A method and device for ore body positioning based on multi-scale aerial magnetic method hierarchical anomaly mapping and spatial weighting

Abstract

The application discloses a kind of ore body positioning method and device based on multiscale aerial magnetic method grading anomaly mapping and spatial weighting, it is related to mineral resources geophysical exploration and magnetic data processing interpretation technical field, the method includes obtaining the aerial magnetic method of whole area in study area, local key area aerial magnetic method and ground magnetic survey profile data, after being preprocessed, it is mapped to unified space grid;The anomaly feature extraction is carried out to whole area aerial magnetic method data, and regional grade prospecting prior map is constructed;Extract candidate key area;Determine aerial magnetic method fine check deployment area;Local high-resolution anomaly feature extraction is carried out to aerial magnetic method fine check deployment area, and local ore body position update map is constructed;The regional grade prospecting prior map and local ore body position update map are spatially weighted and fused;Boundary correction factor is calculated based on ground magnetic survey profile data;The ore body position credibility after fusion is boundary corrected;Final result is generated.The application can realize multiscale magnetic survey data automatic collaborative interpretation and ore body positioning.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration and magnetic data processing and interpretation of mineral resources, and in particular to a method and apparatus for locating ore bodies based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting. Background Technology

[0002] Magnetic exploration is one of the most commonly used geophysical methods in mineral resource exploration. In current work, airborne magnetic surveys are typically used for regional anomaly identification and delineation of prospective mineral areas, while surface magnetic surveys are usually used for local anomaly verification and profile interpretation. Drilling data is used for the final verification of ore body locations. With the development of unmanned aerial vehicle (UAV) technology, airborne magnetic surveys have gradually expanded from traditional small-scale measurements of 1:50,000 to 1:10,000 to more refined exploration at scales of 1:2,000 and even larger. For example, the multi-rotor UAV airborne full-axis magnetic gradient measurement device developed by the Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, has been applied in large-scale, small-area airborne magnetic surveys. This technological advancement has made acquiring multi-scale magnetic data a routine technique. In practice, exploration work typically follows the technical logic of "regional first, local focus, and ground verification": first, 1:10,000 scale airborne magnetic surveys are used to delineate prospective mineralization areas; then, 1:2,000 scale UAV airborne magnetic surveys are conducted in key areas to determine the location of ore bodies; and finally, high-precision ground magnetic surveys are used to define the boundaries of the ore bodies. However, how to organically integrate and synergistically interpret magnetic data at these three scales remains a challenging problem for geological exploration technicians.

[0003] Several existing technologies have been explored for the comprehensive utilization of multi-scale magnetic survey data. For example, a method and system for exploring rich iron ore using multiple mutually constraining exploration methods has been developed. This method integrates and compares the results of airborne magnetic and airborne gravity surveys, achieving rich iron ore exploration through a process of "airborne geophysical anomaly selection → ground geophysical anomaly selection → geophysical profile anomaly point selection → borehole logging point verification." This method emphasizes the mutual constraints of multiple methods (magnetic + gravity + electrical), with its core lying in the cross-validation between data from different physical fields.

[0004] Furthermore, existing technologies also include solutions for fusing airborne magnetic data with ground-based magnetic survey data. For example, the air-to-ground combined high-precision magnetic survey method combines UAV airborne magnetic surveys with ground-based manual magnetic surveys. Ground-based magnetic survey data is used to correct the airborne magnetic survey data, thereby improving the accuracy of airborne magnetic measurements. The core of this method lies in data correction, that is, using high-precision ground-based data as a benchmark to improve the quality of airborne data.

[0005] In summary, existing technologies have the following shortcomings in the comprehensive utilization of multi-scale magnetic survey data: First, existing methods often emphasize the combination of multiple methods or accuracy correction, lacking a quantitative fusion mechanism for single magnetic methods under different scales, carriers, and coverage conditions. Second, the connection between existing airborne and ground magnetic methods largely relies on manual experience interpretation, typically exhibiting a workflow of "large-scale first, small-scale second; airborne first, ground second," lacking repeatable anomaly mapping rules and spatial reliability calculation methods. Third, existing methods mostly remain at the level of "qualitative combination," failing to establish a quantitative mapping model from regional anomalies to local anomalies and then to boundary constraints, and also making it difficult to achieve automatic assessment of the reliability of ore body locations. Summary of the Invention

[0006] The purpose of this application is to provide a method and apparatus for ore body location based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting, which can realize automated collaborative interpretation of multi-scale magnetic survey data and ore body location.

[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for orebody localization based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting, including: We acquired airborne magnetic data for the entire study area, airborne magnetic data for key local areas, and ground magnetic profile data, and preprocessed them to obtain the preprocessed data.

[0008] A unified spatial grid is established, and the preprocessed data is mapped to the unified spatial grid to obtain the mapped grid.

[0009] Based on the mapped grid, regional-level anomaly features are extracted from the aeromagnetic data of the entire study area, and after normalization, a regional-level mineral exploration prior map is constructed.

[0010] Based on the aforementioned regional prospecting prior map, candidate key areas are extracted according to a set regional anomaly threshold. Based on the candidate key areas, the deployment area for detailed airborne magnetic surveys was determined.

[0011] Based on the airborne magnetic data of the key local areas, local high-resolution anomaly features are extracted from the airborne magnetic survey deployment area, and after normalization, a local ore body location update map is constructed.

[0012] The regional prospecting prior map and the local ore body location update map are spatially weighted and fused to obtain the ore body location reliability after airborne magnetic method classification and fusion.

[0013] Based on the mapped grid, abnormal extreme points and gradient extreme points are identified in the ground magnetic profile data, projected onto the unified spatial grid, and influence zones are established on both sides of the ground magnetic profile data. For grid cells within the influence zones, boundary correction factors are calculated.

[0014] Based on the boundary correction factor, the confidence level of the ore body location after the airborne magnetic method classification and fusion is corrected by boundary correction to obtain the final confidence level of the ore body location.

[0015] Based on the credibility of the final ore body location, the most likely location map of the ore body, the ore body boundary line, the target area level map, the suggested direction of the ground survey line layout, the suggested borehole verification location, and the abnormal amplitude, area, boundary constraint strength and credibility score of each target area are generated.

[0016] Optionally, the expression for normalizing regional-level anomaly features is: Norm(X)=(X-X_min) / (X_max-X_min); Where Norm(X) is the normalized regional-level anomaly feature; X is the regional-level anomaly feature; X_min is the minimum regional-level anomaly feature; and X_max is the maximum regional-level anomaly feature.

[0017] Optionally, the expression for the regional-level mineral exploration prior map is: P_1(x,y)=w_a·A_1+w_r·R_1+w_h·HG_1+w_v·VD_1+w_as·AS_1+w_s·S_1+w_c·C_1+w_f·F_1; Wherein, P_1(x,y) is the regional-level prospecting prior map; A_1 is the regional-level magnetic anomaly amplitude; R_1 is the regional-level residual anomaly; HG_1 is the regional-level horizontal gradient magnitude; VD_1 is the regional-level first-order vertical derivative; AS_1 is the regional-level analytical signal; S_1 is the anomaly closure area; C_1 is the anomaly continuity; F_1 is the spatial relationship factor with known rock masses, faults, or mineralization points; w_a is the regional-level feature weight corresponding to the regional-level magnetic anomaly amplitude; w_r is the regional-level feature weight corresponding to the regional-level residual anomaly; w_h is the regional-level feature weight corresponding to the regional-level horizontal gradient magnitude; w_v is the regional-level feature weight corresponding to the regional-level first-order vertical derivative; w_as is the regional-level feature weight corresponding to the regional-level analytical signal; w_s is the regional-level feature weight corresponding to the anomaly closure area; w_c is the regional-level feature weight corresponding to the anomaly continuity; and w_f is the regional-level feature weight corresponding to the spatial relationship factor with known rock masses, faults, or mineralization points.

