Abnormal body trend judgment method and system based on window optimization angle filtering

Abstract

The invention discloses a window optimization angle filtering anomalous body trend determination method and system, and the method comprises the steps: building an anomaly model composed of at least three anomalous bodies, and adding a preset noise amount of Gaussian white noise into the anomaly model, so as to obtain magnetic anomaly data; and performing model verification on the magnetic anomaly data by adopting multiple filtering algorithms to obtain an optimal filtering algorithm, and performing filtering processing on the target magnetic anomaly data based on the optimal filtering algorithm to obtain an output map. Window optimization angle filtering is used as a non-linear direction filtering method, the direction with the highest data consistency is searched in a sliding window through iteration, interpolation averaging is carried out in the direction, the trend feature of an anomalous body can be highlighted during effective denoising, boundary information is reserved, the method is suitable for magnetic anomaly processing with obvious directivity, and the method is suitable for large-scale popularization and application. The window optimization angle filtering can effectively enhance the trend characteristics of the anomalous body, avoids the problems of anomaly superposition and boundary smoothness, and provides a clearer and more accurate data basis for subsequent geological interpretation.

Description

Technical Field

[0001] This invention belongs to the field of magnetic target detection technology, and in particular relates to a method and system for judging the direction of abnormal objects by window optimization angle filtering. Background Technology

[0002] Magnetic anomalies are disturbances of the Earth's magnetic field in localized areas. Their intensity and morphology are mainly controlled by factors such as the geometry of the anomaly, its burial depth, magnetic susceptibility, and the direction of the geomagnetic field. Magnetic anomalies corresponding to linear structures (such as dikes and fault zones) and layered ore bodies often appear as narrow, elongated bands with a clear direction of extension, consistent with the distribution direction of the geological body. The physical basis of magnetic exploration is the magnetic differences (differences in magnetic susceptibility and remanence) between different rocks (ores). Its applications include regional geological structure analysis, delineation of concealed magnetic rock bodies, magnetic stratigraphy, and exploration of magnetic mineral resources. By processing and interpreting magnetic survey data, the distribution and occurrence of underground magnetic bodies can be inferred, thereby retrieving regional geological structure characteristics, identifying concealed magnetic rock bodies, and guiding mineral resource exploration. With the improvement of instrument performance and the development of UAV technology, airborne magnetic surveying has gradually become one of the important means of magnetic exploration. Since the mid-20th century, airborne magnetic surveying has been widely used in mineral resource exploration, regional structural analysis, and oil and gas prospect prediction. In recent years, with the popularization of UAV aeromagnetic systems and the improvement of magnetometer accuracy, the acquisition efficiency and spatial resolution of aeromagnetic data have been significantly improved, driving the demand for high-precision data processing technology.

[0003] However, aeromagnetic data is susceptible to environmental noise interference, making filtering a crucial step in improving data quality. Common noise reduction methods include median filtering, Gaussian filtering, wavelet transform, and adaptive filtering. In magnetic anomaly data processing, different filtering methods exhibit significant differences in noise reduction capabilities, boundary preservation, and directionality enhancement. Mean filtering averages the anomaly amplitude, which can lead to the suppression of signals from shallow, small anomalies (such as skarn-type ore bodies); Gaussian filtering, while isotropically smoothing, cannot distinguish between the linear trend of magnetic anomalies in fault zones and noise fluctuations; wavelet filtering requires a preset threshold function and may over-suppress weak structural anomalies with "low amplitude - high frequency" in aeromagnetic data.

[0004] In summary, existing directional filters (such as directional wavelets and anisotropic kernels) do not fully utilize the geometric orientation characteristics of magnetic anomalies, making it difficult to achieve a balance between noise suppression and preservation of structural information. Summary of the Invention

[0005] In view of this, the present invention provides an anomaly direction judgment method based on window-optimized angle filtering, which can effectively remove noise and highlight the direction characteristics of the anomaly while preserving boundary information. It is suitable for processing magnetic anomalies with obvious directionality and avoids the anomaly superposition and boundary smoothing problems common in traditional filtering methods. The specific technical solution is as follows.

