Air and ground exploration fusion modeling method and system

Abstract

The application discloses an air and ground exploration fusion modeling method and system, and particularly relates to the technical field of fusion modeling; the method is characterized by the following steps: collecting air elevation data and ground normal height data, establishing air-ground heterogeneous vertical data sets, and constructing an initial vertical reference conversion model to perform residual fitting and abnormal area detection and identify initial aliasing feature data; analyzing the spatial coupling relationship between gravity anomalies and vertical reference deviations in a target area, outputting a gravity anomaly height difference model, analyzing the response law of terrain undulation and elevation difference in the target area, and outputting a regional elevation disturbance model; performing regional partitioning and difference modeling on the gravity anomaly height difference model and the regional elevation disturbance model, constructing a multi-source vertical correction parameter field, and applying the multi-source vertical correction parameter field to the air-ground heterogeneous vertical data sets to perform joint coordinate correction and three-dimensional geometric fusion and output a fused three-dimensional space model; and the fusion precision of air and ground exploration data is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of fusion modeling technology, and more specifically, to a method and system for fusion modeling of aerial and ground exploration. Background Technology

[0002] In the current process of fusing aerial and ground exploration data into a model, the inconsistency of reference benchmarks between aerial exploration data and ground measurement data often leads to vertical benchmark differences between the data, resulting in spatial deviations, distortions, and regional errors in the fused spatial model. Traditional modeling methods struggle to accurately identify and eliminate spatial coupling interference between complex factors such as gravity anomalies and terrain undulations and differences in vertical reference, resulting in spatial models that are difficult to meet the requirements of refined spatial analysis and applications in terms of accuracy and reliability. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an aerial and ground exploration fusion modeling method and system to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: The method for fusion modeling of aerial and ground exploration includes the following steps: S1. Collect elevation data with reference to the ellipsoid and orthographic data with reference to the geoid, perform time synchronization and unified spatial regional encoding, and output the vertical dataset of heterogeneous sources in the air and ground. S2. Based on the air-ground heterogeneous vertical dataset, construct an initial vertical benchmark transformation model, perform model residual fitting and anomaly region detection, and output the initial aliasing feature data of the target region. S3. Based on the initial aliasing characteristic data, analyze the distribution of gravity anomalies in the target area and their spatial coupling relationship with the vertical reference deviation, and output a gravity anomaly height difference model driven by vertical deviation. S4. Based on the initial aliasing characteristic data, analyze the response law of terrain undulation and elevation difference in the target area, and output the terrain-induced regional elevation disturbance model. S5. Based on the gravity anomaly elevation difference model and the regional elevation disturbance model, perform regional zoning and differential modeling to construct a multi-source vertical correction parameter field; S6. Apply the multi-source vertical correction parameter field to the air-ground heterogeneous vertical dataset, perform joint coordinate correction and three-dimensional geometric fusion, and output the fused three-dimensional spatial model.

[0005] In a preferred embodiment, S1 specifically refers to: Collect elevation data with an ellipsoidal reference obtained from an aerial exploration platform; Orthometric data with reference to the geoid obtained from a ground measurement platform; The timestamps of elevation data referenced to the ellipsoid and orthographic height data referenced to the geoid are synchronized using a unified time reference. The elevation data and orthographic data after timestamp synchronization are spatially region-unified encoded to generate an air-ground heterogeneous vertical dataset.

[0006] In a preferred embodiment, S2 specifically refers to: Extract elevation values ​​with reference to the ellipsoid and orthographic height values ​​with reference to the geoid from the heterogeneous vertical dataset of open ground, and construct a set of elevation difference samples according to a unified spatial coordinate index; The parameters of the elevation difference sample set are estimated by using the partitioned polynomial surface fitting method, and an initial vertical benchmark transformation model is established. Calculate the fitting residuals of the initial vertical benchmark transformation model to each elevation difference sample, identify abnormal spatial units based on the residual amplitude threshold and spatial connectivity judgment criteria, and generate an abnormal region spatial index. The spatial index of the abnormal region is combined with the corresponding fitting residual for encoding, and the initial aliasing feature data of the target region is output.

[0007] In a preferred embodiment, S3 specifically refers to: Obtain the gravity anomaly grid covering the target area and align it with the initial aliasing feature data using a unified spatial coordinate index. The magnitude of gravity anomaly and the vertical reference deviation value corresponding to the node are calculated at each grid node, and the combined values ​​are used to generate a coupled sample matrix. The vertical deviation sensitivity coefficient is estimated by using multiple linear regression on the coupled sample matrix, and the vertical deviation vector field is inverted. The gravity anomaly height difference correction value is obtained by superimposing the vertical deviation vector field and the gravity anomaly amplitude on a grid-by-grid basis. A gravity anomaly height difference model driven by vertical deviation is constructed by outputting the vertical deviation vector field, gravity anomaly amplitude, and gravity anomaly height difference correction value from the spatial grid.

[0008] In a preferred embodiment, S4 specifically refers to: Extract the terrain undulation data and elevation difference data corresponding to each spatial cell in the initial aliasing feature data, and form terrain undulation and elevation difference data pairs according to a unified spatial coordinate index; The terrain relief and elevation difference data are divided into spatial units. The terrain relief slope value and the corresponding elevation difference statistical value are calculated for each spatial unit. The data are then combined to generate a set of terrain relief and elevation difference response sample data. The least squares method was used to estimate the sensitivity coefficient of topographic relief and the elevation difference response sample data set to determine the elevation disturbance parameters induced by topographic relief. Based on the elevation disturbance parameters induced by terrain undulation, the elevation disturbance values ​​corresponding to terrain undulation are calculated within spatial units to construct a regional elevation disturbance model induced by terrain.

[0009] In a preferred embodiment, S5 specifically refers to: The gravity anomaly elevation difference model and the regional elevation disturbance model are overlaid using a unified spatial coordinate index, and the combined vertical difference of each spatial unit is calculated. The target region is divided based on the combined vertical difference and spatial gradient threshold, and a partition index layer is generated. Within each partition, the combined vertical difference is fitted, the gravity anomaly weight coefficient and the terrain disturbance weight coefficient are calculated, and the partition correction parameter vector is output. All partition correction parameter vectors are encoded according to their spatial location, and the continuity of adjacent partition boundaries is smoothed to construct a multi-source vertical correction parameter field.

