Track data denoising method, device, medium and equipment for marine magnetic survey

Abstract

The application discloses a track data denoising method and device for marine magnetic survey, a medium and equipment, and relates to the technical field of geophysical exploration data processing and digital signal processing. Track data of marine magnetic survey is acquired, and Hampel filtering is applied to the track data to obtain a signal subjected to spike noise suppression. A coarse baseline of the signal subjected to spike noise suppression is calculated through window median value, and the coarse baseline is refined and averaged by sliding mean value to obtain a refined baseline. Residuals between the signal subjected to spike noise suppression and the refined baseline are calculated. The residuals are subjected to spatial and amplitude double-weighted smoothing to obtain smoothed residuals. The sum of the refined baseline and the smoothed residuals is determined as denoised track data. The method improves the denoising precision of the track data of marine magnetic survey.

Description

Technical Field

[0001] This application relates to the fields of geophysical exploration data processing and digital signal processing technology, and in particular to a method, apparatus, medium and equipment for denoising track data for marine magnetic surveys. Background Technology

[0002] Marine magnetic surveying detects anomalies in the total magnetic field or gradient in the marine environment to analyze the characteristics and distribution of underground magnetic bodies. It is one of the key technologies for understanding and developing the ocean and is widely used in mineral resource exploration, marine engineering and geological disaster prevention.

[0003] Marine magnetic survey data is susceptible to various interferences, including high-frequency noise, magnetic anomalies from non-target geological bodies, ship navigation errors, and instrument malfunctions. These interferences introduce noise, alter the morphology of magnetic anomalies, or disrupt the continuity of magnetic stripes, severely degrading the quality of regional data volumes and maps, and significantly impacting the reliability of geological interpretation. Therefore, noise suppression is an indispensable part of marine magnetic survey data processing. However, existing denoising methods suffer from low denoising accuracy. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, medium, and equipment for denoising track data for marine magnetic surveys, addressing the aforementioned technical problems.

[0005] The following technical solution is adopted in this specification: This specification provides a method for denoising track data in marine magnetic surveys, including: The ocean magnetic survey track data is acquired, and Hampel filtering is applied to the track data to obtain a signal with noise suppression. The coarse baseline of the noise-suppressed signal is calculated using the window value, and then the coarse baseline is refined by using the moving average to obtain the refined baseline; the residual between the noise-suppressed signal and the refined baseline is calculated. The residuals are smoothed by spatial and amplitude weighting to obtain the smoothed residuals. The sum of the refined baseline and the smoothed residual is used to determine the denoised track data.

[0006] Optionally, acquire ocean magnetic survey track data, including: The magnetic measurement signal of each sampling point along the flight path is obtained by accumulating the latitude and longitude through Havelsing distance measurement. Based on the median step size, the magnetic measurement signal is linearly interpolated to an equidistant grid to obtain the track data.

[0007] Optionally, the first i Smoothed residuals at each sampling point The calculation formula is: in, For the first i The spatial neighborhood of each sampling point For the first i The location of each sampling point along the route, For the first i Within the spatial neighborhood of the sampling point, the first j Location of each sampling point along the path For the first j The residuals at each sampling point For the first i The residuals at each sampling point For smoothing scale, The amplitude scale , For parameter factors, , For the first i The median of the absolute deviation of the residuals relative to the local median within a small window centered on each sampling point.

[0008] Optionally, the track data includes track magnetometry signals from multiple sampling points; sampling points s Track magnetic measurement signal for: ; in, The target magnetic anomaly; This is a low-frequency baseline or drift; It is sharp noise; It is high-frequency noise.

[0009] This specification provides a noise reduction device for ocean magnetic survey track data, including: The acquisition module is used to acquire track data from ocean magnetic surveys. The noise suppression module is used to apply Hampel filtering to the track data to obtain a noise-suppressed signal; The robust baseline determination module is used to calculate the coarse baseline of the noise-suppressed signal using the window value, and then refine the coarse baseline by using the moving average to obtain the refined baseline. The bilateral filtering module is used to calculate the residual between the noise-suppressed signal and the thinned baseline; the residual is then subjected to spatial and amplitude-weighted smoothing to obtain the smoothed residual. The signal reconstruction module is used to determine the denoised track data by summing the refined baseline and the smoothed residual.

[0010] This specification provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for denoising track data for marine magnetic surveys.

[0011] This specification provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method for denoising track data for marine magnetic surveying.

