Airborne electromagnetic survey comprehensive interpretation method and device

Abstract

The application discloses an airborne electromagnetic detection comprehensive interpretation method and device, and belongs to the technical field of geophysical exploration, hydrology and engineering geological exploration. The application utilizes the difference between the physical property in the detection target body and the geophysical property of the adjacent geological body, mainly utilizes the physical property information provided by the airborne electromagnetic data, obtains the three-dimensional distribution characteristics of the target body, combines the inversion and manual participation division of the airborne electromagnetic data, the magnetic data, the gravity data, the drilling data and other auxiliary data, significantly reduces the multi-solution property of the geophysical inversion, effectively eliminates the shallow and deep interference, improves the accuracy and reliability of the target body boundary definition, and reduces unnecessary drilling workload.

Description

Technical Field

[0001] This application relates to the fields of geophysical exploration, hydrological and engineering geological exploration technology, and in particular to a comprehensive interpretation method and apparatus for airborne electromagnetic detection. Background Technology

[0002] Airborne geophysics is a crucial tool in resource exploration and hydrogeological and environmental fields, offering unique advantages in areas with complex terrain and harsh construction conditions. Airborne electromagnetic methods utilize the electrical differences between near-surface strata and geological bodies to distinguish their distribution in underground space. Airborne magnetic methods detect the deviations of geological bodies from specific magnetic fields to determine their characteristics. Similarly, airborne gravity and radiometric methods utilize density and radioactivity characteristics to obtain information about the distribution of underground targets.

[0003] Ground geophysics is a more refined method than airborne geophysics, capable of obtaining more accurate data at specific locations. However, its low construction efficiency, susceptibility to terrain conditions, and high cost also limit its large-scale application.

[0004] Drilling, through observation of rock cores, allows for direct acquisition of the sample's composition and physical properties, accurately obtaining lithological information at a specified location and depth. The spatial location of the sample is most precise, enabling the determination of corresponding electrical, magnetic, radioactive, density, and material composition information through testing.

[0005] In existing technologies, electromagnetic, magnetic, and radiometric gravity data can be acquired from the ground and air, while airborne electrical resistivity tomography (EMT) parameters generally need to be collected separately. Due to the ambiguity of geophysical methods, the interpretation results of a single parameter often fail to reflect the distribution of the target body. Drilling is costly and yields relatively few core samples, making it difficult to support efficient interpretation over large areas. Multi-data source integrated interpretation methods have poor specificity for particular regions, and the interpretation results do not match the actual application situation well.

[0006] The information disclosed in this background section is intended only to enhance the understanding of the overall background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this application is to provide a comprehensive interpretation method and device for airborne electromagnetic detection, which can significantly reduce the ambiguity of geophysical inversion, effectively eliminate shallow and deep interference, improve the accuracy and reliability of target boundary delineation, and reduce unnecessary drilling workload.

[0008] To achieve the above objectives, this application provides a comprehensive interpretation method for airborne electromagnetic detection, the method comprising:

[0009] Collect airborne electromagnetic data, other multi-source geophysical data, and other auxiliary data within the detection area. The other auxiliary data includes at least one of the following: positioning information, altitude information, and current information.

[0010] Based on the other multi-source geophysical data and geological constraint information, physical property clustering analysis is performed on the strata and target bodies in the exploration area to construct a geological model that includes initial stratigraphic boundaries and physical property assignments.

[0011] The airborne electromagnetic data, the other auxiliary data, and the geological model are used for data processing and resistivity inversion calculation to generate a three-dimensional resistivity distribution model of underground space.

[0012] The response characteristics of the other multi-source geophysical data are used to cross-validate the three-dimensional resistivity distribution model, and to identify and eliminate false anomalies caused by non-target geological bodies or human interference on the surface.

[0013] Based on the three-dimensional resistivity distribution model after removing interference, the other auxiliary data, and the borehole measurement data, the physical property boundary of the target body is dynamically corrected through human-computer interaction, and the three-dimensional spatial distribution morphology of the target body in the detection area is interpreted.

[0014] In some embodiments, the step of performing physical property clustering analysis on the strata and target bodies in the exploration area based on the other multi-source geophysical data and geological constraint information, and constructing a geological model including initial stratigraphic boundaries and physical property assignments, includes:

[0015] Using the electrical, magnetic, density, and radioactive parameters measured from borehole cores, cluster analysis was performed on the other multi-source geophysical data to summarize the characteristic physical fingerprints of different stratigraphic units and target bodies.

[0016] Based on the aforementioned characteristic physical fingerprint, the strata within the detection area are initialized with object information layering, and the initial depth limits, interlayer thicknesses, and material composition of each layer are set.

[0017] The physical property fingerprints of each stratigraphic region obtained from cluster analysis are assigned to the corresponding initial stratigraphic layers to form a geological model containing the initial distribution of resistivity, magnetic susceptibility, density, and radioactivity intensity, which serves as a priori constraints for inversion calculations.

[0018] In some embodiments, the step of using the airborne electromagnetic data, the other auxiliary data, and the geological model to perform data processing and resistivity inversion calculation to generate a three-dimensional resistivity distribution model of underground space includes:

[0019] A lateral constraint inversion algorithm is used to perform channel extraction, superposition, filtering, and horizontal leveling on the airborne electromagnetic data to enhance the signal-to-noise ratio and preserve the continuity of the lateral geological body distribution.

[0020] During the inversion iteration process, the stratigraphic structure of the geological model and other auxiliary data are introduced as rigid constraints to limit the range of abrupt changes in the resistivity model in the vertical direction.

[0021] High-frequency noise points and non-geologically derived abnormal data in the processing results were identified and removed to obtain a three-dimensional resistivity distribution model of underground space.

[0022] In some embodiments, the other multi-source geophysical data includes at least aeromagnetic data, aerogravity data, and aeroradioactive data; the step of cross-validating the three-dimensional resistivity distribution model using the response characteristics of the other multi-source geophysical data to identify and eliminate false anomalies caused by non-target geological bodies or surface human disturbance includes:

[0023] Spatial registration and correlation analysis were performed between the three-dimensional resistivity distribution model and the airborne magnetic data, airborne gravity data, and airborne radioactivity data.

[0024] A multiphysics anomaly discrimination rule is established. The multiphysics anomaly discrimination rule for typical ore bodies is as follows: when the three-dimensional resistivity distribution model shows a low resistivity anomaly, if the corresponding airborne magnetic data shows a low magnetic anomaly and the airborne gravity data shows a low density anomaly, it is judged as a suspected target body; if the corresponding airborne magnetic data shows a high magnetic anomaly, it is marked as igneous rock body interference and removed.

[0025] By combining remote sensing image data and records of human activities on the ground, electromagnetic distortion zones caused by man-made facilities such as shallow buildings and high-voltage lines are identified, and the resistivity anomalies in these zones are removed from the 3D model.

[0026] In some embodiments, the dynamic correction of the physical property boundary of the target body based on the interference-free three-dimensional resistivity distribution model, the other auxiliary data, and borehole measured data, through human-computer interaction, and the interpretation of the three-dimensional spatial distribution morphology of the target body within the detection area, includes:

[0027] Using the positioning information in the other auxiliary data, the grid nodes in the three-dimensional resistivity distribution model are aligned with the borehole measured data, and the spatial distance difference is calculated.

[0028] Based on the spatial distance difference, a distance attenuation function is constructed to determine the influence factors of the borehole data, wherein the influence factors are negatively correlated with distance;

[0029] The influencing factors are imported into the inversion model and combined with the other auxiliary data to perform a secondary constraint inversion, generating a new three-dimensional resistivity distribution model.

