coefficient, wave speed change gradient, stress distribution characteristics, stress change rate, and relative stress value; the multi-index of the vibration field includes time series concentration, space-time diffusion, activity index, time fractal dimension, seismic source concentration, and spatial fractal dimension; the multi-index of the energy field includes impact deformation energy, slope of relationship between earthquake frequency and earthquake energy level, earthquake occurrence rate per unit time, total stress equivalent, and energy fractal dimension; and the multi-index of the seepage field includes daily water inflow, instantaneous water inflow, water level change, and water temperature change.

[0012] Optionally, the core composite index includes stress-seepage coupling coefficient and energy-vibration coupling coefficient, wherein the stress-seepage coupling coefficient is used to represent the interaction between the stress field and the seepage field, and the energy-vibration coupling coefficient is used to represent the synergistic evolution of the energy field and the vibration field.

[0013] Optionally, the stress-seepage coupling coefficient is:

[0014] ;

[0015] wherein, is the stress-seepage coupling coefficient; is the stress concentration coefficient; is the permeability anomaly value; is the water level change; is the prevention zero constant.

[0016] Optionally, the energy-vibration coupling coefficient is:

[0017] ;

[0018] wherein, is the energy-vibration coupling coefficient; is the total acoustic emission energy; is the time series concentration; is the time fractal dimension value; is the activity index.

[0019] Optionally, calculating a comprehensive risk index based on the four-field multi-index and the disaster dominant type includes:

[0020] The multi-element indicators of each field are normalized to obtain the normalized abnormal rate of each field;

[0021] The core composite indicator and the disaster dominant type are analyzed to dynamically assign weights to each field, wherein a high weight is assigned to the seepage field when water inrush is dominant, and a high weight is assigned to the stress field when impact is dominant;

[0022] The comprehensive risk index is calculated based on the normalized abnormal rate of each field and the corresponding weight.

[0023] Optionally, calculating the comprehensive risk index comprises:

[0024] ;

[0025] wherein, is the comprehensive risk index; is the corresponding weight of each field; S is the stress field comprehensive abnormal rate; V is the vibration field comprehensive abnormal rate; E is the energy field comprehensive abnormal rate; and F is the seepage field comprehensive abnormal rate.

[0026] The present application has the following beneficial effects:

[0027] The water inrush and rock burst combined disaster risk multi-source determination method provided by the present application systematically integrates the information of stress field, energy field, vibration field and seepage field, more comprehensively captures the precursors of combined disasters, focuses on solving the coupling mechanism of impact and water inrush, and the early warning is more in line with the actual disaster evolution law. Through early abnormal identification of multi-field information, the early warning time window is extended. Multi-index cross verification reduces the false positive rate of a single index and improves the early warning accuracy; and the dominant factors of disasters (impact dominant, water inrush dominant or strong coupling) can be distinguished, which provides a basis for accurate prevention and control. Based on the existing mature monitoring technology, it is easy to deploy and apply in the mine site. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0029] Figure 1 A flow chart of a water inrush and rock burst combined disaster risk multi-source determination method according to an embodiment of the present application;

[0030] Figure 2 A risk multi-source early warning index diagram according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] This embodiment provides a multi-source determination method for the risk of combined water inrush and rock burst disasters, such as Figure 1 Shown, including:

[0034] Real-time acquisition of four-field raw data of the target downhole area, including stress field, vibration field, energy field, and seepage field;

[0035] Extracting multivariate characteristic indicators from the four fields of original data to obtain four multivariate indicators;

[0036] Based on the four multi-factor indicators, a core composite index is constructed to quantify the coupling intensity between water inrush and rock burst, and to determine the dominant type of disaster.

[0037] A comprehensive risk index is calculated based on the four multi-factor indicators and the dominant types of disasters, and the risk level of the combined disaster of water inrush and rock burst is determined according to the comprehensive risk index.

[0038] Specifically, this embodiment systematically integrates information from four fields: stress, energy, vibration, and seepage. This allows for more comprehensive detection of complex disaster precursors, focusing on the coupling mechanism between impact and water inrush, ensuring that early warnings are more aligned with actual disaster evolution. Early anomaly identification from multiple fields extends the warning window. Cross-validation of multiple indicators reduces the false alarm rate for a single indicator, improving warning accuracy. Furthermore, the system can distinguish the dominant factor in a disaster (primarily impact, primarily water inrush, or strongly coupled), providing a basis for precise prevention and control. Based on established monitoring technology, it is easily deployed and applied on-site in mines.

[0039] Furthermore, the stress field data is obtained by monitoring dual-source CT and three-dimensional stress, the vibration field data is obtained by monitoring the spatiotemporal activity of microseismic activity, the energy field data is obtained by monitoring microseismic energy, and the seepage field data is obtained by monitoring hydrological information.

