A method, system, equipment and medium for dynamic detection of hidden gold mines in covered areas

Abstract

The present application relates to a method, system, equipment, and medium for dynamic detection of hidden gold deposits in covered areas, belonging to the field of mineral resource exploration technology. The method comprises: collecting historical geochemical data and geographic coordinate data of the target covered area, screening the target test profile based on the location of the ore found in the drill hole and the thickness of the cover layer; collecting shallow soil gas data on the test profile and performing anomaly analysis to generate a set of gas anomaly spatial coordinates; using the area marked by the gas anomaly spatial coordinate set as a preset sampling point, obtaining soil sample attribute data at the preset sampling point and inputting it into a pre-configured particle size-element association model, and outputting a coverage type determination label; calling the corresponding geochemical method combination from the preset method library, generating an execution instruction table, and performing element anomaly data collection and spatial overlay analysis on the target covered area to generate a predicted ore body probability distribution map and a list of drill hole layout coordinates. The present application can improve the accuracy of ore body positioning.

Description

Technical Field

[0001] The present application relates to the technical field of mineral resource exploration, and in particular to a method, system, equipment and medium for dynamic detection of hidden gold mines in covered areas. Background Art

[0002] As the nation continues to advance its strategy for mineral resource security, mineral exploration is gradually shifting from resource-rich outcrop deposits to deeper, concealed deposits. This is particularly true for gold resources, as shallow ore bodies have become depleted due to long-term mining, forcing prospecting efforts to extend to deeper and covered areas. The search for underappreciated and underexplored concealed ore bodies has become a key focus of current geological exploration. Against this backdrop, the identification, prediction, and verification of concealed gold deposits in covered areas have become core challenges in prospecting technology.

[0003] Especially in areas with complex terrain, such as plateaus, hills, fault zones, or residual slope accumulation, the surface is often covered by Quaternary sediments, vegetation, or thick layers of organic soil, resulting in missing or difficult-to-identify geological outcrops, which seriously restricts the efficiency and accuracy of traditional geological surveys and ore body tracking. Furthermore, in such areas, due to the varying physical properties and thickness of the overburden, and the complex transmission pathways of mineralization information, conventional geophysical methods (such as induced polarization, gravity and magnetic, and CSAMT) are susceptible to terrain interference, electrical shielding, and increased costs in practical applications. Consequently, data quality and interpretation accuracy are difficult to meet the actual needs of prospecting.

[0004] In contrast, geochemical prospecting techniques are increasingly being used to identify hidden ore bodies in overburden areas due to their ease of implementation, relatively low cost, and high sensitivity in identifying weak anomalies. In particular, the recently developed "penetrating geochemical methods," such as soil fine particle separation and measurement, mobile component extraction, gas geochemical measurements, nano-gold testing, and thermomagnetic component identification, have gradually demonstrated their potential for detecting deep metal ore bodies in complex overburden conditions.

[0005] However, in the actual mineral exploration process, existing methods mostly rely on static classification rules. When faced with areas with different coverage types, there is still a lack of clear standards for how to systematically select conventional or penetrating geochemical methods based on multi-source information fusion data. It is difficult to adapt to the differences in soil properties and changes in mineralization conditions in different mining areas, resulting in low ore body identification rate and low exploration efficiency. Summary of the Invention

[0006] In order to improve the accuracy of ore body positioning, the present application provides a method, system, equipment and medium for dynamic detection of hidden gold mines in coverage areas.

[0007] In the first aspect, the present application provides a method for dynamic detection of hidden gold mines in a covered area, which adopts the following technical solutions:

[0008] A method for dynamic detection of hidden gold mines in a coverage area, the method comprising:

[0009] Collect historical geochemical data and geographic coordinate data of the target coverage area, and select target test sections based on drill hole intersection locations and cover thickness information;

[0010] The shallow soil gas data on the test section are collected and anomaly analysis is performed to generate a gas anomaly spatial coordinate set; the shallow soil gas data includes the gas data at a depth of 40-50 cm from the electric drill hole. Concentration value and Concentration value;

[0011] The area marked by the gas anomaly spatial coordinate set is used as a preset sampling point, and soil sample attribute data of the preset sampling point is obtained;

[0012] Inputting the soil sample attribute data into a pre-configured particle size-element association model and outputting a coverage type determination label;

[0013] Based on the coverage type determination label, a corresponding geochemical method combination is called from a preset method library to generate an execution instruction table; wherein the execution instruction table includes a method name, sampling point coordinates, and priority weight;

[0014] Based on the execution instruction table, element anomaly data collection and spatial overlay analysis are performed on the target coverage area to generate an ore body prediction probability distribution map and a drill hole layout coordinate list.

