A method for positioning a prospecting target area by fusing a geomagnetic constraint and a magnetic gradient structure

Abstract

The application discloses a method for positioning a prospecting target area based on a fusion geologic constraint and a magnetic gradient structure, relates to the technical field of unmanned aerial vehicle (UAV) airborne magnetic survey, and comprises the following steps: obtaining a plurality of abnormal grid units and a plurality of magnetic gradient structure prospecting target areas based on a UAV airborne magnetic survey data set, and determining the object attributes of the magnetic gradient structure prospecting target areas; determining the corresponding outer expansion boundary zone of a current target area; determining the consistent convergence factor, the morphological structure recognition factor and the geologic constraint factor of the current target area; determining a comprehensive score based on the consistent convergence factor, the morphological structure recognition factor, the geologic constraint factor, the horizontal gradient amplitude and the vertical gradient amplitude of each grid unit; determining a priority based on the comprehensive score, the area, the closure degree and the geologic constraint factor, determining a target prospecting target area based on the priority, and completing the positioning of the prospecting target area. The application can improve the objectivity, the reviewability and the automation degree of the positioning of the target area, and effectively distinguish between the ore-bearing abnormality and the non-ore abnormality that are consistent with the geologic constraint.

Description

Technical Field

[0001] This application relates to the field of UAV airborne magnetic surveying technology, and in particular to a method for locating mineral exploration target areas using an aeromagnetic gradient structure that incorporates geological constraints. Background Technology

[0002] Unmanned aerial magnetic surveys (UAVs) are characterized by high mobility, low operating costs, good terrain adaptability, and high data resolution, and have been widely used in mineral exploration, including iron ore, vanadium-titanium magnetite, copper-gold polymetallic deposits, and niobium rare metal deposits. UAV aeromagnetic data at a scale of approximately 1:10,000 can cover large prospecting areas and identify anomalies caused by small, localized, or concealed magnetic bodies, serving as crucial data linking regional screening with ground verification.

[0003] Traditional aeromagnetic mineral exploration interpretation typically relies on total field anomalies, polarization anomalies, upward continuation, vertical derivatives, analytical signals, or enhanced magnetic anomaly boundary maps. It primarily depends on manual anomaly delineation and experience-based target area assessment. Some methods improve the reliability of anomaly interpretation by overlaying UAV aeromagnetic data with ground magnetic surveys, geological maps, remote sensing, borehole data, or other geophysical data. Other methods employ multi-scale or multi-scale aeromagnetic data for mapping, overlaying, or weighted evaluation of anomalies.

[0004] Traditional methods can improve the reliability of anomaly identification or comprehensive mineral exploration, but they still have the following shortcomings: First, the gradient direction structure, boundary direction consistency, directional convergence, and morphological structure of aeromagnetic anomalies themselves are not fully converted into automatic scoring factors; second, in stratabound iron ore, magnetite-ilmenite, and sedimentary metamorphic ore scenarios, ore bodies are often controlled by specific strata, lithological sections, or inter-layer structures, exhibiting thin-layered, quasi-stratified, beaded, or banded distributions, making it easy to misjudge based solely on anomaly peak values ​​or areas; third, target area location and priority classification are highly subjective and lack verifiability, and there is a lack of intelligent location methods that transform geological control knowledge such as strata and structures into calculable geological constraint factors.

[0005] In summary, traditional methods suffer from poor objectivity and insufficient verifiability in locating mineral exploration target areas due to aeromagnetic anomalies, and are unable to effectively distinguish between mineral-bearing anomalies and non-mineral-bearing anomalies that conform to geological constraints. Summary of the Invention

[0006] The purpose of this application is to provide a method for locating mineral exploration target areas based on aeromagnetic gradient structures that integrates geological constraints, so as to improve the objectivity, verifiability and automation of aeromagnetic anomaly mineral exploration target area location, and effectively distinguish between mineral-bearing anomalies and non-mineral anomalies that match geological constraints.

[0007] To achieve the above objectives, this application provides the following solution.

[0008] This application provides a method for locating mineral exploration target areas using aeromagnetic gradient structures that incorporate geological constraints, including: Acquire UAV aeromagnetic measurement dataset and geological constraint parameters for the area to be measured; The UAV aeromagnetic measurement dataset is preprocessed and gridded to generate a regular aeromagnetic grid. The regular aeromagnetic grid is then separated from the background field to obtain a background field grid and a local outlier grid. The grid cells of the regular aeromagnetic grid, the background field grid, and the local outlier grid correspond one-to-one. Gradient calculations are performed on the local outlier grid to obtain the horizontal gradient magnitude, gradient direction, boundary orientation direction, and vertical gradient magnitude of each grid cell in the local outlier grid. Threshold segmentation is performed on the local outlier grid. All grid cells that meet the threshold conditions are extracted from the local outlier grid to obtain multiple outlier grid cells. Connectivity labeling, patch merging, and noise removal are performed on each outlier grid cell to obtain multiple aeromagnetic gradient structure prospecting target areas. The object attributes of each aeromagnetic gradient structure prospecting target area are determined. The object attributes include structure identification parameters, boundary polygon, anomaly mean, centroid, intensity weighting center, major axis direction of the circumscribed ellipse, and anomaly sign. The structure identification parameters include area, perimeter, closed boundary length, major-minor axis ratio of the circumscribed ellipse, and anomaly extrema. Any aeromagnetic gradient structure prospecting target area is determined as the current target area. Along the boundary of the current target area, a preset number of grid cells are extended outward to obtain the corresponding outward boundary zone of the current target area. Based on the horizontal gradient magnitude, gradient direction, boundary orientation direction, intensity weighting center, and anomaly sign of each grid cell in the outer boundary zone corresponding to the current target area, the uniform convergence factor of the current target area is determined; the uniform convergence factor includes the directional uniformity factor and the pointing convergence factor; Based on the structural identification parameters of the current target area, the directional convergence factor, the horizontal gradient magnitude of each grid cell in the current target area, and the regional background magnetic field value of each grid cell in the background field grid corresponding to the spatial position of the current target area, the morphological structure identification factor of the current target area is determined; the morphological structure identification factor includes morphological matching degree, appropriate area factor, and anomaly intensity factor. Based on the geological constraint parameters, boundary polygon, area, major axis direction of the circumscribed ellipse, anomaly extremes, and orientation consistency factor of the current target area, the geological constraint factors of the current target area are determined. The geological constraint factors include stratigraphic coincidence factor, strike consistency factor, zonal continuity factor, and isolated or unrelated geological constraint penalty term. Based on the uniform convergence factor, morphological structure identification factor, geological constraint factor, and the horizontal and vertical gradient magnitudes of each grid cell in the current target area, the comprehensive score of the current target area is determined. Based on the comprehensive score, area, closure degree and geological constraint factors of the current target area, the priority of the current target area is determined; Based on the priority of all aeromagnetic gradient structure prospecting target areas in the area to be tested, the target prospecting target areas corresponding to the area to be tested are determined, and the prospecting target area of ​​the area to be tested is located.

