Intelligent monitoring and visualization method for micro-seismic in mine

Abstract

The invention relates to the technical field of micro-seismic monitoring, in particular to an intelligent monitoring and visualization method for micro-seismic in a mine, and the method comprises the steps: obtaining similar components of all modal components based on the center frequency of the modal components; determining the signal similarity of each modal component based on the similarity between each modal component and all similar components thereof and the similarity of the frequency spectrum, and determining the signal characteristic value of each modal component in combination with the signal similarity so as to reconstruct the microseismic signal; obtaining independent micro-seismic signals in all the reconstructed micro-seismic signals in each micro-seismic sensor array; and based on the autocorrelation of each independent micro-seismic signal, screening out a rock mass fracture signal, and positioning and visualizing the rock mass fracture under the mine. The interference of mechanical vibration and random noise on rock mass fracture position positioning is solved, and the credibility and accuracy of micro-seismic monitoring data are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of microseismic monitoring, in particular to a microseismic intelligent monitoring and visualization method in underground mine. BACKGROUND

[0002] In the process of underground mine exploitation, in order to early warn the ground pressure phenomena such as underground rock mass collapse, landslide and rock burst to ensure the safe and efficient production of mine, the existing method usually uses microseismic technology to monitor the microseismic events generated by the deformation and destruction of underground rock mass, uses microseismic monitoring sensor to collect microseismic signals in real time, and combines with the vibration positioning principle to determine the position of the underground rock mass rupture, so as to display in three-dimensional space to provide three-dimensional visualization interface.

[0003] However, when using the microseismic monitoring system to monitor the microseismic events in the underground mine, due to the complexity of the underground operation environment, the microseismic signals collected by the microseismic sensor are usually the microseismic signals generated by the mixed vibration sources of the underground mechanical equipment such as underground fully mechanized mining machine, drilling machine, fan and other equipment and rock mass rupture, and the random noise is easily introduced in the process of microseismic signal collection and transmission, so that the microseismic signals collected by the microseismic sensor are difficult to accurately reflect the microseismic events caused by the underground rock mass rupture, and the visualization result of the underground rock mass rupture position is deviated, which reduces the credibility and accuracy of the microseismic monitoring data. SUMMARY

[0004] In order to solve the above technical problems, the present application provides a microseismic intelligent monitoring and visualization method in underground mine to solve the existing problems.

[0005] The microseismic intelligent monitoring and visualization method in underground mine of the present application adopts the following technical scheme: One embodiment of the present application provides a microseismic intelligent monitoring and visualization method in underground mine, which comprises the following steps: Obtaining the microseismic signals in each microseismic sensor in each microseismic sensor array in underground mine; Decomposing the microseismic signals in each microseismic sensor into a plurality of modal components, obtaining the similar components of each modal component based on the center frequency of the modal component, determining the waveform similarity of each modal component based on the similarity between each modal component and all its similar components, and combining the similarity of the spectrum between each modal component and all its similar components to determine the signal similarity of each modal component; determining the signal eigenvalue of each modal component based on the confusion degree of each modal component and combining the signal similarity to select the target component from all modal components of each microseismic sensor microseismic signal for microseismic signal reconstruction; The blind source signal separation algorithm is used to obtain independent microseismic signals in all reconstructed microseismic signals in each microseismic sensor array; based on the autocorrelation of each independent microseismic signal, the period characteristic value of each independent microseismic signal is determined, so as to screen out rock mass rupture signals from all independent microseismic signals in each microseismic sensor array, and the rock mass rupture under the mine is positioned and visualized.

[0006] Preferably, the method for obtaining the similar components of the modal components is: Among all the modal components in the microseismic signals of the remaining microseismic sensors other than the microseismic sensor to which the modal component belongs, the modal component closest to the central frequency of the modal component is taken as the similar component of the modal component, and all the similar components of the modal component are obtained by traversing all the microseismic sensors other than the microseismic sensor to which the modal component belongs.

[0007] Preferably, the waveform similarity of the modal component is the average of the similarities between the modal component and all its similar components.

[0008] Preferably, the signal similarity of the modal component is the product of the average of the similarities of the spectra between the modal component and all its similar components and the waveform similarity.

[0009] Preferably, the signal characteristic value of the modal component is the result of dividing the signal similarity of the modal component by the permutation entropy of the modal component.

