Method, system, medium and device for suppressing background noise in das data

Abstract

The application provides a DAS data background noise suppression method, system, medium and equipment, and belongs to the technical field of DAS data processing. The method is as follows: first, ARMA model parameters of DAS background noise are acquired, then the DAS data is denoised by using the ARMA model parameters, and the denoised DAS data is obtained. The application realizes effective suppression of the background noise in the DAS data, can significantly improve the signal-to-noise ratio of the DAS data, and maximally retains effective signals; compared with conventional noise suppression methods, the application has high calculation efficiency and good effect.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of DAS data processing, and particularly relates to a method and system for suppressing background noise in DAS data, a medium and equipment. BACKGROUND

[0002] Distributed acoustic sensing (DAS) is a new sensing technology that can obtain vibration information of each position along the optical fiber in real time, and has unique advantages such as long sensing distance, high spatial resolution and wide frequency band.

[0003] Laser phase noise is a common noise interference type in DAS data. The laser phase noise is divided into two parts. One part is the phase noise caused by the change of the cavity length of the laser due to the change of the environmental temperature or vibration, which causes the center frequency of the laser to drift. The other part is the phase noise caused by the spontaneous emission noise of the laser. The laser frequency drift is caused by the environment and is generally slow, which affects the response of DAS to low-frequency vibration. The spontaneous emission phase noise shows the characteristics of wide frequency and random interference. 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 band-pass filtering, median filtering, etc. However, the band-pass filter is not easy to design, and the median filtering algorithm sometimes causes distortion. SUMMARY

[0005] The purpose of the present application is to solve the problems existing in the prior art, and to provide a method and system for suppressing background noise in DAS data, a medium and equipment, which have high calculation efficiency and good effect, and improve the quality of DAS data.

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

[0007] The second purpose of the present application is to provide a system for suppressing background noise in DAS data.

[0008] The third purpose of the present application is to provide a computer-readable storage medium.

[0009] The fourth purpose of the present application is to provide a computer device.

[0010] The present application is achieved by the following technical solutions:

[0011] The first aspect of the present application provides a method for suppressing background noise in DAS data, which comprises the following steps: obtaining ARMA model parameters of DAS background noise, and using the ARMA model parameters to perform noise reduction processing on DAS data to obtain noise-reduced DAS data.

[0012] The present application is further improved in that:

[0013] The method comprises the following steps:

[0014] Obtaining DAS background noise data;

[0015] Obtaining ARMA model parameters of DAS background noise;

[0016] Using the obtained ARMA model parameters to perform noise reduction processing on DAS data.

[0017] The present application is further improved in that:

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

[0019] The present application is further improved in that:

[0020] The DAS background noise is represented by a difference equation, i.e., an ARMA model, which is as follows:

[0021]

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

[0023] The present application is further improved in that:

[0024] The step of obtaining ARMA model parameters of DAS background noise is as follows:

[0025] (1) For any data x i (t) in DAS background noise, i=1, 2, …, N, N is the total number of data, the ARMA model orders p i ,q i are estimated, wherein any model order determination method such as AIC criterion and BIC criterion can be selected to determine the values of p i ,q i ;

[0026] (2) The median values of p i ,q i are selected as the final ARMA model orders p and q.

[0027] (3) After determining the order p, q of the ARMA model, the ARMA parameters a i (k), b i (k) are estimated for any data x i (t) in the DAS background noise.

[0028] (4) The mean values of a i (k), b i (k) are calculated, which are the final ARMA model parameters a(k), b(k).

[0029] Further improvements of the present application are as follows:

[0030] The DAS data denoising processing is performed by using the obtained ARMA model parameters, and the specific operation includes:

[0031] The DAS original data x(t) to be denoised is inputted.

[0032] The DAS original data x(t) is denoised by using the obtained ARMA model parameters, and the specific expression is as follows:

[0033]

[0034] Wherein, y(t) is the denoised DAS data, x(t) is the DAS original data containing background noise, p and q are the order of the ARMA model, a(k), b(k) are the corresponding ARMA model parameters, x(t-k) is the value of x(t) at t-k time, and k is an integer.