[0009] In one embodiment, the UAV aeromagnetic measurement dataset includes measurement point records for each measurement point in the area to be measured; the measurement point records include coordinates, measured magnetic field values, flight altitude, sampling interval, flight path direction, and quality control markers; Geological constraint parameters include the polygon of the favorable stratum or the buffer zone of the ore-bearing stratum, the strike of the favorable stratum, and the allowable distance between anomalous segments.

[0010] In one embodiment, the UAV aeromagnetic measurement dataset is preprocessed and gridded to generate a regular aeromagnetic grid, and background field separation is performed on the regular aeromagnetic grid to obtain a background field grid and a local outlier grid, including: Based on the quality control markers, the measurement point records of the measurement points marked as invalid records or gross errors are removed from the UAV aeromagnetic measurement dataset to obtain the measurement point records of each valid measurement point. The distance between adjacent valid measurement points is checked using the sampling interval. Overly dense and sparse sampling segments are resampled to obtain the measurement point record after resampling of each valid measurement point. The flight path direction in the resampled measurement record of each effective measurement point is identified to obtain the flight path noise direction. The resampled measurement record of each effective measurement point is leveled according to the flight path noise direction, and diurnal variation correction, international geomagnetic reference field correction and track check are performed to obtain the corrected magnetic field value of each effective measurement point. The corrected magnetic field values ​​of each effective measuring point are used to generate a regular aeromagnetic grid using the Kriging interpolation method. A two-dimensional polynomial trend surface was used to fit a regular aeromagnetic grid to obtain the background field grid; Subtracting the background field grid from the regular aeromagnetic grid yields the difference grid. Gaussian smoothing is then applied to the difference grid to obtain the local outlier grid.

[0011] In one embodiment, gradient calculation is performed on the local outlier grid to obtain the horizontal gradient magnitude, gradient direction, boundary orientation direction, and vertical gradient magnitude of each grid cell in the local outlier grid, including: The central difference method is used to calculate the x-axis and y-axis horizontal derivatives of each grid cell in the local outlier grid. Calculate the horizontal gradient magnitude, gradient direction, and boundary orientation of each grid cell in the local outlier grid based on the x-axis and y-axis horizontal derivatives of each grid cell. The vertical gradient magnitude of each grid cell in the local outlier grid is calculated using the first-order vertical derivative method in the frequency domain.

[0012] In one embodiment, a threshold segmentation is performed on the local outlier grid, and all grid cells that meet the threshold condition are extracted from the local outlier grid to obtain multiple outlier grid cells. Connectivity labeling, patch merging, and noise removal are then performed on each outlier grid cell to obtain multiple aeromagnetic gradient structure prospecting target areas. The object attributes of each aeromagnetic gradient structure prospecting target area are then determined, including: The quantile threshold method is used to determine the positive and negative anomaly thresholds; Mesh cells in the local outlier mesh that meet the threshold condition are identified as outlier mesh cells; the threshold condition is that the local outlier is greater than or equal to the positive outlier threshold or the local outlier is less than or equal to the negative outlier threshold; the positive outlier threshold and the negative outlier threshold are determined using the quantile threshold method; Each anomalous grid cell is labeled with its connected components to obtain multiple initial anomalous patches; Merge adjacent initial abnormal patches whose boundary distance is less than the preset merging distance to obtain merged abnormal patches; Abnormal patches with an area smaller than a preset area threshold are removed to obtain multiple prospecting target areas with aeromagnetic gradient structures. Calculate the object attributes of each prospecting target area with aeromagnetic gradient structure.

[0013] In one embodiment, the uniform convergence factor of the current target region is determined based on the horizontal gradient magnitude, gradient direction, boundary orientation direction, intensity weighting center, and anomaly sign of each grid cell in the outer boundary band corresponding to the current target region, including: The directional consistency factor of the current target area is determined based on the horizontal gradient magnitude and boundary orientation of each grid cell in the outer boundary zone corresponding to the current target area. Based on the horizontal gradient magnitude, gradient direction, intensity weighting center, and anomaly sign of each grid cell in the outer boundary zone corresponding to the current target area, determine the directional convergence factor of the current target area.

[0014] In one embodiment, the morphological structure identification factor of the current target area is determined based on the structure identification parameters of the current target area, the directional convergence factor, the horizontal gradient magnitude of each grid cell in the current target area, and the regional background magnetic field value of each grid cell in the background field grid corresponding to the spatial position of the current target area, including: Determine the compactness of the current target area based on its area and perimeter; Determine the degree of closure of the current target area based on the length and perimeter of the current target area's closed boundary; The boundary gradient continuity of the current target area is determined based on the horizontal gradient magnitude of each grid cell in the current target area. The morphological matching degree of the current target region is determined based on the ratio of major to minor axis, compactness, closure, boundary gradient continuity, and directional convergence factor of the circumscribed ellipse of the current target region. Determine the appropriate area factor for the current target area based on its current area. The abnormal intensity factor of the current target area is determined based on the abnormal extreme value of the current target area and the regional background magnetic field value of each grid cell corresponding to the spatial position of the current target area in the background field grid.

[0015] In one embodiment, the geological constraint factors of the current target area are determined based on the geological constraint parameters, boundary polygon, area, major axis direction of the circumscribed ellipse, anomaly extrema, and orientation consistency factor of the current target area, including: Based on the area, boundary polygon, and favorable stratigraphic or ore-bearing stratigraphic buffer zone polygon of the current target area, determine the stratigraphic fit factor of the current target area; Based on the direction of the major axis of the circumscribed ellipse of the current target area and the favorable stratigraphic orientation, determine the orientation consistency factor of the current target area; Based on the directional consistency factor, favorable stratigraphic strike, and allowable anomalous segment connection distance of the current target area, determine the zonal continuity factor of the current target area; Based on the stratigraphic coincidence factor, strike consistency factor, banded continuity factor, area, and extreme anomalies of the current target area, determine the isolated or unrelated geological constraint penalty terms for the current target area.