[0010] Preferably, the method for screening out the target component from all the modal components of the microseismic signal of each microseismic sensor for microseismic signal reconstruction comprises: The microseismic signals of all the microseismic sensors in the microseismic sensor array are combined into a signal matrix, the signal matrix is taken as the input of the signal source number estimation method, and the output signal source number is recorded as N; The modal components corresponding to the first N signal characteristic values in the ascending order arrangement result of all the signal characteristic values of the modal components in the microseismic signal of each microseismic sensor are taken as the target components.

[0011] Preferably, the method for obtaining independent microseismic signals in all reconstructed microseismic signals in each microseismic sensor array by using the blind source signal separation algorithm comprises: All the reconstructed microseismic signals in each microseismic sensor array are combined into a reconstruction matrix of each microseismic sensor array, and the reconstruction matrix is taken as the input of the blind source signal separation algorithm, wherein the number of independent components is set to N, and all the independent components are output, which are recorded as independent microseismic signals.

[0012] Preferably, the period characteristic value of each independent microseismic signal is the maximum autocorrelation coefficient in the autocorrelation graph of each independent microseismic signal.

[0013] Preferably, the rock mass rupture signal is screened from all independent microseismic signals in each microseismic sensor array, comprising: The period characteristic value of all independent microseismic signals in each microseismic sensor array is taken as the input of the threshold segmentation algorithm, and the segmentation threshold is outputted, and all independent microseismic signals with a period characteristic value less than the segmentation threshold are taken as the rock mass rupture signal.

[0014] Preferably, the rock mass rupture under the mine is positioned and visualized, comprising: Based on the coordinates of the microseismic sensor to which all rock mass rupture signals belong, the microseismic positioning algorithm is used to determine the position of the underground rock mass rupture, and the position is displayed in the three-dimensional visualization interface.

[0015] The present application has at least the following beneficial effects: The present application can effectively reduce the influence of random noise introduced in the collection and transmission process of the collected microseismic signal on the accuracy of subsequent separation of independent microseismic signals generated by different vibration sources from the separated microseismic signal, thereby improving the accuracy of subsequent separation of independent microseismic signals generated by the microseismic event of the target underground rock mass rupture from the collected microseismic signal; further, the present application can effectively separate the independent microseismic signal generated by the underground rock mass rupture of the mine from the collected microseismic signal, avoid the interference of the independent microseismic signal generated by the underground mechanical equipment and the random noise introduced in the collection and transmission process of the microseismic signal on the microseismic signal generated by the microseismic event of the underground rock mass rupture, thereby reducing the deviation of the visualization result of the underground rock mass rupture position, and improving the reliability and accuracy of the microseismic monitoring data. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0017] Figure 1 A flow chart of the steps of a mine microseismic intelligent monitoring and visualization method provided by an embodiment of the present application is provided. Figure 2 A flow chart of the steps of a rock mass fracture signal screening process provided by an embodiment of the present application is provided. Figure 3 A schematic diagram of a microseismic monitoring visualization process provided by an embodiment of the present application is provided. DETAILED DESCRIPTION

[0018] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined purposes, the specific embodiments, structures, features and effects of a mine microseismic intelligent monitoring and visualization method according to the present application are described in detail below in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0020] The specific scheme of a mine microseismic intelligent monitoring and visualization method provided by the present application is described in detail below in combination with the drawings.

[0021] A mine microseismic intelligent monitoring and visualization method provided by an embodiment of the present application is provided, specifically, a mine microseismic intelligent monitoring and visualization method is provided as follows, please refer to Figure 1 The method comprises the following steps: Step S1: Obtain the microseismic signal in each microseismic sensor in each microseismic sensor array under the mine.

[0022] A microseismic sensor array is installed at each predetermined position under the target mine, and each microseismic sensor in the microseismic sensor array is used to collect the microseismic signal in a predetermined time length before the current time, wherein the signal collection frequency is set to f; all the collected microseismic signals are converted from analog electrical signals to digital signals by the data acquisition instrument, and the preprocessed microseismic signals used for analysis are obtained, wherein the number of microseismic sensors in the microseismic sensor array needs to be greater than the number of all mechanical equipment under the mine.

[0023] It should be noted that the preset time length and the value of the signal acquisition frequency f are artificially set, and in the embodiment, the value of the preset time length is 2s, and the value of the signal acquisition frequency f is 8kHz. In actual application, as other implementation manners, the implementer can also set them by himself / herself according to specific conditions, and the embodiment does not have special limitations.