[0006] In a first aspect, the present invention provides a method for determining the trajectory of anomalies using window-optimized angle filtering, comprising the following steps: An anomaly model consisting of at least three anomalies is established to simulate total magnetic field strength data, wherein the anomaly model is constructed based on a regular tensor grid. Gaussian white noise with a preset noise level is added to the anomaly model to obtain magnetic anomaly data. The optimal filtering algorithm was obtained by applying various filtering algorithms to the magnetic anomaly data. These algorithms included window optimization angle filtering algorithm, window mean filtering algorithm, and Gaussian filtering algorithm. The target magnetic anomaly data is filtered using the optimal filtering algorithm to obtain the corresponding output map.

[0007] As a preferred embodiment of the above technical solution, an anomaly model consisting of at least three anomalies is established. This anomaly model is used to simulate total magnetic field strength data and includes: The anomaly model was established by selecting three prism anomalies of different sizes and magnetic susceptibility. The specified tensor mesh is non-uniformly partitioned in both the horizontal and vertical directions.

[0008] As a preferred embodiment of the above technical solution, Gaussian white noise with a preset noise level is added to the anomaly model to obtain magnetic anomaly data, including: The magnetic anomaly data were obtained by adding 2% Gaussian white noise to the anomaly model and performing simulation analysis. The corresponding forward modeling formula is as follows: (1) (2) in, For the first Magnetic field anomalies at each observation point For kernel function, For the first The magnetic susceptibility of each grid cell. The total number of grid cells. The permeability of free space, Unit vector of geomagnetic field direction The distance between the observation point and the source point. Let j be the volume of the j-th grid cell. For gradient operators, For the source point gradient operator, This represents the volume element of the source region.

[0009] As a preferred embodiment of the above technical solution, the execution process of the window optimization angle filtering algorithm includes: Input the total magnetic field anomaly grid data corresponding to the total magnetic field strength data, and define the grid cell of the sliding window (taking a 5×5 window as an example), with the center point being the measurement point to be filtered. ; With the center of the window as the center and the radius as the radius A circle is drawn using 1 grid cell. The magnetic field strength value at any point P on the circle is obtained by interpolation using the nearest surrounding grid point. The position of point P is determined by the angle between the diameter and the x-axis. It has been determined.

[0010] As a preferred embodiment of the above technical solution, the four grid points closest to point P are selected to form a grid cell, and a bilinear interpolation algorithm is used to calculate the magnetic field strength value at point P: (3) in, Let P be the top-left corner node of the grid cell containing point P. Let P be the top-right corner node of the grid cell containing point P. Let P be the bottom left corner node of the grid cell containing point P. Let P be the bottom right corner node of the grid cell containing point P. Let P be the coordinates; According to each included angle Calculate the root mean square error of all interpolation points on the diameter. The mean square error reflects the dispersion of the data along the diameter direction, and the process of finding the optimal angle is accomplished using the gradient descent method. By setting the step size Update angle By iterating and gradually approaching the minimum point, we can find the angle corresponding to the minimum mean square error. (4) in, The mean of K points on the diameter is used to iteratively converge to the optimal angle. The optimal angle will be obtained in the end. The average value of all interpolation points on the corresponding diameter , as a filter measurement point The filtered output value This represents the angle parameter in the current iteration step. This indicates the updated angle parameters. The mean square error function is represented in gradient at, This refers to the location of the observation point to be filtered.

[0011] As a preferred embodiment of the above technical solution, the execution process of the window mean filtering algorithm includes: The window mean filtering algorithm creates a window centered on the data to be processed, calculates the arithmetic mean of all magnetic anomaly values ​​within the window, and uses the arithmetic mean as the filtered magnetic anomaly value of the center data point. The default input data is The output data is If the window size is 5×5, then the window mean filter formula is: (5) in, and These are relative to the central data. The row and column offsets are both in the range of -2 to 2, and i and j represent the row and column indices of the data point, respectively.

