Effective signal filtering and noise reduction method and system for tunnel water and mud inrush disaster micro-seismic event

Abstract

The invention provides an effective signal filtering and noise reduction method and system for a micro-seismic event of a tunnel water and mud inrush disaster, and relates to the technical field of signal processing. Comprising the following steps: acquiring original signals of water-inrush and mud-inrush micro-seismic events; performing modal decomposition on the original signal by using a variational modal decomposition method to obtain a plurality of modal components; calculating a cross correlation coefficient between the original signal and each modal component; based on the center frequency of each modal component and in combination with the cross correlation coefficient, performing primary noise reduction on the plurality of modal components; performing de-trend fluctuation analysis on the modal components after the primary noise reduction screening to obtain scale indexes of the modal components after the primary noise reduction screening, and performing secondary noise reduction based on the scale indexes; and reconstructing the modal component after the secondary noise reduction screening to obtain a micro-seismic signal after noise reduction filtering. According to the method, the problem that noise and effective signals in part of modal components are mixed is solved, and the weak signal extraction capacity is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal processing, and in particular relates to an effective signal filtering and denoising method and system for microseismic events of water and mud inrush disasters in tunnels. Background Art

[0002] During tunnel construction, water and mud bursts occur frequently, seriously affecting the construction progress and endangering the safety of personnel and property. Effective monitoring and early warning work is an important means to avoid major water and mud bursts. The rock microseismic process is the process in which the accumulated energy propagates to the surrounding in the form of elastic waves after tiny cracks appear in the internal structure of the rock mass. Microseismic monitoring is a set of equipment and technology that receives the vibration spectrum of microseismic events through highly sensitive vibration sensors. After data inversion, the time and space of microseismic events can be obtained, and the spatial evolution of the monitored object can be evaluated, thereby providing a basis for predicting and controlling water and mud bursts.

[0003] The microseismic event signals of water and mud inrush caused by the complex environment of the tunnel contain random interference noise, such as construction disturbance. How to eliminate the interference of the tunnel environment and extract the effective microseismic event signals of water inrush has become the primary task of the data processing process. As a classic analysis and processing tool, Fourier transform can effectively represent the effective frequency information contained in the signal and can suppress noise to a certain extent, but its noise reduction effect is poor for complex water and mud inrush microseismic signals. At present, the most widely used non-stationary wave signal noise reduction methods are empirical mode decomposition and wavelet transform. The empirical mode decomposition algorithm has problems such as mode aliasing and endpoint effect. The selection of wavelet function and basis function in the wavelet transform method leads to poor adaptive ability. Both of them have insufficient noise reduction effects on the microseismic signals of water and mud inrush in tunnels. Summary of the invention

[0004] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a method and system for filtering and denoising effective signals of microseismic events caused by water and mud inrush disasters in tunnels. The variational mode decomposition method (VMD) is used to effectively extract the weak high-frequency components of water and mud inrush signals, and the detrended fluctuation analysis method (DFA) is used to identify effective modes, so as to solve the problem of mixing noise and effective signals in some modal components and improve the ability to extract weak signals.

[0005] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0006] The first aspect of the present invention provides an effective signal filtering and noise reduction method for microseismic events of water and mud inrush disasters in tunnels.

[0007] The effective signal filtering and noise reduction method for microseismic events of tunnel water and mud inrush disasters includes the following steps:

[0008] Obtain the original signal of water and mud inrush microseismic events containing noise;

[0009] The original signal is modally decomposed using the variational mode decomposition method to obtain multiple modal components;

[0010] Calculate the cross-correlation coefficient between the original signal and each modal component;

[0011] Based on the center frequency of each modal component and combined with the mutual correlation coefficient, multiple modal components are subjected to primary denoising to obtain the modal components after primary denoising screening;

[0012] Perform detrending fluctuation analysis on the modal components after the initial noise reduction screening to obtain the scaling index of the modal components after the initial noise reduction screening, perform secondary noise reduction based on the scaling index, and obtain the modal components after the secondary noise reduction screening;

[0013] The modal components after secondary noise reduction screening are reconstructed to obtain the microseismic signal after noise reduction filtering.

