A distributed acoustic monitoring signal denoising method based on adaptive wiener filtering

Abstract

The application discloses a distributed acoustic monitoring signal denoising method based on adaptive Wiener filtering, which comprises the following steps: extracting effective acoustic signals of a current signal channel; preprocessing the effective acoustic signals of the current signal channel; converting the preprocessed time domain signals into frequency domain signals; obtaining the logarithmic energy values of each frame of the frequency domain signals; judging whether the current frame of the frequency domain signals is an initial frequency domain signal, and if not, performing groundwater flow sound activity detection according to the logarithmic energy values and a threshold value calculated, and judging whether there is groundwater flow noise; calculating the posterior signal-to-noise ratio of the current frame of the frequency domain signals, updating the prior signal-to-noise ratio by using the posterior signal-to-noise ratio; determining a gain function according to the prior signal-to-noise ratio, filtering and enhancing the frequency domain signals by using the gain function, and obtaining the filtered and enhanced frequency domain signals; and converting all the filtered and enhanced frequency domain signals into time domain signals through inverse short-time Fourier transform, and outputting the obtained time domain signals as a denoising result.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of distributed underground fluid dynamic acoustic wave time domain signal noise reduction processing, and particularly relates to a distributed acoustic wave monitoring signal noise reduction method based on adaptive Wiener filtering. BACKGROUND

[0002] Due to the vertical unevenness of the aquifer, the dynamic monitoring of the aquifer groundwater and the identification of the water-yielding layer section still face great challenges. At present, the identification of the water-yielding layer section is usually carried out by vertical borehole flow profile testing, and the commonly used methods include the segmented pumping / pressure test method, the flow logging method and the salt logging method. However, these existing vertical borehole flow profile testing methods not only have complicated procedures and long test time, but also cannot accurately identify the water-yielding layer section of the aquifer in the vertical direction. In order to facilitate long-term monitoring, it is necessary to use distributed optical fiber acoustic wave sensors to collect data of groundwater in a natural environment. However, in a natural environment, the distributed optical fiber acoustic wave sensor signal is often submerged in background noise, has time-varying characteristics and spatial variation characteristics, has a low signal-to-noise ratio, and thus the identification accuracy of the water-yielding signal is reduced in the submersion of the background noise, which needs to be improved. SUMMARY

[0003] In view of the defects and deficiencies in the prior art, the application provides a distributed acoustic wave monitoring signal noise reduction method based on adaptive Wiener filtering to solve the technical problems of low signal-to-noise ratio and low accuracy in the prior art of collecting underground fluid dynamic acoustic wave data by using a distributed optical fiber acoustic wave sensor.

[0004] To achieve the above-mentioned purpose, the application adopts the following technical scheme:

[0005] A distributed acoustic wave monitoring signal noise reduction method based on adaptive Wiener filtering, comprising the following steps:

[0006] Step 1, using an acoustic wave sensor to collect underground fluid dynamic acoustic wave time domain signals of a plurality of signal channels distributed along the depth direction of the borehole, to obtain the underground fluid dynamic acoustic wave time domain signals of the plurality of signal channels; the interval of adjacent signal channels along the depth direction of the borehole is 0.1-3 meters;

[0007] Step 2, extracting the underground fluid dynamic acoustic wave time domain signal of the current signal channel to obtain the effective acoustic wave time domain signal of the current signal channel;

[0008] Step 3, performing frame windowing processing on the extracted effective acoustic wave time domain signal of the current signal channel;

[0009] Step 4, initializing the effective acoustic wave time domain signal after frame windowing processing to obtain the noise spectrum mean and the noise spectrum variance;

[0010] Step 5, performing a short-time Fourier transform on the current frame time domain signal to convert the current frame time domain signal into a current frame frequency domain signal, and obtaining an amplitude spectrum of the current frame frequency domain signal;

[0011] Step 6, sequentially performing a square operation and a frequency domain integration on the amplitude spectrum of the current frame frequency domain signal to obtain an energy value of the current frame frequency domain signal in a full frequency range; and obtaining a logarithmic energy value of the current frame frequency domain signal by taking a logarithm of the energy value;

[0012] Step 7, judging whether the current frame frequency domain signal is an initial frame frequency domain signal, if not, entering step 8, and if yes, entering step 9;

