A mine microseismic intelligent monitoring and visualization method

Abstract

This application relates to the field of microseismic monitoring technology, specifically to an intelligent monitoring and visualization method for microseismic activity in mines. The method includes: acquiring similar components of each modal component based on the center frequency of each modal component; determining the signal similarity of each modal component based on the similarity between each modal component and all its similar components, as well as the similarity of their spectra; and determining the signal characteristic values ​​of each modal component based on the signal similarity to reconstruct the microseismic signal; acquiring independent microseismic signals from all reconstructed microseismic signals within each microseismic sensor array; and filtering out rock mass fracture signals based on the autocorrelation of each independent microseismic signal to locate and visualize rock mass fractures in the mine. This application solves the interference of mechanical vibration and random noise on the location of rock mass fractures, improving the reliability and accuracy of microseismic monitoring data.

Description

Technical Field

[0001] This application relates to the field of microseismic monitoring technology, specifically to a method for intelligent monitoring and visualization of microseismic activity in underground mines. Background Technology

[0002] In the process of underground mining, in order to provide early warning of underground rock mass collapses, landslides and rock bursts and other ground pressure phenomena to ensure safe and efficient mine production, existing methods usually use microseismic technology to monitor microseismic events generated when underground rock masses deform and break. By using microseismic monitoring sensors to collect microseismic signals in real time, and combining the vibration positioning principle to determine the location of underground rock mass fractures, the location is displayed in three-dimensional space to provide a three-dimensional visualization interface.

[0003] However, when using a microseismic monitoring system to monitor microseismic events in mines, the complexity of the underground working environment means that the microseismic signals collected by the microseismic sensors are usually generated by a mixture of various vibration sources, such as underground mechanical equipment, drilling rigs, ventilation fans, and rock mass fractures. Furthermore, random noise is easily introduced into the microseismic signals during the acquisition and transmission process, making it difficult for the microseismic signals collected by the microseismic sensors to accurately reflect the microseismic events caused by underground rock mass fractures. This leads to deviations in the visualization results of the underground rock mass fracture locations, reducing the reliability and accuracy of the microseismic monitoring data. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method for intelligent monitoring and visualization of microseismic activity in mines, thereby resolving existing issues.

[0005] The intelligent monitoring and visualization method for microseismic events in mines proposed in this application adopts the following technical solution:

[0006] One embodiment of this application provides a method for intelligent monitoring and visualization of microseismic activity in underground mines, the method comprising the following steps:

[0007] Acquire the microseismic signals from each microseismic sensor within each microseismic sensor array in the mine;

[0008] The microseismic signal from each microseismic sensor is decomposed into multiple modal components. Based on the center frequency of each modal component, similar components are obtained. Based on the similarity between each modal component and all its similar components, the waveform similarity of each modal component is determined. Combined with the spectral similarity between each modal component and all its similar components, the signal similarity of each modal component is determined. Based on the disorder of each modal component and combined with the signal similarity, the signal characteristic value of each modal component is determined to screen out target components from all modal components of the microseismic signal from each microseismic sensor for microseismic signal reconstruction.

[0009] A blind source signal separation algorithm is used to obtain independent microseismic signals from all reconstructed microseismic signals in each microseismic sensor array. Based on the autocorrelation of each independent microseismic signal, the periodic characteristic value of each independent microseismic signal is determined so as to screen out rock mass fracture signals from all independent microseismic signals in each microseismic sensor array, and to locate and visualize rock mass fractures in the mine.

[0010] Preferably, the method for obtaining the similar components of each modal component is as follows:

[0011] In all the modal components of the microseismic signals from all the microseismic sensors other than the microseismic sensor to which each modal component belongs, the modal component that is closest to the center frequency of each modal component is taken as the similar component of each modal component. By traversing all the microseismic sensors other than the microseismic sensor to which each modal component belongs, all similar components of each modal component are obtained.

[0012] Preferably, the waveform similarity of each modal component is the average of the similarities between each modal component and all its similar components.

[0013] Preferably, the signal similarity of each modal component is the result of multiplying the mean of the spectral similarity between each modal component and all its similar components by the waveform similarity.

