Multi-scale electromagnetic field component denoising method, device and equipment and storage medium

Abstract

The application provides a multiscale electromagnetic field component denoising method and device, equipment and storage medium, including: using a polynomial function mode, the outer expansion of the original time series two end part data chain is carried out, and the outer expansion data is obtained; the stationary signal in the outer expansion data is extracted by applying a stationary component preprocessing method, and stationary data is obtained; the power frequency harmonic interference in the stationary data is suppressed by using an adaptive wavelet threshold processing mode, and the power frequency removal data is obtained; the EMD decomposition is carried out on the power frequency removal data in the time domain, the high-frequency component and the low-frequency component are denoised respectively, then the denoised high-frequency component, the low-frequency component and the residual modulus are reconstructed by linear reconstruction to obtain the reconstructed denoised signal. To a certain extent, the high-frequency random noise is suppressed; the useful information in the original electromagnetic field component is greatly retained, the signal-to-noise ratio is improved, and the influence of human experience is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geophysical exploration, in particular to a multi-scale electromagnetic field component denoising method, device, equipment and storage medium. BACKGROUND

[0002] The natural electromagnetic field of the magnetotelluric sounding observation has the characteristics of weak signal and wide frequency band, and is easily disturbed by various noises. According to the source of the noise, the noise in the MT signal mainly includes four types of field source noise, human noise, input end noise and static noise. Human noise is the most common, the most serious interference and the most complex noise characteristics. The SNR of the MT data containing noise is low, and the error of the impedance and resistivity calculation results obtained by the final inversion is obvious, thereby causing the inversion result to be poor.

[0003] Improving the SNR of electromagnetic measurement data is of great significance to the calculation of apparent resistivity curves and the interpretation of later data. After decades of development, effective noise analysis and processing methods such as Robust processing, remote reference technology, wavelet transform, Hilbert-Huang transform have been formed. At present, various denoising methods of magnetotelluric have their own advantages, but also have their own shortcomings. The cross power spectrum method and the weighted average cross power spectrum method can effectively suppress and remove unrelated noise signals. However, due to the existence of unrelated noise and correlated noise in the receiving sequence, and the limited recording time, the error of the synthesized signal obtained by superimposing and summing the signals cannot meet the normal distribution, thereby reducing the suppression and elimination effect of the cross power spectrum method on noise, and the effect is very small; the remote reference method effectively avoids the pollution and interference of the same noise on the data recorded by two recording stations, and has obvious suppression effect on eliminating local correlated noise, but the error bar of single-point data in the magnetotelluric data after remote reference processing becomes larger. Especially the electric signal is seriously polluted and interfered by noise; the Robust method requires to reduce the weight of individual 'flying point' and improve the overall quality of the recorded signal; the HHT and EMD (EMD: Empirical Mode Decomposition) method has very good analysis and processing effect on non-stationary and nonlinear multi-component signals, but still has problems such as low calculation efficiency, end wing, noise aliasing and the like; the wavelet analysis plays an important role in time-frequency and multi-resolution analysis, has strong analysis ability, high adaptability and good time-frequency localization characteristics, and can well represent the local characteristics of the signal in the time-frequency domain, but is limited by the selection of wavelet basis function, and the wavelet denoising has certain limitations.

[0004] In view of the above problems, multi-scale combined filtering denoising is carried out based on the different characteristics of electromagnetic noise of different scales in the time domain and the frequency domain, the quality of the magnetotelluric signal is improved, and the comprehensive analysis and denoising processing of the electromagnetic field component of the magnetotelluric recording signal are realized. SUMMARY

[0005] To solve the above problems, the application provides a multi-scale electromagnetic field component denoising method, device, equipment and storage medium.

[0006] The application provides a multi-scale electromagnetic field component denoising method, device, equipment and storage medium.

[0007] S1: using a polynomial function mode, performing outer expansion on the data chain at both ends of the original time sequence to obtain expanded data;

[0008] S2: applying a stationary component preprocessing method, extracting a stationary signal in the expanded data to obtain stationary data;

[0009] S3: using an adaptive wavelet threshold processing mode, suppressing power frequency harmonic interference in the stationary data to obtain power frequency removed data;

[0010] S4: performing EMD decomposition on the power frequency removed data in the time domain, denoising high-frequency components and low-frequency components respectively, and then reconstructing the denoised high-frequency components, low-frequency components and residual components to obtain a reconstructed denoised signal.

[0011] In some embodiments, the specific formula for using the polynomial function mode to perform outer expansion on the data chain at both ends of the original time sequence is:

[0012]

[0013] wherein, is the original signal, represents the signal after expansion, a and b are expansion coefficients, and j is a signal decomposition polynomial; a = [a0…aj-1] and b = [b0…bj-1], m m , M 1 is the number of left expansion terms of the signal, k is the number of original signal terms, n 0 is the number of right expansion terms of the signal.

[0014] In some embodiments, the specific formula for applying the stationary component preprocessing method to extract the stationary signal in the expanded data is:

[0015]

[0016] wherein is a time sequence with a data amount of N, Y'(t) is the extracted stationary component signal, Y(t, 0:M-h) , Y (t, h / 2:M-h / 2), Y(t, h:M) ​In order to intercept time series of different lengths, n is the length of the extended data, M=N+2n, t is time, h is a sliding window, N is the length of the time series, that is, the amount of signal data.

[0017] In some embodiments, before suppressing the power frequency harmonic interference in the stationary data, the method further comprises:

[0018] Transforming the time domain signal to the frequency domain, and performing WT (wavelet transform) transformation on the real part and the imaginary part respectively.

[0019] In some embodiments, the adaptive wavelet threshold selection method comprises:

[0020] The optimal variable threshold is obtained by combining the fixed threshold and the unbiased likelihood estimation soft threshold, which is suitable for the condition that the signal-to-noise ratio is small:

[0021]

[0022] wherein,

[0023] : SURE threshold;

[0024] : global threshold;

[0025] : the i-th wavelet coefficient;

[0026] N: length of time series;

[0027] s, u are the SURE and global threshold calculation parameters respectively.

[0028] In some embodiments, the specific process of performing EMD (empirical mode decomposition) on the de-power-frequency data in the time domain to obtain the high-frequency component and the low-frequency component comprises:

[0029] Performing EMD on the signal in the time domain, and performing secondary CEEMD (complete ensemble empirical mode decomposition) decomposition on the obtained IMF (intrinsic mode function) component to denoise and reconstruct.

[0030] In some embodiments, the specific method of denoising the high-frequency component and the low-frequency component respectively comprises:

[0031] Adopting adaptive truncated threshold denoising for the high-frequency component;

[0032] Adopting zero setting denoising or soft threshold denoising for the low-frequency component.

[0033] The second aspect embodiment of the application provides a multi-scale electromagnetic field component denoising device, comprising:

[0034] a data extension module, a stationary component preprocessing module, an adaptive wavelet threshold processing module, and a data denoising and reconstruction module;

[0035] The data extension module: the data chain at both ends of the original time sequence is extended by using a polynomial function method to obtain extended data.

