Geologic body burial depth detection method and system based on gravity and magnetic Tilt gradient

Abstract

The invention relates to a geologic body burial depth detection method and system based on a gravity and magnetic Tilt gradient, and belongs to the technical field of geological prospecting. The burial depth detection method comprises the following steps: receiving gravity or magnetic field anomaly observation data of a target area; performing gridding processing on the gravity or magnetic field anomaly observation data to obtain gridding data; calculating a Tilt gradient value of each grid point in the grid based on the gridding data to obtain a Tilt gradient value data set; traversing the Tilt gradient value data set, screening a grid point of which the Tilt gradient value meets a preset gradient value requirement as a target point, and obtaining a plane coordinate of the target point in a grid; determining an abnormal center point of the target area according to the gridding data, and determining the position of a corresponding target geologic body; acquiring a plane coordinate of the abnormal center point in the grid, and calculating a corresponding distance value in combination with the plane coordinate of the target point; and obtaining the burial depth value of the target geologic body according to the distance value. According to the invention, the burial depth detection precision of the weak abnormal geologic body can be improved.

Description

Technical Field

[0001] The present application relates to the field of geological exploration technology, and in particular to a method and system for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient. Background Art

[0002] In the field of geological exploration and underground resource development, gravity and magnetic field anomaly analysis is an important geophysical detection method. Due to differences in density and magnetization characteristics, underground geological bodies will affect the earth's gravity field and magnetic field. This difference is manifested through gravity or magnetic field anomaly observation data. By analyzing and processing these anomaly data, the location, shape and burial depth information of underground geological bodies can be inferred. However, due to the complexity of geological bodies and the noise and nonlinear characteristics of the data themselves, existing methods have certain technical bottlenecks in calculating the burial depth of geological bodies.

[0003] Traditional methods for analyzing gravity and magnetic field anomaly data are mostly empirical. A common approach is to preliminarily identify anomaly areas through contour maps or gradient distributions, and use this to determine the approximate range of the geological body. Although these methods are simple and intuitive, they often rely on prior information, such as the density or magnetic susceptibility of the target geological body. However, in actual exploration, accurate acquisition of prior information is not always feasible, especially in unknown or under-explored areas. In addition, for weakly abnormal geological bodies with greater burial depths, the resolution of these methods is significantly reduced, making it difficult to effectively extract anomaly signals and the results of burial depth calculations lack reliability.

[0004] At present, in the process of processing gravity or magnetic field anomaly data, gridding technology has been widely used to discretize the continuous anomaly field distribution into regular grid data. However, the existing methods have limited ability in extracting weak anomaly features, especially when the target geological body is buried deep, the weak anomaly signal is easily masked by noise. At the same time, the existing data processing methods are inefficient in large-scale regional analysis, especially in complex geological environments, and it is often difficult to balance the computational efficiency and the accuracy of the results. Summary of the invention

[0005] In order to improve the accuracy of burial depth detection of weakly abnormal geological bodies, the present application provides a method and system for burial depth detection of geological bodies based on gravity and magnetic Tilt gradient.

[0006] In the first aspect, the present application provides a method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient, which adopts the following technical solution: A method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient, the buried depth detection method comprising: Receiving gravity or magnetic field anomaly observation data of a target area; the observation data includes profile observation data or plane observation data; gridding the gravity or magnetic field anomaly observation data to obtain gridded data; Based on the gridded data, a Tilt gradient value of each grid point in the grid is calculated to obtain a Tilt gradient value data set; Traversing the Tilt gradient value data set, selecting grid points whose Tilt gradient values ​​meet the preset gradient value requirements as target points, and obtaining the plane coordinates of the target points in the grid; Determine an abnormal center point of a target area according to the gridded data, and determine a location of a corresponding target geological body based on the abnormal center point; Obtaining the plane coordinates of the abnormal center point in the grid; Calculate the corresponding distance value according to the plane coordinates of the target point and the plane coordinates of the abnormal center point; The buried depth value of the target geological body is obtained according to the distance value.

[0007] By adopting the above technical solution, the gravity or magnetic field anomaly data of the target area is used as input, and the buried depth value of the target geological body is finally accurately derived through grid processing, Tilt gradient calculation, specific gradient value screening and distance calculation of the center point of the anomaly, thus realizing effective analysis of the complex anomaly field. Through the mathematical model of Tilt gradient, this method can sensitively extract the weak anomaly characteristics of the deeply buried geological body, and can quickly locate the center point of the anomaly even in the case of large noise, and further combine the spatial distribution of the specific Tilt gradient value to accurately screen out the target points related to the buried depth of the geological body, realizing the full process calculation from the acquisition of anomaly data to the output of the buried depth value of the target geological body, and improving the detection accuracy of the buried depth of weakly abnormal geological bodies.

