Method and system for judging geomagnetic anomaly based on full-tensor geomagnetic gradient benchmark

Abstract

The invention discloses a method and a system for judging geomagnetic anomaly based on a full-tensor geomagnetic gradient reference, belongs to the technical field of geomagnetic anomaly detection, and can improve the accuracy of geomagnetic anomaly detection. Comprising the following steps: acquiring geomagnetic sensor arrays mounted on the ground and at different heights in the air, and collecting multi-dimensional geomagnetic field data in real time; wherein data acquired by each geomagnetic sensor in the geomagnetic sensor array comprises geomagnetic component data in three orthogonal directions, and timestamp information is attached; constructing a tensor data set containing time, space coordinates and magnetic field intensity according to the collected multi-dimensional geomagnetic field data; preprocessing the tensor data set; calculating each data point in the preprocessed tensor data set by adopting a central difference method to obtain a geomagnetic gradient tensor; analyzing the geomagnetic gradient tensor by using a preset threshold value, judging whether geomagnetic anomaly exists or not, and obtaining a geomagnetic anomaly judgment result; and generating a visual geomagnetic anomaly report according to the geomagnetic anomaly judgment result.

Description

Technical Field

[0001] The present invention relates to the technical field of geomagnetic anomaly detection, and in particular to a method and system for determining geomagnetic anomaly based on a full tensor geomagnetic gradient benchmark. Background Art

[0002] As a basic physical phenomenon in nature, the Earth's magnetic field is of great significance to the study of the Earth's internal structure and surface environment. In the fields of mineral resource exploration, environmental monitoring and geological disaster warning, the determination of geomagnetic anomalies can provide important geological information, helping researchers to deeply understand the underground mineral distribution, environmental changes and potential geological risks.

[0003] Related geomagnetic anomaly detection methods mostly use one-dimensional or two-dimensional data processing, and these methods are usually based on the analysis of magnetic field data in local areas. Although these technologies can identify geomagnetic anomalies to a certain extent, their limitations are also very obvious. First, these methods often ignore the global information of the geomagnetic field and cannot fully utilize the complexity of spatial distribution, resulting in insufficient accuracy in identifying anomalies. Secondly, since the changes in the geomagnetic field have multi-level and multi-dimensional characteristics, a single-dimensional processing method may miss some important information, thereby affecting the final judgment result.

[0004] The disclosure of the above background technology content is only used to assist in understanding the concept and technical solution of the present invention. It does not necessarily belong to the prior art of this patent application. In the absence of clear evidence that the above content has been disclosed on the filing date of this patent application, the above background technology should not be used to evaluate the novelty and creativity of the present application. Summary of the invention

[0005] The present application provides a method and system for determining geomagnetic anomalies based on a full tensor geomagnetic gradient benchmark, which can improve the accuracy of geomagnetic anomaly detection.

[0006] To achieve the above objectives, the present application discloses the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a method for determining geomagnetic anomalies based on a full tensor geomagnetic gradient benchmark, comprising the following steps:

[0008] Obtain geomagnetic sensor arrays installed at different heights on the ground and in the air to collect multi-dimensional geomagnetic field data in real time; wherein the data collected by each geomagnetic sensor in the geomagnetic sensor array includes geomagnetic component data in three orthogonal directions, and is accompanied by timestamp information;

[0009] Construct a tensor data set containing time, space coordinates and magnetic field intensity based on the collected multi-dimensional geomagnetic field data;

[0010] Preprocess the tensor dataset;

[0011] The central difference method is used to operate each data point in the preprocessed tensor data set to obtain the geomagnetic gradient tensor;

[0012] Analyze the geomagnetic gradient tensor using a preset threshold value to determine whether there is a geomagnetic anomaly and obtain a geomagnetic anomaly determination result;

[0013] Generate a visual geomagnetic anomaly report based on the geomagnetic anomaly determination results.

[0014] In the embodiment of the present application, a geomagnetic sensor array installed at different heights and positions is used to obtain geomagnetic component data in multiple directions in real time, ensuring the comprehensiveness of data collection, thereby obtaining rich spatial information and overcoming the problem of inaccurate abnormality judgment caused by one-sided data in traditional methods.

[0015] In addition, by constructing a tensor data set containing time, spatial coordinates and magnetic field strength, multi-dimensional information is integrated, avoiding data loss caused by focusing only on a single dimension in traditional methods. This allows subsequent analysis to more comprehensively capture changes in the geomagnetic field, further improving the accuracy of anomaly detection.