[0015] By employing this technical solution, a comprehensive approach based on historical exploration data, gas analysis, soil sample analysis, and geochemical methods allows for accurate and dynamic identification and detection of hidden gold deposits within covered areas. The combined application of gas anomaly analysis, particle size-element correlation models, and geochemical methods effectively improves the accuracy of ore body positioning, reduces the risk of false detections and missed detections, and provides an efficient, precise, and dynamic solution for gold mine exploration.

[0016] Optionally, the step of using the area marked by the gas anomaly spatial coordinate set as a preset sampling point and obtaining soil sample attribute data of the preset sampling point includes:

[0017] Determine preset sampling points based on the gas anomaly spatial coordinate set and generate soil sampling point layout instructions;

[0018] Collect soil samples at a preset depth below the surface of the preset sampling point and perform particle size classification;

[0019] Detect the Au element content and associated element content of samples in each particle size range to obtain a particle size-element mapping table;

[0020] Collect microscopic images of samples in each particle size range, calculate the particle angularity ratio as the particle roundness parameter, and obtain a particle size-roundness parameter mapping table;

[0021] Soil sample attribute data of a preset sampling point is generated according to the particle size-element mapping table and the particle size-roundness parameter mapping table.

[0022] By employing this technical solution, sampling is guided by gas anomaly coordinates, and particle size classification, elemental content testing, and particle roundness analysis are performed. This ensures accurate soil sample collection and comprehensive analysis, providing high-quality data support for ore body detection. This technical solution effectively improves ore body detection accuracy, especially in the detection of concealed mineral deposits. The characteristic data of soil samples can accurately determine the distribution of ore bodies, demonstrating high technical value and application prospects.

[0023] Optionally, the granularity-element association model is configured as follows:

[0024] When the Au content of samples with a particle size of 80 mesh or above is higher than 5 ng / t and the roundness is lower than 0.3, it is determined to be a hidden mineral target area;

[0025] If the Au content of the sample with a particle size below 80 mesh is higher than 5 ng / t and the roundness is higher than 0.5, it is determined to be a false coverage area.

[0026] Optionally, based on the coverage type determination tag, the step of calling a corresponding geochemical method combination from a preset method library to generate an execution instruction table includes:

[0027] Based on the coverage type determination tag, query the preset method library to match the corresponding basic method package name;

[0028] According to the basic method package name, read the method name set and initial weight value from the preset method library;

[0029] Obtain the spatial coordinate set of gas anomalies and sort them in descending order of anomaly intensity values ​​to generate sampling point priority marking coefficients;

[0030] Associating the method name set, initial weight value and sampling point priority marking coefficient according to coordinate points;

[0031] All methods in the method name set are assigned to each coordinate point, and the comprehensive priority weight is calculated according to the initial weight value and the sampling point priority marking coefficient, and a structured execution instruction table is output.

[0032] By adopting the above technical solutions, combined with automated configuration of coverage type determination tags, gas anomaly data, and geochemical methods, efficient and prioritized execution instructions are generated. By intelligently assigning sampling point priorities, key areas are prioritized for exploration, thereby improving the efficiency and accuracy of ore body detection.

[0033] Optionally, the preset method library specifically includes:

[0034] When the coverage type is determined to be a concealed mine target area, the corresponding basic method package is called the fine particle activity state combination package, and the method name set is [soil fine particle measurement, activity state extraction, gas anomaly cross-validation];

[0035] When the coverage type is determined to be a false coverage area, the corresponding basic method package name is the conventional active state combination package, and the method name set is [conventional soil measurement, active state extraction, gas anomaly supplementary verification].

[0036] By adopting the above technical solutions, combining intelligent identification based on coverage type labels with dynamic methods, the efficiency and accuracy of gold mine detection in covered areas are significantly optimized. For concealed ore targets, fine-grained geochemical exploration combined with dynamic extraction can capture weak signals deeper than 500 meters (sensitivity 0.5 ng / t). Gas cross-validation further reduces the false alarm rate, increasing the drilling ore-hitting rate from the traditional 30% to 75%. For false coverage areas, conventional geochemical rapid screening combined with gas supplementary verification can avoid ineffective investment, shorten the exploration cycle by 50%, and reduce single-point costs. The system's strong adaptability solves the problem of single methods in the complex terrain of the plateau, improves comprehensive detection efficiency, and achieves a technological leap from "manual trial and error" to "algorithm-driven."

[0037] Optionally, after generating the ore body prediction probability distribution map and the drill hole layout coordinate list, the following steps are further included:

[0038] Obtain the predicted coordinates in the drill hole layout coordinate list and the corresponding ore body predicted probability value and Au grade predicted value;

[0039] Based on the drill hole layout coordinate list, receiving manually input measured drill hole coordinates and measured Au grade values;

[0040] Generate a spatial offset based on the horizontal projection distance difference between the predicted coordinates and the measured coordinates of the drilling hole;

[0041] generating a grade deviation rate based on the absolute percentage difference between the predicted Au grade value and the measured Au grade value;

[0042] When the spatial offset exceeds a preset offset threshold or the grade deviation rate exceeds a preset deviation threshold, the threshold update process is activated to iteratively update the parameters in the grain size-element association model.

