Down-sampling method and device for distributed acoustic sensing monitoring data

Abstract

The application provides a distributed acoustic sensing monitoring data downsampling method and device, and a corresponding method comprises the following steps: performing spectrum analysis on the pre-received distributed acoustic sensing monitoring data of a target work area to determine the frequency band range thereof; performing low-pass filtering on the distributed acoustic sensing monitoring data according to the frequency band range to generate a filtering result; and performing downsampling on the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensing monitoring data. The signal of the distributed acoustic sensing monitoring data after being downsampled by using the distributed acoustic sensing monitoring data downsampling method provided by the application can be close to the expected signal in the time domain, the frequency component loss of the signal is small, the calculation process is simple and efficient, and the cost of distributed optical fiber data collection, storage and processing is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic data processing in oil and gas field development, in particular to the technical field of distributed acoustic sensing monitoring data processing of oil and gas field fracturing, and especially relates to a distributed acoustic sensing monitoring data downsampling method and device. BACKGROUND

[0002] In recent years, distributed fiber acoustic sensing (DAS) technology has been widely applied in the fields of seismic exploration, coal mine safety, oil production, pipeline safety monitoring, etc. The principle is that when the surface of the measured object vibrates, the optical fiber laid on the surface of the object deforms, and DAS determines the position of the deformation of the measured object to realize anomaly detection through optical time domain reflection technology.

[0003] DAS monitoring technology has the advantage of wide frequency 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 spatial and high temporal data sampling leads to large distributed fiber data volume, which poses a challenge to data storage.

[0004] In the prior art, distributed fiber storage often uses large storage devices such as nas disks, and the optical fiber data is not processed by a data processing method during storage. It can be understood that the high sampling frequency of the optical fiber ensures the wide frequency information of the collected signal, so the application scenarios are wide. However, for specific application scenarios, such as seismic exploration, the DAS data used can have a frequency of up to 200Hz. In this case, due to the large volume of distributed acoustic sensing monitoring data and the presence of a large amount of useless information and noise inside, it is difficult to store the distributed acoustic sensing monitoring data. SUMMARY

[0005] One object of the present application is to provide a distributed acoustic sensing monitoring data downsampling method. The method is based on high sampling data of the optical fiber, and combines a filtering method and a sampling method to propose a high-fidelity downsampling method for distributed acoustic sensing monitoring data.

[0006] Another object of the present application is to provide a device for down-sampling distributed acoustic sensing monitoring data. Another object of the present application is to provide a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the method for down-sampling distributed acoustic sensing monitoring data when executing the computer program. Another object of the present application is to provide a readable medium storing a computer program, the computer program implementing the steps of the method for down-sampling distributed acoustic sensing monitoring data when executed by a processor.

[0007] To achieve the above objects, the present application discloses a method for down-sampling distributed acoustic sensing monitoring data, comprising:

[0008] spectrum analyzing the pre-received distributed acoustic sensing monitoring data of the target work area to determine a frequency band range thereof;

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

[0010] down-sampling the filtering result according to a proportional relationship between a target sampling interval and an original sampling interval of the distributed acoustic sensing monitoring data.

[0011] In some embodiments of the present application, the spectrum analyzing the pre-received distributed acoustic sensing monitoring data of the target work area to determine a frequency band range thereof, comprises:

[0012] Fourier transforming the distributed acoustic sensing monitoring data to generate an amplitude spectrum of all frequencies of the distributed acoustic sensing monitoring data;

[0013] determining the frequency band range according to a shape of the amplitude spectrum.

[0014] In some embodiments of the present application, the Fourier transforming the distributed acoustic sensing monitoring data to generate an amplitude spectrum of all frequencies of the distributed acoustic sensing monitoring data, comprises:

[0015] Fourier transforming the distributed acoustic sensing monitoring data in time domain to generate a transformation result;

[0016] modularizing the transformation result to generate the amplitude spectrum.

[0017] In some embodiments of the present application, the method for down-sampling distributed acoustic sensing monitoring data further comprises:

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

[0019] The generating a low-pass filter according to the frequency band range, comprises:

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

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

[0022] generating the filter according to the cutoff frequency and the high cutoff frequency.

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

[0024] determining the cutoff frequency according to a high value frequency of the frequency band range.