[0016] In addition, since the central difference method calculates by considering the neighboring points around the data point, it can effectively reduce the impact of boundary effects and noise, and improve the sensitivity to small changes, thereby improving the accuracy of gradient calculation. Therefore, using the central difference method to calculate the geomagnetic gradient tensor can more accurately reflect the spatial changes of the geomagnetic field. Compared with the traditional simple data processing method, it can more carefully identify abnormal features, thereby reducing the possibility of misjudgment.

[0017] In some possible implementations of the first aspect, the preprocessing includes denoising and normalization;

[0018] The denoising formula is as follows:

[0019]

[0020] Where: D i,j,k : The data point in the i-th row, j-th column, and k-th layer of the original tensor data set represents the intensity of the geomagnetic component at a specific time and space position;

[0021] D′ i,j,k : De-noised data after mean filtering, indicating the intensity of the de-noised geomagnetic component at the same location;

[0022] N: The number of points in the filter window, indicating the total number of data points involved in the mean calculation;

[0023] n: window radius, which indicates the range of neighboring data points considered during filtering;

[0024] m: index variable, indicating the relative position within the window range, used to traverse the surrounding points;

[0025] The normalization formula is as follows:

[0026]

[0027] Where: D″ i,j,k : Normalized data, indicating the intensity of the geomagnetic component at the same location;

[0028] min(D′): the minimum value of all values ​​in the denoised data set, indicating the minimum geomagnetic component intensity in the data set;

[0029] max(D′): The maximum value of all values ​​in the denoised data set, indicating the maximum geomagnetic component intensity in the data set.

[0030] In some possible implementations of the first aspect, the following calculation formula is used to perform a central difference operation on each data point in the preprocessed tensor data set:

[0031]

[0032] Where: G x,y,z , G y,y,z , and G z,y,z Represent the three components of the geomagnetic gradient at the position (x, y, z);

[0033] D″ x,y,z Represents the normalized preprocessed data, and obtains the geomagnetic gradient value of the current point by differential calculation of adjacent data points;

[0034] Δx, Δy, and Δz represent the distance between each data point in three-dimensional space.

[0035] In some possible implementations of the first aspect, whether there is an abnormality is determined by the following formula:

[0036]

[0037] Where: A(i,j,k) represents the result of the geomagnetic anomaly determination at the position (i,j,k). If there is an anomaly, A(i,j,k) = 1, otherwise A(i,j,k) = 0;

[0038] G(i,j,k) represents the geomagnetic gradient value currently calculated;

[0039] G baseline(i, j, k) represents the reference geomagnetic gradient value, which is used to compare with the current value;

[0040] ||·|| represents the norm of the vector, which is used to calculate the difference between the two;

[0041] P represents a preset comparison value.

[0042] In some possible implementations of the first aspect, the step of generating a visualized geomagnetic anomaly report according to the geomagnetic anomaly determination result includes:

[0043] Mark the abnormal points and normal points in the geomagnetic anomaly determination result A(i,j,k) to form an abnormal point set S 异常 and the normal point set S 正常 ,in:

[0044] S 异常 ={(i,j,k)|A(i,j,k)=1}

[0045] S 正常 ={(i,j,k)|A(i,j,k)=0}

[0046] According to the outlier set S 异常 and the normal point set S 正常 Extract the geomagnetic gradient value G(i,j,k) at the corresponding position and generate a visualization data set V 异常 and V 正常 ,in:

[0047] V 异常 ={(i,j,k,G(i,j,k))|(i,j,k)∈S 异常}

[0048] V 正常 ={(i,j,k,G(i,j,k))|(i,j,k)∈S 正常}

[0049] The visualization dataset V 异常 and V 正常 Convert to a graphical representation to obtain a three-dimensional scatter plot showing abnormal points and normal points;

[0050] Generate a visual geomagnetic anomaly report based on the three-dimensional scatter plot, which can improve the comprehensibility of data results and facilitate subsequent decision-making and application.

[0051] In some possible implementations of the first aspect, the method for determining geomagnetic anomalies based on the full tensor geomagnetic gradient benchmark further includes the following steps: sending a visualized geomagnetic anomaly report to a preset terminal device. In this way, the detection results can be communicated to relevant personnel or systems in a timely manner, which ensures that relevant decision makers can quickly obtain geomagnetic anomaly information, thereby reacting and taking corresponding measures more quickly, and improving emergency response capabilities.

