Method and device for extracting background noise bulk waves based on distributed fiber optic acoustic sensing

Abstract

The application discloses a background noise body wave extraction method and device based on a distributed optical fiber acoustic sensing, and the method comprises the following steps: acquiring an original phase difference signal along an optical fiber channel by using a distributed optical fiber acoustic sensing system, and converting the original phase difference signal into strain rate data according to system demodulation parameters; performing noise suppression, frequency energy equalization and other pretreatments on the strain rate data; calculating an autocorrelation function of the pretreated signal, and superimposing autocorrelation results of multiple time segments; performing band-pass filtering on the superimposed autocorrelation function in a target frequency band, and extracting a body wave reflection response in the background noise; in order to eliminate the influence of a zero-time pulse in a shallow layer or a weak reflection signal, performing a mean removal processing on the extraction result along a space direction, and obtaining a zero-offset body wave reflection response. The method can effectively depict discontinuities from a shallow sediment layer, a fracture to a deep Moho surface and even a lithosphere, and makes up for the shortcomings of a conventional seismic station method.

Description

Technical Field

[0001] This invention belongs to the field of structural imaging technology in distributed acoustic sensing seismology, specifically relating to a method and apparatus for extracting background noise volume waves based on distributed fiber optic acoustic sensing. Background Technology

[0002] Seismic background noise imaging is a method for reconstructing the structure of subsurface media using continuous environmental noise signals. In recent years, this method has been widely applied in geophysical exploration, seismic monitoring, and engineering geology. Traditional background noise tomography methods mainly rely on the propagation characteristics of surface waves, extracting phase velocities through cross-correlation or frequency-time analysis to invert large-scale subsurface velocity structures. However, such methods typically produce smooth velocity distributions, have limited ability to identify shallow or small-scale geological bodies, low vertical resolution, and difficulty in accurately characterizing fine geological features.

[0003] In contrast, reflected volume wave signals can penetrate deeper strata and have higher vertical resolution. Therefore, extracting high-quality volume wave reflection responses from background noise will significantly improve the accuracy and resolution of subsurface structure imaging. However, in practical applications, volume wave signals are often weaker and have a lower signal-to-noise ratio than surface wave components, and their energy contribution to the noise field is limited, making the stable extraction of volume wave reflection information from background noise a challenging task.

[0004] Under traditional seismic array observation conditions, researchers typically employ autocorrelation techniques to extract body wave reflection signals from background noise for monitoring dynamic subsurface structures or identifying reflective interfaces. However, such methods rely on discretely distributed seismic stations, and the sparse sensor spacing (hundreds to thousands of meters) limits lateral resolution and fine characterization of small-scale geological structures.

[0005] The emergence of Distributed Acoustic Sensing (DAS) technology offers a new technical approach to solving the aforementioned problems. This technology can convert conventional communication optical fibers into continuous seismic observation arrays with thousands to tens of thousands of spatial sampling points, enabling high-density, long-distance, real-time seismic vibration monitoring. It significantly improves observation resolution and spatial coverage, and offers advantages such as flexible deployment, low cost, and remote monitoring capabilities, providing a potential technical foundation for high-precision noise imaging.

[0006] However, DAS technology still faces numerous technical challenges when applied to extracting volume waves from background noise. First, the physical quantity measured by the DAS system is a strain rate signal along the fiber optic axis, which differs from the three-component velocity or displacement recorded by traditional seismographs. This makes the separation and identification of different types of seismic waves (such as P-waves, S-waves, and surface waves) more complex. Second, DAS records are often accompanied by strong common-mode noise, system noise, and non-uniform spectral energy distribution, significantly masking the volume wave signal. Existing research often employs simple linear methods such as mean removal and filtering when processing DAS noise data, which are insufficient to effectively suppress spatially correlated noise and recover the true reflection response. Furthermore, the physical interpretation mechanism of DAS autocorrelation signals is not fully understood; volume wave reflection information is often aliased with zero-time pulses and surface wave remnants, leading to blurred identification of reflection events and affecting the reliability of the inversion results.

