Broadband triaxial micro-seismic monitoring and early warning method and system based on reconstructed longitudinal and transverse waves
By reconstructing a broadband triaxial microseismic monitoring method based on longitudinal and transverse waves, and utilizing a triaxial microseismic sensor array and particle swarm optimization algorithm, the accuracy of seismic source location was improved, the error problem of existing systems was solved, and accurate early warning of underground engineering disasters was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-23
- Publication Date
- 2026-04-03
AI Technical Summary
The existing microseismic monitoring system has insufficient source location accuracy, with an error of about 30m, and rockburst early warning is prone to false alarms and missed alarms.
A broadband triaxial microseismic monitoring method based on reconstructed P-waves and S-waves is adopted. Multiple triaxial broadband microseismic sensors are used to form a sensing array. By decomposing and reconstructing the seismic wave signal of coal and rock fracture, the arrival time of P-waves and S-waves is determined. An iterative function for source location is constructed, and the source location is located by combining the particle swarm algorithm. A multidimensional microseismic index system is constructed, and a multi-parameter early warning index is generated for monitoring and early warning.
It has improved the accuracy of seismic source location, reduced false alarms and missed alarms in rockburst early warning, realized the real monitoring and early warning of underground engineering disasters, and guided the early warning of geological disasters such as rock bursts, rockbursts and mine tremors in underground engineering.
Smart Images
Figure CN121784826A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of underground engineering disaster monitoring and early warning technology, and in particular to a broadband triaxial microseismic monitoring and early warning method and system based on reconstructed longitudinal and transverse waves. Background Technology
[0002] As mining depths increase, coal seam environments become increasingly complex, and the impact of "high altitude, high pollution, and high risk of disturbance" becomes more severe, leading to a year-on-year increase in the frequency and intensity of rockbursts and other dynamic disasters. Microseismic technology primarily utilizes sensors to collect vibration signals released in the form of stress waves during rock fracturing. By filtering, standardizing, and inverting the collected signals, a three-dimensional visualization of the seismic source location is obtained. The damage state of the surrounding rock is assessed based on the number of microseismic events and their energy distribution characteristics. However, existing microseismic monitoring systems cannot achieve sufficient source location accuracy, with errors reaching approximately 30 meters.
[0003] In addition to three-dimensional positioning visualization, another important function of microseismic systems is to further mine microseismic characteristic parameters to obtain microseismic early warning indicators in order to achieve early warning of rockbursts. Currently, most existing microseismic systems only have two indicators: frequency and energy, and each indicator provides early warning independently, leading to false alarms and missed alarms. Summary of the Invention
[0004] To address the aforementioned technical problems in existing technologies, this invention provides a broadband triaxial microseismic monitoring and early warning method and system based on reconstructed P-waves and S-waves. The technical solution is as follows: On the one hand, a broadband triaxial microseismic monitoring and early warning method based on reconstructed P-waves and S-waves is provided. The method includes: acquiring coal and rock fracture seismic wave signals of the area to be monitored based on a sensor array formed by multiple triaxial broadband microseismic sensors; decomposing and reconstructing the coal and rock fracture seismic wave signals to obtain P-wave signals and S-wave signals, and determining the arrival times of the P-wave signals and the S-wave signals respectively; constructing a source location iterative function based on the first arrival times of the P-wave and S-wave of the same vibration signal at different triaxial broadband microseismic sensor locations within the area to be monitored, and using a particle swarm optimization algorithm to locate the microseismic source; constructing a multidimensional microseismic index system based on the coal and rock fracture seismic wave signals and the microseismic source location; the multidimensional microseismic index system includes multiple microseismic indicators; constructing a multi-parameter early warning index based on the multidimensional microseismic index system, and monitoring and issuing early warnings for target mines within the area to be monitored based on the multi-parameter early warning index.
[0005] Optionally, the triaxial broadband micro-vibration sensor includes three axial vibration sensors (X, Y, and Z) arranged at 90° to each other inside the sensor housing, and the acquired signal frequency range includes 10Hz~10Hz. 5Hz.