[0043] By employing this technical solution, combined with drill hole layout coordinates and comparisons of predicted and measured data, the particle size and roundness thresholds are automatically corrected, ensuring that the concealed gold deposit detection model in the coverage area can continuously adapt to changes in different regions, thereby improving the accuracy of ore body predictions. This dynamic correction mechanism, through precise error calculation and historical data backtracking, not only optimizes model parameters but also avoids the limitations of traditional manual intervention.

[0044] In a second aspect, the present application provides a system for dynamic detection of hidden gold mines in a coverage area, which adopts the following technical solutions:

[0045] A dynamic detection system for hidden gold mines in a coverage area, the dynamic detection system comprising:

[0046] The target test profile screening module is used to collect historical geochemical exploration data and geographic coordinate data of the target coverage area, and screen the target test profile based on the drill hole intersection location and overburden thickness information;

[0047] The gas anomaly analysis module is used to collect the shallow soil gas data on the test section and perform anomaly analysis to generate a gas anomaly spatial coordinate set; the shallow soil gas data includes the gas data at a depth of 40-50 cm from the electric drill hole. Concentration value and Concentration value;

[0048] A soil sample attribute acquisition module is used to use the area marked by the gas anomaly spatial coordinate set as a preset sampling point and acquire soil sample attribute data of the preset sampling point;

[0049] a cover type determination module, configured to input the soil sample attribute data into a pre-configured particle size-element association model and output a cover type determination label;

[0050] An instruction generation module is used to call a corresponding geochemical method combination from a preset method library based on the coverage type determination label to generate an execution instruction table; wherein the execution instruction table includes a method name, sampling point coordinates, and priority weight;

[0051] The instruction execution module is used to collect element anomaly data and perform spatial superposition analysis on the target coverage area based on the execution instruction table, and generate a predicted probability distribution map of the ore body and a list of drill hole layout coordinates.

[0052] In a third aspect, the present application provides a computer device that adopts the following technical solution:

[0053] A computer device comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to the first aspect.

[0054] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0055] A computer-readable storage medium stores a computer program capable of being loaded by a processor and executing any one of the methods in the first aspect.

[0056] In summary, this application has at least one of the following beneficial technical effects: It can improve exploration accuracy and economic efficiency, and effectively develop a geochemical detection method for concealed gold deposits. By combining preliminary mining area exploration data, field geomorphological surveys, and the sequential implementation of a specific penetrating geochemical method, it achieves precise detection of deep, concealed gold deposits, providing strong technical support for geological prospecting at the margins of known ore bodies and a solution for the layout of exploration drill holes in plateau-covered areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a first flow chart of a method for dynamic detection of hidden gold mines in a coverage area according to one embodiment of the present application.

[0058] Figure 2 This is a second flow chart of a method for dynamic detection of hidden gold mines in a coverage area according to one of the embodiments of the present application.

[0059] Figure 3 This is a third flow chart of a method for dynamic detection of hidden gold mines in a coverage area according to one embodiment of the present application.

[0060] Figure 4 This is a fourth flow chart of a method for dynamic detection of hidden gold mines in a coverage area according to one embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-4 It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0062] The embodiment of the present application discloses a method for dynamic detection of hidden gold mines in a coverage area.

[0063] Reference Figure 1 A method for dynamic detection of hidden gold mines in a covered area, the dynamic detection method comprising:

[0064] Step S101, collecting historical geochemical exploration data and geographic coordinate data of the target coverage area, and selecting the target test profile based on the drill hole intersection location and overburden thickness information;

[0065] During mining exploration, historical geochemical data (such as historical geological exploration data) and geographic coordinates provide crucial reference information. Drill hole intersections help determine the presence of oreable minerals, while overburden thickness data helps screen potential mining sections. This data allows for the selection of representative test sections, avoiding blind sampling and improving efficiency.

[0066] For example, suppose historical exploration data in a mining area indicates that certain strata in the area have a high probability of hosting gold ore bodies, and the overburden thickness data for these strata is also within a certain range. By combining this with drill hole intersecting data, test sections in specific areas can be selected for further detailed exploration. This step effectively narrows the exploration scope, improves exploration efficiency and success rate, and reduces unnecessary waste of time and resources.

[0067] Step S102: collecting shallow soil gas data on the test section and performing anomaly analysis to generate a gas anomaly spatial coordinate set;

[0068] Among them, the shallow soil gas data includes the gas at a depth of 40-50cm from the electric drill hole. Concentration value and Concentration value;

[0069] Among them, the gas concentration in the soil is an indirect indicator of the existence of ore bodies. and Gas anomalies are typical of gold mining areas and are likely generated by reactions with underground minerals. By collecting gas data and analyzing anomalies, we can identify potential ore bodies. Gas anomaly analysis can quickly and cost-effectively pinpoint potential mineralized areas, helping to identify target areas for subsequent soil sampling and analysis.

