A progressive composite early warning method for mine water inrush based on microseismic monitoring

Abstract

The present invention relates to a progressive composite early warning method for mine water inrush based on microseismic monitoring. A high-precision microseismic monitoring system with full coverage of the roof and floor plates can effectively and comprehensively collect full vibration field information in real time and greatly improve the vertical positioning accuracy of microseismic events. At the same time, a comprehensive and rich microseismic data indicator library is established, and a calculation method for the core data variation rate is proposed. Different early warning data core indicators and auxiliary indicators are matched for different water inrush types, and corresponding indicator early warning values ​​are calculated. Through quantitative analysis of the degree of damage to the floor aquiclude based on the microseismic monitoring system, the development position and degree of potential water inrush channels are obtained, forming a four-level water inrush degree early warning level. Finally, the indicator early warning value and the channel early warning value are combined to achieve dual early warning of time progress and spatial position, and the corresponding early warning level is determined according to a floor water inrush composite early warning level table, thereby achieving progressive composite early warning and providing evidence-based guidance for safe production.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine safety monitoring, and in particular to a progressive composite early warning method for mine water inrush based on microseismic monitoring. Background Art

[0002] The potential for floor water inrush has become a major factor restricting the safe mining of deep coal resources in coal mines in central and eastern China. The process of coal mine floor water inrush is a complex process of damage and fracture caused by the coupling of mining disturbance and confined water seepage in the floor aquifer. This process significantly impacts coal mine production safety. For a long time, research on the mechanism of coal mine floor water inrush has focused on mining stress fields, floor rock damage, and confined water seepage fields. However, these factors cannot fully explain the mechanism of coal seam floor water inrush. The impact of roof collapse and rock burst on floor water inrush is primarily due to dynamic loads. These dynamic loads release large amounts of energy when they occur, reaching a range of several hundred meters. The resulting dynamic loads can potentially affect the floor aquifer (or impermeable) layers. Therefore, a close relationship exists between floor water inrush and dynamic load disturbance.

[0003] Deep fully mechanized caving working faces require extensive mining. The hard, intact, thick roof strata, due to their high strength, are less susceptible to collapse during mining, often resulting in large areas of overhanging roof. When the working face advances a certain distance, the roof strata can fracture and collapse on a large scale, leading to instantaneous instability of the overburden structure and even avalanche-like chain reaction of large-scale overhanging roof failure, resulting in large-scale roof collapse. The collapsed roof strata rapidly move toward the goaf. Due to their large movement amplitude and high impact energy, the heavy, hard, and thick roof slabs directly act on the floor strata, exerting enormous dynamic loads on the stope floor. These dynamic loads are extremely destructive, sometimes causing the floor slab to suddenly crack, bulge, and connect to the roof, or even triggering water inrush accidents at the working face.

[0004] Previous research has shown that the use of mine floor water inrush monitoring and early warning systems based on microseismic monitoring technology is becoming increasingly widespread. The monitoring principle is that the floor's impermeable rock strata, coupled with mining disturbance and confined water infiltration, generate cracks. The stress waves generated during the rock mass fracture are collected by the microseismic monitoring system. The microseismic system determines the rock mass fracture information through positioning and energy calculation, and then assesses whether the impermeable rock strata can develop into water inrush channels, ultimately providing water inrush early warning. As a powerful geophysical field monitoring method, microseismic monitoring technology establishes a spatial rock mass fracture field evolution model based on microseismic positioning results. By displaying microseismic events from multiple angles and levels, and coupling multiple fields (such as seepage and stress fields), it achieves advanced early warning of floor water inrush monitoring.

[0005] To achieve accurate early warning, the following key issues need to be addressed: ① Currently, most studies on the mechanical mechanism of floor water inrush consider static loads such as the support pressure and confined water pressure of the mining area, but do not consider the impact of strong dynamic disturbances on floor water inrush; floor water inrush is the result of the combined action of dynamic and static loads, and theoretical research using either dynamic or static loads alone cannot most realistically reflect the objective situation. It is necessary to fully analyze the impact of roof rock collapse on the floor water inrush induced by the mining area; ② The vibration information collected by the microseismic monitoring system is very rich, including a large number of small energy events and a smaller proportion of large energy events. If all small energy events caused by mining disturbances and construction effects are simply taken into account during early warning, the accuracy of the early warning results will be greatly affected; ③ The early warning mechanism of floor water inrush under the combined action of dynamic and static loads is very complex. Early warning methods that simply rely on indicator warnings or channel warnings have great limitations. It is urgent to propose a composite early warning method that integrates multiple early warning methods. Summary of the Invention

[0006] In order to make up for the deficiencies in the prior art, the present invention provides a progressive composite early warning method for floor water inrush based on microseismic monitoring, which includes indicator early warning and channel early warning.

[0007] (1) A high-precision microseismic monitoring system that fully covers the coal seam roof and floor

[0008] A broadband and high-precision microseismic monitoring system is used, with a spatial three-dimensional network deployed around the main mining area to collect broadband vibration signals generated by surrounding rock fractures during mining. The system monitoring results cover vibration events caused by micro-fractures of the coal body and surrounding rock (mainly presented as high-frequency, medium-high-frequency, small-energy events), vibration events caused by large-scale fractures of the roof and floor rock layers (mainly presented as medium-low-frequency, low-frequency, large-energy events), and vibration events caused by large-scale structural mining activation (mainly presented as low-frequency, medium-low-frequency, large-energy events).