[0012] As a preferred embodiment of the above technical solution, the execution process of the Gaussian filtering algorithm includes: A two-dimensional Gaussian function is used to generate the convolution kernel. The two-dimensional Gaussian function reaches its maximum value at the center of the data and gradually decays towards the surrounding area. A Gaussian kernel is convolved with the dataset, where the value of each data point in the dataset is replaced by a weighted average of its neighboring data points. The discrete Gaussian filter kernel is obtained by sampling the function within a window. For a window of size (2m+1)×(2m+1), the corresponding Gaussian filter formula is: (6) in, For the input dataset, For the output dataset, the values ​​of x and y are both in the range of -m to m, where m is a parameter that determines the size of the convolution kernel, and i and j represent the row index and column index of the data point, respectively. The standard deviation of the Gaussian function is used to control the smoothness.

[0013] As a preferred embodiment of the above technical solution, the at least three anomalous bodies are three prism anomalous bodies of different sizes and different magnetic susceptibility, and the optimal filtering algorithm is a window optimization angle filtering algorithm.

[0014] As a preferred embodiment of the above technical solution, in the anomaly model, the surface terrain is set to be flat, the observation flight altitude is 5 meters, the observation grid is 33×33, and the range is -120 to 120 meters.

[0015] Secondly, the present invention also provides an anomaly trajectory judgment system for window optimization angle filtering, applied to the above-mentioned anomaly trajectory judgment method for window optimization angle filtering, comprising: The model building module is used to build an anomaly model consisting of at least three anomalies. The anomaly model is used to simulate the total magnetic field strength data. The anomaly model is built based on a regular tensor grid. A noise addition module is used to add Gaussian white noise with a preset noise level to the anomaly model to obtain magnetic anomaly data. The model validation module is used to perform model validation on the magnetic anomaly data using various filtering algorithms to obtain the optimal filtering algorithm. The various filtering algorithms include window optimization angle filtering algorithm, window mean filtering algorithm, and Gaussian filtering algorithm. The noise reduction module is used to filter the target magnetic anomaly data based on the optimal filtering algorithm to obtain the output map corresponding to the target magnetic anomaly data.

[0016] This invention provides a method and system for determining the orientation of anomalies using window-optimized angle filtering. An anomaly model consisting of at least three anomalies is established to simulate total magnetic field strength data. Gaussian white noise with a preset noise level is added to the anomaly model to obtain magnetic anomaly data. Multiple filtering algorithms are applied to the magnetic anomaly data to validate the model and determine the optimal filtering algorithm. Based on the optimal filtering algorithm, the target magnetic anomaly data is filtered to obtain the corresponding output map. Window-optimized angle filtering, as a nonlinear directional filtering method, iteratively finds the direction with the highest data consistency within a sliding window and performs interpolation averaging along that direction. This effectively denoises while highlighting the orientation characteristics of the anomaly and preserving boundary information. It is suitable for processing magnetic anomalies with obvious directionality. Window-optimized angle filtering effectively enhances the orientation characteristics of the anomaly, avoiding the anomaly superposition and boundary smoothing problems common in traditional filtering methods, thus providing a clearer and more accurate data foundation for subsequent geological interpretation. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of the abnormal body trajectory judgment method for window optimization angle filtering provided by the present invention; Figure 2A flowchart for determining the trajectory of abnormal bodies using window optimization angle filtering provided by this invention; Figure 3 A three-dimensional view of the theoretical model provided for this invention; Figure 4 The magnetic anomaly map obtained by forward modeling provided by this invention; Figure 5 A schematic diagram of the window optimization angle filtering method provided by this invention; Figure 6 A comparison diagram of the three filtering methods provided by this invention; Figure 7 The three filtering methods provided by this invention are illustrated in the application effect diagram of actual data. Figure 8 The structural block diagram of the abnormal body trajectory judgment system for window optimization angle filtering provided by the present invention. Detailed Implementation

[0019] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0020] See Figure 1 and Figure 2 This invention provides a method for judging the trajectory of abnormal bodies using window-optimized angle filtering, comprising the following steps: S1: Establish an anomaly model consisting of at least three anomalies, the anomaly model being used to simulate total magnetic field strength data, wherein the anomaly model is constructed based on a regular tensor grid; S2: Gaussian white noise with a preset noise level is added to the anomaly model to obtain magnetic anomaly data; S3: The magnetic anomaly data are subjected to model verification using multiple filtering algorithms to obtain the optimal filtering algorithm. The multiple filtering algorithms include window optimization angle filtering algorithm, window mean filtering algorithm and Gaussian filtering algorithm. S4: Based on the optimal filtering algorithm, the target magnetic anomaly data is filtered to obtain the output map corresponding to the target magnetic anomaly data.