[0035] Further improvements of the present application are as follows:

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

[0037] In the second aspect of the present application, a system for suppressing background noise in DAS data is provided, which includes:

[0038] An acquisition unit is configured to acquire DAS background noise data.

[0039] A model parameter acquisition unit is configured to acquire ARMA model parameters of the DAS background noise.

[0040] A denoising unit is configured to perform DAS data denoising processing by using the obtained ARMA model parameters.

[0041] In a third aspect, the present application provides a computer readable storage medium, which stores at least one program, wherein the at least one program, when executed by a computer, causes the computer to perform the steps of the method for suppressing background noise in DAS data as described above.

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

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

[0044] The present application is based on the actual DAS background noise data, estimates the ARMA model parameters of the background noise, and performs noise reduction processing on the DAS data according to the ARMA model parameters to obtain the denoised DAS data, thereby effectively suppressing the background noise in the DAS data and significantly improving the signal-to-noise ratio of the DAS data while retaining the effective signal to the greatest extent.

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

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

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

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

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

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

[0051] The present application will be further described in detail below with reference to the accompanying drawings:

[0052] The application provides a background noise suppression method for DAS data.

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

[0054] Embodiment 1

[0055] The application provides a background noise suppression method for DAS data, as shown in the following steps. Figure 1

[0056] First, DAS background noise data is obtained.

[0057] The DAS background noise data in this step is DAS background data of any time period without effective signals and containing all acquisition channels.

[0058] Second, ARMA model parameters of the DAS background noise are obtained.

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

[0060]

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

[0062] The ARMA model parameter estimation step of the DAS background noise is as follows:

[0063] (1) For any data x i (t) in the DAS background noise, i=1, 2, …, N, N is the total number of data, the ARMA model orders p i ,q i are estimated, and any model order determination method such as the AIC criterion and the BIC criterion can be selected to determine the p i ,q i values; wherein the AIC criterion and the BIC criterion are obtained by using the prior art method, and will not be described here.

[0064] (2) p i ,q i ​The median of the final ARMA model order p, q;

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

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

[0067] Thirdly, the obtained ARMA model parameters are used for DAS data denoising processing;

[0068] The specific operation includes:

[0069] The DAS original data x(t) to be denoised is input, and the DAS original data x(t) contains background noise;

[0070] The ARMA model parameters obtained in the second step are used for denoising processing of the DAS original data x(t), and the specific expression is:

[0071]

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

[0073] Fourthly, the denoised DAS data is output.

[0074] The method is used for DAS data noise suppression. In the embodiment, the method is verified by using simulated laser phase noise data. As shown in Figure 2 and Figure 3 , they are respectively a simulated DAS phase noise data and its spectrum, and from Figure 2 and Figure 3 , it can be seen that there is serious low-frequency phase interference in the simulated data.

[0075] After the DAS data background noise suppression method of the application is used for processing, the results are as shown in Figure 4 and Figure 5 , they are respectively the denoised DAS data and its spectrum, and from Figure 4 and Figure 5The low-frequency interference can be effectively eliminated by the method of the present application.

[0076] Model test shows that the denoising method of the present application is effective.

[0077]

EXAMPLE 2

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

[0079] An acquisition unit is configured to acquire DAS background noise data, wherein the DAS background noise data is DAS background data of any time period without effective signals and including all acquisition channels.

[0080] A model parameter acquisition unit is configured to acquire ARMA model parameters of the DAS background noise, and specifically performs the following operations:

[0081] The DAS background noise is represented by a difference equation, i.e., an ARMA model, as follows:

[0082]

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

[0084] The step of acquiring the ARMA model parameters of the DAS background noise is as follows:

[0085] (1) For any data x i (t) in the DAS background noise, i=1, 2, …, N, N is the total number of data, the order p i ,q i of the ARMA model is estimated, wherein the p i ,q i value can be determined by using any model order determination method such as AIC criterion or BIC criterion; wherein the AIC criterion and the BIC criterion are determined by using the prior art method, and thus will not be described herein.

[0086] (2) The median of p i ,q i is selected as the final ARMA model order p, q.

[0087] (3) After the order p, q of the ARMA model is determined, the ARMA parameters a i (k), b i (k) are estimated for any data x i (t) in the DAS background noise, wherein the model parameter estimation method can be a conventional maximum likelihood estimation method.