[0024] Further, the microseismic signal recognition based on the energy extremum method is used to detect whether there is an effective microseismic signal in all microseismic signals of the microseismic sensor array at the current time. If there is, the current time is analyzed. If not, the current time is not analyzed. In the embodiment, it is assumed that there is an effective microseismic signal at the current time, and the specific analysis process is as follows.

[0025] The process of using the microseismic signal recognition based on the energy extremum method to detect whether there is an effective microseismic signal in all microseismic signals of the microseismic sensor array at the current time is a known technology, and will not be described in detail.

[0026] Step S2: The rock mass fracture signal is recognized and positioned and visualized by decomposing, screening, reconstructing and separating the microseismic signal.

[0027] S2.1 decompose the microseismic signal in each microseismic sensor into a plurality of modal components, obtain similar components of each modal component based on the center frequency of the modal component, determine the waveform similarity of each modal component based on the similarity between each modal component and all similar components thereof, and determine the signal similarity of each modal component in combination with the similarity of the spectrum between each modal component and all similar components thereof, determine the signal eigenvalue of each modal component based on the confusion degree of each modal component, and combine the signal similarity to screen out target components from all modal components of the microseismic signal of each microseismic sensor for microseismic signal reconstruction.

[0028] Since the microseismic sensors in the same microseismic sensor array are adjacent in position, all the microseismic signals collected by these microseismic sensors at the same time are usually mixed by microseismic signals generated by the same vibration source, such as underground fully mechanized mining machines, drilling machines, fans and other underground mechanical equipment, and rock mass fracture and other vibration sources. Therefore, the blind source signal separation method is used in the embodiment to separate the independent microseismic signals generated by different vibration sources in the microseismic signal, but directly using the Ma Guangyuan separation method to separate the microseismic signal can easily cause the signal components obtained by separation to not only contain the microseismic signal components generated by the vibration source, but also contain random noise components introduced in the collection and transmission process of the microseismic signal.

[0029] Therefore, in order to reduce the influence of random noise introduced in the process of collecting and transmitting the collected microseismic signal on the accuracy of separating the independent microseismic signal components generated by different vibration sources in the subsequent microseismic signal, the microseismic signal in each microseismic sensor is decomposed into a plurality of modal components, the similar components of each modal component are obtained based on the center frequency of the modal component, the waveform similarity of each modal component is determined based on the similarity between each modal component and all its similar components, and the signal similarity of each modal component is determined in combination with the similarity of the spectrum between each modal component and all its similar components; the signal eigenvalue of each modal component is determined based on the confusion degree of each modal component and in combination with the signal similarity, so as to screen out the target component from all modal components of the microseismic signal of each microseismic sensor for microseismic signal reconstruction, and the specific process is as follows: Firstly, in each microseismic sensor array, the microseismic signal in each microseismic sensor is taken as the input of the modal decomposition algorithm, and all modal components of the microseismic signal are output, wherein the modal decomposition algorithm in the embodiment adopts a variational modal decomposition algorithm. In actual application, as other implementation manners, the implementer can also adopt other decomposition methods such as empirical mode decomposition algorithm according to specific conditions. The selection of the modal decomposition algorithm is not specially limited in the embodiment.

[0030] The variational modal decomposition algorithm is a known technology, and the specific process of decomposing the microseismic signal into modal components will not be repeated.

[0031] Further, the similar components of each modal component are obtained based on the center frequency of the modal component, and the specific process is as follows: Among all modal components in the microseismic signal of each microseismic sensor other than the microseismic sensor to which the modal component belongs, the modal component closest to the center frequency of the modal component is taken as the similar component of the modal component, all microseismic sensors other than the microseismic sensor to which the modal component belongs are traversed, and all similar components of each modal component are obtained, which are used to represent the signal components in the microseismic signal that are in the similar frequency band with the modal component.

[0032] The concept of center frequency is a known technology and will not be repeated.