[0009] (2) Indicator warning method based on analysis of water inrush types and dominant factors

[0010] 1) Classify the attributes of microseismic events; intelligently classify them based on the system's calculation results (occurrence time, spatial location, energy, magnitude, main frequency, etc.), mainly into coal seam microfracture events, roof rock stratum microfracture events, floor rock stratum microfracture events, low-level roof rock stratum breakage events, high-level roof rock stratum breakage events, floor rock stratum breakage events, tectonic activation events, etc.;

[0011] 2) Establish a microseismic data index database

[0012] ① Total frequency of events per unit time (optional day, shift, etc.), total energy of events per unit time, average energy of events per unit time, variation rate of total frequency of events per unit time (percentage exceeding or falling below the average), variation rate of total energy of events per unit time, variation rate of average energy of events per unit time, total frequency of events per unit advancement (optional 5m, 10m, etc.), total energy of events per unit advancement, average energy of events per unit advancement, variation rate of total frequency of events per unit advancement, variation rate of total energy of events per unit advancement, variation rate of average energy of events per unit advancement;

[0013] ② Frequency of coal seam microfracture events per unit time, total energy of coal seam microfracture events per unit time, average energy of coal seam microfracture events per unit time, frequency of coal seam microfracture events per unit advance, total energy of coal seam microfracture events per unit advance, average energy of coal seam microfracture events per unit advance, variation rate of frequency of coal seam microfracture events per unit time, variation rate of total energy of coal seam microfracture events per unit time, variation rate of average energy of coal seam microfracture events per unit time, variation rate of frequency of coal seam microfracture events per unit advance, variation rate of total energy of coal seam microfracture events per unit advance, variation rate of average energy of coal seam microfracture events per unit advance;

[0014] ③ Frequency of roof rock stratum microfracture events per unit time, total energy of roof rock stratum microfracture events per unit time, average energy of roof rock stratum microfracture events per unit time, frequency of roof rock stratum microfracture events per unit advance, total energy of roof rock stratum microfracture events per unit advance, average energy of roof rock stratum microfracture events per unit advance, variation rate of frequency of roof rock stratum microfracture events per unit time, variation rate of total energy of roof rock stratum microfracture events per unit time, variation rate of average energy of roof rock stratum microfracture events per unit time, variation rate of frequency of roof rock stratum microfracture events per unit advance, variation rate of total energy of roof rock stratum microfracture events per unit advance, variation rate of average energy of roof rock stratum microfracture events per unit advance;

[0015] ④ The frequency of floor rock microfracture events per unit time, the total energy of floor rock microfracture events per unit time, the average energy of floor rock microfracture events per unit time, the frequency of floor rock microfracture events per unit advancement, the total energy of floor rock microfracture events per unit advancement, the average energy of floor rock microfracture events per unit advancement, the variation rate of the frequency of floor rock microfracture events per unit time, the variation rate of the total energy of floor rock microfracture events per unit time, the variation rate of the average energy of floor rock microfracture events per unit time, the variation rate of the frequency of floor rock microfracture events per unit advancement, the variation rate of the total energy of floor rock microfracture events per unit advancement, and the variation rate of the average energy of floor rock microfracture events per unit advancement;

[0016] ⑤ The frequency of low-roof rock stratum breaking events per unit time, the total energy of low-roof rock stratum breaking events per unit time, the average energy of low-roof rock stratum breaking events per unit time, the frequency of low-roof rock stratum breaking events per unit advance, the total energy of low-roof rock stratum breaking events per unit advance, the average energy of low-roof rock stratum breaking events per unit advance, the variation rate of the frequency of low-roof rock stratum breaking events per unit time, the variation rate of the total energy of low-roof rock stratum breaking events per unit time, the variation rate of the average energy of low-roof rock stratum breaking events per unit time, the variation rate of the frequency of low-roof rock stratum breaking events per unit advance, the variation rate of the total energy of low-roof rock stratum breaking events per unit advance, and the variation rate of the average energy of low-roof rock stratum breaking events per unit advance;

[0017] ⑥ Frequency of high roof rock stratum breakage events per unit time, total energy of high roof rock stratum breakage events per unit time, average energy of high roof rock stratum breakage events per unit time, frequency of high roof rock stratum breakage events per unit advancement, total energy of high roof rock stratum breakage events per unit advancement, average energy of high roof rock stratum breakage events per unit advancement, variation rate of frequency of high roof rock stratum breakage events per unit time, variation rate of total energy of high roof rock stratum breakage events per unit time, variation rate of average energy of high roof rock stratum breakage events per unit time, variation rate of frequency of high roof rock stratum breakage events per unit advancement, variation rate of total energy of high roof rock stratum breakage events per unit advancement, variation rate of average energy of high roof rock stratum breakage events per unit advancement;

[0018] ⑦ Frequency of floor rock breaking events per unit time, total energy of floor rock breaking events per unit time, average energy of floor rock breaking events per unit time, frequency of floor rock breaking events per unit advancement, total energy of floor rock breaking events per unit advancement, average energy of floor rock breaking events per unit advancement, variation rate of frequency of floor rock breaking events per unit time, variation rate of total energy of floor rock breaking events per unit time, variation rate of average energy of floor rock breaking events per unit time, variation rate of frequency of floor rock breaking events per unit advancement, variation rate of total energy of floor rock breaking events per unit advancement, variation rate of average energy of floor rock breaking events per unit advancement;

[0019] ⑧ Frequency of structural activation events per unit time, total energy of structural activation events per unit time, average energy of structural activation events per unit time, frequency of structural activation events per unit advancement, total energy of structural activation events per unit advancement, average energy of structural activation events per unit advancement, variation rate of structural activation event frequency per unit time, variation rate of total energy of structural activation events per unit time, variation rate of average energy of structural activation events per unit time, variation rate of frequency of structural activation events per unit advancement, variation rate of total energy of structural activation events per unit advancement, variation rate of average energy of structural activation events per unit advancement;

[0020] (3) Calculation of core data mutation rate

[0021] The core data anomaly rate of microseismic events is calculated based on the microseismic system monitoring results and the data indicator library.

