Sea wave magnetic interference suppression method and device for ocean electromagnetic detection

Abstract

The invention provides a sea wave magnetic interference suppression method and device for ocean electromagnetic detection, and relates to the technical field of electromagnetic signal processing, and the method comprises the steps: synchronously collecting original electromagnetic signals through a plurality of annularly arranged magnetic sensors, generating interference parameters through employing the calculated interference signal amplitude and phase information, and carrying out the subsequent interference suppression processing. The method comprises the following steps of: calculating a covariance matrix, performing principal component analysis, extracting spatial characteristics of low-frequency magnetic interference, projecting a signal to an orthogonal subspace outside an interference subspace, removing a low-frequency magnetic interference component, retaining a target signal, performing Fourier transform to convert the signal to a frequency domain, analyzing a frequency spectrum by using a band elimination filter, and setting an interference threshold value. According to the identified interference frequency, an interference suppression signal is generated, the interference suppression signal is returned to the time domain through inverse Fourier transform and is superposed with the target signal to generate a standard electromagnetic signal, the magnetic interference in the ocean detection area can be effectively suppressed, and accurate acquisition and analysis of the standard electromagnetic signal are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic signal processing, and in particular to a method and device for suppressing sea wave magnetic interference in marine electromagnetic detection. Background Art

[0002] The movement of ocean waves will produce additional fluctuations in the magnetic field. When these fluctuations are captured by the detection equipment, they will appear as an increase in background noise. This noise can mask the weak electromagnetic signals of underground structures, causing inaccurate results of ocean electromagnetic detection. The magnetic interference of ocean waves may cause electromagnetic signals that should have been coherent to break or deform, making it more difficult to correctly interpret the data, thereby affecting the accurate assessment of the underground structure of the ocean.

[0003] In the prior art, publication number CN111856590A discloses a method for suppressing sea wave magnetic interference in marine magnetotelluric detection. The method reads the marine magnetotelluric field data involved in suppressing sea wave magnetic interference, calculates the initial value of the filter parameter based on the impedance relationship between the filter parameter and the electromagnetic field, the correlation between the input signal and the effective signal in the reference signal, combines the statistical characteristics of the signal and noise that change over time, uses the filter parameter of the previous moment to perform iterative calculation, adaptively calculates the filter parameter of the current moment, reconstructs the prediction signal based on the relationship between the filter parameter and the electric field and magnetic field, and then uses the principle of the minimum mean square error algorithm to calculate the residual between the prediction signal and the reference signal. Finally, it is determined whether the processing requirements are met and outputs the prediction signal that suppresses interference.

[0004] Insufficient existing technology:

[0005] The adaptive filtering algorithm relies on the correlation between the input signal and the reference signal, as well as the statistical characteristics of the signal and noise. The performance of this algorithm is highly dependent on the accurate estimation of these statistical characteristics. In the actual marine environment, the statistical characteristics of the electromagnetic field may be inconsistent with the input signal and the reference signal due to the dynamic changes of the marine environment. The reconstruction of the signal may be affected by environmental noise, resulting in changes in the electric field strength and magnetic field strength, thereby causing inaccurate reconstructed signals.

[0006] When dealing with linear effect problems, the minimum square error algorithm is used. Although it is effective in dealing with linear problems, the LMS algorithm may not be sufficient to provide the optimal solution when facing nonlinear or non-stationary signals, especially when the electromagnetic noise is complex or the signal itself has highly nonlinear characteristics. The use of the LMS algorithm may not be able to effectively minimize the residual between the predicted signal and the reference signal, resulting in unsatisfactory filtering effects. In addition, the LMS algorithm may require more iterations when processing data with high noise levels, thereby increasing the computational burden and delay.

[0007] Therefore, it is necessary to provide a method and device for suppressing sea wave magnetic interference in marine electromagnetic detection to solve the above problem.

[0008] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0009] The object of the present invention is to provide a method and device for suppressing sea wave magnetic interference in marine electromagnetic detection, so as to solve the problems raised in the above-mentioned background technology.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] A method for suppressing sea wave magnetic interference in marine electromagnetic detection, the specific steps comprising:

[0012] Step 1: Arrange multiple magnetic sensors in a circular arrangement in the ocean detection area to collect original electromagnetic signals, synchronize the collected signals of all arranged magnetic sensors, obtain the original electromagnetic signal data of all sensors at each time node, calculate the interference signal amplitude and phase information of the sensor at each time point, and use the interference signal amplitude and phase information to generate interference parameters for subsequent interference suppression processing;

[0013] Step 2: Use spatial filtering technology to process the synchronized electromagnetic signal data, regard the multiple magnetic sensor arrays deployed in the ocean as an overall model, calculate the covariance matrix of the original electromagnetic signal data collected by the sensor array at each moment, perform principal component analysis, and extract the spatial characteristics of the main interference signals;

[0014] Step 3: Identify the main components of the low-frequency magnetic interference signal, construct a subspace containing the interference signal based on the results of the principal component analysis of the low-frequency magnetic interference, project the original electromagnetic signal data into an orthogonal subspace outside the interference subspace, filter out the low-frequency magnetic interference signal component, and retain the target signal matrix;

[0015] Step 4: Perform Fourier transform on the target signal and convert it to frequency domain representation. Use a band-stop filter to analyze the signal spectrum to generate an amplitude spectrum and a phase spectrum, and set an interference threshold. The interference threshold is used to compare with the interference parameter to identify the frequency of the low-frequency magnetic interference signal.