[0036] The stationary component preprocessing module: a stationary signal in the extended data is extracted by using a stationary component preprocessing method to obtain stationary data.

[0037] The adaptive wavelet threshold processing module: the stationary data is processed by using an adaptive wavelet threshold processing method to suppress power frequency harmonic interference to obtain power frequency removed data.

[0038] The data denoising and reconstruction module: the power frequency removed data is subjected to EMD decomposition in the time domain, and high-frequency components and low-frequency components are denoised, respectively, and then the denoised high-frequency components, low-frequency components and residual components are reconstructed to obtain a reconstructed denoised signal.

[0039] The third aspect of the application provides a multi-scale electromagnetic field component denoising device, comprising a memory and a processor, and the memory stores a computer program, which is executed by the processor to execute the multi-scale electromagnetic field component denoising method of the first aspect.

[0040] The fourth aspect of the application provides a storage medium, which stores a computer program that can be executed by one or more processors and can be used to implement the multi-scale electromagnetic field component denoising method of the first aspect.

[0041] The multi-scale electromagnetic field component denoising method, device, equipment and storage medium provided by the application have the following beneficial effects:

[0042] The multi-scale electromagnetic field component denoising method provided by the application improves the shortcomings of single method denoising, respectively performs filtering processing in the frequency domain and the time domain, realizes comprehensive analysis, processing and denoising of the electromagnetic field component of the earth, extends the edge data of the signal in the data preprocessing stage to avoid missing endpoint data processing and the wing effect, extracts the stationary component in the signal and simultaneously performs moving average filtering to suppress high-frequency random noise to a certain extent, effectively solves the aliasing effect in EMD decomposition, improves the EMD threshold denoising effect, can perform denoising processing on continuous acquisition signals, greatly retains useful information in the original electromagnetic field component, improves the signal-to-noise ratio, and designs an adaptive threshold according to the noise characteristics to reduce the influence of human experience. BRIEF DESCRIPTION OF DRAWINGS

[0043] The present application will be described in more detail below based on the embodiments and with reference to the drawings.

[0044] Fig. 1(a)-(b) are the noisy signal containing multi-scale noise and the denoised signal after recovery and reconstruction in the numerical simulation provided by the embodiments of the present application;

[0045] Fig. 2(a)-(c) are the spectrum of the noisy signal containing multi-scale noise, the spectrum of the denoised signal after recovery and reconstruction and the spectrum of the original signal in the numerical simulation provided by the embodiments of the present application;

[0046] Figure 3 The signal-to-noise ratio of the noisy signal containing multi-scale noise, the denoised signal after recovery and reconstruction and the original signal in the numerical simulation provided by the embodiments of the present application;

[0047] Fig. 4(a)-(b) are the noisy signal containing multi-scale noise and the denoised signal after recovery and reconstruction of the electric field component continuously collected in a certain work area in China in the actual data processing provided by the embodiments of the present application;

[0048] Fig. 5(a)-(b) are the noisy signal containing multi-scale noise and the denoised signal after recovery and reconstruction of the magnetic field component continuously collected in a certain work area in China in the actual data processing provided by the embodiments of the present application;

[0049] Fig. 6(a)-(b) are the original signal apparent resistivity map and phase map of a certain work area in China in the actual data processing provided by the embodiments of the present application;

[0050] Fig. 7(a)-(b) are the apparent resistivity map and phase map after multi-scale filtering and denoising of a certain work area in China in the actual data processing provided by the embodiments of the present application;

[0051] Figure 8 The flow chart of the multi-scale electromagnetic field component denoising method provided by the embodiments of the present application.

[0052] In the drawings, the same components use the same reference numerals, and the drawings are not drawn according to the actual scale. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be described in further detail below with reference to the drawings, and the described embodiments should not be regarded as limiting the present application, and all other embodiments obtained by those skilled in the art without making creative efforts fall within the scope of protection of the present application.

[0054] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but which can be understood as a description of all possible embodiments, or as a description of a different subset of all possible embodiments, and which can be combined, mutatis mutandis, with each other.

[0055] If there are similar descriptions of "first\second\third" in the application file, the following description is added: In the following description, the terms "first\second\third" referred to are only to distinguish similar objects, and do not represent a specific order of the objects. Understandably, "first\second\third" can be interchanged in a specific order or sequence as allowed, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only, and is not intended to be limiting of the present application.

[0057] Before introducing a multi-scale electromagnetic field component denoising method provided by the embodiments of the present application, the problems existing in the related art are briefly introduced:

[0058] Improving the signal-to-noise ratio of electromagnetic measurement data is of great significance to the apparent resistivity curve calculation and later data interpretation. After decades of development, effective noise analysis and processing methods such as Robust processing, remote reference technology, wavelet transform, Hilbert-Huang transform, etc. have been formed. At present, each type of noise removal method for magnetotelluric has its own advantages, but also has its own disadvantages. The cross-power spectrum method and the weighted average cross-power spectrum method can effectively suppress and remove unrelated noise signals. However, due to the existence of unrelated noise and correlated noise in the receiving sequence, and the limited recording time, the error of the synthesized signal obtained by superimposing and summing the signals cannot meet the normal distribution, thereby the cross-power spectrum method has a limited effect on noise suppression and elimination, and the effect is very small; the remote reference method effectively avoids the pollution and interference of the same noise on the data recorded by two recording stations, and has obvious suppression effect on eliminating local correlated noise, but the single-point data error bar in the magnetotelluric data processed by the remote reference method becomes larger. Especially the electric signal is seriously polluted by noise and interference; the Robust method requires to reduce the weight of individual "flying points" and improve the overall quality of the recorded signal; the HHT and EMD methods have very good analysis and processing effect on non-stationary and nonlinear multi-component signals, but still have problems such as low calculation efficiency, end wing, noise aliasing and the like; the wavelet analysis has prominent effect in time-frequency and multi-resolution analysis, has strong analysis ability, high adaptability and good time-frequency localization characteristics, and can well represent the local characteristics of the signal in the time-frequency domain, but the wavelet denoising has certain limitations due to the limitation of the selection of wavelet basis function.

[0059] In view of the above problems, multi-scale combined filter denoising is carried out based on different characteristics of electromagnetic noise in time domain and frequency domain, so as to improve the quality of magnetotelluric signal and realize comprehensive analysis and denoising processing of electromagnetic field components of magnetotelluric recorded signal.

[0060] Based on the problems existing in the related art, the embodiment of the present application provides a multi-scale electromagnetic field component denoising method, which is applied to a multi-scale electromagnetic field component denoising device. The multi-scale electromagnetic field component denoising device can be an electronic device, such as a computer, a mobile terminal, etc. The functions realized by the multi-scale electromagnetic field component denoising method provided by the embodiment of the present application can be realized by calling program code by the processor of the electronic device, wherein the program code can be saved in the computer storage medium.

