Method for representing stope face floor rock stratum damage area

Abstract

The invention discloses a method, a system, a medium and equipment for representing a stope floor rock stratum damage area, and relates to the field of geological engineering, and the method comprises the following steps: screening a data set composed of a plurality of rock mass splitting micro-seismic data subsets based on a rock mass fracture seismic source mechanism; classifying the plurality of rock mass fracture micro-seismic data subsets by utilizing spatial spacing and time relevance, and classifying the classified rock mass fracture micro-seismic data subsets by utilizing clustering analysis to obtain a rock mass fracture clustering set; linking the starting points based on the cracking positions together according to a threshold switch to obtain a plurality of communicated cracks; a plurality of spacing distances are preset, a chaotic model is adopted, a possible rock stratum cracking domain is obtained, cracking attributes of the crack cracking domain on different depth layers are quantified, and a characterization result of the stope face floor rock stratum damage area is obtained. The accuracy of rock stratum damage area characterization is improved, and scientific basis and technical support are provided for rock stratum stability evaluation, mining safety management and establishment of a rock stratum damage early warning system.

Description

Technical Field

[0001] The invention relates to the technical field of safety engineering, and in particular to a method for characterizing a rock layer damage area at a mining face bottom. Background Art

[0002] Mine water inrush disasters have always been one of the main geological dynamic disasters that affect the safe production of my country's coal industry. The country continues to increase its investment in monitoring, early warning and prevention of mine water hazards every year, but coal mines across the country still continue to experience water inrush disasters every year. The location, frequency and impact range of mine water inrush disasters are extremely uncertain.

[0003] In response to mine water inrush disasters, some geophysical detection instruments and equipment are widely used. By obtaining and analyzing the data changes of geophysical parameters, some engineering problems have been effectively solved from the aspects of water source, water volume, water pressure, water conduit and other water hazard elements, which to a certain extent meet the safety needs of water inrush disaster detection and prevention in the mining and production process of mines. However, due to the working characteristics of detection instruments and equipment, there are still shortcomings in the real-time monitoring and disaster warning of mine water hazards.

[0004] There are many technical difficulties that are difficult to overcome when applying microseismic monitoring technology to the monitoring and early warning of floor water-conducting fissure channels. It has always been generally believed that the microseismic phenomenon caused by rock mass rupture does not match the rock mass rupture behavior itself, does not have an equal correlation, and cannot achieve a one-to-one effective match. Affected by the limitations of seismological plate movement research, rock mechanics acoustic emission effect research, and the difficulty of reproducing mine microseismic phenomena in the laboratory, most of the microseismic mechanisms are based on the premise that the loaded coal rock mass ruptures. The dynamic behavior of rock rupture is regarded as the only mechanism of microseismic effect, and the source mechanism of microseismic effect is naturally equated with the dynamic behavior of rock rupture. All microseismic events are defined as rock rupture, and microseismic events are considered to be a rock rupture behavior. Based on this "bridging" migration relationship, the concept that microseismic events are equivalent to rock rupture is directly applied in the field of mine water prevention and control. The application of microseismic monitoring technology to the monitoring and early warning of water-conducting fracture channels in the bottom rock layer of the working face threatened by pressurized water is based on microseismic events that can indicate rock fracture behavior. If all monitored microseismic events are considered to be rock fracture behavior, the rock fracture behavior cannot be indicated. The fundamental reason is that rock fracture and microseismic effects are confused without effective distinction, and the analysis and excavation of the source mechanism of the microseismic effect itself are ignored. As a result, microseismic monitoring technology cannot be effectively applied to the monitoring of water-conducting fracture channels, and it is difficult to promote the solution of the problem of early warning of mine water disasters. Summary of the invention

[0005] In order to overcome the shortcomings of the above-mentioned prior art that rock fracture and microseismic effect are confused without making effective distinction, and neglecting the analysis and exploration of the source mechanism of the microseismic effect itself, the main purpose of the present invention is to provide a method, system, medium and equipment for characterizing the rock stratum damage area of ​​the bottom plate of the mining face.

