Method and system for projecting anomaly data from aerial magnetic surveys onto the ground using drone terrain-following flight.
The method and system for drone terrain-following flight improve drone magnetic survey resolution by using spectral information and iterative optimization to stabilize ground projection, addressing low-altitude risks and resolution limitations.
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Patents
- Current Assignee / Owner
- THE 4TH GEOLOGICAL BRIGADE OF SICHUAN
- Filing Date
- 2026-03-17
- Publication Date
- 2026-06-26
Smart Images
Figure 0007880594000001_ABST
Abstract
Description
[Technical Field]
[0001] This invention belongs to the field of processing technology for mineral exploration data from drone aerial magnetic surveys, and more specifically, relates to a method and system for projecting anomaly data from aerial magnetic surveys obtained by drone terrain-following flight onto the ground. [Background technology]
[0002] Drone aerial magnetic surveying has the ability to rapidly acquire data relative to the ground, but because aerial magnetic anomalies attenuate exponentially with flight altitude, the resolution of aerial magnetic anomalies is significantly lower than that of ground surveying, making it unsuitable for identifying local magnetic source anomalies. Therefore, to obtain relatively high-resolution data, drone aerial magnetic surveying often employs low-altitude flight for data collection, but the improvement in effectiveness from this measure is very limited, and the flight risks increase dramatically. One way to effectively improve resolution is to project aerial magnetic anomalies directly onto the ground, thereby obtaining high-resolution data equivalent to that of ground surveying.
[0003] Normally, during drone flight, aeronautical magnetic data is contaminated with various measurement noises and magnetic disturbances. Using these as first-class boundary conditions to solve the Dirichlet lower half-space problem is non-stationary, making it impossible to obtain a stable, regular ground projection solution. This invention employs a calculation method in the opposite direction, namely, a method for solving the Dirichlet upper half-space problem using randomly given ground observation data or aeronautical magnetic survey data as initial boundary conditions. Clearly, analytical calculation under these boundary conditions is an appropriate solution method, yielding a regular and stable solution on the drone flight plane. Therefore, by comparing and fitting the differences between the calculated data and measured data on the drone flight plane, and iteratively correcting the ground data as boundary conditions, it is possible to ultimately obtain ground projection data that satisfies the correction error. [Overview of the Initiative] [Problems that the invention aims to solve]
[0004] The present invention provides the following scheme to solve the shortcomings of the prior art. [Means for solving the problem]
[0005] The method for projecting anomaly data from aerial magnetic surveys using drone terrain-following flight onto the ground is as follows: Steps include acquiring aeromagnetic data at a fixed flight altitude during terrain-following flight, A step to obtain spectral information of the fixed-flight altitude Dirichlet problem solution under initial boundary value conditions for ground magnetic anomalies, The steps include establishing a least-squares iterative optimization solution for the inverse Fourier transform of the aforementioned aeromagnetic data and the aforementioned spectral information, The process includes the step of obtaining ground projection data based on the least-squares iterative optimization solution.
[0006] Preferably, the method for obtaining the spectral information includes the following equation:
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[0007] Preferably, the method for establishing the least-squares iterative optimization solution is: An inverse Fourier transform is performed on the spectral information to calculate magnetic anomaly data for the flight surface based on the initial boundary value conditions:
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[0008] Preferably, the method for obtaining the ground projection data is repeatedly calculating the least-squares iterative optimal solution to obtain the least-squares iterative optimal solution under the nth ground magnetic anomaly boundary value condition
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[0009] The present invention also provides a ground projection system for aeronautical magnetic survey anomaly data obtained by drone terrain-following flight, the system applying the method described in any one of the above items, and including an aeronautical magnetic data acquisition module, a spectral information acquisition module, an optimization solution calculation module, and a projection data calculation module: The aforementioned aeronautical magnetic data acquisition module is used to acquire aeronautical magnetic data at a fixed flight altitude during terrain-following flight. The spectral information acquisition module is used to acquire spectral information of the fixed-flight altitude Dirichlet problem solution under initial boundary value conditions for ground magnetic anomalies. The optimization solution calculation module is used to establish a least-squares iterative optimization solution of the inverse Fourier transform of the aeromagnetic data and the spectral information. The projection data calculation module obtains ground projection data based on the least-squares iterative optimization solution.