[0007] In the prior art, Chinese patent CN118483743B discloses a method for extracting surface wave dispersion curves based on strain fields, including the following steps: extracting strain data from seismic observation data; preprocessing the strain data according to the type of seismic observation data to obtain strain components; converting the strain components from the time-space domain to the frequency-space domain using Fourier transform; converting the strain components from the frequency-space domain to the frequency-phase velocity domain using frequency-Bessel transform to obtain the surface wave dispersion spectrum of the strain components; and picking the surface wave dispersion curve based on the surface wave dispersion spectrum. However, it does not involve the extraction and analysis of body wave reflection response.

[0008] To address the aforementioned issues, current technologies lack a processing method that can effectively suppress system noise, achieve frequency energy balance, and accurately extract the body wave reflection response, taking into account the characteristics of DAS systems. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of existing technologies by providing an autocorrelation imaging method specifically designed for distributed fiber optic acoustic sensing technology. This method can extract zero-offset volume wave reflection responses, thereby supporting reliable geological interpretation. The key to this method lies in selecting the optimal spectral whitening width. By suppressing the peak energy of the dominant frequency and enhancing the contribution of secondary frequency components, the detection capability of weak, non-stationary reflection signals is effectively improved. Simultaneously, combined with spatial mean-reduction processing, the pulse occlusion effect at time zero can be suppressed, successfully recovering high-quality zero-offset volume wave reflection responses of subsurface structures. The processed data provides a solid data foundation for the interpretation of geological structures at multiple scales, from shallow sedimentary layers and fault systems to the deep Moho discontinuity.

[0010] The objective of this invention can be achieved through the following technical solutions: This invention provides a method for extracting volume background noise based on distributed fiber optic acoustic sensing, comprising the following steps: Step S1: Acquire the original phase difference signal along the optical fiber channel using distributed optical fiber acoustic sensing, and convert the original phase difference signal into strain rate data according to the system demodulation parameters; Step S2: Preprocess the strain rate data to obtain a signal that has undergone noise suppression and frequency energy equalization. Step S3: Calculate the autocorrelation function for the signal record of each fiber channel after preprocessing, obtain the autocorrelation function of each channel, and superimpose the autocorrelation results of multiple time segments; Step S4: Select the target frequency band for bandpass filtering of the obtained superimposed autocorrelation function to extract the volume wave reflection response from the background noise; Step S5: For cases where the zero-time pulse dominates in shallow or weak reflection signals, the mean of the body wave reflection response is removed along the spatial direction to obtain the zero-offset body wave reflection response.

[0011] Furthermore, the strain rate data is preprocessed, specifically including: Step S21: Calculate the spatial median of the strain rate data along the spatial direction, and remove fiber common-mode noise by the spatial median weighting method to obtain the denoised data; Step S22: Perform mean and trend removal processing on the denoised data to eliminate long-term trends and DC components, and obtain a stable signal; Step S23: Perform bandpass filtering on the stable signal, select the effective frequency range, and obtain the filtered signal; Step S24: Normalize the filtered signal in the time domain to obtain a normalized signal; Step S25: Convert the normalized signal to the frequency-wavenumber domain through Fourier transform, and set the filtering range in the positive and negative directions according to the energy characteristics to complete the frequency filtering and obtain the filtered frequency domain signal. Step S26: Perform spectral whitening on the frequency domain signal based on the frequency amplitude characteristics of the frequency domain signal to obtain a signal that has undergone noise suppression and frequency energy equalization.

[0012] Furthermore, step S21 specifically includes: Let the strain rate data before pretreatment be... ,in, Indicates the spatial sampling point index of the fiber channel. Indicates the sampling time; At each sampling time Above, calculate the spatial median of all fiber optic channels to obtain the sampling time. median reference signal ; The median reference signal is weighted, and the original strain rate data is subtracted from the weighted median reference signal to remove spatial common-mode noise, resulting in denoised strain rate data. in, This represents the strain rate data after noise reduction; These are preset weighting coefficients.

[0013] Furthermore, step S22 specifically includes: Calculate the time mean of the denoised data obtained in step S21. : in, Indicates fiber optic channel The time mean; This represents the strain rate data after noise reduction; Indicates the total number of sampling times; Subtract the time mean from the denoised strain rate data to obtain the mean-free signal: in, Indicates the mean-removed signal; Mean signal Trend removal is performed by fitting a low-order polynomial. Subtracting this from the signal yields the stable signal: in, For fiber optic channels The fitted trend curve, This indicates a stable signal.

