Method and system for suppressing background noise in DAS data, medium and equipment

Abstract

The invention provides a method, a system, a medium and equipment for suppressing background noise in DAS data, and belongs to the technical field of DAS data processing. The method comprises the steps of firstly obtaining ARMA model parameters of DAS background noise, and then performing noise reduction processing on DAS data by using the ARMA model parameters to obtain DAS data after noise reduction. According to the method, the background noise in the DAS data is effectively suppressed, the signal-to-noise ratio of the DAS data can be remarkably improved, and effective signals are reserved to the maximum extent; compared with a conventional noise suppression method, the method is high in calculation efficiency and good in effect.

Description

Technical Field

[0001] The present invention belongs to the technical field of DAS data processing, and in particular relates to a method, system, medium and equipment for suppressing background noise in DAS data. Background Art

[0002] Distributed acoustic sensing (DAS) is a new type of sensing technology that can obtain vibration information at various locations along the optical fiber in real time, and has unique advantages such as large sensing distance, high spatial resolution and wide bandwidth. This technology uses the characteristics of backward coherent Rayleigh scattering stimulated by narrow linewidth laser in the optical fiber, which is highly sensitive to strain changes, and combines the principle of optical reflectometer to perform high-precision perception of environmental vibration and sound field information that interacts with the optical fiber.

[0003] Laser phase noise is a common type of noise interference in DAS data. The phase noise of the laser is divided into two parts. One part is the phase noise caused by the change of the laser cavity length due to the change of ambient temperature or vibration, which causes the laser center frequency to drift. The other part is caused by the spontaneous radiation noise of the laser. The laser frequency drift is caused by the environment and is generally slow, affecting the response of DAS to low-frequency vibrations; the spontaneous radiation phase noise manifests as a broadband random interference feature. The existence of laser phase noise seriously affects the quality of DAS data.

[0004] There are many existing methods for suppressing background noise, such as bandpass filtering, median filtering, etc., but bandpass filters are not easy to design, and median filtering algorithms sometimes cause distortion. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned problems existing in the prior art and to provide a method, system, medium and device for suppressing background noise in DAS data, which has high calculation efficiency, good effect and improves the quality of DAS data.

[0006] One of the objects of the present invention is to provide a method for suppressing background noise in DAS data.

[0007] A second object of the present invention is to provide a system for suppressing background noise in DAS data.

[0008] A third object of the present invention is to provide a computer-readable storage medium.

[0009] A fourth object of the present invention is to provide a computer device.

[0010] The present invention is achieved through the following technical solutions:

[0011] A first aspect of the present invention provides a method for suppressing background noise in DAS data. First, ARMA model parameters of the DAS background noise are obtained, and then the ARMA model parameters are used to perform denoising on the DAS data to obtain denoised DAS data.

[0012] A further improvement of the present invention is:

[0013] The method comprises the following steps:

[0014] Obtain DAS background noise data;

[0015] Get the ARMA model parameters of DAS background noise;

[0016] The obtained ARMA model parameters are used to perform DAS data denoising.

[0017] A further improvement of the present invention is:

[0018] The DAS background noise data is the DAS background data of any period of time without valid signals and including all acquisition channels.

[0019] A further improvement of the present invention is:

[0020] The DAS background noise is represented by a difference equation, namely the ARMA model:

[0021]

[0022] Among them, v(t) is the driving white noise of the difference equation, x(t) is the DAS laser phase noise, p, q are the ARMA model orders; a(k), b(k) are the corresponding ARMA model parameters.

[0023] A further improvement of the present invention is:

[0024] The steps of obtaining the ARMA model parameters of the DAS background noise are as follows:

[0025] (1) For any data x in the DAS background noise i (t), i = 1, 2...N, N is the total number of data, and the ARMA model order p is estimated i ,q i Here, you can use any model order determination method such as AIC criterion, BIC criterion, etc. to determine p i ,q i value;

[0026] (2) Select p i ,q i The median value of is the final ARMA model order p,q;

[0027] (3) After determining the ARMA model order p, q, for any data x in the DAS background noise i (t), estimate the ARMA parameter a i (k),b i (k);

[0028] (4) Calculate a i (k),b i The mean of (k) is the final ARMA model parameters a(k), b(k).

[0029] A further improvement of the present invention is:

[0030] The DAS data denoising process is performed using the acquired ARMA model parameters, and the specific operations include:

[0031] Input the DAS raw data x(t) that needs to be denoised;

[0032] The obtained ARMA model parameters are used to perform noise reduction on the DAS original data x(t). The specific expression is:

[0033]

[0034] Among them, y(t) is the DAS data after denoising, x(t) is the original DAS data containing background noise, p and q are the ARMA model orders; a(k) and b(k) are the corresponding ARMA model parameters, x(tk) is the value of x(t) at time tk, and k is an integer.

[0035] A further improvement of the present invention is:

[0036] The DAS raw data x(t) contains background noise.