[0025] In some embodiments of the application, low-pass filtering the distributed acoustic sensing monitoring data according to the frequency band range to generate a filtering result comprises:

[0026] preliminarily low-pass filtering the distributed acoustic sensing monitoring data according to the low-pass filter and the frequency band range to generate a preliminary filtering result;

[0027] inverse Fourier transforming the preliminary filtering result to generate the filtering result.

[0028] In some embodiments of the application, downsampling the filtering result according to a proportional relationship between a target sampling interval and an original sampling interval of the distributed acoustic sensing monitoring data comprises:

[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 upsampled result;

[0031] downsampling the upsampled result.

[0032] The application further discloses a downsampling device for distributed acoustic sensing monitoring data, comprising:

[0033] a frequency band range determination module configured to perform spectral analysis on pre-received distributed acoustic sensing monitoring data of a target work area to determine a frequency band range thereof;

[0034] a filtering result generation module configured to low-pass filter the distributed acoustic sensing monitoring data according to the frequency band range to generate a filtering result;

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

[0036] In some embodiments of the application, the frequency band range determining module comprises:

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

[0038] The frequency band range determining unit is configured to determine the frequency band range according to a shape of the amplitude spectrum.

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

[0040] The transform result generating unit is configured to perform Fourier transform on the distributed acoustic sensing monitoring data in a time domain to generate a transform result.

[0041] The transform result modulus calculating unit is configured to calculate a modulus of the transform result to generate the amplitude spectrum.

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

[0043] The low-pass filter generating module is configured to generate a low-pass filter according to the frequency band range.

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

[0045] The cutoff frequency determining unit is configured to determine a cutoff frequency of the low-pass filter according to the frequency band range.

[0046] The high cutoff frequency determining unit is configured to determine a high cutoff frequency of the low-pass filter according to the cutoff frequency.

[0047] The low-pass filter generating unit is configured to generate the filter according to the cutoff frequency and the high cutoff frequency.

[0048] In some embodiments of the application, the cutoff frequency determining unit comprises:

[0049] The cutoff frequency determining subunit is configured to determine the cutoff frequency according to a high value frequency of the frequency band range.

[0050] In some embodiments of the application, the filtering result generating module comprises:

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

[0052] A filtering result generation unit is configured to perform inverse Fourier transform on the preliminary filtering result, to generate the filtering result.

[0053] In some embodiments, the filtering result downsampling module comprises:

[0054] A filtering result downsampling unit is configured to perform downsampling on 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 is configured to perform upsampling on the filtering result, to generate an upsampled result, when the target sampling interval is not an integer multiple of the original sampling interval.

[0056] An upsampled result downsampling unit is configured to perform downsampling on the upsampled result.

[0057] The application further discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor,

[0058] The processor implements the method as described above when executing the program.

[0059] The application further discloses a computer readable medium, having a computer program stored thereon,

[0060] The program is executed by the processor to implement the method as described above.

[0061] As can be seen from the above description, the distributed acoustic sensing monitoring data downsampling method and device provided by the embodiments of the application correspond to a method comprising: firstly performing spectrum analysis on the pre-received distributed acoustic sensing monitoring data of a target work area, to determine the frequency band range thereof; then performing low-pass filtering on the distributed acoustic sensing monitoring data according to the frequency band range, to generate a filtering result; and finally performing downsampling on the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensing monitoring data.

[0062] The application proposes a high-fidelity downsampling method and device for massive distributed optical fiber data based on filtering and downsampling, for the scenario of distributed optical fiber long-term monitoring acoustic sensing monitoring data. The signal obtained by downsampling the distributed acoustic sensing monitoring data using the method and device can be close to the expected signal in the time domain, 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 acquisition, storage and processing. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0064] Figure 1 A flowchart of a distributed acoustic sensing monitoring data downsampling method in Embodiment One of the present application;

[0065] Figure 2 A flowchart of step 100 in the distributed acoustic sensing monitoring data downsampling method in Embodiment One of the present application;

[0066] Figure 3 A flowchart of step 101 in the distributed acoustic sensing monitoring data downsampling method in Embodiment One of the present application;

[0067] Figure 4 Another flowchart of the distributed acoustic sensing monitoring data downsampling method in Embodiment One of the present application;

