A method and device for suppressing ocean wave-induced electromagnetic noise

Abstract

This application relates to the field of marine geophysical exploration technology and provides a method and apparatus for suppressing wave-induced electromagnetic noise. The method comprises: obtaining an optimal structural element; obtaining the results of a multiscale open-close operation and a multiscale close-open operation; performing a weighted linear combination of the multiscale generalized morphological open-close operation and the multiscale generalized morphological close-open operation, iteratively updating the weighting coefficients, and obtaining the entire segment of noise-suppressed marine magnetotelluric data; and evaluating the noise suppression effect. The method of the application improves multiscale adaptive generalized morphological filtering, significantly improving the filtering accuracy of marine magnetotelluric data, and significantly improving the quality of the magnetotelluric data after noise suppression.

Description

Technical Field

[0001] The present application relates to the field of marine geophysical exploration technology, and in particular to a method and device for suppressing ocean wave-induced electromagnetic noise. Background Art

[0002] Marine magnetotelluric (MMT) is a geophysical method that uses natural electromagnetic fields as a source to measure mutually orthogonal electric and magnetic fields using ocean bottom electromagnetic stations (OBEMs) to study the electrical distribution of the Earth. Due to the complex marine environment and the constant motion of the seawater, the moving water cuts into the electromagnetic field, generating secondary electromagnetic fields. Furthermore, electromagnetic receiving instruments on the seafloor are susceptible to vibrations caused by the impact of seawater, which severely interferes with MMT detection and reduces the signal-to-noise ratio (SNR) of MMT data. The induced electromagnetic field generated by wave motion is one of the main noise factors affecting the quality of MMT data. The electromagnetic field intensity is high, and the impact is primarily concentrated around 0.1 Hz, resulting in a reduced SNR in this frequency band and severe distortion of the apparent resistivity and phase curves. Therefore, extracting the characteristics of the wave-induced electromagnetic field is essential when processing MMT data.

[0003] Currently, research on suppressing wave-induced noise is relatively limited. Reference 1 (Wei Wenbo et al., Experimental Study of Submarine Magnetotelluric Sounding in the South Yellow Sea [J]. Chinese Journal of Geophysics) analyzes the characteristics of the wave-induced electromagnetic field to appropriately correct for ocean electromagnetic noise, but this requires high computational accuracy. Reference 2 (Yu Caixia et al., Application of the Hilbert-Huang Transform to Submarine Magnetotelluric Sounding Data Processing [J]. Progress in Geophysics) applies empirical mode decomposition to submarine magnetotelluric data processing, effectively suppressing electromagnetic noise generated by seawater movement, but this also results in "flying spots." Recent wavelet-based ocean electromagnetic data denoising methods, while capable of suppressing wave electromagnetic noise to a certain extent, are highly dependent on the selection of wavelet basis functions. All of these methods have limitations or shortcomings in extracting wave-induced noise. Therefore, finding a method that can effectively separate magnetotelluric signals from wave-induced electromagnetic noise is of great significance. Summary of the Invention

[0004] In response to the problems existing in the prior art, the present application provides a method and device for suppressing ocean wave-induced electromagnetic noise to solve the problem in the prior art that the earth's electromagnetic signals and ocean wave-induced electromagnetic noise cannot be effectively separated.

[0005] In a first aspect, the present application provides a method for suppressing ocean wave-induced electromagnetic noise, the method comprising:

[0006] Step S1: Determine the type and size of the structural element and obtain the optimal structural element;

[0007] Step S2: using the optimal structural element to perform multi-scale operations on the entire segment of original marine magnetotelluric data to obtain a multi-scale open-close operation result and a multi-scale close-open operation result;

[0008] Step S3: performing a weighted linear combination of the multi-scale generalized morphological opening-closing operation and the multi-scale generalized morphological closing-opening operation, and iteratively calculating the weighted coefficients of the multi-scale generalized morphological opening-closing operation and the multi-scale morphological closing-opening operation using a gradient method to update the weighted coefficients, thereby obtaining the entire noise-suppressed marine magnetotelluric data;

[0009] Step S4: Introducing signal-to-noise ratio SNR , root mean square error RMSE and normalized cross-correlation coefficient NCC Three indicators are used to evaluate the noise suppression effect;

[0010] Step S5: Performing robust impedance estimation on the entire noise-suppressed marine magnetotelluric data, calculating apparent resistivity and phase, and evaluating the apparent resistivity and phase data.

[0011] Furthermore, determining the type and size of the structural element and obtaining the optimal structural element includes:

[0012] According to the marine magnetotelluric data and the characteristics of wave-induced electromagnetic noise, the optimal structural element type is set as a triangular structural element;

[0013] The size of the structural element is determined by the following steps:

[0014] S11: perform preliminary filtering to determine the size range of the structural element;

[0015] S12: Input the entire ideal marine magnetotelluric data, perform multi-scale generalized morphological filtering to traverse all structural elements within the size range of the structural elements, and obtain the marine magnetotelluric data after multi-scale generalized morphological filtering of the entire segment and the marine magnetotelluric data after multi-scale generalized morphological filtering of the frequency band that is greatly affected by wave noise;

[0016] Evaluation indicators are set based on the entire ideal ocean magnetotelluric data, the entire ocean magnetotelluric data after multi-scale generalized morphological filtering, the ideal ocean magnetotelluric data in the frequency band greatly affected by wave noise, and the ocean magnetotelluric data after multi-scale generalized morphological filtering in the frequency band greatly affected by wave noise, and the optimal structural element size under the best evaluation indicator is obtained.

[0017] Furthermore, the evaluation index is:

[0018] ,

[0019] Among them, C is the evaluation index, is the normalized cross-correlation coefficient between the entire ideal marine magnetotelluric data and the entire multi-scale generalized morphological filtered marine magnetotelluric data time series, is the normalized cross-correlation coefficient between the ideal ocean magnetotelluric data in the frequency band greatly affected by the ocean wave noise and the ocean magnetotelluric data after multi-scale generalized morphological filtering in the frequency band greatly affected by the ocean wave noise:

[0020] ,

[0021] in, represents the entire ideal ocean magnetotelluric data, represents the entire segment of multi-scale generalized morphological filtered ocean magnetotelluric data, is the sampling point of time series data, Indicates the length of the time series data, Indicates the length of the frequency band affected by waves in the amplitude spectrum.

