Radioactive uranium ore prediction system based on multi-source geophysical data fusion

Abstract

The application discloses a radioactive uranium ore prediction system based on multi-source geophysical data fusion and belongs to the technical field of mineral resources exploration. The system comprises a data acquisition module used for collecting relevant data of a target region, wherein the relevant data comprises radioactive data, and the collected radioactive data is distinguished according to the high and low of the probability of existing uranium ore, including being distinguished into a high-probability radioactive data group and a medium-probability radioactive data group. The system further comprises a data preprocessing module used for preprocessing the collected radioactive data, including cleaning, denoising, format conversion and standardization processing. The application can accurately predict the position of uranium ore, effectively avoid the waste of resources and improve the resource utilization efficiency. Meanwhile, the system also marks the region with high probability of existing uranium ore, provides an important reference for subsequent exploration and development work and further improves the efficiency and benefit of resource development.

Description

Technical Field

[0001] This invention relates to the field of mineral resource exploration technology, and in particular to a radioactive uranium ore prediction system based on multi-source geophysical data fusion. Background Technology

[0002] Currently, with the development of geological exploration technology, the integration and analysis of geophysical, remote sensing data and traditional geological surveys has become a trend in modern society for mineral exploration.

[0003] Regarding this research, application CN201911406792.1 provides a rapid method for locating ore-controlling structures in hard-rock uranium deposits based on multi-source data fusion. This technical solution includes the following steps: aeromagnetic and aerial radiometric data processing; high-resolution remote sensing data preprocessing; data fusion; and confirmation of uranium ore-controlling structures. This technical solution fuses various data related to uranium exploration, such as aeromagnetic, aerial radiometric, and high-resolution remote sensing data, to comprehensively integrate and spatially overlay uranium-related information, thereby identifying ore-controlling structures in hard-rock uranium deposits.

[0004] Another application, CN202510018468.1, provides a multi-dimensional fusion method for mineral exploration prediction based on multi-source data. This technical solution includes: acquiring multi-dimensional data of the study area; constructing a multi-dimensional prediction model; inputting pixel-level geological data, spatial-level geological data, and structural-level geological data into the multi-dimensional prediction model; predicting the geological data of the three dimensions separately using the multi-dimensional prediction model to obtain prediction results for each dimension; introducing corresponding weights into the multi-dimensional prediction model, assigning different weights to the prediction results of the three dimensions, and performing weighted fusion to obtain the final mineral exploration prediction result, used to characterize potential mineral resource areas. This technical solution achieves multi-dimensional feature fusion of multi-source data, improving the prediction accuracy of mineral exploration and the reliability of geological interpretation.

[0005] However, the above-mentioned technical solutions mostly rely on statistical or machine learning algorithms (such as random forests and support vector machines), lacking physical constraints on geological and geophysical processes. This leads to discrepancies between the prediction results and actual geological conditions, resulting in low prediction accuracy and unsatisfactory exploration results for uranium deposits. Summary of the Invention

[0006] In view of the problems existing in the field of mineral resource exploration technology, the present invention is proposed.

[0007] Therefore, one of the objectives of this invention is to provide a radioactive uranium ore prediction system based on the fusion of multi-source geophysical data. This system effectively avoids resource waste and improves resource utilization efficiency by accurately predicting the location of uranium deposits. At the same time, the system also marks areas with a high probability of uranium deposits, providing important reference for subsequent exploration and development work, and further improving the efficiency and benefits of resource development.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0009] This invention provides a radioactive uranium deposit prediction system based on multi-source geophysical data fusion, comprising:

[0010] The data acquisition module is used to collect relevant data of the target area, including radioactive data. The collected radioactive data is distinguished according to the probability of the presence of uranium ore, including distinguishing them into high-probability radioactive data groups and medium-probability radioactive data groups.

[0011] The data preprocessing module is used to preprocess the collected radioactive data, including cleaning, noise reduction, format conversion, and standardization.

[0012] The data acquisition module is used to acquire the topography of the target area and to acquire the topography corresponding to the high-probability radioactivity data group and the medium-probability radioactivity data group based on the distinguished radioactivity data.

[0013] The data fusion processing module is used to classify the terrain and landforms corresponding to the high-probability radioactivity data set; the data fusion processing module includes a classification unit, an analysis unit, a setting unit, and a judgment unit.

[0014] The division unit is used to divide the terrain and landform according to the high probability radiometric data set, including dividing the surface zone, middle zone and deep zone corresponding to the data in the high probability radiometric data set, and obtaining the thickness of the surface zone.