[0030] The human-computer interaction interface displays the degree of matching between the new three-dimensional resistivity distribution model and geological conditions. Based on the adjustment operation of the human-computer interaction interface, the physical property boundary conditions of the target body are adjusted until the model conforms to geological logic, and the three-dimensional spatial distribution morphology of the target body in the detection area is obtained.

[0031] In some embodiments, the method further includes:

[0032] The matching criteria are set, including the residual of the new three-dimensional resistivity distribution model being less than a preset threshold, the target body's orientation being consistent with the known geological structure trend, and the target body's physical properties being consistent with the borehole sampling analysis results.

[0033] If the matching criteria are met, the final three-dimensional distribution range of the mineralization zone or target body will be output.

[0034] If the conformity criterion is not met, an iterative correction process is triggered to redetermine the physical boundary conditions of the target body, restrict the unreasonable expansion of the abnormal region, and return to execute the secondary constraint inversion step until the conformity criterion is met.

[0035] In some embodiments, the method further includes:

[0036] Output the final interpretation results;

[0037] The explanation results include:

[0038] Planar distribution map of the target object, vertical slices at different depths, and a three-dimensional visualization model;

[0039] Precise burial depth range, distribution status, and statistical information on physical properties of the target object;

[0040] Based on the aforementioned three-dimensional spatial distribution pattern, a report is generated recommending mineral resource exploration or water and environmental remediation.

[0041] On the one hand, an integrated interpretation device for airborne electromagnetic detection is provided, the device comprising:

[0042] The collection module is used to collect airborne electromagnetic data, other multi-source geophysical data and other auxiliary data within the detection area. The other auxiliary data includes at least one of the following: positioning information, altitude information and current information.

[0043] The construction module is used to perform physical property clustering analysis on the strata and target bodies in the exploration area based on the other multi-source geophysical data and geological constraint information, and to construct a geological model including initial stratigraphic boundaries and physical property assignments.

[0044] The generation module is used to perform data processing and resistivity inversion calculation using the airborne electromagnetic data, the other auxiliary data and the geological model to generate a three-dimensional resistivity distribution model of underground space.

[0045] The processing module is used to cross-validate the three-dimensional resistivity distribution model using the response characteristics of the other multi-source geophysical data, and to identify and eliminate false anomalies caused by non-target geological bodies or human interference on the surface.

[0046] The interpretation module is used to dynamically correct the physical property boundary of the target body through human-computer interaction based on the three-dimensional resistivity distribution model after removing interference, the other auxiliary data and the borehole measured data, and interpret the three-dimensional spatial distribution morphology of the target body in the detection area.

[0047] In some embodiments, the building module is used for:

[0048] Using the electrical, magnetic, density, and radioactive parameters measured from borehole cores, cluster analysis was performed on the other multi-source geophysical data to summarize the characteristic physical fingerprints of different stratigraphic units and target bodies.

[0049] Based on the aforementioned characteristic physical fingerprint, the strata within the detection area are initialized with object information layering, and the initial depth limits, interlayer thicknesses, and material composition of each layer are set.

[0050] The physical property fingerprints of each stratigraphic region obtained from cluster analysis are assigned to the corresponding initial stratigraphic layers to form a geological model containing the initial distribution of resistivity, magnetic susceptibility, density, and radioactivity intensity, which serves as a priori constraints for inversion calculations.

[0051] In some embodiments, the generation module is used for:

[0052] A lateral constraint inversion algorithm is used to perform channel extraction, superposition, filtering, and horizontal leveling on the airborne electromagnetic data to enhance the signal-to-noise ratio and preserve the continuity of the lateral geological body distribution.

[0053] During the inversion iteration process, the stratigraphic structure of the geological model and other auxiliary data are introduced as rigid constraints to limit the range of abrupt changes in the resistivity model in the vertical direction.

[0054] High-frequency noise points and non-geologically derived abnormal data in the processing results were identified and removed to obtain a three-dimensional resistivity distribution model of underground space.

[0055] In some embodiments, the other multi-source geophysical data includes at least aeromagnetic data, aerogravity data, and aeroradioactive data; the processing module is used for:

[0056] Spatial registration and correlation analysis were performed between the three-dimensional resistivity distribution model and the airborne magnetic data, airborne gravity data, and airborne radioactivity data.

[0057] A multiphysics anomaly discrimination rule is established. The multiphysics anomaly discrimination rule for typical ore bodies is as follows: when the three-dimensional resistivity distribution model shows a low resistivity anomaly, if the corresponding airborne magnetic data shows a low magnetic anomaly and the airborne gravity data shows a low density anomaly, it is judged as a suspected target body; if the corresponding airborne magnetic data shows a high magnetic anomaly, it is marked as igneous rock body interference and removed.

[0058] By combining remote sensing image data and records of human activities on the ground, electromagnetic distortion zones caused by man-made facilities such as shallow buildings and high-voltage lines are identified, and the resistivity anomalies in these zones are removed from the 3D model.

[0059] In some embodiments, the interpretation module is used for:

[0060] Using the positioning information in the other auxiliary data, the grid nodes in the three-dimensional resistivity distribution model are aligned with the borehole measured data, and the spatial distance difference is calculated.

[0061] Based on the spatial distance difference, a distance attenuation function is constructed to determine the influence factors of the borehole data, wherein the influence factors are negatively correlated with distance;

[0062] The influencing factors are imported into the inversion model and combined with the other auxiliary data to perform a secondary constraint inversion, generating a new three-dimensional resistivity distribution model.

[0063] The human-computer interaction interface displays the degree of matching between the new three-dimensional resistivity distribution model and geological conditions. Based on the adjustment operation of the human-computer interaction interface, the physical property boundary conditions of the target body are adjusted until the model conforms to geological logic, and the three-dimensional spatial distribution morphology of the target body in the detection area is obtained.

[0064] In some embodiments, the apparatus further includes:

[0065] The setting module is used to set the matching criteria, which include the residual of the new three-dimensional resistivity distribution model being less than a preset threshold, the target body's attitude being consistent with the known geological structure trend, and the target body's physical properties being consistent with the borehole sampling analysis results.

[0066] If the matching criteria are met, the interpretation module is used to output the final three-dimensional distribution range of the mineralization zone or the target body.

[0067] If the conformity criterion is not met, the interpretation module is used to trigger the iterative correction process, redetermine the physical boundary conditions of the target body, limit the unreasonable expansion of the abnormal region, and return to execute the secondary constraint inversion step until the conformity criterion is met.

[0068] In some embodiments, the apparatus further includes:

[0069] The output module is used to output the final interpretation results;

[0070] The explanation results include:

[0071] Planar distribution map of the target object, vertical slices at different depths, and a three-dimensional visualization model;

[0072] Precise burial depth range, distribution status, and statistical information on physical properties of the target object;

[0073] Based on the aforementioned three-dimensional spatial distribution pattern, a report is generated recommending mineral resource exploration or water and environmental remediation.

[0074] On one hand, an electronic device is provided, comprising one or more processors and one or more memories, wherein at least one computer program is stored in the one or more memories, and the at least one computer program is loaded and executed by the one or more processors to implement various optional implementations of the above-described integrated interpretation method for airborne electromagnetic detection.

[0075] On the one hand, a computer-readable storage medium is provided, wherein at least one computer program is stored in the storage medium, and the at least one computer program is loaded and executed by a processor to implement various optional implementations of the above-described integrated interpretation method for airborne electromagnetic detection.