[0018] Optionally, the expression for the local orebody location update map is: P_2(x,y)=u_a·A_2+u_r·R_2+u_h·HG_2+u_v·VD_2+u_as·AS_2+u_e·E_2+u_l·L_2+u_m·M_2+u_f·F_2; Wherein, P_2(x,y) is the local orebody location update map; A_2 is the local magnetic anomaly amplitude; R_2 is the local residual anomaly; HG_2 is the local horizontal gradient magnitude; VD_2 is the local first-order vertical derivative; AS_2 is the local analytical signal; E_2 is the anomaly center stability; L_2 is the anomaly long axis direction consistency; M_2 is the anomaly closure morphology factor; F_2 is the spatial correspondence factor; u_a is the local anomaly feature weight corresponding to the local magnetic anomaly amplitude; u_r is the local anomaly feature weight corresponding to the local residual anomaly; u_h is the local anomaly feature weight corresponding to the local horizontal gradient magnitude; u_v is the local anomaly feature weight corresponding to the local first-order vertical derivative; u_as is the local anomaly feature weight corresponding to the local analytical signal; u_e is the local anomaly feature weight corresponding to the anomaly center stability; u_l is the local anomaly feature weight corresponding to the anomaly long axis direction consistency; u_m is the local anomaly feature weight corresponding to the anomaly closure morphology factor; and u_f is the local anomaly feature weight corresponding to the spatial correspondence factor.

[0019] Optionally, the expression for the reliability of the ore body location after the airborne magnetic classification and fusion is: P_12(x,y)=[1-I_2k(x,y)]·P_1(x,y)+I_2k(x,y)·[α·P_1(x,y)+β·P_2(x,y)]; Wherein, P_12(x,y) is the reliability of the ore body location after the airborne magnetic method classification and fusion; α is the regional anomaly weight, β is the local anomaly weight; I_2k(x,y) is the coverage indicator function; P_1(x,y) is the regional prospecting prior map; and P_2(x,y) is the local ore body location update map.

[0020] Optionally, the formula for calculating the boundary correction factor is: P_g(x,y)=exp[-d_p 2 / (2σ_p 2 )]·exp[-d_b 2 / (2σ_b 2 )]·Norm(|dM(s) / ds|); Where P_g(x,y) is the boundary correction factor; d_p is the vertical distance from the grid cell to the profile; d_b is the distance from the grid cell to the nearest geomagnetic boundary constraint point; σ_p is the profile influence zone attenuation parameter; σ_b is the boundary distance attenuation parameter; dM(s) / ds is the first derivative along the profile direction; M(s) is the magnetic anomaly; and s is the distance along the profile.

[0021] Optionally, the expression for the confidence level of the final ore body location is: P_final(x,y)=[1-I_g(x,y)]·P_12(x,y)+I_g(x,y)·[λ·P_12(x,y)+γ·P_g(x,y)]; Wherein, P_final(x,y) is the final ore body location confidence; I_g(x,y) is the influence zone indicator function of the ground magnetic profile; P_12(x,y) is the ore body location confidence after air magnetic method graded fusion; λ is the weight of the air magnetic method fusion result; γ is the geomagnetic boundary correction weight; and P_g(x,y) is the boundary correction factor.

[0022] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for ore body location based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting.

[0023] Thirdly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for ore body location based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting.

[0024] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for ore body location based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting.

[0025] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method and apparatus for ore body location based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting. It acquires airborne magnetic data for the entire study area, airborne magnetic data for key local areas, and ground magnetic profile data, and preprocesses the data to obtain preprocessed data. A unified spatial grid is established, and the preprocessed data is mapped onto this grid to obtain a mapped grid. This method solves the core fundamental problems of spatial benchmark mismatch and inability to perform collaborative calculations for multi-scale magnetic data, providing a unified computational carrier for subsequent graded anomaly mapping and spatial weighting calculations. Based on the mapped grid, regional anomaly features are extracted from the airborne magnetic data of the entire study area and normalized to construct a regional prospecting prior map. This transforms traditional manual qualitative delineation of prospective areas into a quantifiable prospecting probability layer with a global grid, eliminating subjective interpretation differences and enabling objective, continuous, and quantitative evaluation of the mineralization potential of the entire study area. Based on the regional prospecting prior map, candidate key areas are extracted according to a set regional anomaly threshold. This enables standardized, repeatable, and automatic screening from regional prospective areas to local detailed exploration areas, significantly reducing the randomness and risk of missed ore deposits in manual delineation. Based on the candidate key areas, the deployment area for detailed airborne magnetic surveys is determined. This allows for precise identification of the large-scale UAV airborne magnetic survey range, eliminating the need for costly large-scale airborne magnetic surveys across the entire region, significantly reducing the fieldwork area, exploration costs, and operation cycle of UAV airborne magnetic surveys. Based on the airborne magnetic data from the local key areas, high-resolution anomaly features are extracted from the deployment area and normalized to construct a local orebody location update map. This fully leverages the high-precision details of large-scale UAV airborne magnetic surveys, compensating for insufficient resolution in regional airborne magnetic surveys and the difficulty in identifying minor orebody anomalies, thus improving the accuracy of local orebody location. Spatially weighted fusion of the regional prospecting prior map and the local orebody location update map yields the reliability of the orebody location after hierarchical fusion using airborne magnetic surveys. This approach balances overall coverage integrity with local location accuracy, solving the problem of inconsistent coverage across multi-scale airborne magnetic data that prevents quantitative fusion. Based on the mapped grid, abnormal extreme points and gradient extreme points are identified in the ground magnetic survey profile data, projected onto the unified spatial grid, and influence zones are established on both sides of the ground magnetic survey profile data. For grid cells within the influence zones, boundary correction factors are calculated. Only a small number of ground magnetic surveys need to be deployed along the key profiles to generate boundary constraint parameters usable throughout the entire domain, eliminating the need for full-coverage ground magnetic surveys and significantly reducing the workload of ground exploration in the field. Based on the boundary correction factors, the reliability of the ore body location after the graded fusion of airborne magnetic methods is corrected to obtain the final ore body location reliability; this can improve the accuracy of ore body boundary and ore-bearing center positioning.Based on the reliability of the final orebody location, the most probable orebody location map, orebody boundary line, target area level map, suggested ground survey line layout direction, suggested borehole verification location, and anomaly amplitude, area, boundary constraint strength, and reliability score of each target area are generated. This provides objective and directly implementable quantitative decision-making basis for subsequent field ground construction and borehole verification. This application completely changes the traditional magnetic exploration mode that relies on manual experience and segmented interpretation without increasing the workload of field exploration. It simultaneously achieves quantitative evaluation of the overall mineralization potential, high-precision positioning of local orebody, and precise constraint of orebody boundaries, outputting standardized quantitative target area results that can be directly used for drilling deployment. This reduces human interpretation errors, shortens exploration costs and cycles, and significantly improves the accuracy of magnetic orebody exploration and positioning and the rationality of engineering deployment. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, 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.