[0021] In this embodiment, an anomaly model consisting of at least three anomalies is established to simulate total magnetic field strength data. This includes: selecting three prism-shaped anomalies of different sizes and magnetic susceptibility to establish the anomaly model; and non-uniformly partitioning the dimensional tensor mesh in both the horizontal and vertical directions. Gaussian white noise with a preset noise level is added to the anomaly model to obtain magnetic anomaly data, including: The magnetic anomaly data were obtained by adding 2% Gaussian white noise to the anomaly model and performing simulation analysis. The corresponding forward modeling formula is as follows: (1) (2) in, For the first Magnetic field anomalies at each observation point For kernel function, For the first The magnetic susceptibility of each grid cell. The total number of grid cells. The permeability of free space, The unit vector representing the direction of the Earth's magnetic field. For magnetic scalar position, | The distance between the observation point and the source point. Let j be the volume of the j-th grid cell. For gradient operators, For the source point gradient operator, This represents the volume element of the source region.

[0022] A three-dimensional magnetic susceptibility model was constructed to simulate the total magnetic field strength data. The model contains three prism anomalies of different sizes and magnetic susceptibility, embedded in a uniform background with low magnetic susceptibility. The size and distribution of the model are as follows: Figure 3 As shown, Figure 3 The image shows a visualization of a triangular prism. Relative to a 3D coordinate system, the red prism's axis is aligned, the green prism is rotated 60 degrees, and the blue prism is rotated 120 degrees. The model is constructed based on a regular tensor mesh, with non-uniform meshing in both the horizontal and vertical directions. To balance computational efficiency and accuracy, the terrain in the anomaly model is set to flat, the observation flight altitude is 5 meters, and the observation mesh is 33×33, ranging from -120 to 120 meters.

[0023] Specifically, the at least three anomalies are three prism-shaped anomalies of different sizes and magnetic susceptibility, and the optimal filtering algorithm is a window-optimized angle filtering algorithm. The entire model design aims to simulate the response characteristics of magnetic anomalies in real geological environments. Regarding noise selection, 0% noise represents ideal data with clear anomaly boundaries; 2% noise is slightly blurred but still allows for the identification of major anomaly features; 5% noise is significantly blurred and may affect the identification of anomaly boundaries; 10% noise is severe interference and may mask small anomalies. Therefore, a 2% noise level was chosen to ensure realism and provide a reliable forward modeling data foundation for subsequent inversion and interpretation. Figure 4 As shown, the left figure is... Figure 4(a) in the figure is the magnetic anomaly diagram obtained from the forward modeling, i.e., the TMI anomaly triangular prism (composed of triangles); the right figure is... Figure 4 (b) in the figure is a magnetic anomaly map with 2% Gaussian white noise added, i.e., a noisy TMI anomaly (2% noise).

[0024] It should be understood that by establishing an anomaly model consisting of at least three anomalies, the anomaly model is used to simulate the total magnetic field strength data. Gaussian white noise with a preset noise level is added to the anomaly model to obtain magnetic anomaly data. Multiple filtering algorithms are applied to the magnetic anomaly data to verify the model and obtain the optimal filtering algorithm. Based on the optimal filtering algorithm, the target magnetic anomaly data is filtered to obtain the corresponding output map. The window optimization angle filtering is used as a nonlinear directional filtering method. Within the sliding window, the direction with the highest data consistency is found iteratively, and interpolation averaging is performed along this direction. This can highlight the orientation characteristics of the anomaly while effectively denoising and preserving boundary information. It is suitable for processing magnetic anomalies with obvious directionality. The window optimization angle filtering can effectively enhance the orientation characteristics of the anomaly and avoid the anomaly superposition and boundary smoothing problems common in traditional filtering methods, providing a clearer and more accurate data foundation for subsequent geological interpretation.