[0040] The multivariate indicators of the stress field include wave velocity anomaly coefficient, stress concentration coefficient, wave velocity change gradient, stress distribution characteristics, stress change rate, and relative stress value; the multivariate indicators of the vibration field include time series concentration, time and space diffusion, activity index, time fractal dimension, source concentration, and space fractal dimension; the multivariate indicators of the energy field include impact deformation energy, the slope of the relationship between the number of earthquakes and the earthquake energy level, the earthquake occurrence rate per unit time, total stress equivalent, and energy fractal dimension; the multivariate indicators of the seepage field include daily water inflow, instantaneous water inflow, water level change, and water temperature change.

[0041] Specifically, this embodiment uses an underground distributed monitoring system to acquire raw data for four fields: stress field, vibration field, energy field, and seepage field. For the stress field, CT detection and triaxial stress sensors are used to monitor surrounding rock stress parameters. For the vibration field, a microseismic monitoring system is used to monitor the spatiotemporal motion parameters of microseismic events. For the energy field, a geoacoustic monitoring system is used to monitor the energy parameters of microseismic events. For the seepage field, a hydrological information monitoring system is used to monitor mine hydrological parameters.

[0042] like Figure 2 As shown in the figure, multivariate characteristic indicators are extracted from the raw data of the four fields to characterize the key features of disaster evolution. Stress field indicators include: wave velocity anomaly coefficient, wave velocity gradient, stress concentration coefficient, stress distribution characteristics, strain change rate, and relative stress value; vibration field indicators include: temporal concentration, spatiotemporal diffusion, activity index, temporal fractal dimension, source concentration, and spatial fractal dimension; energy field indicators include: impact deformation energy, energy fractal dimension, total stress equivalent, the slope of the relationship between the number of earthquakes and the earthquake energy level (b value), and the earthquake occurrence rate per unit time (A(b) value); and seepage field indicators include: daily water inflow, instantaneous water inflow, water level change, and water temperature change.

[0043] Among them, the b value describes the ratio of the number of earthquake events of different magnitudes. The larger the b value, the more small-magnitude events there are. The A(b) value is the number of earthquakes with a magnitude of M=0 or above occurring per unit time under a specific b value. The larger the value, the higher the vibration frequency, reflecting the absolute energy release level of seismic activity in the area.

[0044] Furthermore, the core composite index includes a stress-seepage coupling coefficient and an energy-vibration coupling coefficient, wherein the stress-seepage coupling coefficient is used to characterize the interaction between the stress field and the seepage field, and the energy-vibration coupling coefficient is used to characterize the coordinated evolution of the energy field and the vibration field.

[0045] The stress-seepage coupling coefficient is:

[0046] ;

[0047] in, is the stress-seepage coupling coefficient; is a stress concentration coefficient; is a permeability anomaly value; is a water level change; is a zero constant prevention.

[0048] The energy-vibration coupling coefficient is:

[0049] ;

[0050] wherein, is an energy-vibration coupling coefficient; is total acoustic emission energy; is a time series concentration; is a time fractal value; is an activity index.

[0051] Specifically, the embodiment is based on four-field multi-index, constructs a core composite index, analyzes and quantifies the coupling strength of water inrush and rock burst, and judges the dominant type of disaster. ① Stress-seepage coupling coefficient: representing the interaction of stress field and seepage field, the larger the coefficient, the more significant the stress concentration leads to the increase of permeability and the change of water level, and the stronger the coupling of water inrush and rock burst; ② Energy-vibration coupling coefficient: representing the synergistic evolution of energy field and vibration field, the larger the coefficient, the higher the energy accumulation and vibration event concentration, and the greater the danger of rock burst.

[0052] Further, calculating a comprehensive risk index based on the four-field multi-index and the dominant type of disaster comprises:

[0053] normalizing the multi-index of each field to obtain a normalized anomaly rate of each field;

[0054] allocating weights to each field by analyzing the core composite index and the dominant type of disaster, wherein, when water inrush is dominant, a high weight is allocated to the seepage field, and when impact is dominant, a high weight is allocated to the stress field;

[0055] calculating the comprehensive risk index based on the normalized anomaly rate of each field and the corresponding weight.

[0056] calculating the comprehensive risk index comprises:

[0057] ;

[0058] wherein, is a comprehensive risk index; is a corresponding weight of each field; S is a stress field comprehensive anomaly rate; V is a vibration field comprehensive anomaly rate; E is an energy field comprehensive anomaly rate; and F is a seepage field comprehensive anomaly rate.

[0059] Specifically, the embodiment calculates the abnormal rate after normalizing the multi-index of the "force-vibration-energy-flow" four fields; then takes the average value of multiple indexes of each field to obtain the comprehensive abnormal rate of the field; and then calculates the comprehensive risk index according to the weight assigned to each field. Finally, the risk level (safe, low risk, medium risk, high risk) is determined according to the size of the comprehensive risk index.