[0010] Preferably, the workflow of the spectral information acquisition module includes the following equation:
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[0011] Preferably, the workflow of the optimization solution calculation module is: An inverse Fourier transform is performed on the spectral information to calculate magnetic anomaly data based on the initial boundary value conditions of the flight surface:
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[0012] Preferably, the workflow of the projection data calculation module is: The least-squares iterative optimization solution is repeatedly calculated, and the nth geomagnetic anomaly boundary condition
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[0013] Compared to conventional technology, the beneficial effects of the present invention are as follows:
[0014] This invention is an efficient and practical application method and technology that leverages the advantages of rapid data acquisition efficiency of drone-based aerial magnetics while achieving aerial magnetic anomaly resolution equivalent to that of ground-based acquisition. This method and technology has a significant effect in improving the application effectiveness of low-altitude drone-based aerial magnetic surveys and expanding the flight altitude space for drone-based aerial magnetic surveying. [Brief explanation of the drawing]
[0015] To more clearly illustrate the technical concept of the present invention, the drawings used in the following embodiments are briefly introduced. Clearly, the drawings in the following description represent only some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these without any creative work. [Figure 1] This is a schematic method flow diagram of an embodiment of the present invention. [Figure 2] This is a schematic diagram of the undulating ground and the drone terrain-following flight surface according to an embodiment of the present invention. [Figure 3] This is a schematic diagram of the spatial position of the magnetic source model according to an embodiment of the present invention. [Figure 4] Magnetic anomalies measured on the terrain-following flight surface Sh of an embodiment of the present invention
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[0016] The technical concepts in the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments. Clearly, the embodiments described are only some, and not all, embodiments of the present invention. All other embodiments that a person skilled in the art could obtain without creative work based on the embodiments of the present invention are within the scope of the protection of the present invention.
[0017] To further clarify and facilitate understanding the above-mentioned objectives, features, and advantages of the present invention, the present invention will be described in more detail below with reference to the drawings and specific embodiments.
[0018] Example 1: In this embodiment, as shown in Figure 1, the method for projecting aerial magnetic survey anomaly data onto the ground using drone terrain-following flight includes the following steps.
[0019] S1. Acquire aeronautical magnetic data at a fixed flight altitude during terrain-following flight.
[0020] In this embodiment, aeronautical magnetic data at a fixed altitude is obtained by drone terrain-following flight.
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[0021] S2. Obtain spectral information of the fixed-flight altitude Dirichlet problem solution under initial boundary value conditions for ground magnetic anomalies.
[0022] Type 1 boundary value conditions
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[0023] S3. Establish a least-squares iterative optimization solution based on the inverse Fourier transform of aeromagnetic data and spectral information.
[0024] The method for establishing a least-squares iterative optimization solution involves performing an inverse Fourier transform on spectral information and calculating magnetic anomaly data based on the initial boundary value conditions of the flight surface:
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[0025] S4. Ground projection data is obtained based on the least-squares iterative optimization solution.
[0026] The method for obtaining ground projection data is: Ground data after the first correction
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[0027] Example 2: This embodiment provides a method for projecting aerial magnetic survey anomaly data onto the ground using drone terrain-following flight, and includes the following steps.
[0028] S1. Acquire aeronautical magnetic data at a fixed flight altitude during terrain-following flight.
