Method for eliminating phase drift in distributed acoustic sensing

Abstract

A method for eliminating phase drift in distributed acoustic sensing, comprising: acquiring each signal component in a distributed acoustic sensing system and performing phase demodulation to obtain the phase value of each signal component at the current moment (S1); initializing a Kalman filter and, for each signal component, by means of a Kalman filter time update equation, updating the priori estimate covariance at the current moment on the basis of the posteriori estimate covariance at the previous moment (S2); for each signal component, substituting the priori estimate covariance at the current moment into a Kalman gain equation to calculate a Kalman gain coefficient (S3); on the basis of the phase value and the Kalman gain coefficient of each signal component at the current moment, calculating a phase drift estimated value of each signal component at the current moment and updating the posteriori estimate covariance of each signal component at the current moment (S4); and using the phase value and the phase drift estimated value of each signal component at the current moment to calculate drift-free phase values at the current moment and outputting same, thus eliminating the phase drift of each signal component at the current moment (S5). The method is applicable to various distributed acoustic sensing systems, and can eliminate the phase drift of each signal component to keep the phase consistency of each signal component in real time.

Description

A distributed acoustic sensing phase drift elimination method TECHNICAL FIELD

[0001] The present application relates to the field of optical fiber communication and sensing, and more particularly to a distributed acoustic sensing phase drift elimination method. BACKGROUND

[0002] In the past two decades, distributed optical fiber vibration sensing technology has developed rapidly. Distributed optical fiber vibration sensors include interference sensors and backscattering sensors. The formation of Rayleigh scattering in optical fibers is mainly due to the non-uniformity of material density and refractive index caused by various reasons during the production process of optical fibers. Optical fiber distributed sensors based on interference technology are mainly based on the phase modulation characteristics of light waves in optical fibers under external disturbance signals. The sensing and detection of external disturbance signals are realized by demodulating the phase information changes of the returned light wave signals.

[0003] Distributed optical fiber sensing system based on phase-sensitive optical time domain reflectometry In the signal demodulation process of the distributed optical fiber sensing system based on phase-sensitive optical time domain reflectometry, due to the use of narrow linewidth signals with strong coherence, the local light intensity is weak during the coherence of the signal light and the local oscillator light, that is, the fading noise. This fading noise can cause phase demodulation errors at local positions of the optical fiber, forming a detection blind area. Generally, multiple signal components are obtained by signal multiplexing / demultiplexing to solve the problem.

[0004] For example, an existing patent document discloses a non-fading multi-wavelength distributed acoustic sensing system and a differential rotation vector superposition method. The multi-wavelength light source module is used to generate multiplexed multi-wavelength probe light and multiple independent local oscillator lights. The pulse modulation module is used for pulse modulation and frequency shift of the multi-wavelength probe light to generate short pulse laser. The circulator is used to receive the short pulse laser and output multi-wavelength scattered light. The sensing optical cable is used to scatter the short pulse laser to form multi-wavelength scattered light. The receiving module is used to demultiplex the multi-wavelength scattered light and make each independent local oscillator light interfere with the scattered light signal of the corresponding wavelength to form beat frequency signals through photoelectric conversion. The differential vector superposition module is used for vector merging of the multi-wavelength beat frequency signals. The signal processing module is used for phase demodulation to obtain optical phase information distributed along the sensing optical cable. Although the existing technology can reduce interference fading and polarization fading, the scheme of the existing technology is only applicable to ideal conditions. In ideal conditions, the phases of each signal component should remain consistent after the initial phase is eliminated by the rotation vector. However, due to the frequency drift and random phase noise of the laser, phase errors will occur between each component, and the errors will accumulate over time, causing each component to have cumulative phase errors in addition to the phase changes caused by vibration. The appearance of errors will reduce the modulus of the rotation vector sum, and even when the phase error term of each component exceeds π / 2, cancellation will occur, increasing the occurrence of fading noise and distortion of the phase signal.