[0025] Optionally, the execution process of the window optimization angle filtering algorithm includes: Input the total magnetic field anomaly grid data corresponding to the total magnetic field strength data, and define the grid cell of the sliding window (taking a 5×5 window as an example), with the center point being the measurement point to be filtered. ; With the center of the window as the center and the radius as the radius A circle is drawn using 1 grid cell. The magnetic field strength value at any point P on the circle is obtained by interpolation using the nearest surrounding grid point. The position of point P is determined by the angle between the diameter and the x-axis. It has been determined.

[0026] In this embodiment, at least four grid points closest to point P are selected to form a grid cell, and a bilinear interpolation algorithm is used to calculate the magnetic field strength value at point P. (3) in, Let P be the top-left corner node of the grid cell containing point P. Let P be the top-right corner node of the grid cell containing point P. Let P be the bottom left corner node of the grid cell containing point P. Let P be the bottom right corner node of the grid cell containing point P. Let P be the coordinates; According to each included angle Calculate the root mean square error of all interpolation points on the diameter. The mean square error reflects the dispersion of the data along the diameter direction, and the process of finding the optimal angle is accomplished using the gradient descent method. By setting the step size Update angle By iterating and gradually approximating the minimum point, we can solve for the angle corresponding to the minimum mean square error (MSE). (4) in, The mean of K points on the diameter is used to iteratively converge to the optimal angle. The optimal angle will be obtained in the end. The average value of all interpolation points on the corresponding diameter , as a filter measurement point The filtered output value This represents the angle parameter in the current iteration step. This indicates the updated angle parameters. The mean square error function is represented in gradient at, This refers to the location of the observation point to be filtered.

[0027] It should be noted that, as Figure 5 As shown, input the aeromagnetic total field anomaly grid data, define the sliding window size (e.g., 5×5 grid cell), and set the center point as the measurement point to be filtered. With the center of the window as the center and the radius as the radius Draw a circle using 1 grid cell; for any point P on the circle, its value needs to be obtained by interpolation using the nearest surrounding grid point. The position of point P is determined by the angle between the diameter and the east-west direction (usually the x-axis). The determination was made. To obtain higher accuracy, the four grid points closest to point P were selected, and a bilinear interpolation algorithm was used to calculate the magnetic field strength value; for each... Calculate the root mean square error of all interpolation points on the diameter. ,in The mean of K points along the diameter; the magnitude of the mean square error reflects the degree of dispersion of the data along that diameter. The smaller the value, the better the data consistency along the diameter direction, and the more likely it is to represent the trend of local geological anomalies. The process of finding the optimal angle is accomplished using the gradient descent method. Figure 5 The horizontal axis represents the east-west index (i), and the vertical axis represents the north-south index (j).

[0028] Optionally, the execution process of the window mean filtering algorithm includes: The window mean filtering algorithm creates a window centered on the data to be processed, calculates the arithmetic mean of all magnetic anomaly values ​​within the window, and uses the arithmetic mean as the filtered magnetic anomaly value of the center data point. The default input data is The output data is If the window size is 5×5, then the window mean filter formula is: (5) in, and These are relative to the central data. The row and column offsets are both in the range of -2 to 2, and i and j represent the row and column indices of the data point, respectively.

[0029] In this embodiment, window mean filtering is a classic linear smoothing filtering method. It replaces the original value of each data point with the arithmetic mean of the values ​​in its neighborhood, thereby smoothing noise and suppressing high-frequency components. This filtering method creates a window centered on the data point to be processed, calculates the arithmetic mean of all magnetic anomaly values ​​within the window, and uses this result as the filtered magnetic anomaly value of the central data point.