[0014] Furthermore, step S24 specifically includes: Let the filtered signal be ,in, Indicates the spatial sampling point index of the fiber channel. Indicates the sampling time; The filtered signal is normalized to obtain the normalized signal. : in, This is a normalized signal.

[0015] Furthermore, the spectral whitening process is formulated as follows: in, This represents the frequency domain signal after spectral whitening. This indicates the frequency of the preprocessed single-channel signal. The amplitude; Represents the half-width of the frequency neighborhood window; Indicates the current frequency index; Frequency index The corresponding frequency domain amplitude; This represents the whitening width.

[0016] Furthermore, the whitening width is determined based on the frequency amplitude characteristics of the frequency domain equalization signal.

[0017] Furthermore, the autocorrelation function is formulated as follows: in, Represents the autocorrelation function; Indicates a causal reflection response; Indicates a non-causal reflection response; Indicates a zero-time lag pulse; This represents the convolution operation.

[0018] In another aspect, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the background noise volume wave extraction method based on distributed fiber optic acoustic sensing as described in any of the above-described methods.

[0019] In another aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the background noise volume wave extraction method based on distributed fiber optic acoustic sensing as described in any of the above-described methods.

[0020] Compared with the prior art, the present invention has the following advantages: (1) This invention clarifies, for the first time, the physical mechanism of shallow reflection response in distributed fiber optic acoustic sensing autocorrelation signals from both theoretical and methodological perspectives, confirming that the signal can reliably characterize zero-offset volume wave reflection response, fundamentally eliminating the ambiguity and uncertainty in the prior art's explanation of its formation. This enables clear imaging of multi-scale geological discontinuities from shallow sedimentary layers and fracture systems to deep Moho discontinuities, providing a reliable basis for the fine identification of underground structures.

[0021] (2) Compared with the traditional background noise surface wave tomography method, the present invention fully explores the effective information of the volume wave component in the background noise and breaks through the limitations of surface wave imaging in terms of detection depth and vertical resolution; combined with the high-density spatial sampling characteristics of distributed fiber acoustic sensing technology, it significantly improves the lateral resolution and spatial continuity, and is particularly suitable for fine characterization of small-scale structures and complex geological bodies.

[0022] (3) Compared with traditional autocorrelation imaging techniques that rely on seismic arrays and teleseismic events, this invention does not require an external seismic source and can obtain a stable body wave reflection response based solely on environmental background noise, thereby achieving high-resolution subsurface imaging. This method has the advantages of low implementation cost, flexible deployment, weak environmental dependence, and strong site adaptability, providing a new, efficient, and reliable approach for passive source geophysical exploration. Attached Figure Description

[0023] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the actual strain rate recording in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of Example 2 of the present invention, showing the conversion of strain rate records into the frequency wavenumber domain for filtering (filtering range 0.1~2 km / s). Figure 4 This is a frequency-distance plot of the autocorrelation function of Embodiment 2 of the present invention; Figure 5 This is a schematic diagram of the zero-offset reflection profile obtained by the autocorrelation function in Embodiment 2 of the present invention; Figure 6 This is a schematic diagram of the zero-offset body wave reflection response obtained by removing the zero-time lag pulse after spatial mean removal in Embodiment 2 of the present invention; Figure 7 A preliminary schematic diagram of the Moho surface is shown for the zero-offset volume wave reflection response of Embodiment 3 of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] Example 1: The geometry of fractures plays a crucial role in controlling the dynamics of earthquake rupture and the propagation characteristics of seismic waves. Furthermore, the significant difference in wave impedance between loose sedimentary layers and bedrock can trigger complex wavefield phenomena such as multipath reflection, refraction, and seismic wave amplification. Fault activity easily disrupts the stability of sedimentary layers and can even trigger chain reactions of geological disasters. However, monitoring methods based on traditional seismic stations are limited by station density and the resolution of existing imaging technologies, typically only able to acquire relatively smooth subsurface shear wave velocity structures, making it difficult to identify fine geological structures. Although distributed fiber optic acoustic sensing technology can obtain autocorrelation profiles with high spatial sampling rates, the method itself is still immature, making reliable geological interpretation of this type of data difficult at present.