[0033] Further, the waveform similarity of each modal component is determined based on the similarity between each modal component and all its similar components, so as to evaluate the similarity degree of the waveform between each modal component and its similar components, and the specific process is as follows: The embodiment takes the average of the similarity between each modal component and all similar components thereof as the waveform similarity of each modal component. If the waveform similarity of the current modal component is larger, it indicates that the signal is more regular, which means that the current modal component and the similar components thereof are most likely generated by the same physical vibration source, and are not random noise. On the contrary, if the waveform similarity of the current modal component is smaller, it indicates that the signal is less regular, which means that the correlation between the current modal component and the similar components thereof is weak, and the signal is most likely generated by random noise, device interference, etc., and has low credibility and should be regarded as noise or interference and removed.

[0034] It should be noted that there are many commonly used methods for measuring the similarity between signals. In the embodiment, the cosine similarity between each modal component and all similar components thereof is taken as the similarity between each modal component and all similar components thereof. In actual application, as other implementation manners, the implementer can also use other methods for measuring the similarity between signals, such as the inverse of the Euclidean distance, according to the specific circumstances. The selection of the method for measuring the similarity between signals is not specially limited in the embodiment.

[0035] The calculation method of the cosine similarity is a known technology, and the specific calculation process is not described again.

[0036] It should be noted that, except for special descriptions, the content related to the measurement of the similarity in the embodiment all adopts the cosine similarity.

[0037] Further, the embodiment determines the signal similarity of each modal component based on the waveform similarity of each modal component and the similarity between the frequency spectrum of each modal component and all similar components thereof, which is used to evaluate whether the signal component corresponding to the modal component and the signal component in the microseismic signal of the other microseismic sensor in the similar frequency range have similar signal distribution characteristics. The specific determination process of the signal similarity is as follows: As an implementation manner, in the embodiment, the product of the average of the similarity between the frequency spectrum of each modal component and all similar components thereof and the waveform similarity is taken as the signal similarity of each modal component.

[0038] According to the signal similarity of each modal component, it can be understood that the signal similarity reflects the overall similarity of the signal component, i.e., the modal component, in the two dimensions of waveform and frequency with the corresponding signal component of other microseismic sensors, and is used to indicate whether the modal component and its similar components belong to the real physical vibration source. It is a key indicator for judging whether the signal is an effective signal or a noise signal. If the average of the spectral similarity between the current modal component and all its similar components is greater, it means that the current modal component and its similar components are more similar in the frequency spectrum, which indicates that the microseismic signal is very reliable in the timbre dimension of sound, meaning that the current modal component and its similar components are more likely to come from a real physical vibration source, so the corresponding signal similarity is greater. At the same time, if the waveform similarity of the current modal component is greater, it means that the current modal component and all its similar components are more similar in waveform, which means that the current modal component and all its similar components are more likely to belong to the signal corresponding to the real physical vibration source, so the corresponding signal similarity is greater. On the contrary, if the average of the spectral similarity between the current modal component and all its similar components is smaller, it means that the current modal component and its similar components are less similar in the frequency spectrum, which indicates that there is a significant difference in the timbre dimension of the microseismic signal, meaning that the current modal component and its similar components are more likely to come from random noise, so the corresponding signal similarity is smaller. At the same time, if the waveform similarity of the current modal component is smaller, it means that the current modal component and all its similar components are less similar in waveform, which means that the current modal component and all its similar components are more likely to belong to random interference, so the corresponding signal similarity is smaller.

[0039] Further, the embodiment determines the signal characteristic value of each modal component based on the chaos degree of each modal component and in combination with the signal similarity, so as to screen out the target component from all modal components of the microseismic signal of each microseismic sensor for microseismic signal reconstruction, specifically: In the embodiment, the signal similarity of each modal component is divided by the result of the permutation entropy of the modal component, as the signal characteristic value of each modal component.

[0040] The calculation formula and method of permutation entropy are known technologies, and the specific calculation process is not described again.

[0041] According to the signal characteristic value of each modal component, it can be understood that the signal characteristic value reflects the possibility that the modal component is an effective part of the microseismic signal; the greater the signal similarity of the current modal component, the more similar the current modal component is to a real effective physical signal in waveform and spectrum, rather than a signal component caused by random noise, and therefore the greater the corresponding signal characteristic value; at the same time, the smaller the permutation entropy of the current modal component, the more regular and predictable the current modal component is, indicating that the current modal component is a signal component generated by an effective real physical vibration source, and therefore the greater the signal characteristic value of the corresponding modal component. On the contrary, the smaller the signal similarity of the current modal component, the greater the difference between the current modal component and a real effective physical signal in waveform and spectrum, and the more likely the current modal component is a signal component caused by random noise or irrelevant interference, and therefore the smaller the corresponding signal characteristic value; at the same time, the greater the permutation entropy of the current modal component, the more chaotic and random the current modal component is, and the poorer the predictability, indicating that the current modal component is most likely a signal component generated by noise or sudden interference, and therefore the smaller the signal characteristic value of the corresponding modal component.

