Downsampling method and device for distributed acoustic sensing monitoring data

Abstract

The invention provides a down-sampling method and device for distributed acoustic sensing monitoring data, and the corresponding method comprises the steps: carrying out the spectrum analysis of the pre-received distributed acoustic sensing monitoring data of a target work area, so as to determine the frequency band range; performing low-pass filtering on the distributed acoustic sensing monitoring data according to the frequency band range to generate a filtering result; and down-sampling the filtering result according to a proportional relation between a target sampling interval and an original sampling interval of the distributed acoustic sensing monitoring data. By using the distributed acoustic sensing monitoring data downsampling method provided by the invention, the signal after the distributed acoustic sensing monitoring data is downsampled can be better close to the expected signal of the time domain, meanwhile, the frequency component loss of the signal is small, the calculation process is simple and efficient, and the method is suitable for popularization and application. And thus, the cost of storage and processing of distributed optical fiber acquisition data is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data processing in the process of oil and gas field development, in particular to the technical field of processing distributed acoustic sensor monitoring data of oil and gas field fracturing, and more particularly to a downsampling method and device for distributed acoustic sensor monitoring data. Background Art

[0002] Distributed fiber acoustic sensing (DAS) technology has been widely used in recent years in the fields of earthquake exploration, coal mine safety, oil production, pipeline safety monitoring, etc. Its principle is that when the surface of the object being measured vibrates, the optical fiber laid on the surface of the object will be deformed. DAS uses optical time domain reflectometry technology to determine the position of the deformation of the object being measured to achieve abnormality detection.

[0003] DAS monitoring technology has the advantage of broadband acquisition, and the sampling rate is usually high, generally 2KHz sampling, or even higher. At the same time, the spatial sampling interval of the optical fiber is small, and a spatial sampling rate of every 0.5 meters can be achieved. For dynamic monitoring application scenarios, long-term high-space and high-time domain data sampling leads to a large amount of distributed optical fiber data, which brings challenges to data storage.

[0004] In the prior art, distributed optical fiber storage often uses large storage devices such as NAS disks, and the optical fiber data is not downsampled through data processing methods during the storage process. It is understandable that the high sampling frequency of optical fiber ensures the broadband information of the collected signal, so it has a wide range of application scenarios. However, for specific application scenarios, such as seismic exploration, the DAS data frequency used can be up to 200Hz. In this case, because the distributed acoustic sensor monitoring data is large in volume and contains a lot of useless information and noise, it brings great difficulties to the storage of distributed acoustic sensor monitoring data. Summary of the invention

[0005] One object of the present invention is to provide a downsampling method for distributed acoustic sensor monitoring data. The method aims to propose a high-fidelity downsampling method for distributed acoustic sensor monitoring data based on the characteristics of high sampling rate and large data volume of distributed optical fiber, based on high sampling data of optical fiber, combined with filtering method and sampling method.

[0006] Another object of the present invention is to provide a downsampling device for distributed acoustic sensor monitoring data. Another object of the present invention is to provide a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the above-mentioned downsampling method for distributed acoustic sensor monitoring data when executing the computer program. Another object of the present invention is to provide a readable medium, on which a computer program is stored, and when the computer program is executed by the processor, the steps of the above-mentioned downsampling method for distributed acoustic sensor monitoring data are implemented.

[0007] In order to achieve the above objectives, the present invention discloses a method for downsampling distributed acoustic sensor monitoring data, comprising:

[0008] Perform spectrum analysis on the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range;

[0009] Performing low-pass filtering on the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result;

[0010] The filtering result is downsampled according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data.

[0011] In some embodiments of the invention, the spectral analysis of the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range includes:

[0012] Performing Fourier transformation on the distributed acoustic sensing monitoring data to generate amplitude spectra of all frequencies of the distributed acoustic sensing monitoring data;

[0013] The frequency band range is determined according to the shape of the amplitude spectrum.

[0014] In some embodiments of the invention, Fourier transform is performed on the distributed acoustic sensing monitoring data to generate amplitude spectra of all frequencies of the distributed acoustic sensing monitoring data, including:

[0015] Performing Fourier transform on the distributed acoustic sensor monitoring data in the time domain to generate a transformation result;

[0016] The transformation result is modulo-measured to generate the amplitude spectrum.

[0017] In some embodiments of the invention, the method for downsampling distributed acoustic sensing monitoring data further includes:

[0018] generating a low-pass filter according to the frequency band range;

[0019] Generating a low-pass filter according to the frequency band range includes:

[0020] Determining the cutoff frequency of the low-pass filter according to the frequency band range;

[0021] Determine a high cutoff frequency of the low-pass filter according to the cutoff frequency;

[0022] The filter is generated according to the cut-off frequency and the high-cut-off frequency.

[0023] In some embodiments of the invention, determining the cutoff frequency of the low-pass filter according to the frequency band range includes:

[0024] The cut-off frequency is determined according to a high-value frequency of the frequency band.

[0025] In some embodiments of the invention, low-pass filtering is performed on the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result, including:

[0026] Performing preliminary low-pass filtering on the distributed acoustic sensor monitoring data according to the low-pass filter and the frequency band range to generate preliminary filtering results;

[0027] Performing inverse Fourier transform on the preliminary filtering result to generate the filtering result.