[0029] In this embodiment, as shown in Figure 2, the drone flight surface S z Assuming that the ground S0 is a pair of parallel surfaces, where the drone flight altitude is a fixed altitude z=h that follows the terrain, and the altitude of the ground S0 is defined as z=0 for all. Designing a magnetic source model as shown in Figure 3, the magnetic anomaly measured on the terrain-following flight surface Sh is
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[0030] S2. Obtain spectral information of the fixed-flight altitude Dirichlet problem solution under initial boundary value conditions for ground magnetic anomalies.
[0031] Type 1 boundary value conditions
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[0032] S3. Establish a least-squares iterative optimization solution based on the inverse Fourier transform of aeromagnetic data and spectral information.
[0033] The method for establishing a least-squares iterative optimization solution is as follows: An inverse Fourier transform is performed on the spectral information to calculate magnetic anomaly data for the flight surface based on the initial boundary value conditions. As shown in Figure 5:
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[0034] S4. Ground projection data is obtained based on the least-squares iterative optimization solution.
[0035] The method for obtaining ground projection data is as follows: Ground data after the first correction
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[0036] In Figures 2 to 8, nT represents the unit of airborne magnetic anomaly, X represents the east - west direction of the geographical location, Y represents the north - south direction of the geographical location, Z represents the drone terrain following flight distance, h represents the solution altitude in the upper airspace, and A / M represents the unit of magnetic field strength.
[0037] Example 3: In this example, the ground projection system for airborne magnetic survey anomaly data by drone terrain following flight includes an airborne magnetic data acquisition module, a spectrum information acquisition module, an optimal solution calculation module, and a projection data calculation module.
[0038] The airborne magnetic data acquisition module is used to acquire airborne magnetic data at a fixed flight altitude of terrain following flight.
[0039] The spectrum information acquisition module is used to acquire the spectrum information of the solution of the Dirichlet problem of the fixed flight altitude under the initial boundary value conditions of ground magnetic anomalies.
[0040] The workflow for the spectral information acquisition module includes the following equation:
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[0041] The optimization solution calculation module is used to establish least-squares iterative optimization solutions for the inverse Fourier transforms of aeromagnetic data and spectral information.
[0042] The optimization solution calculation module's workflow involves performing an inverse Fourier transform on spectral information and calculating magnetic anomaly data based on the initial boundary value conditions of the flight surface:
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[0043] The projection data calculation module obtains ground projection data based on least-squares iterative optimization solutions.
[0044] The workflow of the projection data calculation module iteratively calculates the least-squares iteratively optimized solution and the nth ground magnetic anomaly boundary value condition.
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[0045] The embodiments described above illustrate preferred embodiments of the present invention and do not limit the scope of the invention. Various modifications and improvements made by those skilled in the art to the technical proposal of the present invention without departing from the spirit of the invention are all included within the scope of protection defined by the claims of the present invention.
Claims
1. A method for projecting anomaly data from aeronautical magnetic surveys obtained by drone terrain-following flight onto the ground, Steps include acquiring aeromagnetic data at a fixed flight altitude during terrain-following flight, A step to obtain spectral information of the fixed-flight altitude Dirichlet problem solution under initial boundary value conditions for ground magnetic anomalies, The steps include establishing a least-squares iterative optimization solution for the inverse Fourier transform of the aforementioned aeromagnetic data and the aforementioned spectral information, The step of obtaining ground projection data based on the least-squares iterative optimization solution includes, The method for obtaining the spectral information includes the following equation: [Math 1] Here, [Math 2] This represents the spectral information of the drone's flight surface. [Math 3] The initial boundary value conditions [Math 4] This represents the Fourier transform of , where u and v represent spatial frequencies, and z represents the solution altitude in the upper halfspace. The method for establishing the least-squares iterative optimization solution is: An inverse Fourier transform is performed on the spectral information to calculate magnetic anomaly data for the flight surface based on the initial boundary value conditions: [Math 5] Here, [Math 6] This represents magnetic anomaly data, F －１ This represents the inverse Fourier transform, The aforementioned magnetic anomaly data [Number 7] and the aforementioned aeromagnetic data [Number 8] Based on the difference, the initial boundary value conditions are modified to obtain the least-squares iterative optimization solution: [Number 9] Here, [Number 10] represents the least squares iterative optimization solution, where k is the step length, and includes , The method for obtaining ground projection data is: The least-squares iterative optimization solution is calculated repeatedly, and the nth geomagnetic anomaly boundary condition [Math 11] The steps include obtaining the least squares iterative optimization solution below: [Math 12] Here, [Number 13] This represents the magnetic anomaly data under the ground magnetic anomaly boundary value conditions for the nth trial. [Number 14] This represents the Fourier transform of the ground magnetic anomaly boundary value condition in the nth calculation, and at this time: [Number 15] And, Here, [Number 16] This represents the least squares iterative optimization solution in the nth iteration of the solution, [Number 17] This represents the ground magnetic anomaly boundary value conditions in the nth calculation, 【Number 18】 At that time, it will be as follows: [Number 19] [Number 20] In other words, the calculated nth ground magnetic anomaly boundary value conditions are the aeromagnetic data [Math 21] A method for projecting aerial magnetic survey anomaly data by drone terrain-following flight, characterized in that the data is ground projection data on the ground, where ε represents a constant approaching zero.
