A method and device for denoising solid tide signals

Abstract

The embodiments of this specification provide a method and device for denoising solid tide signals. The method includes: collecting the original noisy data of solid tide observations and forming a verification dataset for the denoising model; introducing signal decomposition technology to decompose the original noisy signal into multiple intrinsic mode functions (IMFs) with the same length; constructing a comprehensive evaluation system for intrinsic mode functions, and comprehensively scoring the noise content of each intrinsic mode function IMF based on multiple evaluation indicators; ranking according to the scoring results, and linearly reconstructing the IMFs within a preset ranking from front to back to complete denoising. The present invention has high noise rejection and signal restoration capabilities, so it has high denoising performance and reliability, and can avoid secondary denoising.

Description

Technical Field

[0001] This document relates to the technical field of signal processing, and particularly to a method and device for denoising solid tide signals. Background Art

[0002] Solid tides are generally considered to be the overall periodic deformation of the Earth caused by the tidal forces of the sun or the moon, which directly affect the changes in the data records of deformation observation instruments. Due to the limitations of the complex environment monitored by instruments, solid tide signals often contain a large amount of environmental noise, which is manifested as a large number of burrs, spikes, jumps, or frequent oscillations on the solid tide curve. If the noisy signal is not effectively denoised in time, it may interfere with the normal observation of the solid tide curve and contaminate the earthquake precursor observation records, resulting in prominent problems in earthquake prevention and mitigation such as delayed earthquake warning time and inaccurate earthquake prediction. This places high requirements on the denoising effect of the signal denoising model and is worthy of in-depth study.

[0003] With the rapid development of artificial intelligence technology and refined data analysis, signal denoising methods based on the principle of signal decomposition have attracted more and more attention from scholars. It can decompose a one-dimensional initial signal into several intrinsic mode functions (IMFs) and a residual component of the same length. These components have significant differences in waveform characteristics (amplitude size, oscillation frequency, number of spikes, energy concentration, etc.) and different degrees of noise. How to reasonably select and discard these components is one of the key and difficult problems in signal denoising technology.

[0004] The existing technologies have the following defects: First, some of the existing new denoising methods are extremely complex. They often construct double denoising or double decomposition to denoise the noisy components (IMFs) obtained from the first decomposition, which will greatly waste denoising time and affect signal processing efficiency. Second, the waveforms of the multiple intrinsic mode functions (IMFs) obtained by signal decomposition are different, and traditional methods have not established a reasonable evaluation system to screen and evaluate their noise levels. Third, the evaluation of the effect of the denoising model by traditional methods mainly relies on one or two indicators, with a small number and incomplete consideration, and does not judge from multiple angles such as error size, correlation, and chaos degree. Summary of the Invention

[0005] One or more embodiments of this specification provide a method for denoising solid tide signals, including:

[0006] S1. Collect the original noisy data of solid tide observations and form a verification dataset for the denoising model;

[0007] S2. Introduce signal decomposition technology and decompose the original noisy signal into multiple intrinsic mode functions IMF with the same length;

[0008] S3. Construct an integrated evaluation system of intrinsic mode functions, and comprehensively score the noise content of each intrinsic mode function (IMF) based on multiple evaluation indicators;

[0009] S4. Rank according to the scoring results, and linearly reconstruct the IMFs within the preset ranking from the front to the back according to the ranking results to complete denoising.

[0010] One or more embodiments of this specification provide a solid tide signal denoising device, including:

[0011] Data acquisition module: used to acquire the original noisy solid tide observation data and form a verification dataset for the denoising model;

[0012] Data processing module: used to introduce signal decomposition technology to decompose the original noisy signal into multiple intrinsic mode functions (IMFs) with the same length;

[0013] Integrated scoring module: used to construct an integrated evaluation system of intrinsic mode functions, and comprehensively score the noise content of each intrinsic mode function (IMF) based on multiple evaluation indicators;

[0014] Denoising module: used to rank according to the scoring results, and linearly reconstruct the IMFs within the preset ranking from the front to the back according to the ranking results to complete denoising.

[0015] One or more embodiments of this specification provide an electronic device, including:

[0016] A processor; and,

[0017] A memory arranged to store computer-executable instructions, and when the computer-executable instructions are executed, the processor implements the steps of the above-mentioned solid tide signal denoising method.

[0018] One or more embodiments of this specification provide a storage medium for storing computer-executable instructions, and when the computer-executable instructions are executed, the steps of the above-mentioned solid tide signal denoising method are implemented.