[0052] In a second aspect, an embodiment of the present application provides a system for determining geomagnetic anomalies based on a full tensor geomagnetic gradient benchmark, comprising:

[0053] The first acquisition module is used to acquire geomagnetic sensor arrays installed at different heights on the ground and in the air, and collect multi-dimensional geomagnetic field data in real time; wherein the data collected by each geomagnetic sensor in the geomagnetic sensor array includes geomagnetic component data in three orthogonal directions, and is accompanied by timestamp information;

[0054] A first construction module is used to construct a tensor data set including time, space coordinates and magnetic field intensity according to the multi-dimensional geomagnetic field data collected;

[0055] A preprocessing module, used for preprocessing the tensor data set;

[0056] A first calculation module is used to calculate each data point in the preprocessed tensor data set using a central difference method to obtain a geomagnetic gradient tensor;

[0057] A first judgment module is used to analyze the geomagnetic gradient tensor using a preset threshold value to determine whether there is a geomagnetic anomaly and obtain a geomagnetic anomaly judgment result;

[0058] The first generating module is used to generate a visualized geomagnetic anomaly report according to the geomagnetic anomaly determination result.

[0059] In some possible implementations of the second aspect, the preprocessing includes denoising and normalization; wherein the denoising formula is as follows:

[0060]

[0061] Where: D i,j,k : The data point in the i-th row, j-th column, and k-th layer of the original tensor data set represents the intensity of the geomagnetic component at a specific time and space position;

[0062] D′ i,j,k : De-noised data after mean filtering, indicating the intensity of the de-noised geomagnetic component at the same location;

[0063] N: The number of points in the filter window, indicating the total number of data points involved in the mean calculation;

[0064] n: window radius, which indicates the range of neighboring data points considered during filtering;

[0065] m: index variable, indicating the relative position within the window range, used to traverse the surrounding points;

[0066] The normalization formula is as follows:

[0067]

[0068] Where: D″ i,j,k : Normalized data, indicating the intensity of the geomagnetic component at the same location;

[0069] min(D′): the minimum value of all values ​​in the denoised data set, indicating the minimum geomagnetic component intensity in the data set;

[0070] max(D′): The maximum value of all values ​​in the denoised data set, indicating the maximum geomagnetic component intensity in the data set.

[0071] In some possible implementations of the second aspect, the first computing module performs a central difference operation on each data point in the preprocessed tensor data set using the following calculation formula:

[0072]

[0073] Where: G x,y,z , G y,y,z , and G z,y,z Represent the three components of the geomagnetic gradient at the position (x, y, z);

[0074] D″ x,y,z Represents the normalized preprocessed data, and obtains the geomagnetic gradient value of the current point by differential calculation of adjacent data points;

[0075] Δx, Δy, and Δz represent the distance between each data point in three-dimensional space.

[0076] In some possible implementations of the second aspect, the first judgment module judges whether there is an abnormality by using the following formula:

[0077]

[0078] Where: A(i,j,k) represents the result of the geomagnetic anomaly determination at the position (i,j,k). If there is an anomaly, A(i,j,k) = 1, otherwise A(i,j,k) = 0;

[0079] G(i,j,k) represents the geomagnetic gradient value currently calculated;

[0080] Gbaseline (i, j, k) represents the reference geomagnetic gradient value, which is used to compare with the current value;

[0081] ||·|| represents the norm of the vector, which is used to calculate the difference between the two;

[0082] P represents a preset comparison value.

[0083] In some possible implementations of the second aspect, the first generating module is specifically used to:

[0084] Mark the abnormal points and normal points in the geomagnetic anomaly determination result A(i,j,k) to form an abnormal point set S 异常 and the normal point set S 正常 ,in:

[0085] S 异常 ={(i,j,k)|A(i,j,k)=1}

[0086] S 正常 ={(i,j,k)|A(i,j,k)=0}

[0087] According to the outlier set S 异常 and the normal point set S 正常 Extract the geomagnetic gradient value G(i,j,k) at the corresponding position and generate a visualization data set V 异常 and V 正常 ,in:

[0088] V 异常 ={(i,j,k,G(i,j,k))|(i,j,k)∈S 异常}

[0089] V 正常 ={(i,j,k,G(i,j,k))|(i,j,k)∈S 正常}

[0090] The visualization dataset V 异常 and V 正常 Convert to a graphical representation to obtain a three-dimensional scatter plot showing abnormal points and normal points;

[0091] Generate a visual geomagnetic anomaly report based on a three-dimensional scatter plot.

[0092] In some possible implementations of the second aspect, the system for determining geomagnetic anomaly based on the full tensor geomagnetic gradient benchmark also includes a first sending module for sending a visualized geomagnetic anomaly report to a preset terminal device.

[0093] In a third aspect, an embodiment of the present application provides an electronic device, comprising one or more processors; a storage device on which one or more programs are stored; when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any technical solution of the first aspect.