[0022] Furthermore, the step S2 includes:

[0023] The generalized morphological filter includes a generalized morphological open-closed filter and a generalized morphological open-closed filter, wherein the generalized morphological open-closed filter and the generalized morphological open-closed filter are:

[0024] ,

[0025] Among them, the input signal It is the entire section of raw ocean magnetotelluric data; is the sampling point of time series data, , Indicates the length of the time series data, and Indicates structural elements of different sizes; symbols Indicates the opening operation, symbol Represents closing operation; GOC represents generalized morphological open-close filter, GCO represents generalized morphological close-open filter, ;

[0026] On the basis of the generalized morphological filter, the structural elements are eroded and expanded multiple times to obtain multi-scale generalized morphological operations and multi-scale generalized morphological opening operations. and multi-scale generalized morphological closing operations for:

[0027] ,

[0028] in, It is the entire section of raw ocean magnetotelluric data; and Indicates structural elements of different sizes, symbols Represents the dilation operation, symbol represents the erosion operation, s represents the scale of the multi-scale generalized morphological operation, is the sampling point of time series data, Indicates the length of time series data;

[0029] According to the multi-scale generalized morphological operation, the multi-scale generalized morphological opening-closing operation is obtained. and multi-scale generalized morphological closing-opening operations :

[0030] ,

[0031] in, It is the entire section of raw ocean magnetotelluric data; and Represents structural elements of different sizes, s represents the scale of the multi-scale generalized morphological operation, is the sampling point of time series data, Indicates the length of the time series data.

[0032] Furthermore, the weighted linear combination of the multi-scale generalized morphological opening-closing operation and the multi-scale generalized morphological closing-opening operation includes:

[0033] The weighted linear combination of multi-scale generalized morphological open-close operation and multi-scale generalized morphological close-open operation is performed to obtain the marine magnetotelluric data after noise suppression of the entire segment. for:

[0034] ,

[0035] in, It is a multi-scale generalized morphological opening-closing operation The weighting coefficient of It is a multi-scale morphological closing-opening operation The weighting coefficient of is the sampling point of time series data;

[0036] Assumptions , , then the above formula can be expressed as:

[0037] ,

[0038] in, is the weighting coefficient, , is the sampling point of time series data.

[0039] Furthermore, the gradient method is used to iteratively calculate the weighted coefficients of the multi-scale generalized morphological opening-closing operation and the weighted coefficients of the multi-scale morphological closing-opening operation to update the weighted coefficients, thereby obtaining the entire noise-suppressed marine magnetotelluric data, including:

[0040] Step S31: Perform multi-scale generalized morphological opening-closing operations on the input signal to obtain , perform multi-scale closing-opening operations to obtain , then the output of the multi-scale generalized morphological filter after the kth iteration is for:

[0041] ,

[0042] in, is the weight coefficient of the kth iteration, , is the sampling point of time series data, , Indicates the length of time series data;

[0043] Step S32: Obtain a single error sample after the kth iteration :

[0044] ,

[0045] in, is the output of the multi-scale generalized morphological filter after k-1 iterations, is the output of the multi-scale generalized morphological filter after k iterations, is the sampling point of time series data, , represents the length of the time series data, then the gradient for:

[0046] ,

[0047] ,

[0048] in, is the square of a single error sample at the kth iteration Weighting coefficient The gradient, is the square of a single error sample at the kth iteration Weighting coefficient gradient;

[0049] Step S33: Step S33: Calculate direction vector :

[0050] ,

[0051] in, is the first component of the direction vector at the kth iteration, is the second component of the direction vector at the kth iteration, is the direction vector at the kth iteration A quantity, is the direction vector at the k-1th iteration A quantity, is the square of a single error sample at the kth iteration Weighting coefficient The gradient, is the square of a single error sample at the k-1th iteration Weighting coefficient gradient;

[0052] Step S34: Calculate the weighting coefficient:

[0053] ,

[0054] in, is the weighting coefficient for the k+1th iteration, is the weight coefficient of the kth iteration, is the direction vector of the kth iteration, , is the step size parameter.

[0055] Furthermore, the step S3 further includes:

[0056] Multi-scale adaptive generalized morphological filtering is an iterative filtering process. In the iterative filtering process, the initial input signal is the entire segment of original ocean magnetotelluric data. After each iterative filtering, the filtered ocean magnetotelluric data is reconstructed. The data reconstruction is to perform bandpass filtering on the filtered ocean magnetotelluric data, retaining only the ocean magnetotelluric data in the frequency band that is significantly affected by wave noise in the filtered ocean magnetotelluric data, which is recorded as the intermediate ocean magnetotelluric data in the frequency band that is significantly affected by wave noise. The ocean magnetotelluric data in the remaining frequency bands are the same as the original ocean magnetotelluric data. The intermediate ocean magnetotelluric data in the frequency band that is significantly affected by wave noise is reconstructed with the ocean magnetotelluric data in the remaining frequency bands to obtain reconstructed ocean magnetotelluric data after each iterative filtering. The reconstructed ocean magnetotelluric data is then used as the input signal and the iterative filtering process is repeated. When the iterative stopping condition is met, the noise-suppressed ocean magnetotelluric data in the frequency band that is significantly affected by wave noise and the noise-suppressed ocean magnetotelluric data of the entire segment are obtained.

[0057] Furthermore, the signal-to-noise ratio SNR , the root mean square error RMSE and the normalized cross-correlation coefficient NCC They are:

[0058] ,

[0059] in, represents the entire segment of raw ocean magnetotelluric data, Represents the ocean magnetotelluric data after the entire noise is suppressed. is the sampling point of time series data, Indicates the length of the time series data.

[0060] In a second aspect, the present application provides a device for suppressing ocean wave-induced electromagnetic noise, the device comprising:

[0061] Optimal structural element determination module: determines the type and size of the structural element and obtains the optimal structural element;

[0062] Multi-scale generalized morphological operation module: using the optimal structural element to perform multi-scale operations on the entire segment of original marine magnetotelluric data, and obtaining multi-scale open-close operation results and multi-scale close-open operation results;

[0063] Noise suppression module: Perform weighted linear combination of multi-scale generalized morphological opening-closing operation and multi-scale generalized morphological closing-opening operation, and use gradient method to iteratively calculate the weight coefficients of multi-scale generalized morphological opening-closing operation and multi-scale morphological closing-opening operation to update the weight coefficients, and obtain the entire noise-suppressed marine magnetotelluric data;

[0064] The first noise suppression effect evaluation module: introducing signal-to-noise ratio SNR , root mean square error RMSE and normalized cross-correlation coefficient NCC Three indicators are used to evaluate the noise suppression effect;

[0065] The second noise suppression effect evaluation module: performs robust impedance estimation on the entire noise-suppressed marine magnetotelluric data, calculates apparent resistivity and phase, and evaluates the apparent resistivity and phase data.

[0066] In a third aspect, the present application provides an electronic device, comprising:

[0067] processor;

[0068] Memory;

[0069] and a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, which, when executed by the processor, enable the electronic device to perform the method described in the first aspect.