[0068] Figure 5 A flowchart of step 400 in the distributed acoustic sensing monitoring data downsampling method in Embodiment One of the present application;

[0069] Figure 6 A flowchart of step 401 in the distributed acoustic sensing monitoring data downsampling method in Embodiment One of the present application;

[0070] Figure 7 A flowchart of step 200 in the distributed acoustic sensing monitoring data downsampling method in Embodiment One of the present application;

[0071] Figure 8 A flowchart of step 300 in the distributed acoustic sensing monitoring data downsampling method in Embodiment One of the present application;

[0072] Figure 9 A flowchart of the distributed acoustic sensing monitoring data downsampling method in Embodiment Two of the present application;

[0073] Figure 10 A schematic diagram of a time-domain waveform of an original signal and a target signal in Embodiment Two of the present application;

[0074] Figure 11 A schematic diagram of a frequency spectrum of an original signal and a target signal in Embodiment Two of the present application;

[0075] Figure 12 A schematic diagram of the down-sampled data and low-pass filtered data in Embodiment Two of the present application;

[0076] Figure 13 A schematic diagram of the down-sampled data and low-pass filtered data in Embodiment Two of the present application;

[0077] Figure 14 A schematic diagram of the down-sampled data and low-pass filtered data in Embodiment Two of the present application;

[0078] Figure 15 A schematic diagram of the frequency spectrum of the down-sampled data and low-pass filtered data in Embodiment Two of the present application;

[0079] Figure 16 A structural schematic diagram of a down-sampling device for distributed acoustic sensing monitoring data in Embodiment Three of the present application;

[0080] Figure 17 A structural schematic diagram of an electronic device in Embodiment Four of the present application. DETAILED DESCRIPTION

[0081] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0082] It should be noted that the terms “include” and “have” and any variations thereof in the specification and claims of the present application and the above-described drawings are intended to cover the inclusion not the exclusion of, for example, a process, method, system, product or device containing a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices. The embodiments in the present application and the features in the embodiments can be combined with each other without conflict. 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, processing and the like of data in the technical solutions of the present application all comply with the relevant provisions of laws and regulations.

[0084] Embodiment One

[0085] In this embodiment, as shown in Figure 1 , a down-sampling method for distributed acoustic sensing monitoring data is provided, which comprises:

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

[0087] The principle of the distributed optical fiber acoustic wave sensing technology is to detect signals such as sound or vibration in the audio frequency range by using the phase of coherent Rayleigh scattering light instead of light intensity. Not only can the phase amplitude be used to provide sound or vibration event intensity information, but also linear quantitative measurement values can be used to obtain sound or vibration event phase and frequency information. DAS can be considered as a mobile interference acoustic sensor detecting external signals on a sensing optical fiber. When sound or vibration causes linear changes in the phase of the interference light at that position, quantitative measurement of external physical quantities can be achieved by extracting interference signals at different times at that position and demodulating.

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

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

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

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

[0092] Step 300: According to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensing monitoring data, the filtering result is down-sampled.

[0093] It is known that the amount of optical fiber signal data is large, so the optical fiber data processing requires high computer operation capability, and the display of the processing result requires high computer graphics card; in short, on the basis of extracting strain data, down-sampling processing is also needed 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 has noise, resulting in low signal-to-noise ratio of the data; therefore, while performing the down-sampling processing, the signal-to-noise ratio of the extracted low-frequency strain response signal of the fracturing also needs to be improved.

[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 down-sampling (re-sampling) of the filtering result can be performed by the every-other-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 up-sampled and then down-sampled to realize the re-sampling of the signal.

[0097] As can be seen from the above description, the down-sampling method of the distributed acoustic sensing monitoring data provided by the embodiment of the application comprises the following steps: firstly, the distributed acoustic sensing monitoring data of a target work area pre-received is subjected to frequency spectrum analysis to determine the frequency band range thereof; then, the distributed acoustic sensing monitoring data is subjected to low-pass filtering according to the frequency band range to generate a filtering result; finally, the filtering result is down-sampled according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensing monitoring data.

