Distributed optical fiber temperature measurement system denoising method based on CPO-ICEEMDAN

Abstract

The invention provides a CPO-ICEEMDAN-based de-noising method for a distributed optical fiber temperature measurement system, and the method comprises the steps: enabling an external temperature sensing optical fiber to be correctly connected to a system, enabling the system to sequentially carry out the data collection, the algorithm initialization of a CPO algorithm, the signal decomposition of the ICEEMDAN, the calculation of optimization parameters, and the data de-noising, and finally obtaining a proper de-noised temperature signal. The method has the beneficial effects that 1, the noise amplitude weight and the noise adding frequency of the ICEEMDAN of the self-adaptive white noise are optimized by utilizing a crown porcupine optimization algorithm (CPO), unnecessary calculation in the ICEEMDAN is reduced, convergence is accelerated, and the denoising effect is improved; and 2, compared with EMD, the ICEEMDAN reduces the phenomenon of mode aliasing, and has a more accurate noise adjustment strategy. And 3, the minimum information entropy is adopted as a fitness function to better obtain temperature signal data with stratum signal characteristics. And 4, IMF components with large correlation coefficients are added, and finally obtained de-noised temperature signal data have credibility.

Description

Technical Field

[0001] The invention relates to the field of optical fiber signal data processing of a temperature measurement system, and mainly relates to a denoising method for a distributed optical fiber temperature measurement system based on CPO-ICEEMDAN. Background Art

[0002] The principle of distributed optical fiber temperature measurement technology is to use the Raman scattering effect to measure the temperature changes distributed along the optical fiber. The laser emits a beam of light, which enters the temperature-sensitive optical fiber after being modulated by the optical module. By detecting the backscattered light information in the optical fiber, the temperature signal distributed along the optical fiber can be obtained.

[0003] Distributed fiber optic temperature signal logging is a technology that uses optical fiber to sense temperature signals from different formations to analyze surrounding formation characteristics or downhole events. It has the advantages of strong real-time monitoring results and high reliability of interpretation results. However, the optical signal emitted by the laser is not stable, which leads to the optical signal collected by the optical signal demodulator at different times of the same optical cable is also unstable. There is a lot of noise in the demodulated temperature signal data. It is difficult to accurately interpret the temperature changes of the production layer less than 0.5 meters in the production formation. The temperature signal data collected by the distributed fiber optic temperature measurement system is decomposed by the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN). The reprocessing and addition of the obtained IMF components can achieve a certain denoising effect. On this basis, the crown porcupine optimization algorithm (CPO) is introduced to optimize the parameters that need to be set in the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN) algorithm: noise amplitude weight and the number of noise additions, which can achieve better denoising effect. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for denoising a distributed optical fiber temperature measurement system based on CPO-ICEEMDAN.

[0005] The problem solved by the present invention is to improve the signal-to-noise ratio of temperature signal data of a distributed optical fiber temperature measurement system, optimize the noise amplitude weight and the number of noise additions in a complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN) by a crown porcupine optimization algorithm (CPO), reduce unnecessary calculations in the ICEEMDAN algorithm, improve the decomposition efficiency to a certain extent, accelerate convergence, improve the denoising effect of the ICEEMDAN algorithm, and finally improve the signal-to-noise ratio of temperature signal data, highlight its characteristic signals, and use them for analysis of the production of a generating layer in well logging.

[0006] The object of the present invention is achieved through the following technical solution: A method for denoising a distributed optical fiber temperature measurement system based on CPO-ICEEMDAN, comprising the following steps:

[0007] (1) Equipment connection: The equipment side is normally connected to the long-distance optical fiber for data collection to ensure that the interface is unified and free of foreign matter;

[0008] (2) Data collection: Use a distributed optical fiber temperature measurement system to connect the temperature measurement optical cable to collect and save temperature signal data from production wells;

[0009] (3) CPO algorithm initialization: In order to achieve the effect of optimizing the noise amplitude weight and the number of noise additions of the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN), the initialization parameters of the crown porcupine optimization algorithm (CPO) are set, and the minimum information entropy is used as the fitness function;

[0010] (4) ICEEMDAN decomposition signal: The temperature signal data collected in step (2) is decomposed using the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN) to obtain each IMF component and calculate its minimum information entropy;

[0011] (5) Calculate the optimization parameters: According to the parameters set by the CPO algorithm, iterate the step (4) until the conditions that meet the initialization settings are calculated to obtain the optimal noise amplitude weight and the number of noise additions;

[0012] (6) Data denoising: The optimal parameters obtained in the above steps are used to reset the CPO-ICEEMDAN method, decompose the temperature signal data, obtain the optimal IMF components and reorganize them to obtain the denoised temperature signal data.