[0042] Further, the embodiment filters target components from all modal components of the microseismic signal of each microseismic sensor for microseismic signal reconstruction based on the signal characteristic value, specifically: As an implementation manner, in the embodiment, the microseismic signals of all microseismic sensors in each microseismic sensor array are combined into a signal matrix, the signal matrix is taken as the input of the signal source number estimation method, and the output signal source number is denoted as N. The modal components corresponding to the first N signal characteristic values in the ascending order arrangement result of the signal characteristic values of all modal components in the microseismic signal of each microseismic sensor are taken as target components, which are used to represent the modal components corresponding to all signal components generated by the vibration sources such as underground fully mechanized mining machines, drilling machines, fans and other underground mechanical equipment and rock mass rupture in the microseismic signal.

[0043] It should be noted that there are many commonly used signal source number estimation methods, and in the embodiment, the signal source number estimation method based on the Gerschgorin disc method is adopted to determine the number of signal sources. In actual application, as other implementation manners, the implementer can also use the signal source number estimation method based on the smooth rank sequence or the signal source number estimation method based on information theory to obtain the number of signal sources according to the specific circumstances. The selection of the signal source number estimation method is not specially limited in the embodiment.

[0044] The signal source number estimation method based on the Gerschgorin disc method is a known technology, and the specific process of estimating the number of signal sources by using the method will not be described here.

[0045] So far, the microseismic signal is converted into multiple components by modal decomposition in this embodiment, and the effectiveness and reliability of each component are quantified by innovatively combining waveform similarity, spectral similarity and permutation entropy, so as to accurately screen out the target component representing the real physical vibration source, effectively suppress noise interference, and provide a high-quality and high-SNR signal basis for subsequent blind source separation and accurate positioning and visualization of rock mass fracture signals.

[0046] S2.2. Adopting a blind source signal separation algorithm to obtain independent microseismic signals in all reconstructed microseismic signals in each microseismic sensor array; based on the autocorrelation of each independent microseismic signal, determining the periodic characteristic value of each independent microseismic signal to screen out rock mass fracture signals from all independent microseismic signals in each microseismic sensor array, and positioning and visualizing the rock mass fracture under the mine.

[0047] Further, based on the target component obtained above, each microseismic signal is reconstructed, that is, all target components in each microseismic signal are reconstructed with the residual component obtained by modal decomposition to obtain a reconstructed microseismic signal, and the denoising processing of the microseismic signal is completed.

[0048] The process of reconstructing the modal component into a signal is a known technology, and the specific reconstruction process is not described again.

[0049] Further, the embodiment adopts a blind source signal separation algorithm to obtain independent microseismic signals in all reconstructed microseismic signals in each microseismic sensor array, specifically: All reconstructed microseismic signals in each microseismic sensor array are combined to form a reconstruction matrix of each microseismic sensor array, and the reconstruction matrix is taken as the input of the blind source signal separation algorithm, wherein the number of independent components is set to N, and all independent components are output, which are denoted as independent microseismic signals.

[0050] It should be understood that there are many commonly used blind source signal separation algorithms, and the blind source signal separation algorithm based on FastICA is adopted in this embodiment to separate the signals, and in actual application process, the implementer can also adopt the blind source separation algorithm based on SOBI according to the specific circumstances. The selection of the blind source separation algorithm is not specially limited in this embodiment.

[0051] The blind source signal separation algorithm based on FastICA is a known technology, and the specific process of separating the independent microseismic signal by using the denoising matrix is not described again.

[0052] Further, based on the autocorrelation of each independent microseismic signal, the periodic characteristic value of each independent microseismic signal is determined to screen out rock mass fracture signals from all independent microseismic signals in each microseismic sensor array, and the rock mass fracture under the mine is positioned and visualized, specifically: In the embodiment, each independent microseismic signal is taken as an input of an autocorrelation function, and an autocorrelation graph of each independent microseismic signal is outputted, in which the abscissa is a different time lag, and the ordinate is an autocorrelation coefficient, so that an autocorrelation graph composed of autocorrelation coefficients of each independent microseismic signal under different time lags is obtained, and the maximum value in the autocorrelation graph is taken as a periodic characteristic value of each independent microseismic signal.