[0008] Optionally, the step of calculating the Tilt gradient value of each grid point in the grid based on the gridded data to obtain the Tilt gradient value data set includes: Based on the gridded data, calculating the vertical first-order derivative and the lateral first-order derivative of each grid point in the grid; Calculate the square root of the sum of the squares of the first-order lateral derivatives to obtain the total horizontal derivative of each grid point; The Tilt gradient value of each grid point in the grid is calculated according to the vertical first-order derivative and the total horizontal derivative to obtain a Tilt gradient value data set.

[0009] By adopting the above technical solution, the Tilt gradient value integrates vertical and horizontal change information, which can effectively amplify the signal of weak anomalies in deep areas, especially in complex geological environments, with high recognition accuracy for target geological bodies. This application has the characteristics of high precision, high sensitivity and strong adaptability, laying a solid foundation for the screening of target points and subsequent geological body burial depth analysis.

[0010] Optionally, the step of determining the abnormal center point of the target area according to the gridded data includes: Based on the gridded data, calculating the vertical first-order derivative and the lateral first-order derivative of each grid point in the grid; Calculate the square root of the sum of the squares of the first-order lateral derivatives to obtain the total horizontal derivative of each grid point; Traverse the total horizontal derivative of each grid point and determine the extreme point according to the extreme value of the total horizontal derivative; Performing vertical gradient verification on the extreme point to determine whether the vertical first-order derivative of the extreme point is greater than a preset threshold; If yes, then the extreme point is taken as the abnormal center point; If not, the extreme point is excluded and the next extreme point is sought for vertical gradient verification until the vertical first-order derivative of the extreme point is greater than a preset threshold.

[0011] By adopting the above technical solution, the lateral and vertical first-order derivatives of the grid points are gradually calculated, and combined with the extreme value screening of the total horizontal derivative and the vertical gradient verification, the misjudgment of the single gradient analysis is avoided and the positioning accuracy of the abnormal center point is improved. At the same time, the traversal and verification logic based on matrix calculation is adopted, which can efficiently process large-scale gridded data and reduce human intervention. It can be widely used in gravity and magnetic field anomaly data processing, especially in complex geological environments. It has significant robustness and lays a solid foundation for subsequent geological body location and burial depth analysis.

[0012] Optionally, the step of calculating the corresponding distance value according to the plane coordinates of the target point and the plane coordinates of the abnormal center point includes: According to the plane coordinates of the target point and the plane coordinates of the abnormal center point, the plane distance between the target point and the abnormal center point is calculated based on the Euclidean distance formula to obtain a corresponding distance value.

[0013] By adopting the above technical solution, the distance between the target point and the center point of the anomaly is calculated using the plane coordinates of the two points. With the mathematical model as the core and combined with the Tilt gradient characteristics, the rapid and accurate calculation of the buried depth value of the geological body is achieved.

[0014] Optionally, the preset gradient value requirement includes: a Tilt gradient value of -18.435°.

[0015] Optionally, the preset gradient value requirement includes: the Tilt gradient value satisfies a preset gradient threshold range of -18.435°±0.5°.

[0016] Optionally, the target geological body is an equiaxed body.

[0017] In the second aspect, the present application provides a geological body burial depth detection system based on gravity and magnetic Tilt gradient, which adopts the following technical solution: A geological body burial depth detection system based on gravity and magnetic Tilt gradient, the burial depth detection system comprising: A data receiving module, used to receive gravity or magnetic field anomaly observation data of a target area; the observation data includes profile observation data or plane observation data; A grid processing module, used for gridding the gravity or magnetic field anomaly observation data to obtain grid data; a gradient value calculation module, used for calculating the Tilt gradient value of each grid point in the grid based on the grid data to obtain a Tilt gradient value data set; A target point screening module, used to traverse the Tilt gradient value data set, screen the grid points whose Tilt gradient values ​​meet the preset gradient value requirements as target points, and obtain the plane coordinates of the target points in the grid; An abnormal center point determination module, used to determine the abnormal center point of the target area according to the grid data; A geological body position determination module, used to determine the position of the corresponding target geological body based on the abnormal center point; A plane coordinate acquisition module, used to acquire the plane coordinates of the abnormal center point in the grid; A distance value calculation module, used to calculate the corresponding distance value according to the plane coordinates of the target point and the plane coordinates of the abnormal center point; The burial depth value generating module is used to obtain the burial depth value of the target geological body according to the distance value.

