A method based on WOA-VMD to reduce the impact of frequency drift on Φ-OTDR demodulation

Abstract

Invention Name: A method for reducing the influence of frequency drift on Φ-OTDR demodulation based on WOA-VMD Abstract: The present invention discloses a method for reducing the influence of frequency drift on Φ-OTDR demodulation based on WOA-VMD, which relates to the field of optical fiber sensing technology. Specifically, the method comprises the following steps: obtaining the RBS signal of the Φ-OTDR system by acquisition; demodulating the RBS signal by an IQ demodulation method; obtaining a secondary differential signal by a secondary difference method; obtaining a VMD parameter combination according to WOA-VMD; performing VMD on the secondary differential signal according to the obtained parameter combination, and decomposing it into different IMFs; removing the low-frequency component in the secondary differential signal according to the obtained IMF to obtain a noise-reduced demodulated signal. Without changing the original hardware structure of the Φ-OTDR system, the present invention reduces the influence of frequency drift on Φ-OTDR demodulation, solves the problem of Φ-OTDR demodulation waveform distortion caused by laser frequency drift, and expands the application scope of Φ-OTDR in fields with high requirements for high sensitivity and low-frequency response.

Description

Technical Field

[0001] The present invention relates to the field of optical fiber technology, and in particular to a method for reducing the influence of frequency drift on Φ-OTDR demodulation based on WOA-VMD. Background Art

[0002] Phase-Sensitive Optical Time-Domain Reflectometry (Φ-OTDR) is an optical measurement technology used to detect and analyze changes in the structure or environment around a sensing fiber. This technology measures the intensity of backscattered light and demodulates the phase information, enabling it to detect subtle structural or environmental changes. Due to its high sensitivity, long sensing distance, and excellent dynamic detection capabilities, Φ-OTDR has been widely used in perimeter security, pipeline monitoring, geological exploration, and power equipment monitoring. Compared to point sensors, Φ-OTDR can obtain continuously distributed vibration information along the sensing fiber, making it more suitable for long-distance and large-scale monitoring.

[0003] In reality, Φ-OTDR systems are subject to a variety of interferences during use. Laser frequency drift is a particularly significant interference factor, and its impact cannot be ignored. This can distort the demodulated waveform of the Φ-OTDR, limiting its application in areas requiring high sensitivity and low-frequency response. Summary of the Invention

[0004] In order to solve the technical problems raised by the above background technology, the present invention aims to provide a method for reducing the influence of frequency drift on Φ-OTDR demodulation based on Whale Optimization Algorithm and Variational Mode Decomposition (WOA-VMD), so as to solve the problem of Φ-OTDR demodulation waveform distortion caused by laser frequency drift and expand the application scope of Φ-OTDR.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: a method for reducing the influence of frequency drift on Φ-OTDR demodulation based on WOA-VMD, comprising the following steps:

[0006] S1. Obtain the Rayleigh Backscattering (RBS) signal of the Φ-OTDR system by acquisition;

[0007] S2. Demodulate the RBS signal using an IQ demodulation method;

[0008] S3. Obtaining a secondary differential signal using a secondary differential method;

[0009] S4. Obtain VMD parameter combination according to WOA-VMD;

[0010] S5. Perform VMD on the secondary differential signal according to the obtained parameter combination to decompose it into different intrinsic mode functions (IMFs);

[0011] S6. According to the obtained IMF, the low-frequency component in the secondary differential signal is removed to obtain a noise-reduced demodulated signal.

[0012] As described above, a method for reducing the influence of frequency drift on Φ-OTDR demodulation based on WOA-VMD is characterized in that the RBS signal in S1 is:

[0013] V(t)=RE(t)cos[2πΔft+Φ(t)],

[0014] Where t represents time, R represents a constant, E(t) represents the amplitude, Δf represents the frequency shift added by the AOM, and Φ(t) represents the demodulated signal to be demodulated.

[0015] As described above, a method for reducing the influence of frequency drift on Φ-OTDR demodulation based on WOA-VMD is characterized in that the specific steps of IQ demodulation in S2 are:

[0016] S21. Multiply V(t) by cos(2πΔft) and sin(2πΔft) respectively and pass the resulting signals through a low-pass filter to obtain two orthogonal signals I(t) and Q(t).