[0016] In one embodiment, the comprehensive score of the current target area is determined based on the uniform convergence factor, morphological structure identification factor, geological constraint factor, and the horizontal and vertical gradient magnitudes of each grid cell in the current target area, including: Based on the uniform convergence factor of the current target region, determine the first combination factor of the current target region; The second combination factor of the current target area is determined based on the horizontal gradient magnitude and vertical gradient magnitude of each grid cell in the current target area. The first combination factor, the second combination factor, the morphological structure identification factor, and the geological constraint factor of the current target area are weighted and summed to obtain the comprehensive score of the current target area.

[0017] In one implementation, the priority of the current target area is determined based on its comprehensive score, area, closure, and geological constraint factors, including: If the current target area meets the advanced conditions, the priority of the current target area is set to high priority; the advanced conditions are that the comprehensive score is greater than or equal to the first preset value and the stratigraphic matching factor and the orientation consistency factor are both greater than or equal to the second preset value. If the current target area meets the first intermediate condition, the second intermediate condition, or the third intermediate condition, then the priority of the current target area is determined to be medium priority. The first intermediate condition is that the comprehensive score is greater than or equal to the first preset value, the stratigraphic consistency factor is greater than or equal to the second preset value, but the trajectories consistency factor is less than the second preset value; the second intermediate condition is that the comprehensive score is greater than or equal to the first preset value, the trajectories consistency factor is greater than or equal to the second preset value, but the stratigraphic consistency factor is less than the second preset value; the third intermediate condition is that the comprehensive score is greater than or equal to the third preset value but less than the first preset value. If the current target area meets the low-level condition, then the priority of the current target area is set to low priority; the low-level condition is that the comprehensive score is greater than or equal to the fourth preset value but less than the third preset value. If the current target area meets the first, second, third, or fourth anomaly level conditions, then the priority of the current target area is determined as an anomaly to be excluded. The first anomaly level condition is that the comprehensive score is less than the fourth preset value; the second anomaly level condition is that the area is less than the preset minimum area; the third anomaly level condition is that the closure degree is less than the preset minimum closure degree; and the fourth anomaly level condition is that any of the geological constraint factors does not meet the corresponding preset range.

[0018] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application discloses a method for locating mineral exploration target areas based on aeromagnetic gradient structures that integrates geological constraints. It incorporates multiple dimensions of indicators, including gradient direction consistency, pointing convergence, morphological matching, stratigraphic fit factor, strike consistency factor, zonal continuity factor, penalty for isolated or unrelated geological constraints, and area and closure, into a comprehensive score. This comprehensive score, combined with area, closure, and geological constraint factors, determines priority, automating and quantifying target area priority classification and improving the objectivity and verifiability of the classification results. Furthermore, since area, closure, and geological constraint factors serve as hard conditions for priority determination, it suppresses noisy anomalies with excessively small areas or poor closure, and reduces the priority of anomalies that deviate from favorable stratigraphic positions or are isolated, effectively reducing the possibility of non-mineral anomalies being misclassified as high-priority target areas. Moreover, this application improves the objectivity, verifiability, and automation of aeromagnetic anomaly mineral exploration target area location, effectively distinguishing between mineral-bearing and non-mineral anomalies with geologically consistent fit. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of a mineral exploration target area location method based on aeromagnetic gradient structure incorporating geological constraints, provided in an embodiment of this application; Figure 2 A polygonal overlay image of the local aeromagnetic anomaly and the favorable stratigraphic buffer zone at Yufengshan; Figure 3 The horizontal gradient magnitude and gradient direction pattern within the stratified favorable band; Figure 4 A schematic diagram showing the overlay of aeromagnetic gradient structure target area identification results and geological constraint factors; Figure 5 A diagram illustrating the priority classification and suggested verification directions for target area localization; Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] The purpose of this application is to provide a method for locating mineral exploration target areas based on aeromagnetic gradient structures that integrates geological constraints. This method aims to improve the objectivity, verifiability, and automation of locating mineral exploration target areas based on aeromagnetic anomalies, and to effectively distinguish between mineral-bearing anomalies and non-mineral-bearing anomalies that match geological constraints.

[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] In one exemplary embodiment, such as Figure 1 As shown, a method for locating mineral exploration target areas based on aeromagnetic gradient structures that incorporates geological constraints is provided, comprising the following steps.

[0025] Step 01: Obtain the UAV aeromagnetic measurement dataset and geological constraint parameters for the area to be measured.

[0026] As an optional implementation, in step 01, the UAV aeromagnetic measurement dataset includes measurement point records for each measurement point in the area to be measured; the measurement point records include coordinates, measured magnetic field values, flight altitude, sampling interval, flight path direction, and quality control markers.

[0027] Geological constraint parameters include the polygon of the favorable stratum or the buffer zone of the ore-bearing stratum, the strike of the favorable stratum, and the allowable distance between anomalous segments.

[0028] Specifically, geological constraint parameters include, but are not limited to, strata-controlling geological parameters (favorable strata or ore-bearing strata buffer zone polygons, favorable strata strike, and permissible anomalous segment connection distances). Structural control parameters (such as fault buffer zones, fold axis strikes) or lithological control parameters can also be selected based on the actual geological background. This embodiment uses strata-controlling geological parameters as a preferred implementation method for describing geological constraint parameters.

[0029] Step 02: Preprocess and grid the UAV aeromagnetic measurement dataset to generate a regular aeromagnetic grid, and separate the background field of the regular aeromagnetic grid to obtain the background field grid and the local outlier grid.

[0030] Among them, the grid cells of the regular aeromagnetic grid, the grid cells of the background field grid, and the grid cells of the local outlier grid correspond one-to-one.

[0031] As an optional implementation, step 02 includes the following steps.

[0032] Step 021: Based on the quality control markers, remove the measurement point records of measurement points marked as invalid records or gross errors from the UAV aeromagnetic measurement dataset to obtain the measurement point records of each valid measurement point.

[0033] Step 022: Use the sampling interval to check the distance between adjacent valid measurement points, resample the overly dense and overly sparse sampling segments, and obtain the measurement point record after resampling of each valid measurement point.

[0034] Step 023: Identify the flight path direction in the resampled measurement record of each effective measurement point to obtain the flight path noise direction. Based on the flight path noise direction, perform leveling processing on the resampled measurement record of each effective measurement point, and perform diurnal variation correction, international geomagnetic reference field correction and track check to obtain the corrected magnetic field value of each effective measurement point.

[0035] Step 024: Generate a regular aeromagnetic grid by using the Kriging interpolation method to obtain the corrected magnetic field values ​​of each effective measuring point.