[0061] Embodiment one

[0062] The embodiment of the present application provides a multi-scale electromagnetic field component denoising method, Figure 8 The implementation process schematic diagram of the multi-scale electromagnetic field component denoising method provided by the embodiment of the present application is shown as Figure 8 ​

[0063] S1: extending the data chain at both ends of the original time series by a polynomial function to obtain extended data;

[0064] S2: extracting stationary signals from the extended data by using a stationary component preprocessing method to obtain stationary data;

[0065] S3: suppressing power frequency harmonic interference in the stationary data by using an adaptive wavelet threshold processing method to obtain power frequency removed data;

[0066] S4: performing EMD decomposition on the power frequency removed data in the time domain, denoising the high-frequency component and the low-frequency component respectively, and then reconstructing the denoised high-frequency component, low-frequency component and residual modulus to obtain a reconstructed denoised signal.

[0067] The multi-scale electromagnetic field component denoising method provided by the application improves the shortcomings of single method denoising, respectively performs filtering processing in the frequency domain and the time domain, realizes comprehensive analysis, processing and denoising of the electromagnetic field component of the earth's magnetism, expands the edge data of the signal in the data preprocessing stage to avoid missing endpoint data processing and wing effect, extracts stationary components in the signal and simultaneously performs moving average filtering to suppress high-frequency random noise to a certain extent, effectively solves the aliasing effect in EMD decomposition, improves the effect of EMD threshold denoising, can perform denoising processing on continuous acquisition signals, greatly retains useful information in the original electromagnetic field component, improves the signal-to-noise ratio, and designs an adaptive threshold according to the characteristics of the noise to reduce the influence of human experience.

[0068] Embodiment Two

[0069] Based on the foregoing embodiments, the embodiments of the application further provide a multi-scale electromagnetic field component denoising method, comprising:

[0070] S21: extending the data chain at both ends of the original time series by a polynomial function to obtain extended data;

[0071] In some embodiments, the extension of the data chain at both ends of the original time series by the polynomial function can effectively reflect the attenuation of the signal, and relatively stably restore the change trend of the signal. The specific formula is:

[0072]

[0073] wherein, is the original signal, denotes the signal after extension, a and b are extension coefficients, and j is a signal decomposition polynomial; a = [a0…a m ]; b = [b0…b m ].M 1 is the number of left extension edge items of the signal, k is the number of original signal items, n 0 is the number of right extension edge items of the signal.

[0074] S22: According to the high irrelevance of the input signal and the noise, a stationary component preprocessing method is applied to extract the stationary signal in the extended data, and random noise can be suppressed to a certain extent to obtain stationary data;

[0075] S23: An adaptive wavelet threshold processing method is used to denoise the wavelet coefficients of each layer, suppress the power frequency harmonic interference in the stationary data, and obtain power frequency removed data;

[0076] S24: The EMD decomposition is carried out on the power frequency removed data in the time domain, the high-frequency component and the low-frequency component are denoised respectively, and then the denoised high-frequency component, the denoised low-frequency component and the residual modulus are reconstructed to obtain the reconstructed denoised signal.

[0077] The multi-scale electromagnetic field component denoising method provided by the application improves the shortcomings of single method denoising, respectively carries out filtering processing in the frequency domain and the time domain, realizes comprehensive analysis, processing and denoising of the electromagnetic field component of the earth's magnetism; the signal edge data is extended in the data preprocessing stage to avoid endpoint data processing loss and wing effect; the stationary component in the signal is extracted, and the moving average filtering is carried out at the same time, which suppresses the high-frequency random noise to a certain extent; the aliasing effect in the EMD decomposition is effectively solved, and the effect of the EMD threshold denoising is improved; the continuous acquisition signal can be denoised, the useful information in the original electromagnetic field component is greatly retained, and the signal-to-noise ratio is improved; the adaptive threshold is designed according to the noise characteristics, and the influence of human experience is reduced.

[0078] Embodiment three

[0079] Based on the foregoing embodiment, the embodiment of the application further provides a multi-scale electromagnetic field component denoising method, comprising:

[0080] S31: The data chain at both ends of the original time sequence is extended by using a polynomial function method to obtain extended data;

[0081] In some embodiments, the data chain at both ends of the original time sequence is extended by using a polynomial function method, which can effectively reflect the attenuation of the signal, and relatively stably restore the change trend of the signal. The specific formula is:

[0082]

[0083] wherein, is the original signal, a, b are extension coefficients, j is signal decomposition polynomial; a = [a0…a m ]; b = [b0…b m ], M 1 is the number of left extension terms of the signal, k is the number of original signal terms, n 0 is the number of right extension terms of the signal;

[0084] S32: According to the input signal and the high noise irrelevance, the pre-processing method of stationary component is applied to extract the stationary signal in the external expansion data, and the random noise can be suppressed to a certain extent, and stationary data is obtained;

[0085] In some embodiments, the specific formula of the pre-processing method of stationary component for extracting the stationary signal in the external expansion data is:

[0086]

[0087] Wherein is a time series with data amount N, Y'(t) is the extracted stationary component signal, Y(t, 0:M-h) , Y (t, h / 2:M-h / 2), Y(t, h:M) is the time series with different lengths, n is the length of the extension data, M = N + 2n, t is time, h is the sliding window, and N is the length of the time series, that is, the data amount of the signal.

[0088] S33: The adaptive wavelet threshold processing method is used to denoise the wavelet coefficients of each layer, suppress the power frequency harmonic interference in the stationary data, and obtain the de-power frequency data.

[0089] S34: The EMD decomposition is carried out on the de-power frequency data in the time domain, the high-frequency component and the low-frequency component are denoised respectively, and then the denoised high-frequency component, low-frequency component and residual modulus are reconstructed to obtain the reconstructed denoised signal.

[0090] In the embodiments of the application, the shortcomings of single method denoising are improved, filtering processing is carried out in the frequency domain and the time domain respectively, comprehensive analysis, processing and denoising of the geomagnetic electromagnetic field component are realized, the signal edge data is expanded in the data preprocessing stage to avoid endpoint data processing loss and wing effect, the stationary component in the signal is extracted, and the moving average filtering is carried out at the same time, which suppresses the high-frequency random noise to a certain extent, effectively solves the aliasing effect in EMD decomposition, improves the effect of EMD threshold denoising, and can denoise the continuous acquisition signal, greatly retains the useful information in the original electromagnetic field component.