[0016] Step 5: For the identified interference frequency, the amplitude spectrum and phase spectrum in the obtained digital signal are used to synthesize the interference suppression signal, and the signal processed by the band-stop filter is returned to the time domain through inverse Fourier transform, and the target signal is superimposed with the interference suppression signal to generate a standard electromagnetic signal.

[0017] Furthermore, multiple magnetic sensors are arranged in a circular arrangement in the detection area, and the collected signals of all the arranged magnetic sensors are time synchronized. The specific method is as follows: select a high-precision time source as the synchronization reference for all magnetic sensors, use the precise time signal provided by the GPS satellite as the synchronization time reference, and equip each magnetic sensor with a time synchronization module. The synchronization signal is distributed to all sensors through the time synchronization module to ensure that the time reference of each magnetic sensor is consistent with the time source.

[0018] Furthermore, the original electromagnetic signal data of all sensors at each time node are obtained, the interference signal amplitude and phase information of the sensor at each time point are calculated, and the interference signal amplitude and phase information are used to generate interference parameters for subsequent interference suppression processing. The specific logic is as follows:

[0019] F n (t) = {f t1 , f t2 , f t3 , ...f tn}

[0020]

[0021] Among them, F n (t) represents the data set of all original electromagnetic signals of sensors at each time node, f tn represents the original electromagnetic signal data at the nth time point, E represents the average value of the original electromagnetic signal data of each sensor, V represents the interference signal amplitude at each time point, Q represents the phase information at each time point, and G represents the generated interference parameter.

[0022] Furthermore, the covariance matrix of the original electromagnetic signal data collected by the sensor array at each moment is calculated, and principal component analysis is performed to extract the spatial characteristics of the main interference signal. The specific logic is as follows:

[0023]

[0024] K=(λ 1 ,λ 2 ,λ 3 , …λ n )

[0025] Where A represents the initial matrix composed of the original electromagnetic signal data collected by the sensor model array, AE represents the transition matrix, and each column of the matrix (a 11 , a 21 , …, a i1 ) represents the signal strength of a sensor at consecutive time points, and each row (a 11 , a12 , …, a 1j ) represents the signal of all sensors at the same time point, (AE) T represents the transpose of the transition matrix, n represents the number of time points, C represents the covariance matrix, K represents the eigenvalue set of the covariance matrix, and λ 1 ,λ 2 ,λ 3 , …λ n Represents the eigenvalues ​​of the covariance matrix.

[0026] Furthermore, a subspace containing interference signals is constructed, and the original electromagnetic signal data is projected into an orthogonal subspace outside the interference subspace, and the low-frequency magnetic interference signal components are filtered out, and the target signal is retained. The specific method is as follows: the principal component in the covariance matrix, i.e., the eigenvector, is determined. The eigenvector reflects the main change direction of the original electromagnetic signal, and the eigenvalue in the covariance matrix reflects the degree of change in these directions. The eigenvalue and eigenvector of the covariance matrix are calculated, and the main components in the data are identified. The maximum, minimum, and middle three types of eigenvalues ​​and their corresponding eigenvectors are selected to represent the most significant source of variation in the data. The specific logic is as follows:

[0027] λ max ={max(λ 1 ,λ 2 ,λ 3 , …λ n )}

[0028] λ min ={min(λ 1 ,λ 2 ,λ 3 , …λ n )}

[0029] λ mid = {mid(λ 1 ,λ 2 ,λ 3 , …λ n )}

[0030] ε max =(x 1 , x 2 , x 3 , …x n )

[0031] ε min =(y 1 ,y 2 ,y 3 ,…y n )

[0032] ε mid =(z1 , z 2 , z 3 , …z n )

[0033]

[0034] P=IC S C S T

[0035] C ′ =PC

[0036] Among them, λ max , min , mid Respectively represent the maximum, minimum, and middle eigenvalues ​​in the covariance matrix, ε max , ε min , ε mid Represents the eigenvectors corresponding to the maximum, minimum, and middle eigenvalues, respectively. S represents the subspace of the interference signal, P represents the orthogonal subspace projected outside the interference subspace, and C ′ represents the projection target signal matrix after removing interference, I represents the identity matrix, x n ,y n 、z n They represent the elements in their corresponding feature vectors respectively.

[0037] Furthermore, the time domain signal in the target signal is subjected to Fourier transform, and the digital signal in the target signal is subjected to discrete Fourier transform, and all are converted to frequency domain representation. The specific logic is as follows:

[0038] [X(f)] = FFT(C ′ )

[0039] [X(k)] = DFT(C ′ )

[0040] Among them, [X(f)] represents the time domain signal in the frequency domain after Fourier transform, [X(k)] represents the digital signal in the frequency domain after discrete Fourier transform, FFT represents Fourier transform, and DFT represents discrete Fourier transform.