[0036] Specifically, the search radius of the Kriging interpolation method can be 1 to 3 times the line distance, and the grid spacing is preferably 1 / 5 to 1 / 2 of the line distance; when the aeromagnetic line distance is 100m, the grid spacing is preferably 20m to 50m.

[0037] Step 025: Use a two-dimensional polynomial trend surface to fit a regular aeromagnetic grid to obtain the background field grid.

[0038] Step 026: Subtract the background field grid from the regular aeromagnetic grid to obtain the difference grid. Perform Gaussian smoothing on the difference grid to obtain the local outlier grid.

[0039] Step 03: Perform gradient calculation on the local outlier grid to obtain the horizontal gradient magnitude, gradient direction, boundary orientation direction, and vertical gradient magnitude of each grid cell in the local outlier grid.

[0040] As an optional implementation, step 03 includes the following steps.

[0041] Step 031: Using the central difference method, calculate the x-direction and y-direction horizontal derivatives of each grid cell in the local outlier grid.

[0042] Specifically, the formulas for calculating the horizontal derivatives in the x and y directions are as follows: ; ; in, Let x be the horizontal derivative of the i-th grid cell; This represents the local outlier at the location of the i-th grid cell, offset by one grid spacing along the x-direction. This represents the local outlier at the location where the i-th grid cell is negatively offset by one grid spacing along the x-direction; The grid spacing is in the x-direction; Let be the horizontal derivative in the y-direction of the i-th grid cell; This represents the local outlier at the location of the i-th grid cell, offset by one grid spacing along the y-direction. This represents the local outlier at the location where the i-th grid cell is negatively offset by one grid spacing along the y-direction. The grid spacing is in the y-direction.

[0043] Step 032: Calculate the horizontal gradient magnitude, gradient direction, and boundary orientation of the corresponding grid cell based on the x-axis and y-axis horizontal derivatives of each grid cell in the local outlier grid.

[0044] Specifically, the formulas for calculating the horizontal gradient magnitude, gradient direction, and boundary orientation are as follows: ; ; ; in, Let be the horizontal gradient magnitude of the i-th grid cell; Let be the gradient direction of the i-th grid cell; It is the arctangent function in the four quadrants; Let i be the boundary orientation direction of the i-th grid cell; It is 180 degrees.

[0045] Step 033: Using the first-order vertical derivative method in the frequency domain, calculate the vertical gradient magnitude of each grid cell in the local outlier grid.

[0046] Step 04: Threshold segmentation is performed on the local outlier grid. All grid cells that meet the threshold conditions are extracted from the local outlier grid to obtain multiple outlier grid cells. Connectivity labeling, patch merging, and noise removal are performed on each outlier grid cell to obtain multiple aeromagnetic gradient structure prospecting target areas. The object attributes of each aeromagnetic gradient structure prospecting target area are then determined.

[0047] The object attributes include structural identification parameters, boundary polygons, anomaly mean, centroid, intensity weighting center, direction of the major axis of the circumscribed ellipse, and anomaly sign; the structural identification parameters include area, perimeter, length of closed boundary, ratio of major to minor axis of the circumscribed ellipse, and anomaly extrema.

[0048] As an optional implementation, step 04 includes the following steps.

[0049] Step 041: Use the quantile threshold method to determine the positive and negative outlier thresholds.

[0050] Specifically, the positive outlier threshold is taken as the 95th percentile of all local outliers in the local outlier grid, and the negative outlier threshold is taken as the 5th percentile of all local outliers in the local outlier grid.

[0051] Step 042: Identify the grid cells in the local outlier grid that meet the threshold conditions as outlier grid cells; the threshold conditions are that the local outlier is greater than or equal to the positive outlier threshold or the local outlier is less than or equal to the negative outlier threshold; the positive outlier threshold and the negative outlier threshold are determined using the quantile threshold method.

[0052] Step 043: Mark the connected components of each abnormal grid cell to obtain multiple initial abnormal patches.

[0053] Specifically, 8-neighbor connected components are used to form initial abnormal patches.

[0054] Step 044: Merge adjacent initial abnormal patches whose boundary distance is less than the preset merging distance to obtain merged abnormal patches.

[0055] Specifically, adjacent patches with a boundary distance less than a preset distance dmerge are merged, with the preset distance preferably being 1 to 2 grid spacings.

[0056] Step 045: Remove abnormal patches with an area smaller than the preset area threshold to obtain multiple aeromagnetic gradient structure prospecting target areas.

[0057] Specifically, the preset area threshold is preferably the area of ​​3 grid units.

[0058] Step 046: Calculate the object attributes of each prospecting target area for each aeromagnetic gradient structure.

[0059] Step 05: Determine any aeromagnetic gradient structure prospecting target area as the current target area, and extend the current target area outward by a preset number of grid cells along the boundary of the current target area to obtain the corresponding outward extension boundary zone of the current target area.

[0060] Step 06: Based on the horizontal gradient magnitude, gradient direction, boundary orientation, intensity weighting center, and anomaly sign of each grid cell in the outer boundary zone corresponding to the current target area, determine the uniform convergence factor of the current target area.

[0061] Among them, the uniform convergence factor includes the directional uniformity factor and the direction convergence factor.

[0062] As an optional implementation, step 06 includes the following steps.

[0063] Step 061: Determine the directional consistency factor of the current target area based on the horizontal gradient magnitude and boundary orientation of each grid cell in the outer boundary zone corresponding to the current target area.

[0064] Specifically, the formula for calculating the directional consistency factor is as follows: ; ; in, Let be the directional consistency factor of the prospecting target area for the j-th aeromagnetic gradient structure. ; For the modulo operation of complex numbers; The total number of grid cells in the outer boundary zone of the prospecting target area of ​​the j-th aeromagnetic gradient structure; The gradient magnitude weight is the weight of the i-th grid cell; It is a natural exponential function; It is the square root function; Let be the horizontal gradient magnitude of the a-th grid cell.

[0065] Step 062: Determine the directional convergence factor of the current target region based on the horizontal gradient magnitude, gradient direction, intensity weighting center, and anomaly sign of each grid cell in the outer boundary zone corresponding to the current target region.

[0066] Specifically, the formula for calculating the convergence factor is as follows: ; in, Let be the directional convergence factor of the prospecting target area for the j-th aeromagnetic gradient structure. ; This is a function to find the maximum value. It is a cosine function; The intensity weighting center of the prospecting target area pointing from the i-th grid cell to the j-th aeromagnetic gradient structure. The azimuth angle; Let be the anomaly sign correction term for the j-th aeromagnetic gradient structure prospecting target area. When the anomaly sign of the j-th aeromagnetic gradient structure prospecting target area is a positive anomaly... Set to 0, when the anomaly sign of the j-th aeromagnetic gradient structure prospecting target area is a negative anomaly. Pick .