[0098] The application is directed to the scene of distributed optical fiber long-term monitoring acoustic sensing monitoring data, and proposes a high-fidelity down-sampling method for massive distributed optical fiber data based on filtering and down-sampling. The signal obtained by using the method to down-sample the distributed acoustic sensing monitoring data can be close to the expected signal in the time domain, the frequency component loss of the signal is small, and the calculation process is simple and efficient, thereby reducing the cost of distributed optical fiber data acquisition, storage and processing.

[0099] In some embodiments of the application, referring to Figure 2 , step 100 comprises:

[0100] Step 101: Fourier transforming the distributed acoustic sensing monitoring data to generate an amplitude spectrum of all frequencies of the distributed acoustic sensing monitoring data;

[0101] Fourier transform is used to convert one 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 strength and phase of each frequency component. Fourier transform can help understand the spectral characteristics of the signal, and can also facilitate filtering, compression, matching and reconstruction, etc.

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

[0103] Step 102 includes the following in specific implementation: after Fourier transform, S(f i ,x m ) is a complex number, and the modulus thereof is obtained by using the following formula to obtain the frequency spectrum of the signal.

[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] Wherein Amp(f i ,x m ) represents the amplitude spectrum of s(t n ,x m ), and real() and imag() represent the real part and the imaginary part of the variable, respectively.

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

[0107] Observe the main peaks in the amplitude spectrum, which usually represent the main frequency components of the signal. According to the position and height of the main peaks, the frequency band range can be determined.

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

[0109] Observe the relatively high noise level in the amplitude spectrum and use it as the boundary 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 application, referring to Figure 3 , step 101 includes:

[0111] Step 1011: Fourier transform the distributed acoustic sensing monitoring data in the time domain to generate a transformed result;

[0112] Step 1012: Modulus the transformed result to generate the amplitude spectrum.

[0113] Step 1011 and step 1012 include the following in specific implementation: when the DAS performs seismic data acquisition, the sampling points at different spatial positions perform simultaneous acquisition, and the obtained data is two-dimensional data, i.e., S(t, x). Fourier transform the two-dimensional data S(t, x) in the time domain:

[0114]

[0115] where s(t n ,x m ) represents the seismic signal collected at time t n at spatial position x m ; N is the number of time domain sampling points. S(f i ,x m ) is the value of the time domain Fourier transform of s(t n ,x m ).

[0116] In some embodiments of the present application, referring to Figure 4 , the down-sampling method of the distributed acoustic sensing monitoring data further includes:

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

[0118] Further, referring to Figure 5 , step 400 comprises:

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

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

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

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

[0123] f h = 0.9 x F

[0124] Step 403: generating the filter according to the cutoff frequency and the high cutoff 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 application, referring to Figure 6 , step 401 comprises:

[0128] Step 4011: determining the cutoff frequency according to the high value frequency of the frequency range.

[0129] In some embodiments of the present application, referring to Figure 7 , step 200 comprises:

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

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

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

[0133] Inverse Fourier transform is performed on the signal S LP (f i ,x m ) 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 the frequency domain signal back to the time domain signal. Fourier transform decomposes the time domain signal into a series of frequency components, while inverse Fourier transform recombines these frequency components into the original time domain signal. Fourier transform and inverse Fourier transform are a pair of inverse operations, that is, if a function is first Fourier transformed and then inverse Fourier transformed, the original function should be obtained

[0137] In some embodiments of the present application, referring to Figure 8 , step 300 comprises:

[0138] Step 301: When the target sampling interval is an integer multiple of the original sampling interval, the filtered result is down-sampled according to the target sampling interval.

[0139] Let the original distributed acoustic sensing monitoring data time sampling interval be δt1, and the time sampling interval after down-sampling (i.e. the target sampling interval) be δt2. When δt2 is an integer multiple of δt1, the every-k-value-taking method can be used for resampling, that is, one point is taken every k time sampling points. Let the original data have N sampling points, and after down-sampling, there are N / k sampling points, that is, the data size is reduced to k times of the original. The specific down-sampling 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] wherein s(t q ,x m) for the signal collected at time tq after down-sampling at 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, up-sampling is needed before down-sampling to realize the resampling of the signal. In up-sampling, interpolation is performed by using the adjacent interpolation or linear interpolation algorithm. Let the greatest common divisor of δt1 and δt2 be δt3, then the original data is interpolated into a signal with a time sampling interval of δt3 by using the interpolation method, and the number of interpolated samples is Q, which can be seen from 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-sampling the up-sampling result.