[0094] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in any technical solution of the first aspect is implemented.

[0095] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method described in any technical solution of the first aspect.

[0096] Among them, the technical effects brought about by any design method in the second to fifth aspects can refer to the technical effects brought about by different design methods in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0098] Figure 1 A schematic flow chart of a method for determining geomagnetic anomalies based on a full tensor geomagnetic gradient benchmark provided in some embodiments of the present application;

[0099] Figure 2 A schematic diagram of the structure of a system for determining geomagnetic anomalies based on a full tensor geomagnetic gradient benchmark provided in some embodiments of the present application;

[0100] Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present application. DETAILED DESCRIPTION

[0101] Specific embodiments of the present invention will now be mentioned in detail. Although the present invention is described in conjunction with these specific embodiments, it should be appreciated that it is not intended to limit the present invention to these specific embodiments. On the contrary, these embodiments are intended to cover substitutions, changes or equivalent embodiments that may be included in the spirit and scope of the invention defined by the claims. In the following description, a large number of specific details are set forth in order to provide a comprehensive understanding of the present invention. The present invention may be implemented without some or all of these specific details.

[0102] When used in conjunction with "including," "methods comprising," or similar language in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0103] Application Overview: As a basic physical phenomenon in nature, the Earth's magnetic field is of great significance to the study of the Earth's internal structure and surface environment. In the fields of mineral resource exploration, environmental monitoring and geological disaster warning, the determination of geomagnetic anomalies can provide important geological information and help researchers deeply understand the underground mineral distribution, environmental changes and potential geological risks.

[0104] Related geomagnetic anomaly detection methods mostly use one-dimensional or two-dimensional data processing, and these methods are usually based on the analysis of magnetic field data in local areas. Although these technologies can identify geomagnetic anomalies to a certain extent, their limitations are also very obvious. First, these methods often ignore the global information of the geomagnetic field and cannot fully utilize the complexity of spatial distribution, resulting in insufficient accuracy in identifying anomalies. Secondly, since the changes in the geomagnetic field have multi-level and multi-dimensional characteristics, a single-dimensional processing method may miss some important information, thereby affecting the final judgment result.

[0105] In response to the above technical problems, the overall idea of ​​the technical solution provided by the present application is as follows: a method for determining geomagnetic anomalies based on a full tensor geomagnetic gradient benchmark is provided, comprising the following steps: obtaining a geomagnetic sensor array installed at different heights on the ground and in the air, and collecting multi-dimensional geomagnetic field data in real time; wherein the data collected by each geomagnetic sensor in the geomagnetic sensor array includes geomagnetic component data in three orthogonal directions, and is accompanied by timestamp information; constructing a tensor data set containing time, spatial coordinates and magnetic field intensity based on the collected multi-dimensional geomagnetic field data; preprocessing the tensor data set; using the central difference method to calculate each data point in the preprocessed tensor data set to obtain a geomagnetic gradient tensor; using a preset threshold to analyze the geomagnetic gradient tensor to determine whether there is a geomagnetic anomaly and obtain a geomagnetic anomaly determination result; and generating a visual geomagnetic anomaly report based on the geomagnetic anomaly determination result.

[0106] Among them, an array of geomagnetic sensors installed at different heights and positions is used to obtain geomagnetic component data in multiple directions in real time, ensuring the comprehensiveness of data collection, thereby obtaining rich spatial information and overcoming the problem of inaccurate abnormality judgment caused by one-sided data in traditional methods.

[0107] In addition, by constructing a tensor data set containing time, spatial coordinates and magnetic field strength, multi-dimensional information is integrated, avoiding data loss caused by focusing only on a single dimension in traditional methods. This allows subsequent analysis to more comprehensively capture changes in the geomagnetic field, further improving the accuracy of anomaly detection.

[0108] In addition, since the central difference method calculates by considering the neighboring points around the data point, it can effectively reduce the impact of boundary effects and noise, and improve the sensitivity to small changes, thereby improving the accuracy of gradient calculation. Therefore, using the central difference method to calculate the geomagnetic gradient tensor can more accurately reflect the spatial changes of the geomagnetic field. Compared with the traditional simple data processing method, it can more carefully identify abnormal features, thereby reducing the possibility of misjudgment.