[0070] Based on the above invention content, compared with the prior art, the present application improves the multi-scale adaptive generalized morphological filtering. By setting an evaluation index, the method of obtaining the size of the structural element is improved, so that the obtained structural element is more suitable for filtering marine magnetotelluric data; by performing a weighted linear combination of the multi-scale generalized morphological open-close operation and the multi-scale generalized morphological close-open operation, the filtering accuracy is improved, and the gradient method is used to iteratively calculate the weighted coefficients of the multi-scale generalized morphological open-close operation and the weighted coefficients of the multi-scale generalized morphological close-open operation to update the weighted coefficients, further improving the accuracy of the multi-scale adaptive generalized morphological filter; after each iterative filtering, the filtered data is reconstructed as the input data for the next iteration, further improving the filtering accuracy; the experience of selecting parameters from simulated data is applied to the measured data, and the actual application effect of the method is tested. In summary, the method of the present application significantly improves the filtering accuracy of marine magnetotelluric data, the noise suppression effect is significantly improved, and the quality of magnetotelluric data after noise suppression is significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0072] Figure 1 This is a flow chart of a method for suppressing ocean wave-induced electromagnetic noise provided by an embodiment of the present application;

[0073] Figure 2 The E for simulating ocean magnetotelluric data provided in the embodiment of the present application is y Components and H of marine magnetotelluric data x One-dimensional horizontal layered geoelectric model of components;

[0074] Figure 3a is the E of the simulated ocean magnetotelluric data provided in the embodiment of the present application y Quantity;

[0075] Figure 3b is the H of the simulated ocean magnetotelluric data provided in the embodiment of the present application x Quantity;

[0076] Figure 4a is the time series of the X component of the ocean wave induced magnetic field provided in the embodiment of the present application;

[0077] Figure 4b is the X-component amplitude spectrum of the ocean wave-induced magnetic field provided in the embodiment of the present application;

[0078] Figure 5a The ocean magnetotelluric H before and after noise addition provided in the embodiment of the present application is x Component time series, red is noise-free data, blue is noisy data;

[0079] Figure 5b The ocean magnetotelluric H before and after noise addition provided in the embodiment of the present application is x component amplitude spectrum;

[0080] Figure 6a is the simulated ocean magnetotelluric H provided in the embodiment of the present application. x Comparison of time series before and after component noise suppression;

[0081] Figure 6b is the simulated ocean magnetotelluric H provided in the embodiment of the present application. x Comparison of time series details before and after component noise suppression;

[0082] Figure 6c is the simulated ocean magnetotelluric H provided in the embodiment of the present application. x Comparison of power spectra before and after component noise suppression;

[0083] Figure 7a This is a comparison of the overall time series of the simulated wave-induced magnetic noise provided in the embodiment of the present application and the X component of the wave-induced magnetic noise extracted by filtering;

[0084] Figure 7b It is a comparison of the local time series of the X component of the simulated ocean wave induced magnetic noise and the filtered extracted ocean wave induced magnetic noise provided in the embodiment of the present application;

[0085] Figure 8 Comparison of apparent resistivity curves and phase curves estimated from the original data (blue), noisy data (black), and data denoised by morphological filtering (MMF) (red) and EMD method (green) provided in the examples of this application;

[0086] Figure 9a The measured ocean magnetotelluric data E provided in the embodiment of this application x component time series;

[0087] Figure 9b The measured ocean magnetotelluric data E provided in the embodiment of this application x component amplitude spectrum;

[0088] Figure 9c The measured ocean magnetotelluric data E provided in the embodiment of this application y component time series;

[0089] Figure 9d The measured ocean magnetotelluric data E provided in the embodiment of this application y component amplitude spectrum;

[0090] Figure 9e The measured ocean magnetotelluric data H provided in the embodiment of this application x component time series;

[0091] Figure 9f The measured ocean magnetotelluric data H provided in the embodiment of this application x component amplitude spectrum;

[0092] Figure 9g The measured ocean magnetotelluric data H provided in the embodiment of this application y component time series;

[0093] Figure 9h The measured ocean magnetotelluric data H provided in the embodiment of this application y component amplitude spectrum;

[0094] Figure 10a is the measured horizontal magnetic field H provided in the embodiment of the present application x Time series before and after data denoising;

[0095] Figure 10b is the measured horizontal magnetic field H provided in the embodiment of the present application x Local time series before and after data denoising;

[0096] Figure 10c is the measured horizontal magnetic field H provided in the embodiment of the present application xPower spectral density of data before and after denoising;

[0097] Figure 10d is the measured horizontal magnetic field H provided in the embodiment of the present application y Time series before and after data denoising;

[0098] Figure 10e is the measured horizontal magnetic field H provided in the embodiment of the present application y Time series before and after data denoising;

[0099] Figure 10f is the measured horizontal magnetic field H provided in the embodiment of the present application y Power spectral density of data before and after denoising;

[0100] Figure 11a is the measured horizontal magnetic field H provided in the embodiment of the present application x Time-frequency diagram before data noise suppression;

[0101] Figure 11b is the measured horizontal magnetic field H provided in the embodiment of the present application x Time-frequency diagram after data noise suppression;

[0102] Figure 11c is the measured horizontal magnetic field H provided in the embodiment of the present application y Time-frequency diagram before data noise suppression;

[0103] Figure 11d is the measured horizontal magnetic field H provided in the embodiment of the present application y Time-frequency diagram after data noise suppression;

[0104] Figure 12a The apparent resistivity and phase curves in the XY directions before and after noise suppression of the measured data provided in the embodiment of the present application are as follows;

[0105] Figure 12b The apparent resistivity and phase curves in the Y and X directions before and after noise suppression of the measured data provided in the embodiment of the present application are shown;

[0106] Figure 13 This is a structural block diagram of a device for suppressing ocean wave-induced electromagnetic noise provided by an embodiment of the present application;

[0107] Figure 14 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0108] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0109] It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0110] The terms used in the embodiments of the present application are for the purpose of describing specific embodiments only and are not intended to limit the present application. The singular forms "a", "an", "the" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0111] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the associated objects.

[0112] See Figure 1 The embodiment of the present application provides a method for suppressing ocean wave-induced electromagnetic noise, which may include the following steps during specific implementation.

[0113] Step S1: Determine the type and size of the structural element and obtain the optimal structural element.

[0114] This application suppresses ocean wave-induced electromagnetic noise based on multi-scale adaptive generalized morphological filtering. During the filtering process, the selection of structural element type and size has a significant impact on the results of ocean wave-induced electromagnetic noise suppression.