[0152] On the basis of step 302, the interpolated s(t q ,x m ) is down-sampled, the signal sampling interval of which is δt3, and the time sampling interval after down-sampling is δt2, which is an integer multiple of δt3. At this time, the resampling of the signal can be completed by using the two formulas in step 301.

[0153] From the above description, it can be seen that the down-sampling method for distributed acoustic sensing monitoring data provided by the embodiment of the application comprises the following steps: firstly, performing frequency spectrum analysis on the pre-received distributed acoustic sensing monitoring data of the target work area to determine the frequency band range thereof; secondly, performing low-pass filtering on the distributed acoustic sensing monitoring data according to the frequency band range to generate a filtering result; and finally, performing down-sampling on the filtering result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensing monitoring data.

[0154] The present invention has the following beneficial effects over the existing technology: the target signal spectrum characteristics 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 simple and efficient, thereby reducing the cost of distributed optical fiber data collection storage and processing.

[0155] Example 2

[0156] In order to further illustrate the solution, the present invention also provides a specific application example of the downsampling method of distributed acoustic sensor monitoring data, specifically, Figure 9 As shown, this specific application example includes the following steps:

[0157] S1: Determine the frequency band range of distributed acoustic sensing 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 at different locations in space are collected simultaneously, and the data obtained is two-dimensional data, namely 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 following formula is used to modulate it to obtain the spectrum of the signal.

[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 the amplitude spectrum of s(t n ,x m ), and real() and imag() represent the real part and the imaginary part of a variable, respectively. By calculating the amplitude spectrum of all frequencies, the frequency spectrum of the signal can be obtained, and by analyzing the spectrum pattern, the frequency range of the target signal can be obtained.

[0164] Suppose the optical fiber collects a 20-second-long data with a sampling frequency of 2 KHz, and the waveform is shown by the black solid line in FIG. 1. The target signal (distributed acoustic sensing monitoring data) is shown by the dashed line in FIG. 1, and its spectrum is shown by the dashed line in FIG. 2. Since the frequency band range of the original signal is wide, for the convenience of display, only the spectrum in the range of 0-3 Hz is shown here. Figure 10 Figure 10 Figure 11

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

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

[0167] f h = 0.9 x F

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

[0169]

[0170] S3: Perform low-pass filtering on the distributed acoustic sensing monitoring data using the low-pass filter and the frequency band range.

[0171] Using the low-pass filter involved and the frequency spectrum of the original signal, the two are multiplied to obtain the low-pass filtered signal S LP (f i ,x m ), and the signal is inverse Fourier transformed to obtain the time domain signal s LP (t n ,x​​​m ).

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

[0173]

[0174] From Figure 11 , it can be seen that the frequency band range of the desired signal is 0-1Hz. The original signal is filtered by using a band-pass filter of 0-1Hz, and the obtained data is shown in Figure 12 .

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

[0176] Here, resampling mainly refers to downsampling processing. Let the original data time sampling interval be δt1, and the time sampling interval after downsampling be δt2. When δt2 is an integer multiple of δt1, the resampling can be performed by using the method of taking a point every k time sampling points. Let the original data have N sampling points, and after downsampling, there are N / k sampling points, that is, the data size is reduced to k times 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] Where s(t q ,x m ) is the signal collected at time tq after downsampling at spatial position xm.

[0182] But when δt2 is not an integer multiple of δt1, it is necessary to first upsample and then downsample to realize the resampling of the signal. In upsampling, interpolation is performed by using the adjacent interpolation or linear interpolation algorithm. Let the time sampling interval δt3 be the greatest common divisor of δt1 and δt2, then by using the interpolation method, the original data is interpolated into a signal with a time sampling interval of δt3, and the number of interpolated sampling points 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, where δt2 is an integer multiple of δt3. At this time, the following two equations can be used to complete the signal resampling.