[0018] In a third aspect, the present application provides a computer device, which adopts the following technical solution: A computer device comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the methods in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1It is a flow chart of a method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient in one of the embodiments of the present application.

[0021] Figure 2 It is a flow chart of a method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient according to another embodiment of the present application.

[0022] Figure 3 It is a flow chart of a method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient according to another embodiment of the present application.

[0023] Figure 4 This is a magnetic anomaly distribution map based on a spherical combination model in one of the embodiments of the present application.

[0024] Figure 5 This is a Tilt gradient analysis result diagram based on a sphere combination model in one of the embodiments of the present application.

[0025] Figure 6 This is a magnetic anomaly distribution map based on a cube combination model in one of the embodiments of the present application.

[0026] Figure 7 This is a Tilt gradient analysis result diagram based on a cube combination model in one of the embodiments of the present application. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-7 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0028] The embodiment of the present application discloses a method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient.

[0029] Reference Figure 1 , a geological body burial depth detection method based on gravity and magnetic Tilt gradient, the burial depth detection method includes: Step S101, receiving gravity or magnetic field anomaly observation data of a target area; wherein the observation data includes profile observation data or plane observation data; Among them, gravity or magnetic field anomaly data is obtained by observing the target area through a gravimeter or magnetometer. The anomaly data is caused by the density difference (gravity anomaly) or magnetization intensity difference (magnetic field anomaly) of the geological body in the target area.

[0030] In some embodiments, an array of observation points may be arranged in the target area to measure the gravity anomaly values ​​of the profile or plane. For example, the gravity anomaly data of a certain area may include: profile data (gravity anomaly values ​​of a series of points measured along a straight line) and plane data (grid-arranged points in the area to form a two-dimensional gravity anomaly data distribution).

[0031] Step S102, gridding the gravity or magnetic field anomaly observation data to obtain gridded data; The data of the target area is divided into regular grids, and each grid point corresponds to an observation value (gravity or magnetic field anomaly). The data structure is unified through grid processing, providing an efficient processing method for gradient calculation and target point screening.

[0032] Step S103, based on the gridded data, calculating the Tilt gradient value of each grid point in the grid to obtain a Tilt gradient value data set; Among them, the Tilt gradient value is sensitive to deep anomaly changes and can effectively extract weak anomalies, providing a basis for accurately locating target points.

[0033] Step S104, traversing the Tilt gradient value data set, selecting grid points whose Tilt gradient values ​​meet the preset gradient value requirements as target points, and obtaining the plane coordinates of the target points in the grid; Among them, grid points that meet the preset Tilt gradient value (e.g., -18.435°) or a specific gradient threshold range (e.g., -18.435°±0.5°) are screened, and the plane coordinates of the target points are used for subsequent burial depth calculation.

[0034] Step S105, determining the abnormal center point of the target area according to the gridded data, and determining the location of the corresponding target geological body based on the abnormal center point; Specifically, by calculating the extreme points of the gravity or magnetic field gradient, the position of the anomaly center point (usually corresponding to the center of the target geological body) is determined. The anomaly center point can be used as the preliminary positioning point of the potential target geological body, laying the foundation for subsequent analysis.

[0035] For example, assume that the magnetic field anomaly values ​​measured in a certain area are distributed as follows: the maximum magnetic anomaly value appears at a certain position, and after calculating the gradient, it is confirmed that the gradient extreme point is also at this position. Then this point is the anomaly center point, and the target geological body may be below this anomaly center point.

[0036] In one of the embodiments of the present application, since the calculation logic is based on the special mathematical characteristics of the Tilt gradient, the present application scheme is most suitable for the case where the target geological body is an equiaxed body. The equiaxed geological body can be equivalent to a spherical model during observation and data analysis. This model has symmetry and regularity, making the relationship between the Tilt gradient value and the depth of the geological body clearer and more accurate. In this case, the present application scheme can achieve the highest measurement accuracy, so the present application is particularly suitable for the calculation of the depth of an equiaxed geological body.

[0037] When the shape of the geological body is not an equiaxed body, for example, it is irregular or obviously elongated, although the accuracy of the Tilt gradient model will be affected to a certain extent, the applicability of the data can be significantly improved by properly adjusting the observation distance. Specifically, when the distance between the observation point and the geological body is kept at a relatively long range, the irregular geological body appears as an abnormal distribution close to a circle in the observation data. In this case, the Tilt gradient model can still accurately reflect the burial depth characteristics of the geological body. As the observation distance increases, the closer the abnormal data is to the ideal equiaxed symmetry, the higher the accuracy of the calculation.