[0115] According to the characteristics of marine magnetotelluric data and wave-induced electromagnetic noise, the triangular structural element is more suitable for the characteristics of marine magnetotelluric data. Therefore, the type of the optimal structural element is set to the triangular structural element.

[0116] The size of the structural element is determined by the following steps:

[0117] S11: Perform preliminary filtering to determine the size range of the structural element.

[0118] S12: Input the entire ideal ocean magnetotelluric data and perform multi-scale generalized morphological filtering to traverse all structural elements within the size range of the structural elements, thereby obtaining the entire ocean magnetotelluric data after multi-scale generalized morphological filtering and the ocean magnetotelluric data after multi-scale generalized morphological filtering in the frequency band that is more affected by wave noise.

[0119] Multi-scale generalized morphological filtering is an iterative filtering process. The initial input signal is the entire idealized ocean magnetotelluric data segment. After each iterative filtering, the filtered ocean magnetotelluric data is reconstructed. This data reconstruction involves bandpass filtering the filtered ocean magnetotelluric data. Only the ocean magnetotelluric data in the frequency band significantly affected by wave noise are retained, denoted as the z-mid-band ocean magnetotelluric data in the frequency band significantly affected by wave noise. The ocean magnetotelluric data in the remaining frequency bands are identical to the idealized ocean magnetotelluric data. The mid-band ocean magnetotelluric data in the frequency band significantly affected by wave noise are then reconstructed with the remaining frequency bands to obtain the reconstructed ocean magnetotelluric data after each filtering step. The reconstructed ocean magnetotelluric data are then used as the input signal, and the iterative filtering process is repeated. When the iterative stopping condition is met, the ocean magnetotelluric data for the frequency band significantly affected by wave noise and the entire segment of multi-scale generalized morphological filtered ocean magnetotelluric data are obtained.

[0120] Evaluation indicators are set based on the entire ideal ocean magnetotelluric data, the entire ocean magnetotelluric data after multi-scale generalized morphological filtering, the ideal ocean magnetotelluric data in the frequency band greatly affected by wave noise, and the ocean magnetotelluric data after multi-scale generalized morphological filtering in the frequency band greatly affected by wave noise, and the optimal structural element size under the best evaluation indicator is obtained.

[0121] The evaluation indicators are:

[0122] ,

[0123] Among them, C is the evaluation index, is the normalized cross-correlation coefficient between the entire ideal marine magnetotelluric data and the entire multi-scale generalized morphological filtered marine magnetotelluric data time series, is the normalized cross-correlation coefficient between the ideal ocean magnetotelluric data in the frequency band greatly affected by the ocean wave noise and the ocean magnetotelluric data after multi-scale generalized morphological filtering in the frequency band greatly affected by the ocean wave noise:

[0124] ,

[0125] in, represents the entire ideal ocean magnetotelluric data, represents the entire segment of multi-scale generalized morphological filtered ocean magnetotelluric data, is the sampling point of time series data, Indicates the length of the time series data, Indicates the length of the frequency band affected by waves in the amplitude spectrum.

[0126] whenC The optimal structuring element size is the maximum structuring element size.

[0127] Step S2: using the optimal structural element to perform multi-scale operations on the entire segment of original marine magnetotelluric data to obtain a multi-scale open-close operation result and a multi-scale close-open operation result.

[0128] The generalized morphological filter includes a generalized morphological open-closed filter and a generalized morphological open-closed filter, wherein the generalized morphological open-closed filter and the generalized morphological open-closed filter are:

[0129] ,

[0130] Among them, the input signal It is the entire section of raw ocean magnetotelluric data; is the sampling point of time series data, , Indicates the length of the time series data, and Indicates structural elements of different sizes; symbols Indicates the opening operation, symbol Represents closing operation; GOC represents generalized morphological open-close filter, GCO represents generalized morphological close-open filter, .

[0131] On the basis of the generalized morphological filter, the structural elements are eroded and expanded multiple times to obtain multi-scale generalized morphological operations and multi-scale generalized morphological opening operations. and multi-scale generalized morphological closing operations for:

[0132] ,

[0133] in, It is the entire section of raw ocean magnetotelluric data; and Indicates structural elements of different sizes, symbols Represents the dilation operation, symbol represents the erosion operation, s represents the scale of the multi-scale generalized morphological operation, is the sampling point of time series data, Indicates the length of the time series data.

[0134] According to the multi-scale generalized morphological operation, the multi-scale generalized morphological opening-closing operation is obtained. and multi-scale generalized morphological closing-opening operations :

[0135] ,

[0136] in, It is the entire section of raw ocean magnetotelluric data; and Represents structural elements of different sizes, s represents the scale of the multi-scale generalized morphological operation, is the sampling point of time series data, Indicates the length of the time series data.

[0137] Step S3: Perform a weighted linear combination of the multi-scale generalized morphological opening-closing operation and the multi-scale generalized morphological closing-opening operation, and use the gradient method to iteratively calculate the weighted coefficients of the multi-scale generalized morphological opening-closing operation and the weighted coefficients of the multi-scale morphological closing-opening operation to update the weighted coefficients and obtain the entire section of noise-suppressed marine magnetotelluric data.

[0138] The weighted linear combination of multi-scale generalized morphological open-close operation and multi-scale generalized morphological close-open operation is performed to obtain the marine magnetotelluric data after noise suppression of the entire segment. for:

[0139] ,

[0140] in, It is a multi-scale generalized morphological opening-closing operation The weighting coefficient of It is a multi-scale morphological closing-opening operation The weighting coefficient of is the sampling point of time series data.

[0141] Assumptions , , then the above formula can be expressed as:

[0142] ,

[0143] in, is the weighting coefficient, , is the sampling point of time series data.

[0144] In order to optimize the filtering effect, the gradient method is used to iteratively calculate the weighting coefficients of the multi-scale generalized morphological opening-closing operation and the weighting coefficients of the multi-scale morphological closing-opening operation to obtain the optimal weighting coefficients.

[0145] Assume that the actual input data is the entire segment of original ocean magnetotelluric data for:

[0146] ,

[0147] in, For the ideal ocean magnetotelluric data of the entire section, is the entire simulated wave noise, is the sampling point of time series data, Indicates the length of the time series data.

[0148] The output signal is the ocean magnetotelluric data after the noise is suppressed. , For the entire ideal ocean magnetotelluric data time series Compared with the ocean magnetotelluric data after the entire noise is suppressed The difference between .

[0149] The entire noise-suppressed ocean magnetotelluric data The mean square error is:

[0150] ,

[0151] in, is the mean square error of the output signal, For the ideal ocean magnetotelluric data of the entire section, This is the ocean magnetotelluric data after noise suppression. Ideal marine magnetotelluric data for the entire section Compared with the marine magnetotelluric data after the entire noise is suppressed The difference between is the weighting coefficient, , is the sampling point of time series data, , Indicates the length of the time series data.