[0017] S22. Demodulated signal Φ(t)=arctan[Q(t) / I(t)], where arctan() is an inverse tangent function.

[0018] As described above, the method for reducing the influence of frequency drift on Φ-OTDR demodulation based on WOA-VMD is characterized in that the specific steps of the secondary difference method in S3 include:

[0019] S31, setting the differential length L, where L is greater than the sum of the vibration duration length and the system spatial resolution;

[0020] S32. Select points X2 and X1 before and after the optical fiber vibration region, with X1 > X2 and a distance L between the two points. Differ the demodulated signal at point S1 from the demodulated signal at point S2 to obtain the demodulated signal between the two points, which is recorded as (X1, X2).

[0021] S33. Select two points X4 and X3 with an interval of L in the non-vibration area, and X3>X4, and obtain (X3, X4). Difference (X1, X2) and (X3, X4) to obtain a secondary differential signal.

[0022] As described above, the method for reducing the influence of frequency drift on Φ-OTDR demodulation based on WOA-VMD is characterized in that the specific steps of S4 are:

[0023] S41, constructing fitness function;

[0024] Furthermore, the fitness function is minimum sample entropy;

[0025] S42. Initialize the number of whale populations N, the individual whale positions Z = [k, α], and the maximum number of iterations M;

[0026] Where k and α represent the number of IMFs and penalty factors required to be set in VMD, respectively;

[0027] S43, establish a mathematical model of shrinkage, random search, and spiral shrinkage, specifically:

[0028] Generate a random number p in [0,1], and perform shrinkage when p<0.5 and |A|<1. Shrinkage model:

[0029] D=|CZ * (m)-Z(m)|

[0030] Z(m+1)=Z * (m)-AD

[0031] Among them, D is the distance between the best whale individual position and the average whale individual position in the whale population, Z * (m) is the best individual whale position in the mth iteration. Z(m) is the individual whale position in the mth iteration. A and C are the control coefficients:

[0032] A=2βr-β

[0033] C=2r

[0034] Where β = 2 – (2m / M), β decreases linearly from 2 to 0 during the iteration process. r is a random value in [0, 1].

[0035] When p<0.5 and |A|≥1, a random search is performed. Random search model:

[0036] D r =|CZ r (m)-Z(m)|

[0037] Z(m+1)=Z r (m)-AD r

[0038] Among them, D ris the distance between the best individual whale position and the average individual whale position in the whale population. r (m) represents the position of a random whale individual in the mth iteration.

[0039] When p≥0.5, spiral contraction is performed. Spiral contraction model:

[0040] D p =|Z * (m)-Z(m)|

[0041] Z(m+1)=e bl D p cos(2πl)+Z * (m)

[0042] Among them, D p is the distance between the best individual whale position and the average individual whale position in the whale population. b is the logarithmic spiral coefficient, and l is a random number in the interval [-1, 1].

[0043] S44, performing VMD on the secondary differential signal according to the position Z of each whale individual, calculating the fitness of each whale individual position, selecting the minimum fitness value, and obtaining the best whale individual position in the current whale population;

[0044] S45, updating the individual whale positions in the current whale population;

[0045] S46, repeat steps S33 and S45 to complete the maximum number of iterations M;

[0046] S47. Obtain the optimal individual whale position Z0 = [k0, α0] and output it. [k0, α0] is the VMD parameter combination for this time.

[0047] The advantages and beneficial effects of the present invention are:

[0048] The present invention reduces the influence of frequency drift on Φ-OTDR demodulation without changing the original Φ-OTDR system hardware structure, and solves the problem of Φ-OTDR demodulation waveform distortion caused by laser frequency drift.

[0049] The present invention improves the performance of the Φ-OTDR system and expands the application range of the Φ-OTDR in fields with high requirements for high sensitivity and low frequency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is the structure diagram of the Φ-OTDR system;

[0051] Figure 2 This is a flow chart of a method for reducing the impact of frequency drift on Φ-OTDR demodulation based on WOA-VMD;

[0052] Figure 3 Flowchart for obtaining VMD parameter combinations based on WOA-VMD;

[0053] Figure 4 It is the demodulated signal waveform of the Φ-OTDR system.