[0060] The following is a specific application introduction of a water inrush and rock burst combined disaster risk multi-source judgment method proposed in the embodiment, including the following contents:

[0061] (1) First, according to the mine geological conditions and disaster risk points, four-field monitoring equipment is arranged in the underground mine. Three-axis stress sensors are arranged on the roadway roof and two sides to monitor the three-dimensional stress of the surrounding rock in real time; microseismic probes are arranged in key areas of the mine to collect the space-time parameters of microseismic events; in conjunction with the microseismic system, the energy of each microseismic event is recorded by the acoustic emission instrument; water inflow monitors are installed in the shaft and low-lying places in the roadway, and water level meters and water thermometers are arranged in the water-bearing layer to monitor hydrological parameters in real time. All equipment is connected through the underground optical fiber network to ensure the stability and real-time performance of data transmission.

[0062] (2) After starting the monitoring system, the stress field sensor collects triaxial stress data such as the maximum principal stress and the minimum principal stress of the surrounding rock in real time; the microseismic network synchronously collects the occurrence time, spatial position and event frequency of microseismic events; the acoustic emission module records the energy value of each microseismic event and calculates the total energy in the monitoring period; the hydrological monitoring equipment collects daily water inflow, instantaneous water inflow, water level change and water temperature change data in real time. During the collection process, the time synchronization of the four-field data is ensured to provide a basis for subsequent multi-field data fusion analysis.

[0063] (3) Then, according to the multi-field parameters measured from the scene, the multi-index reflecting the multi-index is calculated. The key indexes such as the permeability abnormal value , stress concentration coefficient , time sequence concentration , spatio-temporal dispersion , total acoustic emission energy cover the whole chain of "stress-deformation-energy-seepage" of disaster evolution, providing multi-dimensional basis for risk judgment. The calculation formula is as follows:

[0064] ;

[0065] Among them, is the permeability abnormal value; is the current permeability; is the geological exploration value; is the stress concentration characteristic; is the maximum principal stress; is the uniaxial compressive strength of rock mass; is the strain rate; is the strain of surrounding rock, is the strain time.

[0066] ;

[0067] wherein, is the time series concentration; is the number of events within the spatial cluster; is the total number of events; is the spatiotemporal dispersion; is the distance of event i to the monitoring center; is the average distance; is the activity index; is the number of events in the monitoring period; is the length of the period.

[0068] ;

[0069] wherein, is the total acoustic emission energy; is the single event energy; is the event pressure value; is the single event impact volume.

[0070] ;

[0071] wherein, b is the b value, reflecting the relationship between energy and the number of events; is the number of events in the monitoring period, here specifically the number of events with energy ≥ E in the monitoring period; is the A(b) value, reflecting the microseismic event activity of the monitoring area; is the reference event number, indicating the number of events with energy ≥ reference value; indicates the average energy released by each microseismic event.

[0072] ;

[0073] wherein, is the water level change; is the current water level; is the water level reference value; is the water temperature change; is the current water temperature; is the temperature reference value.

[0074] (4) Extract key parameters from the four fields of multi-index to construct composite index, analyze the coupling strength of water inrush and rock burst, and thus determine the dominant type of composite disaster. Stress-seepage coupling coefficient , which represents the interaction between stress field and seepage field; energy-vibration coupling coefficient , which represents the synergistic evolution of energy field and vibration field. The formula is:

[0075] ;

[0076] wherein, is the stress concentration coefficient; is the permeability anomaly value; is the water level change; is the prevention zero constant; is the total acoustic emission energy; is the time concentration, which reflects the concentration degree of microseismic events in “time + space” within a period of time; is the time fractal value, which reflects whether the distribution of microseismic events in time is uniform; is the activity index, which is the number of microseismic events occurring in a unit of time.

[0077] (5) Based on the four-field multi-element characteristic index, the linear weighting method is used to calculate the comprehensive risk index. First, normalize the index of each field (convert the index of different units and orders of magnitude into dimensionless anomaly rate to eliminate the influence of units); then, according to the historical disaster data and disaster mechanism analysis, assign weights to each field (analyze the dominant type of composite index and composite disaster, and dynamically assign weights. When water inrush is dominant, the weight of seepage field is high, and when impact is dominant, the weight of stress field is high); finally, multiply the normalized anomaly rate of each field by the corresponding weight, and sum to obtain the comprehensive risk index :

[0078] ;

[0079] wherein, is the comprehensive risk index; is the field weight; S is the stress field comprehensive anomaly rate; V is the vibration field comprehensive anomaly rate; E is the energy field comprehensive anomaly rate; F is the seepage field comprehensive anomaly rate (the anomaly rate is the quantitative value of the anomaly degree. The larger the value is, the farther the index deviates from the normal state, and the higher the disaster risk is. It is obtained through the normalization processing of the core monitoring index of the field).