[0022] ① Basic data: frequency of events per unit time N T , the frequency of events within unit advancement N S , the average frequency unit of events in n unit time N TA , the average frequency unit of events within n unit advancement N SA , the total energy of the event per unit time E TZ , the total energy E of events within a unit of advancement SZ , the total energy unit average value E of the event in n unit time TZA , the total energy unit average value E of events within n unit advancement SZA , the average energy of the event per unit time E TP , the average energy E of events within a unit of propulsion SP , the average energy of the event in n unit time unit average value E TPA , the average energy unit average value E of events within n unit advancement SPA ;

[0023] ②Frequency variation rate δ N , including the frequency variation rate of events per unit time δ NT , the frequency variation rate of events within a unit advancement degree δ NS ;

[0024] ③Energy variation rate δ E , including the total energy variation rate of events per unit time δ ETZ , the total energy variation rate of events within unit advancement δ ESZ , the average energy variation rate of events per unit time δ ETP , the average energy variation rate of events within a unit advancement degree δ ESP .

[0025] The core data mutation rate calculation formula includes the frequency mutation rate formula and the energy mutation rate formula.

[0026] The frequency variation rate calculation formula is:

[0027]

[0028] The calculation formula of total energy variation rate is:

[0029]

[0030] The calculation formula of average energy variation rate is:

[0031]

[0032] Among them: δ-variability, N-frequency, T-time, S-advancement, E-energy, Z-total (energy), P-average (energy), A-average value.

[0033] (4) Analysis of water inrush types and dominant factors

[0034] The formation process of floor water inrush channels is a process in which the floor water-insulating rock strata are damaged and fractured by the coupling effects of mining disturbance and pressurized water infiltration. The mechanism of action is complex and affected by multiple factors. If all data indicators are used for early warning, the data volume will be huge, the processing process will be complex, the pertinence will not be strong, and the early warning efficiency will be low. Therefore, it is necessary to analyze the type of water inrush threat faced by the monitored working face and the main factors restricting this type of water inrush.

[0035] ① Simple floor-driven type: This type of floor water inrush is mainly caused by the rupture of the floor aquiclude during the stress field adjustment process caused by mining disturbance; the geological and hydrogeological characteristics of the working face where this type of water inrush occurs are: shallow burial depth, low roof rock strength, insignificant dynamic load effect, the destruction of the floor rock mainly caused by concentrated stress generated by static load, relatively complete floor aquiclude rock mass (no or few geological defects), high water pressure in the aquifer or high water inrush coefficient, etc.

[0036] ② Roof dynamic load driven type: This type of floor water inrush is mainly caused by the strong dynamic load generated by roof collapse, mine earthquake, etc., which causes impact damage to the floor water-proof rock layer. On the basis of static load, the degree of floor damage increases suddenly, forming a water inrush channel. The geological and hydrogeological characteristics of the working face where this type of water inrush occurs are: large burial depth, single or multiple thick hard rock layers on the roof, obvious dynamic load effect caused by overburden collapse during mining, damage to the floor rock layer caused by the combined action of dynamic and static loads, relatively complete rock mass of the floor water-proof layer (no or few geological defects), high water pressure in the aquifer or high water inrush coefficient, etc.

[0037] ③ Structural activation driven type: This type of floor water inrush is mainly caused by the activation of regional structures under the influence of mining to form water inrush channels. The existence of geological structures has destroyed the integrity of the aquiclude, reducing the strength and water-proof performance of the rock mass in a certain area, and faults and other structures have shortened the distance between the coal seam and the aquifer, resulting in a significant increase in the possibility of the formation of water inrush channels. The geological and hydrogeological characteristics of the working face of this type of water inrush are: there are many structures or hidden structures in the mining area, faults and other structures have shortened the relative distance between the coal seam and the aquifer, the water pressure in the aquifer is large or the water inrush coefficient is large, etc.

[0038] ④ Dynamic load and structure complex type: This type of floor water inrush is dominated by two factors: dynamic load and structure. For example, the structure within a certain working face is complex and the dynamic load effect of mining is obvious. The dynamic load generated by mining activities may cause structural activation to form a water inrush channel.

[0039] (5) Matching of early warning data indicators

[0040] After determining the type of water inrush and the dominant factors at the working face, it is necessary to match the data indicators required for early warning for the working face:

[0041] ①Simple bottom plate driven matching index;

[0042] Core indicators (two categories): 12 sub-indicators in indicator library ④ and 12 sub-indicators in indicator library ⑦;

[0043] Auxiliary indicators (two categories): 12 sub-indicators in indicator library ① and 12 sub-indicators in indicator library ②.