[0067] Step 07: Based on the structural identification parameters of the current target area, the directional convergence factor, the horizontal gradient magnitude of each grid cell in the current target area, and the regional background magnetic field value of each grid cell in the background field grid corresponding to the spatial position of the current target area, determine the morphological structure identification factor of the current target area.

[0068] Among them, the morphological structure identification factors include morphological matching degree, appropriate area factor and abnormal intensity factor.

[0069] As an optional implementation, step 07 includes the following steps.

[0070] Step 071: Determine the compactness of the current target area based on its area and perimeter.

[0071] Specifically, the formula for calculating compactness is: ; in, The compactness of the prospecting target area for the j-th aeromagnetic gradient structure; Let be the area of ​​the prospecting target area for the j-th aeromagnetic gradient structure; Let be the perimeter of the prospecting target area for the j-th aeromagnetic gradient structure.

[0072] Step 072: Determine the closure degree of the current target area based on the length and perimeter of the current target area's closed boundary.

[0073] Specifically, the formula for calculating the degree of closure is: ; in, Let be the closure degree of the prospecting target area of ​​the j-th aeromagnetic gradient structure; Let be the length of the closed boundary of the prospecting target area of ​​the j-th aeromagnetic gradient structure.

[0074] Step 073: Determine the boundary gradient continuity of the current target area based on the horizontal gradient magnitude of each grid cell in the current target area.

[0075] Specifically, boundary gradient continuity is calculated based on the proportion of boundary grid cells in the current target area whose horizontal gradient magnitude exceeds the 75th percentile.

[0076] Step 074: Determine the morphological matching degree of the current target region based on the ratio of major to minor axis, compactness, closure, boundary gradient continuity, and directional convergence factor of the circumscribed ellipse of the current target region.

[0077] Specifically, the formula for calculating morphological matching degree is: ; in, The morphological matching degree of the prospecting target area for the j-th aeromagnetic gradient structure; For normalization function, , To find the minimum value function, This is the preset base value for the ratio of the major and minor axes of the circumscribed ellipse. Take 3 to 6; Let represent the boundary gradient continuity of the prospecting target area for the j-th aeromagnetic gradient structure.

[0078] Step 075: Determine the appropriate area factor for the current target area based on its current area.

[0079] Specifically, the formula for calculating the appropriate area factor is: ; in, The suitable area factor for the prospecting target area of ​​the j-th aeromagnetic gradient structure; This is a preset, suitable area benchmark value; These are the preset area scale parameters.

[0080] Step 076: Determine the anomalous intensity factor of the current target area based on the anomalous extreme values ​​of the current target area and the regional background magnetic field values ​​of each grid cell corresponding to the spatial location of the current target area in the background field grid.

[0081] Specifically, the formula for calculating the anomaly intensity factor is as follows: ; in, Let be the anomalous intensity factor of the j-th aeromagnetic gradient structure prospecting target area; For the j-th aeromagnetic gradient structure prospecting target area, the extreme value is the abnormal value. The value represents the regional background magnetic field at the location corresponding to the prospecting target area of ​​the j-th aeromagnetic gradient structure in the background field grid.

[0082] Step 08: Determine the geological constraint factor of the current target area based on the geological constraint parameters, boundary polygon, area, major axis direction of the circumscribed ellipse, outlier extreme values, and direction consistency factor of the current target area.

[0083] Among them, geological constraint factors include stratigraphic coincidence factor, strike consistency factor, zonal continuity factor, and isolated or unrelated geological constraint penalty term.

[0084] Specifically, geological constraint factors are used to characterize the degree of matching between anomalies and geological control conditions. Different types of geological constraint parameters, such as strata control, structural control, or lithological control, can be selected for calculation based on the actual exploration object. This embodiment uses the stratigraphic coincidence factor, strike consistency factor, zonal continuity factor, and isolated or geological constraint deviation penalty term under the strata control constraint scenario as examples for illustration, and does not constitute the only limitation on the specific composition of geological constraint factors.

[0085] As an optional implementation, step 08 includes the following steps.

[0086] Step 081: Determine the stratigraphic fit factor of the current target area based on the area, boundary polygon, and favorable stratigraphic or ore-bearing stratigraphic buffer zone polygon of the current target area.

[0087] Specifically, the formula for calculating the stratigraphic fit factor is as follows: ; in, Let be the stratigraphic fit factor of the j-th aeromagnetic gradient structure prospecting target area; It is an area function; Let be the boundary polygon of the prospecting target area for the j-th aeromagnetic gradient structure; This is the spatial intersection operator; It is a polygonal shape representing a favorable stratum or a buffer zone of an ore-bearing stratum.

[0088] Step 082: Determine the strike consistency factor of the current target area based on the direction of the major axis of the circumscribed ellipse and the favorable stratigraphic strike.

[0089] Specifically, the formula for calculating the consistency factor is as follows: ; in, Let be the strike consistency factor of the j-th aeromagnetic gradient structure prospecting target area; Let be the direction of the major axis of the circumscribed ellipse of the prospecting target area of ​​the j-th aeromagnetic gradient structure. For favorable strata orientation.

[0090] Step 083: Determine the band continuity factor of the current target area based on the directional consistency factor, favorable stratigraphic direction, and allowable anomalous segment connection distance.

[0091] Specifically, the formula for calculating the band continuity factor is as follows: ; in, Let be the zonal continuity factor of the prospecting target area of ​​the j-th aeromagnetic gradient structure; For the prospecting target area of ​​the j-th aeromagnetic gradient structure, along the favorable stratigraphic strike The projected length; is the minimum distance between the prospecting target area of ​​the j-th aeromagnetic gradient structure and the adjacent anomaly segment in the same direction; This is the preset allowed abnormal segment connection distance.

[0092] Step 084: Based on the stratigraphic coincidence factor, strike consistency factor, zonal continuity factor, area, and extreme anomaly of the current target area, determine the isolated or unrelated geological constraint penalty term for the current target area.

[0093] Specifically, the formula for calculating the penalty for isolated or unrelated geological constraints is as follows: ; in, For the j-th aeromagnetic gradient structure prospecting target area, there is an isolated or unrelated geological constraint penalty term. The value range is 0 to 30, which is used to quantitatively reduce the priority of deviations from the stratigraphic level, inconsistent strikes, poor continuity, or isolated strong magnetic anomalies. For the isolated strong magnetic point index of the j-th aeromagnetic gradient structure prospecting target area, when Smaller than the preset area and The value is set to 1 if it exceeds the 95th percentile of all local anomalies in the prospecting target area of ​​the j-th aeromagnetic gradient structure, otherwise it is set to 0; the preset area is preferably the area of ​​5 to 10 grid cells.