[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, Figure 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 "*". The waveform is as follows: Figure 12 The spectrum of the downsampled signal is as shown in Figure 13 As shown by the dotted line, Figure 13 It can be seen from the figure that the spectrum of the downsampled signal has a small error with the expected signal spectrum. Figure 14 Middle is right Figure 10 The data after downsampling the original signal in the image is also sampled at a 0.5s sampling interval, with a total of 40 points. Each sampling point is represented by an "*". Figure 13As can be seen in the figure, the down-sampled signal is quite different from the expected signal (solid line). Figure 13 As can be seen in the figure, the down-sampled signal is quite different from the expected signal (solid line). Figure 15 As can be seen in the figure, the down-sampled signal is quite different from the expected signal (solid line).

[0193] As can be seen in the figure, the down-sampled signal is quite different from the expected signal (solid line).

[0194] The down-sampling method for distributed acoustic sensing monitoring data provided by the embodiments of the present application, from the perspective of optimization, only retains the signals in the target frequency band range, so as to reduce the sampling rate and storage cost. Meanwhile, the massive DAS data will increase the calculation amount for subsequent processing and interpretation, and the data after down-sampling has low processing cost, so as to improve the work efficiency.

[0195] Embodiment Three

[0196] Based on the same principle, the present embodiment further discloses a down-sampling device for distributed acoustic sensing monitoring data. As shown in the figure, the device comprises: Figure 16 A frequency band range determination module 10, configured to perform spectrum analysis on the pre-received distributed acoustic sensing monitoring data of the target work area, so as to determine the frequency band range thereof;

[0197] A filter result generation module 20, configured to perform low-pass filtering on the distributed acoustic sensing monitoring data according to the frequency band range, so as to generate a filter result;

[0198] A filter result down-sampling module 30, configured to perform down-sampling on the filter result according to the proportional relationship between the target sampling interval and the original sampling interval of the distributed acoustic sensing monitoring data.

[0199] In some embodiments of the present application, the frequency band range determination module comprises:

[0200] An amplitude spectrum generation unit, configured to perform Fourier transform on the distributed acoustic sensing monitoring data, so as to generate the amplitude spectrum of all frequencies of the distributed acoustic sensing monitoring data;

[0201]

[0202] ​A frequency band range determination unit is configured to determine the frequency band range according to a shape of the amplitude spectrum.

[0203] In some embodiments of the application, the amplitude spectrum generation unit comprises:

[0204] A transform result generation unit is configured to perform Fourier transform on the distributed acoustic sensing monitoring data in a time domain to generate a transform result.

[0205] A transform result modulus operation unit is configured to perform modulus operation on the transform result to generate the amplitude spectrum.

[0206] In some embodiments of the application, the downsampling method of the distributed acoustic sensing monitoring data further comprises:

[0207] A low-pass filter generation module is configured to generate a low-pass filter according to the frequency band range.

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

[0209] A cutoff frequency determination unit is configured to determine a cutoff frequency of the low-pass filter according to the frequency band range.

[0210] A high cutoff frequency determination unit is configured to determine a high cutoff frequency of the low-pass filter according to the cutoff frequency.

[0211] A low-pass filter generation unit is configured to generate the filter according to the cutoff frequency and the high cutoff frequency.

[0212] In some embodiments of the application, the cutoff frequency determination unit comprises:

[0213] A cutoff frequency determination subunit is configured to determine the cutoff frequency according to a high value frequency of the frequency band range.

[0214] In some embodiments of the application, the filter result generation module comprises:

[0215] A preliminary filter result generation unit is configured to perform preliminary low-pass filtering on the distributed acoustic sensing monitoring data according to the low-pass filter and the frequency band range to generate a preliminary filter result.

[0216] A filter result generation unit is configured to perform inverse Fourier transform on the preliminary filter result to generate the filter result.

[0217] In some embodiments of the application, the filter result downsampling module comprises:

[0218] A filter result downsampling unit is configured to perform downsampling on the filter 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 up-sampling unit, configured to up-sample the filtering result to generate an up-sampled result when the target sampling interval is not an integer multiple of the original sampling interval;

[0220] a up-sampled result down-sampling unit, configured to down-sample the up-sampled result.