[0076] On one hand, a computer program product or computer program is provided, the computer program product or computer program comprising one or more lines of program code stored in a computer-readable storage medium. One or more processors of an electronic device read the one or more lines of program code from the computer-readable storage medium, and the one or more processors execute the one or more lines of program code, causing the electronic device to perform the airborne electromagnetic detection integrated interpretation method of any of the above possible embodiments.

[0077] This application utilizes the differences in physical properties between the target body and the surrounding adjacent geological bodies. It mainly uses the physical property information provided by airborne electromagnetic data to obtain the three-dimensional distribution characteristics of the target body. Combined with airborne electromagnetic, magnetic, gravity, borehole data and other auxiliary data, it performs inversion and manual delineation, which significantly reduces the ambiguity of geophysical inversion, effectively eliminates shallow and deep interference, improves the accuracy and reliability of target body boundary delineation, and reduces unnecessary drilling workload. Attached Figure Description

[0078] Figure 1 This is a flowchart illustrating the process of a comprehensive interpretation method for airborne electromagnetic detection provided in an embodiment of this application.

[0079] Figure 2 This is an implementation flowchart of an airborne electromagnetic detection comprehensive interpretation method provided in an embodiment of this application.

[0080] Figure 3 This is a schematic diagram of the structure of an integrated interpretation device for airborne electromagnetic detection provided in an embodiment of this application.

[0081] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0082] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0083] In this application, the term "at least one" means one or more, and the term "multiple" means two or more. For example, multiple data packets means two or more data packets.

[0084] It should be understood that the terminology used in the description of the various examples herein is for the purpose of describing particular examples only and is not intended to be limiting. As used in the description of the various examples and the appended claims, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0085] It should also be understood that the term "and / or" as used herein refers to and covers any and all possible combinations of one or more of the associated listed items. The term "and / or" describes an association between related objects, indicating the existence of three relationships; for example, A and / or B means: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " in this application generally indicates that the preceding and following related objects are in an "or" relationship.

[0086] It should also be understood that, in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0087] It should also be understood that determining B based on A does not mean determining B solely based on A, but also based on A and / or other information.

[0088] It should also be understood that the term “comprising” (also referred to as “includes”, “including”, “Comprises” and / or “Comprising”) as used in this specification specifies the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0089] In this application embodiment, considering the ambiguity of existing geophysical methods, the result of interpreting a single parameter often fails to reflect the distribution of the target body. However, multi-data source integrated interpretation methods have poor targeting for specific regions, and the interpretation results do not match the actual application situation well. This application provides a method, the main contents of which include collecting airborne electromagnetic data and auxiliary information within the horizontal and spatial distribution range of the target, collecting regional physical property data, analyzing stratigraphic distribution, hydrogeological characteristics, fault structure distribution, mineral resource distribution, and human interference effects, analyzing the possible burial depth and distribution of the target body, using the electrical, magnetic, radioactive, and gravity information of the target body, inferring the three-dimensional spatial distribution morphology of the target body, and submitting the detection results.

[0090] In some embodiments, the main steps of this method are as follows: Collecting airborne electromagnetic, aeromagnetic, and ground geophysical data for a designated area; using airborne electromagnetic methods to invert and obtain three-dimensional resistivity distribution information of underground space; integrating stratigraphic and fault distribution information; importing magnetic, gravity, and radial information to identify and classify geological bodies with different physical properties; integrating the results into the three-dimensional resistivity distribution information and establishing a model; identifying and eliminating interfering data based on data acquisition records, remote sensing images, etc.; importing borehole electrical parameters to further conduct constrained inversion, maximizing the use of known information; using the constrained inversion model, correcting the inference results from other physical property data, proposing the distribution of underground target bodies, and providing working suggestions for mineral resource exploration and applications in the hydrogeological and environmental fields. This invention can significantly reduce the scope of target bodies in areas with low exploration activity, shorten the exploration cycle, improve the accuracy of target body detection, and save on exploration and ground work costs.

[0091] The following is a detailed description of an airborne electromagnetic detection comprehensive interpretation method provided by an embodiment of this application, with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a comprehensive interpretation method for airborne electromagnetic detection provided in an embodiment of this application. This method is applied to electronic equipment. (See also...) Figure 1 The method includes the following steps.

[0092] Step 101: The electronic device collects airborne electromagnetic data, other multi-source geophysical data and other auxiliary data within the detection area. The other auxiliary data includes at least one of the following: positioning information, altitude information and current information.

[0093] In existing technologies, underground target detection often relies on a single data source, resulting in incomplete information. Furthermore, airborne electromagnetic data is highly susceptible to interference from terrain undulations and human-made structures (such as high-voltage lines and buildings), leading to a low signal-to-noise ratio that directly impacts the accuracy of subsequent inversion. To obtain comprehensive, multi-dimensional underground response data and improve data integrity and anti-interference capabilities from the source, this application adopts a multi-source data support (multi-platform collaborative operation) strategy. Specifically, this step aims to construct a comprehensive database containing main target imaging data and auxiliary discrimination data, providing high-quality input for subsequent modeling.

[0094] In some embodiments, the electronic device may collect information such as altitude, attitude, and emission current as additional auxiliary data. This additional auxiliary data can be used to correct physical properties during data processing.

[0095] In some embodiments, step 101 establishes a "primary-secondary" data fusion strategy. Airborne electromagnetic data is used as the primary imaging source, leveraging its high sensitivity to acquire electrical structures; other multi-source data are simultaneously collected as auxiliary discrimination sources for subsequent multi-dimensional cross-validation.

[0096] In some embodiments, the other multi-source geophysical data may include at least one of airborne magnetic data, airborne gravity data, or airborne radiometric data. This multi-source geophysical data serves as an auxiliary decision source to facilitate subsequent multi-dimensional cross-validation and obtain more reliable processing results.

[0097] In some embodiments, the airborne electromagnetic data can be obtained through acquisition. Other multi-source geophysical data can be obtained by collecting relevant information, through acquisition, or by extracting data stored in a database after acquisition. Other auxiliary data can be obtained through... It can be obtained from relevant data, or extracted from other collected data. Therefore, the embodiments of this application do not specifically limit the method of obtaining these data.

[0098] For example, in one specific embodiment, during the data acquisition phase, a helicopter equipped with a high-frequency electromagnetic transmission and reception system (e.g., the Horizond system) can be used, with a flight altitude set at 50-80 meters and a survey line spacing of 100 meters. Simultaneously, an optically pumped magnetometer and a gamma-ray spectrometer can be carried. Historical data can be directly integrated from a regional geophysical database. This geophysical database can be pre-loaded with the acquired airborne data. For example, stratigraphic distribution data can be statistically analyzed and stored in the database, physical property data can be aggregated and analyzed, and distribution information can be constructed and stored. Then, when comprehensive interpretation is required, this information can be uniformly imported into the model analysis.

[0099] In some embodiments, the above-described data collection process can also be implemented using sensors mounted on a UAV, suitable for small-scale, high-precision detailed exploration, or using a shipborne platform for exploration of water areas. Furthermore, for complex terrain areas, an adaptive flight path planning algorithm can be used to dynamically adjust flight altitude and survey line density based on the digital elevation model (DEM) to optimize coverage and signal-to-noise ratio. This application does not specifically limit the data acquisition carrier, sensor model, or flight path planning method described above; any multi-source geophysical data that meets the required accuracy falls within the protection scope of this application.

[0100] In step 101, the collaborative operation of multiple platforms and sensors is used to obtain comprehensive and multi-dimensional underground response data, which effectively overcomes the limitations of a single data source and lays the foundation for high-precision inversion.