[0006] To achieve the above object, the present invention adopts the following technical scheme, a method for characterizing the rock stratum damage area of ​​the bottom plate of the mining face, comprising the following steps:

[0007] Acquire a microseismic data set in the rock layer at the bottom of the goaf of the working face, and screen out a rock mass fracture microseismic data set based on the source mechanism of rock mass fracture, including multiple rock mass fracture microseismic data subsets;

[0008] By using the spatial spacing and time correlation, the space-time density coefficient is obtained, and multiple rock mass fracture microseismic data subsets are classified according to the space-time density coefficient to obtain the classified rock mass fracture microseismic data subsets;

[0009] Cluster analysis is used to classify the classified rock mass fracture microseismic data subsets to obtain the clustering characteristics of rock mass fractures, and based on the clustering characteristics of rock mass fractures, the clustering clusters of rock mass fractures are obtained;

[0010] Based on the clustering characteristics of the rock mass fracture, the spatial data difference method is used to obtain the corresponding weight of the space where the rock mass crack area is located; the starting points with correlations of the cracking misalignment, cracking scale, and cracking orientation obtained based on the cracking position are linked together according to the threshold switch, and the fracture points connected by the cracks are cross-connected to obtain multiple connected cracks;

[0011] A plurality of spacing distances are preset to divide the tip spacings of the plurality of connected cracks into a plurality of types, and a chaotic model is adopted in combination with a plurality of iterative algorithms to obtain a possible rock stratum cracking domain, and the cracking properties of the crack crazing domain at different depth levels are quantified to obtain the characterization results of the rock stratum damage area of ​​the mining face floor, and the characterization results include the plane range, plane distribution and cracking depth of the crack crazing area.

[0012] The preset multiple spacing distances divide the tip spacings of the multiple connected cracks into multiple types, and adopt a chaotic model in combination with multiple iteration algorithms to obtain possible rock formation cracking domains, including the following steps:

[0013] The preset spacing distances include 2 meters, 5 meters and 10 meters;

[0014] The crack propagation model is constructed based on the chaotic dynamics equation, and the initial position, size and shape of the crack are set;

[0015] Perform the first iteration according to the chaos model and initial conditions, record the crack state, update the crack state according to the result of the previous iteration, and perform the next iteration;

[0016] During the iteration process, the model parameters are adjusted according to the crack growth;

[0017] Through multiple iterations, the main cracking domain formed by crack extension is identified and the possible rock formation cracking domain is obtained.

[0018] The method of obtaining the space-time density coefficient by the spatial spacing and time correlation comprises the following steps:

[0019] Clean and format the collected data;

[0020] Calculate the straight-line distance between two points and the time difference between two events to obtain the time interval;

[0021] Construct the spatiotemporal weight function through Gaussian function;

[0022] According to the spatiotemporal weight function, the spatiotemporal density coefficient between each pair of features is obtained.

[0023] Also includes:

[0024] Quantifying the source attributes of multiple rock mass fracture microseismic data subsets to obtain multi-dimensional attributes of the rock mass fracture microseismic data subsets, wherein the multi-dimensional attributes include time attributes, energy attributes, physical attributes, source mechanism attributes, stress attributes, fracture mechanism attributes and geometric attributes;

[0025] The time attribute is the characteristic component, the time attribute is the time of earthquake occurrence, the energy attribute is the released energy, the physical attribute is the line-of-sight wave velocity, the source mechanism attribute is the initial motion polarity, the stress attribute is the direction of the main stress, the fracture mechanism attribute is the plastic fracture, strike-slip and relaxation elasticity category, and the geometric attribute is the cracking direction and scale.

[0026] Also includes:

[0027] According to the clustering of the rock mass fractures, the geometric and morphological attributes of the fracture points in the rock mass are determined, wherein the geometric and morphological attributes include the cracking position, cracking scale, cracking direction and crack tip extension range.