[0091] Embodiment four

[0092] Based on the foregoing various embodiments, the embodiments of the application further provide a multi-scale electromagnetic field component denoising method, the method comprising:

[0093] Based on the foregoing embodiments, the embodiments of the application further provide a multi-scale electromagnetic field component denoising method, comprising:

[0094] S41: using a polynomial function method, the original time series of two end part data chain is extended, and the extended data is obtained;

[0095] In some embodiments, the polynomial function method is used to extend the original time series of two end part data chain, which can effectively reflect the signal attenuation, and restore the signal change trend more stably. The specific formula is:

[0096]

[0097] wherein, is the original signal, is the signal after extension, a and b are extension coefficients, and j is a signal decomposition polynomial; a = [a0…a m ]; b = [b0…b m ], M 1 is the number of left extension terms of the signal, k is the number of original signal terms, n 0 is the number of right extension terms of the signal;

[0098] S42: according to the input signal and the noise height unrelated, the pre-processing method of stationary component is applied to extract the stationary signal in the extended data, which can also suppress random noise to a certain extent, and stationary data is obtained;

[0099] In some embodiments, the specific formula of the pre-processing method of stationary component for extracting the stationary signal in the extended data is:

[0100]

[0101] wherein is a time series with data amount N, Y'(t) is the extracted stationary component signal, Y(t, 0:M-h) , Y (t, h / 2:M-h / 2), Y(t, h:M) is the time series with different lengths, n is the length of the extended data, M = N + 2n, t is time, h is a sliding window, and N is the length of the time series, i.e. the data amount of the signal.

[0102] S43: using an adaptive wavelet threshold processing method, the wavelet coefficients of each layer are denoised, the power frequency harmonic interference in the stationary data is suppressed, and the power frequency removed data is obtained;

[0103] In some embodiments, before suppressing the power harmonic interference in the stationary data by using the adaptive wavelet threshold processing method, the method further comprises:

[0104] transforming the time domain signal to the frequency domain, i.e. the time domain signal is transformed to the frequency domain by Fourier transform, and the real part and the imaginary part are respectively subjected to WT (wavelet transform) transform;

[0105] S44: performing EMD decomposition on the de-power data in the time domain, respectively denoising the high-frequency component and the low-frequency component, and then obtaining a reconstructed denoised signal by linear reconstruction of the denoised high-frequency component, the denoised low-frequency component and the residual modulus.

[0106] The multi-scale electromagnetic field component denoising method provided by the embodiments of the present application improves the shortcomings of single method denoising, respectively performs filtering processing in the frequency domain and the time domain, realizes comprehensive analysis, processing and denoising of the electromagnetic field component of the earth's magnetism, expands the edge data of the signal in the data preprocessing stage to avoid missing of endpoint data processing and wing effect, extracts the stationary component in the signal and simultaneously performs moving average filtering, which suppresses the high-frequency random noise to a certain extent, effectively solves the aliasing effect in EMD decomposition, improves the effect of EMD threshold denoising, can perform denoising processing on continuous acquisition signals, greatly retains the useful information in the original electromagnetic field component, improves the signal-to-noise ratio, and designs an adaptive threshold according to the characteristics of the noise to reduce the influence of human experience.

[0107] Embodiment five

[0108] Based on the foregoing various embodiments, the embodiments of the present application further provide a multi-scale electromagnetic field component denoising method, which comprises:

[0109] Based on the foregoing embodiments, the embodiments of the present application further provide a multi-scale electromagnetic field component denoising method, which comprises:

[0110] S51: performing outer expansion on the two end part data chains of the original time sequence by using a polynomial function method to obtain outer expanded data;

[0111] In some embodiments, the outer expansion on the two end part data chains of the original time sequence by using the polynomial function method can effectively reflect the attenuation of the signal, and relatively stably restore the change trend of the signal, and the specific formula is:

[0112]

[0113] wherein, is the original signal, denotes the signal after expansion, a and b are expansion coefficients, and j is a signal decomposition polynomial; a = [a0…a m ]; b = [b0…bm ], M 1 is the number of left extension edge items of the signal, k is the number of original signal items, n 0 is the number of right extension edge items of the signal;

[0114] S52: According to the input signal and the high noise, a stationary component preprocessing method is applied to extract the stationary signal in the extended data, and random noise can be suppressed to a certain extent to obtain stationary data;

[0115] In some embodiments, the specific formula of the stationary component preprocessing method for extracting the stationary signal in the extended data is:

[0116]

[0117] wherein is a time series with a data amount of N, Y'(t) is the extracted stationary component signal, Y(t, 0:M-h) 、 Y (t, h / 2:M-h / 2), Y(t, h:M) is a time series with different lengths, n is the length of the extended data, M=N+2n, t is time, h is a sliding window, and N is the length of the time series, i.e., the data amount of the signal.

[0118] S53: An adaptive wavelet threshold processing method is used to denoise the wavelet coefficients of each layer, suppress the power frequency harmonic interference in the stationary data, and obtain power frequency removed data;

[0119] In some embodiments, before the adaptive wavelet threshold processing method is used to suppress the power frequency harmonic interference in the stationary data, the method further comprises:

[0120] transforming the time domain signal to the frequency domain, and performing WT transformation on the real part and the imaginary part, respectively;

[0121] In some embodiments, the selection method of the adaptive wavelet threshold value comprises:

[0122] The optimal variable threshold value is obtained by combining the fixed threshold value and the unbiased likelihood estimation soft threshold value, which is suitable for the condition that the signal-to-noise ratio is small:

[0123]

[0124] wherein,

[0125] : SURE threshold value;

[0126] : global threshold value;

[0127] : the i-th wavelet coefficient;

[0128] N: length of time series;

[0129] s, u respectively, SURE and global threshold calculation parameters.

[0130] S54: performing EMD decomposition on the de-power-frequency data in the time domain, respectively denoising high-frequency components and low-frequency components, and then obtaining a reconstructed denoised signal through linear reconstruction of the denoised high-frequency components, low-frequency components and residual modulus.

[0131] The multi-scale electromagnetic field component denoising method provided by the embodiments of the present application improves the shortcomings of single method denoising, respectively performs filtering processing in the frequency domain and the time domain, realizes comprehensive analysis, processing and denoising of the electromagnetic field components of the earth's magnetism; in the data preprocessing stage, the edge data of the signal is expanded to avoid missing of endpoint data processing and wing effect; the stationary components in the signal are extracted, and the moving average filtering is performed at the same time, which suppresses the high-frequency random noise to a certain extent; the aliasing effect in the EMD decomposition is effectively solved, and the effect of the EMD threshold denoising is improved; the continuous acquisition signal can be denoised, which greatly retains the useful information in the original electromagnetic field components and improves the signal-to-noise ratio; the adaptive threshold is designed according to the noise characteristics, and the influence of human experience is reduced.