[0028] In some embodiments of the invention, downsampling the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data includes:

[0029] When the target sampling interval is an integer multiple of the original sampling interval, downsampling the filtering result according to the target sampling interval;

[0030] When the target sampling interval is not an integer multiple of the original sampling interval, upsampling the filtering result to generate an upsampling result;

[0031] Down-sampling is performed on the up-sampling result.

[0032] The present invention also discloses a downsampling device for distributed acoustic sensor monitoring data, comprising:

[0033] A frequency band range determination module is used to perform spectrum analysis on the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range;

[0034] A filtering result generating module, used for performing low-pass filtering on the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result;

[0035] The filtering result downsampling module is used to downsample the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data.

[0036] In some embodiments of the invention, the frequency band range determination module includes:

[0037] an amplitude spectrum generating unit, configured to perform Fourier transform on the distributed acoustic sensing monitoring data to generate amplitude spectra of all frequencies of the distributed acoustic sensing monitoring data;

[0038] A frequency band range determining unit is used to determine the frequency band range according to the form of the amplitude spectrum.

[0039] In some embodiments of the invention, the amplitude spectrum generating unit comprises:

[0040] A transformation result generating unit, used for performing Fourier transformation on the distributed acoustic sensor monitoring data in the time domain to generate a transformation result;

[0041] The transformation result modulus unit is used to perform modulus calculation on the transformation result to generate the amplitude spectrum.

[0042] In some embodiments of the invention, the method for downsampling distributed acoustic sensing monitoring data further includes:

[0043] A low-pass filter generation module, used for generating a low-pass filter according to the frequency band range;

[0044] The low-pass filter generation module comprises:

[0045] A cut-off frequency determining unit, configured to determine the cut-off frequency of the low-pass filter according to the frequency band range;

[0046] A high cutoff frequency determining unit, used to determine the high cutoff frequency of the low-pass filter according to the cutoff frequency;

[0047] A low-pass filter generating unit is used to generate the filter according to the cut-off frequency and the high-cut-off frequency.

[0048] In some embodiments of the invention, the cut-off frequency determining unit comprises:

[0049] The cut-off frequency determining subunit determines the cut-off frequency according to a high-value frequency of the frequency band.

[0050] In some embodiments of the invention, the filtering result generation module includes:

[0051] A preliminary filtering result generating unit, configured to perform preliminary low-pass filtering on the distributed acoustic sensor monitoring data according to the low-pass filter and the frequency band range to generate a preliminary filtering result;

[0052] The filtering result generating unit is used to perform inverse Fourier transform on the preliminary filtering result to generate the filtering result.

[0053] In some embodiments of the invention, the filtering result downsampling module includes:

[0054] A filtering result downsampling unit, configured to downsample the filtering result according to the target sampling interval when the target sampling interval is an integer multiple of the original sampling interval;

[0055] A filtering result upsampling unit, configured to upsample the filtering result to generate an upsampled result when the target sampling interval is not an integer multiple of the original sampling interval;

[0056] The up-sampling result down-sampling unit is used to down-sample the up-sampling result.

[0057] The present invention also discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0058] When the processor executes the program, the method described above is implemented.

[0059] The present invention also discloses a computer readable medium having a computer program stored thereon.

[0060] When the program is executed by a processor, the method described above is implemented.

[0061] From the above description, it can be seen that the downsampling method and device for distributed acoustic sensor monitoring data provided in the embodiments of the present invention, the corresponding method includes: first, performing spectral analysis on the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range; then, low-pass filtering the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result; finally, downsampling the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data.

[0062] The present invention is aimed at the scenario of distributed optical fiber long-term monitoring acoustic sensor monitoring data, and proposes a high-fidelity downsampling method and device for massive distributed optical fiber data based on filtering and downsampling. The signal after downsampling of distributed acoustic sensor monitoring data using this method and device can be closer to the expected signal in the time domain, while the frequency component loss of the signal is small, and the calculation process is simple and efficient, which can reduce the cost of distributed optical fiber data collection storage and processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A schematic diagram of a process of downsampling method of distributed acoustic sensor monitoring data in Embodiment 1 of the present invention;

[0065] Figure 2 1 is a flow chart of step 100 in the method for downsampling distributed acoustic sensor monitoring data in the first embodiment of the present invention;

[0066] Figure 3 1 is a flow chart of step 101 in the method for downsampling distributed acoustic sensor monitoring data in the first embodiment of the present invention;

[0067] Figure 4 Another schematic diagram of the process of downsampling method of distributed acoustic sensor monitoring data in Embodiment 1 of the present invention;

[0068] Figure 5 4 is a flow chart of step 400 in the method for downsampling distributed acoustic sensor monitoring data in the first embodiment of the present invention;

[0069] Figure 6 4 is a flow chart of step 401 in the method for downsampling distributed acoustic sensor monitoring data in the first embodiment of the present invention;

[0070] Figure 7 Schematic diagram of the process of step 200 in the method for downsampling distributed acoustic sensor monitoring data in the first embodiment of the present invention;

[0071] Figure 8 3 is a flow chart of step 300 in the method for downsampling distributed acoustic sensor monitoring data in the first embodiment of the present invention;

[0072] Fig. 9 Schematic diagram of the process of downsampling method of distributed acoustic sensor monitoring data in Embodiment 2 of the present invention;

[0073] Fig.10 Schematic diagram of the time domain waveforms of the original signal and the target signal in the second embodiment of the present invention;

[0074] Fig.11 is a schematic diagram of the spectrum of the original data and the target signal in the second embodiment of the present invention;