[0028] A system for characterizing rock stratum failure areas in a mining face floor, comprising:

[0029] The data acquisition module is used to acquire the microseismic data set in the rock layer at the bottom of the goaf of the working face, and screen out the rock mass fracture microseismic data set based on the source mechanism of rock mass fracture, including multiple rock mass splitting microseismic data subsets; obtain the space-time density coefficient by using the spatial spacing and time correlation, classify the multiple rock mass fracture microseismic data subsets according to the space-time density coefficient, and obtain the classified rock mass fracture microseismic data subsets;

[0030] The data analysis module uses cluster analysis to classify the classified rock fracture microseismic data subsets to obtain the clustering characteristics of the rock fracture, and obtains the clustering clusters of the rock fracture according to the clustering characteristics of the rock fracture; based on the clustering characteristics of the rock fracture, the spatial data difference method is used to obtain the corresponding weight of the space where the rock fracture area is located; the starting points with correlations of the cracking misalignment, cracking scale, and cracking orientation obtained based on the cracking position are linked together according to the threshold switch, and the fracture points connected by the cracks are cross-connected to obtain multiple connected cracks;

[0031] The characterization result acquisition model is used to preset multiple spacing distances to divide the tip spacings of multiple connected cracks into multiple types, adopt a chaotic model, and cooperate with multiple iterative algorithms to obtain possible rock formation cracking domains, quantify the cracking properties of the crack cracking domains at different depth levels, and obtain the characterization results of the rock formation damage area of ​​the bottom plate of the mining face. The characterization results include the plane range, plane distribution, and cracking depth of the crack cracking area.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] By analyzing the spatiotemporal evolution of microseismic event sets, establishing the correlation between microseismic event sets, clustering microseismic event sets, and establishing the quantitative standard for the development of microseismic event sets into microseismic event clusters, the spatiotemporal transition characteristics of microseismic event clusters are further revealed, as well as the reliability analysis of the spatial transition and penetration dynamic behavior of rock cracking to form water-conducting fracture channels, laying the foundation for the establishment of key parameters and indicators for subsequent water-conducting fracture channel early warning based on the spatiotemporal transition of microseismic event clusters. The connectivity between the fracture channels in the rock mass of the floor of the goaf is determined, and the penetration path and extension trend of the water-conducting fracture channels are quantified.

[0034] The original quantitative relationship expression that uses microseismic intensity to characterize the scale of rock mass cracking is improved and optimized by supplementing parameters, calibrating coefficients, etc., so that the vector element description of the rock mass cracking point by microseismic events directly related to the dynamic behavior of rock mass fracture is more reliable and accurate, and the development characteristics of the rock mass cracking point are established. According to the relationship between the fracture scale and the source energy, the extension scale of the fracture channel is calculated. The location, cracking scale and direction of the fracture point in the rock mass of the goaf floor are determined.

[0035] Analyze the spatiotemporal evolution of individual microseismic events, establish the correlation between individual microseismic events, cluster the microseismic events that are continuous in time and extensible in space, focus on the spatial attributes of microseismic events to quantify the key clustering parameters and corresponding thresholds, obtain the relevant characteristics of the microseismic event set, determine the centroid position of the microseismic event cluster and the cluster tuple load, and reveal the law of the extension and penetration of the rock crack position from scattered points to local areas. Determine the connection between the fracture points in the rock mass of the floor of the goaf, and quantify the development position and extension scale of the "trunk" and "branches" of the fracture channel. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings described herein are used to provide further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0037] Figure 1 It is a top view of the inversion result of the fractured area distribution of the rock stratum in the goaf bottom of the working face in an embodiment of the present invention;

[0038] Figure 2 It is a left view of the inversion result of the fractured area distribution of the rock stratum in the goaf bottom of the working face in the embodiment of the present invention;

[0039] Figure 3 It is a front view of the inversion result of the fractured area distribution of the rock stratum in the goaf bottom of the working face in the embodiment of the present invention;

[0040] Figure 4 It is a stereogram of the inversion result of the fractured area distribution of the rock stratum in the goaf bottom of the working face in the embodiment of the present invention;

[0041] Figure 5 It is a schematic diagram of the process structure of the present invention. DETAILED DESCRIPTION

[0042] Accurately reveal the seismic mechanism and source properties of mine microseismic effects, realize accurate classification and identification of microseismic events, and screen out microseismic events that are truly directly related to the dynamic behavior of rock mass rupture from a massive collection of microseismic events. Improve the ability of microseismic events to characterize water-conducting fracture channels, which will help improve the accuracy and reliability of microseismic monitoring technology in monitoring water-conducting fracture channels in the bottom rock strata of working faces threatened by pressurized water.