2. A ground projection system for aeronautical magnetic survey anomaly data obtained by drone terrain-following flight, The system applies the method described in claim 1 and includes an aeromagnetic data acquisition module, a spectral information acquisition module, an optimization solution calculation module, and a projection data calculation module: The aforementioned aeronautical magnetic data acquisition module is used to acquire aeronautical magnetic data at a fixed flight altitude during terrain-following flight. The spectral information acquisition module is used to acquire spectral information of the fixed-flight altitude Dirichlet problem solution under initial boundary value conditions for ground magnetic anomalies. The optimization solution calculation module is used to establish a least-squares iterative optimization solution of the inverse Fourier transform of the aeromagnetic data and the spectral information. The projection data calculation module obtains ground projection data based on the least-squares iterative optimization solution, The workflow of the aforementioned spectral information acquisition module includes the following equation: [Number 22] Here, [Number 23] This represents the spectral information of the drone's flight surface. [Number 24] The initial boundary value conditions [Number 25] This represents the Fourier transform of , where u and v represent spatial frequencies, and z represents the solution altitude in the upper halfspace. The workflow of the aforementioned optimization solution calculation module is as follows: An inverse Fourier transform is performed on the spectral information to calculate magnetic anomaly data based on the initial boundary value conditions of the flight surface: [Number 26] Here, [Number 27] This represents magnetic anomaly data, F －１ This represents the inverse Fourier transform, The aforementioned magnetic anomaly data [Number 28] and the aforementioned aeromagnetic data [Number 29] Based on the difference, the initial boundary value conditions are modified to obtain the least-squares iterative optimization solution: [Number 30] Here, [Number 31] represents the least squares iterative optimization solution, where k is the step length, and includes , The workflow of the aforementioned projection data calculation module is as follows: The least-squares iterative optimization solution is calculated repeatedly, and the nth geomagnetic anomaly boundary condition [Number 32] The steps include obtaining the least squares iterative optimization solution below: [Number 33] Here, [Number 34] This represents the magnetic anomaly data under the ground magnetic anomaly boundary value conditions for the nth trial. [Number 35] This represents the Fourier transform of the ground magnetic anomaly boundary value condition in the nth calculation, and at this time: [Number 36] And, Here, [Number 37] This represents the least squares iterative optimization solution in the nth iteration of the solution, [Number 38] This represents the ground magnetic anomaly boundary value conditions in the nth calculation, [Number 39] At that time, it will be as follows: [Number 40] [Number 41] In other words, the calculated nth ground magnetic anomaly boundary value conditions are the aeromagnetic data [Number 42] A ground projection system for aerial magnetic survey anomaly data obtained by drone terrain-following flight, characterized in that the ground projection data is on the ground, where ε represents a constant approaching zero.