[0030] Optionally, the execution process of the Gaussian filtering algorithm includes: A two-dimensional Gaussian function is used to generate the convolution kernel. The two-dimensional Gaussian function reaches its maximum value at the center of the data and gradually decays towards the surrounding area. A Gaussian kernel is convolved with the data, and the value of each data point is replaced by a weighted average of its neighboring data points. The discrete form of the Gaussian filter kernel is obtained by sampling the function within a window. For a window of size (2m+1)×(2m+1), the corresponding Gaussian filter formula is: (6) in, For the input dataset, For the output dataset, the values ​​of x and y are both in the range of -m to m, where m is a parameter that determines the size of the convolution kernel, and i and j represent the row index and column index of the data point, respectively. The standard deviation of the Gaussian function is used to control the smoothness.

[0031] In this embodiment, Gaussian filtering is a linear smoothing filtering method based on the Gaussian function. It smooths the data through a weighted average, where data points closer to the center contribute more to the result and have higher weights, while data points farther away have lower weights. A two-dimensional Gaussian function is used to generate a convolution kernel (filter template). This function reaches its maximum value at the center and gradually decays outwards. The Gaussian kernel is convolved with the data, and the value of each data point is replaced by a weighted average of the values ​​of its neighboring data points.

[0032] It should be noted that both the window optimization angle filtering and the window mean filtering use a 5×5 window, and the Gaussian filtering coefficient is 1.5. The noise reduction effects of the three methods are as follows: Figure 6 As shown, the filtering effect of the window optimization angle filtering method is as follows: Figure 6 As shown in (a), this method can remove the effects of Gaussian white noise and preserve the boundary features of the anomaly, and is highly consistent with the magnetic anomaly data obtained by forward modeling; at x=-20, Figure 6 The filtering method (a) in the above examples shows the best effect and preserves the boundary features of the anomalous objects. It avoids excessive smoothing due to filtering, which could blur the boundaries of the anomalous objects and weaken the signal, making it difficult to determine their location. Furthermore, the window-optimized angle filtering effectively enhances the directional features of the anomalous objects, allowing for the observation of two distinct anomalous object directions. The other two filtering methods suffer from anomalous object superposition and boundary smoothing issues, such as... Figure 6 In (c), the anomalies of the two anomalies at (-30, 60) overlap, affecting subsequent geological interpretation; Figure 6 The situation in (b) is similar and will not be repeated here.

[0033] In practical applications, the window-optimized angle filtering method effectively removes noise while preserving the edge information of anomalies. Unlike window mean filtering and Gaussian filtering, which, in their noise removal efforts, result in overly smoothed edges and loss of some anomaly information, window-optimized angle filtering retains the edge information caused by anomalies and identifies their general direction. The direction of the anomaly is most clearly visible in the window-optimized angle filtered image. In the original data, anomalies are obscured and interfered with by numerous noise points, making their boundaries and overall shape blurry and difficult to reliably determine their precise direction. While mean filtering removes noise, it also blurs the boundaries of anomalies, making their outlines smoother and weakening their directional characteristics, making them less clear than those obtained with window-optimized angle filtering. Gaussian filtering has a similar effect to mean filtering, smoothing the image but also losing some detail and edge sharpness. Window mean filtering and Gaussian filtering tend to over-smooth, resulting in insufficient preservation of key anomaly information.

[0034] like Figure 7As shown, at position (526858, 5322419), a clear high-value anomaly is preserved in the window optimization angle filtering, while the other two methods erase this anomaly due to the smoothing effect; at position (525928, 5316744), the window optimization angle filtering retains the boundary features of the anomaly, while the other two methods do not, making it impossible to determine the direction of the anomaly; at position (530280, 5314447), the window optimization angle filtering retains the boundary features of the anomaly, allowing for the determination of its direction, while the other two methods blur the boundary, making it impossible to identify its direction. Among these, Figure 7 (a) in the diagram represents a magnetic anomaly map. Figure 7 In the diagram, (b) represents window-optimized corner filtering. Figure 7 (c) in the text represents window mean filtering. Figure 7 In the example, (d) represents Gaussian filtering.