[0038] Therefore, the technical solution of this application not only has significant applicability and high accuracy for equiaxed geological bodies, but can also expand its scope of application through the reasonable selection of observation distance, so that it can be compatible with the abnormal conditions of geological bodies of other shapes. In particular, when the observation distance is appropriately increased, the vast majority of irregular geological bodies can also be effectively calculated for buried depth through this solution. This feature significantly improves the versatility and practicality of this technical solution, and further enhances the scope of application of the solution in geological exploration.

[0039] Step S106, obtaining the plane coordinates of the abnormal center point in the grid; Among them, the coordinates of the abnormal center point are associated with the grid data to ensure that the abnormal center point is used as a reference point for burial depth calculation.

[0040] Step S107, calculating the corresponding distance value according to the plane coordinates of the target point and the plane coordinates of the abnormal center point; Among them, the plane distance between the target point and the center point of the anomaly can be calculated using the distance formula between two points on the plane, providing a direct basis for the buried depth value of the target geological body.

[0041] Step S108, obtaining the buried depth value of the target geological body according to the distance value.

[0042] Among them, combined with the Tilt gradient model, the buried depth value of the target geological body is equal to the plane distance between the target point and the center point of the anomaly.

[0043] In the above implementation, the gravity or magnetic field anomaly data of the target area is used as input, and the buried depth value of the target geological body is finally accurately derived through grid processing, Tilt gradient calculation, specific gradient value screening and distance calculation of the center point of the anomaly, thereby realizing effective analysis of the complex anomaly field. Through the mathematical model of Tilt gradient, this method can sensitively extract the weak anomaly characteristics of the deeply buried geological body, and can quickly locate the center point of the anomaly even in the case of large noise, and further combine the spatial distribution of the specific Tilt gradient value to accurately screen out the target points related to the buried depth of the geological body, realizing the full process calculation from the acquisition of anomaly data to the output of the buried depth value of the target geological body, and improving the buried depth detection accuracy of weakly abnormal geological bodies.

[0044] This application makes full use of the parallel computing power of modern computers, and can ensure computing efficiency while processing large-scale data. This automated data processing method reduces the need for human intervention, making the method more suitable for exploration applications in large-scale complex geological areas, especially for the identification of deeply buried weakly abnormal target geological bodies. In addition, the method itself does not rely on specific prior information and is applicable to a variety of geological backgrounds and target fields (gravity fields or magnetic fields), showing wide applicability and practical application potential.

[0045] Reference Figure 2 As an implementation of step S103, based on the gridded data, the Tilt gradient value of each grid point in the grid is calculated to obtain the Tilt gradient value data set, which includes: Step S201, based on the gridded data, calculating the vertical first-order derivative and the lateral first-order derivative of each grid point in the grid; Among them, the vertical first-order derivative and the lateral first-order derivative are measures of the rate of change of outliers with spatial position, and are the basis for calculating the Tilt gradient.

[0046] Specifically, and is the transverse first-order derivative of the potential field T, that is, the rate of change of each grid point (x, y) is calculated in the x and y directions respectively. The formula is: Along the x direction: Along the y direction: In the above formula, Δx and Δy are the spacing between grid points; Specifically, is the vertical first-order derivative of the potential field T. The vertical change can be estimated by using the change law of the outlier value, which can be calculated by the numerical difference method: Step S202, calculating the square root of the sum of squares of the horizontal first-order derivatives to obtain the total horizontal derivative of each grid point; in, is the total horizontal derivative of the potential field T, which integrates the scalar value of the change rate of the lateral first-order derivative in the x and y directions, and is used to reflect the overall horizontal change intensity of the abnormal field. The calculation formula is: Step S203, calculating the Tilt gradient value of each grid point in the grid according to the vertical first-order derivative and the total horizontal derivative, and obtaining a Tilt gradient value data set.

[0047] The Tilt gradient value is the inverse tangent of the ratio of the vertical first-order derivative to the total horizontal derivative, which reflects the relative relationship between the changes in the abnormal field in the vertical and horizontal directions. The calculation formula is:

[0048] It can be understood that by generating a Tilt gradient value data set for each grid point, an accurate determination basis is provided for screening target points.

[0049] In the above implementation, the Tilt gradient value integrates the vertical and horizontal change information, which can effectively amplify the signal of deep weak anomalies, especially in complex geological environments, with high recognition accuracy for target geological bodies. This application has the characteristics of high precision, high sensitivity and strong adaptability, laying a solid foundation for the screening of target points and subsequent geological body burial depth analysis.