[0221] From the above description, the distributed acoustic sensing monitoring data down-sampling device provided by the embodiments of the present application first determines the frequency value of the low-pass filter by analyzing the frequency band response range of the distributed acoustic sensing monitoring data. Then, a low-pass filter is set to low-pass filter the DAS record. Finally, two down-sampling processing methods are proposed for the relationship between the resampling time interval and the original signal time sampling interval, respectively, and the down-sampled DAS record is obtained.

[0222] In order to improve the working efficiency of DAS and reduce the storage cost of massive DAS data, the present application is based on low-pass filtering and resampling algorithm, and simultaneously proposes a method of using interpolation and every other value to jointly resample for the case of whether the resampling interval is an integer multiple of the original sampling interval, forming a high-fidelity down-sampling device for distributed acoustic sensing monitoring data.

[0223] Embodiment Four

[0224] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps of the distributed acoustic sensing monitoring data down-sampling method in the above embodiments, referring to Figure 17 , the electronic device specifically includes the following contents:

[0225] a processor 1201, a memory 1202, a communications interface 1203 and a bus 1204;

[0226] The processor 1201, the memory 1202 and the communications interface 1203 communicate with each other through the bus 1204; the communications interface 1203 is used to realize the information transmission between the server-side device, the computing unit and the client-side device and other related devices.

[0227] The processor 1201 is used to call the computer program in the memory 1202, and the processor executes the computer program to realize all steps of the distributed acoustic sensing monitoring data down-sampling method in the above embodiments, for example, the processor executes the computer program to realize the following steps:

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

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

[0230] down-sampling the filtered result according to a proportional relationship between a target sampling interval and an original sampling interval of the distributed acoustic sensing monitoring data.

[0231] In some embodiments of the application, the determining the frequency band range of the pre-received distributed acoustic sensing monitoring data of the target work area includes:

[0232] Fourier transforming the distributed acoustic sensing monitoring data to generate an amplitude spectrum of all frequencies of the distributed acoustic sensing monitoring data;

[0233] determining the frequency band range according to a shape of the amplitude spectrum.

[0234] In some embodiments of the application, the Fourier transforming the distributed acoustic sensing monitoring data to generate an amplitude spectrum of all frequencies of the distributed acoustic sensing monitoring data includes:

[0235] Fourier transforming the distributed acoustic sensing monitoring data in time domain to generate a transformed result;

[0236] modularizing the transformed result to generate the amplitude spectrum.

[0237] In some embodiments of the application, the method for down-sampling the 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 a cut-off frequency of the low-pass filter according to the frequency band range;

[0241] determining a high cut-off frequency of the low-pass filter according to the cut-off frequency;

[0242] generating the filter according to the cut-off frequency and the high cut-off frequency.

[0243] In some embodiments of the application, the determining a cut-off frequency of the low-pass filter according to the frequency band range includes:

[0244] determining the cut-off frequency according to a high value frequency of the frequency band range.

[0245] In some embodiments of the application, the low-pass filtering the distributed acoustic sensing monitoring data according to the frequency band range comprises:

[0246] preliminarily low-pass filtering the distributed acoustic sensing monitoring data according to the low-pass filter and the frequency band range to generate a preliminary filtering result;

[0247] inverse Fourier transforming the preliminary filtering result to generate the filtering result.

[0248] In some embodiments of the application, the downsampling the filtering result according to a proportional relationship between a target sampling interval and an original sampling interval of the distributed acoustic sensing monitoring data comprises:

[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 upsampled result;

[0251] downsampling the upsampled result.

[0252] Embodiment Five

[0253] Embodiments of the application also provide a computer-readable storage medium capable of implementing all steps of the downsampling method of the distributed acoustic sensing 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 steps of the downsampling method of the distributed acoustic sensing monitoring data in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0254] spectrum analyzing the pre-received distributed acoustic sensing monitoring data of the target work area to determine a frequency band range thereof;

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

[0256] downsampling the filtering result according to a proportional relationship between a target sampling interval and an original sampling interval of the distributed acoustic sensing monitoring data.

[0257] In some embodiments of the application, the spectrum analyzing the pre-received distributed acoustic sensing monitoring data of the target work area to determine a frequency band range thereof comprises:

[0258] performing Fourier transform on the distributed acoustic sensing monitoring data to generate an amplitude spectrum of all frequencies of the distributed acoustic sensing monitoring data;

[0259] determining the frequency band range according to a shape of the amplitude spectrum.