[0010] In a preferred embodiment, S6 specifically refers to: According to the unified spatial coordinate index, the elevation data and orthographic data of each spatial unit in the air-ground heterogeneous vertical dataset are matched with the partition correction parameter vector corresponding to the multi-source vertical correction parameter field; Based on the partition correction parameter vector corresponding to each spatial unit, the elevation data and orthographic data in each spatial unit are jointly coordinate corrected to obtain aerial and ground data under a unified vertical datum. Spatial point set fusion is performed on aerial and ground data under a unified vertical reference to establish a fused three-dimensional spatial model.

[0011] On the other hand, the present invention provides a fusion modeling system for aerial and ground exploration, comprising: Data acquisition module: Collects elevation data with reference to the ellipsoid and orthographic height data with reference to the geoid, performs time synchronization and unified spatial regional encoding, and outputs vertical datasets from different sources in the air and ground. The benchmark detection module: Based on the air-ground heterogeneous vertical dataset, it constructs an initial vertical benchmark transformation model, performs model residual fitting and anomaly region detection, and outputs the initial aliasing feature data of the target region; Gravity Analysis Module: Based on the initial aliasing characteristic data, analyze the distribution of gravity anomalies in the target area and their spatial coupling relationship with the vertical reference deviation, and output a gravity anomaly height difference model driven by vertical deviation. Terrain Analysis Module: Based on the initial aliasing feature data, analyze the response law of terrain undulation and elevation difference within the target area, and output a terrain-induced regional elevation disturbance model; Parametric modeling module: Based on the gravity anomaly elevation difference model and the regional elevation disturbance model, it performs regional zoning and differential modeling to construct a multi-source vertical correction parameter field; Fusion Modeling Module: Applies the multi-source vertical correction parameter field to the air-ground heterogeneous vertical dataset, performs joint coordinate correction and 3D geometric fusion, and outputs the fused 3D spatial model.

[0012] The technical effects and advantages of the aerial and ground exploration fusion modeling method and system of this invention are as follows: By collecting aerial elevation data referenced to an ellipsoid and ground orthographic data referenced to a geoid, and performing unified time synchronization and spatial encoding, a structured aggregation of air-ground data sources was achieved. An initial vertical datum transformation model was constructed, and residual fitting and anomaly detection were performed to identify aliasing features affecting fusion accuracy. By analyzing the distribution of gravity anomalies within the target area and their spatial coupling relationship with vertical datum deviations, systematic errors could be accurately captured. By analyzing the response patterns of terrain undulations and elevation differences within the target area, a regional elevation disturbance model was generated to compensate for local deviations caused by terrain. Through regional partitioning and difference modeling, a multi-source vertical correction parameter field was constructed, enabling adaptive allocation of gravity and terrain correction weights. This multi-source vertical correction parameter field was applied to the air-ground heterogeneous vertical dataset, performing joint coordinate correction and 3D geometric fusion to obtain a fused 3D spatial model. This improved the accuracy and reliability of the fusion modeling, as well as the geometric accuracy and representational completeness of the 3D spatial model. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the aerial and ground exploration fusion modeling method of the present invention; Figure 2 This is a schematic diagram of the structure of the aerial and ground exploration fusion modeling system of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0015] Example 1 Figure 1 The present invention provides a method for fusion modeling of aerial and ground exploration, which includes the following steps: S1. Collect elevation data with reference to the ellipsoid and orthographic data with reference to the geoid, perform time synchronization and unified spatial regional encoding, and output the vertical dataset of heterogeneous sources in the air and ground. S2. Based on the air-ground heterogeneous vertical dataset, construct an initial vertical benchmark transformation model, perform model residual fitting and anomaly region detection, and output the initial aliasing feature data of the target region. S3. Based on the initial aliasing characteristic data, analyze the distribution of gravity anomalies in the target area and their spatial coupling relationship with the vertical reference deviation, and output a gravity anomaly height difference model driven by vertical deviation. S4. Based on the initial aliasing characteristic data, analyze the response law of terrain undulation and elevation difference in the target area, and output the terrain-induced regional elevation disturbance model. S5. Based on the gravity anomaly elevation difference model and the regional elevation disturbance model, perform regional zoning and differential modeling to construct a multi-source vertical correction parameter field; S6. Apply the multi-source vertical correction parameter field to the air-ground heterogeneous vertical dataset, perform joint coordinate correction and three-dimensional geometric fusion, and output the fused three-dimensional spatial model.

[0016] S1. Collect elevation data referenced to the ellipsoid and orthographic data referenced to the geoid, perform time synchronization and unified spatial regional encoding, and output an air-ground heterogeneous vertical dataset, including: Collect elevation data with an ellipsoidal reference obtained from an aerial exploration platform; Measurements are taken from various spatial units within a target area using measuring devices such as Global Navigation Satellite System (GNSS) receivers, Inertial Navigation Measurement Units (INS), or airborne laser scanners mounted on aerial exploration platforms. These measurements are used to obtain elevation data with an ellipsoidal reference. The aerial exploration platform can be a fixed-wing manned aircraft, a multi-rotor unmanned aerial vehicle (UAV), or a helicopter. For example, in mountainous geological exploration, a fixed-wing manned aircraft equipped with a GNSS receiver and an INS can be used to cover a large area and collect elevation data with an ellipsoidal reference. Ellipsoidal elevation data refers to elevation values ​​measured using the Earth's ellipsoid as a reference datum. For instance, a combined measurement by a GNSS receiver and an INS might yield an ellipsoidal elevation of 200 meters for a specific spatial unit.

[0017] Orthometric data with reference to the geoid obtained from a ground measurement platform; Using surveying equipment such as ground-based laser scanners, precision electronic levels, or electronic total stations mounted on ground-based surveying platforms, measurements are taken separately for each spatial unit within the same target area measured by the aerial exploration platform to obtain orthographic data with reference to the geoid. The ground-based surveying platform includes fixed base stations or mobile surveying equipment. For example, in the same area of ​​a geological exploration mission in mountainous regions, a ground-based laser scanner installed at a fixed surveying station can be used to measure the surface and features within the area via laser scanning, obtaining orthographic data for each spatial unit with reference to the geoid. Orthographic data with reference to the geoid is the orthographic value obtained by measuring using the local geoid as a benchmark. For example, the orthographic data obtained after a ground-based laser scanner scans the ground position of a spatial unit is 180 meters.