[0041] Furthermore, the signal spectrum is analyzed by using a band-stop filter to generate an amplitude spectrum and a phase spectrum, and an interference threshold is set. The interference threshold is used to compare with the interference parameter to identify the frequency of the low-frequency magnetic interference signal. The specific method is as follows: the signal spectrum is analyzed to generate an amplitude spectrum and a phase spectrum using a digital signal in the frequency domain of a discrete Fourier transform, and an interference threshold is set as an indicator through a time domain signal in the frequency domain of a Fourier transform. After comparing with the interference parameter, it is determined whether these frequency intervals match the known low-frequency magnetic interference signal characteristics. The specific logic is as follows:

[0042] X fd ={[Re([X(k)])] 2 +[Im([X(k)])] 2}

[0043]

[0044] Among them, X fd , X xw Represent the generated amplitude spectrum and phase spectrum respectively, Re([X(k)]) and Im([X(k)]) represent the real part and imaginary part of the digital signal respectively, f[i] represents the frequency of the identified low-frequency magnetic interference signal, and Y represents the interference threshold.

[0045] Furthermore, for the identified interference frequency, the obtained amplitude spectrum and phase spectrum are used to comprehensively generate the interference suppression signal, and the signal processed by the band-stop filter is returned to the time domain through inverse Fourier transform. The specific logic is as follows:

[0046]

[0047] [y(t)] = FFT(C ′ ) -1 {[X(f)]}

[0048] Among them, s(t) represents the interference suppression signal, [y(t)] represents the target signal in the time domain after inverse Fourier transform, and FFT(C ′ ) -1 represents the inverse Fourier transform.

[0049] Furthermore, the target signal is superimposed with the interference suppression signal to remove the interference signal frequency in the target signal. The specific method is: adjusting the phase of the interference suppression signal to make it opposite to the phase of the target signal and ensuring that the two signals are at the same frequency. The specific logic is:

[0050] w(t)=s(t)+y(t)

[0051] Wherein, w(t) represents the standard electromagnetic signal.

[0052] The present invention further provides a device for suppressing sea wave magnetic interference for marine electromagnetic detection, the device being capable of implementing the method for suppressing sea wave magnetic interference as claimed in any one of claims 1 to 9, comprising:

[0053] A synchronous signal acquisition module is used to arrange multiple magnetic sensors in a circular arrangement in the ocean detection area to collect original electromagnetic signals, synchronize the collected signals of all arranged magnetic sensors, obtain the original electromagnetic signal data of all sensors at each time node, calculate the interference signal amplitude and phase information of the sensor at each time point, and generate interference parameters using the interference signal amplitude and phase information for subsequent interference suppression processing;

[0054] The spatial feature extraction module is used to process the synchronized electromagnetic signal data using spatial filtering technology, regard the multiple magnetic sensor arrays arranged in the ocean as an overall model, calculate the covariance matrix of the original electromagnetic signal data collected by the sensor array at each moment, perform principal component analysis, and extract the spatial features of the main interference signals;

[0055] The interference signal suppression module is used to identify the main components of the low-frequency magnetic interference signal, construct a subspace containing the interference signal based on the results of the principal component analysis of the low-frequency magnetic interference, project the original electromagnetic signal data into an orthogonal subspace outside the interference subspace, filter out the low-frequency magnetic interference signal component, and retain the target signal matrix;

[0056] The frequency domain analysis and interference identification module is used to perform Fourier transform on the target signal, convert it to frequency domain representation, use a band-stop filter to analyze the signal spectrum to generate an amplitude spectrum and a phase spectrum, and set an interference threshold. The interference threshold is used to compare with the interference parameter to identify the frequency of the low-frequency magnetic interference signal;

[0057] The signal synthesis and restoration module is used to generate an interference suppression signal based on the identified interference frequency using the amplitude spectrum and phase spectrum in the obtained digital signal, and return the signal processed by the band-stop filter to the time domain through inverse Fourier transform, superimpose the target signal with the interference suppression signal, and generate a standard electromagnetic signal.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] By arranging multiple magnetic sensors in a circle in the ocean and synchronizing their collected signals, it is ensured that the signal data collected by each sensor at the same time point is consistent, which can effectively reduce the signal deviation caused by environmental changes. By using spatial filtering technology, the entire sensor array is regarded as an overall model, the covariance matrix is ​​calculated and analyzed, and principal component analysis is performed, which can effectively extract and identify the spatial characteristics of the main interference signals, solve the problem of changes in electric field strength and magnetic field strength caused by environmental changes, and improve the reliability and stability of the signal;

[0060] Interference signals may be particularly prominent in the marine environment and have nonlinear characteristics, which are difficult to be effectively processed by traditional linear filtering methods. The present invention constructs a subspace containing interference signals through the results of principal component analysis, and projects the original signal data into an orthogonal non-interference subspace, which can effectively filter out the components of the interference signal and retain the effective information of the target signal, thereby improving the accuracy of signal reconstruction.