[0094] Step 09: Determine the comprehensive score of the current target area based on the uniform convergence factor, morphological structure identification factor, geological constraint factor, and the horizontal and vertical gradient magnitudes of each grid cell in the current target area.

[0095] As an optional implementation, step 09 includes the following steps.

[0096] Step 091: Determine the first combination factor of the current target region based on the uniform convergence factor of the current target region.

[0097] Specifically, the formula for calculating the first combination factor is: ; in, is the first combination factor for the prospecting target area of ​​the j-th aeromagnetic gradient structure.

[0098] Step 092: Determine the second combination factor of the current target area based on the horizontal gradient magnitude and vertical gradient magnitude of each grid cell in the current target area.

[0099] Specifically, the second combination factor of the current target area is the normalized combination value of the horizontal gradient magnitude and the vertical gradient magnitude of each grid cell in the current target area.

[0100] Step 093: Perform a weighted summation of the first combination factor, the second combination factor, the morphological structure identification factor, and the geological constraint factor for the current target area to obtain the comprehensive score of the current target area.

[0101] Specifically, the formula for calculating the comprehensive score, i.e., the general scoring model, is as follows: ; in, The comprehensive score for the j-th aeromagnetic gradient structure prospecting target area; , , , , , and These are the weight coefficients of the corresponding factors, and the sum of all weight coefficients is 1.

[0102] Specifically, the comprehensive scoring formula is a general scoring framework used to express the comprehensive relationship between various gradient structure factors, morphological structure factors, and geological constraint factors. The weighting coefficients can be adjusted according to the target mineral type, metallogenic model, and geological background of the study area. Specific weight combinations are only preferred implementation methods and do not constitute a limitation on the scope of protection of this application.

[0103] Step 10: Determine the priority of the current target area based on its comprehensive score, area, closure degree, and geological constraint factors.

[0104] As an optional implementation, step 10 includes the following steps.

[0105] Step 101: If the current target area meets the advanced conditions, then the priority of the current target area is set to high priority; the advanced conditions are that the comprehensive score is greater than or equal to the first preset value and the layer matching factor and the orientation consistency factor are both greater than or equal to the second preset value.

[0106] Specifically, the first preset value is 80, and the second preset value is 0.60.

[0107] Step 102: If the current target area meets the first, second, or third intermediate conditions, then the priority of the current target area is determined to be medium priority. The first intermediate condition is that the comprehensive score is greater than or equal to the first preset value, the stratigraphic alignment factor is greater than or equal to the second preset value, but the trajectories consistency factor is less than the second preset value; the second intermediate condition is that the comprehensive score is greater than or equal to the first preset value, the trajectories consistency factor is greater than or equal to the second preset value, but the stratigraphic alignment factor is less than the second preset value; the third intermediate condition is that the comprehensive score is greater than or equal to the third preset value but less than the first preset value.

[0108] Specifically, the third preset value is 0.60.

[0109] Step 103: If the current target area meets the low-level condition, then the priority of the current target area is determined to be low priority; the low-level condition is that the comprehensive score is greater than or equal to the fourth preset value but less than the third preset value.

[0110] Specifically, the fourth preset value is 40.

[0111] Step 104: If the current target area meets the first, second, third, or fourth anomaly level conditions, then the priority of the current target area is determined as an anomaly to be excluded. The first anomaly level condition is that the comprehensive score is less than the fourth preset value; the second anomaly level condition is that the area is less than the preset minimum area; the third anomaly level condition is that the closure degree is less than the preset minimum closure degree; and the fourth anomaly level condition is that any of the geological constraint factors does not meet the corresponding preset range.

[0112] Step 11: Based on the priority of all aeromagnetic gradient structure prospecting target areas in the area to be tested, determine the target prospecting target areas corresponding to the area to be tested, and complete the prospecting target area location of the area to be tested.

[0113] Furthermore, after step 11, the output also includes high / medium / low priority target area classification results, the boundary polygon of the target prospecting target area, the centroid of the target prospecting target area, the recommended direction of ground magnetic or electrical resistivity tomography profiles, the preliminary borehole locations, and a list of anomalies to be excluded. For near-east-west trending stratabound ore bodies, the verification lines are mainly near-north-south trending, perpendicularly traversing the anomaly zone and its two sides' high gradient boundaries; the preliminary borehole locations are preferentially arranged in the beaded positive magnetic anomaly expansion areas within the high-priority anomaly zone, in locations with clear gradient boundaries and coinciding with favorable strata.

[0114] The method described in this application was used to classify and grade prospecting targets in the Yufengshan stratabound iron ore prospecting area based on aeromagnetic gradient structure. The comprehensive scoring of the Yufengshan stratabound magnetite-ilmenite orebody adopts a special case of the general scoring model: ; The diagrams in the grading process are as follows Figures 2-5 As shown, Figures 2-5In the diagram, the horizontal axis points eastward and the vertical axis points northward. Figures 2-5 This indicates that the Yufengshan aeromagnetic anomaly generally presents as an anomalous band extending from near east-west to slightly northeast in the central part. Locally, beaded positive magnetic anomalies appear within the anomalous band, forming a relatively clear gradient boundary together with the adjacent low magnetic background to the north. Within the stratabound favorable zone, the high gradient boundary mainly extends along a near east-west direction, and the gradient direction exhibits a clear boundary orientation on both sides of the anomalous band.

[0115] In the automatic classification results, H1 is a main east-west trending beaded-band magnetic anomaly in the central part, located within the favorable strata-boundary zone, with clear boundary gradients and good continuity, making it the highest priority target area; H2 is an anomaly segment extending along the same stratum in the west, although small in scale, its direction and stratum match well, making it a high priority verification target area; M1 is a local strong magnetic anomaly in the southeast, with a high amplitude but deviating from the favorable strata-boundary zone and its shape is relatively isolated, so it is adjusted to medium priority; M2 is a relatively wide and gentle background anomaly in the northeast, which may reflect deep or non-target strata magnetic bodies, and is not considered as the first choice ore body target area; L1 mainly reflects the low magnetic background or boundary transition on the north side of the anomaly zone, and is not considered as a direct prospecting target area.