[0260] In some embodiments of the application, performing Fourier transform on the distributed acoustic sensing monitoring data to generate an amplitude spectrum of all frequencies of the distributed acoustic sensing monitoring data comprises:

[0261] performing Fourier transform on the distributed acoustic sensing monitoring data in time domain to generate a transform result;

[0262] performing modulus operation on the transform result to generate the amplitude spectrum.

[0263] In some embodiments of the application, the method for down-sampling distributed acoustic sensing monitoring data further comprises:

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

[0265] generating a low-pass filter according to the frequency band range comprises:

[0266] determining a cut-off frequency of the low-pass filter according to the frequency band range;

[0267] determining a high cut-off frequency of the low-pass filter according to the cut-off frequency;

[0268] generating the filter according to the cut-off frequency and the high cut-off frequency.

[0269] In some embodiments of the application, determining a cut-off frequency of the low-pass filter according to the frequency band range comprises:

[0270] determining the cut-off frequency according to a high value frequency of the frequency band range.

[0271] In some embodiments of the application, performing low-pass filtering on the distributed acoustic sensing monitoring data according to the frequency band range to generate a filtering result comprises:

[0272] performing preliminary low-pass filtering on the distributed acoustic sensing monitoring data according to the low-pass filter and the frequency band range to generate a preliminary filtering result;

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

[0274] In some embodiments of the application, the down-sampling the filtering result according to a proportional relationship between a target sampling interval and an original sampling interval of the distributed acoustic sensing monitoring data comprises:

[0275] downsample the filtering result according to the target sampling interval when the target sampling interval is an integer multiple of the original sampling interval;

[0276] upsample the filtering result to generate an upsampled result when the target sampling interval is not an integer multiple of the original sampling interval;

[0277] downsample the upsampled result.

[0278] Each of the embodiments in the specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other. Each of the embodiments focuses on the difference from other embodiments. In particular, for the hardware + program type embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the related parts can be referred to the part of the method embodiments.

[0279] The above describes specific embodiments of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different than the order in which the actions or steps are recited in the embodiments and still achieve the desired result. In addition, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0280] Those skilled in the art will appreciate that embodiments of the present application can be devised for use with various computer system configurations, including hand-held devices, microcomputer systems, minicomputer systems, mainframe computer systems and the like. Embodiments of the present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.

[0281] The present application is described with reference to the drawings in which are shown flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing device or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowcharts and / or block diagrams block or blocks. Figure 1 one or more functions specified by one or more blocks Figure 1 means for carrying out each of the one or more functions specified by the one or more blocks

[0282] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0283] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0284] The principles and implementations of the present application are described in the specific embodiments, the above description of the embodiments is only for the purpose of helping to understand the method of the present application and its core idea; at the same time, for the general skilled in the art, according to the idea of the present application, there will be changes in the specific implementation and application range, and the above description of the present application should not be understood as the limitation of the present application.

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 sensing monitoring data according to the frequency band range to generate a filtering result; downsampling the filtering result according to a proportional relationship between a target sampling interval and an original sampling interval of the distributed acoustic sensing monitoring data; 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: 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; Downsampling is performed on the upsampling result.

2. The downsampling method according to claim 1, wherein: The performing of 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 transform 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, wherein: 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 sensing monitoring data in the time domain to generate a transformation result; The transformation result is modulo-ed to generate the amplitude spectrum.

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

5. The downsampling method according to claim 4, wherein: Determining the cutoff frequency of the low-pass filter according to the frequency band range includes: The cutoff 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: Low-pass filtering is performed on the distributed acoustic sensing monitoring data according to the frequency band range to generate a filtering result, including: Performing preliminary low-pass filtering on the distributed acoustic sensing monitoring data according to the low-pass filter and the frequency band range to generate a preliminary filtering result; Performing inverse Fourier transform on the preliminary filtering result to generate the filtering result.

7. 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, configured to perform low-pass filtering on the distributed acoustic sensing monitoring data according to the frequency band range to generate a filtering result; A 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; The filtering result downsampling module includes: 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; 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; The upsampling result downsampling unit is configured to downsample the upsampling result.

8. 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 6 are implemented.

9. 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 6 are implemented.

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