[0018] The timestamps of elevation data referenced to the ellipsoid and orthographic height data referenced to the geoid are synchronized using a unified time reference. To achieve spatial consistency and matching between aerial and ground measurement data, it is necessary to add unified timestamps to both the elevation data (referenced to an ellipsoid) obtained from the aerial exploration platform and the orthometric data (referenced to a geoid) obtained from the ground measurement platform, ensuring data synchronization on a time reference basis. Timestamp synchronization involves using a unified high-precision clock source to calibrate the measurement equipment during data acquisition, ensuring data corresponds to the same measurement time or period. For example, the BeiDou satellite time synchronization system can be used to uniformly timestamp the global navigation satellite system receiver on the aerial platform and the ground laser scanner on the ground platform. For instance, when measuring aerial elevation data, a unified standard timestamp of 10:15:20 AM Beijing time on May 12, 2025 is added to each measurement result. Similarly, when measuring ground orthometric data, the same unified standard timestamp of 10:15:20 AM on May 12, 2025 is added simultaneously to ensure accurate matching between elevation and orthometric data.

[0019] The elevation data and orthographic data after timestamp synchronization are uniformly encoded in spatial regions to generate an air-ground heterogeneous vertical dataset; To achieve unified identification and classification management of different spatial units within the measurement area, it is necessary to perform unified spatial region coding on the timestamped elevation data (referenced to the ellipsoid) and orthographic height data (referenced to the geoid). Unified spatial region coding involves pre-setting a unified spatial coding system based on the boundary range and spatial resolution of the measurement area before the target area is measured. For example, if the target measurement area is rectangular, a regular grid division method can be used to evenly divide the measurement area into multiple spatial units with intervals of 10 meters × 10 meters. Each spatial unit is numbered sequentially according to the east-west and north-south directions, starting from the southwest corner of the area and proceeding eastwards from number 1 to 100, then continuing eastwards from number 101 to 200, and so on, until all spatial units within the target area are covered. The elevation data obtained from aerial exploration platform measurements with reference to the ellipsoid and the orthographic data obtained from ground measurement platform measurements with reference to the geoid are coded according to a unified spatial area code, so that the elevation data and orthographic data in each spatial unit correspond to a unique spatial location code.

[0020] After time-stamp synchronization and unified spatial regional coding, the elevation data obtained from the aerial exploration platform, which is time-stamped and spatially coded, and referenced to an ellipsoid, is integrated with the orthometric data obtained from the ground measurement platform, which is time-stamped and spatially coded, and referenced to a geoid. This results in a unified air-ground heterogeneous vertical dataset. The air-ground heterogeneous vertical dataset is a collection of vertical measurement data containing both elevation and orthometric data, based on a unified spatiotemporal reference. For example, for spatial unit numbered 1 in the unified spatial regional coding, the corresponding air-ground heterogeneous vertical dataset includes 200 meters of elevation data (referenced to an ellipsoid) and 180 meters of orthometric data (referenced to a geoid), measured at the unified time of 10:15:20 on May 12, 2025.

[0021] S2. Based on the air-ground heterogeneous vertical dataset, construct an initial vertical baseline transformation model, perform model residual fitting and anomaly region detection, and output the initial aliasing feature data of the target region, including: Extract elevation values ​​with reference to the ellipsoid and orthographic height values ​​with reference to the geoid from the heterogeneous vertical dataset of open ground, and construct a set of elevation difference samples according to a unified spatial coordinate index; Based on the spatial units divided in the unified spatial region coding, for example, spatial units numbered 1 to 100, the elevation and orthographic values ​​corresponding to each spatial unit are extracted. For example, in spatial unit number 1, the elevation value with reference to the ellipsoid is 200 meters, and the orthographic value with reference to the geoid is 180 meters; in spatial unit number 2, the elevation value with reference to the ellipsoid is 205 meters, and the orthographic value with reference to the geoid is 183 meters, and so on, to complete the extraction of data for all spatial units. The extraction process is carried out unit by unit according to the unified spatial coordinate index, and the elevation and orthographic values ​​correspond to the same spatial coordinate index code. After extraction, the elevation and orthographic values ​​together form the elevation difference sample of the spatial unit. For example, the elevation difference sample of spatial unit number 1 is 20 meters, which is obtained by subtracting the geoid orthographic height of 180 meters from the ellipsoid elevation of 200 meters.

[0022] The parameters of the elevation difference sample set are estimated by using the partitioned polynomial surface fitting method, and an initial vertical benchmark transformation model is established. The target area is divided into multiple spatial partitions, each containing several spatial units. For example, a target area for geological exploration in a mountainous region can be divided into several 100m x 100m spatial partitions, each containing several 10m x 10m spatial units. The elevation difference sample sets within each spatial partition are then used for parameter estimation using a polynomial surface fitting method. For instance, for 10 spatial units within a given spatial partition, a quadratic polynomial surface fitting method is used, including calculating the fitting polynomial surface equation for all elevation difference sample data within the spatial partition. For example, the fitting polynomial surface equation might be of the form Z = aX. 2 +bY 2 +cXY+dX+eY+f, where X and Y are the planar coordinates of the corresponding spatial unit, and Z is the corresponding elevation difference sample value; the parameters a, b, c, d, e, and f of the fitted polynomial surface are estimated using the least squares principle; for example, within a certain spatial partition, the fitted polynomial surface equation obtained after parameter estimation is: Z=0.01X 2 -0.02Y 2 +0.005XY+0.1X-0.2Y+18.5, this is used as the initial vertical datum transformation model.

[0023] Calculate the fitting residuals of the initial vertical benchmark transformation model to each elevation difference sample, identify abnormal spatial units based on the residual amplitude threshold and spatial connectivity judgment criteria, and generate an abnormal region spatial index. The theoretical fitted height difference is calculated by substituting the polynomial surface equation corresponding to the initial vertical datum transformation model obtained from the fitting at each spatial unit location. For example, in spatial unit number 1, the theoretical height difference is calculated to be 19.8 meters by substituting the corresponding spatial coordinates X and Y. The theoretical fitted height difference is then subtracted from the actual extracted height difference sample value. For example, the theoretical fitted height difference for spatial unit number 1 is 19.8 meters, while the actual height difference sample value is 20 meters. The resulting fitting residual is 0.2 meters. The fitting residuals for all spatial units within the target area are calculated using the above method to establish the overall fitting residual distribution.

[0024] Set a residual amplitude threshold, for example, set the residual amplitude threshold to 0.5 meters; mark all spatial units whose absolute value of the fitted residual exceeds the residual amplitude threshold of 0.5 meters as initial anomalous spatial units; perform clustering processing on the initial anomalous spatial units based on the spatial connectivity between spatial units; for example, if multiple spatial units have residuals exceeding 0.5 meters and are adjacent to each other, then the multiple spatial units are regarded as a connected anomalous spatial unit cluster; for example, the fitted residuals in three adjacent spatial units numbered 5, 6, and 7 are 0.6 meters, 0.7 meters, and 0.8 meters respectively, all exceeding the set threshold of 0.5 meters, so these three spatial units are jointly determined as an anomalous spatial unit cluster; and establish a unified spatial index for each identified anomalous spatial unit cluster, for example, mark the spatial units numbered 5, 6, and 7 together as the anomalous region spatial index A1.