[0061] The present invention uses multi-level signal processing and analysis modules to effectively extract target signals from complex marine electromagnetic environments and suppress interference, providing an efficient and accurate solution for electromagnetic signal acquisition and processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0063] Figure 2 It is a schematic diagram of the system module flow of the present invention. DETAILED DESCRIPTION

[0064] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0065] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0066] Example:

[0067] See also Figure 1 :A method for suppressing sea wave magnetic interference in marine electromagnetic detection, the specific steps comprising:

[0068] Step 1: Arrange multiple magnetic sensors in a circular arrangement in the ocean detection area to collect original electromagnetic signals, synchronize the collected signals of all arranged magnetic sensors, obtain the original electromagnetic signal data of all sensors at each time node, calculate the interference signal amplitude and phase information of the sensor at each time point, and use the interference signal amplitude and phase information to generate interference parameters for subsequent interference suppression processing;

[0069] Step 2: Use spatial filtering technology to process the synchronized electromagnetic signal data, regard the multiple magnetic sensor arrays deployed in the ocean as an overall model, calculate the covariance matrix of the original electromagnetic signal data collected by the sensor array at each moment, perform principal component analysis, and extract the spatial characteristics of the main interference signals;

[0070] Step 3: Identify the main components of the low-frequency magnetic interference signal, construct a subspace containing the interference signal based on the results of the principal component analysis of the low-frequency magnetic interference, project the original electromagnetic signal data into an orthogonal subspace outside the interference subspace, filter out the low-frequency magnetic interference signal component, and retain the target signal matrix;

[0071] Step 4: Perform Fourier transform on the target signal and convert it to frequency domain representation. Use a band-stop filter to analyze the signal spectrum to generate an amplitude spectrum and a phase spectrum, and set an interference threshold. The interference threshold is used to compare with the interference parameter to identify the frequency of the low-frequency magnetic interference signal.

[0072] Step 5: For the identified interference frequency, the amplitude spectrum and phase spectrum in the obtained digital signal are used to synthesize the interference suppression signal, and the signal processed by the band-stop filter is returned to the time domain through inverse Fourier transform, and the target signal is superimposed with the interference suppression signal to generate a standard electromagnetic signal.

[0073] It should be noted that in the marine environment, arranging multiple magnetic sensors and ensuring that they are arranged in a ring can provide wider and more comprehensive signal coverage. If the sensor data is not synchronized or consistent, it may cause errors in interference signal analysis and suppression, affecting the final signal processing effect. Accurate interference signal analysis can help the system accurately identify and locate the source of interference, thereby effectively suppressing the impact on the target signal and improving the purity and availability of the data.

[0074] Therefore, it is necessary to arrange multiple magnetic sensors in a circular arrangement in the detection area, and synchronize the collected signals of all arranged magnetic sensors. The specific method is as follows:

[0075] Select a high-precision time source as the synchronization reference for all magnetic sensors, use the precise time signal provided by the GPS satellite as the synchronization time reference, and equip each magnetic sensor with a time synchronization module. Distribute the synchronization signal to all sensors through the time synchronization module to ensure that the time reference of each magnetic sensor is consistent with the time source.

[0076] It should be noted that the use of spatial filtering technology to process synchronized electromagnetic signal data can make full use of the spatial distribution information of the sensor array to help identify and separate interference signals. By treating the sensor array as an overall model and calculating the covariance matrix of the signal, the spatial correlation and variation of the signal can be captured. This spatial information analysis helps to accurately identify the source and characteristics of the interference signal, thereby more effectively suppressing interference.

[0077] Therefore, it is necessary to obtain the original electromagnetic signal data of all sensors at each time node, calculate the interference signal amplitude and phase information of the sensor at each time point, and use the interference signal amplitude and phase information to generate interference parameters for subsequent interference suppression processing. The specific logic is as follows:

[0078] F n (t) = {f t1 , f t2 , f t3 , ...f tn}

[0079]

[0080]

[0081] Among them, F n (t) represents the data set of all original electromagnetic signals of sensors at each time node, f tn represents the original electromagnetic signal data at the nth time point, E represents the average value of the original electromagnetic signal data of each sensor, V represents the interference signal amplitude at each time point, Q represents the phase information at each time point, and G represents the generated interference parameter.

[0082] It should be noted that in a complex electromagnetic environment, low-frequency magnetic interference signals may be mixed in the target signal, affecting the accuracy and interpretability of the data. By identifying the main components of the low-frequency magnetic interference signals and constructing a special interference subspace, these interference signals are separated from the original electromagnetic signals, and the original electromagnetic signal data is projected into an orthogonal subspace outside the interference subspace. Through mathematical methods, such as orthogonalization or projection, the components of the low-frequency magnetic interference signals can be effectively filtered out. This method retains the core part of the target signal and avoids the negative impact of interference signals on data analysis and application.