[0035] Among the three filtering methods, the northwest-oriented anomalous objects in the high outlier region on the left are relatively clear. However, in the upper right corner, the window mean filtering and Gaussian filtering methods smooth the image, blurring the boundaries of the anomalous objects and making it impossible to determine their direction. The window optimal angle filtering method performs better in handling boundary issues. At the position (531195, 5325241), it is not affected by the superposition of anomalous objects caused by the anomalous objects, and can clearly distinguish the boundaries of the two anomalous objects and determine their direction. Therefore, the window optimal angle filtering algorithm effectively suppresses noise while preserving or enhancing the morphological features of the anomalous objects to the maximum extent, especially their boundaries and extension directions, making the direction analysis reliable and intuitive.

[0036] See Figure 8 The present invention also provides an anomaly trajectory judgment system based on window optimization angle filtering, applied to the above-mentioned anomaly trajectory judgment method based on window optimization angle filtering, including: The model building module is used to build an anomaly model consisting of at least three anomalies. The anomaly model is used to simulate the total magnetic field strength data. The anomaly model is built based on a regular tensor grid. A noise addition module is used to add Gaussian white noise with a preset noise level to the anomaly model to obtain magnetic anomaly data. The model validation module is used to perform model validation on the magnetic anomaly data using various filtering algorithms to obtain the optimal filtering algorithm. The various filtering algorithms include window optimization angle filtering algorithm, window mean filtering algorithm, and Gaussian filtering algorithm. The noise reduction module is used to filter the target magnetic anomaly data based on the optimal filtering algorithm to obtain the output map corresponding to the target magnetic anomaly data.

[0037] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limitations; therefore, other examples of exemplary embodiments may have different values.

[0038] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0039] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for judging the trajectory of abnormal bodies using window-optimized angle filtering, characterized in that, Includes the following steps: An anomaly model consisting of at least three anomalies is established to simulate total magnetic field strength data, wherein the anomaly model is constructed based on a regular tensor grid. Gaussian white noise with a preset noise level is added to the anomaly model to obtain magnetic anomaly data. The optimal filtering algorithm was obtained by applying various filtering algorithms to the magnetic anomaly data. These algorithms included window optimization angle filtering algorithm, window mean filtering algorithm, and Gaussian filtering algorithm. The target magnetic anomaly data is filtered using the optimal filtering algorithm to obtain the corresponding output map.

2. The method for judging the trajectory of abnormal bodies using window optimization angle filtering according to claim 1, characterized in that, An anomaly model consisting of at least three anomalies is established to simulate the total magnetic field strength data, including: The anomaly model was established by selecting three prism anomalies of different sizes and magnetic susceptibility. The specified tensor mesh is non-uniformly partitioned in both the horizontal and vertical directions.

3. The method for judging the trajectory of abnormal bodies using window optimization angle filtering according to claim 1, characterized in that, Gaussian white noise with a preset noise level is added to the anomaly model to obtain magnetic anomaly data, including: The magnetic anomaly data were obtained by adding 2% Gaussian white noise to the anomaly model and performing simulation analysis. The corresponding forward modeling formula is as follows: (1) (2) in, For the first Magnetic field anomalies at each observation point For kernel function, For the first The magnetic susceptibility of each grid cell. The total number of grid cells. The permeability of free space, The unit vector representing the direction of the Earth's magnetic field. For magnetic scalar position, | The distance between the observation point and the source point. Let j be the volume of the j-th grid cell. For gradient operators, For the source point gradient operator, This represents the volume element of the source region.

4. The method for judging the trajectory of abnormal bodies using window optimization angle filtering according to claim 1, characterized in that, The execution process of the window optimization angle filtering algorithm includes: Input the total magnetic field anomaly grid data corresponding to the total magnetic field strength data, and define the grid cells of the sliding window, with the center point being the measurement point to be filtered. ; With the center of the window as the center and the radius as the radius A circle is drawn using 1 grid cell. The magnetic field strength value at any point P on the circle is obtained by interpolation using the nearest surrounding grid point. The position of point P is determined by the angle between the diameter and the x-axis. It has been determined.