[0025] The spatial index of the abnormal region is combined with the corresponding fitting residual and encoded to output the initial aliasing feature data of the target region. The fitting residual value of each anomalous spatial unit is combined with the spatial index of its corresponding anomalous region. For example, if the anomalous region spatial index A1 contains three spatial units numbered 5, 6, and 7, with corresponding fitting residuals of 0.6 m, 0.7 m, and 0.8 m, respectively, then the fitting residuals of these three spatial units are combined with the anomalous region spatial index A1 to form initial aliasing feature data of (A1, spatial unit 5, 0.6 m), (A1, spatial unit 6, 0.7 m), and (A1, spatial unit 7, 0.8 m). For all identified anomalous spatial unit clusters and their corresponding fitting residuals within the target region, the combination encoding is completed one by one. Finally, the initial aliasing feature dataset is output.

[0026] S3. Based on the initial aliasing characteristic data, analyze the distribution of gravity anomalies within the target area and their spatial coupling relationship with the vertical reference deviation, and output a gravity anomaly elevation difference model driven by vertical deviation, including: Obtain the gravity anomaly grid covering the target area and align it with the initial aliasing feature data using a unified spatial coordinate index. A gravity anomaly grid is a regular grid of spatially distributed gravity anomaly values ​​within a target area, identified and managed based on a unified spatial coordinate index. For example, the target area may be divided into several regular spatial grids, with each grid node corresponding to a unique spatial coordinate index. For instance, for a rectangular target area in mountainous geological exploration, it can be divided into 100 grid nodes along both the east-west and north-south directions, with a node spacing of, for example, 10 meters. The gravity anomaly value at each grid node is recorded at its corresponding coordinate index location within the target area. For example, a grid node at coordinate index location (X=100 meters, Y=200 meters) records a gravity anomaly value of 15 milligal. The gravity anomaly value is the difference between the actually measured gravity field value and the theoretical reference gravity field value, obtained through on-site measurements in the target area using an absolute gravimeter or a relative gravimeter. The unit of gravity anomaly is milligal.

[0027] The acquired gravity anomaly grid data and the initial aliasing feature data of the target area are aligned using a unified spatial coordinate index to achieve precise matching: the spatial coordinate index corresponding to each spatial cell in the initial aliasing feature dataset is matched one-to-one with the spatial coordinate index of the gravity anomaly grid data. For example, if there is a spatial cell in the initial aliasing feature dataset with a spatial coordinate index of (X=100m, Y=200m), during grid alignment, the initial aliasing feature data corresponding to the spatial cell is matched with the gravity anomaly value recorded by the grid node of the gravity anomaly grid at (X=100m, Y=200m).

[0028] The magnitude of gravity anomaly and the vertical reference deviation value corresponding to the node are calculated at each grid node, and the combined values ​​are used to generate a coupled sample matrix. At each grid node location, the gravity anomaly amplitude is the recorded gravity anomaly value at that location. For example, the gravity anomaly amplitude recorded at the grid node with coordinate index (X=100 m, Y=200 m) is 15 milligal. The vertical reference deviation value is obtained through the initial aliasing feature data at the corresponding location. For example, at the spatial coordinate index location (X=100 m, Y=200 m), based on the fitting residual value recorded in the initial aliasing feature data, the vertical reference deviation value is 0.6 m. Similarly, the gravity anomaly amplitude and vertical reference deviation value at all grid node locations within the region are calculated, and the gravity anomaly amplitude and vertical reference deviation value are combined accordingly, for example, to form coupled sample data (X=100 m, Y=200 m, gravity anomaly amplitude 15 milligal, vertical reference deviation value 0.6 m). The coupled sample data corresponding to all grid nodes are summarized to form a coupled sample matrix.

[0029] The vertical deviation sensitivity coefficient is estimated by using multiple linear regression on the coupled sample matrix, and the vertical deviation vector field is inverted. The multiple linear regression method specifically involves using the gravity anomaly amplitude in the coupled sample matrix as the independent variable and the vertical reference deviation value at the corresponding location as the dependent variable. A linear regression relationship is determined using the linear least squares method, and the vertical deviation sensitivity coefficient is estimated. For example, within the target area, after performing multiple linear regression calculations on the coupled sample data of several grid nodes, the linear relationship expression is obtained as: Vertical reference deviation value = Gravity anomaly amplitude × Sensitivity coefficient + Intercept term. The vertical deviation sensitivity coefficient estimated through the above calculation process is 0.04 m / mGa, and the intercept term is, for example, 0.05 m. Using the estimated vertical deviation sensitivity coefficient, the gravity anomaly amplitude at all grid node locations is calculated, thus inverting the vertical deviation value at each grid node location within the target area. The calculated vertical deviation values ​​at each grid node location are then summarized to form a vertical deviation vector field.

[0030] The gravity anomaly height difference correction value is obtained by superimposing the vertical deviation vector field and the gravity anomaly amplitude on a grid-by-grid basis. The superposition calculation method is as follows: At each grid node location, the estimated vertical deviation value and the corresponding gravity anomaly amplitude are linearly superimposed. For example, at the grid node location with coordinate index (X=100 meters, Y=200 meters), the vertical deviation value is 0.65 meters and the gravity anomaly amplitude is 15 milligal. Through multivariate linear relationships, the gravity anomaly height difference correction value is calculated to be 0.65 meters, which is the total vertical height difference contributed by the vertical deviation of 0.65 meters and the gravity anomaly amplitude. The above calculation is performed on all grid node locations to calculate the gravity anomaly height difference correction value for all grid node locations.

[0031] A gravity anomaly height difference model driven by vertical deviation is constructed by outputting the vertical deviation vector field, gravity anomaly amplitude, and gravity anomaly height difference correction value from the spatial grid. The vertical deviation vector field, gravity anomaly amplitude, and gravity anomaly height difference correction value are output separately according to the spatial grid method and then integrated to construct a gravity anomaly height difference model driven by vertical deviation in the target area. The gravity anomaly height difference model is a comprehensive spatial data model that includes the vertical deviation vector field, gravity anomaly amplitude, and gravity anomaly height difference correction value. For example, at the grid node position of the coordinate index position (X=100 meters, Y=200 meters), the model data is (X=100 meters, Y=200 meters, gravity anomaly amplitude 15 milligal, vertical deviation 0.65 meters, gravity anomaly height difference correction value 0.65 meters).