[0014] Preferably, the signal characteristic value of each modal component is the result of the ratio of the signal similarity of each modal component to the entropy of the upper modal component arrangement.

[0015] Preferably, the step of selecting the target component from all modal components of the microseismic signal from each microseismic sensor for microseismic signal reconstruction includes:

[0016] The microseismic signals from all the microseismic sensors in each microseismic sensor array are combined into a signal matrix. The signal matrix is ​​used as the input to the signal source number estimation method, and the output is the number of signal sources, denoted as N.

[0017] The modal components corresponding to the first N signal feature values ​​of all modal components in the microseismic signal of each microseismic sensor in ascending order are taken as the target components.

[0018] Preferably, the step of using a blind source signal separation algorithm to obtain the independent microseismic signals from all reconstructed microseismic signals within each microseismic sensor array includes:

[0019] The reconstructed microseismic signals from each microseismic sensor array are combined to form the reconstruction matrix of each microseismic sensor array. The reconstruction matrix is ​​used as the input to the blind source signal separation algorithm. The number of independent components is set to N, and all independent components are output, denoted as independent microseismic signals.

[0020] Preferably, the periodic characteristic value of each independent microseismic signal is the maximum autocorrelation coefficient in the autocorrelation graph of each independent microseismic signal.

[0021] Preferably, the step of filtering rock mass fracture signals from all independent microseismic signals within each microseismic sensor array includes:

[0022] The periodic characteristic values ​​of all independent microseismic signals within each microseismic sensor array are used as input to the threshold segmentation algorithm, and the segmentation threshold is output. All independent microseismic signals with periodic characteristic values ​​less than the segmentation threshold are taken as rock mass fracture signals.

[0023] Preferably, the process of locating and visualizing rock mass fractures in the mine includes:

[0024] Based on the coordinates of all the microseismic sensors to which the rock mass fracture signals belong, a microseismic positioning algorithm is used to determine the location of the underground rock mass fracture and display it in a three-dimensional visualization interface.

[0025] This application has at least the following beneficial effects:

[0026] This application constructs signal feature values ​​by analyzing the signal distribution characteristics between the signal components generated by vibration sources such as underground mine machinery and rock fracturing, and the signal components generated by random noise in the acquired microseismic signals. Based on the constructed signal feature values, random noise in the acquired microseismic signals is filtered out. This effectively reduces the impact of random noise introduced during the acquisition and transmission of the microseismic signals on the accuracy of subsequently separating independent microseismic signals generated by different vibration sources from the acquired microseismic signals, thereby improving the accuracy of separating independent microseismic signals generated by microseismic events such as underground rock fracturing in the target mine from the acquired microseismic signals. Furthermore, this application analyzes the signal distribution characteristics between the independent microseismic signals generated by underground mine machinery and the random noise generated by the vibration sources. The signal distribution characteristics of independent microseismic signals generated by microseismic events such as rock mass fracturing are analyzed to construct periodic feature values. Based on the constructed periodic feature values, rock mass fracturing signals generated by underground rock mass fracturing are screened from all the separated independent microseismic signals. This can effectively separate independent microseismic signals generated by underground rock mass fracturing events from the collected microseismic signals, avoiding interference from independent microseismic signals generated by underground machinery and random noise introduced during the acquisition and transmission of microseismic signals. This reduces the deviation in the visualization results of underground rock mass fracturing locations and improves the reliability and accuracy of microseismic monitoring data. Attached Figure Description

[0027] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A flowchart illustrating the steps of an intelligent monitoring and visualization method for microseismic activity in mines, as provided in one embodiment of this application;

[0029] Figure 2 A flowchart of a rock mass fracture signal screening process provided in one embodiment of this application;

[0030] Figure 3 This is a schematic diagram of a microseismic monitoring visualization process provided in one embodiment of this application. Detailed Implementation

[0031] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a mine micro-seismic intelligent monitoring and visualization method proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0032] 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 this application pertains.

[0033] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent monitoring and visualization method for microseismic activity in mines provided in this application.

[0034] This application provides an embodiment of an intelligent monitoring and visualization method for microseismic activity in mines. Specifically, it provides the following method for intelligent monitoring and visualization of microseismic activity in mines. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps:

[0035] Step S1: Obtain the microseismic signals from each microseismic sensor in each microseismic sensor array in the mine.