[0012] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects: The denoising method for marine magnetic survey track data provided in this specification firstly removes spike noise from the track data using Hampel filtering, preventing spike noise from contaminating baseline and residual estimation. Further, a combination of median and mean values ​​is used to resist outlier capture trends before smoothing optimization, ensuring the extracted baseline is neither affected by high-frequency interference nor over-smoothed to the point of losing true low-frequency information. Then, the residuals are subjected to spatial and amplitude-weighted smoothing, which maximizes noise removal while preserving meaningful magnetic anomaly signals. Finally, the sum of the refined baseline and the smoothed residuals is determined as the denoised track data, effectively reconstructing the processed components. This significantly improves the signal-to-noise ratio (SNR) of the denoised track data, while simultaneously restoring the morphology, amplitude, and spatial characteristics of signals (especially geological anomalies) with high fidelity. This method improves the denoising accuracy of marine magnetic survey track data. Attached Figure Description

[0013] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0014] Figure 1 This document presents a flowchart illustrating a method for denoising track data from marine magnetic surveys. Figure 2 This specification provides one type of unfiltered track data. Figure 3 The track data is filtered by the method provided by this invention; Figure 4 This manual provides a comparison chart of marine magnetic survey track data before and after filtering. Figure 5 This is a schematic diagram of a computer device used in this specification to implement a method for denoising track data for marine magnetic surveying. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0016] In the existing technology, researchers have developed a variety of denoising methods. For example:

[0017] Fast Fourier Transform (FFT) smoothing can handle high-frequency noise, but its smoothing coefficients depend on the unknown prior noise level: if the coefficients are too small, the noise removal will be incomplete, and if they are too large, the effective high-frequency magnetic anomaly signal will be lost.

[0018] Wavelet thresholding, as a simple and effective method, has been applied to denoising marine magnetic data and has achieved effective separation of noise from real anomalies. However, the criteria for selecting the threshold lack in-depth discussion, and the choice of basis function has a significant impact on the results.

[0019] Empirical Mode Decomposition (EMD) adaptively decomposes a signal into several intrinsic mode functions, making it suitable for processing non-stationary and nonlinear magnetic measurement data. However, this method suffers from mode aliasing and endpoint effects, and is prone to introducing spurious components or distorting signal boundaries during the decomposition process.

[0020] Deep learning-based methods have shown great potential in recent years, enabling them to learn complex noisy features from large amounts of data. However, these methods typically rely on complete and high-quality sample sets for training, and their "black box" nature leads to a lack of physical interpretability in the model's decision-making process and parameters. Furthermore, their generalization ability declines when training data is insufficient or when there are significant differences in the scenarios.

[0021] In summary, existing mainstream filtering methods, whether traditional algorithms or emerging learning methods, still have significant limitations in terms of edge fidelity, low-frequency baseline processing, clarity of parameter physical meaning, and robustness to non-uniformly sampled data. When processing real-world ocean magnetic survey data with both abrupt boundaries and slow-drift backgrounds, these shortcomings can easily lead to problems such as blurred structural boundaries, passband signal distortion, endpoint artifacts, and poor parameter mobility. Therefore, there is an urgent need for a technical solution that can balance edge protection and noise suppression, provide parameters with clear physical scales, and possess strong adaptability and robustness.

[0022] Based on this, the present invention provides a method, apparatus, medium, and equipment for denoising track data in marine magnetic surveys. The method employs a cascade structure of "robust baseline estimation + residual domain bilateral filtering": first, a robust baseline is given using "large window midpoint + smoothing," then the residuals are calculated and subjected to bilateral filtering (spatial kernel × amplitude kernel), and finally the signal is reconstructed. This method improves the denoising accuracy of marine magnetic survey track data.

[0023] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0024] Figure 1 This is a flowchart illustrating a method for denoising track data from marine magnetic surveys, as described in this specification. The method includes the following steps: S101 acquires ocean magnetic survey track data and applies Hampel filtering to the track data to obtain a signal with noise suppression.

[0025] Track data includes track magnetometry signals from multiple sampling points; sampling points s Track magnetic measurement signal for: ; in, The target magnetic anomaly; This is a low-frequency baseline or drift; It is sharp noise; It is high-frequency noise.

[0026] In one embodiment, acquiring ocean magnetic survey track data includes: accumulating the magnetic survey signal at each sampling point along the track based on latitude and longitude using Havelsing distance measurement; and linearly interpolating the magnetic survey signal to an equidistant grid based on the median step size to obtain the track data.