[0050] Reference Figure 3 As an implementation of step S105, the step of determining the abnormal center point of the target area according to the gridded data includes: Step S301, based on the gridded data, calculating the vertical first-order derivative and the lateral first-order derivative of each grid point in the grid; Step S302, calculating the square root of the sum of squares of the first-order lateral derivatives to obtain the total horizontal derivative of each grid point; Step S303, traversing the total horizontal derivative of each grid point, and determining the extreme point according to the extreme value of the total horizontal derivative; Among them, the extreme values ​​of the total horizontal derivative correspond to the boundary points of the abnormal field, and these points are clearly distributed and representative in the abnormal area. By screening the extreme points of the total horizontal derivative, multiple candidate points can be quickly determined to cover the boundary of the abnormal body and its internal area.

[0051] It should be noted that the extreme value points of the total horizontal derivative in the embodiment of the present application may be multiple local extreme value points, rather than a unique global maximum value, because the rate of change of the abnormal field at the boundary varies with different positions, but multiple positions may reach local extreme values. Therefore, due to the limited accuracy of grid division, some derivative values ​​that are close but not exactly the same may also be regarded as independent extreme value points.

[0052] Step S304, verify the vertical gradient of the extreme point to determine whether the vertical first-order derivative of the extreme point is greater than a preset threshold; if so, jump to step S305; if not, jump to step S306; Step S305, taking the extreme point as the abnormal center point; It is understandable that the distribution of the extreme values ​​of the vertical first-order derivative is usually limited and only changes significantly near the center of the anomaly. If you rely directly on the vertical derivative, you may miss the true center of the anomaly due to the sparse grid division. Therefore, using the extreme points of the total horizontal derivative as the initial screening can provide a reliable range, and then combined with the vertical gradient verification to ensure that the final positioning result is more accurate.

[0053] Step S306, exclude the extreme point and find the next extreme point to perform vertical gradient verification until the vertical first-order derivative of the extreme point is greater than a preset threshold.

[0054] Specifically, for the determined extreme point, check whether its vertical first-order derivative meets the preset threshold (e.g. ), only the extreme point with significant vertical change is considered as the abnormal center point, which is the position where the horizontal gradient and vertical gradient change significantly, usually corresponding to the center of the target geological body; if the conditions are not met, the point is excluded, the total horizontal derivative matrix is ​​traversed again to find the next largest value, and the verification process is repeated.

[0055] For example, for the extreme point (1,2), its vertical gradient value is 0.6mGal / m, and the threshold is set to 0.5mGal / m, then the point passes the verification; if the threshold condition is not met, the point (0,1) corresponding to the second largest value of the total horizontal derivative 0.8 is selected for verification.

[0056] It can be understood that through vertical gradient verification, the misjudged extreme points are eliminated to ensure that the physical meaning of the final abnormal center point is accurate.

[0057] In the above implementation, the lateral and vertical first-order derivatives of the grid points are calculated step by step, and combined with the extreme value screening of the total horizontal derivative and the vertical gradient verification, the misjudgment of the single gradient analysis is avoided and the positioning accuracy of the abnormal center point is improved. At the same time, the traversal and verification logic based on matrix calculation is adopted, which can efficiently process large-scale grid data and reduce human intervention. It can be widely used in gravity and magnetic field anomaly data processing, especially in complex geological environments. It has significant robustness and lays a solid foundation for subsequent geological body location and burial depth analysis.

[0058] As an implementation of step S106, the step of calculating the corresponding distance value according to the plane coordinates of the target point and the plane coordinates of the abnormal center point includes: According to the plane coordinates of the target point and the plane coordinates of the center point of the anomaly, the plane distance between the target point and the center point of the anomaly is calculated based on the Euclidean distance formula to obtain the corresponding distance value.

[0059] Specifically, the Euclidean distance formula is: In the above formula, (xt, yt) is the plane coordinate of the target point; (xc, yc) is the plane coordinate of the center point of the anomaly; d is the plane distance between the two points.

[0060] In the above implementation, the distance between the target point and the center point of the anomaly is calculated using the plane coordinates of the two points, and a mathematical model is used as the core, combined with the Tilt gradient characteristics, to achieve rapid and accurate calculation of the buried depth value of the geological body.

[0061] In some embodiments of the present application, the preset gradient value requirement includes: the Tilt gradient value is -18.435° or the Tilt gradient value satisfies a preset gradient threshold range of -18.435°±0.5°.