[0075] Fig.12 It is a schematic diagram of down-sampled data and low-pass filtered data in the second embodiment of the present invention;

[0076] Fig.13 It is a schematic diagram of down-sampled data and low-pass filtered data in the second embodiment of the present invention;

[0077] Fig.14 It is a schematic diagram of down-sampled data and low-pass filtered data in Embodiment 2 of the present invention;

[0078] Fig.15 Schematic diagram of the spectrum of down-sampled data and low-pass filtered data in Embodiment 2 of the present invention;

[0079] Fig.16 Schematic diagram of the structure of the downsampling device for distributed acoustic sensor monitoring data in the third embodiment of the present invention;

[0080] Fig.17 It is a schematic diagram of the structure of an electronic device in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] It should be noted that the terms "including" and "having" in the specification and claims of the present application and the above-mentioned drawings and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. In the absence of conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0083] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of laws and regulations.

[0084] Embodiment 1

[0085] In this embodiment, Figure 1 As shown, a method for downsampling distributed acoustic sensor monitoring data is provided, which includes:

[0086] Step 100: performing spectrum analysis on the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range;

[0087] The principle of distributed fiber acoustic wave sensing technology is to use the phase of coherent Rayleigh scattered light rather than light intensity to detect signals such as sound or vibration within the audio range. It can not only use the phase amplitude to provide information on the intensity of sound or vibration events, but also use linear quantitative measurement values ​​to obtain phase and frequency information of sound or vibration events. DAS can be considered as a mobile interferometric acoustic wave sensor that detects external signals in the sensing fiber. When sound or vibration causes a linear change in the phase of the interference light at that position, quantitative measurement of external physical quantities can be achieved by extracting and demodulating the interference signals at different times at that position.

[0088] Furthermore, the laser emits a light pulse along the optical fiber, and some of the light interferes with the incident light in the pulse in the form of backscattering. After the interference light is reflected back, the backscattered interference light returns to the signal processing device, and at the same time brings the vibration sound wave signal along the optical fiber to the signal processing device. Since the speed of light remains unchanged, the measurement result of the sound wave vibration per meter of optical fiber can be obtained.

[0089] DAS-based in-well fracturing monitoring not only has the advantages of high temperature resistance, high pressure resistance, and corrosion resistance, but also can achieve high-density sampling in the entire well section. It is a new type of fracturing monitoring technology. There is a certain functional relationship between distributed fiber optic acoustic wave data and reservoir rock strain (generated by fracturing of the reservoir). By determining this functional relationship, the rock strain of the target reservoir can be predicted based on the distributed fiber optic acoustic wave data. The reservoir rock strain here is the relative change in length or volume of the reservoir rock under stress (fracturing). It means the ratio of the changed shape or volume to the original.

[0090] Step 200: low-pass filtering the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result;

[0091] It is understandable that step 200 is to avoid the Gibbs phenomenon caused by filtering. The Gibbs phenomenon refers to the phenomenon that when a signal with a sudden change at a boundary is subjected to a Fourier series expansion or a Fourier transform in signal processing, the expansion coefficient will oscillate near the boundary. Specifically, when a signal has a sudden change or jump at the boundary, after the Fourier series expansion or the Fourier transform, the expansion coefficient will have obvious oscillations at the boundary, and these oscillations are called Gibbs oscillations. This is caused by the truncation of the Fourier series expansion or the Fourier transform, which introduces additional high-frequency components and cannot perfectly represent the boundary jump of the signal. The manifestation of the Gibbs phenomenon is the appearance of obvious oscillations at the jump, which cannot be completely eliminated by increasing the number of terms in the series expansion or increasing the window size of the transform.

[0092] Step 300: Down-sampling the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data.

[0093] As we all know, the amount of optical fiber signal data is large, so the optical fiber data processing requires high computer computing power. At the same time, the display of processing results requires high computer graphics cards. In short, on the basis of extracting strain data, downsampling processing is also required to improve the calculation effect and display capability. In addition, due to the influence of environmental noise and internal impurities, the optical fiber measurement data of distributed optical fiber monitoring has noise, resulting in a low signal-to-noise ratio of the data. Therefore, while performing downsampling processing, it is also necessary to improve the signal-to-noise ratio of the extracted low-frequency strain response signal of fracturing.

[0094] Step 300 is divided into two cases: the target sampling interval is an integer multiple of the original sampling interval, and the target sampling interval is not an integer multiple of the original sampling interval.

[0095] When the target sampling interval is an integer multiple of the original sampling interval, the filtering result can be downsampled (resampled) by using the alternate bit value method to reduce the data size of the filtering result.

[0096] When the target sampling interval is not an integer multiple of the original sampling interval, the filtering result needs to be upsampled and then downsampled to achieve signal resampling.

[0097] From the above description, it can be seen that the downsampling method of distributed acoustic sensor monitoring data provided in an embodiment of the present invention includes: first, performing spectral analysis on the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range; then, low-pass filtering the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result; finally, downsampling the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data.

[0098] The present invention is aimed at the scenario of distributed optical fiber long-term monitoring acoustic sensor monitoring data, and proposes a high-fidelity downsampling method for massive distributed optical fiber data based on filtering and downsampling. The signal after downsampling of distributed acoustic sensor monitoring data using this method can be closer to the expected signal in the time domain, while the frequency component loss of the signal is small, and the calculation process is simple and efficient, which can reduce the cost of distributed optical fiber data collection storage and processing.