[0043] Step 1: Classification and identification of microseismic signals

[0044] (1) The rock layer of the working face goaf floor is equivalent to a rock plate of a certain thickness in the laboratory. A horizontal uniaxial pressure is applied around the rock plate to simulate the horizontal geostress compression in the rock layer of the goaf floor. A uniaxial load is applied at the center of the bottom of the rock plate to simulate the vertical geostress with a certain vertical component that the rock layer of the goaf floor is subjected to under the horizontal geostress compression. The closer to the middle of the goaf floor rock layer, the more obvious the vertical component of the geostress is, and the greater the vertical force is. The uniaxial vertical force is applied to the center of the bottom of the rock plate. The stress characteristics of the rock layer of the goaf floor and the occurrence process of microseismic phenomena in the rock layer are simulated. The entire experimental platform is installed on a special reaction support. The reaction support not only plays the role of fixing the rock plate and preventing deviation, but also can control the force loading of the rock plate on demand. During the test, the settings of some parameters will be fine-tuned according to the actual situation.

[0045] (2) Install some detector sensors on the four sides, one top surface, and one bottom surface of the rock slab. There are 3 detector sensors on each long side and 2 detector sensors on each short side, arranged at an equal interval of 35 cm; 3 detector sensors are installed on the top surface and 3 detector sensors are installed on the bottom surface, for a total of 16 detector sensors. The response frequency range of the detector sensor is 0.3Hz~12000Hz, and the signal sampling frequency of a single channel is 32kHz. Each detector sensor is arranged according to a fixed spatial coordinate, and the sensor and the rock slab are rigidly coupled by strong welding glue or bolts φ=5mm, L=12mm to improve the reception quality of the vibration signal. The above detector sensors are used to capture various vibration signals generated by the loaded rock slab during the entire process from pressure to rupture in real time, continuously, and synchronously.

[0046] (3) Identify and compare all vibration signals obtained one by one. Explore the differences between the complete vibration signals generated by the loaded rock plate at different pressure stages, especially in the elastic deformation stage, plastic deformation stage, and instability and decompression stage. Reveal the source mechanism of the rock plate at the elastic deformation stage, plastic deformation stage, and instability and decompression stage, and establish key evaluation parameters, reliable algorithms, and implementation standards for the classification and identification of microseismic signals.

[0047] Step 2: Revealing the mechanism of microseismic effect

[0048] (1) Based on the acquired microseismic wave field time series, the attenuation law of the vibration wave field through the rock layer, the significant energy dissipation characteristics and gradient effect are analyzed to reveal the wave phase consistency of the microseismic wave field. According to the spatial vectorized vibration wave field information of individual microseismic events in the rock layer of the goaf floor captured by each detector, the field source characteristics of the microseismic phenomenon are analyzed to reveal the initial motion polarity of the microseismic wave field. According to the quadrant distribution characteristics of the initial motion of the seismic wave, the parameters such as the particle motion direction and amplitude of the initial motion seismic wave are marked on the Wolf net diagram of the source reference sphere.

[0049] (2) The second-order seismic moment tensor inversion analysis method is used to solve the strike, dip and inclination of the double-force sporadic earthquake model surface of the earthquake source, revealing the parameters such as the direction of the principal compressive stress and the principal principal stress of the earthquake source, revealing the seismogenic mechanism of each microseismic event in the rock layer of the goaf floor, and revealing the diversity of seismogenic mechanisms. Based on the dissipation theory of viscoelastic anisotropic media, the common mechanism of the propagation, attenuation and extinction process of the microseismic field source in the rock layer is revealed.