[0005] Summary of the Invention

[0006] To overcome the shortcomings of existing technologies that cannot eliminate the influence of phase drift generated by the system under non-ideal conditions on the signal, this invention provides a distributed acoustic wave sensing phase drift elimination method that can eliminate phase drift in real time, thereby maintaining the phase consistency of each signal component.

[0007] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0008] A method for eliminating phase drift in distributed acoustic wave sensing includes the following steps:

[0009] S1: Acquire each signal component in the distributed acoustic wave sensing system and demodulate its phase to obtain the phase value of each signal component at the current moment;

[0010] S2: Initialize the Kalman filter. For each signal component, update the prior estimate covariance at the current time according to the posterior estimate covariance at the previous time step using the Kalman filter time update equation.

[0011] S3: For each of the signal components, substitute the prior estimate covariance at the current time into the Kalman gain equation to calculate the Kalman gain coefficient;

[0012] S4: Calculate the phase drift estimate of each signal component at the current moment based on the phase value and Kalman gain coefficient of each signal component at the current moment; and update the posterior estimate covariance of each signal component at the current moment.

[0013] S5: Calculate and output the drift-free phase value at the current moment using the phase value and phase drift estimate of each signal component at the current moment, thus completing the elimination of phase drift of each signal component at the current moment.

[0014] Preferably, step S1 includes:

[0015] Each signal component in the distributed acoustic wave sensing system is acquired and its phase is demodulated. The current time t of each signal component is then calculated. n Argument φ(t) n ), and based on the previous time t n-1 The demodulated phase value u(t) n-1 ) Perform the current time t n Phase demodulation yields the phase value u(t) of each signal component at the current moment. n ).

[0016] Preferably, step S2 includes:

[0017] Initialize the state parameters of the Kalman filter;

[0018] For each of the signal components, the prior estimate covariance at the current time is updated using the Kalman filter time update equation based on the posterior estimate covariance of the previous time step, as shown in the following formula:

[0019] P′(t n )=P(t n-1 )+Q

[0020] Wherein, P′(t) n (t) represents the current time. n The prior estimate of the covariance, P(t) n-1 ) represents the previous time t n-1 The posterior estimated covariance, Q is the process excitation covariance of the Kalman filter.

[0021] Preferably, step S3 includes:

[0022] For each of the signal components, the prior estimate covariance at the current time is substituted into the Kalman gain equation to calculate the Kalman gain coefficient, as shown in the following formula:

[0023] Among them, K g R is the Kalman gain coefficient, and R is the measurement covariance of the Kalman filter.

[0024] Preferably, in step S4, calculating the phase drift estimate of each signal component at the current moment includes:

[0025] The Kalman filter state is updated by calculating the phase drift estimate of each signal component at the current moment based on the phase value and Kalman gain coefficient of each signal component, as shown in the following formula:

[0026] n(t n )=n(t n-1 )+K g (u(t n )-n(t n-1 ))

[0027] Wherein, n(t) n (t) represents the current time. n Phase drift estimate; n(t) n-1 ) represents the previous time t n-1 The estimated phase drift value.

[0028] Preferably, in step S4, the posterior estimated covariance of each signal component at the current time is updated according to the following formula:

[0029] P(t n )=(1-K g )P′(t n )

[0030] wherein P(t n ) is the posterior estimation covariance at the current time t n .

[0031] Preferably, the step S5 comprises:

[0032] The phase value without phase drift at the current time is calculated by using the phase value at the current time and the phase drift estimation value of each signal component, and the formula is as follows:

[0033] θ(t n )=u(t n )-n(t n )

[0034] wherein θ(t n ) is the phase value without phase drift at the current time.

[0035] The phase value without phase drift at the current time θ(t n-1 ) of each signal component is outputted, and the elimination of the phase drift at the current time of each signal component is completed.