[0152] The gradient method is used to gradually modify the weight coefficient value so that the entire noise-suppressed marine magnetotelluric data Continuously approaching the ideal marine magnetotelluric data of the entire section under the condition of minimum mean square error .

[0153] To simplify the calculation, take the square of a single error sample As the mean square error Estimates, The gradient of the weighted coefficient is:

[0154] ,

[0155] in, for Weighting coefficient The gradient, for Weighting coefficient The gradient of n is the sampling point of time series data, is the sampling point of time series data, , Indicates the length of the time series data, , .

[0156] The iterative calculation process of multi-scale adaptive generalized morphological filtering based on the gradient method is:

[0157] Step S31: Perform multi-scale generalized morphological opening-closing operations on the input signal to obtain , perform multi-scale closing-opening operations to obtain , then the output of the multi-scale generalized morphological filter after the kth iteration is for:

[0158] ,

[0159] in, is the weight coefficient of the kth iteration, , is the sampling point of time series data, , Indicates the length of the time series data.

[0160] Step S32: Obtain a single error sample after the kth iteration :

[0161] ,

[0162] in, is the output of the multi-scale generalized morphological filter after k-1 iterations, is the output of the multi-scale generalized morphological filter after k iterations, is the sampling point of time series data, , represents the length of the time series data, then the gradient for:

[0163] ,

[0164] in, is the square of a single error sample at the kth iteration Weighting coefficient The gradient, is the square of a single error sample at the kth iteration Weighting coefficient gradient.

[0165] Step S33: Calculate direction vector :

[0166] ,

[0167] in, is the first component of the direction vector at the kth iteration, is the second component of the direction vector at the kth iteration, is the direction vector at the kth iteration A quantity, is the direction vector at the k-1th iteration A quantity, is the square of a single error sample at the kth iteration Weighting coefficient The gradient, is the square of a single error sample at the k-1th iteration Weighting coefficient gradient.

[0168] Step S34: Calculate the weighting coefficient:

[0169] ,

[0170] in, is the weighting coefficient for the k+1th iteration, is the weight coefficient of the kth iteration, is the direction vector of the kth iteration, , is the step size parameter.

[0171] Multi-scale adaptive generalized morphological filtering is an iterative filtering process. The initial input signal is the entire segment of raw ocean MT data. After each iterative filtering, the filtered MT data is reconstructed. This involves bandpass filtering the filtered MT data, retaining only the MT data in the frequency bands significantly affected by wave noise. This data is denoted as the intermediate MT data in the frequency bands significantly affected by wave noise. The MT data in the remaining frequency bands are the same as the original MT data. The intermediate MT data in the frequency bands significantly affected by wave noise are then reconstructed with the remaining MT data to obtain the reconstructed MT data after each iterative filtering. The reconstructed MT data are then used as the input signal, and the iterative filtering process is repeated. When the iterative stopping condition is met, the noise-suppressed MT data in the frequency bands significantly affected by wave noise and the noise-suppressed MT data for the entire segment are obtained.

[0172] Step S4: Introducing signal-to-noise ratio SNR, root mean square error RMSE and normalized cross-correlation coefficient NCC Three indicators are used to evaluate the noise suppression effect:

[0173] ,

[0174] in, represents the entire segment of raw ocean magnetotelluric data, Represents the ocean magnetotelluric data after the entire noise is suppressed. is the sampling point of time series data, Indicates the length of the time series data.

[0175] Signal-to-noise ratio SNR and normalized cross-correlation coefficient NCC The bigger ， The smaller the root mean square error (RMSE), the better the noise suppression effect.

[0176] Step S5: Performing robust impedance estimation on the entire noise-suppressed marine magnetotelluric data, calculating the apparent resistivity and phase, and evaluating the apparent resistivity and phase data.

[0177] The technical solution of this application is further explained below with reference to specific examples.

[0178] Example 1:

[0179] In this example, we first set up simulation data for the experiment. In order to obtain the ocean magnetotelluric simulation data, we designed Figure 2 The one-dimensional geoelectric model shown in the figure uses the constructed system function method to simulate the marine magnetotelluric data. Assuming the sampling frequency is 10 Hz and the sampling time is 30 min, the E of the marine magnetotelluric field is obtained. y Component time series such as Figure 3a As shown, the H of the ocean magnetotelluric field x Component time series such as Figure 3b shown.

[0180] The simulation of the wave-induced magnetic field begins with Maxwell's equations. Direct differentiation is used to derive the differential equation for the wave-induced magnetic field. This equation, combined with the continuity conditions for the magnetic field at the sea surface and seabed, is used to derive the expression for the wave-induced magnetic field. The assumptions are a geomagnetic field of 50,000 nT, a wind speed of 9 m / s, a sea depth of 50 m, and a sampling rate of 10 Hz. Figure 4a To simulate the 30-minute time series of the wave-induced magnetic field, Figure 4b The amplitude spectrum of the magnetic field induced by ocean waves is obtained by simulation.

[0181] Depend on Figure 4bThe amplitude spectrum shows that the energy of the simulated wave-induced magnetic noise is mainly concentrated in the frequency band of 0.08~0.22 Hz. Figure 3b The H of the simulated ocean magnetotelluric field is shown x From the component time series data, the time series and amplitude spectrum of the simulated ocean magnetotelluric data containing wave-induced magnetic noise are obtained, as shown in Figure 5a and 5b As shown. Figure 5b It can be seen from the figure that a strong energy band appears near 0.1 Hz in the amplitude spectrum of the noisy data, which is the electromagnetic interference of the ocean waves.

[0182] The method of the present invention is used to suppress the noise of the synthesized magnetotelluric data containing wave-induced magnetic noise. Through testing, the type of structural element is determined to be a triangle, and the size of the structural element is determined according to step S1 to obtain the optimal structural element. At the same time, through testing, the scale and initial weighting coefficient are determined. Because too large a scale will not significantly improve the results, but will increase the computational cost, the scale should not be too large. The final selected filtering parameters are shown in Table 1. The H of the simulated ocean magnetotelluric x The overall comparison of the time series before and after component noise suppression and the comparison of the time series details are as follows: Figure 6a and 6b As shown, the power spectrum of the filtered result is compared with that of the pure data. Figure 6c As shown in the figure, the overall time series comparison of the simulated wave-induced magnetic noise and the filtered wave-induced magnetic noise X component is as follows: Figure 7a As shown, the local time series pairs are Figure 7b .from Figure 6a 、 6b It can be seen that the error between the noise suppressed ocean magnetotelluric data and the original ocean magnetotelluric data is small. Figure 6c It can be seen that around 0.1 Hz, the abnormal power spectrum density envelope caused by the wave-induced magnetic noise can be well suppressed. Figure 7a-7b It can be seen that the extracted wave-induced magnetic noise is basically consistent with the simulated wave-induced magnetic noise.