[0053] According to the periodic characteristic value of each independent microseismic signal, it can be understood that the periodic characteristic value is used to measure the regularity of the independent microseismic signal. If the periodic characteristic value of the current independent microseismic signal is larger, it means that the regularity of the current independent microseismic signal is stronger, and the current independent microseismic signal is more likely to be a signal generated by mechanical vibration rather than a signal generated by rock mass rupture. Conversely, if the periodic characteristic value of the current independent microseismic signal is smaller, it means that the regularity of the current independent microseismic signal is weaker, and the current independent microseismic signal is more likely to be a signal generated by rock mass rupture rather than a signal generated by mechanical vibration.

[0054] The calculation method and principle of the autocorrelation function are known technologies, and the specific process of obtaining the autocorrelation graph of the independent microseismic signal will not be repeated.

[0055] Further, in the embodiment, the periodic characteristic values of all independent microseismic signals in each microseismic sensor array are taken as inputs of a threshold segmentation algorithm, and a segmentation threshold is outputted. All independent microseismic signals with a periodic characteristic value smaller than the segmentation threshold are taken as rock mass rupture signals.

[0056] Preferably, the rock mass rupture signal screening process flowchart provided by the embodiment is as shown in Figure 2

[0057] It should be noted that there are many commonly used threshold segmentation algorithms. In the embodiment, the maximum inter-class variance algorithm is used to classify the independent microseismic signals. In actual application, as other implementation manners, implementers can also use other threshold segmentation algorithms according to specific conditions. The selection of the threshold segmentation algorithm is not specially limited in the embodiment.

[0058] The maximum inter-class variance algorithm is a known technology, and the specific process of classifying the independent microseismic signals will not be repeated.

[0059] Further, in the embodiment, based on the coordinates of the microseismic sensors to which all rock mass rupture signals belong, a linear positioning algorithm in a microseismic positioning algorithm is used to determine the position where the underground rock mass rupture occurs, and the position is displayed in a three-dimensional visualization interface.

[0060] The specific positioning process is as follows: assuming that a rock mass rupture occurs at an unknown position P(X, Y, Z) and the occurrence time is t, the time for the rock mass rupture signal to propagate from P to the ith microseismic sensor is ​​, assuming that the coordinates of the ith microseismic sensor are According to the coordinates of the microseismic sensor of the rock mass fracture signal, a distance equation set can be listed: , V represents the propagation speed of the rock mass fracture signal in the rock mass, , k represents the number of microseismic sensors corresponding to all rock mass signals, and the equation set is solved to obtain the position coordinates of the rock mass fracture occurrence.

[0061] The visualization process is: superimposing the calculated position coordinates of the rock mass fracture occurrence on the three-dimensional model of the mine, and using color, size and other graphical properties to encode the time and energy information, forming a dynamic and interactive mine microseismic activity map, which provides intuitive and scientific decision basis for safety production warning.

[0062] Preferably, the microseismic monitoring visualization process provided by the embodiment is as shown in Figure 3 .

[0063] Among them, the linear positioning algorithm in the microseismic positioning algorithm and the acquisition process of the mine three-dimensional model in the visualization process are all known technologies, and the specific principle of the linear positioning algorithm and the specific acquisition method of the mine three-dimensional model are not repeated.

[0064] So far, through modal decomposition and signal feature analysis, the embodiment effectively filters out random noise and mechanical interference in the mine microseismic signal, accurately separates the rock mass fracture signal, and realizes three-dimensional visualization of the fracture position combined with the microseismic positioning algorithm, which significantly improves the accuracy of microseismic monitoring and the authenticity of the visualization result, and provides reliable technical support for mine safety warning.

[0065] It should be noted that: the above-mentioned sequence of the embodiments of the present application is only for description, not representing the advantages and disadvantages of the embodiments. And the above describes the specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0066] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment focuses on the difference from other embodiments.

[0067] The above-described embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; modifying the technical solutions described in the above embodiments, or equivalently replacing some technical features, does not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and all should be included in the protection scope of the present application.