[0036] Preferably, in the step S1, the distributed acoustic sensing system comprises a distributed optical fiber sensing system based on a phase-sensitive optical time domain reflectometer.

[0037] Preferably, in the step S1, each signal component is specifically a multiplexed signal in the distributed acoustic sensing system, and the multiplexing comprises space division multiplexing, frequency division multiplexing, wave division multiplexing, time division multiplexing and polarization multiplexing.

[0038] Or the signal component is specifically a single signal in the distributed acoustic sensing system without using multiplexed signal modulation.

[0039] Preferably, the order of the Kalman filter is one order.

[0040] It is stated herein that the order of the Kalman filter is not specifically limited, and the Kalman filter of one order is only an exemplary description, and the method of the present application can be applied to Kalman filters of all orders (one order, two orders and higher orders).

[0041] Compared with the prior art, the technical scheme of the present application has the beneficial effects that:

[0042] ​The application provides a distributed acoustic sensing phase drift elimination method, which comprises the following steps: firstly, acquiring each signal component in a distributed acoustic sensing system and respectively performing phase demodulation to obtain the phase value of each signal component at the current time; initializing a Kalman filter, and for each signal component, updating the prior estimation covariance at the current time according to the posterior estimation covariance at the last time by using the Kalman filter time update equation; then, substituting the prior estimation covariance at the current time into the Kalman gain equation to calculate the Kalman gain coefficient; subsequently, calculating the phase drift estimation value of each signal component at the current time according to the phase value of each signal component at the current time and the Kalman gain coefficient; and updating the posterior estimation covariance of each signal component at the current time; finally, calculating the phase value without drift at the current time by using the phase value and the phase drift estimation value of each signal component at the current time and outputting the phase value, so as to eliminate the phase drift of each signal component at the current time.

[0043] The application can be applied to the distributed acoustic sensing system with any multiplexing signal quantity, can effectively eliminate the phase drift of each signal component, and can keep the phase consistency of each signal component in real time. BRIEF DESCRIPTION OF DRAWINGS

[0044] Fig. 1 is a flow chart of a distributed acoustic sensing phase drift elimination method provided in embodiment 1.

[0045] Fig. 2 is a structural schematic diagram of a distributed acoustic sensing system provided in embodiment 2.

[0046] Fig. 3 is a logic flow chart of a distributed acoustic sensing phase drift elimination method provided in embodiment 2.

[0047] Fig. 4 is a comparison diagram of the variation of the argument of each signal component with time before and after phase drift elimination provided in embodiment 2. DETAILED DESCRIPTION

[0048] The accompanying drawings are only used for illustrative description, and cannot be understood as limitation to the patent;

[0049] In order to better illustrate the embodiments, some components in the drawings can be omitted, enlarged or reduced, and do not represent the actual product size;

[0050] For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings can be omitted.

[0051] The technical solutions of the application will be further described below in combination with the drawings and embodiments.

[0052] Embodiment 1

[0053] As shown in FIG. 1, the embodiment provides a distributed acoustic sensing phase drift elimination method, comprising the following steps:

[0054] S1: obtaining each signal component in the distributed acoustic sensing system and respectively performing phase demodulation to obtain the phase value of each signal component at the current time;

[0055] S2: initializing the Kalman filter, and for each signal component, updating the prior estimation covariance at the current time according to the posterior estimation covariance at the last time by using the Kalman filter time update equation;

[0056] S3: for each signal component, substituting the prior estimation covariance at the current time into the Kalman gain equation to calculate the Kalman gain coefficient;

[0057] S4: calculating the phase drift estimation value of each signal component at the current time according to the phase value of each signal component at the current time and the Kalman gain coefficient; and updating the posterior estimation covariance of each signal component at the current time;

[0058] S5: calculating the phase value without drift at the current time by using the phase value and the phase drift estimation value of each signal component at the current time and outputting, and completing the elimination of the phase drift of each signal component at the current time.