[0027] Figure 1 This is an application environment diagram of a orebody location method based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting, according to one embodiment of this application. Figure 2 A flowchart illustrating a method for locating ore bodies based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting, provided as an embodiment of this application; Figure 3 A schematic diagram of unified mesh and local update under inconsistent coverage conditions provided in an embodiment of this application; Figure 4 This is a schematic diagram of geomagnetic profile boundary correction provided in an embodiment of this application; Figure 5 A 1:10000 regional prospecting prior map of the Niu Mao Quan area provided in an embodiment of this application; Figure 6 A 1:2000 local high-resolution updated map of the Niumaoquan area provided in an embodiment of this application; Figure 7 This application provides a spatially weighted fusion map of hierarchical anomalies in the Niumaoquan area, as an embodiment of the present application. Figure 8 A boundary correction factor map of the geomagnetic profile in the Niumaoquan area provided in an embodiment of this application; Figure 9A reliability map of the final orebody location before borehole feedback in the Niu Mao Quan area is provided as an embodiment of this application; Figure 10 A target grading map of the Niumaoquan area before borehole feedback is provided as an embodiment of this application; Figure 11 This is a final reliability diagram of the PCZK001 borehole constraint optimization provided in an embodiment of this application; Figure 12 This is a target classification map after drilling constraint optimization provided in an embodiment of this application using PCZK001; Figure 13 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0028] 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.

[0029] This application pertains to magnetic orebody location methods, specifically involving a method for mapping 1:10000 airborne magnetic data, 1:2000 large-scale UAV airborne magnetic data, and ground magnetic profile data to a unified spatial grid, and calculating the orebody location reliability and boundary constraint results through spatial weighting.

[0030] This method is applicable to the exploration and location of magnetic ore bodies, rock masses containing magnetic minerals, or mineralized zones. It is especially suitable for mining areas where regional airborne magnetic data has delineated anomalies, local large-scale UAV airborne magnetic data is available, and ground magnetic profiles or borehole verification data are already available.

[0031] Compared with existing technologies, this application focuses on the multi-scale application of a single magnetic method, namely, establishing a collaborative mechanism among magnetic survey data at three scales: 1:10,000 airborne magnetics, 1:2,000 UAV airborne magnetics, and ground-based magnetic surveys. This approach offers a more refined and focused technical path. The application clearly defines the functional roles: the regional-scale 1:10,000 airborne magnetics is responsible for delineating the prospecting area; the large-scale UAV airborne magnetics is responsible for locating the ore body; and ground-based magnetic surveys are responsible for defining the ore body boundaries—these three roles form a clearly defined, progressive relationship, rather than a simple accuracy correction relationship.

[0032] The technical problem to be solved in this application is: addressing the inconsistencies in spatial coverage, data resolution, and interpretation functions of existing multi-scale magnetic data, and the fact that they often rely on manual experience for connection, this application proposes a ore body location method based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting, which enables quantitative and repeatable processing from delineation of regional prospecting areas and determination of local ore body locations to correction of ground profile boundaries.

[0033] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] Example 1: The orebody localization method based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting provided in this application can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on other servers. Terminal 102 can send the acquired aeromagnetic data of the entire study area, aeromagnetic data of key local areas, and ground magnetic profile data to server 104. After receiving this data, server 104 preprocesses it to obtain preprocessed data; establishes a unified spatial grid and maps the preprocessed data to the unified spatial grid to obtain a mapped grid; based on the mapped grid, it extracts regional-level anomaly features from the aeromagnetic data of the entire study area, normalizes it, and constructs a regional-level prospecting prior map; based on the regional-level prospecting prior map, it extracts candidate key areas according to a set regional anomaly threshold; based on the candidate key areas, it determines the aeromagnetic detailed survey deployment area; based on the aeromagnetic data of the key local areas, it extracts local high-resolution anomaly features from the aeromagnetic detailed survey deployment area, and... After normalization, a local orebody location update map is constructed. The regional exploration prior map and the local orebody location update map are spatially weighted and fused to obtain the orebody location confidence after airborne magnetic survey grading and fusion. Based on the mapped grid, anomaly extreme points and gradient extreme points are identified in the ground magnetic survey profile data, projected onto the unified spatial grid, and influence zones are established on both sides of the ground magnetic survey profile data. For grid cells within the influence zones, boundary correction factors are calculated. Based on the boundary correction factors, the orebody location confidence after airborne magnetic survey grading and fusion is boundary corrected to obtain the final orebody location confidence. Based on the final orebody location confidence, the most probable orebody location map, orebody boundary line, target area level map, suggested ground survey line layout direction, suggested borehole verification location, and the anomaly amplitude, area, boundary constraint strength, and confidence score of each target area are generated. Server 104 can feed back the most likely location map of the ore body, the boundary line of the ore body, the target area level map, the suggested direction of the ground survey line layout, the suggested borehole verification location, and the abnormal amplitude, area, boundary constraint strength and confidence score of each target area to terminal 102.Furthermore, in some embodiments, the orebody location method based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly perform orebody location based on graded anomaly mapping and spatial weighting for the airborne magnetic data of the entire study area, the airborne magnetic data of the local key areas, and the ground magnetic profile data. Alternatively, the server 104 can obtain the airborne magnetic data of the entire study area, the airborne magnetic data of the local key areas, and the ground magnetic profile data from the data storage system, and perform orebody location based on graded anomaly mapping and spatial weighting for the airborne magnetic data of the entire study area, the airborne magnetic data of the local key areas, and the ground magnetic profile data.

[0035] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0036] Example 2: In one exemplary embodiment, such as Figure 2 As shown, a method for orebody location based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the following steps are included: S1: Acquire airborne magnetic data for the entire study area, airborne magnetic data for key local areas, and ground magnetic profile data, and perform preprocessing to obtain preprocessed data.

[0037] S2: Establish a unified spatial grid and map the preprocessed data to the unified spatial grid to obtain the mapped grid.

[0038] S3: Based on the mapped grid, regional-level anomaly features are extracted from the aeromagnetic data of the entire study area, and after normalization, a regional-level prospecting prior map is constructed.

[0039] S4: Based on the aforementioned regional prospecting prior map, candidate key areas are extracted according to the set regional anomaly threshold.

[0040] S5: Based on the candidate key areas, determine the deployment area for detailed airborne magnetic survey.

[0041] S6: Based on the aeromagnetic data of the local key areas, local high-resolution anomaly features are extracted from the aeromagnetic detailed survey deployment area, and after normalization, a local ore body location update map is constructed.

[0042] S7: Spatially weighted fusion of the regional prospecting prior map and the local ore body location update map is performed to obtain the ore body location reliability after airborne magnetic method classification and fusion.

[0043] S8: Based on the mapped grid, identify abnormal extreme points and gradient extreme points in the ground magnetic profile data, project them onto the unified spatial grid, and establish influence zones on both sides of the ground magnetic profile data. For grid cells within the influence zones, calculate boundary correction factors.