[0036] A microseismic sensor array is installed at each predetermined location in the target mine. Each microseismic sensor in the array collects microseismic signals within a predetermined time period prior to the current moment. The signal acquisition frequency is set to f. The data acquisition instrument converts all collected microseismic signals from analog electrical signals into digital signals, resulting in pre-processed microseismic signals for analysis. The number of microseismic sensors in the array needs to be greater than the total number of all mechanical equipment in the mine.

[0037] It should be noted that the preset duration and signal acquisition frequency f are both set manually. In this embodiment, the preset duration is 2s and the signal acquisition frequency f is 8kHz. In actual applications, as other implementation methods, implementers can also set them according to specific circumstances. This embodiment does not impose any special restrictions.

[0038] Furthermore, the microseismic signal identification based on the energy extremum method is used to detect whether there is a valid microseismic signal among all the microseismic signals of the microseismic sensor array at the current moment. If there is, the current moment is analyzed; if not, the current moment is not analyzed. In this embodiment, it is assumed that there is a valid microseismic signal at the current moment, and the specific analysis process is as follows.

[0039] The process of using the energy extremum method to identify and detect whether there is a valid microseismic signal among all the microseismic signals of the microseismic sensor array at the current moment is a well-known technique and will not be described in detail here.

[0040] Step S2: By decomposing, filtering, reconstructing and separating the microseismic signals, the rock mass fracture signals are identified and located and visualized.

[0041] S2.1 The microseismic signal in each microseismic sensor is decomposed into multiple modal components. Based on the center frequency of each modal component, similar components of each modal component are obtained. Based on the similarity between each modal component and all its similar components, the waveform similarity of each modal component is determined. Combined with the spectral similarity between each modal component and all its similar components, the signal similarity of each modal component is determined. Based on the disorder of each modal component and combined with the signal similarity, the signal characteristic value of each modal component is determined, so as to select the target component from all modal components of the microseismic signal of each microseismic sensor for microseismic signal reconstruction.

[0042] Since the microseismic sensors in the same microseismic sensor array are adjacent in position, the microseismic signals collected by these sensors at the same time are usually a mixture of microseismic signals generated by the same vibration source, such as underground mining machinery such as fully mechanized mining machines, drilling rigs, and ventilation fans, as well as rock fracturing and other vibration sources. Therefore, this embodiment uses a blind source signal separation method to separate the independent microseismic signals generated by different vibration sources in the microseismic signal. However, directly using the Ma Guangyuan separation method to separate the microseismic signal can easily result in the separated signal components not only containing the microseismic signal components generated by the vibration source, but also containing random noise components introduced during the acquisition and transmission of the microseismic signal.

[0043] Therefore, to reduce the impact of random noise introduced during the acquisition and transmission of the microseismic signal on the accuracy of the independent microseismic signal components generated by different vibration sources in the subsequent separation of the microseismic signal, the microseismic signal in each microseismic sensor is decomposed into multiple modal components. Based on the center frequency of each modal component, similar components of each modal component are obtained; based on the similarity between each modal component and all its similar components, the waveform similarity of each modal component is determined; and combined with the spectral similarity between each modal component and all its similar components, the signal similarity of each modal component is determined; based on the disorder of each modal component and combined with the signal similarity, the signal characteristic value of each modal component is determined, so as to select the target component from all modal components of the microseismic signal of each microseismic sensor for microseismic signal reconstruction. The specific process is as follows:

[0044] First, in each microseismic sensor array, this embodiment uses the microseismic signal from each microseismic sensor as the input to the mode decomposition algorithm and outputs all modal components of the microseismic signal. In this embodiment, the mode decomposition algorithm adopts the variational mode decomposition algorithm. In practical applications, as other implementation methods, implementers may also adopt other decomposition methods such as empirical mode decomposition algorithms according to specific circumstances. This embodiment does not impose any special restrictions on the selection of mode decomposition algorithms.

[0045] Variational mode decomposition algorithm is a well-known technique, and the specific process of using it to decompose microseismic signals into modal components will not be elaborated here.