[0037] A second aspect of the present invention provides a system for suppressing background noise in DAS data, comprising:

[0038] An acquisition unit, used for acquiring DAS background noise data;

[0039] A model parameter acquisition unit, used for acquiring ARMA model parameters of DAS background noise;

[0040] The denoising unit is used to perform denoising processing on the DAS data using the acquired ARMA model parameters.

[0041] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the method for suppressing background noise in DAS data as described above.

[0042] A fourth aspect of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for suppressing background noise in DAS data as described above.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention estimates the ARMA model parameters of the background noise based on the actual DAS background noise data, and performs denoising processing on the DAS data according to the ARMA model parameters to obtain the denoised DAS data, thereby achieving effective suppression of the background noise in the DAS data, significantly improving the signal-to-noise ratio of the DAS data, and retaining the effective signal to the greatest extent.

[0045] Compared with conventional noise suppression methods, the method of the present invention has high computational efficiency and good effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of a method for suppressing background noise in DAS data in an embodiment of the present invention;

[0047] Figure 2 is the laser phase noise data simulated in the embodiment of the present invention;

[0048] Figure 3 is the spectrum of the laser phase noise data simulated in the embodiment of the present invention;

[0049] Figure 4 is the denoised data in the embodiment of the present invention;

[0050] Figure 5 is the spectrum of the denoised data in the embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0052] The present invention provides a method for suppressing background noise in DAS data. Firstly, ARMA model parameters of DAS background noise are obtained, and then the ARMA model parameters are used to perform denoising on the DAS data to obtain the denoised DAS data. The method effectively suppresses the background noise in the DAS data, can significantly improve the signal-to-noise ratio of the DAS data, and retains the effective signal to the greatest extent.

[0053] The autoregressive moving average (ARMA) model is a commonly used random time series model. It is a time series prediction method with high accuracy and is often used for random signal prediction and random signal denoising.

[0054] [Example 1]

[0055] The embodiment of the present invention provides a method for suppressing background noise in DAS data, such as Figure 1 As shown, the specific steps include:

[0056] Step 1: Obtain DAS background noise data;

[0057] The DAS background noise data in this step refers to the DAS background data of any period of time without valid signals and including all acquisition channels.

[0058] The second step is to obtain the ARMA model parameters of DAS background noise;

[0059] The DAS background noise is expressed by a difference equation, that is, the ARMA model is:

[0060]

[0061] Wherein, v(t) is the driving white noise of the difference equation, x(t) is the DAS laser phase noise, p, q are the ARMA model orders; a(k), b(k) are the corresponding ARMA model parameters;

[0062] Among them, the ARMA model parameter estimation steps of DAS background noise are as follows:

[0063] (1) For any data x in the DAS background noise i (t), i = 1, 2...N, N is the total number of data, and the ARMA model order p is estimated i ,q i Here, you can use any model order determination method such as AIC criterion, BIC criterion, etc. to determine p i ,q i Value; among them, the AIC criterion and the BIC criterion both adopt the existing technical methods and will not be described in detail here.

[0064] (2) Select p i ,q iThe median value of is the final ARMA model order p,q;

[0065] (3) After determining the ARMA model order p, q, for any data x in the DAS background noise i (t), estimate the ARMA parameter a i (k),b i (k), the model parameter estimation method can adopt the conventional maximum likelihood estimation method;

[0066] (4) Calculate a i (k),b i The mean of (k) is the final ARMA model parameters a(k), b(k).

[0067] The third step is to use the obtained ARMA model parameters to perform DAS data noise reduction;

[0068] Specific operations include:

[0069] Inputting DAS raw data x(t) to be denoised, wherein the DAS raw data x(t) contains background noise;

[0070] The ARMA model parameters obtained in the second step are used to perform noise reduction on the DAS original data x(t). The specific expression is:

[0071]

[0072] Among them, y(t) is the DAS data after denoising, x(t) is the original DAS data containing background noise, p and q are the ARMA model orders; a(k) and b(k) are the corresponding ARMA model parameters, x(tk) is the value of x(t) at time tk, and k is an integer.

[0073] The fourth step is to output the denoised DAS data.

[0074] The method of the present invention is used to suppress DAS data noise. In this embodiment, the method is verified using simulated laser phase noise data. Figure 2 and Figure 3 As shown in the figure, they are a simulated DAS phase noise data and its spectrum. Figure 2 and Figure 3 It can be seen that there is serious low-frequency phase interference in the simulation data.

[0075] After the background noise suppression method in the DAS data of the present invention is used, the results are as follows: Figure 4 and Figure 5 , respectively, the denoised DAS data and its spectrum. Figure 4 and Figure 5It can be seen from China that the low-frequency interference is effectively eliminated after the denoising process of the method of the present invention.

[0076] Model testing shows the effectiveness of the denoising method of the present invention.

[0077] [Example 2]

[0078] The embodiment of the present invention further provides a system for suppressing background noise in DAS data, comprising:

[0079] The acquisition unit is used to acquire DAS background noise data; the DAS background noise data is any period of time without valid signals and includes DAS background data of all acquisition channels.