[0013] Step 8, performing an underground fluid dynamic acoustic wave activity detection according to the logarithmic energy value obtained in step 6 and a threshold value, judging whether there is an underground fluid dynamic acoustic wave, if there is an underground fluid dynamic acoustic wave, entering step 9, and if there is not an underground fluid dynamic acoustic wave, re-computing a noise spectrum mean value and a noise spectrum variance, and then entering step 9;

[0014] Step 9, calculating a posterior signal-to-noise ratio of the current frame frequency domain signal according to the noise spectrum variance obtained, and updating a prior signal-to-noise ratio of the current frame frequency domain signal according to the posterior signal-to-noise ratio of the current frame frequency domain signal calculated to obtain an updated prior signal-to-noise ratio of the current frame signal;

[0015] Step 10, determining a gain function according to the updated prior signal-to-noise ratio of the current frame frequency domain signal, filtering and enhancing the amplitude spectrum of the current frame frequency domain signal by using the gain function to obtain a filtered and enhanced amplitude spectrum of the current frame frequency domain signal; and converting the filtered and enhanced current frame frequency domain signal into a time domain signal through an inverse short-time Fourier transform to obtain the time domain signal as a de-noised signal of the current frame;

[0016] Step 11, judging whether the current frame is a last frame of a current signal channel, if yes, taking an underground fluid dynamic acoustic wave time domain signal of a next signal channel as an underground fluid dynamic acoustic wave time domain signal of the current signal channel, and entering steps 2-10, and if not, repeating steps 5-10 until the current frame is the last frame of the current signal channel, then taking an underground fluid dynamic acoustic wave time domain signal of a next signal channel as an underground fluid dynamic acoustic wave time domain signal of the current signal channel, and entering steps 2-10;

[0017] Step 12, judging whether the current signal channel is a last channel of multiple signal channels, if yes, entering step 13, and if not, repeating steps 2-11 until the current signal channel is the last channel of the multiple signal channels, and entering step 13;

[0018] Step 13, outputting all the de-noised signals of the current frames obtained as a de-noising result.

[0019] The application also has the following technical features:

[0020] Specifically, the logarithmic energy value in step 6 is obtained by the following formula:

[0021]

[0022] Wherein,

[0023] E(m) is the logarithmic energy value of the current frame frequency domain signal;

[0024] N is half of the sampling frequency of the acoustic wave sensor;

[0025] is the amplitude value of the current frame frequency domain signal.

[0026] Further, the step 8 determines whether there is an underground fluid dynamic sound wave according to the following criteria:

[0027] If the logarithmic energy value is greater than the threshold value, it is determined that there is an underground fluid dynamic sound wave;

[0028] If the logarithmic energy value is less than or equal to the threshold value, it is determined that there is no underground fluid dynamic sound wave.

[0029] Further, the threshold value in step 8 is determined according to the following formula:

[0030]

[0031] Wherein,

[0032] is the threshold value;

[0033] A is an empirical value, and the value is 2-3;

[0034] N is half of the sampling frequency of the acoustic wave sensor;

[0035] is the probability density function of the first frame frequency domain signal in the state of the existence of the underground fluid dynamic sound wave;

[0036] is the noise spectrum mean value calculated last time.

[0037] Further, the noise spectrum variance in step 8 is calculated by the following formula:

[0038]

[0039] Wherein,

[0040] is the mean of the noise spectrum obtained by recalculation;

[0041] is a forgetting factor, 0≤α≤1;

[0042] is the mean of the noise spectrum obtained by recalculation;

[0043] is the power of the current frame frequency domain signal.

[0044] Further, the noise spectrum variance estimation of step 8 is calculated by the following formula:

[0045]

[0046] In the formula,

[0047] is the updated noise spectrum variance of the current frame frequency domain signal;

[0048] is a forgetting factor, 0≤α≤1;

[0049] is the noise spectrum variance of the current frame frequency domain signal before updating;

[0050] is the power of the current frame frequency domain signal.

[0051] Further, the posterior signal-to-noise ratio of step 8 is determined by the following formula:

[0052]

[0053] is the posterior signal-to-noise ratio of the current frame frequency domain signal;

[0054] is the latest noise spectrum variance;

[0055] is the power of the current frame frequency domain signal.