[0015] The analysis unit is used to analyze the variation characteristics of the data corresponding to the surface zone based on the thickness;

[0016] The setting unit is used to preset a critical value for radioactive data and compare the analyzed change characteristics with the critical value;

[0017] The determination unit is used to determine whether there is uranium ore in the surface zone based on the comparison results. If the data corresponding to the change characteristics exceeds the threshold value, the system determines that there is uranium ore in the surface zone; otherwise, it does not make a determination.

[0018] In a preferred embodiment of the present invention, if the system determines that uranium ore exists in the surface zone, the area of ​​the surface zone is obtained, and a preset value is included in the area. Mining sites Mining sites and Mineral deposits, the sum of the areas of all said mineral deposits being at least 70% of the area of ​​the surface zone, at least 10 to 20 radiometric data points corresponding to each said mineral deposit are collected, and the mineral deposit that exceeds the critical value the most among the radiometric data points corresponding to each said mineral deposit is identified, the mineral deposit is marked as θ1 mining mineral deposit, and radiometric data of the middle zone is collected based on the θ1 mining mineral deposit.

[0019] As a preferred embodiment of the present invention, the method further includes: obtaining the number of points exceeding the critical value in the radiometric data corresponding to each of the mining points; marking the mining point with the most points exceeding the critical value as the θ2 mining point; and collecting radiometric data of the middle zone based on the θ2 mining point.

[0020] In a preferred embodiment of the present invention, the following steps are taken: The radioactive data exceeding the critical value most frequently at the θ2 mining point is obtained; this radioactive data is marked as reference radioactive data; radioactive data adjacent to the reference radioactive data is obtained; and among the adjacent radioactive data, those less than the reference radioactive data are marked as Δ. 小 Data, radioactive data that is greater than the reference radioactive data is marked as Δ. 大 Data; Obtain the Δ 小 Data to the reference radioactive data and the reference radioactive data to the Δ 大 The soil layer thickness corresponding to the data, if it is related to Δ 小 The soil layer thickness corresponding to the reference radioactivity data is greater than the soil layer thickness corresponding to the reference radioactivity data to the Δ 大 If the soil layer thickness corresponding to the data is such that the soil layer thickness at the θ2 excavation point shows a decreasing trend from the point where the uranium ore is found, then no determination is made.

[0021] In a preferred embodiment of the present invention, the analysis steps, based on the variation characteristics of the data corresponding to the surface zone and the thickness analysis in the analysis unit, include:

[0022] The thickness is divided, including dividing it into at least 10 identical thickness layers;

[0023] Radioactivity data for each thickness layer is collected, and the data difference between radioactivity data of adjacent thickness layers is calculated.

[0024] Calculate the average radioactivity of radioactivity data from 10 thickness layers, and identify the thickness layers with lower radioactivity than the average radioactivity among the 10 thickness layers.

[0025] If the thickness layer consists of three or more consecutive thickness layers, and these three or more thickness layers border the middle layer zone, the system determines that the probability of uranium deposits existing in the middle layer zone is low; otherwise, no determination is made.

[0026] In a preferred embodiment of the present invention, when the probability of uranium deposits existing in the intermediate zone is determined to be low, the probability is verified, and the verification steps are as follows:

[0027] Radioactive data exceeding the critical value were collected in the middle layer zone.

[0028] In the middle layer zone, the soil layer thickness corresponding to the radioactivity data is obtained, and 6 to 10 verification points are given in the soil layer thickness to obtain the radioactivity data of each verification point.

[0029] If, among the radioactive data at each verification point, half of the radioactive data exceeds the threshold value, then the probability of uranium deposits existing in the intermediate zone is considered high; otherwise, the probability of uranium deposits existing in the intermediate zone is considered low.

[0030] If no radioactive data exceeding the threshold is collected in the intermediate zone, verification will not be performed.

[0031] In a preferred embodiment of the present invention, the distance between adjacent verification points is obtained at each of the verification points, and the regular changes in radioactivity data are calculated based on the distance, according to the following formula:

[0032] ΔI i =I i+1 -I i ;

[0033] In the formula, ΔI i I represents the change in radioactivity intensity from verification point i to verification point i+1. i This represents the measured value of radioactivity at verification point i.