[0032] S4. Based on the initial aliasing characteristic data, analyze the response patterns of terrain undulation and elevation differences within the target area, and output a terrain-induced regional elevation disturbance model, including: Extract the terrain undulation data and elevation difference data corresponding to each spatial cell in the initial aliasing feature data, and form terrain undulation and elevation difference data pairs according to a unified spatial coordinate index; The initial aliasing feature data consists of the residual data of the vertical reference deviation of each spatial unit in the target area. Each spatial unit corresponds to a specific unified spatial coordinate index. For example, spatial units numbered 1 to 100 in the target area each correspond to a unique spatial coordinate index coordinate position. The terrain undulation data within each spatial unit is the local terrain elevation difference measured in the target area with the center of the spatial unit as the reference. For example, when measuring a spatial unit, if the highest point elevation within the unit is 210 meters and the lowest point elevation is 190 meters, then the corresponding terrain undulation data for the spatial unit is 20 meters. Elevation difference The difference data is the difference between the calculated elevation data (referenced to the ellipsoid) and the orthographic height (referenced to the geoid) in the corresponding spatial cell. For example, in spatial cell number 1, the elevation data (referenced to the ellipsoid) is 200 meters, and the orthographic height (referenced to the geoid) is 180 meters, so the corresponding elevation difference data is 20 meters. The above topographic relief data and elevation difference data are matched according to a unified spatial coordinate index to form topographic relief and elevation difference data pairs. For example, the data pair formed for spatial cell number 1 is (spatial coordinate index: 1, topographic relief value: 20 meters, elevation difference value: 20 meters).

[0033] The terrain relief and elevation difference data are divided into spatial units. The terrain relief slope value and the corresponding elevation difference statistical value are calculated for each spatial unit. The data are then combined to generate a set of terrain relief and elevation difference response sample data. The target area is divided into several independent spatial units according to a unified spatial unit division standard. For example, a target area for geological exploration in a mountainous area can be divided into spatial units of 10 meters × 10 meters in size. Each spatial unit is non-overlapping and covers the entire target area. The slope value of the terrain undulations within each spatial unit is calculated. The slope value is obtained by calculating the ratio between the height difference of the terrain undulations within the spatial unit and the horizontal distance of the spatial unit. For example, if the height difference of the terrain undulations in spatial unit numbered 1 is 20 meters and the horizontal length of the spatial unit is 10 meters, then the slope value is calculated as 20 meters / 10 meters = 2.0. Simultaneously, the elevation within the corresponding spatial unit is also calculated. The difference statistics are calculated as follows: For example, for the elevation difference data in spatial cell number 1, if the elevation difference is 20 meters, then the corresponding elevation difference statistics are 20 meters. The above calculation is performed on all spatial cells in the target area. The terrain undulation slope value and elevation difference statistics obtained from each spatial cell are combined according to a unified spatial coordinate index to form a terrain undulation and elevation difference response sample data set. For example, at the position with spatial coordinate index number 1, the corresponding terrain undulation slope value is 2.0 and the elevation difference statistics are 20 meters, which forms the data sample in the terrain undulation and elevation difference response sample data set (spatial coordinate index: 1, slope value: 2.0, elevation difference statistics: 20 meters).

[0034] The least squares method was used to estimate the sensitivity coefficient of topographic relief and the elevation difference response sample data set to determine the elevation disturbance parameters induced by topographic relief. Using the terrain undulation slope value as the independent variable and the elevation difference statistical value as the dependent variable in the terrain undulation and elevation difference response sample data set, a linear mathematical model expression is constructed. For example, elevation difference statistical value = terrain undulation slope value × terrain undulation sensitivity coefficient + constant term. Based on the linear mathematical model expression, the least squares principle is used for calculation. By performing regression analysis and parameter estimation on the data of all spatial units in the target area, the terrain undulation sensitivity coefficient and constant term are obtained. For example, the terrain undulation sensitivity coefficient is estimated to be 5 meters and the constant term is 0.5 meters in the target area. The terrain undulation sensitivity coefficient obtained above is an important parameter reflecting the degree of influence of terrain undulation on vertical elevation disturbance in the target area, and serves as the elevation disturbance parameter induced by terrain undulation.

[0035] Based on the elevation disturbance parameters induced by terrain undulation, the elevation disturbance values ​​corresponding to terrain undulation are calculated within spatial units to construct a regional elevation disturbance model induced by terrain. Using the estimated terrain undulation-induced elevation disturbance parameter, i.e., the terrain undulation sensitivity coefficient, the elevation disturbance value within each spatial cell is calculated. The expression for calculating the elevation disturbance value is: Elevation disturbance value of spatial cell = Terrain undulation sensitivity coefficient × Terrain undulation slope value + constant term. For example, at spatial cell number 1, the terrain undulation sensitivity coefficient is 5 meters, the corresponding terrain undulation slope value is 2.0, and the constant term is 0.5 meters. Substituting these values ​​into the calculation expression, the calculated elevation disturbance value for the spatial cell is: 5 meters × 2.0 + 0.5 meters = 10.5 meters. The elevation disturbance values ​​for all spatial cells within the target area are calculated using the above method. The calculated elevation disturbance values ​​for all spatial cells are then obtained. The elevation disturbance values ​​are combined with the corresponding spatial coordinate indices to form a set of elevation disturbance values ​​covering all spatial units in the target area. Then, through spatial data organization, a terrain-induced regional elevation disturbance model is constructed. The form of the regional elevation disturbance model is a combination of the spatial coordinate index of each spatial unit and the calculated elevation disturbance value. For example, at the location with spatial coordinate index number 1, the model data is (spatial coordinate index: 1, elevation disturbance value: 10.5 meters). The final set of regional elevation disturbance model data is a comprehensive spatial data model that induces disturbances to the vertical elevation benchmark based on the terrain undulation characteristics in the target area.