[0083] Therefore, it is necessary to calculate the covariance matrix of the original electromagnetic signal data collected by the sensor array at each moment, perform principal component analysis, and extract the spatial characteristics of the main interference signals. The specific logic is as follows:

[0084]

[0085] K=(λ 1 ,λ 2 ,λ 3 , …λ n )

[0086] Where A represents the initial matrix composed of the original electromagnetic signal data collected by the sensor model array, AE represents the transition matrix, and each column of the matrix (a 11 , a 21 ,λ,a i1 ) represents the signal strength of a sensor at consecutive time points, and each row (a 11 , a 12 , …, a 1j ) represents the signal of all sensors at the same time point, (AE) T represents the transpose of the transition matrix, n represents the number of time points, C represents the covariance matrix, K represents the eigenvalue set of the covariance matrix, and λ 1 ,λ 2 ,λ 3 , …λ n Represents the eigenvalues ​​of the covariance matrix.

[0087] It should be noted that low-frequency magnetic interference signals are often one of the main sources of interference in electromagnetic signal processing. These signals may come from power equipment, natural magnetic field changes or other electromagnetic interference sources. By identifying their main components and filtering them out, the impact of interference on the original electromagnetic signal can be significantly reduced. In a complex marine environment, the target signal may be masked or confused by the low-frequency magnetic interference signal, which will affect the extraction and analysis of the target information. By projecting the interference signal outside a specific subspace and retaining part of the target signal, the complete information of the target signal can be effectively protected and extracted.

[0088] Therefore, it is necessary to construct a subspace containing interference signals, project the original electromagnetic signal data into an orthogonal subspace outside the interference subspace, filter out the low-frequency magnetic interference signal components, and retain the target signal. The specific method is as follows:

[0089] Determine the principal components in the covariance matrix, i.e., the eigenvectors. The eigenvectors reflect the main change directions of the original electromagnetic signals. The eigenvalues ​​in the covariance matrix reflect the degree of change in these directions. Calculate the eigenvalues ​​and eigenvectors of the covariance matrix, identify the principal components in the data, and select the largest, smallest, and middle eigenvalues ​​and their corresponding eigenvectors to represent the most significant sources of variation in the data. The specific logic is as follows:

[0090] λ max ={max(λ 1 ,λ 2 ,λ 3 , …λ n )}

[0091] λ min ={min(λ 1 ,λ 2 ,λ 3 , …λ n )}

[0092] λ mid = {mid(λ 1 ,λ 2 ,λ 3 , …λ n )}

[0093] ε max =(x 1 , x 2 , x 3 , …x n )

[0094] ε min =(y 1 ,y 2 ,y 3 ,…y n )

[0095] ε mid =(z 1 , z 2 , z 3 , …z n )

[0096]

[0097] P=IC S C S T

[0098] C ′ =PC

[0099] Among them, λ max , min ,mid Respectively represent the maximum, minimum, and middle eigenvalues ​​in the covariance matrix, ε max , ε min , ε mid Represents the eigenvectors corresponding to the maximum, minimum, and middle eigenvalues, respectively. S represents the subspace of the interference signal, P represents the orthogonal subspace projected outside the interference subspace, and C ′ represents the projection target signal matrix after removing interference, I represents the identity matrix, x n ,y n 、z n They represent the elements in their corresponding feature vectors respectively.

[0100] It should be noted that the Fourier transform converts the time domain signal into a frequency domain signal, making the frequency characteristics of the signal more obvious. The frequency domain representation of the target signal can effectively reveal its spectral structure, including the distribution of frequency components and energy distribution. This analysis helps to more accurately identify and understand the characteristics of the target signal.

[0101] Therefore, the time domain signal in the target signal is subjected to Fourier transform, and the digital signal in the target signal is subjected to discrete Fourier transform, and all are converted to frequency domain representation. The specific logic is as follows:

[0102] [X(f)] = FFT(C ′ )

[0103] [X(k)] = DFT(C ′ )

[0104] Among them, [X(f)] represents the time domain signal in the frequency domain after Fourier transform, [X(k)] represents the digital signal in the frequency domain after discrete Fourier transform, FFT represents Fourier transform, and DFT represents discrete Fourier transform.

[0105] It should be noted that the band-stop filter can selectively block or weaken the signal components within a specific frequency range. In this step, the signal spectrum is analyzed using a band-stop filter to suppress low-frequency magnetic interference signals in a targeted manner. By setting an appropriate impedance band, the frequency components of the interference signal can be effectively filtered out and the effective part of the target signal can be retained. The amplitude spectrum and phase spectrum are important outputs of frequency domain analysis. They describe the intensity and phase characteristics of the signal in frequency. By analyzing the amplitude spectrum, the contribution and importance of different frequency components in the signal can be evaluated, while the phase spectrum provides the phase information of the signal in the frequency domain, which helps to more accurately reconstruct and process the original signal. The interference threshold is set to effectively identify the frequency of the low-frequency magnetic interference signal. By setting an appropriate threshold, noise and interference can be distinguished from the target signal. This comparison can help determine which frequency ranges of signals should be regarded as interference, thereby further optimizing the accuracy and reliability of signal processing.