5. The method for judging the trajectory of abnormal bodies using window optimization angle filtering according to claim 4, characterized in that, Also includes: The grid cells are composed of at least four grid points closest to point P, and the magnetic field strength at point P is calculated using a bilinear interpolation algorithm. The corresponding expression is: (3) in, Let P be the top-left corner node of the grid cell containing point P. Let P be the top-right corner node of the grid cell containing point P. Let P be the bottom left corner node of the grid cell containing point P. Let P be the bottom right corner node of the grid cell containing point P. Let P be the coordinates; According to each included angle Calculate the root mean square error of all interpolation points on the diameter. The mean square error reflects the dispersion of the data along the diameter direction, and the process of finding the optimal angle is accomplished using the gradient descent method. By setting the step size Update angle By iterating and gradually approaching the minimum point, we can find the angle corresponding to the minimum mean square error. (4) in, The mean of K points on the diameter is used to iteratively converge to the optimal angle. The final optimal angle will be obtained. The average value of all interpolation points on the corresponding diameter , as a filter measurement point The filtered output value This represents the angle parameter in the current iteration step. This indicates the updated angle parameters. The mean square error function is represented in gradient at, This refers to the location of the observation point to be filtered.

6. The method for judging the trajectory of abnormal bodies using window optimization angle filtering according to claim 1, characterized in that, The execution process of the window mean filtering algorithm includes: The window mean filtering algorithm creates a window centered on the data to be processed, calculates the arithmetic mean of all magnetic anomaly values ​​within the window, and uses the arithmetic mean as the filtered magnetic anomaly value of the center data point. The default input data is The output data is If the window size is 5×5, then the window mean filter formula is: （5） in, and These are relative to the central data. The row offset and column offset are both in the range of -2 to 2, and i and j represent the row index and column index of the data point, respectively.

7. The method for judging the trajectory of abnormal bodies using window optimization angle filtering according to claim 1, characterized in that, The execution process of the Gaussian filtering algorithm includes: A two-dimensional Gaussian function is used to generate the convolution kernel. The two-dimensional Gaussian function reaches its maximum value at the center of the data and gradually decays towards the surrounding area. A Gaussian kernel is convolved with the dataset, where the value of each data point in the dataset is replaced by a weighted average of its neighboring data points. The discrete Gaussian filter kernel is obtained by sampling the function within a window. For a window of size (2m+1)×(2m+1), the corresponding Gaussian filter formula is: （6） in, For the input dataset, For the output dataset, the values ​​of x and y are both in the range of -m to m, where m is a parameter that determines the size of the convolution kernel, and i and j represent the row index and column index of the data point, respectively. The standard deviation of the Gaussian function is used to control the smoothness.

8. The method for judging the trajectory of abnormal bodies using window optimization angle filtering according to claim 1, characterized in that, The at least three anomalous bodies are three prism anomalous bodies of different sizes and different magnetic susceptibility, and the optimal filtering algorithm is a window-optimized angle filtering algorithm.

9. The method for judging the trajectory of abnormal bodies using window optimization angle filtering according to claim 8, characterized in that, In the anomaly model, the surface terrain is set to flat, the observation flight altitude is 5 meters, the observation grid is 33×33, and the range is -120 to 120 meters.

10. An anomaly trajectory judgment system based on window optimization angle filtering, characterized in that, The method for determining the trajectory of anomalies using window optimization angle filtering as described in any one of claims 1-9 includes: The model building module is used to build an anomaly model consisting of at least three anomalies. The anomaly model is used to simulate the total magnetic field strength data. The anomaly model is built based on a regular tensor grid. A noise addition module is used to add Gaussian white noise with a preset noise level to the anomaly model to obtain magnetic anomaly data. The model validation module is used to perform model validation on the magnetic anomaly data using various filtering algorithms to obtain the optimal filtering algorithm. The various filtering algorithms include window optimization angle filtering algorithm, window mean filtering algorithm, and Gaussian filtering algorithm. The noise reduction module is used to filter the target magnetic anomaly data based on the optimal filtering algorithm to obtain the output map corresponding to the target magnetic anomaly data.

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