[0019] The solid tide signal denoising method proposed by the present invention has high noise rejection and signal restoration capabilities, taking into account the retention of the main components of the signal and the smoothness effect of the curve. Based on a multi-dimensional IMF evaluation index system, a reasonable criterion for judging the quality of IMFs is constructed, which can better distinguish the effective components and noise components from several intrinsic mode functions, and recombine half or a small number of intrinsic mode functions with almost no noise. It has high denoising performance and reliability, avoids secondary denoising, is more accurate than some traditional signal denoising models, and meets the requirements of practicality and innovation.

[0020] The above description is only an overview of the technical solution of the present invention. In order to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are specifically exemplified below. Description of the Drawings

[0021] In order to more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0022] Figure 1 Flowchart of a method for denoising solid tide signals provided for one or more embodiments of this specification;

[0023] Figure 2 Specific flowchart of a method for denoising solid tide signals provided for one or more embodiments of this specification;

[0024] Figure 3 Schematic diagram of the final denoising result of a method for denoising solid tide signals provided for one or more embodiments of this specification;

[0025] Figure 4 Schematic diagram of the composition of a device for denoising solid tide signals provided for one or more embodiments of this specification;

[0026] Figure 5 Schematic diagram of the structure of an electronic device provided for one or more embodiments of this specification. Detailed Embodiments

[0027] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will clearly and completely describe the technical solutions in one or more embodiments of this specification in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only some embodiments of this specification, rather than all embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this document.

[0028] Method Embodiment

[0029] According to an embodiment of the present invention, a method for denoising solid tide signals is provided. Figure 1 Flowchart of a method for denoising solid tide signals provided for one or more embodiments of this specification, asFigure 1 As shown in Figure 1 , the solid tide signal denoising method according to an embodiment of the present invention specifically includes:

[0030] S1. Collect the original noisy solid tide observation data and form a verification dataset for the denoising model.

[0031] Under special environmental conditions, collect N pieces of original noisy solid tide observation data obtained by the ground deformation monitoring instrument to form a verification dataset for the denoising model; wherein, the special environment includes thunderstorm weather, high temperature and cold, artificial blasting, etc.; the ground deformation monitoring instrument includes a VP tiltmeter, a VS tiltmeter, a DSQ water tube tiltmeter, or an SSY extensometer, etc.

[0032] S2. Introduce a signal decomposition technique to decompose the original noisy signal into multiple intrinsic mode functions IMF with the same length.

[0033] Methods such as empirical mode decomposition EMD, wavelet transform WT, EEMD, CEEMD, etc. can be used to decompose the original noisy data. In this embodiment, the improved complete ensemble empirical mode decomposition with adaptive noise ICEEMDAN technology is adopted. The specific process is as Figure 2 shown. Decompose the i-th (i = 1, 2,..., K) solid tide signal, that is, the original noisy data, into m intrinsic mode functions IMF i,j and 1 residual signal R i , specifically as follows:

[0034]

[0035] Among them, X i is the solid tide signal; IMF i,j is the j-th intrinsic mode function decomposed from the i-th solid tide signal, j = 1, 2,..., m; R i is the residual signal. When using methods such as empirical mode decomposition EMD, wavelet transform WT, EEMD, CEEMD, etc. for signal decomposition, there is a residual signal component. When using the ICEEMDAN technology for signal decomposition, there is no residual signal component and it can be directly linearly reconstructed (restored) to X i , that is:

[0036]

[0037] S3. Construct an integrated evaluation system for intrinsic mode functions, and comprehensively score the noise content of each intrinsic mode function IMF based on multiple evaluation indicators.

[0038] In this embodiment, a series of characteristic parameters that can characterize the relationship between the one-dimensional vector and the original signal or evaluate the complexity of its own signal are introduced and refined to construct an integrated evaluation system for intrinsic mode functions. The evaluation indicators of the integrated evaluation system for intrinsic mode functions include: the correlation coefficient, mutual information MI, sample entropy SampEn, mean absolute error MAE, mean absolute percentage error MAPE, determination coefficient R i,j , root mean square error RMSE, mean square error MSE, variance SSE, and adjusted determination coefficient Adj-R 2 ; 2

[0039] The specific calculation method of the mean absolute error MAE is as follows:

[0040]

[0041] The specific calculation method of the mean absolute percentage error MAPE is as follows:

[0042]

[0043] The specific calculation method of the mean square error MSE is as follows:

[0044]

[0045] The specific calculation method of the root mean square error RMSE is as follows:

[0046]

[0047] Determination coefficient R 2 The specific calculation method is as follows:

[0048]