[0106] Therefore, it is necessary to use a band-stop filter to analyze the signal spectrum to generate an amplitude spectrum and a phase spectrum, and set an interference threshold. The interference threshold is used to compare with the interference parameter to identify the frequency of the low-frequency magnetic interference signal. The specific method is as follows:

[0107] The signal spectrum is analyzed by using discrete Fourier transform to generate amplitude spectrum and phase spectrum of digital signal in frequency domain. The interference threshold is set as an indicator by Fourier transform of time domain signal in frequency domain. After comparing with the interference parameter, it is determined whether these frequency intervals match the known low-frequency magnetic interference signal characteristics. The specific logic is as follows:

[0108] X fd ={[Re([X(k)])] 2 +[Im([X(k)])] 2}

[0109]

[0110] Among them, X fd , X xw Represent the generated amplitude spectrum and phase spectrum respectively, Re([X(k)]) and Im([X(k)]) represent the real part and imaginary part of the digital signal respectively, f[i] represents the frequency of the identified low-frequency magnetic interference signal, and Y represents the interference threshold.

[0111] It should be noted that by using the amplitude spectrum and phase spectrum to generate suppressed interference signals, and combining the inverse Fourier transform to return the processed signal to the time domain, the influence of low-frequency magnetic interference signals can be effectively removed or weakened. This method can ensure the suppression of the signal at the interference frequency while retaining the original characteristics of the target signal as much as possible, so that the final restored standard electromagnetic signal is closer to the actual environmental conditions.

[0112] Therefore, for the identified interference frequency, the obtained amplitude spectrum and phase spectrum are used to synthesize the interference suppression signal, and the signal processed by the band-stop filter is returned to the time domain through inverse Fourier transform. The specific logic is as follows:

[0113]

[0114] [y(t)] = FFT(C ′ ) -1 {[X(f)]}

[0115] Among them, s(t) represents the interference suppression signal, [y(t)] represents the target signal in the time domain after inverse Fourier transform, and FFT(C ′ ) -1 represents the inverse Fourier transform.

[0116] It should be noted that in the process of signal processing, especially in complex environments, protecting and restoring the integrity of the target signal is a key task. By processing the anti-interference signal in the frequency domain and converting the processed signal back to the time domain, the core information of the target signal can be retained as much as possible, ensuring its traceability and credibility in the signal processing process.

[0117] Therefore, the target signal is superimposed with the interference suppression signal, and the specific method for removing the interference signal frequency in the target signal is:

[0118] Adjust the phase of the interference suppression signal to make it opposite to the target signal and ensure that the two signals are at the same frequency. The specific logic is as follows:

[0119] w(t)=s(t)+y(t)

[0120] Wherein, w(t) represents the standard electromagnetic signal.

[0121] The present invention also provides a device for suppressing sea wave magnetic interference for marine electromagnetic detection, comprising:

[0122] A synchronous signal acquisition module is used to arrange multiple magnetic sensors in a circular arrangement in the ocean detection area to collect original electromagnetic signals, synchronize the collected signals of all arranged magnetic sensors, obtain the original electromagnetic signal data of all sensors at each time node, calculate the interference signal amplitude and phase information of the sensor at each time point, and generate interference parameters using the interference signal amplitude and phase information for subsequent interference suppression processing;

[0123] The spatial feature extraction module is used to process the synchronized electromagnetic signal data using spatial filtering technology, regard the multiple magnetic sensor arrays arranged in the ocean as an overall model, calculate the covariance matrix of the original electromagnetic signal data collected by the sensor array at each moment, perform principal component analysis, and extract the spatial features of the main interference signals;

[0124] The interference signal suppression module is used to identify the main components of the low-frequency magnetic interference signal, construct a subspace containing the interference signal based on the results of the principal component analysis of the low-frequency magnetic interference, project the original electromagnetic signal data into an orthogonal subspace outside the interference subspace, filter out the low-frequency magnetic interference signal component, and retain the target signal matrix;

[0125] The frequency domain analysis and interference identification module is used to perform Fourier transform on the target signal, convert it to frequency domain representation, use a band-stop filter to analyze the signal spectrum to generate an amplitude spectrum and a phase spectrum, and set an interference threshold. The interference threshold is used to compare with the interference parameter to identify the frequency of the low-frequency magnetic interference signal;

[0126] The signal synthesis and restoration module is used to generate an interference suppression signal based on the identified interference frequency using the amplitude spectrum and phase spectrum in the obtained digital signal, and return the signal processed by the band-stop filter to the time domain through inverse Fourier transform, superimpose the target signal with the interference suppression signal, and generate a standard electromagnetic signal.