[0059] In the specific implementation process, first, each signal component in the distributed acoustic sensing system is obtained and respectively performs phase demodulation to obtain the phase value of each signal component at the current time;

[0060] Initializing the Kalman filter, and for each signal component, updating the prior estimation covariance at the current time according to the posterior estimation covariance at the last time by using the Kalman filter time update equation;

[0061] Then, the prior estimation covariance at the current time is substituted into the Kalman gain equation to calculate the Kalman gain coefficient;

[0062] Subsequently, the phase drift estimation value of each signal component at the current time is calculated according to the phase value of each signal component at the current time and the Kalman gain coefficient; and the posterior estimation covariance of each signal component at the current time is updated, and the updated posterior estimation covariance at the current time is used for calculating the Kalman gain coefficient at the next time;

[0063] Finally, the phase value without drift at the current time is calculated by using the phase value and the phase drift estimation value of each signal component at the current time, and outputting, and the elimination of the phase drift of each signal component at the current time is completed;

[0064] The above steps are repeated to complete the elimination of the real-time phase drift of each signal component of the distributed acoustic sensing system;

[0065] The method can be applied to the distributed acoustic sensing system with any number of multiplexed signals, effectively eliminates the phase drift of each signal component, and maintains the phase consistency of each signal component in real time.

[0066] Embodiment 2

[0067] The embodiment provides a distributed acoustic sensing phase drift elimination method, including the following steps:

[0068] S1: obtaining each signal component in the distributed acoustic sensing system and performing phase demodulation on each signal component respectively to obtain the phase value of each signal component at the current time, including:

[0069] Obtaining each signal component in the distributed acoustic sensing system and performing phase demodulation on each signal component respectively to obtain the argument of each signal component at the current time t n φ(t n ), and performing phase demodulation on the current time t n-1 according to the demodulated phase value u(t n-1 ) at the previous time t n to obtain the phase value u(t n ) of each signal component at the current time;

[0070] S2: initializing the Kalman filter, and for each signal component, updating the prior estimation covariance at the current time according to the posterior estimation covariance at the previous time by using the Kalman filter time update equation, including:

[0071] Initializing the state parameters of the Kalman filter;

[0072] For each signal component, the prior estimation covariance at the current time is updated according to the posterior estimation covariance at the previous time by using the Kalman filter time update equation, and the formula is as follows:

[0073] P'(t n ) = P(t n-1 ) + Q

[0074] Wherein, P'(t n ) is the prior estimation covariance at the current time t n , P(t n-1 ) is the posterior estimation covariance at the previous time t n-1 , and Q is the process excitation covariance of the Kalman filter;

[0075] S3: for each signal component, substituting the prior estimation covariance of the current time into the Kalman gain equation, calculating the Kalman gain coefficient, including:

[0076] for each signal component, substituting the prior estimation covariance of the current time into the Kalman gain equation, calculating the Kalman gain coefficient, the formula is as follows:

[0077] wherein, K g is the Kalman gain coefficient, and R is the measurement covariance of the Kalman filter;

[0078] S4: according to the phase value of each signal component at the current time and the Kalman gain coefficient, calculating the phase drift estimation value of each signal component at the current time, including:

[0079] updating the state of the Kalman filter, according to the phase value of each signal component at the current time and the Kalman gain coefficient, calculating the phase drift estimation value of each signal component at the current time, the formula is as follows:

[0080] n(t n )=n(t n-1 )+K g (u(t n )-n(t n-1 ))

[0081] wherein, n(t n ) is the phase drift estimation value at the current time t n ; n(t n-1 ) is the phase drift estimation value at the last time t n-1 ;

[0082] and updating the posterior estimation covariance of each signal component at the current time:

[0083] P(t n )=(1-K g )P′(t n )

[0084] wherein, P(t n ) is the posterior estimation covariance at the current time t n ;