[0027] By interpolating the magnetic measurement signal to an approximately equidistant grid, the impact of non-uniform sampling on frequency domain and window operations can be reduced.

[0028] track data Applying Hampel filtering, the window of the Hampel filter If it is an odd number, the scale factor is . For track data The specific steps for applying Hampel filtering include: targeting track data any sampling point Select width as Given a symmetrical sliding window, calculate the median of all sampling points within the window. Calculate the median for each sample point within the calculation window. The absolute deviations are calculated, and the median of these absolute deviations is called the median absolute deviation. , scale factor Absolute deviation from the median The product of the two is used as the threshold. If sampling points With median The absolute value of the difference between them is greater than the threshold. Then determine the sampling point. For outliers, replace with the median. This sampling point The value of is used to determine all sampling points after Hampel filtering as the signal with noise suppression. .

[0029] S102, calculate the coarse baseline of the noise-suppressed signal using the window value, and refine the coarse baseline by using the moving average to obtain the refined baseline; calculate the residual between the noise-suppressed signal and the refined baseline.

[0030] Use large window midpoint (background window length) A coarse baseline was obtained by using a moving average (smoothing window length) (km); then the moving average (smoothing window length) was used to obtain the coarse baseline. The baseline is refined by (km) to obtain the refined baseline. .

[0031] Optionally, the background window length The value of can be 2–4 times the minimum wavelength of the background field. ; The horizontal scale of the target geological body is expressed in km; the smoothing window simply smooths out the "steps" created by the median, without changing the overall trend. The smoothing window length is... The value can be the background window length. 1 / 3 to 1 / 6 times, .

[0032] by To calculate the rolling median for the noise-suppressed signal, the median is calculated for each point within the window. This median is then used to replace the value of the center point within the window, creating a smooth curve that serves as a coarse baseline. The number of points within the window represents the background window length. The ratio between the sampling interval and the sampling interval.

[0033] Coarse baseline refinement includes: applying a small-window sliding mean to the coarse baseline, that is, calculating the mean of all points within the window for each point, and replacing the value of the center point within the window with this mean to form the refined baseline. The number of points within the window is the average window length. The ratio between the sampling interval and the sampling interval.

[0034] Calculate the residual between the noise-suppressed signal and the thinned baseline. Defined as: .

[0035] S103 performs spatial and amplitude-weighted smoothing on the residuals to obtain the smoothed residuals.

[0036] For each sampling point, within a radius of approximately Calculated within the neighborhood of ; the th i Smoothed residuals at each sampling point The calculation formula is: ; in, For the first i The spatial neighborhood of each sampling point For the first i The position along the path of the sampling point, i.e. the first sampling point i The distance in kilometers from each sampling point to the starting point. For the first i Within the spatial neighborhood of the sampling point, the first j The position along the path of the sampling point, i.e. the first sampling point j The distance in kilometers from each sampling point to the starting point. For the first j The residuals at each sampling point For the first i The residuals at each sampling point To determine the smoothing scale, the smoothing range of the filter is determined. The amplitude scale , For parameter factors, , For the first i The median of the absolute deviation of the residuals relative to the local median within a small window centered on each sampling point. Smoothing scale. From the target cutoff space frequency Mapped from; optionally, ; .

[0037] S104 determines the denoised track data by summing the refined baseline and the smoothed residual.

[0038] Denoising-reduced flight track data for: ; This is the residual after smoothing.

[0039] In one embodiment, to verify the effectiveness of the method provided by the present invention, experiments were conducted, such as... Figure 2-4 As shown, Figure 2 This is a type of unfiltered track data; Figure 3 The track data is filtered by the method provided by this invention; Figure 4 This is a comparison chart of ocean magnetic survey track data before and after filtering; the track data used to test this method comes from the Magellanic Highlands and adjacent seamounts in the central and western Pacific Ocean. The denoising effect is demonstrated here using the MGL1206 expedition in the central and western Pacific as an example. Figure 2 As can be seen, there are both global outliers that deviate significantly from the overall dataset and contextual outliers that deviate from the local data only within a specific time period. Compared to global outliers, contextual outliers are consistent with the overall data trend and are hidden within the local data features, making global statistical methods inapplicable and increasing the risk of misjudgment. Figure 3 The denoised track data obtained using the method provided in this invention shows that global outliers and contextual outliers are almost completely filtered out, and the curves become smoother. A comparison of the data before and after filtering is also shown (…). Figure 4 As can be seen in the data, the data range before filtering is (-4000, 800); while the data range after filtering is (-300, 200), and the overall dataset is very concentrated, which demonstrates the superiority of this method.