[0036] S5. Based on the gravity anomaly elevation difference model and the regional elevation disturbance model, perform regional zoning and difference modeling to construct a multi-source vertical correction parameter field, including: The gravity anomaly elevation difference model and the regional elevation disturbance model are overlaid using a unified spatial coordinate index, and the combined vertical difference of each spatial unit is calculated. The raster overlay process is as follows: Based on the established unified spatial coordinate index, each spatial cell records a gravity anomaly elevation difference correction value in the gravity anomaly elevation difference model driven by vertical deviation. For example, the spatial cell with spatial coordinate index number 1 records a gravity anomaly elevation difference correction value of 0.65 meters. At the same time, the regional elevation disturbance model records the elevation disturbance value induced by terrain undulation corresponding to the same spatial cell. For example, the spatial cell with spatial coordinate index number 1 records an elevation disturbance value of 10.5 meters. The spatial cells are matched and overlaid one by one according to the spatial coordinate index. The overlay calculation is as follows: the combined vertical difference value of each spatial cell is the linear superposition value of the gravity anomaly elevation difference correction value and the elevation disturbance value induced by terrain undulation. For example, the combined vertical difference value calculation expression for the spatial cell with spatial coordinate index number 1 is: 0.65 meters (gravity anomaly elevation difference correction value) + 10.5 meters (elevation disturbance value) = 11.15 meters. The raster overlay calculation is completed for all spatial cells in the target area to generate a combined vertical difference data set.

[0037] The target region is divided based on the combined vertical difference and spatial gradient threshold, and a partition index layer is generated. A spatial gradient threshold is set, which depends on the spatial variation characteristics of the vertical difference in the target area. For example, the spatial gradient threshold can be set to 0.5 meters. The combined vertical difference between each spatial unit and its adjacent spatial units is calculated. This difference is the absolute difference between the combined vertical differences of adjacent spatial units. For example, if the combined vertical difference of spatial unit number 1 is 11.15 meters, and its combined vertical difference with its adjacent spatial unit number 2 is 11.55 meters, then the spatial gradient of the combined vertical difference is calculated to be 0.4 meters. If the spatial gradient of the vertical difference between spatial units is less than the set spatial gradient threshold of 0.5 meters, then the adjacent spatial units are divided into the same zone; if the spatial gradient of the combined vertical difference between spatial units is greater than... If the value equals a set threshold, the spatial unit is divided into different partitions. For example, the vertical difference between the spatial unit combination with spatial coordinate index number 2 is 11.55 meters, and the vertical difference between the spatial unit combination with spatial coordinate index number 3 is 12.10 meters. Their spatial gradient is calculated to be 0.55 meters, which is greater than the spatial gradient threshold of 0.5 meters. Therefore, spatial units numbered 2 and 3 are divided into different partitions. By dividing all spatial units in the entire target area using the above method, several spatial partitions are generated. Each spatial partition is marked with a unique partition code, and the partition code is recorded in the form of spatial coordinate index to form a partition index layer. For example, spatial units numbered 1 to 20 form partition code B1, and spatial units numbered 21 to 40 form partition code B2.

[0038] Within each partition, the combined vertical difference is fitted, the gravity anomaly weight coefficient and the terrain disturbance weight coefficient are calculated, and the partition correction parameter vector is output. Within each spatial partition, based on the combined vertical difference, linear mathematical model expressions for the contributions of gravity anomaly and terrain disturbance are established: Combined vertical difference = Gravity anomaly weight coefficient × Gravity anomaly elevation difference correction value + Terrain disturbance weight coefficient × Terrain undulation disturbance value + Intercept term; For example, within partition code B1, using data containing 20 spatial units, parameter estimation is performed using the least squares linear regression method, calculating the gravity anomaly weight coefficient as 0.2, the terrain disturbance weight coefficient as 0.8, and the intercept term as 0.05 meters. The parameter coefficients for each spatial partition within the target area are calculated using the above method, and the parameters are combined to form a partition correction parameter vector. For example, the partition correction parameter vector for partition code B1 is (gravity anomaly weight coefficient: 0.2, terrain disturbance weight coefficient: 0.8, intercept term: 0.05 meters), forming a set of partition correction parameter vectors.

[0039] All partition correction parameter vectors are encoded according to their spatial location, and the continuity of adjacent partition boundaries is smoothed to construct a multi-source vertical correction parameter field. The continuous smoothing process is as follows: There are usually differences in correction parameters between adjacent spatial partitions. For example, the terrain disturbance weighting coefficient for partition B1 is 0.8, while that for adjacent partition B2 is 0.7. Therefore, there is a parameter difference of 0.1 at the boundary between partitions B1 and B2. To avoid abrupt parameter changes at spatial boundaries, a boundary buffer smoothing method is used. For example, a buffer zone with a width of one spatial unit (10 meters) is defined within the boundary area. Within this buffer zone, smoothing transition parameters are calculated through progressive linear interpolation. The interpolation smoothing algorithm is as follows: Correction parameters for spatial units are calculated through linear interpolation within the boundary buffer zone, gradually changing to achieve a smooth transition. For example, after calculating the interpolation at the spatial unit position at the boundary between B1 and B2… The terrain disturbance weighting coefficient is 0.75 (the average of 0.8 and 0.7), and it gradually transitions into the interior of the two partitions until it is restored to the original parameter values ​​of the partitions. The boundary of all spatial partitions within the target area is processed sequentially using the above method. After the boundary smoothing of all spatial partitions is completed, a multi-source vertical correction parameter field with continuous parameters and smooth boundaries is formed. The multi-source vertical correction parameter field is a spatial data field covering the entire target area, which includes the gravity anomaly weighting coefficient, terrain disturbance weighting coefficient, and intercept term corresponding to the spatial coordinate index position. For example, the data corresponding to the spatial unit with spatial coordinate index number 1 is (gravity anomaly weighting coefficient: 0.2, terrain disturbance weighting coefficient: 0.8, intercept term: 0.05 meters).

[0040] S6. Apply the multi-source vertical correction parameter field to the air-to-ground heterogeneous vertical dataset, perform joint coordinate correction and 3D geometric fusion, and output the fused 3D spatial model, including: According to the unified spatial coordinate index, the elevation data and orthographic data of each spatial unit in the air-ground heterogeneous vertical dataset are matched with the partition correction parameter vector corresponding to the multi-source vertical correction parameter field; Based on a unified spatial coding system, each spatial unit has a unique unified spatial coordinate index. For example, in a geological exploration task in a mountainous area, each spatial unit has an area of ​​10 meters × 10 meters. The spatial coordinate index is numbered consecutively from 1 until it covers all spatial units in the entire target area. For example, the spatial unit with spatial coordinate index number 1 corresponds to an elevation of 200 meters with reference to the ellipsoid and an orthographic height of 180 meters with reference to the geoid. In the multi-source vertical correction parameter field, each spatial unit also records its corresponding partition correction parameter vector according to the same set of unified spatial coordinate indexes. For example, the partition correction parameter vector recorded for the spatial unit with spatial coordinate index number 1 is... The weighting coefficients are: (gravity anomaly weighting coefficient: 0.2, terrain disturbance weighting coefficient: 0.8, intercept term: 0.05 meters). For each spatial unit, elevation data, orthographic height data, and zonal correction parameter vectors are matched according to the spatial coordinate index. The matching result ensures that each spatial unit accurately corresponds to a unique correction parameter. For example, at the location of spatial unit number 1, the elevation data of 200 meters (referenced to the ellipsoid) and the orthographic height data of 180 meters (referenced to the geoid) are matched with the zonal correction parameter vector (gravity anomaly weighting coefficient 0.2, terrain disturbance weighting coefficient 0.8, intercept term 0.05 meters) to form a set of matched data. The matching work for the entire target area is completed unit by unit using the above method.