[0099] In some embodiments of the present invention, see Figure 2 , step 100 comprises:

[0100] Step 101: performing Fourier transform on the distributed acoustic sensor monitoring data to generate amplitude spectra of all frequencies of the distributed acoustic sensor monitoring data;

[0101] Fourier transform is used to convert a function (time domain) into another function (frequency domain). It expresses a function as a series of basic sine and cosine functions (sine and cosine components) and shows the intensity and phase of each frequency component. Fourier transform can help understand the spectral characteristics of the signal, and can also facilitate operations such as filtering, compression, matching and reconstruction.

[0102] Step 102: Determine the frequency band range according to the shape of the amplitude spectrum.

[0103] Step 102 includes the following steps during implementation: After Fourier transformation, S(f i ,x m ) is a complex number, and the frequency spectrum of the signal is obtained by modulating it using the following formula.

[0104] Amp(f i ,x m )=sqrt(real(S(f i ,x m )) 2 +imag(S(f i ,x m )) 2 ),i=1,2,…N

[0105] Where Amp(f i ,x m ) represents s(t n ,x m ), real() and imag() represent the real and imaginary parts of the variable respectively.

[0106] By calculating the amplitude spectrum of all frequencies, the frequency band range (spectrum) of the signal can be obtained, and by analyzing the spectrum morphology, the frequency range of the target signal can be obtained. In this application, the frequency band range can be determined by the following methods:

[0107] Observe the main peaks in the amplitude spectrum. These peaks usually represent the main frequency components of the signal. The frequency band range can be determined based on the position and height of the main peaks.

[0108] Calculate the energy distribution of each frequency interval in the amplitude spectrum. If the energy of a frequency interval is relatively high, then the interval may contain important frequency components.

[0109] Observe the relatively high noise levels in the amplitude spectrum and use them as the boundaries of the frequency band range. The noise level can usually be estimated by the average amplitude of the background noise.

[0110] In some embodiments of the present invention, see Figure 3 , step 101 comprises:

[0111] Step 1011: performing Fourier transform on the distributed acoustic sensor monitoring data in the time domain to generate a transformation result;

[0112] Step 1012: Modulo the transformation result to generate the amplitude spectrum.

[0113] Step 1011 and step 1012 include the following contents when implemented: When DAS collects seismic data, sampling points located at different positions in space are collected simultaneously, and the data obtained is two-dimensional data, that is, S(t,x). Fourier transform is performed on the two-dimensional data S(t,x) in the time domain:

[0114]

[0115] Among them, s(t n ,x m ) represents the space x m Position, t n The seismic signal collected at the moment; N is the number of sampling points in the time domain. S(f i ,x m ) is s(t n ,x m )The value of the time-domain Fourier transform.

[0116] In some embodiments of the present invention, see Figure 4 , the downsampling method of distributed acoustic sensor monitoring data also includes:

[0117] Step 400: generating a low-pass filter according to the frequency band range;

[0118] Further, see Figure 5 , step 400 comprises:

[0119] Step 401: determining the cutoff frequency of the low-pass filter according to the frequency band range;

[0120] Based on the frequency range of the target signal, the high frequency of the frequency range is used as the cutoff frequency F of the low-pass filter.

[0121] Step 402: determining a high cutoff frequency of the low-pass filter according to the cutoff frequency;

[0122] In order to avoid the Gibbs phenomenon caused by filtering, it is necessary to set a high cutoff frequency f of the low-pass filter. h , set as follows

[0123] f h =0.9×F

[0124] Step 403: Generate the filter according to the cut-off frequency and the high-cut-off frequency.

[0125] Based on step 401 and step 402, the mathematical expression of the low-pass filter H(f) is:

[0126]

[0127] In some embodiments of the present invention, see Figure 6 , step 401 comprises:

[0128] Step 4011: Determine the cutoff frequency according to the high-value frequency of the frequency band.

[0129] In some embodiments of the present invention, see Figure 7 , step 200 comprises:

[0130] Step 201: performing preliminary low-pass filtering on the distributed acoustic sensor monitoring data according to the low-pass filter and the frequency band range to generate a preliminary filtering result;

[0131] Using the low-pass filter in step 201 and the frequency band of the original signal, the two are multiplied to obtain the low-pass filtered signal S LP (f i ,x m ),

[0132] Step 202: Perform inverse Fourier transform on the preliminary filtering result to generate the filtering result.

[0133] For signal S LP (f i ,x m ) performs inverse Fourier transform to obtain the time domain signal sLP (t n ,x m ).

[0134] S LP (f i ,x m )=H(f i )*S(f i ,x m ),i=1,2,…N

[0135]

[0136] Inverse Fourier transform is the inverse operation of Fourier transform, which is used to convert frequency domain signals back to time domain signals. Fourier transform decomposes time domain signals into a series of frequency components, while inverse Fourier transform resynthesizes these frequency components into the original time domain signal. Fourier transform and inverse Fourier transform are a pair of mutually inverse operations, that is, if a function is first Fourier transformed and then inverse Fourier transformed, the original function itself should be obtained.