[0026] To address the aforementioned technical bottlenecks, this invention proposes a method for extracting volumetric background noise based on distributed fiber optic acoustic sensing. The overall process of this method is similar to traditional background noise tomography, but the key differences are: frequency wavenumber filtering is introduced in the preprocessing stage to improve data quality; the contribution of secondary frequency components is enhanced while preserving formant characteristics by optimizing the spectral whitening width; and spatial mean removal is employed to effectively suppress interference from zero-time pulses. This method is simple, highly operable, and the overall steps are as follows: Figure 1 As shown: S1. Obtain the raw observation phase difference data, and convert the phase difference data into strain rate data for subsequent preprocessing according to the parameters of the distributed fiber acoustic demodulator. S2. Perform data preprocessing, including the following steps: Step S21: Calculate the spatial median of the strain rate data along the spatial direction, and remove fiber common-mode noise by the spatial median weighting method to obtain the denoised data; Step S21 specifically includes: Let the strain rate data before pretreatment be... ,in, Indicates the spatial sampling point index of the fiber channel. Indicates the sampling time; At each sampling time Above, calculate the spatial median of all fiber optic channels to obtain the sampling time. median reference signal ; The median reference signal is weighted, and the original strain rate data is subtracted from the weighted median reference signal to remove spatial common-mode noise, resulting in denoised strain rate data. in, This represents the strain rate data after noise reduction; These are preset weighting coefficients.

[0027] Step S22: Perform mean and trend removal processing on the denoised data to eliminate long-term trends and DC components, and obtain a stable signal; Step S22 specifically includes: Calculate the time mean of the denoised data obtained in step S21. : in, Indicates fiber optic channel The time mean; This represents the strain rate data after noise reduction; Indicates the total number of sampling times; Subtract the time mean from the denoised strain rate data to obtain the mean-free signal: in, Indicates the mean-removed signal; Mean signal Trend removal is performed by fitting a low-order polynomial. Subtracting this from the signal yields the stable signal: in, For fiber optic channels The fitted trend curve, This indicates a stable signal.

[0028] Step S23: Perform bandpass filtering on the stable signal, select the effective frequency range, and obtain the filtered signal; Step S23 specifically includes: Perform a Fourier transform on the stable signal to obtain its frequency domain expression: in, Indicates fiber optic channel In frequency Spectral amplitude at that location; This represents the Fourier transform operation; In the frequency domain, based on the frequency characteristics of the target volume wave signal, the effective frequency range of the bandpass filter is selected, and a bandpass filter operator is constructed. : in, This represents the bandpass filter operator. This indicates the low-frequency cutoff frequency of the bandpass filter; This indicates the high-frequency cutoff frequency of the bandpass filter; Based on the bandpass filter operator for frequency domain signals Perform filtering: in, This represents the filtered frequency domain signal; The filtered frequency domain signal is then converted back to the time domain using an inverse Fourier transform to obtain the filtered signal: in, This represents the filtered signal. This indicates the inverse Fourier transform operation; Step S24: Normalize the filtered signal in the time domain to obtain a normalized signal; Step S24 specifically includes: The filtered signal is normalized to obtain the normalized signal. : in, This is a normalized signal.

[0029] Step S25: Convert the normalized signal to the frequency-wavenumber domain through Fourier transform, and set the filtering range in the positive and negative directions according to the energy characteristics to complete the frequency filtering and obtain the filtered frequency domain signal. Step S26: Perform spectral whitening on the frequency domain signal based on the frequency amplitude characteristics of the frequency domain signal to obtain a signal that has undergone noise suppression and frequency energy equalization.

[0030] Spectral whitening treatment, the formula is: in, This represents the frequency domain signal after spectral whitening. This indicates the frequency of the preprocessed single-channel signal. The amplitude; Represents the half-width of the frequency neighborhood window; Indicates the current frequency index; Frequency index The corresponding frequency domain amplitude; This represents the whitening width.

[0031] S3. Calculate the autocorrelation function for a single seismic record. ,in and They represent causal and non-causal reflexes, respectively. The zero-time pulse is then superimposed on the autocorrelation functions calculated after segmentation; The autocorrelation function, given by the formula: S4. Select the target frequency band for bandpass filtering on the superimposed result to extract the effective reflection components; S5. For weak near-surface reflection response ( The zero-time lag pulse dominates and masks the reflection response. Spatial demeaning is used to remove the influence of the zero-time lag pulse and obtain the zero-offset body wave reflection response.