[0183] Table 1:

[0184] ,

[0185] H of simulated ocean magnetotelluric data x The changes in the signal-to-noise ratio (SNR), root mean square error (RMSE), and normalized cross-correlation coefficient (NCC) of the components before and after noise suppression are shown in Table 2. The normalized cross-correlation coefficient between the extracted ocean waves and the added ocean-induced magnetic noise also reaches 0.9832, indicating that the quality of magnetotelluric data is significantly improved after noise suppression.

[0186] Table 2:

[0187] ,

[0188] In order to further verify the improvement of the marine magnetotelluric data after noise suppression, robust impedance estimation was performed on simulated marine magnetotelluric data, simulated marine magnetotelluric data containing wave-induced magnetic noise, and marine magnetotelluric data after noise suppression obtained by the method of the present invention. Apparent resistivity and phase were calculated and compared with the results calculated by the empirical mode decomposition (EMD) method. Figure 8 As shown in the figure, before noise suppression, the magnetotelluric apparent resistivity and phase curves are significantly distorted within a period of 5 to 10 s due to the influence of wave-induced magnetic noise. The EMD method can restore the distortion of the apparent resistivity curve to a certain extent. After multi-scale adaptive generalized morphological filtering denoising, the magnitude of the apparent resistivity can be better restored, and the apparent resistivity and phase curves are more continuous and reasonable.

[0189] Example 2:

[0190] The method of the present invention is used to process the measured marine magnetotelluric data of a station in the South Yellow Sea to further verify the practical application effect of the method. In order to minimize the electromagnetic noise interference caused by tidal movement, the data of the slack tide period are selected for noise suppression. The time series and amplitude spectra of the two selected horizontal components of the electric field are shown as follows: Figures 9a-9d As shown, the time series and amplitude spectra of the two horizontal components of the magnetic field are respectively as follows: Figures 9e~9h As shown in the figure, the sampling rate is 500 Hz and the sampling time is 100 minutes. As can be seen from the amplitude spectrum, the horizontal component of the electric field is less affected by waves and can be assumed to be a noise-free signal. However, the horizontal component of the magnetic field is significantly affected by seawater movement, and the amplitude spectrum exhibits noise interference within the frequency range of 0.08-0.3 Hz. Therefore, this application only performs noise suppression on the magnetic field component of the measured data.

[0191] The method of the present invention is used to Figure 9e and 9g The measured data shown in the figure are processed and analyzed, and the measured ocean magnetotelluric data H x Component and measured marine magnetotelluric data H y The filtering parameters used for the components are shown in Table 3. x The overall time series comparison and local time series comparison before and after component noise suppression are as follows: Figures 10a-10b As shown, the power spectrum density is Figure 10c As shown, the measured ocean magnetotelluric data H y The overall time series comparison and local time series comparison before and after component noise suppression are as follows: Figure 10d and10e As shown, the power spectrum density is Figure 10f As shown in the power spectrum density, the measured ocean magnetotelluric data H x Compared with the measured marine magnetotelluric data H y The abnormally strong energy in the range of 0.08~0.3Hz is suppressed, and the energy of the power spectrum density returns to normal levels. Figure 11a It is the measured ocean magnetotelluric data H x Time-frequency diagram before component noise suppression, Figure 11b It is the measured ocean magnetotelluric data H x Time-frequency diagram after component noise suppression, Figure 11c It is the measured ocean magnetotelluric data H y Time-frequency diagram before component noise suppression, Figure 11d It is the measured ocean magnetotelluric data H y The time-frequency diagram after component noise suppression also shows the measured ocean magnetotelluric data H after noise suppression. y Component and measured marine magnetotelluric data H y The abnormally strong energy within the black frame is effectively suppressed.

[0192] Table 3:

[0193] ,

[0194] The robust impedance estimation method is used to estimate the impedance of the measured data before and after noise suppression and calculate the apparent resistivity and phase. The results in the XY direction are as follows: Figure 12a As shown, the results in the YX direction are as follows Figure 12b As shown in the figure, it can be seen that under the influence of the wave-induced magnetic field, the apparent resistivity curve and phase curve are greatly distorted between periods of 3 and 10 s. After noise suppression, the apparent resistivity curve and phase curve are significantly improved, becoming more continuous and reasonable.

[0195] This application improves the multi-scale adaptive generalized morphological filtering. By setting evaluation indicators, the method of obtaining the size of structural elements is improved, making the obtained structural elements more suitable for filtering marine magnetotelluric data; by performing weighted linear combination of multi-scale generalized morphological opening-closing operation and multi-scale generalized morphological closing-opening operation, the filtering accuracy is improved, and the gradient method is used to iteratively calculate the weighted coefficients of the multi-scale generalized morphological opening-closing operation and the weighted coefficients of the multi-scale generalized morphological closing-opening operation to update the weighted coefficients, further improving the accuracy of the multi-scale adaptive generalized morphological filter; after each iterative filtering, the filtered data is reconstructed as the input data for the next iteration, further improving the filtering accuracy; the experience of selecting parameters for simulation data is applied to the measured data, and the actual application effect of this method is tested. In summary, the filtering accuracy of marine magnetotelluric data is significantly improved by using the method of this application, the noise suppression effect is significantly improved, and the quality of magnetotelluric data after noise suppression is significantly improved.

[0196] Corresponding to the above embodiments, the present application also provides a device for suppressing ocean wave-induced electromagnetic noise.

[0197] See also Figure 13 , is a structural block diagram of a device for suppressing ocean wave-induced electromagnetic noise provided by an embodiment of the present application. Figure 13 As shown, it mainly includes the following modules.

[0198] Optimal structural element determination module 1301: determines the type and size of the structural element and obtains the optimal structural element;

[0199] Multi-scale generalized morphological operation module 1302: using the optimal structural element to perform multi-scale operation on the entire segment of original marine magnetotelluric data, to obtain multi-scale open-close operation results and multi-scale close-open operation results;

[0200] Noise suppression module 1303: Performs a weighted linear combination of the multi-scale generalized morphology opening-closing operation and the multi-scale generalized morphology closing-opening operation, and iteratively calculates the weight coefficients of the multi-scale generalized morphology opening-closing operation and the multi-scale morphology closing-opening operation using a gradient method to update the weight coefficients, thereby obtaining the entire noise-suppressed marine magnetotelluric data.