[0044] ②Top plate dynamic load drive type matching index;

[0045] Core indicators (two categories): 12 sub-indicators in indicator library ⑥ and 12 sub-indicators in indicator library ④;

[0046] Auxiliary indicators (two categories): 12 sub-indicators in indicator library ⑦ and 12 sub-indicators in indicator library ⑤.

[0047] ③Construct activation-driven matching indicators;

[0048] Core indicators (two categories): 12 sub-indicators in indicator library ⑧ and 12 sub-indicators in indicator library ④;

[0049] Auxiliary indicators (two categories): 12 sub-indicators in indicator library ⑦ and 12 sub-indicators in indicator library ①.

[0050] ④ Dynamic load structure composite matching index;

[0051] Core indicators (two categories): 12 sub-indicators in indicator library ⑥ and 12 sub-indicators in indicator library ⑧;

[0052] Auxiliary indicators (two categories): 12 sub-indicators in indicator library ④ and 12 sub-indicators in indicator library ⑦.

[0053] (6) Determine the indicator warning level

[0054] For different types of water inrush, appropriate indicator database data is selected according to the early warning data indicator matching standard to calculate the indicator warning and obtain the indicator warning value:

[0055] δ=δ N +δ EZ +δ EP

[0056] Based on laboratory tests and engineering experience, the initial value of the indicator warning is determined to be X. When δ<X, no warning is given. When X<δ<2X, a first-level warning is given. When 2X<δ<3X, a second-level warning is given. When δ>3X, a third-level warning is given.

[0057] (7) Quantitative analysis of the degree of damage to the bottom plate aquiclude and early warning of water inrush channels

[0058] The vertical penetration of the fracture network within each combined rock layer of the floor aquiclude is a potential water inrush channel. Quantitatively calculating the extent of damage to the entire aquiclude and predicting the location of the potential water inrush channel is done in the following steps:

[0059] 1. Based on the geological drilling data within the working face, the aquiclude is vertically divided into several aquicludes of equal thickness. The lithology of each aquiclude needs to be defined during the quantitative analysis process.

[0060] 2. For the microseismic events captured by the microseismic monitoring system during the mining process of the working face, they are divided into each water-proof layer according to their spatial location. The planar distribution results of the energy kernel density value of each water-proof layer are calculated. On this basis, contour lines are drawn and filled into a cloud map using gradient color bars. On the premise of clarifying the lithology of the water-proof layer, the initial damage warning value WJ / m2 of the corresponding water-proof layer is determined through laboratory tests and engineering experience (that is, the area ≥ the warning value has met the conditions for being a water inrush channel). In the process of applying the initial warning value, it is continuously optimized and revised in combination with the engineering situation.

[0061] 3. Maintaining the consistency of plane coordinates, the plane cloud map results of all water-blocking layers are superimposed vertically to display them in a three-dimensional effect. According to the damage areas delineated by each water-blocking layer, their vertical overlap is analyzed to obtain the development location and degree of potential water inrush channels.

[0062] 4. Based on the analysis results of the third step (i.e., the overlap of the damaged areas of each water-proof layer in the vertical position), the bottom plate water inrush warning level is set, and the ratio of the number of water-proof layers with overlapping damaged areas in the vertical position to the total number of water-proof layers is set as K. When K < 1 / 4, it is considered that there is no water inrush risk at this location (no warning); when 1 / 4 ≤ K < 1 / 2, it is considered that there is a weak water inrush risk at this location (level 1 warning); when 1 / 2 ≤ K < 3 / 4, it is considered that there is a moderate water inrush risk at this location (level 2 warning); and when K ≥ 3 / 4, it is considered that there is a strong water inrush risk at this location (level 3 warning).

[0063] 5 As the working face advances, the number of microseismic events in the aquiclude collected by the microseismic monitoring system continues to increase. It is necessary to recalculate the distribution of the energy kernel density values ​​of each aquiclude at a certain interval (which can be set), and repeat the third and fourth steps to achieve dynamic early warning during mining.

[0064] (8) A progressive composite warning method for bottom water inrush based on microseismic monitoring, which includes indicator warning and channel warning, is used to realize dual warning of time progress and spatial position by integrating indicator warning values ​​and channel warning values. A composite warning level table for bottom water inrush is defined, and the corresponding warning level can be determined based on the composite superposition results of the two values, thus realizing a progressive composite warning.

[0065] The beneficial effects of the present invention are as follows: the system and analysis method mainly carry out comprehensive monitoring of the roof movement and floor damage during the working face mining process. The high-precision microseismic monitoring system with full coverage of the roof and floor can effectively and comprehensively collect the full vibration field information in real time, and can greatly improve the vertical positioning accuracy of microseismic events; at the same time, the present invention establishes a comprehensive and rich microseismic data indicator library, proposes a calculation method for the core data variation rate, matches different early warning data core indicators and auxiliary indicators for different water inrush types, and calculates corresponding indicator warning values; through quantitative analysis of the degree of damage to the floor aquiclude based on the microseismic monitoring system, the development position and degree of potential water inrush channels are obtained, forming a four-level water inrush degree warning level; finally, the comprehensive indicator warning value and channel warning value are used to realize dual warning of time progress and spatial position, and according to the floor water inrush composite warning level table, the corresponding warning level is determined to realize progressive composite warning, so as to provide guidance for safe production. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 Schematic diagram of the structure of the full-coverage high-precision microseismic monitoring system of the present invention;

[0067] Figure 2 This is a three-dimensional rendering of the water-inrush channel development in the bottom plate of the present invention;

[0068] Figure 3 This is a composite early warning flow chart for monitoring water inrush from the bottom plate of the present invention;

[0069] Figure 4 It is the warning level table of the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in a variety of different configurations.