[0132] Embodiment six

[0133] Based on the foregoing embodiments, the embodiments of the present application further provide a multi-scale electromagnetic field component denoising method, which comprises:

[0134] Based on the foregoing embodiments, the embodiments of the present application further provide a multi-scale electromagnetic field component denoising method, which comprises:

[0135] S61: performing outer expansion on the two end part data chains of the original time series by using a polynomial function method to obtain expanded data;

[0136] In some embodiments, the outer expansion on the two end part data chains of the original time series by using the polynomial function method can effectively reflect the signal attenuation and relatively stably restore the change trend of the signal, and the specific formula is:

[0137]

[0138] wherein, is the original signal, represents the signal after expansion, a and b are expansion coefficients, and j is a signal decomposition polynomial; a = [a0…a m ]; b = [b0…b m ], M 1 is the number of left expansion terms of the signal, k0 is the number of right extension items of the original signal, n 0 is the number of right extension items of the original signal;

[0139] S62: According to the input signal and the high noise, the pre-processing method of stationary component is applied to extract the stationary signal in the extended data, and random noise can be suppressed to a certain extent to obtain stationary data;

[0140] In some embodiments, the specific formula of the pre-processing method of stationary component applied to extract the stationary signal in the extended data is:

[0141]

[0142] Wherein is a time series with data amount N, Y'(t) is the extracted stationary component signal, Y(t, 0:M-h) , Y (t, h / 2:M-h / 2), Y(t, h:M) is the time series with different lengths, n is the length of the extended data, M=N+2n, t is time, h is the sliding window, and N is the length of the time series, that is, the data amount of the signal.

[0143] S63: Adopting the adaptive wavelet threshold processing method, the noise of each layer wavelet coefficient is removed, and the power frequency harmonic interference in the stationary data is suppressed to obtain the de-power frequency data;

[0144] In some embodiments, before the adaptive wavelet threshold processing method is used to suppress the power frequency harmonic interference in the stationary data, the method further comprises:

[0145] Transforming the time domain signal to the frequency domain, and respectively performing WT transformation on the real part and the imaginary part;

[0146] In some embodiments, the selection method of the adaptive wavelet threshold value comprises:

[0147] The optimal variable threshold value is obtained by combining the fixed threshold value and the unbiased likelihood estimation soft threshold value, which is suitable for the condition that the signal-to-noise ratio is small:

[0148]

[0149] Wherein,

[0150] : SURE threshold value;

[0151] : Global threshold value;

[0152] : The i-th wavelet coefficient;

[0153] N: Length of time series;

[0154] s, u SURE and global threshold calculation parameters, respectively.

[0155] S64: Perform EMD decomposition on the de-power frequency data in the time domain, denoise the high-frequency component and the low-frequency component respectively, and then obtain a reconstructed denoised signal by linear reconstruction of the denoised high-frequency component, low-frequency component and residual modulus;

[0156] In some embodiments, the specific process of performing EMD decomposition on the de-power frequency data in the time domain, i.e., the signal processing method, includes:

[0157] Perform EMD decomposition on the signal in the time domain, and perform secondary CEEMD decomposition denoising and reconstruction on the IMF components obtained by decomposition, i.e., perform CEEMD decomposition on the intrinsic modal component (IMF) as the original signal based on EMD decomposition.

[0158] The multi-scale electromagnetic field component denoising method provided by the embodiments of the present application improves the shortcomings of single method denoising, respectively performs filtering processing in the frequency domain and the time domain, realizes comprehensive analysis, processing and denoising of the electromagnetic field components of the earth's magnetism; in the data preprocessing stage, the signal edge data is expanded to avoid endpoint data processing loss and wing effect; the stationary component in the signal is extracted, and the moving average filtering is performed at the same time, which suppresses the high-frequency random noise to a certain extent; the aliasing effect in EMD decomposition is effectively solved, and the effect of EMD threshold denoising is improved; the continuous acquisition signal can be denoised, which greatly retains the useful information in the original electromagnetic field component and improves the signal-to-noise ratio; the adaptive threshold is designed according to the noise characteristics, and the influence of human experience is reduced.

[0159] Embodiment seven

[0160] Based on the foregoing various embodiments, the embodiments of the present application further provide a multi-scale electromagnetic field component denoising method, which comprises:

[0161] Based on the foregoing embodiments, the embodiments of the present application further provide a multi-scale electromagnetic field component denoising method, which comprises:

[0162] S71: Perform external expansion on the two end part data chains of the original time series by using a polynomial function method to obtain external expansion data;

[0163] In some embodiments, the external expansion on the two end part data chains of the original time series by using a polynomial function method can effectively reflect the signal attenuation and relatively stably restore the change trend of the signal, and the specific formula is:

[0164]

[0165] wherein, is the original signal, represents the signal after extension, a, b are extension coefficients, and j is a signal decomposition polynomial; a = [a0…a m ]; b = [b0…b m ], M 1 is the number of left extension terms of the signal, k is the number of original signal terms, n 0 is the number of right extension terms of the signal;

[0166] S72: According to the input signal and the high noise irrelevance, a pre-processing method of stationary component is applied to extract the stationary signal in the external expansion data, and random noise can also be suppressed to a certain extent to obtain stationary data;

[0167] In some embodiments, the specific formula of the pre-processing method of stationary component applied to extract the stationary signal in the external expansion data is:

[0168]

[0169] wherein is a time series with a data amount of N, Y'(t) is a stationary component signal extracted, Y(t, 0:M-h) , Y (t, h / 2:M-h / 2), Y(t, h:M) is a time series with different lengths, n is the length of the extension data, M = N + 2n, t is time, h is a sliding window, and N is the length of the time series, i.e., the data amount of the signal.

[0170] S73: An adaptive wavelet threshold processing method is used to denoise the wavelet coefficients of each layer, suppress the power frequency harmonic interference in the stationary data, and obtain power frequency removed data;

[0171] In some embodiments, before the adaptive wavelet threshold processing method is used to suppress the power frequency harmonic interference in the stationary data, the method further comprises:

[0172] The time domain signal is transformed into the frequency domain, and WT transformation is performed on the real part and the imaginary part respectively, i.e., a discrete wavelet transform method is used.

[0173] In some embodiments, the selection method of the adaptive wavelet threshold value comprises:

[0174] The optimal variable threshold value is obtained by combining the fixed threshold value and the unbiased likelihood estimation soft threshold value, which is suitable for the condition that the signal-to-noise ratio is small:

[0175]

[0176] wherein,

[0177] : SURE threshold value;

[0178] : global threshold value;

[0179] : the i-th wavelet coefficient;

[0180] N: length of time series;

[0181] s, u respectively SURE and global threshold value calculation parameters.

[0182] S74: performing EMD decomposition on the de-power-frequency data in the time domain, respectively denoising high-frequency components and low-frequency components, and then reconstructing a reconstructed denoised signal through linear reconstruction of the denoised high-frequency components and low-frequency components and residual modulus;

[0183] In some embodiments, the specific process of performing EMD decomposition on the de-power-frequency data in the time domain to obtain the high-frequency components and low-frequency components includes:

[0184] performing EMD decomposition on the signal in the time domain, and performing secondary CEEMD decomposition denoising and reconstruction on the IMF components obtained by decomposition;

[0185] In some embodiments, the specific method of respectively denoising the high-frequency components and the low-frequency components includes:

[0186] adopting adaptive truncated threshold denoising for the high-frequency components;

[0187] adopting zero setting denoising or soft threshold denoising for the low-frequency components;

[0188] that is, for the high-frequency components, the adaptive truncated threshold is calculated, the part higher than the threshold is returned to the threshold, and the part lower than the threshold remains unchanged; the low-frequency part remains unchanged, or a soft threshold is set, the part higher than the threshold is returned to the threshold, and the part lower than the threshold remains unchanged.