[0101] Step 102: Based on the other multi-source geophysical data and geological constraint information, the electronic equipment performs physical property clustering analysis on the strata and target bodies in the exploration area, and constructs a geological model including initial stratigraphic boundaries and physical property assignments.

[0102] In existing technologies, geological models often rely on general assumptions or simple stratigraphic divisions, failing to fully utilize detailed physical property data from borehole measurements to guide the inversion process. This results in significant discrepancies between the model and the actual geological environment, affecting the inversion convergence speed and accuracy, and even leading to local extrema. To reduce the inversion search space and improve the accuracy of the initial model, this application proposes a data-driven modeling method. Specifically, this step aims to use statistical methods to generalize discrete borehole point physical property data to the entire region, constructing a "prior knowledge base" that conforms to geological logic.

[0103] In some embodiments, step 102 may include three steps: feature extraction, classification mapping, and spatial interpolation. The feature extraction step can extract continuous feature vectors (resistivity, magnetic susceptibility, density, radioactivity intensity) from discrete borehole points. The classification mapping step can use clustering algorithms to group similar rock strata into one class, establishing a "lithology-physical property" mapping table. The spatial interpolation step can expand the point-like physical property data into a planar / volume-like initial model, assigning stratigraphic attributes.

[0104] In some embodiments, step 102 can be implemented through the following steps: the electronic device can use the electrical, magnetic, density, and radioactive parameters measured from borehole cores to perform cluster analysis on other multi-source geophysical data, summarize the characteristic physical property fingerprints of different stratigraphic units and target bodies, and then the electronic device initializes the object information layering of the strata in the detection area according to the characteristic physical property fingerprints, sets the initial depth limit, interlayer thickness, and material composition of each layer, and finally assigns the characteristic physical property fingerprints of each stratum obtained from the cluster analysis to the corresponding initial layer to form a geological model containing the initial distribution of resistivity, magnetic susceptibility, density, and radioactivity intensity, which serves as the prior constraint condition for inversion calculation.

[0105] In some embodiments, the above-described layer initialization process requires assigning initial layer boundaries, interlayer thicknesses, the depths corresponding to the start and end points of each layer, and the physical properties of the material within each layer to the model. The physical properties of each layer need to be assigned values ​​based on the results of the above cluster analysis to more closely approximate the actual situation and reduce the problems caused by multiple solutions in the inversion.

[0106] In other embodiments, the above-described physical property clustering analysis process can also be implemented using the Fuzzy C-Means (FCM) clustering algorithm. Specifically, this allows a rock sample to belong to multiple stratigraphic categories simultaneously and assigns different membership degrees; for example, a borehole point might have 60% clay layers and 40% silt layers. The advantage of this approach is that it better reflects the complexity of geological transition zones and avoids information loss due to hard boundaries, making it particularly suitable for fractured geological zones or lithologically gradual transition zones.

[0107] In other embodiments, the density-based DBSCAN algorithm can also be used. Instead of pre-setting the number of cluster centers, it automatically identifies core points, boundary points, and noise points based on the density distribution of data points, thereby automatically eliminating outliers caused by measurement errors.

[0108] In other embodiments, those skilled in the art can also employ unsupervised deep learning techniques, such as autoencoders or t-SNE dimensionality reduction combined with clustering, to mine nonlinear features in high-dimensional physical property data. This application does not specifically limit the choice of the above-mentioned clustering algorithm; any algorithm that can achieve the summarization and mapping of physical property features falls within the protection scope of this application.

[0109] In step 102, the discrete borehole point physical property data are extended to the entire region, which significantly reduces the inversion search space, makes the inversion results closer to the real geological structure, improves the accuracy of the initial model, and accelerates the convergence speed of the algorithm.

[0110] Step 103: The electronic equipment uses the airborne electromagnetic data, the other auxiliary data and the geological model to perform data processing and resistivity inversion calculation to generate a three-dimensional resistivity distribution model of underground space.

[0111] In existing technologies, traditional electromagnetic inversion often employs unconstrained or only vertically constrained algorithms, which easily overlook the lateral continuity of the formation. To eliminate spurious anomalies caused by noise and obtain a smooth and continuous geological structure, this application introduces a lateral continuity constraint mechanism. Specifically, this step aims to utilize the principle of lateral continuity of the formation to force the resistivity to maintain a smooth change in the horizontal direction during the inversion process, while allowing abrupt changes at known stratigraphic interfaces.

[0112] In some embodiments, step 103 is implemented through data preprocessing, constraint application, and layer locking. Data preprocessing can be a standardization process to remove random noise. The constraint application process can involve adding a horizontal gradient penalty term to the inversion objective function to force a smooth change in resistivity between adjacent grids. Layer locking can force the inversion results to maintain abrupt property changes at known layer interfaces, while maintaining smoothness within layers.

[0113] In some embodiments, step 103 can be implemented through the following steps: The electronic device employs a lateral constraint inversion algorithm to perform channel extraction, stacking, filtering, and horizontal leveling on the airborne electromagnetic data to enhance the signal-to-noise ratio and preserve the continuity of the lateral geological body distribution. During the inversion iteration process, the electronic device introduces the stratigraphic structure of the geological model and other auxiliary data as rigid constraints to limit the range of abrupt changes in the resistivity model in the vertical direction. Finally, the electronic device identifies and removes high-frequency noise points and non-geologically derived anomalous data in the processing results to obtain a three-dimensional resistivity distribution model of the subsurface space. By utilizing the principle of lateral continuity of strata to constrain resistivity changes during the inversion process, a smooth and continuous three-dimensional resistivity structure map is obtained, eliminating jagged false anomalies caused by noise and providing a high-quality base map for subsequent target body identification. Introducing other auxiliary data during the inversion process can correct the physical properties in the constructed geological model, facilitating more accurate subsequent analysis and interpretation.

[0114] For example, in some embodiments, airborne electromagnetic data is inverted, including raw data extraction, filtering, atmospheric noise removal, and importing auxiliary information. Detectable noise points in the data are eliminated, and the reliability of the data is determined based on the auxiliary information. A lateral constraint inversion method is used to establish the correspondence between depth and resistivity, and an electrical structure diagram of the subsurface half-space is drawn based on this. The data analysis results are then imported into subsequent steps to determine the three-dimensional extent of subsurface anomalies, using geological, drilling, and other auxiliary data for constraint. The reliability of each part of the data is determined by comparing the borehole data.

[0115] In other embodiments, the above-mentioned lateral constraint inversion process can also be implemented using anisotropic constraints or structure-guided constraints. Specifically, when there are obvious folds or fault structures in the detection area, an anisotropic smoothing factor can be introduced to dynamically adjust the constraint strength according to the geological structure: strong constraints are applied in the direction perpendicular to the stratum strike to maintain continuity, while weak constraints are applied in the direction parallel to the stratum strike to allow abrupt changes at faults. Alternatively, a structure tensor can be constructed using gradient tensors to guide the inversion along geological interfaces, thereby obtaining a more accurate model at dipping strata. In other embodiments, those skilled in the art can also use a Bayesian inversion framework, which not only outputs the optimal solution but also the uncertainty range (variance) of each grid point, providing a statistical basis for subsequent manual corrections. This application does not specifically limit the above-mentioned specific inversion constraint strategies; any technical means that can achieve lateral geological continuity maintenance is applicable.

[0116] Step 104: The electronic device uses the response characteristics of other multi-source geophysical data to cross-validate the three-dimensional resistivity distribution model, and identifies and eliminates false anomalies caused by non-target geological bodies or human interference on the surface.