[0034] In a preferred embodiment of the present invention, the following formula is also included:

[0035]

[0036] In the formula, R i I represents the relative rate of change of radioactivity intensity from verification point i to verification point i+1. i This represents the measured value of radioactivity at verification point i.

[0037] In a preferred embodiment of the present invention, the area consisting of all verification points is obtained, and based on the calculated results, the region corresponding to the radioactive data greater than the critical value is obtained from the area, and the region is marked as a region with a high probability of uranium ore presence.

[0038] Beneficial effects:

[0039] 1. By categorizing the collected radioactive data according to the probability of uranium deposits, including high-probability and medium-probability radioactive data groups, and by performing differentiated processing on the data of different probability groups, the accuracy of uranium deposit prediction is significantly improved, enabling the system to more accurately identify areas where uranium deposits may exist and reducing misjudgments and omissions.

[0040] 2. By dividing the data into units, analyzing units, setting units, and judging units, the topography and landforms corresponding to the high probability radioactive data set are analyzed in multiple dimensions. The data change characteristics are analyzed based on the thickness of the surface zone and compared with the preset critical value, so as to scientifically determine whether there is uranium deposit in the surface zone. This multi-dimensional analysis method enhances the scientificity and reliability of the judgment.

[0041] 3. After determining that there is uranium ore in the surface area, the system further presets the mining point and collects the corresponding radioactive data. By comparing the number of radioactive data exceeding the critical value, the mining point is accurately located to improve mining efficiency, reduce ineffective mining, and lower mining costs.

[0042] 4. The system achieves quantitative analysis of radioactive data changes by calculating and verifying the changes in radioactivity intensity and relative rate of change between points. At the same time, based on the changing trend of radioactivity data around the mining site, it predicts the changing trend of soil layer thickness when uranium ore is mined, providing strong support for the mining work. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0044] Figure 1 This is a schematic diagram of the modular structure of the radioactive uranium ore prediction system based on multi-source geophysical data fusion according to an embodiment of the present invention.

[0045] Figure 2 This is a schematic diagram of the system flow structure according to an embodiment of the present invention;

[0046] The diagram is labeled as follows: 110 - Data acquisition module; 120 - Data preprocessing module; 130 - Data acquisition module; 140 - Data fusion processing module; 1401 - Division unit; 1402 - Analysis unit; 1403 - Setting unit; 1404 - Judgment unit. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0048] Because existing technologies rely heavily on statistical or machine learning algorithms (such as random forests and support vector machines), they lack physical constraints on geological and geophysical processes, resulting in prediction results that do not match actual geological conditions. This leads to low prediction accuracy and unsatisfactory exploration results for uranium deposits.

[0049] Based on this, the present invention proposes a radioactive uranium ore prediction system based on multi-source geophysical data fusion. By accurately predicting the location of uranium ore, it effectively avoids resource waste and improves resource utilization efficiency. At the same time, the system also marks areas with a high probability of uranium ore presence, providing important reference for subsequent exploration and development work, and further improving the efficiency and benefits of resource development.

[0050] The present solution will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0051] Reference Figures 1 to 2 As one embodiment of the present invention, this embodiment provides a radioactive uranium ore prediction system based on multi-source geophysical data fusion, comprising:

[0052] The data acquisition module 110 is used to collect relevant data of the target area, including radioactive data. The collected radioactive data is distinguished according to the probability of the presence of uranium ore, including distinguishing it into a high probability radioactive data group and a medium probability radioactive data group.

[0053] In this embodiment, in one feasible implementation, radioactivity in the target area can be collected by selectively adsorbing radioactive gases using solid adsorbents (such as activated carbon, silica gel, or molecular sieves). Activated carbon is loaded into the sampling device, and when the gas passes through, the radioactive gas (such as 131I) is adsorbed. Subsequently, the radioactivity intensity of the activated carbon is measured by high-temperature desorption or by direct measurement.

[0054] By distinguishing data groups with different probabilities, the system can focus more on high-potential areas, reduce data processing volume, and improve analysis efficiency.

[0055] Meanwhile, special processing of high-probability data sets helps to more accurately identify uranium ore locations and improve prediction accuracy;

[0056] The data preprocessing module 120 is used to preprocess the collected radioactive data, including cleaning, noise reduction, format conversion and standardization.

[0057] In this embodiment, cleaning and noise reduction processes reduce errors and interference in the data, improving the reliability and accuracy of the data; while format conversion and standardization processes enable the data to be processed and analyzed uniformly by various modules of the system, improving the system's compatibility and efficiency.