[0041] Based on the partition correction parameter vector corresponding to each spatial unit, the elevation data and orthographic data in each spatial unit are jointly coordinate corrected to obtain aerial and ground data under a unified vertical datum. The joint coordinate correction specifically involves using the gravity anomaly weight coefficient, terrain disturbance weight coefficient, and intercept term recorded in the partition correction parameter vector corresponding to the spatial cell. Based on the gravity anomaly elevation difference correction value and the terrain undulation-induced elevation disturbance value of the corresponding spatial cell, the joint correction parameter value of the spatial cell is calculated. The correction formula is: Joint correction parameter value = Gravity anomaly weight coefficient × Gravity anomaly elevation difference correction value + Terrain disturbance weight coefficient × Terrain undulation elevation disturbance value + Intercept term. For example, in the spatial cell with spatial coordinate index number 1, the gravity anomaly elevation difference correction value is 0.65 meters, and the terrain undulation elevation disturbance value is 10.5 meters. Substituting these values ​​into the weight coefficients and intercept term in the corresponding partition correction parameter vector, the calculation is as follows: Joint correction parameter value = 0.2 × 0.65 meters + 0.8 × 10.5 meters + 0.05 meters = 0.13 meters + 8.4 meters + 0.05 meters = 8.58 meters; Using the calculated joint correction parameter value, coordinate correction is performed on the original elevation data and orthographic data within the spatial unit. For example, subtracting the joint correction parameter value of 8.58 meters from the elevation data of 200 meters with the ellipsoid as the reference yields an elevation data of 191.42 meters under the unified vertical datum; simultaneously, subtracting the joint correction parameter value of 8.58 meters from the orthographic data of 180 meters with the geoid as the reference yields an orthographic data of 171.42 meters under the unified vertical datum. The above calculations are performed in all spatial units within the target area, and the corrected elevation data and orthographic data under the unified vertical datum for each spatial unit are recorded to form an aerial and ground dataset under the unified vertical datum covering the entire target area. The aerial and ground dataset under the unified vertical datum enables accurate fusion of aerial exploration data and ground measurement data under unified datum conditions.

[0042] Spatial point set fusion is performed on aerial and ground data under a unified vertical reference to establish a fused three-dimensional spatial model; The three-dimensional coordinate point data of corresponding spatial units in the aerial and ground data are integrated to form a dense and unified three-dimensional spatial point set. For example, in the spatial unit with spatial coordinate index number 1, the aerial data under the unified vertical datum is 191.42 meters after correction to the ellipsoidal datum elevation, and the ground data under the unified vertical datum is 171.42 meters after correction to the geoid orthographic elevation. The aerial and ground data within the spatial unit are recorded in the form of three-dimensional spatial coordinates, including the horizontal spatial coordinate position and the corrected unified vertical elevation. For example, the aerial data is recorded as spatial coordinates (X coordinate value: 100 meters, Y coordinate value: 200 meters, unified vertical elevation: 191.42 meters), and the ground data is recorded as spatial coordinates (X coordinate value: 100 meters, Y coordinate value: 200 meters, unified vertical elevation: 171.42 meters). By performing the above fusion on a spatial unit-by-spatial basis, a dense and unified three-dimensional data set covering the target area is established. Spatial point set; After the spatial point set is fused, 3D modeling technology is used to reconstruct the spatial surface of the fused unified 3D spatial point set. For example, the 3D Delaunay triangulation algorithm is used, with the fused 3D spatial point set with unified spatial coordinate index as input data, to generate a 3D spatial surface model covering the target area. The 3D spatial surface model records the 3D coordinates and surface topological relationships of all units in the space. For example, in the spatial unit with spatial coordinate index number 1 in the 3D spatial model, the surface model topology is formed by connecting several adjacent spatial points to form a triangular network structure, showing the 3D spatial morphology of the spatial unit. By reconstructing the 3D surface model of each spatial unit and connecting them into a continuous 3D curved surface structure, a fused 3D spatial model covering the entire target area is finally formed. The fused 3D spatial model has a unified vertical spatial reference and can reflect the real 3D spatial structure characteristics of the target area, which is widely applicable to geological, engineering and environmental exploration tasks.

[0043] Example 2 The difference between Embodiment 2 and Embodiment 1 is that this embodiment introduces a fusion modeling system for aerial and ground exploration.

[0044] Figure 2 A schematic diagram of the aerial and ground exploration fusion modeling system of the present invention is provided. The aerial and ground exploration fusion modeling system includes: Data acquisition module: Collects elevation data with reference to the ellipsoid and orthographic height data with reference to the geoid, performs time synchronization and unified spatial regional encoding, and outputs vertical datasets from different sources in the air and ground. The benchmark detection module: Based on the air-ground heterogeneous vertical dataset, it constructs an initial vertical benchmark transformation model, performs model residual fitting and anomaly region detection, and outputs the initial aliasing feature data of the target region; Gravity Analysis Module: Based on the initial aliasing characteristic data, analyze the distribution of gravity anomalies in the target area and their spatial coupling relationship with the vertical reference deviation, and output a gravity anomaly height difference model driven by vertical deviation. Terrain Analysis Module: Based on the initial aliasing feature data, analyze the response law of terrain undulation and elevation difference within the target area, and output a terrain-induced regional elevation disturbance model; Parametric modeling module: Based on the gravity anomaly elevation difference model and the regional elevation disturbance model, it performs regional zoning and differential modeling to construct a multi-source vertical correction parameter field; Fusion Modeling Module: Applies the multi-source vertical correction parameter field to the air-ground heterogeneous vertical dataset, performs joint coordinate correction and 3D geometric fusion, and outputs the fused 3D spatial model.