[0014] The second aspect of the present invention provides an effective signal filtering and noise reduction system for microseismic events of tunnel water and mud inrush disasters.

[0015] The effective signal filtering and noise reduction system for microseismic events of tunnel water and mud inrush disasters includes:

[0016] The original signal acquisition module is configured to: acquire the original signal of the water-sudden mud-sudden microseismic event containing noise;

[0017] The modal decomposition module is configured to: perform modal decomposition on the original signal using a variational modal decomposition method to obtain multiple modal components;

[0018] The cross-correlation coefficient calculation module is configured to: calculate the cross-correlation coefficient between the original signal and each modal component;

[0019] The primary noise reduction module is configured to: perform primary noise reduction on multiple modal components based on the center frequency of each modal component and in combination with the mutual correlation coefficient to obtain modal components after primary noise reduction screening;

[0020] The secondary noise reduction module is configured to: perform a detrending fluctuation analysis on the modal components after the primary noise reduction screening, obtain a scaling index of the modal components after the primary noise reduction screening, perform secondary noise reduction based on the scaling index, and obtain the modal components after the secondary noise reduction screening;

[0021] The reconstruction module is configured to reconstruct the modal components after secondary noise reduction screening to obtain the microseismic signal after noise reduction filtering.

[0022] The third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the effective signal filtering and denoising method for microseismic events of tunnel water and mud inrush disasters as described in the first aspect of the present invention.

[0023] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for filtering and denoising effective signals of microseismic events of tunnel water and mud inrush disasters as described in the first aspect of the present invention are implemented.

[0024] One or more of the above technical solutions have the following beneficial effects:

[0025] The present invention provides a method and system for filtering and denoising effective signals of microseismic events of water and mud inrush disasters in tunnels. Aiming at the serious interference problem of water and mud inrush signals in tunnels, a VMD-DFA denoising method is proposed:

[0026] The VMD algorithm has good stability in the process of separating similar modes, can avoid mode aliasing, has better modal decomposition ability for high-frequency components of signals, and has a simple algorithm with strong real-time performance, which can better and faster extract the effective high-frequency components of water and mud inrush in the original signal;

[0027] At the same time, the DFA method is used to identify effective modes, solve the problem of noise and effective signals mixing in some modal components, and improve the ability to extract weak signals.

[0028] The present invention can effectively perform noise reduction filtering on the original microseismic noisy signals picked up in the tunnel, and extract the high-frequency signal components of water and mud inrush, and has great technical value and application prospects in the field of tunnel water and mud inrush disaster monitoring and early warning.

[0029] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0031] Figure 1 This is an overall flow chart of Embodiment 1 of the present invention.

[0032] FIG. 2( a ) is a waveform diagram of the original signal f-1 in the first embodiment.

[0033] FIG2( b ) is a spectrum diagram of the original signal f-1 in the first embodiment.

[0034] FIG2( c ) is a waveform diagram of the original signal f-2 in the first embodiment.

[0035] FIG2( d ) is a spectrum diagram of the original signal f-2 in the first embodiment.

[0036] FIG3( a ) is a waveform diagram of the signal f-1 processed by the filtering and noise reduction method of the first embodiment.

[0037] FIG3( b ) is a spectrum diagram of the signal f-1 processed by the filtering and noise reduction method of the first embodiment.

[0038] FIG3( c ) is a waveform diagram of the signal f-2 processed by the filtering and noise reduction method of the first embodiment.