[0044] S9: Based on the boundary correction factor, perform boundary correction on the ore body location confidence after the airborne magnetic method classification and fusion to obtain the final ore body location confidence.

[0045] S10: Based on the credibility of the final ore body location, generate the most likely location map of the ore body, the ore body boundary line, the target area level map, the suggested direction of the ground survey line layout, the suggested borehole verification location, and the abnormal amplitude, area, boundary constraint strength and credibility score of each target area.

[0046] Implementing steps S1 to S10 above is not simply about superimposing different magnetic data, nor is it about manually interpreting data in the conventional order of "regional - local - ground". Instead, it involves mapping magnetic anomaly data of different scales and coverage areas into a unified spatial grid and calculating the reliability of the ore body location based on the data scale, spatial location, anomaly characteristics, and profile constraint relationships.

[0047] More specifically, this application can be divided into the following steps: (1) Data acquisition and preprocessing.

[0048] We acquired 1:10,000 airborne magnetic data for the study area, 1:2,000 large-scale UAV airborne magnetic data for key local areas, and ground magnetic profile data. The 1:10,000 airborne magnetic data was used for regional anomaly identification across the entire area; the 1:2,000 large-scale UAV airborne magnetic data was used for detailed local investigation of candidate key areas; and the ground magnetic profile data was used to correct anomaly centers and orebody boundaries along key profiles.

[0049] The above data were subjected to coordinate unification, diurnal variation correction, normal field correction, leveling, fine-tuning, anomaly separation, gridding, and anomaly enhancement processing. Anomaly enhancement processing may include pole transformation, analytical signals, horizontal gradient modulus, first-order vertical derivative, tilt derivative, or other methods that can enhance the anomaly center and boundary. For low-latitude regions or regions with remanent magnetization, analytical signals, magnetic anomaly modulus, normalized source strength, or gradient-like parameters can be preferentially used to reduce the influence of magnetization direction on anomaly location determination.

[0050] The selectable parameter ranges are as follows: the side length Δ of the uniform grid unit can be 5m to 50m, preferably 10m to 25m; the local grid for 1:2000 UAV airborne magnetic survey can be 5m to 10m; the spacing between measuring points in the ground magnetic profile can be 2m to 20m, preferably 5m to 10m; the width Dg of the influence zone in the geomagnetic profile can be 20m to 200m, preferably 50m to 100m. These parameters can be adjusted according to the survey line spacing, flight altitude, terrain conditions, and ore body scale.

[0051] (2) Establish a unified spatial grid.

[0052] A unified spatial grid G ​​is established in the study area, with grid cells denoted as cell_i and corresponding planar coordinates (x_i, y_i). 1:10000 airborne magnetic data is interpolated or resampled onto the unified spatial grid to form a regional magnetic anomaly grid covering the entire area. 1:2000 UAV airborne magnetic data is mapped to grid cells within its local coverage area. Ground magnetic profiles are projected onto the unified spatial grid according to their location, orientation, and influence zone width.

[0053] Define I_2k(x,y) as the 1:2000 UAV airborne magnetic survey coverage indication function. I_2k(x,y) = 1 when the grid cell is within the 1:2000 coverage area; otherwise, I_2k(x,y) = 0. Define I_g(x,y) as the influence zone indication function for the ground magnetic profile. I_g(x,y) = 1 when the grid cell is within the influence zone of the geomagnetic profile; otherwise, I_g(x,y) = 0.

[0054] (3) Regional anomaly mapping and mineral exploration prior map generation.

[0055] Regional anomaly features are extracted based on 1:10000 airborne magnetic data. These features include, but are not limited to: regional magnetic anomaly amplitude A_1, regional residual anomaly R_1, regional horizontal gradient magnitude HG_1, regional first-order vertical derivative VD_1, regional analytical signal AS_1, anomaly closure area S_1, anomaly continuity C_1, and spatial relationship factor F_1 with known rock masses, faults, or mineralization points.

[0056] Various regional anomaly features are normalized to the range of 0 to 1 to obtain normalized feature values. Normalization methods can include max-min normalization, quantile normalization, or standardization methods based on the background field mean and standard deviation.

[0057] Norm(X)=(X-X_min) / (X_max-X_min); Where Norm(X) is the normalized regional-level anomaly feature; X is the regional-level anomaly feature; X_min is the minimum regional-level anomaly feature; and X_max is the maximum regional-level anomaly feature.

[0058] Construct a regional prospecting prior map P_1(x,y) based on normalized features: P_1(x,y)=w_a·A_1+w_r·R_1+w_h·HG_1+w_v·VD_1+w_as·AS_1+w_s·S_1+w_c·C_1+w_f·F_1; Wherein, P_1(x,y) is the regional-level prospecting prior map; w_a is the regional-level feature weight corresponding to the regional-level magnetic anomaly amplitude; w_r is the regional-level feature weight corresponding to the regional-level residual anomaly; w_h is the regional-level feature weight corresponding to the regional-level horizontal gradient magnitude; w_v is the regional-level feature weight corresponding to the regional-level first-order vertical derivative; w_as is the regional-level feature weight corresponding to the regional-level analytical signal; w_s is the regional-level feature weight corresponding to the anomaly closure area; w_c is the regional-level feature weight corresponding to the anomaly continuity; and w_f is the regional-level feature weight corresponding to the spatial relationship factor with known rock masses, faults, or mineralization points. Each weight ranges from 0 to 1, and the sum of all weights is 1. The closer P_1(x,y) is to 1, the higher the probability that the grid cell is a candidate location for regional prospecting.

[0059] Based on P_1(x,y), set an anomaly threshold T_1 for the region and extract candidate key regions R_2k. The threshold T_1 can be 0.5 to 0.9, preferably 0.6 to 0.75; alternatively, a quantile threshold can be used, for example, selecting the top 10% to 30% of regions with the highest P_1(x,y) as candidate regions.

[0060] R_2k={(x,y)|P_1(x,y)≥T_1}; Where (x,y) are the coordinates of the candidate region.

[0061] A buffer distance B is extended outside the candidate key area to form a 1:2000 UAV airborne magnetic survey deployment area. The buffer distance B can be 50m to 500m, preferably 100m to 300m.

[0062] (4) Local anomaly mapping and high-resolution update graph generation.

[0063] Within the 1:2000 UAV airborne magnetic survey coverage area, local high-resolution anomaly features are extracted, including local magnetic anomaly amplitude A_2, local residual anomaly R_2, local horizontal gradient magnitude HG_2, local first-order vertical derivative VD_2, local analytical signal AS_2, anomaly center stability E_2, anomaly long axis direction consistency L_2, anomaly closure morphology factor M_2, and spatial correspondence factor F_2 with the anomaly center in the 1:10000 region.

[0064] Among them, the anomaly center stability E_2 is used to characterize the consistency of the anomaly center location under different anomaly enhancement methods; the anomaly long axis direction consistency L_2 is used to characterize the consistency between the 1:2000 local anomaly distribution direction and the regional structure, ore body strike or 1:10000 anomaly distribution direction; the spatial correspondence factor F_2 is used to characterize the spatial distance relationship between the 1:2000 anomaly center and the 1:10000 candidate anomaly center.