[0137] In some embodiments of the present invention, see Figure 8 , step 300 comprises:

[0138] Step 301: when the target sampling interval is an integer multiple of the original sampling interval, downsampling the filtering result according to the target sampling interval;

[0139] Assume that the original time sampling interval of the distributed acoustic sensor monitoring data is δt1, and the time sampling interval after downsampling (i.e., the target sampling interval) is δt2. When δt2 is an integer multiple of δt1, the alternate value method can be used for resampling, that is, one point is taken every k time sampling points. Assume that the original data has a total of N sampling points, and after downsampling, there are a total of N / k sampling points, that is, the data size is reduced to one kth of the original. The specific downsampling expression is:

[0140] s(t q ,x m )=s LP (t n ,x m )

[0141] t q =qδt2

[0142] t n =nδt1

[0143] q=1,2,…N / k

[0144] Among them, s(t q ,x m) is the signal collected at time tq after downsampling at the spatial position xm.

[0145] Step 302: When the target sampling interval is not an integer multiple of the original sampling interval, up-sampling the filtering result to generate an up-sampling result;

[0146] When δt2 is not an integer multiple of δt1, it is necessary to perform upsampling and then downsampling to achieve signal resampling. When upsampling, interpolation is performed using the neighboring interpolation or linear interpolation algorithm. Assuming that the time sampling interval δt3 is the common divisor of δt1 and δt2, the interpolation method is used to interpolate the original data into a signal with a time sampling interval of δt3. The number of samples after interpolation is Q. For details, see the following two formulas:

[0147] s(t q ,x m )=s(t n ,x m )+(t q -t n )*[s(t n+1 ,x m )-s(t n ,x m )] / (δt1)

[0148] t q =q*δt3,q=1,2,…Q

[0149] t n =n*δt1,n=1,2,…N

[0150] Q=(N*δt1 / δt3)

[0151] Step 303: down-sample the up-sampling result.

[0152] Based on step 302, the interpolated s(t q ,x m ) is downsampled, the signal sampling interval is δt3, and the time sampling interval after downsampling is δt2, and δt2 is an integer multiple of δt3. At this time, the two formulas in step 301 can be used to complete the resampling of the signal.

[0153] From the above description, it can be seen that the downsampling method of distributed acoustic sensor monitoring data provided in an embodiment of the present invention includes: first, performing spectral analysis on the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range; then, low-pass filtering the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result; finally, downsampling the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data.

[0154] The present invention has the following beneficial effects over the prior art: the spectrum characteristics of the target signal are taken into account when downsampling the distributed acoustic sensor monitoring data, ensuring that the downsampled signal meets the subsequent processing requirements without signal distortion, and the calculation process is concise and efficient, thereby reducing the cost of distributed optical fiber data acquisition storage and processing.

[0155] Embodiment 2

[0156] To further illustrate the solution, the present invention also provides a specific application example of the downsampling method for distributed acoustic sensor monitoring data, specifically, Fig. 9 As shown, the specific application example includes the following steps:

[0157] S1: Determine the frequency band range of distributed acoustic sensor monitoring data.

[0158] The high sampling rate of DAS makes its data frequency band range very wide. Based on the sampling theorem, a sampling rate of 2KHz can obtain a signal with a frequency range of 0-1000Hz. Since the frequency band range of the effective signal of DAS is different in different application scenarios, it is necessary to clearly monitor the frequency band range of the target signal for different application scenarios to provide a basis for subsequent filtering. For example, when DAS is used in seismic exploration, the data received by the detector in the work area can be subjected to spectral analysis to obtain the frequency band range of the target signal. In this application, the main method of spectral analysis is to perform Fourier transform on the signal and obtain its amplitude spectrum, specifically:

[0159] When DAS collects seismic data, sampling points located at different positions in space are collected simultaneously, and the data obtained is two-dimensional data, that is, S(t,x). The two-dimensional data signal S(t,x) is Fourier transformed in the time domain:

[0160]

[0161] Among them, s(t n ,x m ) represents the space x m Position, t n The seismic signal collected at the moment; N is the number of sampling points in the time domain. S(f i ,x m ) is s(t n ,x m ) is the value of the time domain Fourier transform. After Fourier transform, S(f i ,x m ) is a complex number, and the frequency spectrum of the signal is obtained by modulating it using the following formula.

[0162] Amp(f i ,xm )=sqrt(real(S(f i ,x m )) 2 +imag(S(f i ,x m )) 2 ),i=1,2,…N

[0163] Where Amp(f i ,x m ) represents s(t n ,x m ), real() and imag() represent the real and imaginary parts of the variable respectively. By calculating the amplitude spectrum of all frequencies, the spectrum of the signal can be obtained, and by analyzing the spectrum shape, the frequency range of the target signal can be obtained.

[0164] The optical fiber collected data for 20 seconds with a sampling frequency of 2KHz. The waveform is as follows: Fig.10 The target signal (distributed acoustic sensor monitoring data) is shown as Fig.10 The dotted line shows its spectrum. Fig.11 As shown by the dotted line. Since the original signal has a wide frequency band, for the sake of convenience, only the spectrum within the range of 0-3Hz is shown here.

[0165] S2: Set a low-pass filter according to the frequency band range of the distributed acoustic sensor monitoring data.

[0166] Based on the frequency range of the target signal, the high frequency of the frequency range is used as the cutoff frequency F of the low-pass filter. In order to avoid the Gibbs phenomenon caused by filtering, it is necessary to set a high cutoff frequency f of the low-pass filter. h , set as follows

[0167] f h =0.9×F

[0168] Based on this, the mathematical expression of the low-pass filter H(f) is:

[0169]

[0170] S3: Low-pass filtering the distributed acoustic sensor monitoring data using a low-pass filter and a frequency band range.