[0039] FIG3( d ) is a spectrum diagram of the signal f-2 processed by the filtering and noise reduction method of the first embodiment. DETAILED DESCRIPTION

[0040] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0041] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0042] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0043] Embodiment 1

[0044] The present embodiment discloses a method for filtering and denoising effective signals of microseismic events caused by sudden water and mud in tunnels. The method adopts a variational mode decomposition (VMD) method to effectively extract weak high-frequency components of sudden water and mud signals, and utilizes a detrended fluctuation analysis (DFA) method to identify effective modes, thereby solving the problem of the mixing of noise and effective signals in some modal components, improving the ability to extract weak signals, and being able to effectively perform noise reduction filtering on noisy original microseismic signals. The method has the characteristics of good stability and strong ability to decompose high-frequency components.

[0045] like Figure 1 As shown, the effective signal filtering and noise reduction method for microseismic events of water and mud inrush disasters in tunnels provided in this embodiment may include the following steps:

[0046] Step S1: reading the original signal f(t) of the water and mud inrush microseismic event containing noise;

[0047] Step S2: Using the VMD method to perform modal decomposition on the original signal f(t) and determine the optimal decomposition modal number K;

[0048] Step S3: Calculate the original signal f(t) and each modal component {u k}'s cross-correlation function;

[0049] Step S4: filtering out the variational modal components with a center frequency greater than 800 Hz and a mutual correlation coefficient less than 0.3 as noise;

[0050] Step S5: Perform detrending analysis on the modal components preliminarily screened to obtain the scaling index γ, determine whether the mode is dominated by random noise or effective fluctuation signal, and remove the noise-dominated mode;

[0051] Step S6: Reconstruct the remaining variational modal components to finally obtain the microseismic time signal f′(t) after noise reduction filtering.

[0052] in:

[0053] Step S2: Use the VMD method to perform modal decomposition on the original signal f(t) and determine the optimal decomposition mode number K.

[0054] Furthermore, step S2 includes the following sub-steps:

[0055] S201: Decompose the noisy original signal into K finite-bandwidth variational mode components using VMD.

[0056] Assume u k For the resolved modal BLIMF sequence, each BLIMF mode is solved by Hilbert transform with each mode u k The analytical signal of the component is:

[0057]

[0058] For each mode u k Add all index items Move the center frequency of the mode and convert the spectrum of the mode into the frequency band:

[0059]

[0060] Where δ(t) is the Dirac function,

[0061] Modal u k The corresponding analytical signal is processed by Gaussian smoothing to estimate the bandwidth of the signal, and then the center frequency of different modes is obtained. After adding the equality constraint condition to equation (2), the solution process is converted into a constrained optimization problem, which is called the variational constraint model, and the expression is:

[0062]

[0063] Where f is the original signal of the noisy microseismic event, {w k} is each BLIMF modal component u kThe center frequency, k = 1, 2, ...K, "*" represents the convolution operation, and t represents the time variable.

[0064] S202: Introduce Lagrange multipliers to convert the constrained optimization problem containing variables and constraints into the solution form of formula (4):

[0065]

[0066] Where δ(t) is the Dirac function, f(t) is the original signal, α is the quadratic penalty factor, and λ is the Lagrange multiplier.

[0067] S203: Using the alternating direction multiplier method (ADMM) to solve equation (4), the two optimization variables are {u k} and {w k}. The specific solution steps are as follows:

[0068] 1) For the parameter {u k},{w k}, λ and loop count n are initialized, and the initial value of n is defined as 0;

[0069] 2) Set up the loop process: n = n + 1, update u k and w k The update process is as follows:

[0070]

[0071] in, Represents the Fourier transform of the original signal in the time domain at f(t).

[0072]

[0073] 3) Update the multiplication operator λ. The update formula of λ is as follows:

[0074]

[0075] 4) Determine whether the modal component meets the constraints If it is satisfied, the solution process stops, otherwise the above steps are repeated. Among them, ε is the solution accuracy.