[0054] Figure 5 The demodulated signal waveform of the Φ-OTDR system processed by the present invention is shown. DETAILED DESCRIPTION

[0055] The present invention will be further described in detail below with reference to the accompanying drawings and through examples. The following examples are intended to explain the present invention but the present invention is not limited to the following examples.

[0056] The present invention provides a method for reducing the influence of frequency drift on Φ-OTDR demodulation based on WOA-VMD, the flow chart of which is as follows: Figure 2 As shown, please follow the steps below:

[0057] Figure 2 As shown in S1, the Rayleigh Backscattering (RBS) signal of the Φ-OTDR system is obtained by acquisition.

[0058] Figure 1 The following diagram shows the structure of the Φ-OTDR system. In this embodiment, a narrow linewidth laser (NLL) with a linewidth of 3 kHz emits laser light, which is split into two beams by coupler 1 (OC1). One beam is modulated into pulsed light by an acousto-optic modulator (AOM), and the other beam serves as a local reference beam. The frequency shift of the AOM is 80 MHz. The pulsed light is amplified by an optical fiber amplifier (EDFA) and injected into a single-mode fiber (SMF) through a circulator (CIR). The generated RBS light is then mixed with the local reference beam through coupler 2 (OC2). The RBS signal is detected by a photoelectric balanced detector (BPD), filtered by a bandpass filter (BPF), and collected by an acquisition card (DAQ) before being displayed on a personal computer (PC). A piezoelectric ceramic (PZT) is placed somewhere in the fiber to generate vibrations. The total length of the single-mode fiber is 3.84 km, and approximately 3.6 to 3.64 km of the fiber is wrapped around the PZT. The probe pulse width was set to 80 ns and the frequency to 400 Hz. A 1 V, 0.2 Hz sine wave was applied to the PZT. The DAQ sampling rate was set to 125 MSPS, and 400 RBS signals were sampled.

[0059] V(t)=RE(t)cos[2πΔft+Φ(t)],

[0060] Where t represents time, R represents a constant, E(t) represents the amplitude, Δf represents the frequency shift added by the AOM, and Φ(t) represents the demodulated signal to be demodulated.

[0061] Figure 2 As shown in S2, the RBS signal is demodulated by the IQ demodulation method. The specific steps are:

[0062] S21. Multiply V(t) by cos(2πΔft) and sin(2πΔft) respectively, and then pass them through a low-pass filter to obtain two orthogonal signals I(t) and Q(t).

[0063] S22. Demodulated signal Φ(t)=arctan[Q(t) / I(t)], where arctan() is an inverse tangent function.

[0064] In this embodiment, the demodulated signal waveform of the Φ-OTDR system is as follows: Figure 4 shown.

[0065] Figure 2 As shown in S3, a quadratic differential signal is obtained by using the quadratic differential method. The specific steps of the quadratic differential method include:

[0066] S31. Set the differential length L, which is greater than the sum of the vibration duration length and the system spatial resolution. In this embodiment, L=60m.

[0067] S32. Select points X2 and X1, respectively, before and after the optical fiber vibration region, with X1 > X2 and a distance L between the two points. Differ the demodulated signals at point S1 and S2 to obtain the demodulated signal between the two points, denoted as (X1, X2). In this embodiment, X1 = 3650 and X2 = 3590. (3650, 3590) is the demodulated signal between the two points.

[0068] S33. Select two points X4 and X3 in the non-vibration region, separated by L, with X3 > X4. Calculate (X3, X4). Difference (X1, X2) and (X3, X4) to obtain a secondary differential signal. In this embodiment, X3 = 3590 and X4 = 3530. The secondary differential signal is the difference between (3650, 3590) and (3590, 3520).

[0069] Figure 2 As shown in S4, the VMD parameter combination is obtained according to WOA-VMD. The flow chart of step S4 is as follows Figure 3 As shown, the specific steps are:

[0070] S41. Construct a fitness function.