[0065] Construct a local orebody location update map P_2(x,y): P_2(x,y)=u_a·A_2+u_r·R_2+u_h·HG_2+u_v·VD_2+u_as·AS_2+u_e·E_2+u_l·L_2+u_m·M_2+u_f·F_2; Wherein, P_2(x,y) is the local orebody location update map; u_a is the local anomaly feature weight corresponding to the local magnetic anomaly amplitude; u_r is the local anomaly feature weight corresponding to the local residual anomaly; u_h is the local anomaly feature weight corresponding to the local horizontal gradient magnitude; u_v is the local anomaly feature weight corresponding to the local first-order vertical derivative; u_as is the local anomaly feature weight corresponding to the local analytical signal; u_e is the local anomaly feature weight corresponding to the anomaly center stability; u_l is the local anomaly feature weight corresponding to the anomaly long axis direction consistency; u_m is the local anomaly feature weight corresponding to the anomaly closure morphology factor; and u_f is the local anomaly feature weight corresponding to the spatial correspondence factor. Each weight ranges from 0 to 1, and the sum of all weights is 1. The closer P_2(x,y) is to 1, the more likely that the grid cell corresponds to the orebody location in the local high-resolution airborne magnetic data.

[0066] (5) Hierarchical anomaly mapping and spatial weighted fusion.

[0067] The 1:10000 regional prior map P_1(x,y) and the 1:2000 local update map P_2(x,y) are mapped to a unified spatial grid. Considering that the 1:2000 data only covers local key areas, this invention uses a coverage indicator function I_2k(x,y) to achieve local updates: P_12(x,y)=[1-I_2k(x,y)]·P_1(x,y)+I_2k(x,y)·[α·P_1(x,y)+β·P_2(x,y)]; Where P_12(x,y) represents the reliability of the ore body location after grading and fusion using airborne magnetic methods; α represents the regional anomaly weight, and β represents the local anomaly weight. Both α and β range from 0 to 1, and α + β = 1. Within a 1:2000 coverage area, α is preferably 0.2 to 0.5, and β is preferably 0.5 to 0.8; in uncovered areas, I_2k(x,y) = 0, therefore P_12(x,y) = P_1(x,y), which preserves the regional exploration prior results.

[0068] The above formula solves the problem of inconsistent coverage of airborne magnetic data at different scales: the entire area has a 1:10000 prior value, and high-resolution local updates are only performed within the 1:2000 coverage area. It does not require full 1:2000 airborne magnetic data coverage and avoids invalid interpolation due to missing local data. A schematic diagram of unified grid and local updates under inconsistent coverage conditions is shown below. Figure 3 (This figure shows the full-area coverage of 1:10000 airborne magnetic survey, the partial coverage of 1:2000 UAV airborne magnetic survey, the linear coverage of ground magnetic survey profiles, and their mapping methods in a unified spatial grid.)

[0069] (6) Ground magnetic profile boundary correction (e.g.) Figure 4 As shown in the figure, this diagram illustrates the process of extracting anomalous extreme points and gradient extreme points along the geomagnetic profile and converting them into orebody center constraints and boundary constraints.

[0070] For a ground magnetic survey profile, the magnetic anomaly M(s) is extracted along the profile distance s. The first derivative dM(s) / ds or the horizontal gradient value along the profile direction is calculated to identify the anomaly extremum C_g and the gradient extremum B_g. The anomaly extremum C_g can be used as a constraint for the center of the magnetic body or the projection center of the ore body, and the gradient extremum B_g can be used as a candidate point for the boundary of the magnetic body.

[0071] C_g={s|M(s) is a local maximum or local minimum}; B_g={s||dM(s) / ds| is a local maximum}; C_g and B_g are projected onto a unified spatial grid, and influence zones are established on both sides of the ground magnetic profile. For grid cells within the influence zone, the boundary correction factor P_g(x,y) is calculated. P_g(x,y) can be determined by the distance d_b from the grid cell to the nearest geomagnetic boundary constraint point, the perpendicular distance d_p to the profile, and the intensity of the geomagnetic anomaly or gradient. P_g(x,y)=exp[-d_p 2 / (2σ_p 2)]·exp[-d_b 2 / (2σ_b 2 )]·Norm(|dM(s) / ds|); Where σ_p is the profile influence zone attenuation parameter, which can range from 10m to 100m; σ_b is the boundary distance attenuation parameter, which can range from 10m to 80m. The larger P_g(x,y) is, the higher the degree of support of the grid cell from the boundary constraints of the ground magnetic profile.

[0072] Within the influence zone of the ground magnetic profile, boundary correction is performed on P_12(x,y) to obtain the final ore body location confidence level P_final(x,y): P_final(x,y)=[1-I_g(x,y)]·P_12(x,y)+I_g(x,y)·[λ·P_12(x,y)+γ·P_g(x,y)]; In the formula, λ is the weight of the airborne magnetic fusion result, and γ is the weight of the geomagnetic boundary correction. Both λ and γ are 0 to 1, and λ + γ = 1. Preferably, λ is 0.6 to 0.8, and γ is 0.2 to 0.4. When I_g(x,y) = 0 outside the geomagnetic profile influence zone, P_final(x,y) = P_12(x,y); when I_g(x,y) = 1 inside the geomagnetic profile influence zone, the geomagnetic profile boundary constraint participates in the ore body confidence correction.

[0073] For the generation of the ore body boundary line, contour lines can be extracted as the initial boundary around the high-value area of ​​P_final(x,y). Then, the gradient extreme points B_g of the geomagnetic profile are used to locally adsorb or translate the initial boundary so that the final boundary passes through or approaches the geomagnetic constraint point. The boundary adsorption distance D_b can be taken from 10m to 100m, preferably from 20m to 50m.

[0074] As an optional implementation, the method may further include: (7) Target classification, boundary output and borehole verification correction.

[0075] Target areas are classified based on the final orebody location confidence level P_final(x,y). Preferably, areas with P_final(x,y) ≥ 0.70 are classified as Level 1 target areas, areas with 0.50 ≤ P_final(x,y) < 0.70 are classified as Level 2 target areas, and areas with 0.35 ≤ P_final(x,y) < 0.50 are classified as Level 3 target areas; the specific thresholds can be adjusted according to the mining area background, known ore bodies, and borehole verification results.

[0076] The output includes: the most likely location map of the ore body, the boundary line of the ore body, the target area level map, the suggested direction of the ground survey line layout, the suggested borehole verification location, and the anomaly amplitude, area, boundary constraint strength and confidence score of each target area.

[0077] Drilling results can be used as a posterior correction basis for weights and thresholds. When a borehole encountering minerals falls into a high-confidence zone, the weight of the corresponding anomalous combination is increased; when a borehole not encountering minerals falls into a high-confidence zone, the weight of the anomalous features that lead to misjudgment is decreased. Weight correction can be performed manually, through grid search, cross-validation, or optimization methods based on ROC / AUC.

[0078] Therefore, this application has the following advantages: (1) Solve the problem of inconsistent coverage of multi-scale magnetic data. The 1:10000 airborne magnetic method is used as the whole area prior, the 1:2000 UAV airborne magnetic method is only updated in high resolution within the local coverage area, and the ground magnetic profile is only used for boundary correction within the influence zone. It is not required that all data cover the whole area.