[0201] First noise suppression effect evaluation module 1304: introducing signal-to-noise ratio SNR , root mean square error RMSE and normalized cross-correlation coefficient NCC Three indicators are used to evaluate the noise suppression effect;

[0202] The second noise suppression effect evaluation module 1305 performs robust impedance estimation on the entire noise-suppressed marine magnetotelluric data, calculates apparent resistivity and phase, and evaluates the apparent resistivity and phase data.

[0203] It should be pointed out that the specific contents involved in the embodiments of the present application can be found in the description of the above method embodiments. For the sake of brevity, they will not be repeated here.

[0204] Corresponding to the above embodiment, an embodiment of the present application further provides an electronic device.

[0205] See also Figure 14 , is a structural diagram of an electronic device provided in an embodiment of the present application. Figure 14 As shown, the electronic device 1400 may include: a processor 1401, a memory 1402, and a communication unit 1403. These components communicate via one or more buses. Those skilled in the art will appreciate that the electronic device structure shown in the figure does not limit the embodiments of the present application. It may be a bus structure or a star structure, and may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0206] The communication unit 1403 is used to establish a communication channel so that the electronic device can communicate with other devices.

[0207] The processor 1401 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It runs or executes software programs and / or modules stored in the memory 1402, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 1401 can only include a central processing unit (CPU). In the embodiment of the present application, the CPU can be a single computing core or multiple computing cores.

[0208] Memory 1402 is used to store execution instructions of processor 1401. Memory 1402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0209] When the execution instructions in the memory 1402 are executed by the processor 1401 , the electronic device 1400 is enabled to execute part or all of the steps in the above method embodiments.

[0210] Corresponding to the above embodiment, embodiments of the present application further provide a computer-readable storage medium, wherein the computer-readable storage medium may store a program. When the program is executed, the program may control the device containing the computer-readable storage medium to execute some or all of the steps of the above method embodiments. In a specific implementation, the computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0211] Corresponding to the above embodiment, an embodiment of the present application further provides a computer program product, which includes executable instructions. When the executable instructions are executed on a computer, the computer executes some or all of the steps in the above method embodiment.

[0212] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0213] Those skilled in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented using a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0214] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0215] In the several embodiments provided in this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0216] The above description is merely a specific embodiment of the present application. Any person skilled in the art may easily conceive of variations or substitutions within the technical scope disclosed in this application, and such variations or substitutions shall be within the scope of protection of this application. The scope of protection of this application shall be subject to the scope of protection of the claims.

Claims

1. A method for suppressing ocean wave-induced electromagnetic noise, characterized in that: include: Step S1: Determine the type and size of the structural element and obtain the optimal structural element; Determining the type and size of the structural element and obtaining the optimal structural element includes: According to the marine magnetotelluric data and the characteristics of wave-induced electromagnetic noise, the optimal structural element type is set as a triangular structural element; The size of the structural element is determined by the following steps: S11: perform preliminary filtering to determine the size range of the structural element; S12: Input the entire ideal marine magnetotelluric data, perform multi-scale generalized morphological filtering to traverse all structural elements within the size range of the structural elements, and obtain the marine magnetotelluric data after multi-scale generalized morphological filtering of the entire segment and the marine magnetotelluric data after multi-scale generalized morphological filtering of the frequency band that is greatly affected by wave noise; Evaluation indicators are set based on the entire ideal ocean magnetotelluric data, the entire ocean magnetotelluric data after multi-scale generalized morphological filtering, the ideal ocean magnetotelluric data in the frequency band greatly affected by wave noise, and the ocean magnetotelluric data after multi-scale generalized morphological filtering in the frequency band greatly affected by wave noise, and the optimal structural element size under the optimal evaluation indicator is obtained; the structural element size when the evaluation indicator reaches the maximum is the optimal structural element size; Step S2: using the optimal structural element to perform multi-scale operations on the entire segment of original marine magnetotelluric data to obtain a multi-scale open-close operation result and a multi-scale close-open operation result; Step S3: performing a weighted linear combination of the multi-scale generalized morphological opening-closing operation and the multi-scale generalized morphological closing-opening operation, and iteratively calculating the weighted coefficients of the multi-scale generalized morphological opening-closing operation and the multi-scale morphological closing-opening operation using a gradient method to update the weighted coefficients, thereby obtaining the entire noise-suppressed marine magnetotelluric data; Multi-scale adaptive generalized morphological filtering is an iterative filtering process. In the iterative filtering process, the initial input signal is the entire segment of original ocean magnetotelluric data. After each iterative filtering, the filtered ocean magnetotelluric data is reconstructed. The data reconstruction is to perform bandpass filtering on the filtered ocean magnetotelluric data, retaining only the ocean magnetotelluric data in the frequency band that is significantly affected by wave noise in the filtered ocean magnetotelluric data, which is recorded as the intermediate ocean magnetotelluric data in the frequency band that is significantly affected by wave noise. The ocean magnetotelluric data in the remaining frequency bands are the same as the original ocean magnetotelluric data. The intermediate ocean magnetotelluric data in the frequency band that is significantly affected by wave noise is reconstructed with the ocean magnetotelluric data in the remaining frequency bands to obtain reconstructed ocean magnetotelluric data after each iterative filtering. The reconstructed ocean magnetotelluric data is then used as the input signal, and the iterative filtering process is repeated. When the iterative stopping condition is met, the noise-suppressed ocean magnetotelluric data in the frequency band that is significantly affected by wave noise and the noise-suppressed ocean magnetotelluric data of the entire segment are obtained. Step S4: Introduce three indicators, namely signal-to-noise ratio (SNR), root mean square error (RMSE) and normalized cross-correlation coefficient (NCC), to evaluate the noise suppression effect; Step S5: Performing robust impedance estimation on the entire noise-suppressed marine magnetotelluric data, calculating apparent resistivity and phase, and evaluating the apparent resistivity and phase data.

2. The method according to claim 1, characterized in that The evaluation indicators are: Where C is the evaluation index, NCC1 is the normalized cross-correlation coefficient between the entire ideal ocean magnetotelluric data and the entire ocean magnetotelluric data time series after multi-scale generalized morphological filtering, and NCC2 is the normalized cross-correlation coefficient between the ideal ocean magnetotelluric data in the frequency band that is greatly affected by wave noise and the amplitude spectrum of the ocean magnetotelluric data after multi-scale generalized morphological filtering in the frequency band that is greatly affected by wave noise: Where s(n) represents the entire ideal ocean magnetotelluric data, γ(n) represents the entire ocean magnetotelluric data after multi-scale generalized morphological filtering, n is the sampling point of the time series data, N represents the length of the time series data, and N1 represents the length of the frequency band affected by waves in the amplitude spectrum.