[0171] Using the spectrum of the low-pass filter and the original signal involved, multiply the two to obtain the low-pass filtered signal S LP (f i ,x m ), and perform inverse Fourier transform on the signal to obtain the time domain signal s LP (t n ,xm ).

[0172] S LP (f i ,x m )=H(f i )*S(f i ,x m ),i=1,2,…N

[0173]

[0174] from Fig.11 It can be seen that the frequency band of the desired signal is 0-1Hz. The original signal is filtered using a 0-1Hz bandpass filter, and the data obtained is as follows Fig.12 shown.

[0175] S4: Resample the distributed acoustic sensor monitoring data after low-pass filtering.

[0176] Here, resampling mainly refers to downsampling. Suppose the time sampling interval of the original data is δt1, and the time sampling interval after downsampling is δt2. When δt2 is an integer multiple of δt1, the alternate value method can be used for resampling, that is, one point is taken every k time sampling points. Suppose the original data has a total of N sampling points, and after downsampling there are a total of N / k sampling points, that is, the data size is reduced to one kth of the original. The specific downsampling expression is:

[0177] s(t q ,x m )=s LP (t n ,x m )

[0178] t q =qδt2

[0179] t n =nδt1

[0180] q=1,2,…N / k

[0181] Among them, s(t q ,x m ) is the signal collected at time tq after downsampling at the spatial position xm.

[0182] However, when δt2 is not an integer multiple of δt1, it is necessary to perform upsampling and then downsampling to achieve signal resampling. When upsampling, interpolation is performed using the neighboring interpolation or linear interpolation algorithm. Assuming that the time sampling interval δt3 is the common divisor of δt1 and δt2, the original data is interpolated into a signal with a time sampling interval of δt3 using the interpolation method, and the number of samples after interpolation is Q.

[0183] s(t q ,x m )=s(t n ,x m )+(t q -t n )*[s(t n+1 ,x m )-s(t n ,x m )] / (δt1)

[0184] t q =q*δt3,q=1,2,…Q

[0185] t n =n*δt1,n=1,2,…N

[0186] Q=(N*δt1 / δt3)

[0187] For the interpolated s(t q ,x m ) is downsampled, the signal sampling interval is δt3, and the time sampling interval after downsampling is δt2, and δt2 is an integer multiple of δt3. At this time, the signal resampling can be completed using the following two formulas.

[0188] s(t q ,x m )=s LP (t n ,x m )

[0189] t q =qδt2

[0190] t n =nδt1

[0191] q=1,2,…N / k

[0192] Using the method in step S3, Fig.12 The filtered data is downsampled with a sampling interval of 0.5s. Therefore, the downsampled signal has 40 sampling points, each of which is represented by a "*". The waveform is as follows: Fig.12 The spectrum of the downsampled signal is shown in Fig.13 As shown by the dotted line, Fig.13 It can be seen that the spectrum of the downsampled signal has a small error with the expected signal spectrum. Fig.14 Middle for Fig.10 The data after the original signal is downsampled also uses a sampling interval of 0.5s, with a total of 40 sampling points, each of which is represented by a "*". Fig.13It can be seen that the signal after downsampling by this method is similar to the expected signal ( Fig.13 The difference is large. Fig.15 The spectra of the two signals are shown, and it can be seen that the spectrum of the downsampled signal is also far from the spectrum of the expected signal. Therefore, using the downsampling method for distributed acoustic sensor monitoring data provided by this application, the expected signal can be restored with high fidelity and little loss of frequency components.

[0193] From the above description, it can be seen that the downsampling method of distributed acoustic sensor monitoring data provided by the embodiment of the present invention first determines the frequency value of the low-pass filter by analyzing the frequency band response range of the distributed acoustic sensor monitoring data. Then a low-pass filter is set to perform low-pass filtering on the DAS record. Finally, two downsampling processing methods are proposed for the filtered records based on the relationship between the resampling time interval and the original signal time sampling interval, and the downsampled DAS records are obtained.

[0194] The downsampling method for distributed acoustic sensor monitoring data provided by the embodiment of the present invention only retains signals within the target frequency band from the perspective of optimization to reduce the sampling rate and storage cost. At the same time, the massive amount of DAS data will increase the amount of calculation for subsequent processing and interpretation, while the processing cost of the downsampled data is low, thereby improving work efficiency.

[0195] Embodiment 3

[0196] Based on the same principle, this embodiment also discloses a downsampling device for distributed acoustic sensor monitoring data. Fig.16 As shown, the device comprises:

[0197] The frequency band range determination module 10 is used to perform spectrum analysis on the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range;

[0198] A filtering result generating module 20, configured to perform low-pass filtering on the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result;

[0199] The filtering result downsampling module 30 is used to downsample the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data.

[0200] In some embodiments of the invention, the frequency band range determination module includes:

[0201] an amplitude spectrum generating unit, configured to perform Fourier transform on the distributed acoustic sensing monitoring data to generate amplitude spectra of all frequencies of the distributed acoustic sensing monitoring data;

[0202] A frequency band range determining unit is used to determine the frequency band range according to the form of the amplitude spectrum.

[0203] In some embodiments of the invention, the amplitude spectrum generating unit comprises:

[0204] A transformation result generating unit, used for performing Fourier transformation on the distributed acoustic sensor monitoring data in the time domain to generate a transformation result;

[0205] The transformation result modulus unit is used to perform modulus calculation on the transformation result to generate the amplitude spectrum.