[0006] Optionally, the coal and rock fracturing seismic wave signal is decomposed and reconstructed to obtain longitudinal wave and transverse wave signals, including: obtaining the instantaneous frequency distribution characteristics of the coal and rock fracturing seismic wave signal based on synchronous compression transform; determining the first arrival time of the transverse wave and the first arrival time of the longitudinal wave based on the instantaneous frequency distribution characteristics; determining the triaxial seismic wave signal of the transverse wave after the first arrival of the longitudinal wave and the transverse wave based on the first arrival time of the transverse wave; constructing the covariance matrix of the triaxial seismic wave signal and solving the eigenvalues of the covariance matrix and the corresponding matrix eigenvectors; separating the triaxial seismic wave signal based on the matrix eigenvectors to obtain the longitudinal wave and transverse wave signals.
[0007] Optionally, the source location iteration function includes:
[0008] In the formula, The initial arrival time of the longitudinal wave is given. The initial arrival time of the shear wave is given by i, where i represents the triaxial broadband micro-vibration sensor number. n The number of triaxial broadband micro-vibration sensors. x The coordinates of the earthquake source are... When the longitudinal wave theory is completed, The theoretical arrival time difference between the longitudinal wave signal and the transverse wave signal. and This is the weighting factor.
[0009] Optionally, the microseismic multidimensional index system includes temporal indexes, spatial indexes, and intensity indexes; wherein, the temporal indexes include the cumulative frequency of microseismic events reflecting the rock mass fracturing activity per unit time; the spatial indexes include the microseismic event cluster density characterizing the degree of microseismic event aggregation per unit volume; and the intensity indexes include the energy ratio of shear waves to longitudinal waves characterizing the degree of coal and rock fracturing.
[0010] Optionally, the microseismic multidimensional index system also includes: energy, total daily energy, average daily energy, maximum daily energy, energy deviation value, energy density, total daily frequency, average daily frequency, frequency deviation value, frequency ratio, and focal concentration. a value, b Value, seismic failure, A(b) value, P(b) Value, total fault area, microseismic activity degree, microseismic activity scale, algorithm complexity, normalized microseismic spatiotemporal diffusion, void parameters, energy density index, and temporal information entropy.
[0011] Optionally, a multi-parameter early warning index is constructed based on the microseismic multidimensional index system, including: determining a combination of early warning indicators applicable to target mines within the monitored area based on the sensitivity of the multiple microseismic indicators to the precursors of coal and rock dynamic disasters in the monitored area; normalizing the combination of early warning indicators and assigning indicator weights based on the entropy weight method; and performing a weighted summation of the normalized combination of early warning indicators based on the indicator weights to obtain the multi-parameter early warning index.
[0012] On the other hand, a broadband triaxial microseismic monitoring and early warning system based on reconstructed P-waves and S-waves is also provided, including: an acquisition module, a decomposition module, a positioning module, a construction module, and an early warning module; wherein, the acquisition module is used to acquire the coal and rock fracture seismic wave signal of the area to be monitored based on a sensing array formed by multiple triaxial broadband microseismic sensors; the decomposition module is used to decompose and reconstruct the coal and rock fracture seismic wave signal to obtain P-wave signals and S-wave signals, and determine the arrival time of the P-wave signal and the arrival time of the S-wave signal respectively; the positioning module is used to locate the same vibration signal within the area to be monitored. The source location iterative function is constructed based on the first arrival time of the P-wave, the first arrival time of the S-wave, and the arrival time difference between the P-wave and S-wave signals at different triaxial broadband microseismic sensor locations. The particle swarm optimization algorithm is then used to locate the microseismic source. The construction module is used to construct a multi-dimensional microseismic index system based on the coal and rock fracture seismic wave signal and the microseismic source location. This multi-dimensional microseismic index system includes multiple microseismic indicators. The early warning module is used to construct a multi-parameter early warning index based on the multi-dimensional microseismic index system and to monitor and issue early warnings for target mines within the monitored area based on the multi-parameter early warning index.
[0013] On the other hand, an electronic device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method provided in the embodiments of the present invention.
[0014] On the other hand, a computer-readable storage medium is also provided, wherein program code is stored in the computer-readable storage medium, and the program code can be called by a processor to execute the method provided in the embodiments of the present invention.