[0079] (2) The traditional empirical “airborne magnetic survey – local detailed investigation – geomagnetic verification” process is transformed into a quantifiable and repeatable hierarchical anomaly mapping and spatial weighted calculation process, reducing subjective differences among different interpreters.

[0080] (3) It can simultaneously output the prospecting area, the most likely location of the ore body and the boundary constraint results, so that airborne magnetic data and geomagnetic profile data can play different roles in a unified spatial grid.

[0081] (4) Reduce the need for full coverage of ground magnetic methods. Ground magnetic methods only need to be deployed along key profiles, and can participate in the correction of ore body boundaries through profile influence zones and boundary correction factors, which helps to reduce the amount of fieldwork.

[0082] (5) It can provide quantitative basis for borehole deployment. Through the P_final(x,y) confidence map and boundary line, high confidence anomaly centers, the inner side of the boundary or the location of anomaly gradient transformation can be selected for drilling verification.

[0083] In summary, this application includes the following key points: Hierarchical anomaly mapping mechanism: regional anomalies generated by 1:10000 airborne magnetic method are converted into a global prospecting prior map P_1(x,y), local anomalies generated by 1:2000 UAV airborne magnetic method are converted into a local update map P_2(x,y), and ground magnetic profiles are converted into boundary correction factors P_g(x,y).

[0084] The local update mechanism controlled by the coverage indicator function: I_2k(x,y) is used to distinguish between the 1:2000 UAV airborne magnetic survey coverage area and the uncovered area, so as to achieve hierarchical fusion of "updating when there is local high-resolution data and preserving the regional prior when there is no local data".

[0085] Spatial weighted orebody location credibility model: The fusion of regional anomaly weights and local anomaly weights is achieved by P_12(x,y)=[1-I_2k(x,y)]·P_1(x,y)+I_2k(x,y)·[α·P_1(x,y)+β·P_2(x,y)].

[0086] Ground magnetic profile boundary correction mechanism: The profile anomaly extreme points and gradient extreme points are transformed into boundary correction factors P_g(x,y) within the influence zone, and the orebody confidence and boundary are corrected by P_final(x,y)=[1-I_g(x,y)]·P_12(x,y)+I_g(x,y)·[λ·P_12(x,y)+γ·P_g(x,y)].

[0087] A target classification and boundary output method based on P_final(x,y) automatically generates first-level, second-level, and third-level target areas according to the confidence threshold, and outputs the most likely location of the ore body and its boundary line.

[0088] Borehole verification feedback correction mechanism: The results of whether the borehole encounters or does not encounter minerals are used to correct the weights and thresholds of abnormal features, forming a parameter combination applicable to similar mining areas.

[0089] Additionally, it should be noted that this application is not limited to the two fixed scales of 1:10000 and 1:2000. Regional-level data can be replaced with airborne magnetic data of 1:50000, 1:25000, 1:10000, etc., and local detailed survey data can be replaced with low-altitude high-precision airborne magnetic data of UAVs or manned aircraft of 1:5000, 1:2000, 1:1000, etc.

[0090] The anomalous features in this application are not limited to magnetic anomaly amplitude, horizontal gradient magnitude and first-order vertical derivative, but may also include analytical signals, magnetic anomaly magnitude, normalized source strength, tilt derivative, total gradient magnitude, edge enhancement filtering results, inverted magnetic susceptibility or magnetization intensity and other parameters.

[0091] The weights in this application can be set using expert experience, or determined using entropy weighting, analytic hierarchy process (AHP), logistic regression, random forest, XGBoost, or Bayesian update methods. Any alternative implementation method that achieves hierarchical mapping and weighted fusion of regional anomalies, local anomalies, and profile boundary constraints within a unified grid is acceptable.

[0092] Surface magnetic profiles can be replaced or supplemented with high-precision magnetic gradient profiles, borehole magnetic surveys, magnetic susceptibility profiles, gravity profiles, electrical profiles, or borehole physical property constrained profiles; these data can be incorporated into a spatial weighted model according to similar influence zones and boundary correction factors.

[0093] In addition to being used for ore body location, this application can also be used for delineating magnetic rock mass boundaries, identifying fracture structures, tracing rock mass contact zones, evaluating mineralization zone priorities, and optimizing borehole deployment.

[0094] Example 3: Multi-scale magnetic method for ore body location in the Niu Mao Quan area.

[0095] The Niumaoquan area already has 1:10,000 airborne magnetic data, local 1:2,000 large-scale UAV airborne magnetic data, ground magnetic profile data, and borehole verification results. First, all data were unified to the same coordinate system and a unified spatial grid. Regional anomaly enhancement processing was performed on the 1:10,000 airborne magnetic data to obtain data layers including the amplitude of the magnetic anomaly across the entire area, the horizontal gradient magnitude, and the first-order vertical derivative.

[0096] Secondly, the aforementioned regional anomaly characteristics were normalized, and P_1(x,y) was calculated according to indicators such as regional anomaly amplitude, gradient intensity, anomaly scale, and continuity. Candidate key areas were extracted based on the high-value areas of P_1(x,y), and compared with geological knowledge, known rock masses, or mineralization clues to determine the local detailed investigation range of 1:2000 UAV airborne magnetic method.

[0097] Then, within the 1:2000 UAV airborne magnetic survey coverage area, local anomaly amplitudes, anomaly centers, high-value horizontal gradient bands, vertical derivative boundaries, and anomaly morphology parameters are extracted, and the local update map P_2(x,y) is calculated. For grid cells located within the 1:2000 coverage area, αP_1(x,y)+βP_2(x,y) is used for local updates; for grid cells not covered by the 1:2000 scale, P_1(x,y) is kept unchanged.

[0098] Next, ground magnetic survey data were read along key anomaly profiles. Anomaly extreme points and gradient extreme points were calculated for each geomagnetic profile. Anomaly extreme points were used as constraints for the orebody center, and gradient extreme points were used as candidate points for the orebody boundary. The geomagnetic profile boundary correction diagram is shown below. Figure 4 As shown. Based on the width of the influence zone on both sides of the profile, the above points are projected onto a unified grid to form the geomagnetic boundary correction factor P_g(x,y).

[0099] Finally, within the influence zone of the geomagnetic profile, P_g(x,y) and the fusion result P_12(x,y) from the airborne magnetic method are spatially weighted to obtain the final orebody location confidence level P_final(x,y). High-confidence orebody location areas are extracted based on the P_final(x,y) threshold, and the boundary lines are locally corrected using geomagnetic gradient extreme points. The output most probable orebody location and boundary lines are compared with the borehole exposure results to validate the model parameters and adjust the weights.

[0100] In this embodiment, the regional anomaly weight α can be set to 0.3, and the local anomaly weight β can be set to 0.7; the weight λ of the airborne magnetic fusion result can be set to 0.7, and the geomagnetic boundary correction weight γ can be set to 0.3. The above values ​​are preferred initial values, and in practical applications, they can be adjusted according to the borehole verification results, the accuracy of ground magnetic surveys, and the anomaly type.