[0076] S204: Determine the optimal decomposition mode number K by observing the modal center frequency and spectrum band of the microseismic signal; select different K values ​​to solve the distribution of the modal main frequency of the original microseismic signal in the required frequency range, and use the signal-to-noise ratio value after signal processing to measure the noise filtering effect, so that the K value with uniform distribution of the center frequency in the whole frequency band and high signal-to-noise ratio value is the optimal decomposition mode number.

[0077] Step S3: Calculate the original signal f(t) and each modal component {u k}'s cross-correlation function;

[0078]

[0079] Where N is the number of sampling points,

[0080] Step S4: filtering out the variational modal components with a center frequency greater than 800 Hz and a mutual correlation coefficient less than 0.3 as noise;

[0081] Step S5: Perform detrending analysis on the modal components preliminarily screened to obtain the scaling index γ, determine whether the mode is dominated by random noise or effective fluctuation signal, and remove the noise-dominated mode;

[0082] Furthermore, the step S5 includes the following sub-steps:

[0083] S501: Microseismic signal time series f(t), t = 1, 2, 3, ..., N, N represents the number of sampling points, where f(t) is the time series of each modal component after the initial noise reduction screening. The cumulative deviation of the sequence f(t) is calculated by formula (9);

[0084]

[0085] in, is the mean sequence of f(t).

[0086] S502: y(k) is the cumulative deviation sequence. Divide y(k) into K non-overlapping intervals with n sampling points, then k = N / n (k is an integer). The trend equation of each window can be expressed as a p-order exponential form with time t:

[0087]

[0088] In the formula, β j (j=0,1,2,…,p) are the coefficients of the least squares fitting of different windows, and finally the curve y of different segments is obtained n (k);

[0089] S503: Eliminate the trend term y in the time series y(k) within the window n (k)Equation (11):

[0090] Δy n (k) = y(k) - y n (k) (11)

[0091] S504: Calculate time series Δy n RMS volatility of (k):

[0092]

[0093] S505: Increase the window size n by a certain step length, and repeat steps S502 to S504 to obtain the function F(n) curve, that is, the fluctuation function curve. The curve equation is written in power exponential form, and there exists

[0094] F(n)=An γ (13)

[0095] By changing the form of formula (13), we can get formula (14):

[0096]

[0097] S506: Eliminate the modal components with a scaling index γ between 0.45 and 0.55.

[0098] Step S6: Reconstruct the remaining variational modal components to finally obtain the microseismic time signal f′(t) after noise reduction filtering.

[0099] Experimental verification:

[0100] This embodiment uses an experimental verification method to illustrate the effect of the above-mentioned filtering and noise reduction method. The actual data of the experiment comes from the microseismic response test of tunnel water and mud intrusion damage carried out in the laboratory. Two noisy microseismic signals are randomly selected from the monitoring data for analysis, namely: the original signal f-1 and the original signal f-2. The waveform and spectrum diagram before noise reduction are shown in Figure 2(a), Figure 2(b), Figure 2(c), and Figure 2(d).

[0101] The present invention is applied to filter and de-noise the microseismic signal waveform, extract characteristic information, and verify that the modal decomposition number K of this embodiment is 8. The DFA scaling index of each decomposed modal component of the two groups of signals is calculated as shown in Table 1. The components with a scaling index between 0.45 and 0.55 are removed as noise. The waveform and spectrum diagram obtained after waveform reconstruction are shown in Figures 3(a), 3(b), 3(c), and 3(d).

[0102] Table 1 DFA scaling index of each mode of microseismic signal

[0103]

[0104]

[0105] The signal-to-noise ratio and root mean square error of the two groups of microseismic signals before and after noise reduction are calculated. The calculation results are shown in Table 2.