[0049] Adjusted determination coefficient Adj-R 2 The specific calculation method is as follows:

[0050]

[0051] The specific calculation method of the sample entropy SampEn is as follows:

[0052]

[0053] In the above formulas (1)-(7), y i represents the i-th value in the original noisy signal X i ; represents the i-th element value in the IMF i,j vector; y i ​represents the mean of all values in an original noisy signal; n is the length of the signal, i.e., the number of elements in the IMF; p is the original noisy signal X i and the IMF i,j The number of model variables after linear regression; w is the reconstruction dimension, usually preset to 1 or 2; r is the threshold, generally preset to 0.1 times or 0.2 times the standard deviation of the IMF i,j ; B (w) (r) is the overall arithmetic mean of the matching degrees between each reconstruction vector and other reconstruction vectors; B (w+1) (r) is the overall arithmetic mean of the matching degrees between each reconstruction vector and other reconstruction vectors after the dimension is increased to w + 1.

[0054] Based on multiple evaluation indicators, a comprehensive score is given to the noise level of each intrinsic mode function IMF. The specific method is as follows:

[0055] First, according to the different meanings of the evaluation indicators, the evaluation indicators are divided into benefit-type indicators and cost-type indicators. The benefit-type indicators include the correlation coefficient, mutual information, determination coefficient, and adjusted determination coefficient. The larger the value of the benefit-type indicator, the higher the correlation or similarity between the IMF and the original signal; the cost-type indicators include the mean absolute error, mean absolute percentage error, root mean square error, mean square error, and variance. The smaller the value of the cost-type indicator, the smaller the error between the IMF and the original signal, and more signal amplitude energy is retained; the sample entropy is also used as a cost-type indicator. The smaller the value of the sample entropy, the smoother and more stable the IMF curve, and the less noise content;

[0056] Calculate the parameter values of each evaluation indicator for each intrinsic mode function IMF i,j to obtain the characteristic matrix S of the solid tide signal IMF component 0 ; For the component characteristic matrix S 0 perform standardization processing to obtain the standardized characteristic matrix S std ;

[0057] Set the weight values occupied by each indicator to form a one-dimensional weight vector w = [w 1 , w 2 , …, w 10 , where w k (k = 1, 2, …, n) represents the weight value of the kth evaluation indicator, that is, the importance of this indicator for noise evaluation. In this embodiment, equal weights are taken, that is, it is considered that the importance of each evaluation indicator is equal; Set the reference sequence x 0 = {x 0 (k)|k = 1, 2, …, 10} and the comparison sequence x i = {x i(k)|k = 1, 2, …, 10; i = 1, 2, …, m}, where each element value of the reference sequence is the maximum value 1 of the standardized vector, and the comparison sequence is the i-th row of the standardized feature matrix S std Define the proportionality coefficient ρ ∈ [0, 1]. In this embodiment, the value of the proportionality coefficient is defined as 0.5;

[0058] Calculate the evaluation object IMF according to the weight vector, reference sequence and comparison sequence i,j The correlation coefficient ξ between IMF and each evaluation index i (k) is as follows:

[0059]

[0060] Obtain m × 10 ξ i (k) values to form the correlation coefficient matrix C m×10 , and its element value represents the association measurement value between m evaluation object IMFs i,j And 10 evaluation indexes;

[0061] Weightedly sum the 10 ξ i,j corresponding to each evaluation object IMF i (k) values with the respective element values of the weight vector w to obtain the r i,j value of each evaluation object IMF i , that is, the scoring result. The specific calculation is as follows:

[0062]

[0063] S4. Rank according to the scoring result, and select the IMFs within the preset ranking from front to back for linear reconstruction to complete denoising.

[0064] Arrange the above-obtained evaluation object IMFs i,j in descending order according to their r i values, and perform linear reconstruction on the IMFs ranked in the top R = round(m / 2). Then the final denoising result can be obtained. When the calculation result of R is not an integer, round it to an integer; i,j

[0065] Repeat the above steps to complete the denoising work for all K solid tide signals. The final denoising result of the solid tide noisy signal by the method of this embodiment is as Figure 3 shown.

[0066] The beneficial effects of the present invention are as follows:

[0067] The solid tide signal denoising method proposed by the present invention has high noise rejection and signal restoration capabilities, taking into account the retention of the main components of the signal and the smoothness effect of the curve. A reasonable evaluation criterion for the quality of IMF is constructed based on a multi-dimensional IMF evaluation index system, which can better distinguish the effective components and noise components from several intrinsic mode functions, and recombine half or a small number of intrinsic mode functions with almost no noise, having high denoising performance and reliability, avoiding secondary denoising, and being more accurate than some traditional signal denoising models, meeting the requirements of practicality and innovation.