[0058] The data acquisition module 130 is used to acquire the topography of the target area and acquire the topography corresponding to the high-probability radioactivity data group and the medium-probability radioactivity data group based on the distinguished radioactivity data.

[0059] In this embodiment, combining radioactive data with topography helps to more comprehensively understand the relationship between uranium deposit distribution and the geological environment, and improves the scientific accuracy of predictions.

[0060] Based on the topographic features, more reasonable exploration strategies can be formulated, reducing ineffective exploration and improving exploration efficiency.

[0061] The data fusion processing module 140 is used to classify the terrain and landforms corresponding to the high probability radioactivity data set; the data fusion processing module 140 includes a division unit 1401, an analysis unit 1402, a setting unit 1403 and a judgment unit 1404.

[0062] The division unit 1401 is used to divide the topography according to the high probability radiometric data set, including dividing the surface zone, middle zone and deep zone corresponding to the data in the high probability radiometric data set, and obtaining the thickness of the surface zone.

[0063] In this embodiment, through layered processing, the system can analyze geological features at different depth levels, improving the precision and accuracy of the analysis.

[0064] Furthermore, obtaining the thickness of the surface zone provides important parameters for subsequent analysis, which helps to more accurately determine the location of uranium deposits;

[0065] The analysis unit 1402 is used to analyze the variation characteristics of the data corresponding to the surface zone based on the thickness;

[0066] In this embodiment, by analyzing the characteristics of data changes, the system can identify potential uranium deposit distribution areas, thereby improving the accuracy of predictions.

[0067] The setting unit 1403 is used to preset a critical value for radioactive data and compare the analyzed change characteristics with the critical value;

[0068] The determination unit 1404 is used to determine whether there is uranium ore in the surface zone based on the comparison results. If the data corresponding to the change characteristics exceeds the threshold value, the system determines that there is uranium ore in the surface zone; otherwise, it does not make a determination.

[0069] In this embodiment, by setting a threshold value and making a comparison, the system can scientifically determine whether uranium ore exists or not, reducing the influence of subjective judgment.

[0070] Furthermore, the automated comparison process improves the efficiency of judgment, enabling the system to quickly provide prediction results;

[0071] If the system determines that uranium ore exists in the surface zone, it obtains the area of ​​the surface zone and presets a value within that area. Mining sites Mining sites and Mineral points, the sum of the areas of all said mineral points is at least 70% of the area of ​​the surface zone, at least 10 to 20 radiometric data corresponding to each said mineral point are collected, the mineral point that exceeds the critical value the most among the radiometric data corresponding to each said mineral point is obtained, the mineral point is marked as θ1 mining point, and radiometric data of the middle zone is collected based on the θ1 mining point.

[0072] It also includes obtaining the number of radioactive data points exceeding the threshold value in the radioactive data corresponding to each of the aforementioned mining points, marking the mining point with the most exceeding the threshold value as the θ2 mining point, and collecting radioactive data of the middle zone based on the θ2 mining point.

[0073] Obtain the radioactivity data that exceeds the critical value most in the θ2 mining point, and mark the radioactivity data as reference radioactivity data. Obtain the radioactivity data adjacent to the reference radioactivity data, and mark the radioactivity data smaller than the reference radioactivity data as Δ. 小 Data, radioactive data that is greater than the reference radioactive data is marked as Δ. 大 Data; Obtain the Δ 小 Data to the reference radioactive data and the reference radioactive data to the Δ 大 The soil layer thickness corresponding to the data, if it is related to Δ 小 The soil layer thickness corresponding to the reference radioactivity data is greater than the soil layer thickness corresponding to the reference radioactivity data to the Δ大 If the soil layer thickness corresponding to the data is such that the soil layer thickness at the θ2 excavation point shows a decreasing trend from the point where the uranium ore is found, then no determination is made.

[0074] In the analysis unit, based on the variation characteristics of the thickness analysis and the data corresponding to the surface zone, the analysis steps include:

[0075] The thickness is divided, including dividing it into at least 10 identical thickness layers;

[0076] Radioactivity data for each thickness layer is collected, and the data difference between radioactivity data of adjacent thickness layers is calculated.

[0077] Calculate the average radioactivity of radioactivity data from 10 thickness layers, and identify the thickness layers with lower radioactivity than the average radioactivity among the 10 thickness layers.

[0078] If the thickness layer consists of three or more consecutive thickness layers, and these three or more thickness layers border the middle layer zone, the system determines that the probability of uranium deposits existing in the middle layer zone is low; otherwise, no determination is made.