[0056] Further, the update of the prior signal-to-noise ratio of step 9 is realized by the following formula:

[0057]

[0058] In the formula,

[0059] is the updated prior signal-to-noise ratio of the current frame frequency domain signal;

[0060] is an updating factor, 0≤β≤1;

[0061] a posteriori signal-to-noise ratio of the current frame frequency domain signal;

[0062] a posteriori signal-to-noise ratio of the previous frame frequency domain signal.

[0063] Further, the gain function in step 10 is:

[0064]

[0065] wherein,

[0066] an amplitude value of the current frame frequency domain signal after filtering enhancement;

[0067] a gain function;

[0068] an amplitude value of the current frame frequency domain signal.

[0069] Compared with the prior art, the present application has the following technical effects:

[0070] (1) The method of the present application can dynamically respond to signal and noise changes by adaptively adjusting filter parameters through underground fluid dynamic sound activity detection. The activity detection mechanism enables the filter to quickly adjust when signal changes suddenly, thereby maintaining good noise reduction effect. The adaptive forgetting factor is adjusted according to energy changes. The introduction of the forgetting factor reduces the dependence on historical data, reduces the computational complexity, improves the processing speed, enables the filter to quickly adapt to changes in the noise environment, and enhances the ability to process complex noise environments.

[0071] (2) The method of the present application uses activity detection based on Gaussian distribution, which can more accurately distinguish between signal and noise, thereby improving the accuracy of noise estimation.

[0072] (3) The method of the present application uses dynamic updating of prior signal-to-noise ratio and a posteriori signal-to-noise ratio, which improves the noise reduction performance of the filter under different signal-to-noise ratios, and can effectively suppress noise while preserving the details of the acoustic signal.

[0073] (4) The method of the present application is suitable for different types and intensities of noise, and improves the robustness and stability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0074] Figure 1 a flowchart of the method of the present application;

[0075] Figure 2 a single-channel filtering effect diagram obtained in Example 1. DETAILED DESCRIPTION

[0076] The application will be described in greater detail with reference to the drawings and embodiments. In the description of the embodiments of the application, the terms used in the following embodiments are only intended to describe the specific embodiments and are not intended to be limiting to the application.

[0077] The terms "comprise", "contain", "have" and their conjugates do not mean "consist of" unless otherwise specifically indicated. The term "connected" includes direct and indirect connections unless otherwise specified. "First", "second" are only for the purpose of description and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features.

[0078] In the embodiments of the application, the words "exemplary" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of "exemplary" or "for example" is intended to present concepts in a concrete manner.

[0079] The method provided by the application can be applied to a computer device, which can be a terminal or a server. It can be understood that the voice keyword recognition method provided by the application can also be applied to a system comprising a terminal and a server, and the interaction of the terminal and the server is realized.

[0080] Embodiment 1

[0081] This embodiment takes the identification of the groundwater outflow layer section of the BK1 groundwater exploration borehole in the south Ordovician limestone in the central mining area of Zhungeer, Ordos, Inner Mongolia Autonomous Region as an example, and uses a distributed acoustic sensing (DAS) system based on TGD-OFDR to collect distributed acoustic data from the BK1 borehole. The data collection equipment is the HiFi-DAS system of Pankou (Shanghai) Technology Co., Ltd. The principle is to use optical fiber as a sensing medium, based on optical reflectometer technology, by detecting the phase change of Rayleigh scattering light signals, the amplitude and phase information of the acoustic (vibration) waveform at any position in the optical fiber is obtained. Based on the coherent Rayleigh scattering principle, a single-mode optical cable is used as a front-end sensor, a laser pulse signal is continuously injected at one end of the optical cable, and the optical fiber is sensitive to acoustic (vibration). When the hydrodynamic acoustic waves in the hydrogeological exploration borehole act on the sensing optical fiber, the small deformation of the optical fiber changes the scattering body spacing and the refractive index inside the optical fiber, and then causes the phase and intensity changes of the backscattered signal. By analyzing the phase and intensity of the backscattered signal, the deformation of the optical cable caused by the hydrodynamic acoustic waves in the hydrogeological exploration borehole can be detected, and all acoustic (vibration) signals along the optical cable can be obtained at one time. The system has low noise and high fidelity characteristics in the entire sensing optical fiber length. Through data processing and interpretation, the outflow layer section of the limestone aquifer in the borehole can be accurately identified, providing geological basis for mine water prevention and control measures.