[0071] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely intended to represent selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0072] Figure 1-Figure 4 This is a specific embodiment of the present invention, which is mainly aimed at coal seams threatened by bottom plate pressurized water. By adopting the bottom plate water inrush composite warning method based on microseismic monitoring and dynamic load analysis described in this embodiment, it is possible to comprehensively monitor the roof movement and bottom plate damage during the working face mining process, and perform effective indicator warnings; through the bottom plate water inrush channel warning method, the degree of damage in different areas of the bottom plate is quantitatively calculated, and graded warnings are performed; using the indicator warning results and channel warning results, dual warnings of spatial position and time progress are achieved, and according to the bottom plate water inrush composite warning level table, the corresponding warning level is determined to achieve progressive composite warnings. The specific implementation methods are as follows:

[0073] Step 1: Use a broadband and high-precision microseismic monitoring system. The system structure is as follows: Figure 1 As shown in the figure, a three-dimensional network of stations is deployed around the main mining area to collect wide-band vibration signals generated by surrounding rock fractures during mining. The system monitoring results cover vibration events caused by micro-fractures of the coal body and surrounding rock (mainly manifested as high-frequency and medium-high-frequency low-energy events), vibration events caused by large-scale fractures of roof and floor strata (mainly manifested as medium-low-frequency and low-frequency high-energy events), and vibration events caused by large-scale mining-induced structural activation (mainly manifested as low-frequency and medium-low-frequency high-energy events).

[0074] Step 2: Classify the attributes of microseismic events

[0075] According to the system's calculation results (occurrence time, spatial location, energy, magnitude, main frequency, etc. of microseismic events), intelligent classification is performed, mainly divided into coal seam micro-fracture events, roof rock stratum micro-fracture events, floor rock stratum micro-fracture events, low-level roof rock stratum breakage events, high-level roof rock stratum breakage events, floor rock stratum breakage events, tectonic activation events, etc.

[0076] Step 3: Establish a microseismic data index database

[0077] Eight microseismic data indicator libraries are established for overall events, coal seam microfracture events, roof rock stratum microfracture events, floor rock stratum microfracture events, low-level roof rock stratum breakage events, high-level roof rock stratum breakage events, floor rock stratum breakage events, and tectonic activation events.

[0078] Step 4: Determine the water inrush type, analyze the dominant factors, and match the early warning data indicator database

[0079] Analyze the types of water inrush threats faced by the monitored working face and the main factors restricting this type of water inrush, and match the early warning data indicator library.

[0080] ①Simple bottom plate driven matching index;

[0081] Core indicators (two categories): 12 sub-indicators in indicator library ④ and 12 sub-indicators in indicator library ⑦;

[0082] Auxiliary indicators (two categories): 12 sub-indicators in indicator library ① and 12 sub-indicators in indicator library ②.

[0083] ②Top plate dynamic load drive type matching index;

[0084] Core indicators (two categories): 12 sub-indicators in indicator library ⑥ and 12 sub-indicators in indicator library ④;

[0085] Auxiliary indicators (two categories): 12 sub-indicators in indicator library ⑦ and 12 sub-indicators in indicator library ⑤.

[0086] ③Construct activation-driven matching indicators;

[0087] Core indicators (two categories): 12 sub-indicators in indicator library ⑧ and 12 sub-indicators in indicator library ④;

[0088] Auxiliary indicators (two categories): 12 sub-indicators in indicator library ⑦ and 12 sub-indicators in indicator library ①.

[0089] ④ Dynamic load structure composite matching index;

[0090] Core indicators (two categories): 12 sub-indicators in indicator library ⑥ and 12 sub-indicators in indicator library ⑧;

[0091] Auxiliary indicators (two categories): 12 sub-indicators in indicator library ④ and 12 sub-indicators in indicator library ⑦.

[0092] Step 5: Calculation of core data mutation rate

[0093] The core data anomaly rate of microseismic events is calculated based on the microseismic system monitoring results and the data indicator library. The core data anomaly rate calculation formula includes the frequency anomaly rate formula and the energy anomaly rate formula.

[0094] The frequency variation rate calculation formula is:

[0095]

[0096] The calculation formula of total energy variation rate is:

[0097]

[0098] The calculation formula of average energy variation rate is:

[0099]

[0100] Among them: δ-variability, N-frequency, T-time, S-advancement, E-energy, Z-total (energy), P-average (energy), A-average value.

[0101] Step 6: Determination of indicator warning level

[0102] For different types of water inrush, appropriate indicator database data is selected according to the early warning data indicator matching standard to calculate the indicator warning and obtain the indicator warning value:

[0103] δ=δ N +δ EZ +δ EP

[0104] Based on laboratory tests and engineering experience, the initial value of the indicator warning is determined to be X. When δ<X, no warning is given. When X<δ<2X, a first-level warning is given. When 2X<δ<3X, a second-level warning is given. When δ>3X, a third-level warning is given.

[0105] Step 7: Quantitatively calculate the degree of damage to each aquiclude and determine the location of potential water inrush channels

[0106] The fracture network within each combined rock layer of the bottom plate aquiclude is vertically penetrated and becomes a potential water inrush channel.