[0101] This embodiment can generate a orebody location confidence map, boundary constraint map, and target area classification map for the Niu Mao Quan area. If the borehole ore-bearing location matches the high value area of ​​P_final(x,y) and the boundary constraint range, it indicates that the method can effectively utilize multi-scale airborne magnetic and geomagnetic profile data to locate the orebody. If local deviations exist, iterative optimization can be performed by adjusting the weights of various anomaly features and boundary influence zone parameters.

[0102] To verify the feasibility of the orebody location method based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting described in this application, an example calculation was performed on the calculation process described in steps (3) to (7) of Example 2 using existing 1:10000 airborne magnetic anomaly grid, 1:10000 airborne magnetic polarization anomaly grid, 1:2000 UAV airborne magnetic anomaly grid, ground magnetic survey profile, and PCZK001 borehole data from the Niumaoquan area. All data were unified to the same plane coordinate system, and a 20m×20m unified spatial grid was used for resampling and fusion calculation.

[0103] (1) Based on the 1:10000 airborne magnetic anomaly grid and the 1:10000 polarization anomaly grid, the regional magnetic anomaly amplitude A_1, the regional residual anomaly R_1, the regional horizontal gradient magnitude HG_1, the regional first-order vertical derivative VD_1, and the regional analytical signal AS_1 are extracted and normalized to 0-1 respectively. In this embodiment, the regional prospecting prior map P_1(x,y) is calculated with equal weights: P_1(x,y)=(A_1+R_1+HG_1+VD_1+AS_1) / 5, as shown in the figure. Figure 5 (This figure shows the regional anomaly mapping result generated by combining 1:10000 aeromagnetic anomalies, polarization anomalies, and regional enhancement parameters.) The calculated P_1(x,y) is used to represent the regional prospecting probability of the entire area, and T1=0.60 is used as the threshold for candidate key areas.

[0104] (2) Based on the 1:2000 UAV aeromagnetic anomaly grid, the local anomaly amplitude A_2, local horizontal gradient magnitude HG_2, local first-order vertical derivative VD_2, and local analytical signal AS_2 are extracted, and the spatial correspondence factor F_2 between the 1:2000 local anomaly and the 1:10000 candidate high-value area is calculated. In this embodiment, the local update map P_2(x,y) is calculated with equal weight: P_2(x,y)=(A_2+HG_2+VD_2+AS_2+F_2) / 5, as shown. Figure 6 (This figure shows the local update result generated by combining local anomaly amplitude, gradient, vertical derivative, analytical signal, and corresponding regional factors within the 1:2000 UAV airborne magnetic field coverage area.) P_2 is only valid within the 1:2000 UAV airborne magnetic field coverage area; no unfounded extrapolation is performed outside the coverage area.

[0105] (3) Map P_1(x,y) and P_2(x,y) to a unified spatial grid, and use the coverage indicator function I_2k(x,y) to control local updates. In this embodiment, the region-level weight α is 0.3, the local update weight β is 0.7, and P_12(x,y) = [1-I_2k(x,y)]P_1(x,y) + I_2k(x,y)(0.3P_1(x,y) + 0.7P_2(x,y)) is calculated. Figure 7 (This figure shows the fusion result with local updates of αP_1(x,y)+βP_2(x,y) in the 1:2000 coverage area and retention of P_1(x,y) in the uncovered area.) This result achieves hierarchical fusion of the entire region's prior and local high-resolution anomalies.

[0106] (4) Based on the x, y, and Magnet data of geomagnetic lines 1, 2, 5, 6, 7, 8, 9, 10, and 11, calculate the magnetic anomaly gradient along each survey line, and extract the anomaly center point and candidate boundary points with large absolute gradients. Construct the geomagnetic boundary correction factor P_g using the geomagnetic survey line distance d_p, the boundary point distance d_b, and the normalized gradient intensity value, as follows: Figure 8 (This figure shows the boundary constraint strength calculated from the extreme points of the geomagnetic survey line anomaly gradient and the influence zone of the profile.) In this embodiment, a 100m influence zone of the profile is used, with σ_p=80m, σ_b=50m, λ=0.7, and γ=0.3. The final confidence level P_final before borehole feedback is calculated, as shown below. Figure 9 (This figure shows the reliability of the ore body location after weighting the results of the airborne magnetic method classification and fusion with the geomagnetic boundary correction factor.)

[0107] (5) Divide the target areas into Level 1, Level 2, and Level 3 according to P_final≥0.70, 0.50≤P_final<0.70, and 0.35≤P_final<0.50 respectively, such as Figure 10 (This figure represents the primary, secondary, and tertiary target regions divided according to the P_final threshold) and Figure 12 (This figure shows the high-confidence target area range after borehole feedback correction and optimization.) Subsequently, borehole PCZK001 was used as a positive verification point for posterior correction. Borehole PCZK001 revealed that the upper part is mainly gabbro, with granite only in the bottom 50m, indicating that the borehole location corresponds to the target magnetic rock mass. To avoid the low P_g(x,y) value in the geomagnetic profile causing unreasonable weakening of P_12(x,y) within the profile influence zone, this embodiment uses a non-penalty boundary correction P_boundary=max(P_12(x,y),0.7P_12(x,y)+0.3P_g(x,y)), and constructs a borehole constraint factor P_drill=exp[-d] using PCZK001 as the positive verification point. 2 / (2σ_d 2 ]], where σ_d=180m, borehole feedback weight η=0.40, and finally the borehole constraint optimization result P_opt=(1-η)P_boundary+ηP_drill is obtained, as follows Figure 11 (This figure shows the orebody location confidence after using the PCZK001 ore-seeking result as a positive verification point for posterior constraint.) The main parameters used in the Niu Mao Quan example calculation are shown in Table 1.

[0108] Table 1. Main parameters used in the calculation of the Niu Mao Quan example.

[0109] Table 2 Comparison of PCZK001 position reliability before and after borehole feedback

[0110] As shown in Table 2, before borehole feedback, the P_final value of the PCZK001 location was 0.3971, placing it within the third-level target area. After applying non-penalty boundary correction and adding the positive validation constraint for PCZK001, P_opt increased to 0.7393, adjusting it to the first-level target area. This result indicates that borehole feedback can perform posterior correction on weights and spatial reliability, reasonably enhancing the validated orebody location in the final model.

[0111] Table 3 Statistical analysis of target area before and after borehole feedback

[0112] As shown in Table 3, after borehole constraint optimization, the area of ​​the first-level target region increased from 0.8844 km². 2 Shrinking to 0.0308km 2 The secondary target area increased from 3.3224 km². 2 Shrinking to 0.2636km 2This indicates that the positive feedback of borehole verification can significantly narrow the range of high-confidence target areas, making the final results more suitable for subsequent borehole deployment and target area priority evaluation.

[0113] The above calculation results show that this application can not only achieve spatial fusion of 1:10000 regional anomalies, 1:2000 local anomalies and geomagnetic profile boundary constraints, but also perform a posteriori correction through borehole positive verification to form an iteratively updated orebody location confidence map and target classification results.

[0114] Example 4: 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 13 As shown, the 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 operating system and computer programs stored in the non-volatile storage media. The database stores aeromagnetic data for the entire study area, aeromagnetic data for key local areas, and ground magnetic profile data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a orebody location method based on multi-scale aeromagnetic graded anomaly mapping and spatial weighting.