[0082] The borehole structure of this embodiment is shown in Table 1. In the environmental state, the distributed acoustic sensor is used to monitor the groundwater outflow for 2 hours. The test section of the borehole is 785.66~875.53m deep, the test layer is the Ordovician Majiagou Formation (O2m), the test optical cable is 878m deep, the test data sampling frequency is 4000Hz, and 1 set of data is stored every 30s. 240 sets of effective differential acoustic data are obtained from the continuous differential acoustic signals in the 2-hour monitoring period of the BK1 borehole, and the data size is more than 180G.

[0083] BK1 borehole structure

[0084]

[0085] According to the above technical solution, as shown in Figure 1 The embodiment provides a distributed acoustic monitoring signal denoising method based on adaptive Wiener filtering, which comprises the following steps:

[0086] Step 1, using an acoustic sensor to collect the time-domain signals of the groundwater hydrodynamic acoustic waves in a plurality of signal channels distributed along the depth direction of the borehole, obtaining the time-domain signals of the groundwater hydrodynamic acoustic waves in the plurality of signal channels; the interval of adjacent signal channels along the depth direction of the borehole is 0.1~3 meters;

[0087] Step 2, extracting the effective acoustic time-domain signal of the current signal channel from the underground fluid dynamic acoustic signal of the current signal channel to obtain an effective acoustic time-domain signal of the current signal channel;

[0088] Step 3, performing frame windowing processing on the extracted effective acoustic time-domain signal of the current signal channel;

[0089] Step 4, initializing the effective acoustic signal after the frame windowing processing to obtain a noise spectrum mean value and a noise spectrum variance;

[0090] Step 5, performing short-time Fourier transform on the current frame time-domain signal to convert the current frame time-domain signal into a current frame frequency-domain signal and obtain an amplitude spectrum of the current frame frequency-domain signal; in the frequency domain, the amplitude spectrum is a collection of amplitude values of all frequency components of the signal.

[0091] Step 6, sequentially performing square operation and frequency-domain integration processing on the amplitude spectrum of the current frame frequency-domain signal to obtain an energy value of the current frame frequency-domain signal in the full frequency range; taking the logarithm of the energy value to obtain a logarithmic energy value of the current frame frequency-domain signal;

[0092] Specifically, the logarithmic energy value of the current frame (the mth frame) is determined by the following formula:

[0093]

[0094] Wherein,

[0095] E(m) is the logarithmic energy value of the current frame frequency-domain signal;

[0096] N is one-half of the sampling frequency of the acoustic sensor;

[0097] is the amplitude value of the current frame frequency-domain signal, that is, the amplitude value of the short-time Fourier transform of the current frame (the mth frame) frequency-domain signal at the th frequency.

[0098] Step 7, judging whether the current frame frequency-domain signal is an initial frame frequency-domain signal, if not, entering step 8, if yes, entering step 9;

[0099] Step 8, performing underground fluid dynamic acoustic activity detection according to the logarithmic energy value obtained in step 6 and a threshold value to judge whether there is an underground fluid dynamic acoustic wave, if there is an underground fluid dynamic acoustic wave, entering step 9, if there is no underground fluid dynamic acoustic wave, re-computing the noise spectrum mean value and the noise spectrum variance, and then entering step 9;

[0100] The filter parameters are adaptively adjusted by the groundwater sound activity detection, which can dynamically respond to signal and noise changes, and the activity detection mechanism enables the filter to quickly adjust when signal changes suddenly, thereby maintaining good noise reduction effect.

[0101] Specifically, whether the underground fluid dynamic sound wave exists is determined according to the following criteria:

[0102] If the logarithmic energy value is greater than the threshold value, it is determined that the underground fluid dynamic sound wave exists.

[0103] If the logarithmic energy value is less than or equal to the threshold value, it is determined that the underground fluid dynamic sound wave does not exist.

[0104] The threshold value is determined according to the following formula:

[0105]

[0106] In the formula,

[0107] is the threshold value;

[0108] A is an empirical value, and the value is 2-3;

[0109] N is one-half of the sampling frequency of the sound wave sensor;

[0110] is the probability density function of the current frame frequency domain signal in the state of the existence of the underground fluid dynamic sound wave; is the probability density function of the current frame frequency domain signal in the state of the existence of the underground fluid dynamic sound wave;

[0111] is the noise spectrum mean value calculated last time.