[0088] (4) Calculate a i (k),b i (k) are the final ARMA model parameters a(k),b(k).

[0089] A denoising unit is configured to perform DAS data denoising processing using the acquired ARMA model parameters, and the denoising unit performs the following operations:

[0090] The DAS original data x(t) containing background noise is inputted.

[0091] The DAS original data x(t) is denoised using the ARMA model parameters acquired in the second step, and the specific expression is as follows:

[0092]

[0093] wherein y(t) is the denoised DAS data, x(t) is the DAS original data containing background noise, p and q are the ARMA model orders, a(k),b(k) are the corresponding ARMA model parameters, x(t-k) is the value of x(t) at t-k, and k is an integer.

[0094] An output unit is configured to output the denoised DAS data.

[0095]

Embodiment 3

[0096] The embodiment of the present application further provides a computer readable storage medium, which stores at least one computer executable program, and the at least one program is executed by a computer to make the computer perform the steps of the method for suppressing background noise in DAS data according to the embodiment 1.

[0097]

Embodiment 4

[0098] The embodiment of the present application further provides a computer device, which comprises a memory and a processor, and the memory stores a computer program, and the computer program is executed by the processor to make the processor perform the steps of the method for suppressing background noise in DAS data according to the embodiment 1.

[0099] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0100] SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0101] The above technical solutions are only one embodiment of the present application. For those skilled in the art, on the basis of the principles disclosed in the present application, various types of improvements or modifications can be easily made, and are not limited to the technical solutions described in the above-mentioned embodiments of the present application. Therefore, the above description is only preferred, and is not limited in meaning.

Claims

1. A method of suppressing background noise in DAS data, characterized by, First, the ARMA model parameters of the DAS background noise are acquired, and then the DAS data are denoised by using the ARMA model parameters to obtain the denoised DAS data. The ARMA model parameters of the DAS background noise are acquired by the following steps: (1) For any one channel data in DAS background noise , i =1, 2 …… N, N is the total number of data, estimate its ARMA model order ; (2) Selecting the median as the final ARMA model order ; (3) determining the order of the ARMA model Afterwards, for any one data in the DAS background noise , the ARMA parameters are estimated ; (4) Calculation The mean of these values ​​is the final ARMA model parameter. ; The DAS data denoising by using the acquired ARMA model parameters includes the following steps: Input DAS raw data that needs to be denoised ; The acquired ARMA model parameters are used to denoise the DAS original data The specific expression is as follows: wherein, is the de-noised DAS data, is the DAS raw data containing background noise, is the order of the ARMA model; is the corresponding ARMA model parameters.

2. The method of claim 1, wherein, The method includes the following steps: Acquiring DAS background noise data; Acquiring the ARMA model parameters of the DAS background noise; Denoising the DAS data by using the acquired ARMA model parameters.

3. The method of claim 2, wherein, The DAS background noise data is the DAS background data of any period of time without effective signals and containing all acquisition channels.

4. The method of claim 3, wherein, The DAS background noise is represented by a difference equation, i.e., the ARMA model is as follows: wherein, is the driving white noise of the difference equation, is the DAS raw data containing background noise, is the order of the ARMA model; is the corresponding ARMA model parameters.

5. A system for suppressing background noise in DAS data, characterized by, The system for executing the method for suppressing the background noise in the DAS data according to any one of claims 1-4 includes: An acquisition unit for acquiring DAS background noise data; A model parameter acquisition unit for acquiring the ARMA model parameters of the DAS background noise; A denoising unit for denoising the DAS data by using the acquired ARMA model parameters.

6. A computer-readable storage medium, characterized in that, The computer readable storage medium stores at least one computer executable program, and the at least one program is executed by the computer to make the computer execute the steps in the method for suppressing the background noise in the DAS data according to any one of claims 1-4.

7. A computer device, comprising: The computer readable storage medium stores at least one computer executable program, and the at least one program is executed by the computer to make the computer execute the steps in the method for suppressing the background noise in the DAS data according to any one of claims 1-4. The computer readable storage medium stores at least one computer executable program, and the at least one program is executed by the computer to make the computer execute the steps in the method for suppressing the background noise in the DAS data according to any one of claims 1-4.

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