[0116] Since the target ore body is generally distributed in a near-east-west zonal pattern, it is recommended that the ground magnetic survey, electrical method or trenching profile be mainly in a near-north-south direction, vertically crossing the H1 and H2 anomaly zones and their high gradient boundaries on both sides; the initial selection of borehole locations should be prioritized in the beaded positive magnetic anomaly expansion area within the H1 anomaly zone, in locations with clear gradient boundaries and coinciding with favorable strata.

[0117] In one exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for locating mineral exploration target areas based on aeromagnetic gradient structures that incorporate geological constraints.

[0118] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements a method for locating mineral exploration target areas using aeromagnetic gradient structures that incorporate geological constraints.

[0119] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements a method for locating mineral exploration target areas based on aeromagnetic gradient structures that incorporate geological constraints.

[0120] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for locating mineral exploration target areas based on aeromagnetic gradient structures that incorporate geological constraints.

[0121] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0122] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0123] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0124] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0125] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for locating mineral exploration target areas using aeromagnetic gradient structures that incorporate geological constraints, characterized in that, The method for locating mineral exploration target areas using aeromagnetic gradient structures that incorporate geological constraints includes: Acquire UAV aeromagnetic measurement dataset and geological constraint parameters for the area to be measured; The UAV aeromagnetic measurement dataset is preprocessed and gridded to generate a regular aeromagnetic grid. The regular aeromagnetic grid is then separated from the background field to obtain a background field grid and a local outlier grid. The grid cells of the regular aeromagnetic grid, the background field grid, and the local outlier grid correspond one-to-one. Gradient calculations are performed on the local outlier grid to obtain the horizontal gradient magnitude, gradient direction, boundary orientation direction, and vertical gradient magnitude of each grid cell in the local outlier grid. Threshold segmentation is performed on the local outlier grid. All grid cells that meet the threshold conditions are extracted from the local outlier grid to obtain multiple outlier grid cells. Connectivity labeling, patch merging, and noise removal are performed on each outlier grid cell to obtain multiple aeromagnetic gradient structure prospecting target areas. The object attributes of each aeromagnetic gradient structure prospecting target area are determined. The object attributes include structure identification parameters, boundary polygon, anomaly mean, centroid, intensity weighting center, major axis direction of the circumscribed ellipse, and anomaly sign. The structure identification parameters include area, perimeter, closed boundary length, major-minor axis ratio of the circumscribed ellipse, and anomaly extrema. Any aeromagnetic gradient structure prospecting target area is determined as the current target area. Along the boundary of the current target area, a preset number of grid cells are extended outward to obtain the corresponding outward boundary zone of the current target area. Based on the horizontal gradient magnitude, gradient direction, boundary orientation direction, intensity weighting center, and anomaly sign of each grid cell in the outer boundary zone corresponding to the current target area, the uniform convergence factor of the current target area is determined; the uniform convergence factor includes the directional uniformity factor and the pointing convergence factor; Based on the structural identification parameters of the current target area, the directional convergence factor, the horizontal gradient magnitude of each grid cell in the current target area, and the regional background magnetic field value of each grid cell in the background field grid corresponding to the spatial position of the current target area, the morphological structure identification factor of the current target area is determined; the morphological structure identification factor includes morphological matching degree, appropriate area factor, and anomaly intensity factor. Based on the geological constraint parameters, boundary polygon, area, major axis direction of the circumscribed ellipse, anomaly extremes, and orientation consistency factor of the current target area, the geological constraint factors of the current target area are determined. The geological constraint factors include stratigraphic coincidence factor, strike consistency factor, zonal continuity factor, and isolated or unrelated geological constraint penalty term. Based on the uniform convergence factor, morphological structure identification factor, geological constraint factor, and the horizontal and vertical gradient magnitudes of each grid cell in the current target area, the comprehensive score of the current target area is determined. Based on the comprehensive score, area, closure degree and geological constraint factors of the current target area, the priority of the current target area is determined; Based on the priority of all aeromagnetic gradient structure prospecting target areas in the area to be tested, the target prospecting target areas corresponding to the area to be tested are determined, and the prospecting target area of ​​the area to be tested is located.

2. The method for locating mineral exploration target areas using aeromagnetic gradient structures that incorporate geological constraints, as described in claim 1, is characterized in that... The UAV aeromagnetic measurement dataset includes measurement point records for each measurement point in the area to be measured; the measurement point records include coordinates, measured magnetic field values, flight altitude, sampling interval, flight path direction, and quality control markers; Geological constraint parameters include the polygon of the favorable stratum or the buffer zone of the ore-bearing stratum, the strike of the favorable stratum, and the allowable distance between anomalous segments.

3. The method for locating mineral exploration target areas using aeromagnetic gradient structures that integrate geological constraints, as described in claim 2, is characterized in that... The UAV aeromagnetic measurement dataset is preprocessed and gridded to generate a regular aeromagnetic grid. Background field separation is then performed on the regular aeromagnetic grid to obtain a background field grid and a local outlier grid, including: Based on the quality control markers, the measurement point records of the measurement points marked as invalid records or gross errors are removed from the UAV aeromagnetic measurement dataset to obtain the measurement point records of each valid measurement point. The distance between adjacent valid measurement points is checked using the sampling interval. Overly dense and sparse sampling segments are resampled to obtain the measurement point record after resampling of each valid measurement point. The flight path direction in the resampled measurement record of each effective measurement point is identified to obtain the flight path noise direction. The resampled measurement record of each effective measurement point is leveled according to the flight path noise direction, and diurnal variation correction, international geomagnetic reference field correction and track check are performed to obtain the corrected magnetic field value of each effective measurement point. The corrected magnetic field values ​​of each effective measuring point are used to generate a regular aeromagnetic grid using the Kriging interpolation method. A two-dimensional polynomial trend surface was used to fit a regular aeromagnetic grid to obtain the background field grid; Subtracting the background field grid from the regular aeromagnetic grid yields the difference grid. Gaussian smoothing is then applied to the difference grid to obtain the local outlier grid.

4. The method for locating mineral exploration target areas using aeromagnetic gradient structures that incorporate geological constraints, as described in claim 1, is characterized in that... Gradient calculations are performed on the local outlier mesh to obtain the horizontal gradient magnitude, gradient direction, boundary orientation direction, and vertical gradient magnitude of each mesh cell in the local outlier mesh, including: The central difference method is used to calculate the x-axis and y-axis horizontal derivatives of each grid cell in the local outlier grid. Calculate the horizontal gradient magnitude, gradient direction, and boundary orientation of each grid cell in the local outlier grid based on the x-axis and y-axis horizontal derivatives of each grid cell. The vertical gradient magnitude of each grid cell in the local outlier grid is calculated using the first-order vertical derivative method in the frequency domain.