[0112] As a preferred, the noise spectrum mean value is calculated by the following formula:

[0113]

[0114] In the formula,

[0115] is the noise spectrum mean value recalculated;

[0116] α is a forgetting factor, 0≤α≤1;

[0117] is the noise spectrum mean value calculated last time;

[0118] is the power of the current frame frequency domain signal.

[0119] As a preferred, the noise spectrum variance estimation is calculated by the following formula:

[0120]

[0121] wherein,

[0122] is the newly calculated noise spectrum variance;

[0123] is a forgetting factor, 0≤ ≤1;

[0124] is the noise spectrum variance calculated last time;

[0125] is the power of the current frame frequency domain signal.

[0126] The introduction of the forgetting factor reduces the dependence on historical data, reduces the calculation complexity, improves the processing speed, enables the filter to quickly adapt to changes in the noise environment, and enhances the ability to process complex noise environments. 0≤α≤1, and the closer α is to 1, the greater the dependence of the noise spectrum mean of the current frame on the noise spectrum mean of the previous frame of the current frame, thereby achieving smoother estimation. On the contrary, the closer α is to 0, the faster the noise spectrum mean reacts to new data, and the worse the smoothing effect. In the present embodiment, α is 0.99.

[0127] The activity detection based on Gaussian distribution can more accurately distinguish between signal and noise, thereby improving the accuracy of noise estimation.

[0128] Step 9, calculating the posterior signal-to-noise ratio of the current frame frequency domain signal according to the noise spectrum variance, and updating the prior signal-to-noise ratio of the current frame frequency domain signal according to the calculated posterior signal-to-noise ratio of the current frame frequency domain signal to obtain the updated prior signal-to-noise ratio of the current frame signal;

[0129] Specifically, the posterior signal-to-noise ratio is determined by the following formula:

[0130]

[0131] is the posterior signal-to-noise ratio of the current frame frequency domain signal;

[0132] is the noise spectrum variance calculated last time;

[0133] is the power of the current frame frequency domain signal.

[0134] The updating of the prior signal-to-noise ratio is realized by the following formula:

[0135]

[0136] wherein,

[0137] an updated prior signal-to-noise ratio of the current frame frequency domain signal;

[0138] β is an update factor, 0≤β≤1;

[0139] an updated posterior signal-to-noise ratio of the current frame frequency domain signal;

[0140] a posterior signal-to-noise ratio of a previous frame frequency domain signal.

[0141] The closer β is to 1, the more the updated noise spectrum mean value depends on the noise spectrum mean value of the previous frame of the current frame, thereby achieving a smoother estimation. Conversely, the closer β is to 0, the more the updated noise spectrum mean value depends on the noise power estimation of the current frame, and the faster the reaction to new information, but the smoothing effect is weakened. Therefore, in the embodiment, β is 0.98.

[0142] Step 10, determining a gain function according to the updated prior signal-to-noise ratio of the current frame frequency domain signal, filtering and enhancing the amplitude spectrum of the current frame frequency domain signal by using the gain function, to obtain the amplitude spectrum of the filtered and enhanced current frame frequency domain signal; and converting the filtered and enhanced current frame frequency domain signal into a time domain signal through inverse short-time Fourier transform, to obtain the time domain signal as the de-noised signal of the current frame;

[0143] The gain function is:

[0144]

[0145] In the formula, Y is the amplitude value of the filtered and enhanced current frame frequency domain signal;

[0146] Y is the amplitude value of the filtered and enhanced current frame frequency domain signal;

[0147] G is the gain function;

[0148] Y is the amplitude value of the current frame frequency domain signal.

[0149] Step 11, determining whether the current frame is the last frame of the current signal channel, if yes, taking the time domain signal of the next signal channel as the time domain signal of the underground fluid dynamic sound wave of the current signal channel, and entering steps 2-10, if not, repeating steps 5-10 until the current frame is the last frame of the current signal channel, and then taking the time domain signal of the next signal channel as the time domain signal of the underground fluid dynamic sound wave of the current signal channel, and entering steps 2-10;

[0150] Step 12: Determine if the current signal channel is the last of multiple signal channels. If yes, proceed to step 13. If no, repeat steps 2 to 11 until the current signal channel is the last of multiple signal channels, then proceed to step 13.