[0109] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced in conjunction with the accompanying drawings. Figure 1 The embodiment of the present application provides a method for determining geomagnetic anomalies based on a full tensor geomagnetic gradient benchmark, comprising the following steps:

[0110] S101: Obtain geomagnetic sensor arrays installed at different heights on the ground and in the air to collect multi-dimensional geomagnetic field data in real time; wherein the data collected by each geomagnetic sensor in the geomagnetic sensor array includes geomagnetic component data in three orthogonal directions, and is accompanied by timestamp information;

[0111] S102: constructing a tensor data set including time, space coordinates and magnetic field intensity according to the collected multi-dimensional geomagnetic field data;

[0112] S103: Preprocessing the tensor data set;

[0113] Specifically, in some embodiments, the preprocessing includes denoising and normalization;

[0114] The denoising formula is as follows:

[0115]

[0116] Where: D i,j,k : The data point in the i-th row, j-th column, and k-th layer of the original tensor data set represents the intensity of the geomagnetic component at a specific time and space position;

[0117] D′ i,j,k : De-noised data after mean filtering, indicating the intensity of the de-noised geomagnetic component at the same location;

[0118] N: The number of points in the filter window, indicating the total number of data points involved in the mean calculation;

[0119] n: window radius, which indicates the range of neighboring data points considered during filtering;

[0120] m: index variable, indicating the relative position within the window range, used to traverse the surrounding points;

[0121] The normalization formula is as follows:

[0122]

[0123] Where: D″ i,j,k : Normalized data, representing the intensity of the geomagnetic component at the same location; it can be understood that after normalization, the value range is usually between 0 and 1;

[0124] min(D′): the minimum value of all values ​​in the denoised data set, indicating the minimum geomagnetic component intensity in the data set;

[0125] max(D′): The maximum value of all values ​​in the denoised data set, indicating the maximum geomagnetic component intensity in the data set.

[0126] S104: using the central difference method to calculate each data point in the preprocessed tensor data set to obtain a geomagnetic gradient tensor;

[0127] Specifically, in some embodiments, the following calculation formula is used to perform a central difference operation on each data point in the preprocessed tensor data set:

[0128]

[0129] Where: G x,y,z , G y,y,z , and G z,y,z Represent the three components of the geomagnetic gradient at the position (x, y, z);

[0130] D″ x,y,z Represents the normalized preprocessed data, and obtains the geomagnetic gradient value of the current point by differential calculation of adjacent data points;

[0131] Δx, Δy, and Δz represent the distance between each data point in three-dimensional space.

[0132] S105: Analyze the geomagnetic gradient tensor using a preset threshold value to determine whether there is a geomagnetic anomaly, and obtain a geomagnetic anomaly determination result;

[0133] Specifically, the following formula can be used to determine whether there is an abnormality:

[0134]

[0135] Where: A(i,j,k) represents the result of the geomagnetic anomaly determination at the position (i,j,k). If there is an anomaly, A(i,j,k) = 1, otherwise A(i,j,k) = 0;

[0136] G(i,j,k) represents the geomagnetic gradient value currently calculated;

[0137] G baseline (i, j, k) represents the reference geomagnetic gradient value, which is used to compare with the current value;

[0138] ||·|| represents the norm of the vector, which is used to calculate the difference between the two;

[0139] P represents a preset comparison value.

[0140] S106: Generate a visualized geomagnetic anomaly report based on the geomagnetic anomaly determination result.

[0141] Specifically, in some embodiments, a visualized geomagnetic anomaly report may be generated according to the geomagnetic anomaly determination result through the following steps:

[0142] The first step is to mark the abnormal points and normal points in the geomagnetic anomaly determination result A(i,j,k) to form an abnormal point set S 异常 and the normal point set S 正常 ,in:

[0143] S 异常 ={(i,j,k)|A(i,j,k)=1}

[0144] S 正常 ={(i,j,k)|A(i,j,k)=0}

[0145] The second step is to use the outlier set S 异常 and the normal point set S 正常 Extract the geomagnetic gradient value G(i,j,k) at the corresponding position and generate a visualization data set V 异常 and V 正常 ,in:

[0146] V 异常 ={(i,j,k,G(i,j,k))|(i,j,k)∈S 异常}

[0147] V 正常 ={(i,j,k,G(i,j,k))|(i,j,k)∈S 正常}

[0148] The third step is to visualize the dataset V 异常 and V 正常Convert to a graphical representation to obtain a three-dimensional scatter plot showing abnormal points and normal points;

[0149] The fourth step is to generate a visual geomagnetic anomaly report based on the three-dimensional scatter plot.

[0150] For example, the geomagnetic anomaly report may include, but is not limited to, a three-dimensional scatter plot, anomaly location, and anomaly intensity, which can improve the comprehensibility of the data results and facilitate subsequent decision-making and application.