[0046] Furthermore, in this embodiment, based on the center frequency of the modal components, similar components of each modal component are obtained, specifically as follows:

[0047] In all the modal components of the microseismic signals from all the microseismic sensors other than the microseismic sensor to which each modal component belongs, the modal component that is closest to the center frequency of each modal component is taken as the similar component of each modal component. By traversing all the microseismic sensors other than the microseismic sensor to which each modal component belongs, all similar components of each modal component are obtained. These are used to characterize the signal components in the microseismic signal that are in the same frequency band as each modal component.

[0048] The concept of center frequency is a well-known technique and will not be elaborated further.

[0049] Furthermore, this embodiment determines the waveform similarity of each modal component based on the similarity between each modal component and all its similar components, in order to evaluate the degree of waveform similarity between each modal component and its similar components, specifically as follows:

[0050] In this embodiment, the average similarity between each modal component and all its similar components is used 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, suggesting that the current modal component and its similar components are very likely generated by the same physical vibration source and are not random noise. Conversely, if the waveform similarity of the current modal component is smaller, it indicates that the signal lacks regularity, suggesting that the correlation between the current modal component and its similar components is weak, and it is very likely a signal generated by random noise, equipment interference, etc., with low credibility, and should be regarded as noise or interference and eliminated.

[0051] It should be noted that there are many commonly used methods for measuring the similarity between signals. In this embodiment, the cosine similarity between each modal component and all its similar components is used as the similarity between each modal component and all its similar components. In practical applications, as other implementation methods, implementers may also use other methods such as the reciprocal of the Euclidean distance to measure the similarity between signals, depending on the specific circumstances. This embodiment does not impose any special restrictions on the selection of methods for measuring the similarity between signals.

[0052] The method for calculating cosine similarity is a well-known technique, and its specific calculation process will not be elaborated here.

[0053] It should be noted that, unless otherwise specified, all content involving the measurement of similarity in this embodiment uses cosine similarity.

[0054] Furthermore, this embodiment determines the signal similarity of each modal component based on the waveform similarity of each modal component and the spectral similarity between each modal component and all its similar components. This signal similarity is used to evaluate whether the signal component corresponding to the modal component has similar signal distribution characteristics to the signal components in the same frequency range of the microseismic signals from other microseismic sensors. The specific process for determining the signal similarity is as follows:

[0055] As one implementation method, in this embodiment, the result of multiplying the mean of the spectral similarity between each modal component and all its similar components with the waveform similarity is used as the signal similarity of each modal component.

[0056] Based on the signal similarity of each modal component, it can be understood that signal similarity reflects the overall similarity of a signal component, that is, a modal component, to other corresponding signal components of other microseismic sensors in both waveform and frequency dimensions. It is used to indicate whether a modal component and its similar components belong to a real physical vibration source. It is a key indicator for judging whether a signal is a valid signal or noise. If the average similarity of the spectrum 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 spectrum. This indicates that the microseismic signal is very reliable in terms of sound timbre, meaning that the current modal component and its similar components are more likely to come from a real physical vibration source. Therefore, 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 its similar components are more similar in waveform. This means that the current modal component and its similar components are more likely to belong to a signal corresponding to a real physical vibration source. Therefore, the corresponding signal similarity is greater.

[0057] Conversely, if the mean similarity of the spectrum between the current modal component and all its similar components is smaller, it indicates that the current modal component and its similar components have lower similarity in the spectrum. This suggests that there is a significant difference in the timbre dimension of the micro-vibration signal, meaning that the current modal component and its similar components are very likely to come from random noise. Therefore, the corresponding signal similarity is smaller. At the same time, if the waveform similarity of the current modal component is smaller, it indicates that the current modal component and its similar components have lower similarity in the waveform. This suggests that the current modal component and its similar components are more likely to belong to random interference. Therefore, the corresponding signal similarity is smaller.

[0058] Furthermore, this embodiment determines the signal characteristic value of each modal component based on the disorder level of each modal component and in combination with the signal similarity, so as to select the target component from all modal components of the microseismic signal of each microseismic sensor for microseismic signal reconstruction, specifically as follows:

[0059] In this embodiment, the signal similarity of each modal component is divided by the entropy of the modal component arrangement, and the result is used as the signal feature value of each modal component.