[0151] Step 13: Output the denoised signals of all current frames as the denoising result.

[0152] In this embodiment, the filtering effect obtained by a single signal channel during the noise reduction process is as follows: Figure 2 As shown.

[0153] from Figure 2 The original time series plot shows that the original signal may contain significant noise, which could interfere with the accurate detection of hydrodynamic acoustic waves. The hydrodynamic acoustic waves may be masked by background noise, making them difficult to observe directly. After noise reduction processing, the background noise level of the signal is significantly reduced, and the characteristics of the hydrodynamic acoustic waves are more clearly visible. The processed signal is clearer, which is helpful for subsequent signal analysis and feature extraction. Comparing the original signal and the noise-reduced signal shows that the method of this invention can effectively suppress background noise while preserving the main characteristics of hydrodynamic acoustic waves. Applying this method to the real-time monitoring and analysis of groundwater flow can effectively improve the accuracy and reliability of the monitoring system.

[0154] The above-described implementation process is merely an example to clearly illustrate this application and is not intended to limit the implementation methods. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively list all possible implementation methods here. However, obvious variations or modifications derived therefrom are still within the protection scope of this application.

Claims

1. A method for distributed acoustic monitoring signal denoising with adaptive Wiener filtering, characterized in that, The method comprises the following steps: Step 1, collecting the underground fluid dynamic acoustic time domain signals of multiple signal channels distributed along the borehole depth direction by using the acoustic wave sensor, to obtain the underground fluid dynamic acoustic time domain signals of the multiple signal channels; the interval of adjacent signal channels along the borehole depth direction is 0.1-3 meters; Step 2, extracting the underground fluid dynamic acoustic time domain signals of the current signal channel to obtain the effective acoustic time domain signals of the current signal channel; Step 3, performing frame windowing processing on the extracted effective acoustic time domain signals of the current signal channel; Step 4, initializing the effective acoustic time domain signals after frame windowing processing to obtain the noise spectrum mean value and the noise spectrum variance; Step 5, performing short-time Fourier transform on the current frame time domain signal, converting the current frame time domain signal into a current frame frequency domain signal, and obtaining the amplitude spectrum of the current frame frequency domain signal; Step 6, sequentially performing square operation and frequency domain integration processing on the amplitude spectrum of the current frame frequency domain signal to obtain the energy value of the current frame frequency domain signal in the full frequency range; taking the logarithm of the energy value to obtain the logarithmic energy value of the current frame frequency domain signal; Step 7, judging whether the current frame frequency domain signal is an initial frame frequency domain signal, if not, entering step 8, if yes, entering step 9; Step 8, performing underground fluid dynamic acoustic activity detection according to the logarithmic energy value obtained in step 6 and a threshold value, judging whether there is underground fluid dynamic acoustic wave, if there is underground fluid dynamic acoustic wave, entering step 9, if there is no underground fluid dynamic acoustic wave, re-calculating the noise spectrum mean value and the noise spectrum variance, and then entering step 9; Step 9, calculating the posterior signal-to-noise ratio of the current frame frequency domain signal according to the noise spectrum variance, and updating the prior signal-to-noise ratio of the current frame frequency domain signal according to the calculated posterior signal-to-noise ratio of the current frame frequency domain signal to obtain the updated prior signal-to-noise ratio of the current frame signal; Step 10, determining a gain function according to the updated prior signal-to-noise ratio of the current frame frequency domain signal, filtering and enhancing the amplitude spectrum of the current frame frequency domain signal by using the gain function to obtain the amplitude spectrum of the filtered and enhanced current frame frequency domain signal; Converting the filtered and enhanced current frame frequency domain signal into a time domain signal through inverse short-time Fourier transform to obtain the time domain signal as the denoised signal of the current frame; Step 11, judging whether the current frame is the last frame of the current signal channel, if yes, taking the underground fluid dynamic acoustic time domain signals of the next signal channel as the underground fluid dynamic acoustic time domain signals of the current signal channel, and entering steps 2-10, if not, repeating steps 5-10 until the current frame is the last frame of the current signal channel, then taking the underground fluid dynamic acoustic time domain signals of the next signal channel as the underground fluid dynamic acoustic time domain signals of the current signal channel, and entering steps 2-10; Step 12, judging whether the current signal channel is the last channel of the multiple signal channels, if yes, entering step 13, if not, repeating steps 2-11 until the current signal channel is the last channel of the multiple signal channels, and entering step 13; Step 13, outputting all the obtained denoised signals of the current frame as the denoising result.