[0068] Device Embodiment

[0069] According to an embodiment of the present invention, there is provided a solid tide signal denoising device. Figure 4 As shown in the schematic diagram of the composition of a solid tide signal denoising device provided for one or more embodiments of this specification, Figure 4 as shown, the solid tide signal denoising device according to the embodiment of the present invention specifically includes:

[0070] Data acquisition module 40: used to acquire the original noisy data of solid tide observations and form a verification data set for the denoising model;

[0071] Data processing module 42: used to introduce signal decomposition technology to decompose the original noisy signal into multiple intrinsic mode functions IMF with the same length;

[0072] Comprehensive scoring module 44: used to construct a comprehensive evaluation system for intrinsic mode functions and comprehensively score the noise content of each intrinsic mode function IMF based on multiple evaluation indicators;

[0073] Denoising module 46: used to rank according to the scoring results, and linearly reconstruct the IMF within a preset ranking from front to back according to the ranking results to complete denoising.

[0074] The embodiment of the present invention is a device embodiment corresponding to the above method embodiment. The specific operations of each module can be understood with reference to the description of the method embodiment and will not be elaborated here.

[0075] Device Embodiment 1

[0076] The embodiment of the present invention provides an electronic device, as Figure 5 shown, including: a memory 50, a processor 52, and a computer program stored on the memory 50 and executable on the processor 52. When the computer program is executed by the processor 52, the following method steps are implemented:

[0077] S1. Acquire the original noisy data of solid tide observations and form a verification data set for the denoising model;

[0078] S2. Introduce signal decomposition technology to decompose the original noisy signal into multiple intrinsic mode functions (IMFs) with the same length;

[0079] S3. Construct a comprehensive evaluation system for intrinsic mode functions, and comprehensively score the noise level of each intrinsic mode function (IMF) based on multiple evaluation indicators;

[0080] S4. Rank according to the scoring results, and select the IMFs within the preset ranking from front to back for linear reconstruction to complete denoising.

[0081] Device Embodiment II

[0082] An embodiment of the present invention provides a computer-readable storage medium, on which an implementation program for information transmission is stored. When the program is executed by a processor 52, the following method steps are implemented:

[0083] S1. Collect the original noisy data of solid tide observations and form a validation dataset for the denoising model;

[0084] S2. Introduce signal decomposition technology to decompose the original noisy signal into multiple intrinsic mode functions (IMFs) with the same length;

[0085] S3. Construct a comprehensive evaluation system for intrinsic mode functions, and comprehensively score the noise level of each intrinsic mode function (IMF) based on multiple evaluation indicators;

[0086] S4. Rank according to the scoring results, and select the IMFs within the preset ranking from front to back for linear reconstruction to complete denoising.

[0087] The computer-readable storage medium described in this embodiment includes, but is not limited to, ROM, RAM, magnetic disk, optical disc, etc.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for denoising solid tide signals, characterized in that, it includes: S1. Collect the original noisy data of solid tide observations and form a verification dataset for the denoising model; S2. Introduce signal decomposition technology to decompose the original noisy signal into multiple intrinsic mode functions (IMFs) with the same length; the specific method is: Using the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) technique, the th solid tide signal is decomposed into m intrinsic mode functions and one residual signal , as follows: ； wherein, is the solid tide signal; is the intrinsic mode function, ; is the residual signal; S3. Construct a comprehensive evaluation system for intrinsic mode functions, and comprehensively score the noise content of each intrinsic mode function (IMF) based on multiple evaluation indicators; The evaluation indicators of the intrinsic mode function comprehensive evaluation system include: each intrinsic mode function 's correlation coefficient, mutual information MI, sample entropy SampEn, mean absolute error MAE, mean absolute percentage error MAPE, determination coefficient R 2 , root mean square error RMSE, mean square error MSE, variance SSE, and adjusted determination coefficient Adj-R 2 ; The specific method for comprehensively scoring the noise content of each intrinsic mode function (IMF) based on multiple evaluation indicators is: Calculate each intrinsic mode function The parameter values of each evaluation index to obtain the IMF component feature matrix of the solid tide signal ; For the component feature matrix Perform normalization processing to obtain the normalized feature matrix ; Set the weight values occupied by each index to form a one-dimensional weight vector , where represents the weight value of the th evaluation index; set the reference sequence and the comparison sequence , where each element value of the reference sequence is the maximum value 1 of the standardized vector, and the comparison sequence is the i-th row of the standardized feature matrix ; define the proportionality coefficient ; Calculate the evaluation object based on the weight vector, reference sequence, and comparison sequence and the correlation coefficient between each evaluation index , and the obtained values form a correlation coefficient matrix ; For each evaluation object corresponding value and the weight vector each element value of, weighted summation can be performed to obtain the value of each evaluation object , that is, the scoring result; S4. Rank according to the scoring results, and select the IMFs within the preset ranking from front to back for linear reconstruction to complete denoising.