[0106] Table 2 Analysis of noise reduction effect of microseismic signals

[0107] Signal category Signal-to-noise ratio (dB) Root mean square error Original signal f-1 7.66 0.051 Processed signal f-1 17.76 0.021 Original signal f-2 5.72 0.042 Processed signal f-2 15.04 0.018

[0108] As shown in Figure 2, the original signals f-1 and f-2 contain microseismic responses caused by high-frequency rock hydraulic fracturing, but due to the interference of environmental noise and drilling rig vibration, the effective signal outline is unclear, especially f-2, which contains a large amount of random noise and the dominant frequency distribution is not obvious.

[0109] As shown in Figure 3, the waveform and spectrum of the microseismic signal after noise reduction are shown. After noise reduction processing by the method of the present invention, the waveform of the microseismic signal is clearer and the dominant spectrum distribution is obvious. From the effect comparison in Table 2, it can be seen that the signal-to-noise ratio of the signal is improved and the root mean square error level is reduced after processing.

[0110] In summary, it is shown that the embodiment of the present invention can better extract the effective components of the microseismic signal of water and mud inrush, improve the signal-to-noise ratio of the signal, reduce the root mean square error, and obtain a better filtering and noise reduction effect.

[0111] Embodiment 2

[0112] This embodiment discloses an effective signal filtering and noise reduction system for microseismic events of water and mud inrush disasters in tunnels.

[0113] The effective signal filtering and noise reduction system for microseismic events of tunnel water and mud inrush disasters includes:

[0114] The original signal acquisition module is configured to: acquire the original signal of the water-sudden mud-sudden microseismic event containing noise;

[0115] The modal decomposition module is configured to: perform modal decomposition on the original signal using a variational modal decomposition method to obtain multiple modal components;

[0116] The cross-correlation coefficient calculation module is configured to: calculate the cross-correlation coefficient between the original signal and each modal component;

[0117] The primary noise reduction module is configured to: perform primary noise reduction on multiple modal components based on the center frequency of each modal component and in combination with the mutual correlation coefficient to obtain modal components after primary noise reduction screening;

[0118] The secondary noise reduction module is configured to: perform a detrending fluctuation analysis on the modal components after the primary noise reduction screening, obtain a scaling index of the modal components after the primary noise reduction screening, perform secondary noise reduction based on the scaling index, and obtain the modal components after the secondary noise reduction screening;

[0119] The reconstruction module is configured to reconstruct the modal components after secondary noise reduction screening to obtain the microseismic signal after noise reduction filtering.

[0120] Embodiment 3

[0121] The purpose of this embodiment is to provide a computer-readable storage medium.

[0122] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for filtering and denoising effective signals of microseismic events of tunnel water and mud inrush disasters as described in Example 1 of the present disclosure.

[0123] Embodiment 4

[0124] The purpose of this embodiment is to provide an electronic device.

[0125] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for filtering and denoising effective signals of microseismic events of tunnel water and mud inrush disasters as described in Example 1 of the present disclosure are implemented.

[0126] The steps involved in the apparatuses of the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0127] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0128] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for filtering and denoising effective signals of microseismic events of water and mud inrush disasters in tunnels, characterized in that: The following steps are involved: Obtain the original signal of water and mud inrush microseismic events containing noise; The original signal is modally decomposed using the variational mode decomposition method to obtain multiple modal components; Calculate the cross-correlation coefficient between the original signal and each modal component; Based on the center frequency of each modal component and combined with the mutual correlation coefficient, multiple modal components are subjected to primary denoising to obtain the modal components after primary denoising screening; Perform detrending fluctuation analysis on the modal components after the initial noise reduction screening to obtain the scaling index of the modal components after the initial noise reduction screening, perform secondary noise reduction based on the scaling index, and obtain the modal components after the secondary noise reduction screening; The modal components after secondary noise reduction screening are reconstructed to obtain the microseismic signal after noise reduction filtering.