[0062] As an implementation method for calculating the preset gradient value requirement, the specific theoretical derivation process includes: First, the mathematical relationship of the Tilt gradient (also called oblique derivative) in an equiaxed geological body (equivalent to a spherical model) is as follows: In the above formula, is the vertical first-order derivative of the potential field T, is the total horizontal derivative of the potential field T; where the total horizontal derivative is defined as: In the rectangular coordinate system x, y, z, the target geological body is assumed to be a spherical model with its center coordinates (0, 0, R). Assuming that the sphere is vertically magnetized, the first-order derivative expressions in each direction when calculating the spherical magnetic anomaly are as follows: Derivative along the x direction: Derivative along the y direction: Derivative along the z direction (vertical): In the above formula, μ0 is the vacuum magnetic permeability, and m is the magnetic moment of the sphere. Combining formulas (7)-(11), we can obtain: make Combined with the properties of the magnetic anomaly Tilt gradient, equation (12) can be expressed as a piecewise function with n as the variable, as follows: If the period n = 1, we can get: T dr =arctan(-1 / 3)≈-18.435°, and it can be deduced that when the Tilt gradient value is -18.435°, the distance from the corresponding position to the center of the sphere (i.e. the center of the anomaly mentioned above) is exactly equal to the burial depth of the sphere. This feature can be used to quickly determine the burial depth of equiaxed geological bodies and is suitable for the calculation of gravity anomalies and magnetic field anomalies.

[0063] Reference Figure 4 The figure shows the magnetic anomaly distribution map based on the spherical combination model, which is the result of simulating the influence of underground equiaxed geological bodies on the geomagnetic field. The horizontal and vertical axes represent the plane position of the target area in meters, and the contour lines show the spatial distribution of the magnetic field anomaly value (ΔT, unit: nT). There are two obvious magnetic anomaly areas in the figure, representing the magnetic anomaly responses of the two groups of spherical combinations.

[0064] In this simulation model, magnetic anomalies are presented as closed contour lines, and their centers correspond to the approximate projected positions of geological bodies. The larger the magnetic anomaly value, the denser the contour lines, indicating a higher anomaly intensity. As can be seen from the figure, the anomaly in the upper right (area 2) is stronger than the anomaly in the lower left (area 1), indicating that the geological body in area 2 may be closer to the surface or have a higher magnetization intensity, while the geological body in area 1 may be buried deeper or have a weaker magnetization.

[0065] Through this magnetic anomaly distribution map, the spatial position of the geological body and its strength characteristics can be preliminarily determined. The symmetry and regularity of the contour lines indicate that the shapes of these geological bodies conform to the assumptions of the spherical combination model, providing effective input data for subsequent Tilt gradient analysis.

[0066] Reference Figure 5 The figure shows the Tilt gradient analysis results based on the spherical combination model, and the target geological body is deeply interpreted in combination with the buried depth calculation. The horizontal and vertical coordinates still represent the plane position of the target area. The figure contains two parts of information: the contour distribution of the Tilt gradient and the direction vector field.

[0067] Among them, the contour distribution of the Tilt gradient reflects the vertical and horizontal variation relationship of the anomaly field, among which a specific contour line (such as -18.435°) is clearly marked. This specific value corresponds to the burial depth of the geological body, indicating that on this contour line, the ratio of the vertical derivative to the total horizontal derivative reaches the theoretical characteristic value of the Tilt gradient. By analyzing this contour line, the burial depth of the geological body can be directly determined.

[0068] The directional vector field of the Tilt gradient shows the gradient change trend of the anomaly field, and the vector direction points to the center of the anomaly body. Combined with the anomaly center points (R1 and R2) clearly marked in the figure, it can be found that the anomaly field has the largest gradient change near these center points. These points correspond to the theoretical center of the geological body and are an important basis for the calculation of the buried depth.

[0069] also, Figure 5 The differences in the characteristics of the geological bodies in Area 1 and Area 2 are also revealed. The Tilt gradient range in Area 2 is larger and the distribution range of the -18.435° contour line is also wider, indicating that the geological body in Area 2 is close to the surface, while the -18.435° contour line range in Area 1 is smaller, which means that it is buried deeper.

[0070] In summary, Figure 4 and Figure 5 The characteristics of the spherical combination model are revealed from two perspectives: magnetic anomaly distribution and Tilt gradient analysis. Figure 4 The magnetic anomaly contours provide preliminary information on the location and anomaly intensity of geological bodies; Figure 5 The specific characteristic value and direction vector of the Tilt gradient are used to determine the location of the anomaly center and calculate the buried depth of the geological body. This method of combining magnetic anomaly and Tilt gradient not only verifies the existence of the geological body, but also efficiently quantifies its buried depth, providing theoretical and technical support for the detection of anomalies in complex geological environments.