[0079] When the probability of uranium deposits existing in the intermediate zone is determined to be low, the probability is verified. The verification steps are as follows:

[0080] Radioactive data exceeding the critical value were collected in the middle layer zone.

[0081] In the middle layer zone, the soil layer thickness corresponding to the radioactivity data is obtained, and 6 to 10 verification points are given in the soil layer thickness to obtain the radioactivity data of each verification point.

[0082] If, among the radioactive data at each verification point, half of the radioactive data exceeds the threshold value, then the probability of uranium deposits existing in the intermediate zone is considered high; otherwise, the probability of uranium deposits existing in the intermediate zone is considered low.

[0083] If no radioactive data exceeding the threshold is collected in the middle layer, verification will not be performed.

[0084] In this embodiment, after determining that there is uranium ore in the surface zone, a mining point is preset and corresponding radioactive data is collected to locate the mining point; at the same time, the probability of uranium ore existing in the middle zone is verified.

[0085] By pre-setting mining locations and collecting corresponding data, the system can accurately locate mining locations and improve mining efficiency.

[0086] The verification of the middle zone ensures the comprehensiveness and accuracy of the prediction results, reducing the possibility of missed and misjudgments;

[0087] The distance between adjacent verification points is obtained at each of the aforementioned verification points. Based on the distance, the regular changes in radioactivity data are calculated, and the result is obtained according to the following formula:

[0088] ΔI i =I i+1 -I i ;

[0089] In the formula, ΔI i I represents the change in radioactivity intensity from verification point i to verification point i+1. i This represents the measured value of radioactivity at verification point i.

[0090] It also includes calculations based on the following formula:

[0091]

[0092] In the formula, R i I represents the relative rate of change of radioactivity intensity from verification point i to verification point i+1. i This represents the measured value of radioactivity at verification point i;

[0093] In this embodiment, quantitative analysis is achieved by calculating the change in radioactivity intensity and the relative rate of change between verification points; at the same time, the change trend of soil layer thickness when uranium ore is excavated is predicted based on the change trend of radioactivity data around the mining site.

[0094] Quantitative analysis provides more accurate data support, which helps in scientific decision-making;

[0095] Trend forecasting provides forward-looking guidance for mining operations, helping to optimize mining strategies and resource allocation;

[0096] Obtain the area composed of all verification points, and based on the calculated results, obtain the region corresponding to the radioactive data greater than the threshold value in the area, and mark the region as the region with a high probability of uranium ore presence;

[0097] In this embodiment, areas with a high probability of uranium deposits are marked based on the calculation results, providing a reference for subsequent exploration and development.

[0098] By marking areas with high probability, subsequent exploration and development work can be effectively guided, thereby improving resource utilization efficiency.

[0099] At the same time, accurate forecasting and labeling reduce unnecessary exploration and development activities, lowering development costs and environmental risks.

[0100] In summary, this application effectively avoids resource waste and improves resource utilization efficiency by accurately predicting the location of uranium deposits. At the same time, the system also marks areas with a high probability of uranium deposits, providing important reference for subsequent exploration and development work, and further improving the efficiency and effectiveness of resource development.