[0151] Preferably, the method for determining geomagnetic anomaly based on full tensor geomagnetic gradient benchmark further includes the following steps: sending a visualized geomagnetic anomaly report to a preset terminal device. In this way, the detection results can be communicated to relevant personnel or systems in a timely manner, which ensures that relevant decision makers can quickly obtain geomagnetic anomaly information, thereby reacting and taking corresponding measures more quickly, thereby improving emergency response capabilities.

[0152] See also Figure 2 Based on the same inventive concept as the method for determining geomagnetic anomalies based on a full tensor geomagnetic gradient benchmark in the aforementioned embodiment, the embodiment of the present application provides a system for determining geomagnetic anomalies based on a full tensor geomagnetic gradient benchmark, comprising:

[0153] The first acquisition module 201 is used to acquire geomagnetic sensor arrays installed at different heights on the ground and in the air, and collect multi-dimensional geomagnetic field data in real time; wherein the data collected by each geomagnetic sensor in the geomagnetic sensor array includes geomagnetic component data in three orthogonal directions, and is accompanied by timestamp information;

[0154] A first construction module 202 is used to construct a tensor data set including time, space coordinates and magnetic field intensity according to the collected multi-dimensional geomagnetic field data;

[0155] A preprocessing module 203, used for preprocessing the tensor data set;

[0156] A first calculation module 204 is used to calculate each data point in the preprocessed tensor data set using a central difference method to obtain a geomagnetic gradient tensor;

[0157] The first judgment module 205 is used to analyze the geomagnetic gradient tensor using a preset threshold value to determine whether there is a geomagnetic anomaly and obtain a geomagnetic anomaly determination result;

[0158] The first generating module 206 is used to generate a visualized geomagnetic anomaly report according to the geomagnetic anomaly determination result.

[0159] In some embodiments, the preprocessing includes denoising and normalization; wherein the denoising formula is as follows:

[0160]

[0161] Where: D i,j,k : The data point in the i-th row, j-th column, and k-th layer of the original tensor data set represents the intensity of the geomagnetic component at a specific time and space position;

[0162] D′ i,j,k : De-noised data after mean filtering, indicating the intensity of the de-noised geomagnetic component at the same location;

[0163] N: The number of points in the filter window, indicating the total number of data points involved in the mean calculation;

[0164] n: window radius, which indicates the range of neighboring data points considered during filtering;

[0165] m: index variable, indicating the relative position within the window range, used to traverse the surrounding points;

[0166] The normalization formula is as follows:

[0167]

[0168] Where: D″ i,j,k : Normalized data, representing the intensity of the geomagnetic component at the same location; it can be understood that after standardization, the value range is usually between 0 and 1.

[0169] min(D′): the minimum value of all values ​​in the denoised data set, indicating the minimum geomagnetic component intensity in the data set;

[0170] max(D′): The maximum value of all values ​​in the denoised data set, indicating the maximum geomagnetic component intensity in the data set.

[0171] In some embodiments, the first calculation module 204 uses the following calculation formula to perform a central difference operation on each data point in the preprocessed tensor data set:

[0172]

[0173] Where: G x,y,z , G y,y,z , and G z,y,z Represent the three components of the geomagnetic gradient at the position (x, y, z);

[0174] D″ x,y,z Represents the normalized preprocessed data, and obtains the geomagnetic gradient value of the current point by differential calculation of adjacent data points;

[0175] Δx, Δy, and Δz represent the distance between each data point in three-dimensional space.

[0176] In some embodiments, the first determination module 205 determines whether there is an abnormality by using the following formula:

[0177]

[0178] Where: A(i,j,k) represents the result of the geomagnetic anomaly determination at the position (i,j,k). If there is an anomaly, A(i,j,k) = 1, otherwise A(i,j,k) = 0;

[0179] G(i,j,k) represents the geomagnetic gradient value currently calculated;

[0180] G baseline (i, j, k) represents the reference geomagnetic gradient value, which is used to compare with the current value;

[0181] ||·|| represents the norm of the vector, which is used to calculate the difference between the two;

[0182] P represents a preset comparison value.

[0183] In some embodiments, the first generating module 206 is specifically used to:

[0184] Mark the abnormal points and normal points in the geomagnetic anomaly determination result A(i,j,k) to form an abnormal point set S 异常 and the normal point set S 正常 ,in:

[0185] S 异常 ={(i,j,k)|A(i,j,k)=1}

[0186] S 正常 ={(i,j,k)|A(i,j,k)=0}

[0187] According to the outlier set S 异常 and the normal point set S 正常 Extract the geomagnetic gradient value G(i,j,k) at the corresponding position and generate a visualization data set V 异常 and V 正常 ,in:

[0188] V 异常 ={(i,j,k,G(i,j,k))|(i,j,k)∈S 异常}

[0189] V 正常 ={(i,j,k,G(i,j,k))|(i,j,k)∈S 正常}

[0190] The visualization dataset V 异常 and V 正常Convert to a graphical representation to obtain a three-dimensional scatter plot showing abnormal points and normal points;

[0191] Generate a visual geomagnetic anomaly report based on a three-dimensional scatter plot.