[0071] Furthermore, the fitness function is minimum sample entropy.

[0072] S42. Initialize the number of whale populations N, the individual whale positions Z = [k, α], and the maximum number of iterations M.

[0073] In this embodiment, N = 20, k is in the range of [2, 9], α is in the range of [100, 5000], and M = 30. k and α represent the number of IMFs and the penalty factor required to be set in VMD, respectively.

[0074] S43, establish a shrinking surround, random search, and spiral shrinking model. Specifically:

[0075] Generate a random number p in [0,1], and perform shrinkage when p<0.5 and |A|<1. Shrinkage model:

[0076] D=|CZ * (m)-Z(m)|

[0077] Z(m+1)=Z * (m)-AD

[0078] Among them, D is the distance between the best whale individual position and the average whale individual position in the whale population, Z * (m) is the best individual whale position in the mth iteration. Z(m) is the individual whale position in the mth iteration. A and C are the control coefficients:

[0079] A=2βr-β

[0080] C=2r

[0081] Where β = 2 – (2m / M), β decreases linearly from 2 to 0 during the iteration process. r is a random value in [0, 1].

[0082] When p<0.5 and |A|≥1, a random search is performed. Random search model:

[0083] D r =|CZ r (m)-Z(m)|

[0084] Z(m+1)=Z r (m)-AD r

[0085] Among them, D r is the distance between the best individual whale position and the average individual whale position in the whale population. r (m) represents the position of a random whale individual in the mth iteration.

[0086] When p≥0.5, spiral contraction is performed. Spiral contraction model:

[0087] D p =|Z * (m)-Z(m)|

[0088] Z(m+1)=e bl Dp cos(2πl)+Z * (m)

[0089] Among them, D p is the distance between the best individual whale position and the average individual whale position in the whale population. b is the logarithmic spiral coefficient, and l is a random number in the interval [-1, 1].

[0090] S44. Perform VMD on the secondary differential signal according to the position Z of each whale individual, calculate the fitness of each whale individual position, select the minimum fitness value, and obtain the best whale individual position in the current whale population.

[0091] S45. Update the individual whale positions in the current whale population according to step S43.

[0092] S46. Repeat steps S44 and S45 to complete the maximum number of iterations M.

[0093] S47. Obtain the optimal individual whale position Z0 = [k0, α0] and output it. [k0, α0] is the VMD parameter combination for this time.

[0094] Figure 2 As shown in S5, VMD is performed on the secondary differential signal according to the obtained parameter combination to decompose it into different IMFs.

[0095] Figure 2 As shown in S6, based on the obtained IMF, the low-frequency component in the secondary differential signal is removed to obtain a noise-reduced demodulated signal.

[0096] In this embodiment, the waveform of the demodulated signal of the Φ-OTDR system processed by the present invention is as follows: Figure 5 As shown. Figure 4 By comparing the waveforms of the original demodulated signals, it can be found that the present invention solves the problem of Φ-OTDR demodulated waveform distortion caused by laser frequency drift.

[0097] In this embodiment, the Φ-OTDR system has a simple structure and reduces the impact of frequency drift on Φ-OTDR demodulation without changing its hardware structure, thereby expanding the application range of Φ-OTDR in fields requiring high sensitivity and low-frequency response.

[0098] The above is only one embodiment of the present invention, not all or the only embodiment. Any equivalent transformation of the technical solution of the present invention made by ordinary technicians in this field after reading the specification of the present invention is covered by the claims of the present invention.

Claims

1. A method for reducing the effect of frequency drift on phase-sensitive optical time-domain reflectometry (Φ-OTDR) demodulation based on Whale Optimization Algorithm and Variational Mode Decomposition (WOA-VMD), characterized by: The specific implementation steps are as follows: S1. Obtain the Rayleigh Backscattering (RBS) signal of the Φ-OTDR system by acquisition; S2. Demodulate the RBS signal using an IQ demodulation method; S3. Obtaining a secondary differential signal using a secondary differential method; S4. Obtain VMD parameter combination according to WOA-VMD; S5. Perform VMD on the secondary differential signal according to the obtained parameter combination to decompose it into different intrinsic mode functions (IMFs); S6. According to the obtained IMF, the low-frequency component in the secondary differential signal is removed to obtain a noise-reduced demodulated signal.