[0050] (3) Based on other models such as three-dimensional dynamic source and dislocation model, the properties of microseismic effect such as seismic direction, source wave velocity, stress drop, and structural elastic-plastic deformation are analyzed to reveal the seismic factors and physical processes of the microseismic source. According to the azimuth, incident angle and initial motion type of the microseismic wave field obtained by each detector, the beach ball method is applied, combined with the surrounding rock structure and stress drop characteristics, to reveal the focal mechanism solution of individual microseismic events. The differences in the seismic mechanism of individual microseismic events are analyzed, all microseismic events obtained by monitoring in the rock layer of the goaf floor are classified, and microseismic events related to the development behavior and extension trend of the cracks in the rock layer of the goaf floor are identified and screened.

[0051] Step 3: Characterization of rock mass cracking behavior by microseismic events

[0052] (1) The original quantitative relationship expression that uses microseismic intensity to characterize the scale of rock mass cracking is improved and optimized by supplementing parameters, calibrating coefficients, etc., so that the vector element description of the rock mass cracking point by microseismic events directly related to the dynamic behavior of rock mass fracture is more reliable and accurate, and the development characteristics of the rock mass cracking point are established. According to the relationship between the fracture scale and the source energy, the extension scale of the fracture channel is calculated. The location, cracking scale and direction of the fracture point in the rock mass of the goaf floor are determined.

[0053] (2) Analyze the spatiotemporal evolution of individual microseismic events, establish the correlation between individual microseismic events, cluster the microseismic events that are continuous in time and extensible in space, focus on quantifying the key clustering parameters and corresponding thresholds on the spatial attributes of microseismic events, obtain the relevant characteristics of the microseismic event set, determine the centroid position of the microseismic event cluster and the cluster tuple load, and reveal the law of the extension and penetration of the rock crack position from scattered points to local areas. Determine the connection between the fracture points in the rock mass of the goaf floor, and quantify the development position and extension scale of the "trunk" and "branches" of the fracture channel.

[0054] (3) Analyze the spatiotemporal evolution of microseismic event sets, establish the correlation between microseismic event sets, perform cluster analysis on microseismic event sets, establish quantitative standards for the development of microseismic event sets into microseismic event clusters, further reveal the spatiotemporal transition characteristics of microseismic event clusters, and the reliability analysis of the spatial transition and penetration dynamic behavior of rock cracking to form water-conducting fracture channels, laying the foundation for the establishment of key parameters and indicators for subsequent water-conducting fracture channel early warning based on the spatiotemporal transition of microseismic event clusters. Determine the connectivity between the fracture channels in the rock mass of the goaf floor, and quantify the penetration path and extension trend of the water-conducting fracture channels.

[0055] A method for characterizing the rock stratum damage area of ​​the mining face floor, combined with Figure 1 , including the following steps:

[0056] Acquire a microseismic data set in the rock layer at the bottom of the goaf of the working face, and screen out a rock mass fracture microseismic data set based on the source mechanism of rock mass fracture, including multiple rock mass fracture microseismic data subsets;

[0057] By using the spatial spacing and time correlation, the space-time density coefficient is obtained, and multiple rock mass fracture microseismic data subsets are classified according to the space-time density coefficient to obtain the classified rock mass fracture microseismic data subsets;

[0058] Cluster analysis is used to classify the classified rock mass fracture microseismic data subsets to obtain the clustering characteristics of rock mass fractures, and based on the clustering characteristics of rock mass fractures, the clustering clusters of rock mass fractures are obtained;

[0059] Based on the clustering characteristics of the rock mass fracture, the spatial data difference method is used to obtain the corresponding weight of the space where the rock mass crack area is located; the starting points with correlations of the cracking misalignment, cracking scale, and cracking orientation obtained based on the cracking position are linked together according to the threshold switch, and the fracture points connected by the cracks are cross-connected to obtain multiple connected cracks;

[0060] A plurality of spacing distances are preset to divide the tip spacings of the plurality of connected cracks into a plurality of types, and a chaotic model is adopted in combination with a plurality of iterative algorithms to obtain a possible rock stratum cracking domain, and the cracking properties of the crack crazing domain at different depth levels are quantified to obtain the characterization results of the rock stratum damage area of ​​the bottom plate of the mining face, wherein the characterization results include the plane range, plane distribution and cracking depth of the crack crazing area.