[0127] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0128] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0129] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0130] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for suppressing sea wave magnetic interference in marine electromagnetic detection, characterized in that: The interference suppression method comprises: Step 1: Arrange multiple magnetic sensors in a circular arrangement in the ocean detection area to collect original electromagnetic signals, synchronize the collected signals of all arranged magnetic sensors, obtain the original electromagnetic signal data of all sensors at each time node, calculate the interference signal amplitude and phase information of the sensor at each time point, and use the interference signal amplitude and phase information to generate interference parameters for subsequent interference suppression processing; Step 2: Use spatial filtering technology to process the synchronized electromagnetic signal data, regard the multiple magnetic sensor arrays deployed in the ocean as an overall model, calculate the covariance matrix of the original electromagnetic signal data collected by the sensor array at each moment, perform principal component analysis, and extract the spatial characteristics of the main interference signals; Step 3: Identify the main components of the low-frequency magnetic interference signal, construct a subspace containing the interference signal based on the results of the principal component analysis of the low-frequency magnetic interference, project the original electromagnetic signal data into an orthogonal subspace outside the interference subspace, filter out the low-frequency magnetic interference signal component, and retain the target signal matrix; Step 4: Perform Fourier transform on the target signal and convert it to frequency domain representation. Use a band-stop filter to analyze the signal spectrum to generate an amplitude spectrum and a phase spectrum, and set an interference threshold. The interference threshold is used to compare with the interference parameter to identify the frequency of the low-frequency magnetic interference signal. Step 5: For the identified interference frequency, the amplitude spectrum and phase spectrum in the obtained digital signal are used to synthesize the interference suppression signal, and the signal processed by the band-stop filter is returned to the time domain through inverse Fourier transform, and the target signal is superimposed with the interference suppression signal to generate a standard electromagnetic signal.

2. The method for suppressing sea wave magnetic interference in marine electromagnetic detection according to claim 1, characterized in that: In the detection area, multiple magnetic sensors are arranged in a circular pattern, and the collected signals of all the arranged magnetic sensors are time synchronized to ensure the consistency of the data of each sensor. The specific method is as follows: select a high-precision time source as the synchronization reference for all magnetic sensors, use the precise time signal provided by the GPS satellite as the synchronization time reference, and equip each magnetic sensor with a time synchronization module. The synchronization signal is distributed to all sensors through the time synchronization module to ensure that the time reference of each magnetic sensor is consistent with the time source.

3. The method for suppressing sea wave magnetic interference in marine electromagnetic detection according to claim 2, characterized in that: Obtain the original electromagnetic signal data of all sensors at each time node, calculate the interference signal amplitude and phase information of the sensor at each time point, and use the interference signal amplitude and phase information to generate interference parameters for subsequent interference suppression processing. The specific logic is as follows: F n (t)={f t1 ,f t2 ,f t3 ,……f tn } Among them, F n (t) represents the data set of all original electromagnetic signals of sensors at each time node, f tn represents the original electromagnetic signal data at the nth time point, E represents the average value of the original electromagnetic signal data of each sensor, V represents the interference signal amplitude at each time point, Q represents the phase information at each time point, and G represents the generated interference parameter.

4. The method for suppressing sea wave magnetic interference in marine electromagnetic detection according to claim 1, characterized in that: Calculate the covariance matrix of the original electromagnetic signal data collected by the sensor array at each moment, perform principal component analysis, and extract the spatial characteristics of the main interference signal. The specific logic is as follows: K=(λ1,λ2,λ3,…λ n ) Where A represents the initial matrix composed of the original electromagnetic signal data collected by the sensor model array, AE represents the transition matrix, and each column of the matrix (a 11 , a 21 ,λ,a i1 ) represents the signal strength of a sensor at consecutive time points, and each row (a 11 , a 12 , …, a 1j ) represents the signal of all sensors at the same time point, (AE) T represents the transpose of the transition matrix, n represents the number of time points, C represents the covariance matrix, K represents the eigenvalue set of the covariance matrix, λ1, λ2, λ3, …λ n Represents the eigenvalues ​​of the covariance matrix.

5. The method for suppressing sea wave magnetic interference in marine electromagnetic detection according to claim 4, characterized in that: Construct a subspace containing interference signals, project the original electromagnetic signal data to an orthogonal subspace outside the interference subspace, filter out low-frequency magnetic interference signal components, and retain the target signal. The specific method is: determine the principal component in the covariance matrix, that is, the eigenvector. The eigenvector reflects the main change direction of the original electromagnetic signal. The eigenvalue in the covariance matrix reflects the degree of change in these directions. Calculate the eigenvalue and eigenvector of the covariance matrix, identify the main components in the data, select the maximum, minimum, and middle three types of eigenvalues ​​and their corresponding eigenvectors to represent the most significant source of variation in the data. The specific logic is: l max ={max(λ1,λ2,λ3,…λ n )} l min ={min(λ1,λ2,λ3,…λ n )} l mid ={mid(λ1,λ2,λ3,…λ n )} ε max =(x1,x2,x3,…x n ) ε min =(y1,y2,y3,…y n ) e mid =(z1,z2,z3,…z n ) P=I-C S C S T C ′ =PC Among them, λ max , min , mid Respectively represent the maximum, minimum, and middle eigenvalues ​​in the covariance matrix, ε max , ε min , ε mid Represents the eigenvectors corresponding to the maximum, minimum, and middle eigenvalues, respectively. S represents the subspace of the interference signal, P represents the orthogonal subspace projected outside the interference subspace, and C ′ represents the projection target signal matrix after removing interference, I represents the identity matrix, x n ,y n 、z n They represent the elements in their corresponding feature vectors respectively.