[0115] Figure 13 The structures shown are merely block diagrams of some structures related to the present application and do 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 shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0116] Example 5: In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0117] Example 6: In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0118] 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 related data must comply with relevant regulations and be authorized by the owner of the corresponding device.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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 orebody localization based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting, characterized in that, include: We acquired airborne magnetic data for the entire study area, airborne magnetic data for key local areas, and ground magnetic profile data, and preprocessed them to obtain the preprocessed data. A unified spatial grid is established, and the preprocessed data is mapped to the unified spatial grid to obtain the mapped grid. Based on the mapped grid, regional-level anomaly features are extracted from the aeromagnetic data of the entire study area, and after normalization, a regional-level mineral exploration prior map is constructed. Based on the aforementioned regional prospecting prior map, candidate key areas are extracted according to a set regional anomaly threshold. Based on the candidate key areas, the deployment area for detailed airborne magnetic surveys was determined; Based on the airborne magnetic data of the local key areas, local high-resolution anomaly features are extracted from the airborne magnetic survey deployment area, and after normalization, a local ore body location update map is constructed. The regional prospecting prior map and the local ore body location update map are spatially weighted and fused to obtain the ore body location reliability after airborne magnetic method classification and fusion. Based on the mapped grid, abnormal extreme points and gradient extreme points are identified in the ground magnetic profile data, projected onto the unified spatial grid, and influence zones are established on both sides of the ground magnetic profile data. For grid cells within the influence zones, boundary correction factors are calculated. Based on the boundary correction factor, the confidence of the ore body location after the airborne magnetic method classification and fusion is corrected by boundary to obtain the final ore body location confidence. Based on the credibility of the final ore body location, the most likely location map of the ore body, the ore body boundary line, the target area level map, the suggested direction of the ground survey line layout, the suggested borehole verification location, and the abnormal amplitude, area, boundary constraint strength and credibility score of each target area are generated.

2. The orebody location method based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting according to claim 1, characterized in that, The expression for normalizing regional-level anomaly features is: Norm(X)=(X-X_min) / (X_max-X_min); Where Norm(X) is the normalized regional-level anomaly feature; X is the regional-level anomaly feature; X_min is the minimum regional-level anomaly feature; and X_max is the maximum regional-level anomaly feature.

3. The orebody location method based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting according to claim 1, characterized in that, The expression for the regional-level mineral exploration prior map is: P_1(x,y)=w_a·A_1+w_r·R_1+w_h·HG_1+w_v·VD_1+w_as·AS_1+w_s·S_1+w_c·C_1+w_f·F_1; Wherein, P_1(x,y) is the regional-level prospecting prior map; A_1 is the regional-level magnetic anomaly amplitude; R_1 is the regional-level residual anomaly; HG_1 is the regional-level horizontal gradient magnitude; VD_1 is the regional-level first-order vertical derivative; AS_1 is the regional-level analytical signal; S_1 is the anomaly closure area; C_1 is the anomaly continuity; F_1 is the spatial relationship factor with known rock masses, faults, or mineralization points; w_a is the regional-level feature weight corresponding to the regional-level magnetic anomaly amplitude; w_r is the regional-level feature weight corresponding to the regional-level residual anomaly; w_h is the regional-level feature weight corresponding to the regional-level horizontal gradient magnitude; w_v is the regional-level feature weight corresponding to the regional-level first-order vertical derivative; w_as is the regional-level feature weight corresponding to the regional-level analytical signal; w_s is the regional-level feature weight corresponding to the anomaly closure area; w_c is the regional-level feature weight corresponding to the anomaly continuity; and w_f is the regional-level feature weight corresponding to the spatial relationship factor with known rock masses, faults, or mineralization points.

4. The orebody location method based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting according to claim 1, characterized in that, The expression for the local ore body location update map is: P_2(x,y)=u_a·A_2+u_r·R_2+u_h·HG_2+u_v·VD_2+u_as·AS_2+u_e·E_2+u_l·L_2+u_m·M_2+u_f·F_2; Wherein, P_2(x,y) is the local orebody location update map; A_2 is the local magnetic anomaly amplitude; R_2 is the local residual anomaly; HG_2 is the local horizontal gradient magnitude; VD_2 is the local first-order vertical derivative; AS_2 is the local analytical signal; E_2 is the anomaly center stability; L_2 is the anomaly long axis direction consistency; M_2 is the anomaly closure morphology factor; F_2 is the spatial correspondence factor; u_a is the local anomaly feature weight corresponding to the local magnetic anomaly amplitude; u_r is the local anomaly feature weight corresponding to the local residual anomaly; u_h is the local anomaly feature weight corresponding to the local horizontal gradient magnitude; u_v is the local anomaly feature weight corresponding to the local first-order vertical derivative; u_as is the local anomaly feature weight corresponding to the local analytical signal; u_e is the local anomaly feature weight corresponding to the anomaly center stability; u_l is the local anomaly feature weight corresponding to the anomaly long axis direction consistency; u_m is the local anomaly feature weight corresponding to the anomaly closure morphology factor; and u_f is the local anomaly feature weight corresponding to the spatial correspondence factor.

5. The orebody location method based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting according to claim 1, characterized in that, The expression for the reliability of the ore body location after the airborne magnetic method classification and fusion is as follows: P_12(x,y)=[1-I_2k(x,y)]·P_1(x,y)+I_2k(x,y)·[α·P_1(x,y)+β·P_2(x,y)]; Wherein, P_12(x,y) is the reliability of the ore body location after the airborne magnetic method classification and fusion; α is the regional anomaly weight, β is the local anomaly weight; I_2k(x,y) is the coverage indicator function; P_1(x,y) is the regional prospecting prior map; and P_2(x,y) is the local ore body location update map.

6. The orebody localization method based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting according to claim 1, characterized in that, The formula for calculating the boundary correction factor is as follows: P_g(x,y)=exp[-d_p 2 / (2σ_p 2 )]·exp[-d_b 2 / (2σ_b 2 )]·Norm(|dM(s) / ds|); Where P_g(x,y) is the boundary correction factor; d_p is the vertical distance from the grid cell to the profile; d_b is the distance from the grid cell to the nearest geomagnetic boundary constraint point; σ_p is the profile influence zone attenuation parameter; σ_b is the boundary distance attenuation parameter; dM(s) / ds is the first derivative along the profile direction; M(s) is the magnetic anomaly; and s is the distance along the profile.

7. The orebody location method based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting according to claim 1, characterized in that, The expression for the confidence level of the final ore body location is: P_final(x,y)=[1-I_g(x,y)]·P_12(x,y)+I_g(x,y)·[λ·P_12(x,y)+γ·P_g(x,y)]; Wherein, P_final(x,y) is the final ore body location confidence; I_g(x,y) is the influence zone indicator function of the ground magnetic profile; P_12(x,y) is the ore body location confidence after air magnetic method graded fusion; λ is the weight of the air magnetic method fusion result; γ is the geomagnetic boundary correction weight; and P_g(x,y) is the boundary correction factor.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to implement the ore body location method based on multi-scale aeromagnetic graded anomaly mapping and spatial weighting as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the ore body location method based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the ore body location method based on multi-scale airborne magnetic graded anomaly mapping and spatial weighting as described in any one of claims 1-7.

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