2. The method according to claim 1, characterized in that, the collection of the original noisy data of solid tide observations and the formation of a verification dataset for the denoising model are specifically: Under special environmental conditions, collect N pieces of original noisy data of solid tide observations obtained by terrain deformation monitoring instruments to form a verification dataset for the denoising model; among them, the special environment includes thunderstorm weather, high temperature and cold, and artificial blasting; the terrain deformation monitoring instruments include VP tilt meters, VS tilt meters, DSQ water tube tilt meters or SSY extensometers.

3. The method according to claim 1, characterized in that, the evaluation indicators are divided into benefit-type indicators and cost-type indicators. The benefit-type indicators include correlation coefficient, mutual information, determination coefficient, and adjusted determination coefficient. The larger the value of the benefit-type indicator, the higher the correlation or similarity between the IMF and the original signal; the cost-type indicators include mean absolute error, mean absolute percentage error, root mean square error, mean square error, and variance. The smaller the value of the cost-type indicator, the smaller the error between the IMF and the original signal, and more signal amplitude energy is retained; sample entropy is also used as a cost-type indicator. The smaller the value of the sample entropy, the smoother and more stable the IMF curve, and the less noise content.

4. The method according to claim 1, characterized in that, the ranking according to the scoring results and the selection of the IMFs within the preset ranking from front to back for linear reconstruction are specifically: Sort each in descending order according to its value, and perform linear reconstruction on the top R = round(m / 2) to obtain the final denoising result. When the calculation result of R is not an integer, round it to an integer; Repeat the above steps to complete the denoising of all K solid tide signals.

5. A device for denoising solid tide signals, characterized in that, it includes: A data acquisition module: used to collect the original noisy data of solid tide observations and form a verification dataset for the denoising model; A data processing module: used to introduce signal decomposition technology to decompose the original noisy signal into multiple intrinsic mode functions (IMFs) with the same length; specifically: The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) technique is used to decompose the th solid tide signal into m intrinsic mode functions and one residual signal , as follows: ； Among them, is the solid tide signal; is the intrinsic mode function, ; is the residual signal; A comprehensive scoring module: used to construct a comprehensive evaluation system for intrinsic mode functions and comprehensively score the noise content of each intrinsic mode function (IMF) based on multiple evaluation indicators; The evaluation indicators of the comprehensive evaluation system of the intrinsic mode functions include: each intrinsic mode function 's correlation coefficient, mutual information MI, sample entropy SampEn, mean absolute error MAE, mean absolute percentage error MAPE, determination coefficient R 2 , root mean square error RMSE, mean square error MSE, variance SSE, and adjusted determination coefficient Adj-R 2 ; The specific method for comprehensively scoring the noise content of each intrinsic mode function (IMF) based on multiple evaluation indicators is: Calculate each intrinsic mode function The parameter values of each evaluation index to obtain the IMF component feature matrix of the solid tide signal ; For the component feature matrix Perform normalization processing to obtain the normalized feature matrix ; Set the weight values occupied by each index to form a one-dimensional weight vector , where represents the weight value of the th evaluation index; set the reference sequence and the comparison sequence , where each element value of the reference sequence is the maximum value 1 of the standardized vector, and the comparison sequence is the th row of the standardized feature matrix; define the proportionality coefficient ; Calculate the correlation coefficients between the evaluation object and each evaluation index based on the weight vector, reference sequence, and comparison sequence The obtained values form a correlation coefficient matrix ; ; For each evaluation object corresponding value and the weight vector each element value of, weighted summation can be performed to obtain the value of each evaluation object , that is, the scoring result; A denoising module: used to rank according to the scoring results and select the IMFs within the preset ranking from front to back for linear reconstruction to complete denoising.

6. An electronic device, characterized in that, it includes: A processor; And, A memory arranged to store computer-executable instructions that, when executed, cause the processor to implement the steps of the solid tide signal denoising method according to any one of claims 1 to 4.

7. A storage medium, characterized in that it is used to store computer-executable instructions that, when executed, implement the steps of the solid tide signal denoising method according to any one of claims 1 to 4.

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