[0015] This invention provides a broadband triaxial microseismic monitoring and early warning method and system based on reconstructed P-waves and S-waves. It alleviates the technical problems of low source positioning accuracy and easy false alarms and missed alarms in rockburst early warning in existing technologies, improves the positioning accuracy of traditional microseismic monitoring systems, and achieves real monitoring and early warning of disasters such as underground rockbursts and rockbursts. Furthermore, the microseismic monitoring system can be used to explore the propagation law of real seismic waves or earthquake waves in rock masses, reveal the characteristics of rock mass failure under their action and the precursor information for monitoring and early warning. It can be used to guide the monitoring and early warning of geological disasters such as rockbursts, rockbursts and mine tremors in underground engineering. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a flowchart of a broadband triaxial microseismic monitoring and early warning method based on reconstructed P-waves and S-waves provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of an arrangement scheme for a triaxial broadband micro-vibration sensor provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the coal and rock fracturing seismic wave signal and the arrival time of the transverse wave and the arrival time of the longitudinal wave provided in the embodiment of the present invention; Figure 4 This is a schematic diagram of the separation results of the transverse wave signal and the longitudinal wave signal provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of a broadband triaxial microseismic monitoring and early warning system based on reconstructed longitudinal and transverse waves provided in an embodiment of the present invention. Detailed Implementation
[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.
[0019] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.
[0020] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.
[0021] Figure 1 This is a flowchart of a broadband triaxial microseismic monitoring and early warning method based on reconstructed P-waves and S-waves, according to an embodiment of the present invention. Figure 1 As shown, the method specifically includes the following steps: Step S102: Based on a sensing array formed by multiple triaxial broadband micro-seismic sensors, acquire the coal and rock fracture vibration wave signal of the area to be monitored.
[0022] Step S104: Decompose and reconstruct the coal and rock fracture vibration wave signal to obtain the longitudinal wave signal and the transverse wave signal, and determine the arrival time of the longitudinal wave signal and the arrival time of the transverse wave signal respectively.
[0023] Step S106: Based on the first arrival time of the P-wave, the first arrival time of the S-wave, and the arrival time difference between the P-wave and S-wave signals at different triaxial broadband microseismic sensor locations within the monitoring area, an iterative function for source localization is constructed, and the microseismic source location is located using the particle swarm optimization algorithm.
[0024] Step S108: Based on the coal and rock fracture seismic wave signal and the location of the microseismic source, construct a multi-dimensional microseismic index system; the multi-dimensional microseismic index system includes multiple microseismic indices.
[0025] Step S110: Construct a multi-parameter early warning index based on the microseismic multi-dimensional index system, and conduct monitoring and early warning of target mines in the monitoring area based on the multi-parameter early warning index.
[0026] Specifically, the triaxial broadband micro-vibration sensor includes three axial vibration sensors (X, Y, and Z) arranged at 90° angles to each other inside the sensor housing, with a signal frequency range of 10Hz to 10Hz. 5 Hz.
[0027] Figure 2 This is a schematic diagram of an arrangement scheme for a triaxial broadband micro-vibration sensor according to an embodiment of the present invention. Figure 2 As shown, an array of five triaxial broadband microseismic sensors is used to form a three-dimensional surrounding network for key monitoring areas on site to monitor the seismic wave signals of coal and rock fractures.
[0028] Specifically, step S104 further includes the following steps: Step S1041: Based on synchronous compression transformation, the instantaneous frequency distribution characteristics of the coal and rock fracture vibration wave signal are obtained.
[0029] Step S1042: Based on the instantaneous frequency distribution characteristics, determine the first arrival time of the shear wave and the first arrival time of the p-wave; the results are as follows. Figure 3 As shown, where, Figure 3 This is a schematic diagram of the coal and rock fracturing vibration wave signal and the arrival time of the transverse wave and the longitudinal wave, provided according to an embodiment of the present invention.
[0030] Step S1043: Based on the first arrival time of the shear wave, determine the wake of the mixed P-wave after the first arrival of the shear wave. and transverse waves The three-axis vibration wave signal; specifically as follows:
[0031] In the formula, It is a triaxial vibration wave signal. , and These are the signal components corresponding to the X, Y, and Z axis sensors, respectively.
[0032] Step S1044: Construct the covariance matrix of the triaxial vibration wave signal, and solve for the eigenvalues and corresponding eigenvectors of the covariance matrix; wherein, the covariance matrix C is:
[0033] Then, the eigenvalues of the covariance matrix are solved, and the equation is as follows:
[0034] The eigenvectors of the matrix are obtained as follows: , and The corresponding eigenvalues are respectively , and ,in .
[0035] Step S1045: Based on the matrix eigenvectors, separate the triaxial vibration wave signals to obtain the longitudinal wave signal and the transverse wave signal.