[0032] Example 2: To verify the feasibility of this invention, this embodiment uses a distributed fiber optic acoustic sensor demodulator to continuously collect background noise data on a 29-kilometer-long submarine optical cable for 7 days. The acquisition parameters were set as follows: gauge length 10 meters, sampling rate 200 Hz, and channel spacing 4.09 meters. Figure 2 An example of strain rate recordings ranging from 500 to 7000 channels over a 5-minute period is presented. The raw data was divided into 5-minute segments and then preprocessed sequentially, including common-mode optical noise removal, mean and trend elimination, cone windowing, bandpass filtering (0.7–8 Hz), and downsampling to 20 Hz. Subsequently, time-domain normalization was used for further data processing. To suppress residual wave-related noise, frequency-wavenumber filtering was applied. Figure 3 The wavefield components with apparent velocities between 0.1 and 2 km / s are preserved. Spectral whitening is performed using a moving average method with a width of 0.1 Hz to enhance the contribution of secondary frequency components while preserving the characteristics of the main resonance peaks.

[0033] The reflection response was obtained by linearly superimposing the 7-day autocorrelation function. Figure 4 The autocorrelation function frequency-distance plot shown indicates that a 0.1 Hz spectral whitening width effectively maintains the formant structure while increasing the contribution of secondary frequencies. Figure 5 It can be seen that the shallow reflection was originally blocked by the zero-time pulse; by selecting the 0.7-2 Hz frequency band for bandpass filtering and combining it with spatial mean removal processing, the coherent shallow shear wave reflection response was successfully extracted. Figure 6 This method demonstrates good effectiveness on distributed fiber acoustic sensing datasets, enabling higher-precision underground imaging.

[0034] Based on the discontinuity of the phase axis in the reflected signal, regional fault structures were successfully identified: fault 1 was identified at approximately 7 km of the optical cable, and another significant discontinuity was found in the interface reflected signal at approximately 16 km, marked as fault 2. The geological interpretation results of the above reflection responses demonstrate that this imaging method can effectively extract volume wave information from background noise and obtain reliable shallow sedimentary layer structures.

[0035] Example 3: Besides extracting shallow transverse wave reflection responses, the method of this invention can also be used to obtain deep body wave reflection signals. This embodiment differs slightly from Embodiment 2 in its preprocessing steps: because the P-wave and S-wave velocities of deep structures are typically higher than 2 km / s, the frequency-wavenumber filtering retains wavefield components with apparent velocities greater than 0.1 km / s. After bandpass filtering and spatial mean removal, the zero-offset body wave reflection response was successfully extracted. It should be noted that the deep body wave reflection signal simultaneously contains P-wave and S-wave components from the Moho surface. The regional P-wave to S-wave velocity ratio is approximately 1.7, while the P-wave and S-wave reflection interfaces identified in the reflection response are located at 9.6 seconds and 16.29 seconds, respectively. Figure 7 The time ratio is basically consistent with the theoretical velocity ratio, thus verifying that the reflected signal originates from the reflection of longitudinal and transverse waves from the Mohorovičić discontinuity.

[0036] In summary, the method proposed in this invention can effectively extract zero-offset body wave reflection response from background noise, and has the advantages of high resolution, ease of implementation and low cost. It can not only finely characterize the structure of shallow sedimentary layers, but also preliminarily reveal the reflection information of the Moho discontinuity, providing a powerful supplement to the imaging of discontinuous structures in the deep lithosphere and making up for the shortcomings of traditional seismic station imaging in deep exploration.

[0037] Furthermore, this invention also provides a background noise autocorrelation volume wave imaging device based on distributed fiber optic acoustic sensing technology, including a central processing unit (CPU), which can execute various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM can also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0038] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0039] The processing unit executes the various methods and processes described above, such as methods S1 to S5. For example, in some embodiments, methods S1 to S5 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S5 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S5 by any other suitable means (e.g., by means of firmware).