[0080] (6) According to the size of the comprehensive risk index, the risk grade of water inrush and rock burst composite disaster in the region is determined. Combined with the statistical data of historical dangerous events, the mine area is divided into four risk grades: safe ( <0.25), indicating that all four field indexes are stable and there is no sign of disaster; low risk (0.25≤ <0.5), indicating that the index of single field or two fields is slightly abnormal, and the disaster risk is low; medium risk (0.5≤ <0.8), indicating that multiple field indicators are abnormal, and the disaster risk is higher; high risk ( ≥0.8), indicating that four field indicators deteriorate explosively, and the disaster risk is extremely high.

[0081] The above-described embodiments are only descriptions of the preferred modes of the present application, and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements to the technical solutions of the present application made by those skilled in the art shall fall within the protection scope determined by the claims of the present application.

Claims

1. A multi-source determination method for the risk of combined water inrush and rock burst disaster, characterized in that: include: Real-time acquisition of four-field raw data of the target downhole area, including stress field, vibration field, energy field, and seepage field; Extracting multivariate characteristic indicators from the four fields of original data to obtain four multivariate indicators; Based on the four multi-factor indicators, a core composite index is constructed to quantify the coupling intensity between water inrush and rock burst, and to determine the dominant type of disaster. A comprehensive risk index is calculated based on the four multi-factor indicators and the dominant types of disasters, and the risk level of the combined disaster of water inrush and rock burst is determined according to the comprehensive risk index.

2. The multi-source determination method for the combined disaster risk of water inrush and rock burst according to claim 1 is characterized in that: The stress field data is obtained by monitoring dual-source CT and three-dimensional stress, the vibration field data is obtained by monitoring the spatiotemporal activity of microseismic events, the energy field data is obtained by monitoring microseismic energy, and the seepage field data is obtained by monitoring hydrological information.

3. The multi-source determination method for the combined disaster risk of water inrush and rock burst according to claim 1 is characterized in that: The multivariate indicators of the stress field include wave velocity anomaly coefficient, stress concentration coefficient, wave velocity change gradient, stress distribution characteristics, stress change rate, and relative stress value; the multivariate indicators of the vibration field include time series concentration, time and space diffusion, activity index, time fractal dimension, source concentration, and space fractal dimension; the multivariate indicators of the energy field include impact deformation energy, the slope of the relationship between the number of earthquakes and the earthquake energy level, the earthquake occurrence rate per unit time, total stress equivalent, and energy fractal dimension; the multivariate indicators of the seepage field include daily water inflow, instantaneous water inflow, water level change, and water temperature change.

4. The multi-source determination method for the combined disaster risk of water inrush and rock burst according to claim 1 is characterized in that: The core composite indicators include the stress-seepage coupling coefficient and the energy-vibration coupling coefficient, wherein the stress-seepage coupling coefficient is used to characterize the interaction between the stress field and the seepage field, and the energy-vibration coupling coefficient is used to characterize the coordinated evolution of the energy field and the vibration field.

5. The multi-source determination method for the risk of combined water inrush and rock burst disaster according to claim 4 is characterized in that: The stress-seepage coupling coefficient is: ； in, is the stress-seepage coupling coefficient; is the stress concentration factor; is the permeability anomaly; For water level changes; To prevent division by zero constant.

6. The multi-source determination method for the risk of combined water inrush and rock burst disaster according to claim 4 is characterized in that: The energy-vibration coupling coefficient is: ； in, is the energy-vibration coupling coefficient; is the total acoustic emission energy; is the temporal concentration; is the time fractal dimension value; An activity indicator.

7. The multi-source determination method for the combined disaster risk of water inrush and rock burst according to claim 1 is characterized in that: The comprehensive risk index calculated based on the four multi-factor indicators and the dominant disaster type includes: Normalize the multivariate indicators of each field to obtain the normalized abnormality rate of each field; By analyzing the core composite indicators and the dominant type of disaster, a weight is dynamically assigned to each field. When water inrush dominates, a high weight is assigned to the seepage field, and when impact dominates, a high weight is assigned to the stress field. The comprehensive risk index is calculated based on the normalized abnormality rate of each field and the corresponding weight.

8. The multi-source determination method for the combined disaster risk of water inrush and rock burst according to claim 7 is characterized in that: Calculating the comprehensive risk index includes: ； in, is the comprehensive risk index; is the weight corresponding to each field; S is the comprehensive anomaly rate of stress field; V is the comprehensive anomaly rate of vibration field; E is the comprehensive anomaly rate of energy field; F is the comprehensive anomaly rate of seepage field.

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