[0061] The iterative algorithm includes the following specific iterative steps:

[0062] Take the rupture tip distance as 20m as an example:

[0063] Step 1: determine the spatial position and opening of each rupture point;

[0064] Step 2: quantify the spatial distance between the crack tips of every two rupture points and establish a temporary database;

[0065] Step 3: Perform linear interpolation processing of the meshgrid function on the area not covered by the rupture point to obtain data coverage of the area outside the rupture area;

[0066] Part 4: According to the set fracture tip distance scale, a three-dimensional data volume to be expressed is established, the correlation between each fracture point is calculated, and the data space is covered twice; the newly covered data replaces the data in the database established in the above step 2.

[0067] Step 5: Repeat steps 1 to 4 above at 1 / 2 of the distance to the rupture tip, reduce the calculation grid of the three-dimensional data body, improve the calculation accuracy, and perform repeated iterative operations until the desired distance scale is reached.

[0068] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0069] Embodiment 1:

[0070] A method for characterizing the rock stratum damage area of ​​a mining face floor comprises the following specific steps:

[0071] (1) Find all microseismic event sets A located in the rock layer at the bottom of the goaf of the working face from the microseismic event database;

[0072] (2) Select a set of microseismic events B whose focal mechanisms belong to the rock mass rupture type from the microseismic event set A;

[0073] (3) The source attributes of each microseismic event in the microseismic event set B are calculated and quantified, including the characteristic components of the microseismic event, the time of occurrence, the released energy, the line-of-sight wave velocity, the polarity of the initial motion, the direction of the principal stress, the types of plastic cracking, strike-slip and relaxation elasticity, the cracking direction and scale, and other element information.

[0074] (4) Classify each microseismic event in the microseismic event set B, determine the space-time density coefficient between each event according to the spatial spacing and time correlation, select the spacing of 1m, 2m, 3m, and Nm, and establish the exclusive event combination C1, C2, C3, and Cn for each spacing.

[0075] (5) For each unique event combined with C, the cluster analysis method is used to find the clustering characteristics of the microseismic event set, find the cluster clusters D1~Dn, and determine the cracking position, cracking scale, cracking direction and crack tip extension range of the rupture point in the rock mass.

[0076] (6) Use spatial data interpolation methods such as Kriging, moving average, and special value inverse square coefficient method to calculate the corresponding weight of the space where the rock crack area is located, link the starting points with correlations in crack offset, crack scale, and crack orientation together according to the threshold switch, and cross-connect the fracture points connected by the cracks.

[0077] (7) The crack tip spacing is divided into multiple types, such as 2 m, 5 m, and 10 m. A chaotic model is used in combination with multiple iterative algorithms to obtain the possible rock formation cracking domain, quantify the cracking properties of the crack formation domain at different depth levels, and obtain the plane range, plane distribution, and cracking depth of the crack formation area.

[0078] Example 2

[0079] The following is a specific embodiment of a method for characterizing a rock formation damage area at a mining face floor, including data and steps:

[0080] First, the microseismic data set is screened: Based on the focal mechanism of rock mass rupture, the rock mass rupture microseismic data set is screened from the monitoring system, which contains 100 microseismic data subsets. Then the microseismic data subsets are classified: the microseismic data subsets are classified using the spatial spacing (1m, 2m, 3m) and time correlation (1d, 2d, 3d). For example, under the conditions of spatial spacing of 2m and time correlation of 2d, a proprietary event set C2 is established. Cluster analysis is performed on the classification results: cluster analysis is performed on the classified microseismic data subsets to obtain 5 clusters of rock mass rupture D1~D5. Then spatial data difference and crack connection are performed: based on clustering characteristics, the spatial data difference method is used to obtain the spatial weight of the rock mass cracking area. Assume that the crack opening offset is 1.5m, the cracking scale is 4m, and the cracking direction is 45° north-east. According to the threshold switch, the associated starting points are linked to form multiple connected cracks.