[0040] The functions described above in this embodiment can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0041] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0042] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0043] 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 person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered 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 method for extracting background noise volume waves based on distributed fiber optic acoustic sensing, characterized in that, Includes the following steps: Step S1: Acquire the original phase difference signal along the optical fiber channel using distributed optical fiber acoustic sensing, and convert the original phase difference signal into strain rate data according to the system demodulation parameters; Step S2: Preprocess the strain rate data to obtain a signal that has undergone noise suppression and frequency energy equalization. The strain rate data is preprocessed, specifically including: Step S21: Calculate the spatial median of the strain rate data along the spatial direction, and remove fiber common-mode noise by the spatial median weighting method to obtain the denoised data; Step S22: Perform mean and trend removal processing on the denoised data to eliminate long-term trends and DC components, and obtain a stable signal; Step S23: Perform bandpass filtering on the stable signal, select the effective frequency range, and obtain the filtered signal; Step S24: Normalize the filtered signal in the time domain to obtain a normalized signal; Step S25: Convert the normalized signal to the frequency-wavenumber domain through Fourier transform, and set the filtering range in the positive and negative directions according to the energy characteristics to complete the frequency filtering and obtain the filtered frequency domain signal. Step S26: Perform spectral whitening on the frequency domain signal based on the frequency amplitude characteristics of the frequency domain signal to obtain a signal that has undergone noise suppression and frequency energy equalization. Step S3: Calculate the autocorrelation function for the signal record of each fiber channel after preprocessing, obtain the autocorrelation function of each channel, and superimpose the autocorrelation results of multiple time segments; Step S4: Select the target frequency band for bandpass filtering of the obtained superimposed autocorrelation function to extract the volume wave reflection response from the background noise; Step S5: For cases where the zero-time pulse dominates in shallow or weak reflection signals, the mean of the body wave reflection response is removed along the spatial direction to obtain the zero-offset body wave reflection response.

2. The method for extracting background noise volume waves based on distributed fiber optic acoustic sensing according to claim 1, characterized in that, Step S21 specifically includes: Let the strain rate data before pretreatment be... ,in, Indicates the spatial sampling point index of the fiber channel. Indicates the sampling time; At each sampling time Above, calculate the spatial median of all fiber optic channels to obtain the sampling time. median reference signal ; The median reference signal is weighted, and the original strain rate data is subtracted from the weighted median reference signal to remove spatial common-mode noise, resulting in denoised strain rate data. in, This represents the strain rate data after noise reduction; These are preset weighting coefficients.

3. The method for extracting background noise volume waves based on distributed fiber optic acoustic sensing according to claim 1, characterized in that, Step S22 specifically includes: Calculate the time mean of the denoised data obtained in step S21. : in, Indicates fiber optic channel The time mean; This represents the strain rate data after noise reduction; Indicates the total number of sampling times; Subtract the time mean from the denoised strain rate data to obtain the mean-free signal: in, Indicates the mean-removed signal; Mean signal Trend removal is performed by fitting a low-order polynomial. Subtracting this from the signal yields the stable signal: in, For fiber optic channels The fitted trend curve, This indicates a stable signal.

4. The method for extracting background noise volume waves based on distributed fiber optic acoustic sensing according to claim 1, characterized in that, Step S24 specifically includes: Let the filtered signal be ,in, Indicates the spatial sampling point index of the fiber channel. Indicates the sampling time; The filtered signal is normalized to obtain the normalized signal. : in, This is a normalized signal.

5. The method for extracting background noise volume waves based on distributed fiber optic acoustic sensing according to claim 1, characterized in that, The formula for the spectral whitening process is: in, This represents the frequency domain signal after spectral whitening. This indicates the frequency of the preprocessed single-channel signal. The amplitude; Represents the half-width of the frequency neighborhood window; Indicates the current frequency index; Frequency index The corresponding frequency domain amplitude; This represents the whitening width.

6. The method for extracting background noise volume waves based on distributed fiber optic acoustic sensing according to claim 5, characterized in that, The whitening width is determined based on the frequency peak characteristics of the frequency domain equalization signal.

7. The method for extracting background noise volume waves based on distributed fiber optic acoustic sensing according to claim 1, characterized in that, The autocorrelation function is given by the following formula: in, Represents the autocorrelation function; Indicates a causal reflection response; Indicates a non-causal reflection response; Indicates a zero-time lag pulse; This represents the convolution operation.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the background noise volume wave extraction method based on distributed fiber optic acoustic sensing as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the background noise volume wave extraction method based on distributed fiber optic acoustic sensing as described in any one of claims 1 to 7.

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