[0036] Specifically, it is determined based on the signal polarization principle and the energy ratio of transverse and longitudinal waves in the vibration wave signal. and These are the characteristic values corresponding to the shear wave component in the seismic wave signal. Let be the eigenvalues corresponding to the longitudinal wave component, then the separated transverse wave component in the signal is:
[0037] The longitudinal wave wake component in the signal is:
[0038] This allows us to obtain the accurate arrival times of the transverse and longitudinal wave components. The separation results of the transverse and longitudinal wave signals are as follows: Figure 4 As shown.
[0039] Specifically, in step S106, the source location iteration function includes:
[0040] In the formula, The initial arrival time of the longitudinal wave. The first arrival time of the shear wave is given by , and i represents the triaxial broadband micro-vibration sensor number. n The number of triaxial broadband micro-vibration sensors. x The coordinates of the earthquake source are... When the longitudinal wave theory is completed, The theoretical arrival time difference between the longitudinal wave signal and the transverse wave signal. and This is the weighting factor.
[0041] In one optional embodiment of the present invention, the microseismic multidimensional index system includes time-series indices, spatial indices, and intensity indices; wherein... Time-series indicators include the cumulative frequency of microseismic events, reflecting the rock mass fracturing activity per unit time. The mathematical expressions include:
[0042] in, For the first i The number of micro-seismic times within a time window, where T is the number of time windows per unit time, and N is the number of micro-seismic times within a time window. t It is a time-series indicator.
[0043] Spatial indicators include microseismic event cluster density, which characterizes the degree of microseismic event aggregation per unit volume. The mathematical expressions for this density include:
[0044] in, The number of microseismic events within the cluster; The volume contained in the cluster. For spatial indicators.
[0045] Strength indicators include the energy ratio of shear waves to longitudinal waves, which characterizes the degree of coal and rock fracturing. The mathematical expressions include:
[0046] in, and R represents the energy of the transverse and longitudinal waves in the microseismic signal, respectively. e This is an intensity indicator.
[0047] Optionally, the microseismic multidimensional index system also includes: energy. E Total daily energy E T Daily average energy E A Daily maximum energy E m Energy deviation value ED Energy density E Total daily frequency P T Daily average frequency P A Frequency deviation value P D Frequency ratio F r Seismic focal concentration M C , a value, b Value, seismic absence M m , A(b) value, P(b) Value, total fault area A(t) Microseismic activity S Microseismic activity scale △F Algorithm complexity AC Normalized microseismic spatiotemporal diffraction ds Empty zone parameters σ H (n) Energy density index M e Time information entropy Q t Including 24 microseismic multidimensional parameters.
[0048] Specifically, step S110, which involves constructing a multi-parameter early warning index based on a microseismic multidimensional index system, also includes the following steps: Step S1101: Based on the precursor sensitivity of multiple microseismic indices to coal and rock dynamic disasters in the area to be monitored, determine the combination of early warning indices applicable to the target mine in the area to be monitored; Step S1102: Normalize the combination of early warning indicators and assign indicator weights based on the entropy weight method. Step S1103: Based on the indicator weights, the normalized combination of early warning indicators is weighted and summed to obtain a multi-parameter early warning index.
[0049] Specifically, the multi-parameter early warning indicator L y The expression is as follows:
[0050] in, This refers to the number of indicators in the early warning indicator combination; As the indicator weight; f ( I i ) represents the value of the i-th index at a certain time after normalization.
[0051] Specifically, in step S110, monitoring and early warning of target mines within the monitoring area are carried out based on a multi-parameter early warning index, including: dividing the disaster early warning level into four different levels based on the multi-parameter early warning index Ly, with each level determined by the early warning index threshold y, as shown in Table 1: Table 1
[0052] In Table 1, y1, y2, and y3 represent different warning index thresholds.
[0053] As described above, the embodiments of the present invention provide a broadband triaxial microseismic monitoring and early warning method based on reconstructed longitudinal and transverse waves, which improves the positioning accuracy of traditional microseismic monitoring systems and enables real monitoring and early warning of disasters such as underground rock bursts and impacts. Furthermore, the microseismic monitoring system can be used to explore the propagation law of real seismic waves or earthquake waves in rock masses, reveal the characteristics of rock mass failure under their action and the precursor information for monitoring and early warning, and can be used to guide the monitoring and early warning of geological disasters such as rock bursts, impacts and mine tremors in underground engineering.