2. The method for reducing the influence of frequency drift on Φ-OTDR demodulation based on WOA-VMD according to claim 1, characterized in that: The RBS signal in S1 is: V(t)=RE(t)cos[2πΔft+Φ(t)], Where t represents time, R represents a constant, E(t) represents the amplitude, Δf represents the frequency shift added by the AOM, and Φ(t) represents the demodulated signal to be demodulated.

3. The method for reducing the influence of frequency drift on Φ-OTDR demodulation based on WOA-VMD according to claim 1, characterized in that: The specific steps of IQ demodulation in S2 are: S21. Multiply V(t) by cos(2πΔft) and sin(2πΔft) respectively and pass the resulting signals through a low-pass filter to obtain two orthogonal signals I(t) and Q(t). S22. Demodulated signal Φ(t)=arctan[Q(t) / I(t)], where arctan() is an inverse tangent function.

4. The method for reducing the influence of frequency drift on Φ-OTDR demodulation based on WOA-VMD according to claim 1, characterized in that: The specific steps of the quadratic difference method in S3 include: S31, setting the differential length L, where L is greater than the sum of the vibration duration length and the system spatial resolution; S32. Select points X2 and X1 before and after the optical fiber vibration region, with X1 > X2 and a distance L between the two points. Differ the demodulated signal at point S1 from the demodulated signal at point S2 to obtain the demodulated signal between the two points, which is recorded as (X1, X2). S33. Select two points X4 and X3 with an interval of L in the non-vibration area, and X3>X4, and obtain (X3, X4). Difference (X1, X2) and (X3, X4) to obtain a secondary differential signal.

5. The method for reducing the influence of frequency drift on Φ-OTDR demodulation based on WOA-VMD according to claim 1, characterized in that: The specific steps of S4 are: S41, constructing fitness function; Furthermore, the fitness function is minimum sample entropy; S42. Initialize the number of whale populations N, the individual whale positions Z = [k, α], and the maximum number of iterations M; Where k and α represent the number of IMFs and penalty factors required to be set in VMD, respectively; S43, establish a mathematical model of shrinkage, random search, and spiral shrinkage, specifically: Generate a random number p in [0,1]. When p<0.5 and |A|<1, perform shrinkage and encirclement. The shrinkage and encirclement model is: D=|CZ * (m)-Z(m)| Z(m+1)=Z * (m)–AD Among them, D is the distance between the best whale individual position and the average whale individual position in the whale population, Z * (m) is the best whale individual position in the mth iteration, Z(m) is the whale individual position in the mth iteration, and A and C are the control coefficients: A＝2βr–β C＝2r Where β = 2 – (2m / M), β decreases linearly from 2 to 0 during the iteration process, and r is a random value in [0, 1]; When p<0.5 and |A|≥1, perform random search, random search model: D r =|CZ r (m)-Z(m)| Z(m+1)=Z r (m)-AD r Among them, D r is the distance between the best individual whale position and the average individual whale position in the whale population, Z r (m) represents the position of a random whale individual in the mth iteration; When p≥0.5, spiral contraction is performed, spiral contraction model: D p =|Z * (m)-Z(m)| Z(m+1)=e bl D p cos(2πl)+Z * (m) Among them, D p is the distance between the best individual whale position and the average individual whale position in the whale population, b is the logarithmic spiral coefficient, and l is a random number in the interval [-1,1]; S44, performing VMD on the secondary differential signal according to the position Z of each whale individual, calculating the fitness of each whale individual position, selecting the minimum fitness value, and obtaining the best whale individual position in the current whale population; S45, updating the individual whale positions in the current whale population; S46, repeat steps S33 and S45 to complete the maximum number of iterations M; S47. Obtain the optimal individual whale position Z0 = [k0, α0] and output it. [k0, α0] is the VMD parameter combination for this time.

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