[0045] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0046] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0047] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0048] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0049] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0050] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0051] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0052] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0053] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0054] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for fusion modeling of aerial and ground exploration, characterized in that, Includes the following steps: S1. Collect elevation data with reference to the ellipsoid and orthographic data with reference to the geoid, perform time synchronization and unified spatial regional encoding, and output the vertical dataset of heterogeneous sources in the air and ground. S2. Based on the air-ground heterogeneous vertical dataset, construct an initial vertical benchmark transformation model, perform model residual fitting and anomaly region detection, and output the initial aliasing feature data of the target region. S3. Based on the initial aliasing characteristic data, analyze the distribution of gravity anomalies in the target area and their spatial coupling relationship with the vertical reference deviation, and output a gravity anomaly height difference model driven by vertical deviation. S4. Based on the initial aliasing characteristic data, analyze the response law of terrain undulation and elevation difference in the target area, and output the terrain-induced regional elevation disturbance model. S5. Based on the gravity anomaly elevation difference model and the regional elevation disturbance model, perform regional zoning and differential modeling to construct a multi-source vertical correction parameter field; S6. Apply the multi-source vertical correction parameter field to the air-ground heterogeneous vertical dataset, perform joint coordinate correction and three-dimensional geometric fusion, and output the fused three-dimensional spatial model.

2. The aerial and ground exploration fusion modeling method according to claim 1, characterized in that, S1, specifically: Collect elevation data with an ellipsoidal reference obtained from an aerial exploration platform; Orthometric data with reference to the geoid obtained from a ground measurement platform; The timestamps of elevation data referenced to the ellipsoid and orthographic height data referenced to the geoid are synchronized using a unified time reference. The elevation data and orthographic data after timestamp synchronization are spatially region-unified encoded to generate an air-ground heterogeneous vertical dataset.

3. The aerial and ground exploration fusion modeling method according to claim 2, characterized in that, S2, specifically: Extract elevation values ​​with reference to the ellipsoid and orthographic height values ​​with reference to the geoid from the heterogeneous vertical dataset of open ground, and construct a set of elevation difference samples according to a unified spatial coordinate index; The parameters of the elevation difference sample set are estimated by using the partitioned polynomial surface fitting method, and an initial vertical benchmark transformation model is established. Calculate the fitting residuals of the initial vertical benchmark transformation model to each elevation difference sample, identify abnormal spatial units based on the residual amplitude threshold and spatial connectivity judgment criteria, and generate an abnormal region spatial index. The spatial index of the abnormal region is combined with the corresponding fitting residual for encoding, and the initial aliasing feature data of the target region is output.

4. The aerial and ground exploration fusion modeling method according to claim 3, characterized in that, S3, specifically: Obtain the gravity anomaly grid covering the target area and align it with the initial aliasing feature data using a unified spatial coordinate index. The magnitude of gravity anomaly and the vertical reference deviation value corresponding to the node are calculated at each grid node, and the combined values ​​are used to generate a coupled sample matrix. The vertical deviation sensitivity coefficient is estimated by using multiple linear regression on the coupled sample matrix, and the vertical deviation vector field is inverted. The gravity anomaly height difference correction value is obtained by superimposing the vertical deviation vector field and the gravity anomaly amplitude on a grid-by-grid basis. A gravity anomaly height difference model driven by vertical deviation is constructed by outputting the vertical deviation vector field, gravity anomaly amplitude, and gravity anomaly height difference correction value from the spatial grid.

5. The aerial and ground exploration fusion modeling method according to claim 4, characterized in that, S4, specifically: Extract the terrain undulation data and elevation difference data corresponding to each spatial cell in the initial aliasing feature data, and form terrain undulation and elevation difference data pairs according to a unified spatial coordinate index; The terrain relief and elevation difference data are divided into spatial units. The terrain relief slope value and the corresponding elevation difference statistical value are calculated for each spatial unit. The data are then combined to generate a set of terrain relief and elevation difference response sample data. The least squares method was used to estimate the sensitivity coefficient of topographic relief and the elevation difference response sample data set to determine the elevation disturbance parameters induced by topographic relief. Based on the elevation disturbance parameters induced by terrain undulation, the elevation disturbance values ​​corresponding to terrain undulation are calculated within spatial units to construct a regional elevation disturbance model induced by terrain.

6. The method for fusion modeling of aerial and ground exploration according to claim 5, characterized in that, S5, specifically: The gravity anomaly elevation difference model and the regional elevation disturbance model are overlaid using a unified spatial coordinate index, and the combined vertical difference of each spatial unit is calculated. The target region is divided based on the combined vertical difference and spatial gradient threshold, and a partition index layer is generated. Within each partition, the combined vertical difference is fitted, the gravity anomaly weight coefficient and the terrain disturbance weight coefficient are calculated, and the partition correction parameter vector is output. All partition correction parameter vectors are encoded according to their spatial location, and the continuity of adjacent partition boundaries is smoothed to construct a multi-source vertical correction parameter field.

7. The aerial and ground exploration fusion modeling method according to claim 6, characterized in that, S6, specifically: According to the unified spatial coordinate index, the elevation data and orthographic data of each spatial unit in the air-ground heterogeneous vertical dataset are matched with the partition correction parameter vector corresponding to the multi-source vertical correction parameter field; Based on the partition correction parameter vector corresponding to each spatial unit, the elevation data and orthographic data in each spatial unit are jointly coordinate corrected to obtain aerial and ground data under a unified vertical datum. Spatial point set fusion is performed on aerial and ground data under a unified vertical reference to establish a fused three-dimensional spatial model.

8. An aerial and ground exploration fusion modeling system, used to implement the aerial and ground exploration fusion modeling method according to any one of claims 1-7, characterized in that, include: Data acquisition module: Collects elevation data with reference to the ellipsoid and orthographic height data with reference to the geoid, performs time synchronization and unified spatial regional encoding, and outputs vertical datasets from different sources in the air and ground. The benchmark detection module: Based on the air-ground heterogeneous vertical dataset, it constructs an initial vertical benchmark transformation model, performs model residual fitting and anomaly region detection, and outputs the initial aliasing feature data of the target region; Gravity Analysis Module: Based on the initial aliasing characteristic data, analyze the distribution of gravity anomalies in the target area and their spatial coupling relationship with the vertical reference deviation, and output a gravity anomaly height difference model driven by vertical deviation. Terrain Analysis Module: Based on the initial aliasing feature data, analyze the response law of terrain undulation and elevation difference within the target area, and output a terrain-induced regional elevation disturbance model; Parametric modeling module: Based on the gravity anomaly elevation difference model and the regional elevation disturbance model, it performs regional zoning and differential modeling to construct a multi-source vertical correction parameter field; Fusion Modeling Module: Applies the multi-source vertical correction parameter field to the air-ground heterogeneous vertical dataset, performs joint coordinate correction and 3D geometric fusion, and outputs the fused 3D spatial model.

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