[0189] The multi-scale electromagnetic field component denoising method provided by the embodiments of the present application improves the shortcomings of single method denoising, respectively performs filtering processing in the frequency domain and the time domain, realizes comprehensive analysis, processing and denoising of the ground electromagnetic field components, expands the edge data of the signal in the data preprocessing stage to avoid missing of endpoint data processing and wing effect, extracts the stationary components in the signal and simultaneously performs moving average filtering, which suppresses the high-frequency random noise to a certain extent, effectively solves the aliasing effect in EMD decomposition, improves the effect of EMD threshold denoising, can perform denoising processing on continuous acquisition signals, greatly retains the useful information in the original electromagnetic field components, improves the signal-to-noise ratio, and designs an adaptive threshold according to the noise characteristics, thereby reducing the influence of human experience.

[0190] Embodiment Eight

[0191] Based on the method of Embodiment Seven, the present application gives the following embodiments according to simulation data:

[0192] S81: using a polynomial function method to extend the data chain at both ends of the original time series to obtain extended data;

[0193] In some embodiments, the use of a polynomial function method to extend the data chain at both ends of the original time series can effectively reflect the signal attenuation and more stably restore the signal trend. The specific formula is:

[0194]

[0195] wherein, is the original signal, represents the signal after extension, a and b are extension coefficients, and j is the signal decomposition polynomial; a = [a0…a m ]; b = [b0…b m ], M 1 is the number of left extension terms of the signal, k is the number of terms of the original signal, n 0 is the number of right extension terms of the signal;

[0196] S82: according to the input signal and the high noise irrelevance, applying the pre-processing method of stationary component, extracting the stationary signal in the extended data, and can suppress random noise to a certain extent, to obtain stationary data;

[0197] In some embodiments, the specific formula of the pre-processing method of stationary component for extracting the stationary signal in the extended data is:

[0198]

[0199] wherein is a time series with data amount N, Y'(t) is the extracted stationary component signal, Y(t, 0:M-h) , Y (t, h / 2:M-h / 2), Y(t, h:M) is the time series with different lengths, n is the length of the extended data, M = N + 2n, t is time, h is the sliding window, and N is the length of the time series, i.e. the data amount of the signal.

[0200] S83: using an adaptive wavelet threshold processing method to denoise the wavelet coefficients of each layer, to suppress the power frequency harmonic interference in the stationary data, to obtain the de-power frequency data;

[0201] In some embodiments, before suppressing the power harmonic interference in the stationary data by using the adaptive wavelet threshold processing method, the method further comprises:

[0202] Transforming the time domain signal to the frequency domain, and performing WT transformation on the real part and the imaginary part respectively;

[0203] In some embodiments, the adaptive wavelet threshold selection method comprises:

[0204] The optimal variable threshold is obtained by combining the fixed threshold and the unbiased likelihood estimation soft threshold, which is suitable for the condition that the signal-to-noise ratio is small:

[0205]

[0206] wherein,

[0207] : SURE threshold;

[0208] : global threshold;

[0209] : the i-th wavelet coefficient;

[0210] N: time series length;

[0211] s, u are the SURE and global threshold calculation parameters respectively;

[0212] S84: performing EMD decomposition on the de-power-frequency data in the time domain, respectively denoising the high-frequency component and the low-frequency component, and then reconstructing the de-noised high-frequency component, low-frequency component and residual modulus to obtain the reconstructed de-noised signal;

[0213] In some embodiments, the specific process of performing EMD decomposition on the de-power-frequency data in the time domain to obtain the high-frequency component and the low-frequency component comprises:

[0214] Performing EMD decomposition on the signal in the time domain, and performing secondary CEEMD decomposition denoising and reconstruction on the obtained IMF components;

[0215] In some embodiments, the specific method of respectively denoising the high-frequency component and the low-frequency component comprises:

[0216] Adopting adaptive truncated threshold denoising for the high-frequency component;

[0217] Adopting zero setting denoising or soft threshold denoising for the low-frequency component.

[0218] As shown in FIG. 1(a)-(b), the noisy signal containing multi-scale noise and the de-noised signal recovered and reconstructed in the numerical simulation of the present application, respectively, the abscissa is the sampling point, and the ordinate is the amplitude value. The simulated signal is a Gaussian signal as the original signal, containing harmonic noise, impulse noise and square wave noise, and the signal recovered after de-noising restores the time domain characteristics of the Gaussian signal.

[0219] As shown in FIG. 2(a)-(c), the noisy signal spectrum containing multi-scale noise, the de-noised signal spectrum recovered and reconstructed, and the original signal spectrum in the numerical simulation of the present application, respectively, the abscissa is frequency (Hz), and the ordinate is amplitude value. The noise frequency domain characteristics of the noisy signal are obvious, especially the amplitude value of the spectrum characteristics of the harmonic noise at its specific frequency is obviously higher than that of other frequencies. The frequency spectrum of the signal after de-noising has high consistency with the original signal spectrum.

[0220] As shown in FIG. 2(a)-(c), the noisy signal spectrum containing multi-scale noise, the de-noised signal spectrum recovered and reconstructed, and the original signal spectrum in the numerical simulation of the present application, respectively, the abscissa is frequency (Hz), and the ordinate is amplitude value. The noise frequency domain characteristics of the noisy signal are obvious, especially the amplitude value of the spectrum characteristics of the harmonic noise at its specific frequency is obviously higher than that of other frequencies. The frequency spectrum of the signal after de-noising has high consistency with the original signal spectrum. Figure 3 As shown in FIG. 2(a)-(c), the noisy signal spectrum containing multi-scale noise, the de-noised signal spectrum recovered and reconstructed, and the original signal spectrum in the numerical simulation of the present application, respectively, the abscissa is frequency (Hz), and the ordinate is amplitude value. The noise frequency domain characteristics of the noisy signal are obvious, especially the amplitude value of the spectrum characteristics of the harmonic noise at its specific frequency is obviously higher than that of other frequencies. The frequency spectrum of the signal after de-noising has high consistency with the original signal spectrum.