[0081] Preset spacing and chaos model iteration: The preset spacing distances are 2 meters, 5 meters and 10 meters. Construct a crack extension model, set the initial crack position at the coordinates (100m, 200m), the size is 2m, and the shape is a straight line. Perform the first iteration and record the crack state. Assume that the crack has extended by 0.5m. According to the results of the previous iteration, update the crack state and perform the second iteration. After 10 iterations, the crack domain formed by crack extension is identified. Then the space-time density coefficient is calculated: For the two microseismic events in the C2 set, the spatial spacing is 2m, the time correlation is 2d, and the calculated space-time density coefficient is 2 / 2=1. Then quantify the source attributes: quantify the multi-dimensional attributes of the rock fracture microseismic data subset, and obtain the following data:

[0082] Time attribute (earthquake time): 2023-04-0112:00:00

[0083] Energy attribute (released energy): 5.2×10^6J

[0084] Physical properties (line-of-sight wave speed): 5000m / s

[0085] Focal mechanism properties (first motion polarity): compression wave

[0086] Stress properties (direction of principal stress): 30° east of north

[0087] Fracture mechanism attributes: plastic fracture and strike-slip

[0088] Geometric properties (crack direction and scale): 45° north by east, 4m

[0089] Determine the rupture point geometry and morphological properties:

[0090] According to cluster D1, the geometric and morphological properties of the fracture points in the rock mass were determined, including the crack location (100m, 200m), the crack scale 4m, the crack direction 45° north-east, and the crack tip extension range 5m.

[0091] Quantify cluster characteristics: Based on clusters D1 to D5, determine the cluster characteristics of the rock mass fracture area, including:

[0092] The rupture range of the concentrated damage zone: 50m×50m; crack density: 10 cracks / square meter; cracking degree: medium.

[0093] Through the above embodiments, we can see the specific application and data of the method, so as to better understand how to characterize the damaged area of ​​the bottom rock layer of the mining face through microseismic data analysis.

[0094] It should be noted that, in the present invention, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0095] The above embodiments are merely examples of the present invention and do not limit the protection scope of the present invention. All designs that are the same or similar to the present invention fall within the protection scope of the present invention.

Claims

1. A method for characterizing the rock stratum damage area of ​​the mining face floor, characterized in that: The following steps are involved: Based on the focal mechanism of rock mass rupture, a rock mass rupture microseismic data set is screened, including multiple rock mass rupture microseismic data subsets; Using the spatial spacing and time correlation of rock mass fracture microseismic data, multiple rock mass fracture microseismic data subsets are classified to obtain classified rock mass fracture microseismic data subsets; Cluster analysis is performed on the classified rock mass fracture microseismic data subsets to obtain the clustering clusters of rock mass fractures. Based on the clustering characteristics of the rock mass fracture, a spatial data difference method is used to obtain the corresponding weight of the space where the rock mass cracking area is located; The starting points of the cracking misalignment, cracking scale, and cracking orientation obtained based on the cracking position are linked together according to the threshold switch, and the rupture points connected to the cracks are cross-connected to obtain multiple connected cracks; A plurality of spacing distances are preset to divide the tip spacings of the plurality of connected cracks into a plurality of types, and a chaotic model is adopted in combination with multiple iterations to obtain an estimated rock stratum cracking domain, quantify the cracking properties of the crack stratum at different depth levels, and obtain the characterization results of the rock stratum damage area of ​​the mining face floor.

2. The method for characterizing the rock stratum damage area of ​​the mining face floor according to claim 1, characterized in that: The characterization results include the plane range, plane distribution and crack depth of the crack opening area.

3. The method for characterizing the rock stratum damage area of ​​the mining face floor according to claim 1, characterized in that: The method of obtaining a possible rock formation cracking domain comprises the following steps: The preset spacing distances include 2 meters, 5 meters and 10 meters; The crack propagation model is constructed based on the chaotic dynamics equation, and the initial position, size and shape of the crack are set; Perform the first iteration according to the chaos model and initial conditions, record the crack state, update the crack state according to the result of the previous iteration, and perform the next iteration; During the iteration process, the crack growth model parameters are adjusted according to the crack growth situation; Through multiple iterations, the cracking domain formed by crack extension is identified and the possible rock formation cracking domain is obtained.