[0013] Furthermore, the crested porcupine optimization algorithm (CPO) comprises the following specific steps:

[0014] The crown porcupine optimization algorithm (CPO) is used to optimize the noise amplitude weight and the parameter values ​​of the noise addition times of the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN). First, the initialization settings are performed:

[0015]

[0016] Where N′ means the population size is set to 4, is the i-th candidate solution in the search space, and are the lower and upper limits of the search range, respectively. The search range is set as follows: the optimization range of the noise amplitude weight is set to [0.1, 0.3], and the optimization range of the number of noise additions is [20, 100]. is a random number between 0 and 1.

[0017] The number of optimizations performed using the Cyclic Population Reduction (CPR) technique in the Crowned Porcupine Optimization (CPO) algorithm:

[0018]

[0019] Where T is a variable that determines the number of loops, t is the current function evaluation, and T max is the maximum number of function evaluations, % represents the remainder or modulo operator, N min is the minimum number of individuals in the newly generated population, so the population size cannot be less than N min The population size in the algorithm is set to 4, the maximum number of iterations is 8, and the number of optimization variables is 2;

[0020]

[0021] in is the best solution to the evaluation function (minimum information entropy), is a vector generated between the current CP and a randomly selected CP from the population, used to represent the position with a maximum setting of 8 at algorithm iteration t, τ 1 is a random number based on normal distribution, τ 2 is a random value in the interval [0,1]. The mathematical formula is as follows:

[0022]

[0023] Furthermore, the ICEEMDAN decomposes the signal, and the specific steps are as follows:

[0024] The collected temperature signal data is subjected to the complete ensemble empirical mode decomposition of adaptive white noise (CEEMDAN). 1 [w (i) ] is added to the original temperature signal data x, where w (i) Represents the i-th white noise added:

[0025] x (i) =x+β 0 E 1 [w (i) ]

[0026] Use Empirical Mode Decomposition (EMD) to calculate x (i) The local mean of Calculate each k IMF value in

[0027] Furthermore, the calculation optimization parameters include the following specific steps:

[0028] According to the initial set number of iterations 8, the CPO algorithm is used to calculate the minimum information entropy of the results of each complete ensemble empirical mode decomposition (ICEEMDAN) of adaptive white noise, and its correlation coefficient is solved. The optimal correlation coefficient is then brought back into the crown porcupine optimization algorithm (CPO) algorithm.

[0029] Furthermore, the data denoising comprises the following specific steps:

[0030] The crested porcupine optimization algorithm (CPO) algorithm with reset parameters is combined with the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN) to decompose the temperature signal data, remove the relevant IMF components with correlation coefficients lower than 0.08, and add the remaining IMFs to obtain the final denoised temperature signal data.

[0031] The beneficial effects of the present invention are:

[0032] 1. The complete ensemble empirical mode decomposition (ICEEMDAN) with adaptive white noise adds noise processing compared to the ensemble empirical mode decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMDAN). It can improve the decomposition quality by adaptively adjusting the noise level added to the temperature signal data.

[0033] 2. The complete ensemble empirical mode decomposition (ICEEMDAN) with adaptive white noise reduces the phenomenon of modal aliasing compared with ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition (CEEMDAN);

[0034] 3. Use the iterative calculation of the crown porcupine optimization algorithm (CPO) to calculate the more important parameters in the decomposition effect of the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN): white noise amplitude weight and the number of noise additions. Reduce the amount of calculation in ICEEMDAN and better find the optimal decomposition effect within the setting range. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process of the present invention.

[0036] Figure 2 This is the original temperature signal data graph.

[0037] Figure 3 It is the IMF component map after the optimal ICEEMDAN decomposition.