[0101] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A radioactive uranium deposit prediction system based on multi-source geophysical data fusion, characterized in that, include: The data acquisition module is used to collect relevant data of the target area, including radioactive data. The collected radioactive data is distinguished according to the probability of the presence of uranium ore, including distinguishing them into high-probability radioactive data groups and medium-probability radioactive data groups. The data preprocessing module is used to preprocess the collected radioactive data, including cleaning, noise reduction, format conversion, and standardization. The data acquisition module is used to acquire the topography of the target area and to acquire the topography corresponding to the high-probability radioactivity data group and the medium-probability radioactivity data group based on the distinguished radioactivity data. The data fusion processing module is used to classify the terrain and landforms corresponding to the high-probability radioactivity data set; the data fusion processing module includes a classification unit, an analysis unit, a setting unit, and a judgment unit. The division unit is used to divide the terrain and landform according to the high probability radiometric data set, including dividing the surface zone, middle zone and deep zone corresponding to the data in the high probability radiometric data set, and obtaining the thickness of the surface zone. The analysis unit is used to analyze the variation characteristics of data corresponding to the surface zone based on the thickness, and the analysis steps include: The thickness is divided, including dividing it into at least 10 identical thickness layers; Radioactivity data for each thickness layer is collected, and the data difference between radioactivity data of adjacent thickness layers is calculated. Calculate the average radioactivity of radioactivity data from 10 thickness layers, and identify the thickness layers with lower radioactivity than the average radioactivity among the 10 thickness layers. If the thickness layer consists of three or more consecutive thickness layers, and these three or more thickness layers border the middle layer zone, the system determines that the probability of uranium deposits in the middle layer zone is low; otherwise, no determination is made. When the probability of uranium deposits existing in the intermediate zone is determined to be low, the probability is verified. The verification steps are as follows: Radioactive data exceeding the critical value were collected in the middle layer zone. In the middle layer zone, the soil layer thickness corresponding to the radioactivity data is obtained, and 6 to 10 verification points are given in the soil layer thickness to obtain the radioactivity data of each verification point. If, among the radioactive data at each verification point, half of the radioactive data exceeds the threshold value, then the probability of uranium deposits existing in the intermediate zone is considered high; otherwise, the probability of uranium deposits existing in the intermediate zone is considered low. If no radioactive data exceeding the threshold is collected in the middle layer, verification will not be performed. The distance between adjacent verification points is obtained at each of the aforementioned verification points. Based on the distance, the regular changes in radioactivity data are calculated, and the result is obtained according to the following formula: ； In the formula, Indicates verification point to The change in the intensity of radioactivity at each verification point Indicates verification point The measured value of radioactivity; The setting unit is used to preset a critical value for radioactive data and compare the analyzed change characteristics with the critical value; The determination unit is used to determine whether there is uranium ore in the surface zone based on the comparison results. If the data corresponding to the change characteristics exceeds the threshold value, the system determines that there is uranium ore in the surface zone; otherwise, it does not make a determination.

2. The radioactive uranium deposit prediction system based on multi-source geophysical data fusion as described in claim 1, characterized in that, If the system determines that uranium ore exists in the surface zone, it obtains the area of ​​the surface zone and presets a value within that area. Mining sites Mining sites and Mineral deposits, the sum of the areas of all said mineral deposits being at least 70% of the surface area, at least 10 to 20 radiometric data points corresponding to each mineral deposit are collected, and the mineral deposit exceeding the threshold the most is identified from the radiometric data corresponding to each mineral deposit, and marked as [missing information]. Excavate the mining site, and with the aforementioned Radioactive data from the middle layer of the mineral deposits were collected.

3. The radioactive uranium deposit prediction system based on multi-source geophysical data fusion as described in claim 2, characterized in that, This also includes obtaining the number of instances exceeding the threshold value in the radioactivity data corresponding to each of the aforementioned mining sites, and marking the mining site with the largest number of instances exceeding the threshold value as... Excavate the mining site, and with the aforementioned Radioactive data from the middle layer of the mineral deposits were collected.

4. The radioactive uranium ore prediction system based on multi-source geophysical data fusion as described in claim 3, characterized in that, Obtain the The radioactive data exceeding the critical value most frequently in the mining site is marked as reference radioactive data. Next, radioactive data adjacent to the reference radioactive data are obtained, and among these adjacent radioactive data, those smaller than the reference radioactive data are marked as... Data, radioactive data that is greater than the reference radioactive data is marked as Data; Obtain the Data to the reference radioactive data and the reference radioactive data to the The soil layer thickness corresponding to the data, if it is related to The soil layer thickness corresponding to the reference radioactivity data is greater than the soil layer thickness corresponding to the reference radioactivity data. The system determines the soil layer thickness corresponding to the data. The thickness of the soil layer closest to the uranium deposit decreases with increasing distance from the mining site; conversely, no determination is made if the soil layer thickness increases with increasing distance.

5. The radioactive uranium deposit prediction system based on multi-source geophysical data fusion as described in claim 1, characterized in that, The distance between adjacent verification points is obtained at each of the aforementioned verification points, and the regular changes in radioactivity data are calculated based on the distance. This also includes calculating the data according to the following formula: ； In the formula, Indicates verification point to The relative rate of change of radioactivity intensity at each verification point. Indicates verification point The measured value of radioactivity.

6. The radioactive uranium deposit prediction system based on multi-source geophysical data fusion as described in claim 5, characterized in that, Obtain the area composed of all verification points, and based on the calculated results, obtain the region corresponding to the radioactivity data greater than the threshold value within the area, and mark the region as a region with a high probability of uranium ore presence.

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