[0192] In some embodiments, the system for determining geomagnetic anomaly based on the full tensor geomagnetic gradient benchmark further includes a first sending module 207 for sending a visualized geomagnetic anomaly report to a preset terminal device.

[0193] It can be understood that the modules recorded in the system for determining geomagnetic anomaly based on full tensor geomagnetic gradient benchmark are similar to those in the reference Figure 1 The steps in the method for determining geomagnetic anomalies based on the full tensor geomagnetic gradient benchmark described above correspond to each other. Therefore, the operations, features and beneficial effects described above for the method are also applicable to the system for determining geomagnetic anomalies based on the full tensor geomagnetic gradient benchmark and the modules contained therein, and will not be repeated here.

[0194] See also Figure 3 , based on the inventive concept of a method for determining geomagnetic anomalies based on a full tensor geomagnetic gradient benchmark in the aforementioned embodiment, an embodiment of the present application provides an electronic device. The electronic device may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), etc., and fixed terminals such as digital TVs, desktop computers, etc. The electronic device includes a processing device 301 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a ROM 302 (read-only memory) or a program loaded from a storage device 308 into a RAM 303 (random access memory). In RAM 303, various programs and data required for the operation of the electronic device are also stored. The processing device 301, ROM 302, and RAM 303 are connected to each other via a bus 304. The input / output interface (i.e., an I / O interface 305) is also connected to the bus 304.

[0195] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data.

[0196] In particular, according to some embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present application are executed.

[0197] It should be noted that the computer-readable medium recorded in some embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In some embodiments of the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0198] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an adhoc peer-to-peer network), as well as any currently known or future developed network.

[0199] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains geomagnetic sensor arrays installed at different heights on the ground and in the air, and collects multi-dimensional geomagnetic field data in real time; wherein the data collected by each geomagnetic sensor in the geomagnetic sensor array includes geomagnetic component data in three orthogonal directions, and is accompanied by timestamp information; constructs a tensor data set containing time, spatial coordinates and magnetic field intensity based on the collected multi-dimensional geomagnetic field data; pre-processes the tensor data set; uses the central difference method to calculate each data point in the pre-processed tensor data set to obtain a geomagnetic gradient tensor; uses a preset threshold to analyze the geomagnetic gradient tensor to determine whether there is a geomagnetic anomaly and obtain a geomagnetic anomaly determination result; generates a visual geomagnetic anomaly report based on the geomagnetic anomaly determination result.

[0200] Computer program code for performing the operations of some embodiments of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0201] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0202] The modules described in some embodiments of the present application may be implemented by software or by hardware. The modules described may also be arranged in a processor: for example, they may be described as: a first acquisition module, a first construction module, a preprocessing module, a first calculation module, a first judgment module, a first generation module, and a first sending module. The names of these modules do not, in some cases, constitute limitations on the modules themselves. For example, the first acquisition module may also be described as a "multi-dimensional geomagnetic field data acquisition module".

[0203] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0204] Some embodiments of the present application also provide a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for determining geomagnetic anomalies based on a full tensor geomagnetic gradient benchmark.

[0205] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for determining geomagnetic anomalies based on full tensor geomagnetic gradient benchmark, characterized in that: The following steps are involved: Obtain geomagnetic sensor arrays installed at different heights on the ground and in the air to collect multi-dimensional geomagnetic field data in real time; wherein the data collected by each geomagnetic sensor in the geomagnetic sensor array includes geomagnetic component data in three orthogonal directions, and is accompanied by timestamp information; Constructing a tensor data set including time, space coordinates and magnetic field intensity according to the multi-dimensional geomagnetic field data collected; Preprocessing the tensor data set; The central difference method is used to operate each data point in the preprocessed tensor data set to obtain the geomagnetic gradient tensor; Analyzing the geomagnetic gradient tensor using a preset threshold value to determine whether there is a geomagnetic anomaly and obtain a geomagnetic anomaly determination result; A visualized geomagnetic anomaly report is generated according to the geomagnetic anomaly determination result.