[0038] Figure 4 It is the spectrum diagram of each IMF component after the optimal ICEEMDAN decomposition.

[0039] Figure 5This is the correlation coefficient diagram of each IMF component after the optimal ICEEMDAN decomposition.

[0040] Figure 6 This is the fitness trend chart calculated after each ICEEMDAN decomposition under the CPO algorithm.

[0041] Figure 7 This is a graph showing how the hyperparameters of ICEEMDAN change with the number of iterations.

[0042] Figure 8 This is the CPO-ICEEMDAN denoised signal image. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the technical solution of the present invention, the preferred implementation scheme of the present invention is described below in conjunction with the accompanying drawings and specific examples.

[0044] The present invention provides a method for denoising a distributed optical fiber temperature measurement system based on CPO-ICEEMDAN, the process is as follows: Figure 1 As shown, after confirming that the temperature-sensitive optical fiber is correctly connected, the device side first performs a temperature stability test on the internal constant temperature module. After ensuring that the temperature is stable within a certain period of time, the system collects and demodulates the optical original signal into temperature signal data. Then, by setting the parameters of the crown porcupine optimization algorithm (CPO), such as population initialization, number of iterations, and total population size, the optimization range of the two optimization parameters, the white noise amplitude weight and the number of noise additions of the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN), needs to be set. Next, the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN) is used to decompose the signal, calculate each IMF component, and judge it with the minimum information entropy as the fitness function. The CPO algorithm is used for iterative calculation. If the termination is not reached, the population position is updated and the signal is decomposed again. After the number of iterations is completed, the optimal decomposition parameter combination is saved and brought back into the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN). The CPO-ICEEMDAN method is used to decompose the signal and obtain the optimal IMF component. The correlation coefficient is calculated, and those with a correlation coefficient less than 0.08 are removed. The remaining ones are added to obtain the final denoised temperature signal data.

[0045] 1. CPO algorithm initialization:

[0046] The crown porcupine optimization algorithm (CPO) is used to optimize the noise amplitude weight and the parameter values ​​of the noise addition times of the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN). First, the initialization settings are performed:

[0047]

[0048] Where N′ represents the population size, is the i-th candidate solution in the search space, and are the lower and upper limits of the search range, respectively. is a random number between 0 and 1. Input: Set the optimization range of the noise amplitude weight to [0.1, 0.3], the optimization range of the number of noise additions to [20, 100], the number of iterations to 8, and the population size to 4.

[0049] 2. ICEEMDAN decomposes the signal:

[0050] The collected temperature signal data is subjected to the complete ensemble empirical mode decomposition of adaptive white noise (CEEMDAN). 1 [w (i) ] is added to the original temperature signal data x, where w (i) Represents the i-th white noise added:

[0051] x (i) =x+β 0 E 1 [w (i) ]

[0052] Use Empirical Mode Decomposition (EMD) to calculate x (i) The local mean of Calculate each k IMF value in Input: optical fiber temperature signal data, output: IMF components after ICEEMDAN decomposes the optical fiber temperature signal data. Figure 5 、IMF spectrum diagram as follows Figure 6 , the correlation coefficient is Figure 7 and minimum information entropy;

[0053] 3. Calculate optimization parameters:

[0054] The number of iterations set for the CPO algorithm is 8. The temperature signal data is decomposed using the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN) for 8 cycles, and the minimum information entropy is calculated for the decomposition results to solve the correlation coefficient until the 8th iteration is completed. The optimal correlation coefficient is brought back into the crown porcupine optimization algorithm (CPO) algorithm. Input: Each IMF component of the temperature signal data decomposed by the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN) for 8 times and its minimum information entropy and correlation coefficient. Output: The noise amplitude weight and the number of noise additions of the optimal complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN).

[0055] 4. Data denoising:

[0056] The optimal parameters obtained after the number of iterations are completed are brought into the crown porcupine optimization algorithm (CPO) algorithm and combined with the complete ensemble empirical mode decomposition of adaptive white noise (ICEEMDAN) to decompose the temperature signal data, remove the relevant IMF components with correlation coefficients lower than 0.08, and add the remaining IMFs to obtain the final denoised temperature signal data. Input: Complete ensemble empirical mode decomposition (ICEEMDAN) of adaptive white noise with optimal noise amplitude weights and noise addition times and CPO algorithm. Output: Denoised temperature signal data obtained by adding the IMF components with correlation coefficients greater than 0.08 after decomposition. Figure 8 .