Claims

1. A method for intelligent monitoring and visualization of microseismic events in a mine, characterized in that, The method comprises the following steps: Obtaining microseismic signals in each microseismic sensor in each microseismic sensor array under a mine; Decomposing the microseismic signals in each microseismic sensor into a plurality of modal components, obtaining similar components of each modal component based on the center frequency of the modal component, determining the waveform similarity of each modal component based on the similarity between each modal component and all similar components thereof, and combining the similarity of the spectrum between each modal component and all similar components thereof to determine the signal similarity of each modal component; determining the signal eigenvalue of each modal component based on the degree of chaos of each modal component and in combination with the signal similarity, to screen target components from all modal components of the microseismic signals of each microseismic sensor for microseismic signal reconstruction; Obtaining independent microseismic signals in all reconstructed microseismic signals in each microseismic sensor array by using a blind source signal separation algorithm; determining the periodic eigenvalue of each independent microseismic signal based on the autocorrelation of each independent microseismic signal, to screen a rock mass rupture signal from all independent microseismic signals in each microseismic sensor array, to locate and visualize the rock mass rupture under the mine.

2. The method of intelligent microseismic monitoring and visualization in a mine according to claim 1, characterized in that, The method for obtaining similar components of each modal component is as follows: In all modal components in the microseismic signals of the remaining microseismic sensors other than the microseismic sensor to which each modal component belongs, the modal component closest to the center frequency of each modal component is taken as the similar component of each modal component, and all similar components of each modal component are obtained by traversing all microseismic sensors other than the microseismic sensor to which each modal component belongs.

3. The method of intelligent microseismic monitoring and visualization in a mine according to claim 1, characterized in that, The waveform similarity of each modal component is the average of the similarity between each modal component and all similar components thereof.

4. The method of intelligent microseismic monitoring and visualization in a mine according to claim 1, characterized in that, The signal similarity of each modal component is the product of the average of the similarity of the spectrum between each modal component and all similar components thereof and the waveform similarity.

5. The method of intelligent microseismic monitoring and visualization in a mine according to claim 1, characterized in that, The signal eigenvalue of each modal component is the ratio of the signal similarity of each modal component to the permutation entropy of the modal component.

6. The method of intelligent microseismic monitoring and visualization in a mine according to claim 1, characterized in that, The screening of target components from all modal components of the microseismic signals of each microseismic sensor for microseismic signal reconstruction comprises the following steps: Grouping the microseismic signals of all microseismic sensors in each microseismic sensor array into a signal matrix, taking the signal matrix as the input of a signal source number estimation method, and outputting the number of signal sources, denoted as N; Taking the modal components corresponding to the first N signal eigenvalues in the ascending order arrangement result of the signal eigenvalues of all modal components in the microseismic signals of each microseismic sensor as the target components.

7. A method of microseismic intelligent monitoring and visualization in a mine according to claim 6, characterized in that, The method for obtaining independent microseismic signals in all reconstructed microseismic signals in each microseismic sensor array by using a blind source signal separation algorithm comprises the following steps: Grouping all reconstructed microseismic signals in each microseismic sensor array into a reconstruction matrix of each microseismic sensor array, taking the reconstruction matrix as the input of a blind source signal separation algorithm, wherein the number of independent components is set to N, and all independent components are output, denoted as independent microseismic signals.

8. The method of intelligent microseismic monitoring and visualization in a mine according to claim 1, characterized in that, The periodic eigenvalue of each independent microseismic signal is the maximum autocorrelation coefficient in the autocorrelation diagram of each independent microseismic signal.

9. A method of microseismic intelligent monitoring and visualization in a mine according to claim 1, characterized in that, The screening of a rock mass rupture signal from all independent microseismic signals in each microseismic sensor array comprises the following steps: Periodic characteristic values of all independent microseismic signals in each microseismic sensor array are taken as inputs of a threshold segmentation algorithm, and a segmentation threshold is outputted, and all independent microseismic signals with a periodic characteristic value less than the segmentation threshold are taken as rock mass fracture signals.

10. The method of intelligent microseismic monitoring and visualization in a mine according to claim 1, characterized in that, The positioning and visual processing of the rock mass fracture under the mine include: Based on the coordinates of the microseismic sensors to which all rock mass fracture signals belong, a microseismic positioning algorithm is used to determine the position where the underground rock mass fracture occurs, and the position is displayed in a three-dimensional visual interface.

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