[0117] In existing technologies, a single electrical anomaly often corresponds to multiple geological interpretations and is easily affected by human interference such as surface buildings and high-voltage power lines. Existing methods lack an effective multi-source cross-validation mechanism to distinguish the authenticity of anomalies, which can easily lead to misjudgments. In order to effectively distinguish between the target body and the interfering body and reduce the false positive rate, this application can utilize the response characteristics of other multi-source geophysical data to cross-validate the three-dimensional resistivity distribution model, identify and eliminate false anomalies caused by non-target geological bodies or surface human interference.

[0118] In some embodiments, this application establishes a discrimination system based on multi-physics fingerprints. Utilizing the differences in the responses of different physical fields to the same geological body (i.e., "fingerprint" features), a unique solution is identified through multi-dimensional matching, effectively distinguishing the target body from interfering bodies, significantly reducing the false positive rate, and ensuring the geological reliability of the inversion results. Specifically, this step aims to utilize the differences in the responses of different physical fields to the same geological body to identify a unique solution through multi-dimensional matching, eliminating interfering terms.

[0119] Specifically, the other multi-source geophysical data includes at least airborne magnetic data, airborne gravity data, and airborne radioactive data. In step 104, the electronic device can perform spatial registration and correlation analysis between the three-dimensional resistivity distribution model and the airborne magnetic data, airborne gravity data, and airborne radioactive data. Then, the electronic device establishes multi-physics anomaly discrimination rules. For example, the multi-physics anomaly discrimination rules for typical ore bodies are as follows: when the three-dimensional resistivity distribution model shows a low resistivity anomaly, if the corresponding airborne magnetic data shows a low magnetic anomaly and the airborne gravity data shows a low density anomaly, it is determined to be a suspected target body; if the corresponding airborne magnetic data shows a high magnetic anomaly, it is marked as igneous rock body interference and removed. Finally, the electronic device combines remote sensing image data and ground human activity records to identify electromagnetic distortion zones caused by man-made facilities such as shallow buildings and high-voltage lines, and removes the resistivity anomaly data of these areas from the three-dimensional model.

[0120] In some embodiments, determining the distribution boundary of the target body requires identifying interfering distributions, such as rock masses, faults, aquifers, and associated mineral zones. Considering the boundary distortion caused by surface interference, experienced technicians need to compare and eliminate these factors one by one, which is the content of subsequent step 105. Finally, the distribution range of the target body is obtained, including planar distribution, depth distribution, and the distribution state at different depths.

[0121] In some embodiments, step 104 includes spatial alignment, establishing a logical rule base, and interference source localization. The spatial alignment step can be a unified coordinate system to ensure pixel-level matching of multi-source data. The logical rule base establishment step can be to establish an IF-THEN judgment tree based on geological common sense. The interference source localization step can be to accurately locate the interference source using external data sources (remote sensing, human records).

[0122] For example, in some specific embodiments, spatial registration can employ the control point method to align the aeromagnetic data grid with the resistivity model grid, controlling the error within the target distance (e.g., 5 meters). The system can automatically traverse each grid cell; if certain conditions are met, the cell is marked as "interference" and not included in subsequent statistics. In a specific example, historical imagery can also be loaded, and building outlines can be extracted using image recognition algorithms. If the center point of the electromagnetic anomaly falls within the building outline and the distance is less than 10 meters, it is considered shallow human interference and is directly set to zero.

[0123] In other embodiments, the above-mentioned multiphysics anomaly discrimination process can also be implemented using a machine learning classifier. Specifically, known typical geological samples (including ore bodies, igneous rocks, normal strata, etc.) within the region can be collected, and their corresponding multi-source geophysical response data can be used as a training set to construct a Random Forest or Support Vector Machine (SVM) classification model. During the interpretation phase, the three-dimensional model data of the area to be tested is input into the model, and the model directly outputs the probability distribution of the area belonging to various geological bodies. This method can automatically learn complex nonlinear discrimination boundaries and is particularly suitable for areas with variable geological backgrounds and complex interference factors. In addition, a coupled forward model can be constructed to determine the authenticity of anomalies by minimizing the joint residuals of multiphysics fields, or time series analysis can be used to compare data from different expeditions to distinguish between static human interference and dynamic natural interference. The embodiments of this application do not specifically limit the above-mentioned discrimination algorithms. All technical means that can achieve cross-validation of multi-source data and interference removal are within the protection scope of this application.

[0124] Step 105: Based on the three-dimensional resistivity distribution model after interference removal, the other auxiliary data, and the borehole measurement data, the electronic device dynamically corrects the physical property boundary of the target object through human-computer interaction, and interprets the three-dimensional spatial distribution morphology of the target object in the detection area.

[0125] While borehole data and other auxiliary data are used for correction, a simple distance-weighted average is often employed, lacking a dynamic evaluation mechanism for the reliability of the borehole data. To achieve deep integration of borehole data and airborne data, respecting data authority while considering the uncertainties of spatial extrapolation, this application proposes a quantitative soft-constraint correction mechanism and a closed-loop iterative process. Specifically, this step aims to quantify the "influence" of borehole data through mathematical functions and perform final "fine-tuning" based on expert experience, ultimately outputting intuitive decision support results.

[0126] In some embodiments, step 105 can analyze the spatial distance difference between the grid node positions in the three-dimensional resistivity distribution model and the borehole measured data positions, using this as a basis for inversion, making certain adjustments, and simultaneously providing manual corrections to improve the accuracy of the results. Specifically, the electronic device uses the positioning information in the other auxiliary data to align the grid nodes in the three-dimensional resistivity distribution model with the borehole measured data, calculates the spatial distance difference, and then constructs a distance decay function based on the spatial distance difference to determine the influence factor of the borehole data, wherein the influence factor is negatively correlated with distance. That is, the closer the distance, the larger the influence factor. The influence factor is used to weight and constrain the resistivity model of the corresponding area. Then, the electronic device can import the influence factor into the inversion model and combine it with the other auxiliary data for secondary constraint inversion to generate a new three-dimensional resistivity distribution model. Finally, the electronic device can display the degree of matching between the new three-dimensional resistivity distribution model and the geological conditions through a human-computer interaction interface, and adjust the physical boundary conditions of the target body based on the adjustment operations of the human-computer interaction interface until the model conforms to geological logic, obtaining the three-dimensional spatial distribution morphology of the target body in the detection area. Among them, the human-computer interaction interface allows technicians to manually adjust the physical boundary conditions of the target body or the influence factors of borehole data based on regional metallogenic theories or hydrogeological and environmental experience.

[0127] The above process uses quantified weights to apply "soft constraints" from borehole information back to the inversion model, achieving deep fusion of borehole and airborne data, correcting inversion errors, and ensuring that the final model conforms to both the mathematical optimal solution and geological reality. Simultaneously, the closed-loop iterative mechanism guarantees the geological rationality of the results and avoids blind extrapolation. The diverse output formats enhance the readability and practicality of the exploration results, directly providing a scientific basis for resource exploration and hydrogeological and environmental remediation.

[0128] In some embodiments, the construction process of the distance decay function can also be implemented using a Gaussian kernel function or a Kriging interpolation weighting method. Regarding human-computer interaction, reinforcement learning agents can be introduced to automatically generate recommendation schemes. For iterative decision-making, digital twin simulation feedback technology can be combined to simulate mining or groundwater seepage. In terms of output, it can be expanded to interface data formats with other professional software (such as OBJ, GRD, XYZ), or historical trend analysis charts can be directly exported. This application does not specifically limit the above-mentioned weight calculation methods, interaction strategies, iteration conditions, and output formats. Any method that can achieve data fusion correction, closed-loop optimization, and intuitive decision support falls within the protection scope of this application.