[0206] In some embodiments of the invention, the method for downsampling distributed acoustic sensing monitoring data further includes:

[0207] A low-pass filter generation module, used for generating a low-pass filter according to the frequency band range;

[0208] The low-pass filter generation module comprises:

[0209] A cut-off frequency determining unit, configured to determine the cut-off frequency of the low-pass filter according to the frequency band range;

[0210] A high cutoff frequency determining unit, used to determine the high cutoff frequency of the low-pass filter according to the cutoff frequency;

[0211] A low-pass filter generating unit is used to generate the filter according to the cut-off frequency and the high-cut-off frequency.

[0212] In some embodiments of the invention, the cut-off frequency determining unit comprises:

[0213] The cut-off frequency determining subunit determines the cut-off frequency according to a high-value frequency of the frequency band.

[0214] In some embodiments of the invention, the filtering result generation module includes:

[0215] A preliminary filtering result generating unit, configured to perform preliminary low-pass filtering on the distributed acoustic sensor monitoring data according to the low-pass filter and the frequency band range to generate a preliminary filtering result;

[0216] The filtering result generating unit is used to perform inverse Fourier transform on the preliminary filtering result to generate the filtering result.

[0217] In some embodiments of the invention, the filtering result downsampling module includes:

[0218] A filtering result downsampling unit, configured to downsample the filtering result according to the target sampling interval when the target sampling interval is an integer multiple of the original sampling interval;

[0219] A filtering result upsampling unit, configured to upsample the filtering result to generate an upsampled result when the target sampling interval is not an integer multiple of the original sampling interval;

[0220] The up-sampling result down-sampling unit is used to down-sample the up-sampling result.

[0221] From the above description, it can be seen that the downsampling device for distributed acoustic sensor monitoring data provided by the embodiment of the present invention first determines the frequency value of the low-pass filter by analyzing the frequency band response range of the distributed acoustic sensor monitoring data. Then a low-pass filter is set to perform low-pass filtering on the DAS record. Finally, two downsampling processing methods are proposed for the filtered records based on the relationship between the resampling time interval and the original signal time sampling interval, and the downsampled DAS records are obtained.

[0222] In order to improve the working efficiency of DAS and reduce the storage cost of DAS massive data, the present invention is based on low-pass filtering and resampling algorithms. At the same time, according to the situation whether the resampling interval is an integer multiple of the original sampling interval, a method of resampling by combining interpolation and alternate bit value taking is proposed, forming a high-fidelity downsampling device for distributed acoustic sensor monitoring data.

[0223] Embodiment 4

[0224] The embodiment of the present application also provides a specific implementation of an electronic device capable of implementing all steps in the downsampling method of distributed acoustic sensor monitoring data in the above embodiment, see Fig.17 , electronic equipment specifically includes the following:

[0225] Processor (processor) 1201, memory (memory) 1202, communication interface (CommunicationsInterface) 1203 and bus 1204;

[0226] The processor 1201, the memory 1202, and the communication interface 1203 communicate with each other via the bus 1204; the communication interface 1203 is used to realize information transmission between related devices such as server-side devices, computing units, and client devices.

[0227] The processor 1201 is used to call the computer program in the memory 1202. When the processor executes the computer program, all the steps in the method for downsampling the distributed acoustic sensor monitoring data in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0228] Perform spectrum analysis on the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range;

[0229] Performing low-pass filtering on the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result;

[0230] The filtering result is downsampled according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data.

[0231] In some embodiments of the invention, the spectral analysis of the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range includes:

[0232] Performing Fourier transformation on the distributed acoustic sensing monitoring data to generate amplitude spectra of all frequencies of the distributed acoustic sensing monitoring data;

[0233] The frequency band range is determined according to the shape of the amplitude spectrum.

[0234] In some embodiments of the invention, Fourier transform is performed on the distributed acoustic sensing monitoring data to generate amplitude spectra of all frequencies of the distributed acoustic sensing monitoring data, including:

[0235] Performing Fourier transform on the distributed acoustic sensor monitoring data in the time domain to generate a transformation result;

[0236] The transformation result is modulo-measured to generate the amplitude spectrum.

[0237] In some embodiments of the invention, the method for downsampling distributed acoustic sensing monitoring data further includes:

[0238] generating a low-pass filter according to the frequency band range;

[0239] Generating a low-pass filter according to the frequency band range includes:

[0240] Determining the cutoff frequency of the low-pass filter according to the frequency band range;

[0241] Determine a high cutoff frequency of the low-pass filter according to the cutoff frequency;

[0242] The filter is generated according to the cut-off frequency and the high-cut-off frequency.

[0243] In some embodiments of the invention, determining the cutoff frequency of the low-pass filter according to the frequency band range includes:

[0244] The cut-off frequency is determined according to a high-value frequency of the frequency band.

[0245] In some embodiments of the invention, low-pass filtering is performed on the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result, including:

[0246] Performing preliminary low-pass filtering on the distributed acoustic sensor monitoring data according to the low-pass filter and the frequency band range to generate preliminary filtering results;

[0247] Performing inverse Fourier transform on the preliminary filtering result to generate the filtering result.