[0107] ① Based on the geological drilling data within the working face, the aquiclude is vertically divided into several aquicludes of equal thickness. The lithology of each aquiclude needs to be defined during the quantitative analysis process.

[0108] ② Microseismic events captured by the microseismic monitoring system during the working face mining process were divided into each water-repellent layer according to their spatial location. The planar distribution of the energy kernel density value of each water-repellent layer was calculated. Based on this, contour lines were drawn and filled into a cloud map using gradient color bars. On the premise of clarifying the lithology of the water-repellent layer, the initial damage warning value (WJ / m2) of the corresponding water-repellent layer was determined through laboratory tests and engineering experience (i.e., the area ≥ the warning value meets the conditions for being a water inrush channel). During the application of the initial warning value, it was continuously optimized and revised based on the engineering situation.

[0109] ③ Keep the plane coordinates consistent, and superimpose the plane cloud map results of all water-blocking layers in the vertical position to display them in a three-dimensional effect. According to the damage area circled by each water-blocking layer, analyze its overlap in the vertical position to obtain the development position and development degree of potential water inrush channels. Figure 2 shown.

[0110] Step 8: Quantitatively calculate the damage degree of the entire aquiclude and determine the channel warning level

[0111] (1) Based on the analysis results of step 7 (i.e., the vertical overlap of the damaged areas of each water-insulating layer), set the floor water inrush warning level, such as Figure 4 As shown in the figure, the ratio of the number of water-insulating layers overlapping the damaged areas in the vertical position to the total number of water-insulating layers is set as K. When K < 1 / 4, it is considered that there is no water inrush hazard at this location (no warning); when 1 / 4 ≤ K < 1 / 2, it is considered that there is a weak water inrush hazard at this location (level 1 warning); when 1 / 2 ≤ K < 3 / 4, it is considered that there is a moderate water inrush hazard at this location (level 2 warning); and when K ≥ 3 / 4, it is considered that there is a strong water inrush hazard at this location (level 3 warning).

[0112] (2) As the working face advances, the number of microseismic events in the aquiclude collected by the microseismic monitoring system continues to increase. It is necessary to recalculate the distribution of the energy kernel density values ​​of each aquiclude at a certain interval (which can be set), and repeat the third and fourth steps to achieve dynamic early warning during mining.

[0113] Step 9: Based on microseismic monitoring, a progressive composite warning of floor water inrush is provided, including indicator warning and channel warning. The combined indicator warning value and channel warning value are used to realize dual warning of time progress and spatial position. The floor water inrush composite warning level table is defined. The corresponding warning level can be determined according to the composite superposition result of the two values ​​to realize progressive composite warning. The specific composite warning process is shown in Figure 3 .

Claims

1. A progressive composite early warning method for mine water inrush based on microseismic monitoring, characterized in that: The following steps are involved: S1 uses a broadband, high-precision microseismic monitoring system, deployed in a three-dimensional network around the main mining area, to collect broadband vibration signals generated by surrounding rock fractures during mining. The system's monitoring results cover vibration events caused by microfractures in the coal body and surrounding rock, large-scale fractures in roof and floor strata, and vibration events caused by mining-induced activation of large structures. S2, classify the attributes of microseismic events. Based on the system's calculation results, intelligent classification is performed into coal seam microfracture events, roof rock stratum microfracture events, floor rock stratum microfracture events, low-level roof rock stratum breakage events, high-level roof rock stratum breakage events, floor rock stratum breakage events, and tectonic activation events. S3. Establish a microseismic data index library, and establish eight microseismic data index libraries for overall events, coal seam microfracture events, roof rock stratum microfracture events, floor rock stratum microfracture events, low-level roof rock stratum breakage events, high-level roof rock stratum breakage events, floor rock stratum breakage events, and tectonic activation events; S4: Determine the type of water inrush, analyze the dominant factors, and match the early warning data indicator database. Analyze the type of water inrush threat faced by the monitored working face and the main factors that restrict this type of water inrush, and match it with the early warning data indicator database. S5, calculate the core data anomaly rate, and calculate the core data anomaly rate of microseismic events based on the microseismic system monitoring results and the data indicator library; S6, determine the indicator warning level. For different water inrush types, select appropriate indicator library data according to the warning data indicator matching standard to calculate the indicator warning and obtain the indicator warning value: in, is the indicator warning value, is the frequency variation rate, is the total energy variation rate, is the average energy variation rate; According to laboratory tests and engineering experience, the initial warning value of the indicator is determined to be X. No warning, when It is a Level 1 warning. It is a Level 2 warning. It is a level 3 warning; S7, quantitatively calculate the degree of damage to the aquiclude and determine the location of potential water inrush channels. The potential water inrush channels are those where the fracture network within each combined rock layer of the floor aquiclude is vertically penetrated. S8, quantitatively calculate the damage degree of the entire aquiclude and determine the channel warning level; S9, based on microseismic monitoring, includes progressive composite warning of bottom water inrush, including indicator warning and channel warning. It integrates indicator warning value and channel warning value to realize dual warning of time progress and spatial position, and defines the composite warning level table of bottom water inrush. According to the composite superposition result of the two values, the corresponding warning level is determined to realize progressive composite warning.