[0129] In some embodiments, step 105 includes distance weight calculation, soft constraint injection, visualization interaction, and iterative closure steps. In the distance weight calculation step, a mathematical function is designed to quantify the "influence" of the borehole data. The soft constraint injection process integrates weights into the inversion equation, rather than rigidly covering them. The visualization interaction process provides graphical tools to support manual intervention. The iterative closure process can set multi-dimensional judgment criteria (residuals, attitude, physical properties) to drive the system to self-correct.

[0130] In some embodiments, in step 105 above, the electronic device may also set a conformity judgment criterion, which includes the residual of the new three-dimensional resistivity distribution model being less than a preset threshold, the target body's occurrence being consistent with the known geological structure trend, and the target body's physical properties being consistent with the borehole sampling analysis results.

[0131] If the matching criteria are met, the final three-dimensional distribution range of the mineralized zone or target body will be output.

[0132] If the conformity criterion is not met, an iterative correction process is triggered to redetermine the physical boundary conditions of the target body, restrict the unreasonable expansion of the abnormal region, and return to execute the secondary constraint inversion step until the conformity criterion is met.

[0133] In some embodiments, the final results can also be output, for example, multi-dimensional views, data statistical reports, and textual suggestions can be generated. Specifically, after step 105, the electronic device can also output the final interpretation results. These interpretation results may include a planar distribution map of the target body, vertical slices at different depths, and a three-dimensional visualization model; the precise burial depth range, distribution status, and physical property parameter statistics of the target body; and a mineral resource exploration suggestion or hydrogeological and environmental remediation work suggestion report generated based on the three-dimensional spatial distribution morphology.

[0134] The following is a specific example of the above method. The purpose of this invention is to interpret the distribution of underground geological bodies using airborne geophysical methods, thereby improving the overall interpretation accuracy and detection efficiency of the region. The interpretation process comprehensively utilizes airborne geophysics, surface geophysics, surface geological data, drilling data, etc., and employs a human-computer interactive method to interpret the spatial distribution of underground geological bodies, inferring the three-dimensional distribution information of the target body within the detection range.

[0135] The steps to implement this application are as follows:

[0136] 1. Perform quality control and screening on the collected airborne geophysical data, select reliable data for inversion, and identify areas with poor data quality and interference.

[0137] 2. Using a uniform half-space initialization model, altitude information is introduced to invert the airborne electromagnetic data.

[0138] 3. Perform cluster analysis on the physical properties of the target object to summarize the differences between its electrical properties, magnetism, density, radioactivity, and other information and the background field.

[0139] 4. Perform lateral constraint inversion on electromagnetic data to obtain the underground three-dimensional electrical structure.

[0140] 5. Utilize data acquired from air and ground sources to further determine the distribution of strata and fractures. Incorporate borehole data into airborne electromagnetic inversion data as an important reference for correction. Simultaneously, examine inconsistencies with other physical property parameters and propose interpretation results based on comprehensive analysis.

[0141] 6. After completing the above steps, estimate the three-dimensional distribution information of the underground geological bodies and mark the scope of the target body.

[0142] Specifically, layer initialization requires assigning initial layer boundaries, interlayer thicknesses, the depths corresponding to the start and end points of each layer, and the physical properties of the material within each layer to the model. The physical properties of each layer need to be assigned values ​​based on the cluster analysis results in step 3, so as to be closer to the actual situation and reduce the problems caused by multiple solutions in the inversion.

[0143] Electromagnetic data inversion is performed, including raw data extraction, filtering, atmospheric noise removal, and importing auxiliary information. Detectable noise points are eliminated from the data, and the reliability of the data is determined based on the auxiliary information. A lateral constraint inversion method is used to establish the correspondence between depth and resistivity, and an electrical structure diagram of the subsurface half-space is drawn based on this. The data analysis results from step 5 are imported to determine the three-dimensional range of subsurface anomalies, using geological, drilling, and other auxiliary information for constraint. The reliability of each part of the data is determined by comparing it with borehole data.

[0144] Determining the distribution boundary of a target body requires identifying interfering factors such as rock masses, faults, aquifers, and associated mineral zones. Considering the boundary distortion caused by surface interference, experienced technicians must individually compare and eliminate these factors. The final result is the distribution range of the target body, including its planar distribution, depth distribution, and its distribution at different depths.

[0145] In a specific example, steps 1 through 5 below may be included.

[0146] Step 1: Collect data on the interpretation area, classify electrical, magnetic, gravity, radioactive and drilling data in detail, perform cluster analysis on sample physical property data, and generalize the physical property characteristics of strata and target bodies as the initial conditions for subsequent inversion.

[0147] Step 2: Based on the orientation of the geological body, design flight route data for the exploration area, ensuring the route covers the entire anomaly zone. Use specialized equipment to collect electromagnetic field response data and normalize the data. Organize all auxiliary information and match it with corresponding altitude data. After processing all data, store it in the database and perform inversion by inputting the appropriate parameters.

[0148] Step 3: Remove interfering information from the data and perform preliminary inversion using a uniform half-space model. For specific areas, perform constrained inversion using electrical clustering analysis and models constrained by electrical, magnetic, and gravity data to obtain the electrical distribution of underground space.

[0149] Step 4: For areas with borehole distribution and geological profile control, further constrained inversion is performed.

[0150] Step 5: Import the inversion results into the database to generate an electrical structure map. Check the consistency between the inversion results and the stratigraphic distribution, and perform a consistency analysis. Based on project requirements and geological conditions, fully understand the differences between the target body and the background field, and propose the distribution range of the target body.

[0151] For example, Figure 2 This is a flowchart illustrating the implementation of an airborne electromagnetic detection integrated interpretation method provided in this application. Figure 2 As shown, we can first comprehensively collect data from the working area, including electromagnetic data and auxiliary information, then perform interference removal, followed by preliminary inversion, and then constrained inversion. After inversion, we perform an electrical check. If it matches reality, we output the electrical model to infer the interpretation results; if it does not match reality, we return to perform constrained inversion.

[0152] This application utilizes the differences in physical properties between the target body and the surrounding adjacent geological bodies. It mainly uses the physical property information provided by airborne electromagnetic data to obtain the three-dimensional distribution characteristics of the target body. Combined with airborne electromagnetic, magnetic, gravity, borehole data and other auxiliary data, it performs inversion and manual delineation, which significantly reduces the ambiguity of geophysical inversion, effectively eliminates shallow and deep interference, improves the accuracy and reliability of target body boundary delineation, and reduces unnecessary drilling workload.

[0153] All the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this application, and will not be described in detail here.

[0154] Figure 3 This is a schematic diagram of the structure of an integrated interpretation device for airborne electromagnetic detection provided in an embodiment of this application. See also... Figure 3 The device includes:

[0155] The collection module is used to collect airborne electromagnetic data, other multi-source geophysical data and other auxiliary data within the detection area. The other auxiliary data includes at least one of the following: positioning information, altitude information and current information.

[0156] The construction module is used to perform physical property clustering analysis on the strata and target bodies in the exploration area based on the other multi-source geophysical data and geological constraint information, and to construct a geological model including initial stratigraphic boundaries and physical property assignments.

[0157] The generation module is used to perform data processing and resistivity inversion calculation using the airborne electromagnetic data, the other auxiliary data and the geological model to generate a three-dimensional resistivity distribution model of underground space.

[0158] The processing module is used to cross-validate the three-dimensional resistivity distribution model using the response characteristics of the other multi-source geophysical data, and to identify and eliminate false anomalies caused by non-target geological bodies or human interference on the surface.