6. The method for suppressing sea wave magnetic interference in marine electromagnetic detection according to claim 1, characterized in that: Perform Fourier transform on the time domain signal in the target signal, perform discrete Fourier transform on the digital signal in the target signal, and convert all to frequency domain representation. The specific logic is as follows: [X(f)]=FFT(C ′ ) [X(k)]=DFT(C ′ ) Among them, [X(f)] represents the time domain signal in the frequency domain after Fourier transform, [X(k)] represents the digital signal in the frequency domain after discrete Fourier transform, FFT represents Fourier transform, and DFT represents discrete Fourier transform.

7. A method for suppressing sea wave magnetic interference in marine electromagnetic detection according to claim 6, characterized in that: The signal spectrum is analyzed by a band-stop filter to generate an amplitude spectrum and a phase spectrum, and an interference threshold is set. The interference threshold is used to compare with the interference parameter to identify the frequency of the low-frequency magnetic interference signal. The specific method is as follows: the signal spectrum is analyzed to generate an amplitude spectrum and a phase spectrum using a digital signal in the frequency domain of a discrete Fourier transform, and the interference threshold is set as an indicator through the time domain signal in the frequency domain of the Fourier transform. After comparing with the interference parameter, it is determined whether these frequency intervals match the known low-frequency magnetic interference signal characteristics. The specific logic is as follows: X fd ={[Re([X(k)])] 2 +[Im([X(k)])] 2 } Among them, X fd , X xw Represent the generated amplitude spectrum and phase spectrum respectively, Re([X(k)]) and Im([X(k)]) represent the real part and imaginary part of the digital signal respectively, f[i] represents the frequency of the identified low-frequency magnetic interference signal, and Y represents the interference threshold.

8. The method for suppressing sea wave magnetic interference in marine electromagnetic detection according to claim 1, characterized in that: For the identified interference frequency, the obtained amplitude spectrum and phase spectrum are used to synthesize the interference suppression signal, and the signal processed by the band-stop filter is returned to the time domain through inverse Fourier transform. The specific logic is as follows: [y(t)]=FFT(C ′ ) -1 {[X(f)]} Among them, s(t) represents the interference suppression signal, [y(t)] represents the target signal in the time domain after inverse Fourier transform, and FFT(C ′ ) -1 represents the inverse Fourier transform.

9. The method for suppressing sea wave magnetic interference in marine electromagnetic detection according to claim 8, characterized in that: The target signal and the interference suppression signal are superimposed to remove the interference signal frequency in the target signal. The specific method is to adjust the phase of the interference suppression signal to make it opposite to the phase of the target signal and ensure that the two signals are at the same frequency. The specific logic is: w(t)=s(t)+y(t) Wherein, w(t) represents the standard electromagnetic signal.

10. The present invention further provides a device for suppressing sea wave magnetic interference for marine electromagnetic detection, the device being capable of implementing the method for suppressing sea wave magnetic interference as claimed in any one of claims 1 to 9, comprising: A synchronous signal acquisition module is used to arrange multiple magnetic sensors in a circular arrangement in the ocean detection area to collect original electromagnetic signals, synchronize the collected signals of all arranged magnetic sensors in time, ensure the consistency of data of each sensor, obtain the original electromagnetic signal data of all sensors at each time node through data synchronization, calculate the interference signal amplitude and phase information of the sensor at each time point, and use the interference signal amplitude and phase information to generate interference parameters for subsequent interference suppression processing; The spatial feature extraction module is used to process the synchronized electromagnetic signal data using spatial filtering technology, regard the multiple magnetic sensor arrays arranged in the ocean as an overall model, calculate the covariance matrix of the original electromagnetic signal data collected by the sensor array at each moment, perform principal component analysis, and extract the spatial features of the main interference signals; The interference signal suppression module is used to identify the main components of the low-frequency magnetic interference signal, construct a subspace containing the interference signal based on the results of the principal component analysis of the low-frequency magnetic interference, project the original electromagnetic signal data into an orthogonal subspace outside the interference subspace, filter out the low-frequency magnetic interference signal component, and retain the target signal matrix; The frequency domain analysis and interference identification module is used to perform Fourier transform on the target signal, convert it to frequency domain representation, use a band-stop filter to analyze the signal spectrum to generate an amplitude spectrum and a phase spectrum, and set an interference threshold. The interference threshold is used to compare with the interference parameter to identify the frequency of the low-frequency magnetic interference signal; The signal synthesis and restoration module is used to generate an interference suppression signal based on the identified interference frequency using the amplitude spectrum and phase spectrum in the obtained digital signal, and return the signal processed by the band-stop filter to the time domain through inverse Fourier transform, superimpose the target signal with the interference suppression signal, and generate a standard electromagnetic signal.

Images