[0071] Reference Figure 6 The figure shows the magnetic anomaly distribution map based on the cube combination model, using contour lines to express the spatial variation characteristics of the magnetic field anomaly in the target area. The horizontal and vertical axes represent the plane coordinates of the study area in meters, and the contour lines in the figure represent the magnitude of the magnetic field anomaly in nanotesla (nT). The figure contains two significant anomaly areas, located in the lower left and upper right, marked as 1 and 2.

[0072] As can be seen from the figure, the abnormal area presents symmetrical and closed contour features, reflecting the abnormal magnetic field response of the target geological body. This symmetry shows that the geological body conforms to the characteristics of the cube combination model. Among them, the magnetic field anomaly value of area 2 is higher, the contour lines are denser, and the magnetic anomaly value in the central area reaches 600nT, indicating that the geological body may be close to the surface or have a high magnetization intensity; while the magnetic anomaly value of area 1 is relatively small, and the magnetic anomaly value in the central area is about 300nT, which may correspond to a deeper buried geological body or a lower magnetization intensity.

[0073] Reference Figure 7 As shown, Figure 7The calculation results of the Tilt gradient distribution and the buried depth of the geological body of the cube combination model are shown. The buried depth of the geological body is further revealed by combining the key information of the Tilt gradient characteristics and the contour lines. The horizontal and vertical axes represent the plane coordinates of the target area in meters. The center point of the anomaly (R1 and R2) is clearly marked in the figure, and the buried depth of the geological body is determined by the contour line of the specific value of the Tilt gradient (-18.435°).

[0074] Among them, the contour lines of the Tilt gradient present a regular closed shape and form a clear boundary at the specific value of -18.435°. This boundary corresponds to the burial depth characteristics of the geological body. Through the Tilt gradient calculation, the ratio of the vertical gradient to the horizontal gradient can be converted into a burial depth value. The range of the -18.435° contour line of abnormal area 2 is significantly larger than that of area 1, indicating that the geological body in area 2 is shallowly buried, while the geological body in area 1 is deeply buried. In addition, the direction of the vector field points to the center of the abnormal body, showing the changing trend of the abnormal field, which further verifies the accuracy of the abnormal center point.

[0075] In summary, Figure 6 The Tilt gradient distribution and buried depth calculation results are Figure 7 Combined with the magnetic anomaly distribution, it provides more accurate information on the location and burial depth of geological bodies. This method can effectively identify the morphology and burial characteristics of geological bodies, providing reliable technical support for actual geological exploration.

[0076] The embodiment of the present application also discloses a geological body burial depth detection system based on gravity and magnetic Tilt gradient.

[0077] A geological body burial depth detection system based on gravity and magnetic Tilt gradient, the burial depth detection system comprising: A data receiving module is used to receive gravity or magnetic field anomaly observation data of the target area; the observation data includes profile observation data or plane observation data; A grid processing module is used to grid the gravity or magnetic field anomaly observation data to obtain grid data; A gradient value calculation module is used to calculate the Tilt gradient value of each grid point in the grid based on the grid data to obtain a Tilt gradient value data set; The target point screening module is used to traverse the Tilt gradient value data set, screen the grid points whose Tilt gradient values ​​meet the preset gradient value requirements as target points, and obtain the plane coordinates of the target points in the grid; An abnormal center point determination module is used to determine the abnormal center point of the target area based on the grid data; A geological body position determination module is used to determine the position of the corresponding target geological body based on the abnormal center point; A plane coordinate acquisition module is used to obtain the plane coordinates of the abnormal center point in the grid; The distance value calculation module is used to calculate the corresponding distance value according to the plane coordinates of the target point and the plane coordinates of the abnormal center point; the burial depth value generation module is used to obtain the burial depth value of the target geological body according to the distance value.

[0078] In the above implementation, the target point and the abnormal center point are accurately extracted by grid processing and gradient value calculation, the characteristic points related to the buried depth are screened out by the high sensitivity of the Tilt gradient, and the buried depth value of the target geological body is quickly estimated by combining the spatial distance between the target point and the abnormal center point. The technical solution of this application is characterized by high efficiency, accuracy and strong adaptability, and is particularly suitable for the identification and analysis of deeply buried weak abnormal geological bodies in complex geological environments, providing reliable technical support and practical value for mineral resource exploration, geological surveys and underground structure research.

[0079] The geological body burial depth detection system based on gravity-magnetic Tilt gradient of the embodiment of the present application can implement any of the above-mentioned geological body burial depth detection methods based on gravity-magnetic Tilt gradient, and the specific working process of each module in the geological body burial depth detection system based on gravity-magnetic Tilt gradient can refer to the corresponding process in the above-mentioned method embodiment.

[0080] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are only illustrative; for example, the division of a certain module is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0081] The embodiment of the present application also discloses a computer device.