[0159] The interpretation module is used to dynamically correct the physical property boundary of the target body through human-computer interaction based on the three-dimensional resistivity distribution model after removing interference, the other auxiliary data and the borehole measured data, and interpret the three-dimensional spatial distribution morphology of the target body in the detection area.

[0160] In some embodiments, the building module is used for:

[0161] Using the electrical, magnetic, density, and radioactive parameters measured from borehole cores, cluster analysis was performed on the other multi-source geophysical data to summarize the characteristic physical fingerprints of different stratigraphic units and target bodies.

[0162] Based on the aforementioned characteristic physical fingerprint, the strata within the detection area are initialized with object information layering, and the initial depth limits, interlayer thicknesses, and material composition of each layer are set.

[0163] The physical property fingerprints of each stratigraphic region obtained from cluster analysis are assigned to the corresponding initial stratigraphic layers to form a geological model containing the initial distribution of resistivity, magnetic susceptibility, density, and radioactivity intensity, which serves as a priori constraints for inversion calculations.

[0164] In some embodiments, the generation module is used for:

[0165] A lateral constraint inversion algorithm is used to perform channel extraction, superposition, filtering, and horizontal leveling on the airborne electromagnetic data to enhance the signal-to-noise ratio and preserve the continuity of the lateral geological body distribution.

[0166] During the inversion iteration process, the stratigraphic structure of the geological model and other auxiliary data are introduced as rigid constraints to limit the range of abrupt changes in the resistivity model in the vertical direction.

[0167] High-frequency noise points and non-geologically derived abnormal data in the processing results were identified and removed to obtain a three-dimensional resistivity distribution model of underground space.

[0168] In some embodiments, the other multi-source geophysical data includes at least aeromagnetic data, aerogravity data, and aeroradioactive data; the processing module is used for:

[0169] Spatial registration and correlation analysis were performed between the three-dimensional resistivity distribution model and the airborne magnetic data, airborne gravity data, and airborne radioactivity data.

[0170] A multiphysics anomaly discrimination rule is established. The multiphysics anomaly discrimination rule for typical ore bodies is as follows: when the three-dimensional resistivity distribution model shows a low resistivity anomaly, if the corresponding airborne magnetic data shows a low magnetic anomaly and the airborne gravity data shows a low density anomaly, it is judged as a suspected target body; if the corresponding airborne magnetic data shows a high magnetic anomaly, it is marked as igneous rock body interference and removed.

[0171] By combining remote sensing image data and records of human activities on the ground, electromagnetic distortion zones caused by man-made facilities such as shallow buildings and high-voltage lines are identified, and the resistivity anomalies in these zones are removed from the 3D model.

[0172] In some embodiments, the interpretation module is used for:

[0173] Using the positioning information in the other auxiliary data, the grid nodes in the three-dimensional resistivity distribution model are aligned with the borehole measured data, and the spatial distance difference is calculated.

[0174] Based on the spatial distance difference, a distance attenuation function is constructed to determine the influence factors of the borehole data, wherein the influence factors are negatively correlated with distance;

[0175] The influencing factors are imported into the inversion model and combined with the other auxiliary data to perform a secondary constraint inversion, generating a new three-dimensional resistivity distribution model.

[0176] The human-computer interaction interface displays the degree of matching between the new three-dimensional resistivity distribution model and geological conditions. Based on the adjustment operation of the human-computer interaction interface, the physical property boundary conditions of the target body are adjusted until the model conforms to geological logic, and the three-dimensional spatial distribution morphology of the target body in the detection area is obtained.

[0177] In some embodiments, the apparatus further includes:

[0178] The setting module is used to set the matching criteria, which include the residual of the new three-dimensional resistivity distribution model being less than a preset threshold, the target body's attitude being consistent with the known geological structure trend, and the target body's physical properties being consistent with the borehole sampling analysis results.

[0179] If the matching criteria are met, the interpretation module is used to output the final three-dimensional distribution range of the mineralization zone or the target body.

[0180] If the conformity criterion is not met, the interpretation module is used to trigger the iterative correction process, redetermine the physical boundary conditions of the target body, limit the unreasonable expansion of the abnormal region, and return to execute the secondary constraint inversion step until the conformity criterion is met.

[0181] In some embodiments, the apparatus further includes:

[0182] The output module is used to output the final interpretation results;

[0183] The explanation results include:

[0184] Planar distribution map of the target object, vertical slices at different depths, and a three-dimensional visualization model;

[0185] Precise burial depth range, distribution status, and statistical information on physical properties of the target object;

[0186] Based on the aforementioned three-dimensional spatial distribution pattern, a report is generated recommending mineral resource exploration or water and environmental remediation.

[0187] The device provided in this application utilizes the differences in physical properties between the target body and the surrounding adjacent geological bodies. It mainly uses the physical property information provided by airborne electromagnetic data to obtain the three-dimensional distribution characteristics of the target body. Combined with airborne electromagnetic, magnetic, gravity, borehole data and other auxiliary data, it performs inversion and manual delineation, which significantly reduces the ambiguity of geophysical inversion, effectively eliminates shallow and deep interference, improves the accuracy and reliability of target body boundary delineation, and reduces unnecessary drilling workload.

[0188] It should be noted that the airborne electromagnetic detection integrated interpretation device provided in the above embodiments is only illustrated by the division of the above functional modules when processing data. In actual applications, the above functions may be assigned to different functional modules as needed, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the functions described above. In addition, the airborne electromagnetic detection integrated interpretation device and the airborne electromagnetic detection integrated interpretation method embodiments are based on the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0189] Figure 4 This is a schematic diagram of an electronic device 400 provided in an embodiment of this application. The electronic device 400 can vary considerably depending on its configuration or performance. It includes one or more Central Processing Units (CPUs) 401 and one or more memories 402. The memories 402 store at least one computer program, which is loaded and executed by the processors 401 to implement the airborne electromagnetic detection integrated interpretation method provided in the various method embodiments described above. The electronic device also includes other components for implementing its functions. For example, it may have wired or wireless network interfaces and input / output interfaces for input and output. Details regarding these components are not elaborated upon here.

[0190] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including at least one computer program, which is executable by a processor to perform the airborne electromagnetic detection integrated interpretation method in the above embodiments. For example, the computer-readable storage medium is a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0191] In an exemplary embodiment, a computer program product or computer program is also provided, the computer program product or computer program comprising one or more lines of program code stored in a computer-readable storage medium. One or more processors of an electronic device read the one or more lines of program code from the computer-readable storage medium, and the one or more processors execute the one or more lines of program code, causing the electronic device to perform the above-described airborne electromagnetic detection integrated interpretation method.

[0192] In some embodiments, the computer program involved in the present application embodiments may be deployed and executed on a computer device, or executed on multiple computer devices located in one location, or executed on multiple computer devices distributed in multiple locations and interconnected through a communication network. Multiple computer devices distributed in multiple locations and interconnected through a communication network may constitute a blockchain system.

[0193] Those skilled in the art will understand that all or part of the steps of the above embodiments are implemented by hardware, or by a program instructing related hardware to implement them. The program is stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0194] The above description is only an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0195] The foregoing description of specific exemplary embodiments of this application is for illustrative and explanatory purposes. These descriptions are not intended to limit this application to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of this application and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of this application, as well as various different choices and variations. The scope of this application is intended to be defined by the claims and their equivalents.