[0248] In some embodiments of the invention, downsampling the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data includes:

[0249] When the target sampling interval is an integer multiple of the original sampling interval, downsampling the filtering result according to the target sampling interval;

[0250] When the target sampling interval is not an integer multiple of the original sampling interval, upsampling the filtering result to generate an upsampling result;

[0251] Down-sampling is performed on the up-sampling result.

[0252] Embodiment 5

[0253] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the method for downsampling the distributed acoustic sensor monitoring data in the above embodiments. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps in the method for downsampling the distributed acoustic sensor monitoring data in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0254] Perform spectrum analysis on the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range;

[0255] Performing low-pass filtering on the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result;

[0256] The filtering result is downsampled according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data.

[0257] In some embodiments of the invention, the spectral analysis of the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range includes:

[0258] Performing Fourier transformation on the distributed acoustic sensing monitoring data to generate amplitude spectra of all frequencies of the distributed acoustic sensing monitoring data;

[0259] The frequency band range is determined according to the shape of the amplitude spectrum.

[0260] In some embodiments of the invention, Fourier transform is performed on the distributed acoustic sensing monitoring data to generate amplitude spectra of all frequencies of the distributed acoustic sensing monitoring data, including:

[0261] Performing Fourier transform on the distributed acoustic sensor monitoring data in the time domain to generate a transformation result;

[0262] The transformation result is modulo-measured to generate the amplitude spectrum.

[0263] In some embodiments of the invention, the method for downsampling distributed acoustic sensing monitoring data further includes:

[0264] generating a low-pass filter according to the frequency band range;

[0265] Generating a low-pass filter according to the frequency band range includes:

[0266] Determining the cutoff frequency of the low-pass filter according to the frequency band range;

[0267] Determine a high cutoff frequency of the low-pass filter according to the cutoff frequency;

[0268] The filter is generated according to the cut-off frequency and the high-cut-off frequency.

[0269] In some embodiments of the invention, determining the cutoff frequency of the low-pass filter according to the frequency band range includes:

[0270] The cut-off frequency is determined according to a high-value frequency of the frequency band.

[0271] In some embodiments of the invention, low-pass filtering is performed on the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result, including:

[0272] Performing preliminary low-pass filtering on the distributed acoustic sensor monitoring data according to the low-pass filter and the frequency band range to generate preliminary filtering results;

[0273] Performing inverse Fourier transform on the preliminary filtering result to generate the filtering result.

[0274] In some embodiments of the invention, downsampling the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data includes:

[0275] When the target sampling interval is an integer multiple of the original sampling interval, downsampling the filtering result according to the target sampling interval;

[0276] When the target sampling interval is not an integer multiple of the original sampling interval, upsampling the filtering result to generate an upsampling result;

[0277] Down-sampling is performed on the up-sampling result.

[0278] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0279] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0280] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0281] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0282] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0283] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0284] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for downsampling distributed acoustic sensor monitoring data, characterized in that: include: Perform spectrum analysis on the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range; Performing low-pass filtering on the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result; The filtering result is downsampled according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data.

2. The downsampling method according to claim 1, characterized in that: The performing spectrum analysis on the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range includes: Performing Fourier transformation on the distributed acoustic sensing monitoring data to generate amplitude spectra of all frequencies of the distributed acoustic sensing monitoring data; The frequency band range is determined according to the shape of the amplitude spectrum.

3. The downsampling method according to claim 2, characterized in that: Performing Fourier transform on the distributed acoustic sensing monitoring data to generate amplitude spectra of all frequencies of the distributed acoustic sensing monitoring data, including: Performing Fourier transform on the distributed acoustic sensor monitoring data in the time domain to generate a transformation result; The transformation result is modulo-measured to generate the amplitude spectrum.

4. The downsampling method according to claim 1, characterized in that: Also includes: generating a low-pass filter according to the frequency band range; Generating a low-pass filter according to the frequency band range includes: Determining the cutoff frequency of the low-pass filter according to the frequency band range; Determine a high cutoff frequency of the low-pass filter according to the cutoff frequency; The filter is generated according to the cut-off frequency and the high-cut-off frequency.

5. The downsampling method according to claim 4, characterized in that: Determining the cutoff frequency of the low-pass filter according to the frequency band range includes: The cut-off frequency is determined according to a high-value frequency of the frequency band.

6. The downsampling method according to any one of claims 4 to 5, characterized in that: The distributed acoustic sensor monitoring data is low-pass filtered according to the frequency band range to generate a filtering result, including: Performing preliminary low-pass filtering on the distributed acoustic sensor monitoring data according to the low-pass filter and the frequency band range to generate preliminary filtering results; Performing inverse Fourier transform on the preliminary filtering result to generate the filtering result.

7. The downsampling method according to any one of claims 1 to 3, characterized in that: The downsampling of the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data includes: When the target sampling interval is an integer multiple of the original sampling interval, downsampling the filtering result according to the target sampling interval; When the target sampling interval is not an integer multiple of the original sampling interval, upsampling the filtering result to generate an upsampling result; Down-sampling is performed on the up-sampling result.

8. A downsampling device for distributed acoustic sensor monitoring data, characterized in that: include: A frequency band range determination module is used to perform spectrum analysis on the pre-received distributed acoustic sensor monitoring data of the target work area to determine its frequency band range; A filtering result generating module, used for performing low-pass filtering on the distributed acoustic sensor monitoring data according to the frequency band range to generate a filtering result; The filtering result downsampling module is used to downsample the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensor monitoring data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images