[0060] The formula and method for calculating permutation entropy are well-known techniques, and the specific calculation process will not be elaborated here.

[0061] Based on the signal characteristic values ​​of each modal component, it can be understood that the signal characteristic values ​​reflect the probability that the modal component is an effective part of the microseismic signal. If the signal similarity of the current modal component is greater, it means that the current modal component is very similar to a real and effective physical signal in terms of waveform and spectrum, rather than a signal component caused by random noise. Therefore, its corresponding signal characteristic value is larger. At the same time, if the arrangement entropy of the current modal component is smaller, it means that the current modal component is more regular and more predictable, indicating that the current modal component is a signal component generated by an effective real physical vibration source. Therefore, the corresponding signal characteristic value of the modal component is larger.

[0062] Conversely, the smaller the signal similarity of the current mode component, the greater the difference between the current mode component and the real effective physical signal in terms of waveform and spectrum. It is more likely to be a signal component caused by random noise or irrelevant interference. Therefore, its corresponding signal characteristic value is smaller. At the same time, if the permutation entropy of the current mode component is larger, it indicates that the current mode component is more chaotic and random, with extremely poor predictability. This suggests that the current mode component is very likely a signal component generated by noise or sudden interference. Therefore, the corresponding signal characteristic value of the mode component is smaller.

[0063] Furthermore, in this embodiment, based on the aforementioned signal feature values, target components are selected from all modal components of the microseismic signal from each microseismic sensor for microseismic signal reconstruction. Specifically:

[0064] As one implementation method, in this embodiment, the microseismic signals of all microseismic sensors in each microseismic sensor array are combined into a signal matrix, and the signal matrix is ​​used as the input of the signal source number estimation method, and the number of signal sources is output, denoted as N.

[0065] The modal components corresponding to the first N signal feature values ​​of all modal components in the microseismic signal of each microseismic sensor in ascending order are taken as target components. These target components are used to characterize the modal components corresponding to all signal components generated by vibration sources such as underground mining machinery and equipment such as fully mechanized mining machines, drilling rigs, and ventilation fans, as well as rock mass fractures in the microseismic signal.

[0066] It should be noted that there are many commonly used methods for estimating the number of signal sources. In this embodiment, the signal source number estimation method based on the Gaussian circle method is used to determine the number of signal sources. In practical applications, as other implementation methods, implementers may also use the signal source number estimation method based on smooth order sequence or the signal source number estimation method based on information theory to obtain the number of signal sources, depending on the specific circumstances. This embodiment does not impose any special restrictions on the selection of the signal source number estimation method.

[0067] Among them, the signal source number estimation method based on the Gaussian circle method is a well-known technique, and the specific process of using it to estimate the number of signal sources will not be elaborated here.

[0068] Thus, this embodiment transforms the microseismic signal into multiple components through modal decomposition and innovatively combines waveform similarity, spectral similarity, and permutation entropy to quantify the effectiveness and reliability of each component. This allows for the precise selection of target components representing real physical vibration sources, effectively suppressing noise interference and providing a high-quality, high-signal-to-noise ratio signal foundation for subsequent blind source separation and accurate positioning and visualization of rock mass fracture signals.

[0069] S2.2 The blind source signal separation algorithm is used to obtain the independent microseismic signals from all reconstructed microseismic signals in each microseismic sensor array; based on the autocorrelation of each independent microseismic signal, the periodic characteristic value of each independent microseismic signal is determined, so as to screen out the rock mass fracture signal from all independent microseismic signals in each microseismic sensor array, and to locate and visualize the rock mass fracture in the mine.

[0070] Furthermore, based on the target components obtained above, each microseismic signal is reconstructed, that is, all target components in each microseismic signal are reconstructed with the residual components obtained from mode decomposition to obtain the reconstructed microseismic signal, thus completing the denoising process of the microseismic signal.

[0071] The process of reconstructing modal components into signals is a well-known technique, and its specific reconstruction process will not be elaborated here.