[0080] The model parameter acquisition unit is used to obtain the ARMA model parameters of the DAS background noise, and specifically performs the following operations:

[0081] The DAS background noise is expressed by a difference equation, that is, the ARMA model is:

[0082]

[0083] Wherein, v(t) is the driving white noise of the difference equation, x(t) is the DAS laser phase noise, p, q are the ARMA model orders; a(k), b(k) are the corresponding ARMA model parameters;

[0084] The steps for obtaining the ARMA model parameters of DAS background noise are as follows:

[0085] (1) For any data x in the DAS background noise i (t), i = 1, 2...N, N is the total number of data, and the ARMA model order p is estimated i ,q i Here, you can use any model order determination method such as AIC criterion, BIC criterion, etc. to determine p i ,q i Value; among them, the AIC criterion and the BIC criterion both adopt the existing technical methods and will not be described in detail here.

[0086] (2) Select p i ,q i The median value of is the final ARMA model order p,q;

[0087] (3) After determining the ARMA model order p, q, for any data x in the DAS background noise i (t), estimate the ARMA parameter a i (k),b i (k), the model parameter estimation method can adopt the conventional maximum likelihood estimation method;

[0088] (4) Calculate a i (k),b i The mean of (k) is the final ARMA model parameters a(k), b(k).

[0089] The denoising unit is used to perform denoising on the DAS data using the acquired ARMA model parameters. Specifically, the following operations are performed:

[0090] Inputting DAS raw data x(t) to be denoised, wherein the DAS raw data x(t) contains background noise;

[0091] The ARMA model parameters obtained in the second step are used to denoise the DAS original data x(t). The specific expression is:

[0092]

[0093] Among them, y(t) is the DAS data after denoising, x(t) is the original DAS data containing background noise, p and q are the ARMA model orders; a(k) and b(k) are the corresponding ARMA model parameters, x(tk) is the value of x(t) at time tk, and k is an integer.

[0094] The output unit is used to output the denoised DAS data.

[0095] [Example 3]

[0096] An embodiment of the present invention also provides a computer-readable storage medium, which stores at least one computer-executable program. When the at least one program is executed by the computer, the computer executes the steps in the method for suppressing background noise in DAS data as described in Example 1.

[0097] [Example 4]

[0098] An embodiment of the present invention further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for suppressing background noise in DAS data as described in Example 1.

[0099] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate

[0100] SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM), etc.

[0101] The above technical solution is only one implementation mode of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the principles disclosed in the present invention, and it is not limited to the technical solution described in the above specific embodiments of the present invention. Therefore, the above description is only preferred and does not have a restrictive meaning.

Claims

1. A method for suppressing background noise in DAS data, characterized in that: Firstly, the ARMA model parameters of DAS background noise are obtained, and then the ARMA model parameters are used to perform denoising on the DAS data to obtain the denoised DAS data.

2. The method for suppressing background noise in DAS data according to claim 1, characterized in that: The method comprises the following steps: Obtain DAS background noise data; Get the ARMA model parameters of DAS background noise; The obtained ARMA model parameters are used to perform DAS data denoising.

3. The method for suppressing background noise in DAS data according to claim 2, characterized in that: The DAS background noise data is the DAS background data of any period of time without valid signals and including all acquisition channels.

4. The method for suppressing background noise in DAS data according to claim 3, characterized in that: The DAS background noise is represented by a difference equation, namely the ARMA model: Among them, v(t) is the driving white noise of the difference equation, x(t) is the DAS laser phase noise, p, q are the ARMA model orders; a(k), b(k) are the corresponding ARMA model parameters.

5. The method for suppressing background noise in DAS data according to claim 4, characterized in that: The steps of obtaining the ARMA model parameters of the DAS background noise are as follows: (1) For any data x in the DAS background noise i (t), i = 1, 2...N, N is the total number of data, and the ARMA model order p is estimated i ,q i ; (2) Select p i ,q i The median value of is the final ARMA model order p,q; (3) After determining the ARMA model order p, q, for any data x in the DAS background noise i (t), estimate the ARMA parameter a i (k),b i (k); (4) Calculate a i (k),b i The mean of (k) is the final ARMA model parameters a(k), b(k).

6. The method for suppressing background noise in DAS data according to claim 5, characterized in that: The DAS data denoising process is performed using the acquired ARMA model parameters, and the specific operations include: Input the DAS raw data x(t) that needs to be denoised; The obtained ARMA model parameters are used to denoise the DAS original data x(t). The specific expression is: Among them, y(t) is the DAS data after noise, x(t) is the original DAS data containing background noise, p and q are the ARMA model orders; a(k) and b(k) are the corresponding ARMA model parameters.

7. The method for suppressing background noise in DAS data according to claim 6, characterized in that: The DAS raw data x(t) contains background noise.

8. A system for suppressing background noise in DAS data, characterized in that: include: An acquisition unit, used for acquiring DAS background noise data; A model parameter acquisition unit, used for acquiring ARMA model parameters of DAS background noise; The denoising unit is used to perform denoising processing on the DAS data using the acquired ARMA model parameters.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps of the method for suppressing background noise in DAS data according to any one of claims 1 to 7.

10. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for suppressing background noise in DAS data according to any one of claims 1 to 7.

Images