[0057] It is understandable that, for those skilled in the art, any equivalent replacement or change to the technical solution and inventive concept of the present invention should fall within the protection scope of the claims attached to the present invention.

Claims

1. A method for denoising a distributed optical fiber temperature measurement system based on CPO-ICEEMDAN, characterized in that: The steps include: (1) Equipment connection: The equipment side is normally connected to the long-distance optical fiber for data collection; (2) Data collection: Use a distributed optical fiber temperature measurement system to connect the temperature measurement optical cable to collect and save temperature signal data from production wells; (3) CPO algorithm initialization: set the initialization parameters of the CPO algorithm and use the minimum information entropy as the fitness function; (4) ICEEMDAN decomposition signal: The temperature signal data collected in step (2) is decomposed using the complete ensemble empirical mode decomposition (ICEEMDAN) of adaptive white noise to obtain each IMF component and calculate its minimum information entropy; (5) Calculate the optimization parameters: According to the parameters set by the CPO algorithm, iterate the step (4) until the conditions that meet the initialization settings are calculated to obtain the optimal noise amplitude weight and the number of noise additions; (6) Data denoising: The optimal parameters obtained in the above steps are reset to decompose the temperature signal data, obtain the best IMF components and reorganize them to obtain the denoised temperature signal data.

2. The method for denoising a distributed optical fiber temperature measurement system based on CPO-ICEEMDAN according to claim 1, characterized in that: The CPO algorithm is used to optimize the parameter values ​​of the noise amplitude weight and the number of noise additions of the complete ensemble empirical mode decomposition (ICEEMDAN) of adaptive white noise. First, perform the initialization settings: Where N′ means the population size is set to 4, is the i-th candidate solution in the search space, and are the lower and upper limits of the search range, respectively. The search range is set as follows: the optimization range of the noise amplitude weight is set to [0.1, 0.3], and the optimization range of the number of noise additions is [20, 100]. is a random number between 0 and 1; The CPO algorithm uses the cyclic population reduction technique CPR to perform optimization times: Where T is a variable that determines the number of loops, t is the current function evaluation, and T max is the maximum number of function evaluations, % represents the remainder or modulo operator, N min is the minimum number of individuals in the newly generated population. The population size cannot be less than N min ; The population size in the algorithm is set to 4, the maximum number of iterations is 8, and the number of optimization variables is 2; in is the best solution to the evaluation function, is a vector generated between the current CP and a randomly selected CP from the population, used to represent the position of the maximum setting of 8 at algorithm iteration t, τ1 is a random number based on a normal distribution, τ2 is a random value in the interval [0,1], generated The mathematical formula is as follows:

3. The method for denoising a distributed optical fiber temperature measurement system based on CPO-ICEEMDAN according to claim 2, characterized in that: The collected temperature signal data is subjected to the complete set empirical mode decomposition CEEMDAN of adaptive white noise, and E1[w (i) ] is added to the original temperature signal data x, where w (i) Represents the i-th white noise added: x (i) =x+β0E1[w (i) ] Use EMD to calculate x (i) The local mean of Calculate each k IMF value in 4. The method for denoising a distributed optical fiber temperature measurement system based on CPO-ICEEMDAN according to claim 3 is characterized in that: Calculate the optimization parameters. The specific steps are as follows: according to the initial set number of iterations 8, use the CPO algorithm to calculate the minimum information entropy of the results of each complete ensemble empirical mode decomposition (ICEEMDAN) of adaptive white noise, solve its correlation coefficient, and get the optimal correlation coefficient and bring it back into the CPO algorithm.

5. The method for denoising a distributed optical fiber temperature measurement system based on CPO-ICEEMDAN according to claim 4, characterized in that: Data denoising, the specific steps are: combining the CPO algorithm with reset parameters with the complete set empirical mode decomposition of adaptive white noise to decompose the temperature signal data, removing the relevant IMF components with correlation coefficients lower than 0.08, and adding the remaining IMFs to obtain the final denoised temperature signal data.

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