4. The method for characterizing the rock stratum damage area of ​​the mining face floor according to claim 1, characterized in that: The method of obtaining the space-time density coefficient by utilizing the spatial spacing and time correlation of the rock mass fracture microseismic data comprises the following steps: According to the spatial spacing between two microseismic events, 1m, 2m, 3m, ~, Nm are selected; According to the temporal correlation of two microseismic events, 1d, 2d, 3d, ~, and Md are selected; According to the spatial spacing and time correlation, the formula M / N is used to quantify the spatiotemporal density coefficient of each pair of microseismic events, and the exclusive event sets C1, C2, C3, ..., C under different spacing conditions are established. n .

5. The method for characterizing the rock stratum damage area of ​​the mining face floor according to claim 1, characterized in that: Also includes: Quantifying the source attributes of multiple rock mass fracture microseismic data subsets to obtain multi-dimensional attributes of the rock mass fracture microseismic data subsets, wherein the multi-dimensional attributes include time attributes, energy attributes, physical attributes, source mechanism attributes, stress attributes, fracture mechanism attributes and geometric attributes; The time attribute is the characteristic component, the time attribute is the time of earthquake occurrence, the energy attribute is the released energy, the physical attribute is the line-of-sight wave velocity, the focal mechanism attribute is the initial motion polarity, the stress attribute is the direction of the main stress, the fracture mechanism attribute is the plastic fracture, strike-slip and relaxation elasticity category, and the geometric attribute is the cracking direction and scale.

6. The method for characterizing the rock stratum damage area of ​​the mining face floor according to claim 1, characterized in that: Also includes: According to the clustering of the rock mass fractures, the geometric and morphological attributes of the fracture points in the rock mass are determined, wherein the geometric and morphological attributes include the cracking position, cracking scale, cracking direction and crack tip extension range.

7. The method for characterizing the rock stratum damage area of ​​the mining face floor according to claim 1, characterized in that: The method of obtaining the clustering characteristics of rock mass fracture comprises the following steps: According to the microseismic event type determined by the microseismic focal mechanism, the clustering of microseismic events D1~D n ; According to the matching coupling between the microseismic source attributes and the rock mass cracking attributes, the cracking position, cracking scale, cracking direction and crack tip extension range of the rupture point in the rock mass are determined; Based on the quantitative clustering clusters D1~D n , determine the clustering characteristics of the rock mass fracture area, wherein the clustering characteristics include the fracture range, crack density, and cracking degree of the concentrated damage area.

8. A system for characterizing rock stratum damage areas at the bottom of a mining face, characterized in that: include: The data acquisition module is used to acquire the microseismic data set in the rock layer at the bottom of the goaf of the working face, and screen out the rock mass fracture microseismic data set based on the source mechanism of rock mass fracture, including multiple rock mass splitting microseismic data subsets; obtain the space-time density coefficient by using the spatial spacing and time correlation, classify the multiple rock mass fracture microseismic data subsets according to the space-time density coefficient, and obtain the classified rock mass fracture microseismic data subsets; The data analysis module uses cluster analysis to classify the classified rock mass fracture microseismic data subsets to obtain the clustering characteristics of the rock mass fracture, and obtains the clustering clusters of the rock mass fracture according to the clustering characteristics of the rock mass fracture; Based on the clustering characteristics of the rock mass fracture, a spatial data difference method is used to obtain the corresponding weight of the space where the rock mass cracking area is located; The starting points of the cracking misalignment, cracking scale, and cracking orientation obtained based on the cracking position are linked together according to the threshold switch, and the rupture points connected to the cracks are cross-connected to obtain multiple connected cracks; The characterization result acquisition model is used to preset multiple spacing distances to divide the tip spacings of multiple connected cracks into multiple types, adopt a chaotic model, and cooperate with multiple iterative algorithms to obtain possible rock formation cracking domains, quantify the cracking properties of the crack cracking domains at different depth levels, and obtain the characterization results of the rock formation damage area of ​​the bottom plate of the mining face. The characterization results include the plane range, plane distribution, and cracking depth of the crack cracking area.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

10. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any one of claims 1 to 5 is implemented.

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