5. The method for locating mineral exploration target areas using aeromagnetic gradient structures that incorporate geological constraints, as described in claim 1, is characterized in that... Threshold segmentation is performed on the local outlier grid. All grid cells satisfying the threshold condition are extracted from the local outlier grid to obtain multiple outlier grid cells. Connectivity labeling, patch merging, and noise removal are then performed on each outlier grid cell to obtain multiple aeromagnetic gradient structure prospecting target areas. The object attributes of each aeromagnetic gradient structure prospecting target area are then determined, including: The quantile threshold method is used to determine the positive and negative anomaly thresholds; Mesh cells in the local outlier mesh that meet the threshold condition are identified as outlier mesh cells; the threshold condition is that the local outlier is greater than or equal to the positive outlier threshold or the local outlier is less than or equal to the negative outlier threshold; the positive outlier threshold and the negative outlier threshold are determined using the quantile threshold method; Each anomalous grid cell is labeled with its connected components to obtain multiple initial anomalous patches; Merge adjacent initial abnormal patches whose boundary distance is less than the preset merging distance to obtain merged abnormal patches; Abnormal patches with an area smaller than a preset area threshold are removed to obtain multiple prospecting target areas with aeromagnetic gradient structures. Calculate the object attributes of each prospecting target area with aeromagnetic gradient structure.

6. The method for locating mineral exploration target areas using aeromagnetic gradient structures that incorporate geological constraints, as described in claim 1, is characterized in that... Based on the horizontal gradient magnitude, gradient direction, boundary orientation, intensity weighting center, and anomaly sign of each grid cell in the outer boundary zone corresponding to the current target region, the uniform convergence factor of the current target region is determined, including: The directional consistency factor of the current target area is determined based on the horizontal gradient magnitude and boundary orientation of each grid cell in the outer boundary zone corresponding to the current target area. Based on the horizontal gradient magnitude, gradient direction, intensity weighting center, and anomaly sign of each grid cell in the outer boundary zone corresponding to the current target area, determine the directional convergence factor of the current target area.

7. The method for locating mineral exploration target areas using aeromagnetic gradient structures that incorporate geological constraints, as described in claim 1, is characterized in that... Based on the structural identification parameters of the current target area, the convergence factor, the horizontal gradient magnitude of each grid cell in the current target area, and the regional background magnetic field value of each grid cell in the background field corresponding to the spatial location of the current target area, the morphological structure identification factors of the current target area are determined, including: Determine the compactness of the current target area based on its area and perimeter; Determine the degree of closure of the current target area based on the length and perimeter of the current target area's closed boundary; The boundary gradient continuity of the current target area is determined based on the horizontal gradient magnitude of each grid cell in the current target area. The morphological matching degree of the current target region is determined based on the ratio of major to minor axis, compactness, closure, boundary gradient continuity, and directional convergence factor of the circumscribed ellipse of the current target region. Determine the appropriate area factor for the current target area based on its current area. The abnormal intensity factor of the current target area is determined based on the abnormal extreme value of the current target area and the regional background magnetic field value of each grid cell corresponding to the spatial position of the current target area in the background field grid.

8. The method for locating mineral exploration target areas using aeromagnetic gradient structures that incorporate geological constraints, as described in claim 1, is characterized in that... Based on the current geological constraint parameters, boundary polygon, area, major axis direction of the circumscribed ellipse, outlier extreme values, and orientation consistency factor of the target area, the geological constraint factors of the current target area are determined, including: Based on the area, boundary polygon, and favorable stratigraphic or ore-bearing stratigraphic buffer zone polygon of the current target area, determine the stratigraphic fit factor of the current target area; Based on the direction of the major axis of the circumscribed ellipse of the current target area and the favorable stratigraphic orientation, determine the orientation consistency factor of the current target area; Based on the directional consistency factor, favorable stratigraphic strike, and allowable anomalous segment connection distance of the current target area, determine the zonal continuity factor of the current target area; Based on the stratigraphic coincidence factor, strike consistency factor, banded continuity factor, area, and extreme anomalies of the current target area, determine the isolated or unrelated geological constraint penalty terms for the current target area.

9. The method for locating mineral exploration target areas using aeromagnetic gradient structures that incorporate geological constraints, as described in claim 1, is characterized in that... Based on the current target area's uniform convergence factor, morphological structure identification factor, geological constraint factor, and the horizontal and vertical gradient magnitudes of each grid cell within the current target area, a comprehensive score for the current target area is determined, including: Based on the uniform convergence factor of the current target region, determine the first combination factor of the current target region; The second combination factor of the current target area is determined based on the horizontal gradient magnitude and vertical gradient magnitude of each grid cell in the current target area. The first combination factor, the second combination factor, the morphological structure identification factor, and the geological constraint factor of the current target area are weighted and summed to obtain the comprehensive score of the current target area.

10. The method for locating mineral exploration target areas using aeromagnetic gradient structures that incorporate geological constraints, as described in claim 1, is characterized in that... Based on the current target area's comprehensive score, area, closure, and geological constraint factors, the priority of the current target area is determined, including: If the current target area meets the advanced conditions, the priority of the current target area is set to high priority; the advanced conditions are that the comprehensive score is greater than or equal to the first preset value and the stratigraphic matching factor and the orientation consistency factor are both greater than or equal to the second preset value. If the current target area meets the first intermediate condition, the second intermediate condition, or the third intermediate condition, then the priority of the current target area is determined to be medium priority. The first intermediate condition is that the comprehensive score is greater than or equal to the first preset value, the stratigraphic consistency factor is greater than or equal to the second preset value, but the trajectories consistency factor is less than the second preset value; the second intermediate condition is that the comprehensive score is greater than or equal to the first preset value, the trajectories consistency factor is greater than or equal to the second preset value, but the stratigraphic consistency factor is less than the second preset value; the third intermediate condition is that the comprehensive score is greater than or equal to the third preset value but less than the first preset value. If the current target area meets the low-level condition, then the priority of the current target area is set to low priority; the low-level condition is that the comprehensive score is greater than or equal to the fourth preset value but less than the third preset value. If the current target area meets the first, second, third, or fourth anomaly level conditions, then the priority of the current target area is determined as an anomaly to be excluded. The first anomaly level condition is that the comprehensive score is less than the fourth preset value; the second anomaly level condition is that the area is less than the preset minimum area; the third anomaly level condition is that the closure degree is less than the preset minimum closure degree; and the fourth anomaly level condition is that any of the geological constraint factors does not meet the corresponding preset range.

Images