[0072] Furthermore, this embodiment employs a blind source signal separation algorithm to obtain the independent microseismic signals from all reconstructed microseismic signals within each microseismic sensor array. Specifically:

[0073] The reconstructed microseismic signals from each microseismic sensor array are combined to form the reconstruction matrix of each microseismic sensor array. The reconstruction matrix is ​​used as the input to the blind source signal separation algorithm. The number of independent components is set to N, and all independent components are output, denoted as independent microseismic signals.

[0074] It should be understood that there are many commonly used blind source signal separation algorithms. In this embodiment, a blind source signal separation algorithm based on FastICA is used to separate the signal. In practical applications, implementers may also use a blind source separation algorithm based on SOBI depending on the specific circumstances. This embodiment does not impose any special restrictions on the selection of blind source separation algorithms.

[0075] Among them, the blind source signal separation algorithm based on FastICA is a well-known technology, and the specific process of using it to separate the denoising matrix to obtain independent microseismic signals will not be elaborated here.

[0076] Furthermore, this embodiment determines the periodic characteristic value of each independent microseismic signal based on the autocorrelation of each independent microseismic signal, so as to filter out the rock mass fracture signal from all independent microseismic signals in each microseismic sensor array, and to locate and visualize the rock mass fracture in the mine. Specifically:

[0077] In this embodiment, each independent microseismic signal is used as the input of the autocorrelation function, and the autocorrelation graph of each independent microseismic signal is output. The horizontal axis of the autocorrelation graph represents different time delays, and the vertical axis represents the autocorrelation coefficient. The autocorrelation graph of each independent microseismic signal under different time delays is obtained, and the maximum value in the autocorrelation graph is used as the periodic characteristic value of each independent microseismic signal.

[0078] Based on the periodic characteristic values ​​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 indicates 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 indicates 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.

[0079] The calculation method and principle of the autocorrelation function are well-known techniques, and the specific process of obtaining the autocorrelation plot of independent microseismic signals using it will not be elaborated here.

[0080] Furthermore, in this embodiment, the periodic feature values ​​of all independent microseismic signals in each microseismic sensor array are used as the input of the threshold segmentation algorithm, and the segmentation threshold is output. All independent microseismic signals with periodic feature values ​​less than the segmentation threshold are used as rock mass fracture signals.

[0081] Preferably, the flowchart of the rock mass fracture signal screening process provided in this embodiment is as follows: Figure 2 As shown.

[0082] It should be noted that there are many commonly used threshold segmentation algorithms. In this embodiment, the maximum inter-class variance algorithm is used to classify independent microseismic signals. In practical applications, as other implementation methods, implementers may also use other threshold segmentation algorithms according to specific circumstances. This embodiment does not impose any special restrictions on the selection of threshold segmentation algorithms.

[0083] Among them, the Otsu's inter-class variance algorithm is a well-known technique, and the specific process of using it to classify independent microseismic signals will not be elaborated here.

[0084] Furthermore, in this embodiment, based on the coordinates of all the microseismic sensors to which the rock mass fracture signals belong, the linear positioning algorithm in the microseismic positioning algorithm is used to determine the location where the underground rock mass fracture occurs, and the location is displayed in a three-dimensional visualization interface.

[0085] The specific location process is as follows: Assume a rock mass fracture occurs at an unknown location P(X,Y,Z), and the time of occurrence is... The time it takes for the rock mass fracture signal to propagate from P to the i-th microseismic sensor is Assume the coordinates of the i-th microseismic sensor are Based on the coordinates of the microseismic sensor provided by the rock mass fracture signal, a set of distance equations can be enumerated: V represents the propagation speed of the rock mass fracture signal within the rock mass. , k represents the number of microseismic sensors corresponding to all rock mass signals. Solve the system of equations to obtain the coordinates of the location where the rock mass fracture occurred.

[0086] The visualization process involves overlaying the calculated location coordinates of rock mass fractures onto a 3D model of the mine, and encoding their time and energy information using graphic attributes such as color and size to create a dynamic and interactive mine microseismic activity map, providing an intuitive and scientific basis for safety production early warning.

[0087] Preferably, the schematic diagram of the microseismic monitoring visualization process provided in this embodiment is as follows: Figure 3 As shown.