2. The method of claim 1, wherein the method is a distributed acoustic wave monitoring signal denoising method of adaptive Wiener filtering, characterized in that, The logarithmic energy value in step 6 is obtained by the following formula: wherein, E(m) is the log energy value of the current frame frequency domain signal; N is half of the sampling frequency of the acoustic sensor; Y(m,f k ) represents the amplitude value of the current frame frequency domain signal.

3. The method of claim 1, wherein the adaptive Wiener filter is applied to the distributed acoustic wave monitoring signal to reduce noise. The step 8 judges whether the underground fluid dynamic acoustic wave exists according to the following criteria: If the log energy value is greater than the threshold value, it is judged that the underground fluid dynamic acoustic wave exists; If the log energy value is less than or equal to the threshold value, it is judged that the underground fluid dynamic acoustic wave does not exist.

4. The method of claim 1, wherein the method is a distributed acoustic wave monitoring signal denoising method of adaptive Wiener filtering, characterized in that, The threshold value in step 8 is determined according to the following formula: In the formula, η is the threshold value; A is an empirical value, and the value is 2-3; N is half of the sampling frequency of the acoustic sensor; X(m,f k ) is the probability density function of the mth frame of frequency domain signal in the presence of underground fluid dynamic acoustic wave λ μ (l,f k ) is the noise spectrum mean value from the last calculation.

5. The method of claim 1, wherein, The noise spectrum variance of the current frame in step 8 is calculated by the following formula: λ μ (m,f k ) = a - λ μ (l,f k ) + (1 - a) - P mm (m,f k ) In the formula, λ μ (m,f k ) is the recalculated noise spectrum mean; α is the forgetting factor, 0≤α≤1; λ μ (l,f k ) is the mean of the noise spectrum from the last calculation; P mm (m,f k ) is the power of the current frame frequency domain signal.

6. The method of claim 1, wherein the method is a distributed acoustic wave monitoring signal denoising method of adaptive Wiener filtering, characterized in that, The noise spectrum variance estimation in step 8 is calculated by the following formula: λ σ (m,f k ) = a - λ σ (l,f k ) + (1 - a) - P mm (m,f k ) In the formula, λ σ (m,f k ) is the recalculated noise spectrum variance; α is the forgetting factor, 0≤α≤1; λ σ (l,f k ) is the noise spectrum variance from the last calculation. P mm (m,f k ) is the power of the current frame frequency domain signal.

7. The method of claim 1, wherein the adaptive Wiener filtering of the distributed acoustic wave monitoring signal noise reduction is performed by a processor. The posterior signal-to-noise ratio in step 8 is determined by the following formula: In the formula, γ(m,f k ) is the posterior signal-to-noise ratio of the current frame frequency domain signal; λ σ (l,f k ) last computed noise spectrum variance; P mm (m,f k ) is the power of the current frame frequency domain signal.

8. The method of claim 1, wherein the adaptive Wiener filtering of distributed acoustic sensing signal denoising is characterized by, The update of the prior signal-to-noise ratio in step 9 is realized by the following formula: ξ(m, f k ) = β · γ(m - 1, f k ) + (1 - β) max[γ(m, f k ) - 1, 0] In the formula, ξ(m,f k ) is the updated prior signal-to-noise ratio of the current frame in the frequency domain; β is the update factor, 0≤β≤1; γ(m,f k ) is the posterior signal-to-noise ratio of the current frame in the frequency domain; γ(m - 1, f k ) is the posterior signal-to-noise ratio of the previous frame in the frequency domain.

9. The method of claim 1, wherein, The gain function in step 9 is: In the formula, H(m,f k ) is a gain function; ξ(m,f k ) is the updated a priori SNR for the current frame in the frequency domain.

10. The method of claim 1, wherein, The filtering enhancement of the current frame frequency domain signal by using the gain function in step 10 is realized by the following formula: In the formula, to filter the amplitude values of the current frame in the frequency domain; H(m,f k ) is a gain function; Y(m,f k ) is the amplitude value of the current frame frequency domain signal.

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