[0054] Figure 5 This is a schematic diagram of a broadband triaxial microseismic monitoring and early warning system based on reconstructed P-waves and S-waves, according to an embodiment of the present invention. Figure 5 As shown, it includes: acquisition module 10, decomposition module 20, positioning module 30, construction module 40 and early warning module 50.
[0055] Specifically, the acquisition module 10 is used to acquire the coal and rock fracture vibration wave signal of the monitored area based on a sensing array formed by multiple triaxial broadband micro-seismic sensors. The decomposition module 20 is used to decompose and reconstruct the coal and rock fracture vibration wave signal to obtain the longitudinal wave signal and the transverse wave signal, and to determine the arrival time of the longitudinal wave signal and the arrival time of the transverse wave signal respectively. The positioning module 30 is used to construct a source positioning iterative function based on the first arrival time of the P-wave, the first arrival time of the S-wave, and the arrival time difference between the P-wave signal and the S-wave signal at different triaxial broadband microseismic sensor locations within the monitored area, and to locate the microseismic source using the particle swarm algorithm. Module 40 is used to construct a multi-dimensional microseismic index system based on the coal and rock fracture seismic wave signal and the location of the microseismic source; the multi-dimensional microseismic index system includes multiple microseismic indices; The early warning module 50 is used to construct a multi-parameter early warning index based on the microseismic multi-dimensional index system, and to monitor and issue early warnings for target mines within the monitoring area based on the multi-parameter early warning index.
[0056] Specifically, the decomposition module 20 is also used for: Based on synchronous compression transform, the instantaneous frequency distribution characteristics of the coal and rock fracture seismic wave signal are obtained; Based on the instantaneous frequency distribution characteristics, the first arrival time of the shear wave and the first arrival time of the longitudinal wave are determined. Based on the first arrival time of the shear wave, the triaxial seismic wave signal of the mixed longitudinal wave tail and the shear wave after the first arrival of the shear wave is determined. Construct the covariance matrix of the triaxial vibration wave signal, and solve for the eigenvalues and corresponding eigenvectors of the covariance matrix; Based on matrix eigenvectors, triaxial vibration wave signals are separated to obtain longitudinal wave signals and transverse wave signals.
[0057] Specifically, the early warning module 50 is also used for: Based on the precursor sensitivity of multiple microseismic indices to coal and rock dynamic disasters in the area to be monitored, a combination of early warning indices suitable for target mines in the area to be monitored is determined. The combination of early warning indicators is normalized, and the indicator weights are assigned based on the entropy weight method. The normalized combination of early warning indicators is weighted and summed based on the indicator weights to obtain a multi-parameter early warning index.
[0058] The present invention also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method provided in the embodiments of the present invention.
[0059] The present invention also provides a computer-readable storage medium storing program code, which can be called by a processor to execute the method provided in the embodiments of the present invention.
[0060] It should be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).
[0061] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.
[0062] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
[0063] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.
[0064] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0065] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.
[0066] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0067] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0068] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0069] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A broadband triaxial microseismic monitoring and early warning method based on reconstructed P-waves and S-waves, characterized in that, The method includes: Based on a sensing array formed by multiple triaxial broadband microseismic sensors, the vibration wave signal of coal and rock fracture in the monitored area is acquired. The coal and rock fracture seismic wave signal is decomposed and reconstructed to obtain longitudinal wave signal and transverse wave signal, and the arrival time of the longitudinal wave signal and the arrival time of the transverse wave signal are determined respectively. Based on the arrival times of the P-wave and the S-wave of the same vibration signal in the monitored area at different triaxial broadband microseismic sensor locations, the arrival time difference between the P-wave signal and the S-wave signal is used to construct an iterative function for source localization and to locate the microseismic source using a particle swarm optimization algorithm. Based on the coal and rock fracture seismic wave signal and the location of the microseismic source, a multi-dimensional microseismic index system is constructed; the multi-dimensional microseismic index system includes multiple microseismic indices. A multi-parameter early warning index is constructed based on the aforementioned microseismic multi-dimensional index system, and the target mines within the monitored area are monitored and warned based on the multi-parameter early warning index.