[0085] S5: using the phase value and the phase drift estimation value of each signal component at the current time to calculate the phase value without drift at the current time, the formula is as follows:

[0086] θ(t n )=u(t n )-n(t n )

[0087] wherein, θ(t n) is a phase value without phase drift at the current time;

[0088] Outputting the phase value θ(t n ) of each signal component at the current time, and eliminating the phase drift of each signal component at the current time;

[0089] In the step S1, the distributed acoustic sensing system comprises a phase-sensitive optical time domain reflectometer based distributed optical fiber sensing system;

[0090] In the step S1, each signal component is specifically a multiplexed signal in the distributed acoustic sensing system, and the multiplexing comprises space division multiplexing, frequency division multiplexing, wavelength division multiplexing, time division multiplexing and polarization multiplexing;

[0091] Or the signal component is specifically a single signal in the distributed acoustic sensing system without using multiplexed signal modulation;

[0092] In the embodiment, the order of the Kalman filter is one order;

[0093] The method of the embodiment can also be applied to Kalman filters of all orders (one order, two orders and higher orders).

[0094] In the specific implementation process, as shown in FIG. 2, FIG. 2 is an exemplary distributed acoustic sensing system, which comprises a narrow line width continuous laser 1, a first optical fiber coupler 2, a second optical fiber coupler 3, a first acousto-optic modulator 4, a second acousto-optic modulator 5, a signal sending device 6, a delay optical fiber 7, a third optical fiber coupler 8, a first erbium-doped fiber amplifier 9, a first optical filter 10, an optical circulator 11, a second erbium-doped fiber amplifier 12, a second optical filter 13, a fourth optical fiber coupler 14, an optical balanced receiver 15 and a signal processing device 16;

[0095] The output end of the narrow line width continuous laser 1 is connected with the input end of the first fiber coupler 2, the first output end of the first fiber coupler 2 is connected with the first input end of the fourth fiber coupler 14, and the second output end is connected with the input end of the second fiber coupler 3; the output end of the second fiber coupler 3 is connected with the first acousto-optic modulator 4 and the second acousto-optic modulator 5 respectively, and the signal sending device 6, the continuous light is modulated by emitting an electric pulse, and the two continuous lights are modulated into light pulses with frequency shift amounts of 80MHz and 200MHz respectively; the output end of the second acousto-optic modulator 5 is connected with the delay fiber 7, and the pulse light generated by the second acousto-optic modulator 5 is delayed; the output end of the first acousto-optic modulator 4 and the delay fiber 7 are connected with the input end of the third fiber coupler 8; the input end of the third fiber coupler 8 is connected with the input end of the first erbium-doped fiber amplifier 9, and the pulse light is optically amplified; the output end of the first erbium-doped fiber amplifier 9 is connected with the input end of the first optical filter 10, and the pulse light is filtered; the output end of the first optical filter 10 is connected with the first port of the optical circulator 11; the second port of the optical circulator 11 is connected with the to-be-measured light, and the optical pulse enters the to-be-measured fiber through the optical circulator; the third port of the optical circulator 11 is connected with the input end of the second erbium-doped fiber amplifier 12, and the backscattered Rayleigh scattering light enters the second erbium-doped fiber amplifier 12 through the optical circulator and is optically amplified; the output end of the second erbium-doped fiber amplifier 12 is connected with the second optical filter 13, and the amplified scattering light is filtered; the second optical filter 13 is connected with the second input end of the fourth fiber coupler 14; the output end of the fourth fiber coupler 14 is connected with the photoelectric balance receiver 15, and the scattering light signal is optically converted; the photoelectric balance receiver 15 is connected with the signal processing device 16, and the signal processing device 16 is provided with the phase drift elimination method in the embodiment, and the received signal is processed by using the method;

[0096] As shown in FIG. 3, FIG. 3 is a logic flow diagram of the phase drift elimination method in the embodiment, which includes the following steps:

[0097] 1) Parameter initialization: set the Kalman filter parameters P, Q and R, and start counting the pulse counter N;

[0098] 2) Signal acquisition: the Rayleigh scattering signal r(N, z) is obtained by the Nth pulse detection, and the signal is generated by the 80MHz and 200MHz optical pulses modulated by the AOM (the first acousto-optic modulator 4 and the second acousto-optic modulator 5);

[0099] 3) First layer demultiplexing: Rayleigh scattering signals r(N,z) are respectively passed through band-pass filters with center frequencies of 80MHz and 200MHz, so as to separate the Rayleigh scattering signals generated by the two pulses into independent components and perform IQ demodulation; wherein, the 200MHz signal is passed through delay alignment, so as to make the physical positions corresponding to the 80MHz and 200MHz signals consistent, and signal vectors r1(N,z) and r2(N,z) are respectively obtained;

[0100] 4) Second layer demultiplexing: two signal components r1 and r2 are decomposed into six components r1, r2, r3, r4, r5 and r6 through spectral decomposition, and spatial difference vectors Δr1, Δr2, Δr3, Δr4, Δr5 and Δr6 are obtained by taking a space difference with a gauge length g; ij (i = 1, 2; j = 1, 2, 3), and a space difference vector Δr ij is obtained;

[0101] 5) Phase drift elimination: the phase u ij of each component is obtained through four-quadrant arctangent and unwrapping, and then the phase drift at the current time is estimated through Kalman filtering, and the current phase drift is subtracted;

[0102] 6) Rotated vector sum algorithm: each component is initialized by subtracting the initial phase, a new space difference vector Δr ij ′ is constructed by taking the rotated phase as the argument and the modulus of the space difference vector as the modulus, and then a vector combination is performed to obtain o(N,z);

[0103] 7) Third layer multiplexing algorithm: for the signal vector o(N,z i ) in the neighborhood window of the spatial position z, a combination is performed to obtain o′(N,z);

[0104] 8) Phase demodulation: four-quadrant arctangent and unwrapping are performed on o′(N,z) to output the phase value θ u (N,z) at the current time;

[0105] 9) Entering the next time, repeating steps 2) to 8);

[0106] As shown in FIG. 4, FIG. 4(a) is the change of the argument of each signal component Δr ij ′ with time when the above step 5) is not performed, and FIG. 4(b) is the change of the argument of each signal component Δr ij ′ with time when the above step 5) is performed; it can be seen that when the Kalman filtering is not performed to eliminate the phase drift, the phase error of each signal component is gradually accumulated with time; after the Kalman filtering, the phase error is eliminated, thereby proving the effectiveness of the method;

[0107] The method can be applied to the distributed acoustic sensing system with any multiplexed signal number, can effectively eliminate the phase drift of each signal component, and can keep the phase consistency of each signal component in real time.

[0108] The same or similar reference signs correspond to the same or similar components;

[0109] The terms describing the positional relationship in the drawings are only used for illustrative description, and should not be understood as a limitation on the patent;

[0110] Obviously, the above embodiments of the present application are only examples for clearly illustrating the present application, and are not intended to limit the implementation modes of the present application. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, it is not necessary and also impossible to exhaust all the implementation modes. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the claims of the present application.

Claims

1. A method for eliminating phase drift in distributed acoustic wave sensing, characterized in that, Includes the following steps: S1: Acquire each signal component in the distributed acoustic wave sensing system and demodulate its phase to obtain the phase value of each signal component at the current moment; S2: Initialize the Kalman filter. For each signal component, update the prior estimate covariance at the current time according to the posterior estimate covariance of the previous time step using the Kalman filter time update equation. S3: For each of the signal components, substitute the prior estimate covariance at the current time into the Kalman gain equation to calculate the Kalman gain coefficient; S4: Calculate the phase drift estimate of each signal component at the current moment based on the phase value and Kalman gain coefficient of each signal component at the current moment; and update the posterior estimate covariance of each signal component at the current moment. S5: Calculate and output the drift-free phase value at the current moment using the phase value and phase drift estimate of each signal component at the current moment, thus completing the elimination of phase drift of each signal component at the current moment.