Claims

1. A comprehensive interpretation method for airborne electromagnetic detection, characterized in that, The method includes: Collect airborne electromagnetic data, other multi-source geophysical data, and other auxiliary data within the detection area. The other auxiliary data includes at least one of the following: positioning information, altitude information, and current information. Based on the other multi-source geophysical data and geological constraint information, physical property clustering analysis is performed on the strata and target bodies in the exploration area to construct a geological model that includes initial stratigraphic boundaries and physical property assignments. The airborne electromagnetic data, the other auxiliary data, and the geological model are used for data processing and resistivity inversion calculation to generate a three-dimensional resistivity distribution model of underground space. The response characteristics of the other multi-source geophysical data are used to cross-validate the three-dimensional resistivity distribution model, and to identify and eliminate false anomalies caused by non-target geological bodies or human interference on the surface. Based on the three-dimensional resistivity distribution model after removing interference, the other auxiliary data, and the borehole measurement data, the physical property boundary of the target body is dynamically corrected through human-computer interaction, and the three-dimensional spatial distribution morphology of the target body in the detection area is interpreted.

2. The integrated interpretation method for airborne electromagnetic detection as described in claim 1, characterized in that, Based on the other multi-source geophysical data and geological constraint information, the geological model is constructed by performing physical property clustering analysis on the strata and target bodies in the exploration area, including initial stratigraphic boundaries and physical property assignments: Using the electrical, magnetic, density, and radioactive parameters measured from borehole cores, cluster analysis was performed on the other multi-source geophysical data to summarize the characteristic physical fingerprints of different stratigraphic units and target bodies. Based on the aforementioned characteristic physical fingerprint, the strata within the detection area are initialized with object information layering, and the initial depth limits, interlayer thicknesses, and material composition of each layer are set. The physical property fingerprints of each stratigraphic region obtained from cluster analysis are assigned to the corresponding initial stratigraphic layers to form a geological model containing the initial distribution of resistivity, magnetic susceptibility, density, and radioactivity intensity, which serves as a priori constraints for inversion calculations.

3. The integrated interpretation method for airborne electromagnetic detection as described in claim 1, characterized in that, The process of using the airborne electromagnetic data, the other auxiliary data, and the geological model to perform data processing and resistivity inversion calculations to generate a three-dimensional resistivity distribution model of underground space includes: A lateral constraint inversion algorithm is used to perform channel extraction, superposition, filtering, and horizontal leveling on the airborne electromagnetic data to enhance the signal-to-noise ratio and preserve the continuity of the lateral geological body distribution. During the inversion iteration process, the stratigraphic structure of the geological model and other auxiliary data are introduced as rigid constraints to limit the range of abrupt changes in the resistivity model in the vertical direction. High-frequency noise points and non-geologically derived abnormal data in the processing results were identified and removed to obtain a three-dimensional resistivity distribution model of underground space.

4. The integrated interpretation method for airborne electromagnetic detection as described in claim 1, characterized in that, The other multi-source geophysical data includes at least airborne magnetic data, airborne gravity data, and airborne radiometric data; the step of cross-validating the three-dimensional resistivity distribution model using the response characteristics of the other multi-source geophysical data to identify and eliminate false anomalies caused by non-target geological bodies or surface human interference includes: Spatial registration and correlation analysis were performed between the three-dimensional resistivity distribution model and the airborne magnetic data, airborne gravity data, and airborne radioactivity data. A multiphysics anomaly discrimination rule is established. The multiphysics anomaly discrimination rule for typical ore bodies is as follows: when the three-dimensional resistivity distribution model shows a low resistivity anomaly, if the corresponding airborne magnetic data shows a low magnetic anomaly and the airborne gravity data shows a low density anomaly, it is judged as a suspected target body; if the corresponding airborne magnetic data shows a high magnetic anomaly, it is marked as igneous rock body interference and removed. By combining remote sensing image data and records of human activities on the ground, electromagnetic distortion zones caused by man-made facilities such as shallow buildings and high-voltage lines are identified, and the resistivity anomalies in these zones are removed from the 3D model.

5. The integrated interpretation method for airborne electromagnetic detection according to claim 1, characterized in that, The three-dimensional resistivity distribution model after interference removal, the other auxiliary data, and the borehole measured data are used to dynamically correct the physical property boundaries of the target object through human-computer interaction, interpreting the three-dimensional spatial distribution morphology of the target object within the detection area, including: Using the positioning information in the other auxiliary data, the grid nodes in the three-dimensional resistivity distribution model are aligned with the borehole measured data, and the spatial distance difference is calculated. Based on the spatial distance difference, a distance attenuation function is constructed to determine the influence factors of the borehole data, wherein the influence factors are negatively correlated with distance; The influencing factors are imported into the inversion model and combined with the other auxiliary data to perform a secondary constraint inversion, generating a new three-dimensional resistivity distribution model. The human-computer interaction interface displays the degree of matching between the new three-dimensional resistivity distribution model and geological conditions. Based on the adjustment operation of the human-computer interaction interface, the physical property boundary conditions of the target body are adjusted until the model conforms to geological logic, and the three-dimensional spatial distribution morphology of the target body in the detection area is obtained.

6. The integrated interpretation method for airborne electromagnetic detection as described in claim 5, characterized in that, The method further includes: The matching criteria are set, including the residual of the new three-dimensional resistivity distribution model being less than a preset threshold, the target body's orientation being consistent with the known geological structure trend, and the target body's physical properties being consistent with the borehole sampling analysis results. If the matching criteria are met, the final three-dimensional distribution range of the mineralization zone or target body will be output. If the conformity criterion is not met, an iterative correction process is triggered to redetermine the physical boundary conditions of the target body, restrict the unreasonable expansion of the abnormal region, and return to execute the secondary constraint inversion step until the conformity criterion is met.

7. The integrated interpretation method for airborne electromagnetic detection as described in any one of claims 1-6, characterized in that, The method further includes: Output the final interpretation results; The explanation results include: Planar distribution map of the target object, vertical slices at different depths, and a three-dimensional visualization model; Precise burial depth range, distribution status, and statistical information on physical properties of the target object; Based on the aforementioned three-dimensional spatial distribution pattern, a report is generated recommending mineral resource exploration or water and environmental remediation.

8. A comprehensive interpretation device for airborne electromagnetic detection, characterized in that, The device includes: The collection module is used to collect airborne electromagnetic data, other multi-source geophysical data and other auxiliary data within the detection area. The other auxiliary data includes at least one of the following: positioning information, altitude information and current information. The construction module is used to perform physical property clustering analysis on the strata and target bodies in the exploration area based on the other multi-source geophysical data and geological constraint information, and to construct a geological model including initial stratigraphic boundaries and physical property assignments. The generation module is used to perform data processing and resistivity inversion calculation using the airborne electromagnetic data, the other auxiliary data and the geological model to generate a three-dimensional resistivity distribution model of underground space. The processing module is used to cross-validate the three-dimensional resistivity distribution model using the response characteristics of the other multi-source geophysical data, and to identify and eliminate false anomalies caused by non-target geological bodies or human interference on the surface. The interpretation module is used to dynamically correct the physical property boundary of the target body through human-computer interaction based on the three-dimensional resistivity distribution model after removing interference, the other auxiliary data and the borehole measured data, and interpret the three-dimensional spatial distribution morphology of the target body in the detection area.

9. An electronic device, characterized in that, The electronic device includes one or more processors and one or more memories, wherein the one or more memories store at least one computer program, which is loaded and executed by the one or more processors to implement the integrated interpretation method for airborne electromagnetic detection as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one computer program, which is loaded and executed by a processor to implement the integrated interpretation method for airborne electromagnetic detection as described in any one of claims 1 to 7.

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