[0221] The multi-scale electromagnetic field component de-noising method provided by the embodiments of the present application improves the shortcomings of single method de-noising, respectively performs filtering processing in the frequency domain and the time domain, realizes comprehensive analysis, processing and de-noising of the earth's electromagnetic field components, expands the signal edge data in the data preprocessing stage to avoid missing of endpoint data processing and wing effect, extracts the stable components in the signal and simultaneously performs moving average filtering, which suppresses the high-frequency random noise to a certain extent, effectively solves the aliasing effect in EMD decomposition, improves the effect of EMD threshold de-noising, can de-noise the continuous acquisition signal, greatly retains the useful information in the original electromagnetic field components, improves the signal-to-noise ratio, and designs an adaptive threshold according to the noise characteristics, thereby reducing the influence of human experience.

[0222] Embodiment Nine

[0223] Based on the method of embodiment seven, the present application gives the following embodiments according to real data:

[0224] S91: The original time series data chain at both ends is expanded out by using a polynomial function method to obtain expanded data;

[0225] In some embodiments, the original time series data chain at both ends is expanded out by using a polynomial function method, which can effectively reflect the signal attenuation, and relatively stably restore the signal trend. The specific formula is:

[0226]

[0227] wherein, is the original signal, represents the signal after extension, a, b are extension coefficients, and j is a signal decomposition polynomial; a = [a0…a m ]; b = [b0…b m ], M 1 is the number of left extension terms of the signal, k is the number of original signal terms, n 0 is the number of right extension terms of the signal;

[0228] S92: According to the input signal and the noise height being irrelevant, a pre-processing method of stationary component is applied to extract the stationary signal in the external expansion data, and random noise can also be suppressed to a certain extent to obtain stationary data;

[0229] In some embodiments, the specific formula of the pre-processing method of stationary component applied to extract the stationary signal in the external expansion data is:

[0230]

[0231] wherein is a time series with a data amount of N, Y'(t) is a stationary component signal extracted, Y(t, 0:M-h) , Y (t, h / 2:M-h / 2), Y(t, h:M) is a time series with different lengths, n is the length of the extension data, M = N + 2n, t is time, h is a sliding window, and N is the length of the time series, i.e., the data amount of the signal.

[0232] S93: An adaptive wavelet threshold processing method is used to denoise the wavelet coefficients of each layer, suppress the power frequency harmonic interference in the stationary data, and obtain de-power frequency data;

[0233] In some embodiments, before the adaptive wavelet threshold processing method is used to suppress the power frequency harmonic interference in the stationary data, the method further comprises:

[0234] Transforming the time domain signal to the frequency domain, and performing WT transformation on the real part and the imaginary part, respectively;

[0235] In some embodiments, the selection method of the adaptive wavelet threshold value comprises:

[0236] The optimal variable threshold value is obtained by combining the fixed threshold value and the unbiased likelihood estimation soft threshold value, which is suitable for the condition that the signal-to-noise ratio is small:

[0237]

[0238] wherein,

[0239] : SURE threshold

[0240] : global threshold

[0241] : the i-th wavelet coefficient

[0242] N: length of time series

[0243] s, u : SURE and global threshold calculation parameters, respectively

[0244] S94: performing EMD decomposition on the de-power-frequency data in the time domain, respectively denoising the high-frequency component and the low-frequency component, and then obtaining a reconstructed denoised signal by linear reconstruction of the denoised high-frequency component, the denoised low-frequency component and the residual modulus

[0245] In some embodiments, the specific process of performing EMD decomposition on the de-power-frequency data in the time domain to obtain the high-frequency component and the low-frequency component includes:

[0246] performing EMD decomposition on the signal in the time domain, and performing secondary CEEMD decomposition denoising and reconstruction on the IMF components obtained by decomposition

[0247] In some embodiments, the specific method of respectively denoising the high-frequency component and the low-frequency component includes:

[0248] adopting adaptive truncated threshold denoising for the high-frequency component

[0249] adopting zeroing denoising or soft threshold denoising for the low-frequency component

[0250] As shown in FIGS. 4(a)-(b), it is a noisy signal containing multi-scale noise of an electric field component continuously collected in a certain work area in the country and a denoised signal recovered and reconstructed, the horizontal coordinate is the sampling point, and the vertical coordinate is the amplitude value. The signal harmonic noise characteristics are obvious, and is simultaneously affected by square wave noise and pulse noise. After denoising, the signal restores the Gaussian signal characteristics, and the noise characteristics disappear in the time domain.

[0251] As shown in FIGS. 5(a)-(b), it is a noisy signal containing multi-scale noise of a magnetic field component continuously collected in a certain work area in the country and a denoised signal recovered and reconstructed, the horizontal coordinate is the sampling point, and the vertical coordinate is the amplitude value. The signal harmonic noise characteristics are obvious, and is simultaneously affected by low-frequency triangular noise and pulse noise, the noise amplitude is much higher than the original signal, and the energy is relatively strong. After denoising, the signal restores the Gaussian signal characteristics, and the noise characteristics disappear in the time domain.

[0252] As shown in FIG. 6(a)-(b), the original apparent resistivity and phase diagrams of a certain work area in China processed by the present application are shown, the horizontal coordinate represents frequency (Hz), and the vertical coordinate represents the logarithmic apparent resistivity (oho.m) and phase (deg) respectively. The original apparent resistivity curve has flying points at the high frequency part, the curve is distorted, the human noise in the observation data has a great influence, the low frequency curve is lifted, the phase is biased to 0° and 180°, and the near field effect is obvious.

[0253] As shown in FIG. 7(a)-(b), the apparent resistivity and phase diagrams of a certain work area in China processed by the present application after multi-scale filtering and denoising are shown, the horizontal coordinate represents frequency (Hz), and the vertical coordinate represents the logarithmic apparent resistivity (oho.m) and phase (deg) respectively. After denoising of the multi-scale electromagnetic field component, the flying points disappear, and the curve characteristics are obviously restored. At the same time, the near field effect is improved to a certain extent.

[0254] The multi-scale electromagnetic field component denoising method provided by the embodiments of the present application improves the shortcomings of single method denoising, respectively performs filtering processing in the frequency domain and the time domain, realizes comprehensive analysis, processing and denoising of the electromagnetic field component of the earth, expands the edge data of the signal in the data preprocessing stage to avoid missing of end point data processing and wing effect, extracts the stationary component in the signal and simultaneously performs moving average filtering, which suppresses the high frequency random noise to a certain extent, effectively solves the aliasing effect in EMD decomposition, improves the effect of EMD threshold denoising, can perform denoising processing on the continuous acquisition signal, greatly retains the useful information in the original electromagnetic field component, improves the signal-to-noise ratio, and designs an adaptive threshold according to the noise characteristics, thereby reducing the influence of human experience.

[0255] Embodiment ten

[0256] Based on the foregoing embodiments, the embodiments of the present application provide a multi-scale electromagnetic field component denoising device, a data expansion module, a stationary component preprocessing module, an adaptive wavelet threshold processing module and a data denoising and reconstruction module.

[0257] The data expansion module: the original time series data chain at both ends is expanded by using a polynomial function to obtain expanded data.