2. The distributed acoustic wave sensing phase drift elimination method according to claim 1, characterized in that, Step S1 includes: Each signal component in the distributed acoustic wave sensing system is acquired and its phase is demodulated. The current time t of each signal component is then calculated. n Argument φ(t) n ), and based on the previous time t n-1 The demodulated phase value u(t) n-1 ) Perform the current time t n Phase demodulation yields the phase value u(t) of each signal component at the current moment. n ).

3. The distributed acoustic wave sensing phase drift elimination method according to claim 2, characterized in that, Step S2 includes: Initialize the state parameters of the Kalman filter; For each of the signal components, the prior estimate covariance at the current time is updated using the Kalman filter time update equation based on the posterior estimate covariance of the previous time step, as shown in the following formula: P′(t n )=P(t n-1 )+Q Wherein, P′(t) n (t) represents the current time. n The prior estimate of the covariance, P(t) n-1 ) represents the previous time t n-1 The posterior estimated covariance, Q is the process excitation covariance of the Kalman filter.

4. The distributed acoustic wave sensing phase drift elimination method according to claim 3, characterized in that, Step S3 includes: For each of the signal components, the prior estimate covariance at the current time is substituted into the Kalman gain equation to calculate the Kalman gain coefficient, as shown in the following formula: Among them, K g R is the Kalman gain coefficient, and R is the measurement covariance of the Kalman filter.

5. The distributed acoustic wave sensing phase drift elimination method according to claim 4, characterized in that, In step S4, calculating the phase drift estimate of each signal component at the current moment includes: The Kalman filter state is updated by calculating the phase drift estimate of each signal component at the current moment based on the phase value and Kalman gain coefficient of each signal component, as shown in the following formula: n(t n )=n(t n-1 )+K g (u(t n )-n(t n-1 )) Wherein, n(t) n (t) represents the current time. n Phase drift estimate; n(t) n-1 ) represents the previous time t n-1 The estimated phase drift value.

6. The distributed acoustic wave sensing phase drift elimination method according to claim 4, characterized in that, In step S4, the posterior estimated covariance of each signal component at the current time is updated according to the following formula: P(t n )=(1-K g )P′(t n ) Wherein, P(t) n (t) represents the current time. n The posterior estimate of the covariance.

7. The distributed acoustic wave sensing phase drift elimination method according to claim 5, characterized in that, Step S5 includes: The drift-free phase value at the current moment is calculated using the current phase value and phase drift estimate of each signal component, as shown in the following formula: θ(t n )=u(t n )-n(t n ) Where θ(t) n () represents the phase value without drift at the current moment; Output the drift-free phase value θ(t) of each signal component at the current time. n This completes the elimination of phase drift of each signal component at the current moment.

8. A distributed acoustic wave sensing phase drift elimination method according to any one of claims 1 to 7, characterized in that, In step S1, the distributed acoustic wave sensing system includes: a phase-sensitive optical time-domain reflectometer. A distributed fiber optic sensing system.

9. A distributed acoustic wave sensing phase drift elimination method according to any one of claims 1 to 7, characterized in that, In step S1, each signal component is specifically a multiplexed signal in a distributed acoustic wave sensing system, and the multiplexing includes: space division multiplexing, frequency division multiplexing, wavelength division multiplexing, time division multiplexing, and polarization multiplexing. Or the signal component specifically refers to a single component in a distributed acoustic sensing system that does not use multiplexed signal modulation. A signal.

10. A distributed acoustic wave sensing phase drift elimination method according to any one of claims 1 to 7, characterized in that, The Kalman filter is of order one.

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