2. The method according to claim 1, characterized in that, The triaxial broadband micro-vibration sensor includes three axial vibration sensors (X, Y, and Z) arranged at 90° angles to each other inside the sensor housing, and the acquired signal frequency range includes 10Hz~10Hz. 5 Hz.
3. The method according to claim 1, characterized in that, The coal and rock fracture seismic wave signal is decomposed and reconstructed to obtain longitudinal wave signal and transverse wave signal, including: Based on synchronous compression transformation, the instantaneous frequency distribution characteristics of the coal and rock fracture vibration wave signal are obtained; Based on the instantaneous frequency distribution characteristics, the arrival time of the shear wave and the arrival time of the longitudinal wave are determined. Based on the initial arrival time of the shear wave, the triaxial vibration wave signal of the mixed longitudinal wave tail and the shear wave after the initial arrival of the shear wave is determined. Construct the covariance matrix of the triaxial vibration wave signal, and solve for the eigenvalues and corresponding eigenvectors of the covariance matrix; Based on the matrix eigenvectors, the triaxial vibration wave signals are separated to obtain longitudinal wave signals and transverse wave signals.
4. The method according to claim 1, characterized in that, The source location iteration function includes: In the formula, The initial arrival time of the longitudinal wave is given. The initial arrival time of the shear wave is given by i, where i represents the triaxial broadband micro-vibration sensor number. n The number of triaxial broadband micro-vibration sensors. x The coordinates of the earthquake source are... When the longitudinal wave theory is completed, The theoretical arrival time difference between the longitudinal wave signal and the transverse wave signal. and This is the weighting factor.
5. The method according to claim 1, characterized in that, The microseismic multidimensional index system includes time-series indices, spatial indices, and intensity indices; among which... The time series indicators include the cumulative frequency of microseismic events that reflect the rock mass fracturing activity per unit time. The spatial indicators include the microseismic event cluster density, which characterizes the degree of microseismic event aggregation per unit volume. The strength index includes the energy ratio of transverse waves to longitudinal waves, which characterizes the degree of coal and rock fracturing.
6. The method according to claim 1, characterized in that, The microseismic multidimensional index system also includes: energy, total daily energy, average daily energy, maximum daily energy, energy deviation value, energy density, total daily frequency, average daily frequency, frequency deviation value, frequency ratio, and focal concentration. a value, b Value, seismic failure, A(b) value, P(b) Value, total fault area, microseismic activity degree, microseismic activity scale, algorithm complexity, normalized microseismic spatiotemporal diffusion, void parameters, energy density index, and temporal information entropy.
7. The method according to claim 1, characterized in that, A multi-parameter early warning index is constructed based on the aforementioned microseismic multidimensional index system, including: Based on the sensitivity of the multiple microseismic indices to the precursors of coal and rock dynamic disasters in the monitored area, a combination of early warning indices suitable for target mines in the monitored area is determined; The combination of early warning indicators is normalized, and the indicator weights are assigned based on the entropy weight method. Based on the aforementioned indicator weights, a weighted summation is performed on the normalized combination of early warning indicators to obtain a multi-parameter early warning index.
8. A broadband triaxial microseismic monitoring and early warning system based on reconstructed P-waves and S-waves, characterized in that, include: The module includes an acquisition module, a decomposition module, a location module, a construction module, and an early warning module; among which, The acquisition module is used to acquire the coal and rock fracture vibration wave signal of the monitored area based on a sensing array formed by multiple triaxial broadband micro-vibration sensors. The decomposition module is used to decompose and reconstruct the coal and rock fracture vibration wave signal to obtain longitudinal wave signal and transverse wave signal, and to determine the arrival time of the longitudinal wave signal and the arrival time of the transverse wave signal respectively. The positioning module is used to construct a source positioning iterative function based on the first arrival time of the P-wave, the first arrival time of the S-wave, and the time difference between the arrival times of the P-wave signal and the S-wave signal at different triaxial broadband microseismic sensor locations within the monitored area, and to locate the microseismic source using a particle swarm optimization algorithm. The construction module is used to construct a multi-dimensional microseismic index system based on the coal and rock fracture seismic wave signal and the location of the microseismic source; the multi-dimensional microseismic index system includes multiple microseismic indices; The early warning module is used to construct a multi-parameter early warning index based on the microseismic multidimensional index system, and to monitor and issue early warnings for target mines within the monitored area based on the multi-parameter early warning index.
9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 7.