2. The progressive composite early warning method for mine water inrush based on microseismic monitoring according to claim 1 is characterized by: The system monitoring results of the vibration events caused by the micro-fracture of the coal body and surrounding rock in S1 are presented as high-frequency and medium-high-frequency small-energy events, the system monitoring results of the vibration events caused by the large-scale fracture of the top and bottom plate rock layers are presented as medium-low-frequency and low-frequency large-energy events, and the system monitoring results of the vibration events caused by large-scale structural mining activation are presented as low-frequency and medium-low-frequency large-energy events.

3. The progressive composite early warning method for mine water inrush based on microseismic monitoring according to claim 1 is characterized by: The microseismic data index library in S3 includes: Index library ①: total frequency of events per unit time, total energy of events per unit time, average energy of events per unit time, variation rate of total frequency of events per unit time, variation rate of total energy of events per unit time, variation rate of average energy of events per unit time, total frequency of events per unit advancement, total energy of events per unit advancement, average energy of events per unit advancement, variation rate of total frequency of events per unit advancement, variation rate of total energy of events per unit advancement, variation rate of average energy of events per unit advancement; Index database ②: frequency of coal seam microfracture events per unit time, total energy of coal seam microfracture events per unit time, average energy of coal seam microfracture events per unit time, frequency of coal seam microfracture events per unit advance, total energy of coal seam microfracture events per unit advance, average energy of coal seam microfracture events per unit advance, variation rate of frequency of coal seam microfracture events per unit time, variation rate of total energy of coal seam microfracture events per unit time, variation rate of average energy of coal seam microfracture events per unit time, variation rate of frequency of coal seam microfracture events per unit advance, variation rate of total energy of coal seam microfracture events per unit advance, variation rate of average energy of coal seam microfracture events per unit advance; Index library ③: frequency of roof rock microfracture events per unit time, total energy of roof rock microfracture events per unit time, average energy of roof rock microfracture events per unit time, frequency of roof rock microfracture events per unit advancement, total energy of roof rock microfracture events per unit advancement, average energy of roof rock microfracture events per unit advancement, variation rate of frequency of roof rock microfracture events per unit time, variation rate of total energy of roof rock microfracture events per unit time, variation rate of average energy of roof rock microfracture events per unit time, variation rate of frequency of roof rock microfracture events per unit advancement, variation rate of total energy of roof rock microfracture events per unit advancement, variation rate of average energy of roof rock microfracture events per unit advancement; Index database ④: frequency of floor rock microfracture events per unit time, total energy of floor rock microfracture events per unit time, average energy of floor rock microfracture events per unit time, frequency of floor rock microfracture events per unit advancement, total energy of floor rock microfracture events per unit advancement, average energy of floor rock microfracture events per unit advancement, variation rate of floor rock microfracture frequency per unit time, variation rate of total energy of floor rock microfracture events per unit time, variation rate of average energy of floor rock microfracture events per unit time, variation rate of frequency of floor rock microfracture events per unit advancement, variation rate of total energy of floor rock microfracture events per unit advancement, variation rate of average energy of floor rock microfracture events per unit advancement; Index library ⑤: frequency of low-level roof rock stratum breakage events per unit time, total energy of low-level roof rock stratum breakage events per unit time, average energy of low-level roof rock stratum breakage events per unit time, frequency of low-level roof rock stratum breakage events per unit advance, total energy of low-level roof rock stratum breakage events per unit advance, average energy of low-level roof rock stratum breakage events per unit advance, variation rate of frequency of low-level roof rock stratum breakage events per unit time, variation rate of total energy of low-level roof rock stratum breakage events per unit time, variation rate of average energy of low-level roof rock stratum breakage events per unit time, variation rate of frequency of low-level roof rock stratum breakage events per unit advance, variation rate of total energy of low-level roof rock stratum breakage events per unit advance, variation rate of average energy of low-level roof rock stratum breakage events per unit advance; Index library ⑥: frequency of high roof rock stratum breakage events per unit time, total energy of high roof rock stratum breakage events per unit time, average energy of high roof rock stratum breakage events per unit time, frequency of high roof rock stratum breakage events per unit advancement, total energy of high roof rock stratum breakage events per unit advancement, average energy of high roof rock stratum breakage events per unit advancement, variation rate of frequency of high roof rock stratum breakage events per unit time, variation rate of total energy of high roof rock stratum breakage events per unit time, variation rate of average energy of high roof rock stratum breakage events per unit time, variation rate of frequency of high roof rock stratum breakage events per unit advancement, variation rate of total energy of high roof rock stratum breakage events per unit advancement, variation rate of average energy of high roof rock stratum breakage events per unit advancement; Index library ⑦: frequency of floor rock breakage events per unit time, total energy of floor rock breakage events per unit time, average energy of floor rock breakage events per unit time, frequency of floor rock breakage events per unit advancement, total energy of floor rock breakage events per unit advancement, average energy of floor rock breakage events per unit advancement, variation rate of frequency of floor rock breakage events per unit time, variation rate of total energy of floor rock breakage events per unit time, variation rate of average energy of floor rock breakage events per unit time, variation rate of frequency of floor rock breakage events per unit advancement, variation rate of total energy of floor rock breakage events per unit advancement, variation rate of average energy of floor rock breakage events per unit advancement; Index library ⑧: frequency of structural activation events per unit time, total energy of structural activation events per unit time, average energy of structural activation events per unit time, frequency of structural activation events per unit advancement, total energy of structural activation events per unit advancement, average energy of structural activation events per unit advancement, variation rate of frequency of structural activation events per unit time, variation rate of total energy of structural activation events per unit time, variation rate of average energy of structural activation events per unit time, variation rate of frequency of structural activation events per unit advancement, variation rate of total energy of structural activation events per unit advancement, variation rate of average energy of structural activation events per unit time, variation rate of frequency of structural activation events per unit advancement, variation rate of total energy of structural activation events per unit advancement, variation rate of average energy of structural activation events per unit advancement.