[0258] The stationary component preprocessing module: a stationary signal in the expanded data is extracted by using a stationary component preprocessing method to obtain stationary data.

[0259] The adaptive wavelet threshold processing module: an adaptive wavelet threshold processing method is used to suppress the power frequency harmonic interference in the stationary data to obtain power frequency removed data.

[0260] Data denoising and reconstruction module: the de-industrial frequency data is subjected to EMD decomposition in time domain, the high frequency component and the low frequency component are denoised respectively, and then the denoised high frequency component, the denoised low frequency component and the residual modulus are reconstructed to obtain a reconstructed denoised signal.

[0261] It should be noted that, in the embodiments of the present application, if the multi-scale electromagnetic field component denoising method described above is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a magnetic disk or an optical disk, and various storage media that can store program codes. Thus, the embodiments of the present application are not limited to any specific hardware and software combination.

[0262] Correspondingly, the embodiments of the present application provide a storage medium having a computer program stored thereon, characterized in that the computer program is executed by a processor to implement the steps of the multi-scale electromagnetic field component denoising method provided in the above embodiments.

[0263] Embodiment eleven

[0264] The embodiments of the present application provide a multi-scale electromagnetic field component denoising device including a memory and a processor, the memory having a computer program stored thereon, and the computer program being executed by the processor, the processor being configured to execute the program of the multi-scale electromagnetic field component denoising method stored in the memory to implement the steps of the multi-scale electromagnetic field component denoising method provided in the above embodiments.

[0265] The above description of the display device and the storage medium embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the computer device and storage medium embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

[0266] It should be noted that: the above description of the storage medium and device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

[0267] It should be understood that every feature, structure, or characteristic described herein is within a preferred embodiment of the present application. Thus, it is meant that the features, structures, or characteristics can be combined with each other in any manner within a preferred embodiment of the present application. In addition, it is contemplated that each feature, structure, or characteristic can be implemented in hardware, software, or a combination thereof.

[0268] It should be noted that, as used herein, the terms "includes," "including," or "includes" are intended to be open-ended terms that specifically permit the inclusion of other elements not specifically recited. As used herein, the term "comprises" or "comprising" or "includes" or "including" means specifically including, but not limited to.

[0269] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The above-described device embodiments are only illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each component part shown or discussed can be through some interface, indirect coupling or communication connection between devices or units, which can be electrical, mechanical or other forms.

[0270] The units described above as separate components can or can not be physically separated, and the components shown as units can or can not be physical units; they can be located in one place or distributed on multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0271] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be realized in the form of hardware or hardware plus software functional unit.

[0272] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware, the foregoing program can be stored in a computer readable storage medium, and the program executes the steps of the method embodiments when executed.

[0273] Alternatively, the integrated units of the present application can be stored in a computer readable storage medium if they are realized in the form of software function modules and sold or used as independent products. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of software products, and the computer software products are stored in a storage medium, including a plurality of instructions for causing a controller to execute all or part of the methods described in the embodiments of the present application. The foregoing storage medium includes mobile storage devices, ROM, magnetic discs or optical discs, and various media that can store program codes.

[0274] The above is only an embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for denoising multi-scale electromagnetic field components, characterized in that, include: S1: Using a polynomial function approach, the data chains at both ends of the original time series are expanded to obtain expanded data; S2: Apply the preprocessing method for stationary components to extract the stationary signal from the extended data to obtain stationary data; S3: Adaptive wavelet thresholding is used to suppress power frequency harmonic interference in the stationary data to obtain power frequency-free data. S4: Perform EMD decomposition on the power frequency removed data in the time domain, denoise the high-frequency components and low-frequency components respectively, and then obtain the reconstructed denoised signal by linear reconstruction of the denoised high-frequency components, low-frequency components and residual modulus.

2. The method according to claim 1, characterized in that, The specific formula for expanding the data chain at both ends of the original time series using a polynomial function approach is as follows: in, The original signal, This represents the signal after expansion, where a and b are the expansion coefficients, and j is the signal decomposition polynomial; a = [a0…a1] m b = [b0…b] m ], M 1 represents the number of left expansion terms of the signal. k The original number of signal terms. n 0 represents the number of right-side extension terms of the signal.

3. The method according to claim 1, characterized in that, The specific formula for extracting the stationary signal from the extended data using the preprocessing method for applying stationary components is as follows: in, Y'(t) To extract stationary component signals, Y(t,0:Mh) , Y(t,h / 2:Mh / 2), Y(t,h:M) To extract time series of different lengths, where n is the length of the expanded data, M=N+2n t is time, h is the sliding window, and N is the length of the time series, i.e., the amount of data in the signal.

4. The method according to claim 1, characterized in that, Before employing adaptive wavelet thresholding to suppress power frequency harmonic interference in the stationary data, the method further includes: Transform the time-domain signal to the frequency domain, and then perform W-T transformation on the real and imaginary parts respectively.

5. The method according to claim 1, characterized in that, The method for selecting the adaptive wavelet threshold includes: By combining a fixed threshold with an unbiased likelihood estimation soft threshold, the optimal variable threshold can be obtained, which is suitable for conditions with low signal-to-noise ratio. in, SURE threshold; Global threshold; : The i-th wavelet coefficient; N: Length of the time series; s, u These are the parameters for calculating SURE and the global threshold, respectively.

6. The method according to claim 1, characterized in that, The specific process of performing EMD decomposition on the power frequency-degraded data in the time domain to obtain the high-frequency and low-frequency components includes: EMD decomposition is performed on the signal in the time domain, and secondary CEEMD decomposition, denoising, and reconstruction are performed on the IMF components obtained from the decomposition.

7. The method according to claim 6, characterized in that, The specific methods for denoising high-frequency components and low-frequency components separately include: Adaptive truncation threshold denoising is applied to high-frequency components; For low-frequency components, use zeroing or soft thresholding for noise reduction.

8. A multi-scale electromagnetic field component noise reduction device, characterized in that, include: The system includes a data expansion module, a stationary component preprocessing module, an adaptive wavelet thresholding module, and a data denoising and reconstruction module. Data expansion module: Using a polynomial function approach, the data links at both ends of the original time series are expanded to obtain expanded data; Stationary component preprocessing module: Applying a stationary component preprocessing method, extracting stationary signals from the extended data to obtain stationary data; Adaptive wavelet thresholding module: The adaptive wavelet thresholding method is used to suppress power frequency harmonic interference in the stationary data to obtain power frequency removed data; Data denoising and reconstruction module: Perform EMD decomposition on the power frequency data in the time domain, denoise the high-frequency components and low-frequency components respectively, and then obtain the reconstructed denoised signal by linear reconstruction of the denoised high-frequency components, low-frequency components and residual modulus.

9. A multi-scale electromagnetic field component noise reduction device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the multi-scale electromagnetic field component denoising method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the multi-scale electromagnetic field component denoising method as described in any one of claims 1 to 7.

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