[0082] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient as described above is implemented.

[0083] The embodiment of the present application also discloses a computer-readable storage medium.

[0084] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute any one of the above-mentioned methods for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient.

[0085] Among them, computer-readable storage media can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus or device; the program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0086] It should be noted that in the above embodiments, the description of each embodiment has different emphases, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0087] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient, characterized in that: The buried depth detection method comprises: Receiving gravity or magnetic field anomaly observation data of a target area; the observation data includes profile observation data or plane observation data; Gridding the gravity or magnetic field anomaly observation data to obtain gridded data; Based on the gridded data, a Tilt gradient value of each grid point in the grid is calculated to obtain a Tilt gradient value data set; Traversing the Tilt gradient value data set, selecting grid points whose Tilt gradient values ​​meet the preset gradient value requirements as target points, and obtaining the plane coordinates of the target points in the grid; Determine an abnormal center point of a target area according to the gridded data, and determine a location of a corresponding target geological body based on the abnormal center point; Obtaining the plane coordinates of the abnormal center point in the grid; Calculate the corresponding distance value according to the plane coordinates of the target point and the plane coordinates of the abnormal center point; The buried depth value of the target geological body is obtained according to the distance value.

2. The method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient according to claim 1, characterized in that: The step of calculating the Tilt gradient value of each grid point in the grid based on the gridded data to obtain a Tilt gradient value data set comprises: Based on the gridded data, calculating the vertical first-order derivative and the lateral first-order derivative of each grid point in the grid; Calculate the square root of the sum of the squares of the first-order lateral derivatives to obtain the total horizontal derivative of each grid point; The Tilt gradient value of each grid point in the grid is calculated according to the vertical first-order derivative and the total horizontal derivative to obtain a Tilt gradient value data set.

3. The method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient according to claim 1, characterized in that: The step of determining the abnormal center point of the target area according to the gridded data comprises: Based on the gridded data, calculating the vertical first-order derivative and the lateral first-order derivative of each grid point in the grid; Calculate the square root of the sum of the squares of the first-order lateral derivatives to obtain the total horizontal derivative of each grid point; Traverse the total horizontal derivative of each grid point and determine the extreme point according to the extreme value of the total horizontal derivative; Performing vertical gradient verification on the extreme point to determine whether the vertical first-order derivative of the extreme point is greater than a preset threshold; If yes, then the extreme point is taken as the abnormal center point; If not, the extreme point is excluded and the next extreme point is sought for vertical gradient verification until the vertical first-order derivative of the extreme point is greater than a preset threshold.

4. The method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient according to claim 3, characterized in that: The step of calculating the corresponding distance value according to the plane coordinates of the target point and the plane coordinates of the abnormal center point comprises: According to the plane coordinates of the target point and the plane coordinates of the abnormal center point, the plane distance between the target point and the abnormal center point is calculated based on the Euclidean distance formula to obtain a corresponding distance value.

5. The method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient according to claim 1, characterized in that: The preset gradient value requirement includes: the Tilt gradient value is -18.435°.

6. The method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient according to claim 1, characterized in that: The preset gradient value requirement includes: the Tilt gradient value satisfies a preset gradient threshold range of -18.435°±0.5°.

7. A method for detecting the buried depth of a geological body based on gravity and magnetic Tilt gradient according to any one of claims 1 to 6, characterized in that: The target geological body is an equiaxed body.

8. A geological body burial depth detection system based on gravity and magnetic tilt gradient, characterized in that: The buried depth detection system comprises: A data receiving module, used to receive gravity or magnetic field anomaly observation data of a target area; the observation data includes profile observation data or plane observation data; A grid processing module, used for gridding the gravity or magnetic field anomaly observation data to obtain grid data; A gradient value calculation module, used to calculate the Tilt gradient value of each grid point in the grid based on the gridded data to obtain a Tilt gradient value data set; A target point screening module, used to traverse the Tilt gradient value data set, screen the grid points whose Tilt gradient values ​​meet the preset gradient value requirements as target points, and obtain the plane coordinates of the target points in the grid; An abnormal center point determination module, used to determine the abnormal center point of the target area according to the grid data; A geological body position determination module, used to determine the position of the corresponding target geological body based on the abnormal center point; A plane coordinate acquisition module, used to acquire the plane coordinates of the abnormal center point in the grid; A distance value calculation module, used to calculate the corresponding distance value according to the plane coordinates of the target point and the plane coordinates of the abnormal center point; The burial depth value generating module is used to obtain the burial depth value of the target geological body according to the distance value.

9. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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