4. The progressive composite early warning method for mine water inrush based on microseismic monitoring according to claim 1 is characterized by: The following parameters are set in S5: the frequency of events per unit time N T , the frequency of events within a unit advancement N S , the average frequency unit of events in n unit time N TA , the average frequency unit of events within n units of advancement N SA , the total energy of the event per unit time E TZ , the total energy of events within a unit advancement E SZ , the average value of the total energy of events in n unit time E TZA , the average total energy unit of events within n unit advancement E SZA , the average energy of an event per unit time E TP , the average energy of events within a unit propulsion degree E SP , the average energy unit average value of events in n unit time E TPA , the average energy unit average value of events within n unit advancement E SPA ; Frequency variation rate , including the frequency variation rate of events per unit time , the frequency variation rate of events within a unit advancement ; Energy mutation rate , including the total energy variation rate of events per unit time , the total energy variation rate of events within a unit advancement degree , the average energy variation rate of events per unit time , the average energy variation rate of events within a unit advancement degree ; The core data mutation rate calculation formula in S5 includes the frequency mutation rate formula and the energy mutation rate formula; The frequency variation rate calculation formula is: The calculation formula of total energy variation rate is: The calculation formula of average energy variation rate is: 。 5. The progressive composite early warning method for mine water inrush based on microseismic monitoring according to claim 3 is characterized by: The water inrush types in S4 include simple bottom plate driven type, top plate dynamic load driven type, structural activation driven type and dynamic load structural composite type; The water inrush type matching warning data indicator library is as follows: Pure bottom plate driven matching indicators: Two types of core indicators: 12 sub-indicators in indicator library ④ and 12 sub-indicators in indicator library ⑦; Two types of auxiliary indicators: 12 sub-indicators in indicator library ① and 12 sub-indicators in indicator library ②; Top plate dynamic load drive matching indicators: Two types of core indicators: 12 sub-indicators in indicator library ⑥ and 12 sub-indicators in indicator library ④; Two types of auxiliary indicators: 12 sub-indicators in indicator library ⑦ and 12 sub-indicators in indicator library ⑤; Structural activation driven matching indicators: Two types of core indicators: 12 sub-indicators in indicator library ⑧ and 12 sub-indicators in indicator library ④; Two types of auxiliary indicators: 12 sub-indicators in indicator library ⑦ and 12 sub-indicators in indicator library ①; Dynamic load structure composite matching index: Two types of core indicators: 12 sub-indicators in indicator library ⑥ and 12 sub-indicators in indicator library ⑧; Two types of auxiliary indicators: 12 sub-indicators in indicator library ④ and 12 sub-indicators in indicator library ⑦.

6. The progressive composite early warning method for mine water inrush based on microseismic monitoring according to claim 1 is characterized by: The S7 is specifically: S701: Based on the geological drilling data within the working face, the aquiclude is vertically divided into several aquiclude layers of equal thickness. The lithology of each aquiclude layer must be defined during the quantitative analysis process. S702: Microseismic events captured by the microseismic monitoring system during the working face mining process are divided into each water-repellent layer according to their spatial location. The planar distribution of the energy kernel density value of each water-repellent layer is calculated. Based on this distribution, contour lines are drawn and a cloud map is filled with gradient color bars. Based on the lithology of the water-repellent layer, the initial damage warning value for the corresponding water-repellent layer is determined through laboratory testing and engineering experience. That is, areas with energy kernel density values ​​greater than or equal to the initial damage warning value meet the conditions for serving as water inrush channels. During the application of the initial damage warning value, it is continuously optimized and revised based on the engineering situation. S703: Keeping the plane coordinates consistent, the plane cloud map results of all water-blocking layers are superimposed in the vertical position to display them in a three-dimensional effect. According to the damaged areas circled by each water-blocking layer, their overlap in the vertical position is analyzed to obtain the development position and development degree of the potential water inrush channel.

7. The progressive composite early warning method for mine water inrush based on microseismic monitoring according to claim 6 is characterized by: The S8 is specifically: S801: Based on the analysis results of S7, i.e., the overlap of the damaged areas of each water-insulating layer in the vertical position, set the floor water inrush warning level. The ratio of the number of water-insulating layers with overlapping damaged areas in the vertical position to the total number of water-insulating layers is set as K. When K is less than 1 / 4, it is considered that there is no water inrush risk at this location, i.e., no warning is issued. When 1 / 4≤K<1 / 2, it is considered that there is a weak water inrush risk at this location, i.e., a level 1 warning is issued. When 1 / 2≤K<3 / 4, it is considered that there is a moderate water inrush risk at this location, i.e., a level 2 warning is issued. When K≥3 / 4, it is considered that there is a strong water inrush risk at this location, i.e., a level 3 warning is issued. S802: As the working face advances, the number of microseismic events in the aquiclude collected by the microseismic monitoring system continues to increase. The distribution of the energy kernel density values ​​of each aquiclude layer needs to be recalculated at regular intervals. S703 and S801 are repeated to achieve dynamic early warning during mining.

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