[0088] Among them, the linear positioning algorithm in the microseismic positioning algorithm and the process of obtaining the three-dimensional model of the mine in the visualization process are well-known technologies. The specific principles of the linear positioning algorithm and the specific methods of obtaining the three-dimensional model of the mine will not be elaborated here.

[0089] Thus, this embodiment effectively filters out random noise and mechanical interference in mine microseismic signals through modal decomposition and signal feature analysis, accurately separates rock mass fracture signals, and achieves three-dimensional visualization of fracture locations by combining microseismic positioning algorithms. This significantly improves the accuracy of microseismic monitoring and the authenticity of visualization results, providing reliable technical support for mine safety early warning.

[0090] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0091] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0092] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for intelligent monitoring and visualization of microseismic activity in underground mines, characterized in that, The method includes the following steps: Acquire the microseismic signals from each microseismic sensor within each microseismic sensor array in the mine; The microseismic signal from each microseismic sensor is decomposed into multiple modal components. Based on the center frequency of each modal component, similar components are obtained. Based on the similarity between each modal component and all its similar components, the waveform similarity of each modal component is determined. Combined with the spectral similarity between each modal component and all its similar components, the signal similarity of each modal component is determined. Based on the disorder of each modal component and combined with the signal similarity, the signal characteristic value of each modal component is determined to screen out target components from all modal components of the microseismic signal from each microseismic sensor for microseismic signal reconstruction. A blind source signal separation algorithm is used to obtain independent microseismic signals from all reconstructed microseismic signals in each microseismic sensor array. Based on the autocorrelation of each independent microseismic signal, the periodic characteristic value of each independent microseismic signal is determined so as to screen out rock mass fracture signals from all independent microseismic signals in each microseismic sensor array, and to locate and visualize rock mass fractures in the mine. The waveform similarity of each modal component is the average of the similarity between each modal component and all its similar components. The signal similarity of each modal component is the result of multiplying the mean of the spectral similarity between each modal component and all its similar components by the waveform similarity. The signal feature value of each modal component is the result of the ratio of the signal similarity of each modal component to the entropy of the upper modal component arrangement; The periodic characteristic value of each independent microseismic signal is the maximum autocorrelation coefficient in the autocorrelation graph of each independent microseismic signal.

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

3. The intelligent monitoring and visualization method for microseismic events in mines as described in claim 1, characterized in that, The process of selecting target components from all modal components of the microseismic signal from each microseismic sensor for microseismic signal reconstruction includes: The microseismic signals from all the microseismic sensors in each microseismic sensor array are combined into a signal matrix. The signal matrix is ​​used as the input to the signal source number estimation method, and the output is the number of signal sources, denoted as N. The modal components corresponding to the first N signal feature values ​​of all modal components in the microseismic signal of each microseismic sensor in ascending order are taken as the target components.

4. The intelligent monitoring and visualization method for microseismic events in mines as described in claim 3, characterized in that, The method of using a blind source signal separation algorithm to obtain independent microseismic signals from all reconstructed microseismic signals within each microseismic sensor array includes: The reconstructed microseismic signals from each microseismic sensor array are combined to form the reconstruction matrix of each microseismic sensor array. The reconstruction matrix is ​​used as the input of the blind source signal separation algorithm. The number of independent components is set to N, and all independent components are output, denoted as independent microseismic signals.

5. The intelligent monitoring and visualization method for microseismic events in mines as described in claim 1, characterized in that, The process of filtering rock mass fracture signals from all independent microseismic signals within each microseismic sensor array includes: The periodic characteristic values ​​of all independent microseismic signals within each microseismic sensor array are used as input to the threshold segmentation algorithm, and the segmentation threshold is output. All independent microseismic signals with periodic characteristic values ​​less than the segmentation threshold are taken as rock mass fracture signals.

6. The intelligent monitoring and visualization method for microseismic events in mines as described in claim 1, characterized in that, The process of locating and visualizing rock mass fractures in underground mines includes: Based on the coordinates of all the microseismic sensors to which the rock mass fracture signals belong, a microseismic positioning algorithm is used to determine the location of the underground rock mass fracture and display it in a three-dimensional visualization interface.

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