[0040] The execution subject of the methods provided in this specification can be a server, which can be a server set up on a business platform, or a device such as a desktop computer or laptop computer that can execute the solutions in this specification.

[0041] When applying the denoising method for ocean magnetic survey track data provided in this manual, it is not necessary to consider... Figure 1 The steps shown are executed in sequence. The specific execution order of each step can be determined as needed, and this manual does not impose any restrictions on it.

[0042] The above are one or more embodiments of the method for denoising track data for marine magnetic surveys provided in this specification. Based on the same idea, this specification also provides a corresponding device for denoising track data for marine magnetic surveys, which includes: The acquisition module is used to acquire track data from ocean magnetic surveys. The noise suppression module is used to apply Hampel filtering to the track data to obtain a noise-suppressed signal; The robust baseline determination module is used to calculate the coarse baseline of the noise-suppressed signal using the window value, and then refine the coarse baseline by using the moving average to obtain the refined baseline. The bilateral filtering module is used to calculate the residual between the noise-suppressed signal and the thinned baseline; the residual is then subjected to spatial and amplitude-weighted smoothing to obtain the smoothed residual. The signal reconstruction module is used to determine the denoised track data by summing the refined baseline and the smoothed residual.

[0043] Specific limitations regarding the denoising device for ocean magnetic survey track data can be found in the limitations of the denoising method for ocean magnetic survey track data described above, and will not be repeated here. Each module in the aforementioned denoising device for ocean magnetic survey track data can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0044] This specification also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 A method for denoising track data for ocean magnetic surveys is provided.

[0045] This instruction manual also provides Figure 5 The schematic diagram of the computer device shown is as follows: Figure 5 At the hardware level, the computer device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to achieve the above-mentioned functions. Figure 1 A method for denoising track data for ocean magnetic surveys is provided.

[0046] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0047] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for denoising track data in marine magnetic surveys, characterized in that, include: The ocean magnetic survey track data is acquired, and Hampel filtering is applied to the track data to obtain a signal with noise suppression. The coarse baseline of the noise-suppressed signal is calculated by using the window value, and the coarse baseline is refined by using the moving average to obtain the refined baseline. Calculate the residual between the noise-suppressed signal and the thinned baseline; The residuals are smoothed by spatial and amplitude weighting to obtain the smoothed residuals. The sum of the refined baseline and the smoothed residual is used to determine the denoised track data.

2. The method according to claim 1, characterized in that, Acquire ocean magnetic survey track data, including: The magnetic measurement signal of each sampling point along the flight path is obtained by accumulating the latitude and longitude through Havelsing distance measurement. Based on the median step size, the magnetic measurement signal is linearly interpolated to an equidistant grid to obtain the track data.

3. The method according to claim 1, characterized in that, No. i Smoothed residuals at each sampling point The calculation formula is: in, For the first i The spatial neighborhood of each sampling point For the first i The location of each sampling point along the route, For the first i Within the spatial neighborhood of the sampling point, the first j The location of each sampling point along the route, For the first j The residuals at each sampling point For the first i The residuals at each sampling point For smoothing scale, The amplitude scale , For parameter factors, , For the first i The median of the absolute deviation of the residuals relative to the local median within a small window centered on each sampling point.

4. The method according to claim 1, characterized in that, Track data includes track magnetometry signals from multiple sampling points; sampling points s Track magnetic measurement signal for: ； in, The target magnetic anomaly; This is a low-frequency baseline or drift; It is sharp noise; It is high-frequency noise.

5. A noise reduction device for ocean magnetic survey track data, characterized in that, include: The acquisition module is used to acquire track data from ocean magnetic surveys. The noise suppression module is used to apply Hampel filtering to the track data to obtain a noise-suppressed signal; The robust baseline determination module is used to calculate the coarse baseline of the noise-suppressed signal using the window value, and then refine the coarse baseline by using the moving average to obtain the refined baseline. The bilateral filtering module is used to calculate the residual between the noise-suppressed signal and the thinned baseline; The residuals are smoothed by spatial and amplitude weighting to obtain the smoothed residuals. The signal reconstruction module is used to determine the denoised track data by summing the refined baseline and the smoothed residual.

6. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method described in any one of claims 1 to 4.

7. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any one of claims 1 to 4.

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