3. The method according to claim 1, characterized in that The step S2 comprises: The generalized morphological filter includes a generalized morphological open-closed filter and a generalized morphological open-closed filter, wherein the generalized morphological open-closed filter and the generalized morphological open-closed filter are: Wherein, the input signal f(n) is the entire segment of raw ocean magnetotelluric data; n is the sampling point of the time series data, n = 0, 1, 2, ..., N-1, N represents the length of the time series data, g1 and g2 represent structural elements of different sizes; the symbol represents an opening operation, and the symbol · represents a closing operation; GOC represents a generalized morphological open-closed filter, GCO represents a generalized morphological closed-open filter, and f(n) = f; On the basis of the generalized morphological filter, the structural elements are eroded and expanded multiple times to obtain multi-scale generalized morphological operations and multi-scale generalized morphological opening operations. and multiscale generalized morphological closing operation (f·g2) s (n) is: Where f is the entire segment of raw ocean magnetotelluric data; g1 and g2 represent structural elements of different sizes, and the symbol represents the expansion operation, the symbol Θ represents the erosion operation, s represents the scale of the multi-scale generalized morphological operation, n is the sampling point of the time series data, and N represents the length of the time series data; According to the multi-scale generalized morphological operation, the multi-scale generalized morphological opening-closing operation M is obtained. GOC (f) s (n) and the multi-scale generalized morphological closing-opening operation M GCO (f) s (n): Where f is the entire segment of raw ocean magnetotelluric data; g1 and g2 represent structural elements of different sizes, s represents the scale of the multi-scale generalized morphological operation, n is the sampling point of the time series data, and N represents the length of the time series data.

4. The method according to claim 3, characterized in that The weighted linear combination of the multi-scale generalized morphological opening-closing operation and the multi-scale generalized morphological closing-opening operation includes: The weighted linear combination of the multi-scale generalized morphological open-close operation and the multi-scale generalized morphological close-open operation is performed to obtain the marine magnetotelluric data y(n) after noise suppression for the entire segment: <h2 style=";text-align:left;direction:ltr">y(n) = a1M<h2 style=";text-align:left;direction:ltr"> GOC <h2 style=";text-align:left;direction:ltr"> (f)<h2 style=";text-align:left;direction:ltr"> s <h2 style=";text-align:left;direction:ltr"> (n)+a2M<h2 style=";text-align:left;direction:ltr"> GCO <h2 style=";text-align:left;direction:ltr"> (f)<h2 style=";text-align:left;direction:ltr"> s <h2 style=";text-align:left;direction:ltr"> (n) Where a1 is the multi-scale generalized morphological opening-closing operation M GOC (f) s (n) is the weighting coefficient, a2 is the multi-scale morphological closing-opening operation M GCO (f) s (n) weighting coefficient, n is the sampling point of time series data; Assume M GOC (f) s (n)=y1(n),M GCO (f) s (n) = y2(n), then the above formula can be expressed as: Among them, a i (n) is the weighting coefficient, i=1, 2, and n is the sampling point of the time series data.

5. The method according to claim 4, characterized in that The gradient method is used to iteratively calculate the weighted coefficients of the multi-scale generalized morphological opening-closing operation and the weighted coefficients of the multi-scale morphological closing-opening operation to update the weighted coefficients, and obtain the entire section of noise-suppressed marine magnetotelluric data, including: Step S31: Perform a multi-scale generalized morphological open-close operation on the input signal to obtain y1(n), and perform a multi-scale close-open operation to obtain y2(n). The output y of the multi-scale generalized morphological filter after the kth iteration is k (n) is: in, is the weighting coefficient of the kth iteration, i = 1, 2, n is the sampling point of the time series data, n = 1, 2, 3, ..., N, N represents the length of the time series data; Step S32: Obtain a single error sample [e(n)] after the kth iteration 良 : [in)] k =and k-1 (n)-y k (n) Among them, y k-1 (n) is the output of the multi-scale generalized morphological filter after k-1 iterations, y k (n) is the output of the multi-scale generalized morphological filter after k iterations, n is the sampling point of the time series data, n = 1, 2, 3, ..., N, N represents the length of the time series data, then the gradient for: in, is the square of a single error sample at the kth iteration e 2 (n) Gradient of weighting coefficient a1, is the square of a single error sample at the kth iteration e 2 (n) Gradient of the weighting coefficient a2; Step S33: Step S33: Calculate direction vector p k : in, is the first component of the direction vector at the kth iteration, is the second component of the direction vector at the kth iteration, is the i-th component of the direction vector at the k-th iteration, is the i-th component of the direction vector at the k-1th iteration, is the square of a single error sample at the kth iteration e 2 (n) for the weighting coefficient a i The gradient, is the square of a single error sample at the k-1th iteration e 2 (n) for the weighting coefficient a i gradient; Step S34: Calculate the weighting coefficient: in, is the weighting coefficient for the k+1th iteration, is the weight coefficient of the kth iteration, is the direction vector of the kth iteration, i = 1, 2, and μ is the step size parameter.

6. The method according to claim 1, wherein The signal-to-noise ratio SNR, the root mean square error RMSE and the normalized cross-correlation coefficient NCC are respectively: Where f(n) represents the entire segment of original ocean magnetotelluric data, y(n) represents the entire segment of noise-suppressed ocean magnetotelluric data, n is the sampling point of the time series data, and N represents the length of the time series data.

7. A device for suppressing ocean wave-induced electromagnetic noise for implementing the method for suppressing ocean wave-induced electromagnetic noise according to claim 1, characterized in that: include: Optimal structural element determination module: determines the type and size of the structural element and obtains the optimal structural element; Multi-scale generalized morphological operation module: using the optimal structural element to perform multi-scale operations on the entire segment of original marine magnetotelluric data, and obtaining multi-scale open-close operation results and multi-scale close-open operation results; Noise suppression module: Perform weighted linear combination of multi-scale generalized morphological opening-closing operation and multi-scale generalized morphological closing-opening operation, and use gradient method to iteratively calculate the weight coefficients of multi-scale generalized morphological opening-closing operation and multi-scale morphological closing-opening operation to update the weight coefficients, and obtain the entire noise-suppressed marine magnetotelluric data; The first noise suppression effect evaluation module: introduces three indicators: signal-to-noise ratio (SNR), root mean square error (RMSE), and normalized cross correlation coefficient (NCC) to evaluate the noise suppression effect; The second noise suppression effect evaluation module: performs robust impedance estimation on the entire noise-suppressed marine magnetotelluric data, calculates apparent resistivity and phase, and evaluates the apparent resistivity and phase data.

8. An electronic device, characterized in that: include: processor; Memory; and a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, which, when executed by the processor, enable the electronic device to perform the method according to any one of claims 1 to 6.

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