2. The effective signal filtering and noise reduction method for microseismic events of tunnel water and mud inrush disasters according to claim 1 is characterized in that: The variational mode decomposition method is used to perform mode decomposition on the original signal, including: Solve the analytical signal of each modal component, move the center frequency of the mode based on the analytical signal, convert the spectrum of the mode into the preset frequency band, and construct a variational constraint model; The Lagrange multiplier is introduced to transform the solution form of the variational constraint model, and the alternating direction multiplier method is used to solve the transformed variational constraint model to obtain K modal components. Observe the modal center frequency and spectrum banding of the microseismic signal to determine the optimal number of decomposed modes.

3. The effective signal filtering and noise reduction method for microseismic events of tunnel water and mud inrush disasters as claimed in claim 2 is characterized by: Different K values ​​are selected to solve the distribution of the main frequency of the original signal mode in the required frequency range. At the same time, the signal-to-noise ratio after signal processing is used to measure the noise filtering effect. The K value with a uniform distribution of center frequency in the full frequency band and a high signal-to-noise ratio is selected as the optimal decomposition mode number.

4. The effective signal filtering and noise reduction method for microseismic events of tunnel water and mud inrush disasters according to claim 1 is characterized in that: The modal components with center frequencies greater than 800 Hz and cross-correlation coefficients less than 0.3 are filtered out as noise to complete the initial noise reduction.

5. The effective signal filtering and noise reduction method for microseismic events of tunnel water and mud inrush disasters as claimed in claim 1 is characterized in that: The detrended fluctuation analysis is performed on the modal components after the initial noise reduction screening, including: S501: setting the time series of the modal component after the initial noise reduction screening as f(t), and calculating the cumulative deviation y(k) of the time series f(t); S502: Divide y(k) into K non-overlapping interval windows with n sampling points, express the trend equation of each interval window as a p-order exponential form with time t, and obtain the curve y of different interval windows n (k); S503: Eliminate the trend term y in the cumulative deviation y(k) of the time series f(t) within the interval window n (k), we get Δy n (k); S504: Calculate time series Δy n (k) the root mean square volatility F(n); S505: Increase the window size in a certain step size, loop S502 to S504, obtain the fluctuation F(n) function curve, write the fluctuation F(n) function curve equation into a power exponential form, and finally obtain the scaling index of the modal component after the initial noise reduction screening.

6. The effective signal filtering and noise reduction method for microseismic events of tunnel water and mud inrush disasters as claimed in claim 5 is characterized in that: The modal components with scaling index between 0.45 and 0.55 are removed to complete the secondary noise reduction.

7. The effective signal filtering and noise reduction method for microseismic events of tunnel water and mud inrush disasters as claimed in claim 1 is characterized in that: Calculate the original signal f(t) and each modal component {u k The cross-correlation function of} is as follows: Where N is the number of sampling points, 8. Tunnel water and mud inrush disaster microseismic event effective signal filtering and noise reduction system, characterized by: include: The original signal acquisition module is configured to: acquire the original signal of the water-sudden mud-sudden microseismic event containing noise; The modal decomposition module is configured to: perform modal decomposition on the original signal using a variational modal decomposition method to obtain multiple modal components; The cross-correlation coefficient calculation module is configured to: calculate the cross-correlation coefficient between the original signal and each modal component; The primary noise reduction module is configured to: perform primary noise reduction on multiple modal components based on the center frequency of each modal component and in combination with the mutual correlation coefficient to obtain modal components after primary noise reduction screening; The secondary noise reduction module is configured to: perform a detrending fluctuation analysis on the modal components after the primary noise reduction screening, obtain a scaling index of the modal components after the primary noise reduction screening, perform secondary noise reduction based on the scaling index, and obtain the modal components after the secondary noise reduction screening; The reconstruction module is configured to reconstruct the modal components after secondary noise reduction screening to obtain the microseismic signal after noise reduction filtering.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for filtering and denoising effective signals of microseismic events of water and mud inrush disasters in tunnels as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for filtering and denoising effective signals of microseismic events of tunnel water and mud inrush disasters as described in any one of claims 1 to 7 are implemented.

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