2. The method for determining geomagnetic anomaly based on full tensor geomagnetic gradient benchmark according to claim 1, characterized in that: The preprocessing includes denoising and normalization; The denoising formula is as follows: Where: D i,j,k : The data point in the i-th row, j-th column, and k-th layer of the original tensor data set represents the intensity of the geomagnetic component at a specific time and space position; D′ i,j,k : De-noised data after mean filtering, indicating the intensity of the de-noised geomagnetic component at the same location; N: The number of points in the filter window, indicating the total number of data points involved in the mean calculation; n: window radius, which indicates the range of neighboring data points considered during filtering; m: index variable, indicating the relative position within the window range, used to traverse the surrounding points; The normalization formula is as follows: Where: D″ i,j,k : Normalized data, indicating the intensity of the geomagnetic component at the same location; min(D′): the minimum value of all values ​​in the denoised data set, indicating the minimum geomagnetic component intensity in the data set; max(D′): The maximum value of all values ​​in the denoised data set, indicating the maximum geomagnetic component intensity in the data set.

3. The method for determining geomagnetic anomaly based on full tensor geomagnetic gradient benchmark according to claim 2 is characterized in that: The following calculation formula is used to perform central difference operation on each data point in the preprocessed tensor data set: Where: G x,y,z , G y,y,z , and G z,y,z Represent the three components of the geomagnetic gradient at the position (x, y, z); D″ x,y,z Represents the normalized preprocessed data, and obtains the geomagnetic gradient value of the current point by differential calculation of adjacent data points; Δx, Δy, and Δz represent the distance between each data point in three-dimensional space.

4. The method for determining geomagnetic anomaly based on full tensor geomagnetic gradient benchmark according to claim 3 is characterized in that: Use the following formula to determine whether there is an abnormality: Where: A(i,j,k) represents the result of the geomagnetic anomaly determination at the position (i,j,k). If there is an anomaly, A(i,j,k) = 1, otherwise A(i,j,k) = 0; G(i,j,k) represents the geomagnetic gradient value currently calculated; G baseline (i, j, k) represents the reference geomagnetic gradient value, which is used to compare with the current value; ||·|| represents the norm of the vector, which is used to calculate the difference between the two; P represents a preset comparison value.

5. The method for determining geomagnetic anomaly based on full tensor geomagnetic gradient benchmark according to claim 4 is characterized in that: The step of generating a visualized geomagnetic anomaly report according to the geomagnetic anomaly determination result comprises: Mark the abnormal points and normal points in the geomagnetic anomaly determination result A(i,j,k) to form an abnormal point set S 异常 and the normal point set S 正常 ,in: S 异常 ={(i,j,k)|A(i,j,k)=1} S 正常 ={(i,j,k)|A(i,j,k)=0} According to the outlier set S 异常 and the normal point set S 正常 Extract the geomagnetic gradient value G(i,j,k) at the corresponding position and generate a visualization data set V 异常 and V 正常 ,in: V 异常 ={(i,j,k,G(i,j,k))|(i,j,k)∈S 异常 } V 正常 ={(i,j,k,G(i,j,k))|(i,j,k)∈S 正常 } The visualization dataset V 异常 and V 正常 Convert to a graphical representation to obtain a three-dimensional scatter plot showing abnormal points and normal points; A visualized geomagnetic anomaly report is generated according to the three-dimensional scatter plot.

6. The method for determining geomagnetic anomalies based on full tensor geomagnetic gradient benchmark according to any one of claims 1 to 5, characterized in that: The following steps are also included: The visualized geomagnetic anomaly report is sent to a preset terminal device.

7. A system for determining geomagnetic anomalies based on full tensor geomagnetic gradient benchmark, characterized in that: include: The first acquisition module is used to acquire geomagnetic sensor arrays installed at different heights on the ground and in the air, and collect multi-dimensional geomagnetic field data in real time; wherein the data collected by each geomagnetic sensor in the geomagnetic sensor array includes geomagnetic component data in three orthogonal directions, and is accompanied by timestamp information; A first construction module is used to construct a tensor data set including time, space coordinates and magnetic field intensity according to the multi-dimensional geomagnetic field data collected; A preprocessing module, used for preprocessing the tensor data set; A first calculation module is used to calculate each data point in the preprocessed tensor data set using a central difference method to obtain a geomagnetic gradient tensor; A first judgment module is used to analyze the geomagnetic gradient tensor using a preset threshold value to determine whether there is a geomagnetic anomaly and obtain a geomagnetic anomaly judgment result; The first generating module is used to generate a visualized geomagnetic anomaly report according to the geomagnetic anomaly determination result.

8. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processing device, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processing device, the method according to any one of claims 1 to 6 is implemented.

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