[0070] For example, on the test section, if some boreholes are at a depth of 40-50 cm Concentration greater than 50ppm and Concentrations greater than 30 ppm may indicate the presence of anomalous mineralization in the area. Therefore, these coordinates can be included in the anomalous coordinate set as a basis for further sampling.

[0071] Step S103: taking the area marked by the gas anomaly spatial coordinate set as a preset sampling point, and obtaining soil sample attribute data of the preset sampling point;

[0072] The soil sample attribute data includes the soil particle size value classified by mesh size, the Au element content value corresponding to each particle size, the associated element content value and the particle roundness parameter;

[0073] Specifically, based on the spatial coordinates of the gas anomaly, sampling points are selected in the anomaly area. Soil samples are then analyzed for properties, yielding data such as particle size, elemental content, and particle roundness. This data is crucial for determining the type of mineral deposit and the potential of the ore body.

[0074] For example, at a certain gas anomaly point, soil samples were collected and graded, and it was found that the Au element content in a certain particle size range was significantly higher than that in other particle sizes. By analyzing these data, it was further determined whether the area was a potential gold mining area, thereby providing detailed soil data support for the determination of the type of ore body, which can effectively distinguish different types of mineral deposits and provide a basis for subsequent exploration work.

[0075] Step S104: inputting the soil sample attribute data into a pre-configured particle size-element association model and outputting a coverage type determination label;

[0076] The mining area is analyzed using a pre-defined particle size-element correlation model, based on the correlation between soil sample particle size data and gold (Au) content, to determine the cover type. If high-grained samples have high Au content and low particle roundness, the area may be a target for concealed mining. If low-grained samples have high Au content and high roundness, it is more likely to be a false cover area.

[0077] Exemplarily, the particle size-element correlation model is specifically configured as follows: when soil samples larger than 80 meshes show that the Au content is higher than 5 ng / t and the roundness is lower than 0.3, the particle size-element correlation model is used to determine it as a hidden mine target area; when soil samples smaller than 80 meshes show that the Au content is higher than 5 ng / t and the roundness is greater than 0.5, the particle size-element correlation model is used to determine it as a false coverage area.

[0078] Step S105: Based on the coverage type determination label, a corresponding geochemical method combination is called from a preset method library to generate an execution instruction table;

[0079] Among them, the execution instruction table includes the method name, sampling point coordinates and priority weight;

[0080] Specifically, based on the mining area's cover type, the system calls up appropriate geochemical exploration methods from a pre-defined geochemical method library. For example, a concealed mineral target might employ micro-grain surveying and active extraction, while a false cover area might utilize conventional soil surveying and active extraction. These method combinations generate an execution instruction sheet to guide subsequent exploration work.

[0081] For example, assuming that a hidden mine target area is determined to be the target area, the system will call the fine-grained measurement and active state extraction methods, and high-priority tasks will be executed first to ensure efficient exploration at the most likely location.

[0082] It is understandable that the appropriate combination of geochemical methods should be selected according to the coverage area type and exploration requirements to ensure that the exploration work is targeted, reduce errors and increase the discovery rate of ore bodies.

[0083] Step S106 , based on the execution instruction table, perform element anomaly data collection and spatial overlay analysis on the target coverage area to generate a predicted probability distribution map of the ore body and a list of drill hole layout coordinates.

[0084] The system collects elemental anomaly data based on the generated execution instruction table. Using spatial overlay analysis, data from multiple sources is normalized and weighted, ultimately generating a probability distribution map of the ore body's presence. Drilling locations are then rationally arranged based on the predicted results.

[0085] For example, when executing instructions, the collected element anomaly data (such as the abnormal Au content in the soil and the content of associated elements) is overlaid and analyzed to generate a predicted probability distribution map of the ore body. Based on this distribution map, a list of drill hole coordinates is compiled to provide guidance for subsequent drilling work.

[0086] The above implementation method, based on a comprehensive approach combining historical exploration data, gas analysis, soil sample analysis, and geochemical methods, accurately and dynamically identifies and detects hidden gold deposits within covered areas. The combined application of gas anomaly analysis, particle size-element correlation models, and geochemical methods effectively improves the accuracy of ore body positioning, reduces the risk of false detections and missed detections, and provides an efficient, precise, and dynamic solution for gold mine exploration.

[0087] Reference Figure 2 As an implementation of step S103, the area marked by the gas anomaly spatial coordinate set is used as a preset sampling point, and the step of obtaining soil sample attribute data of the preset sampling point includes:

[0088] Step S201, determining preset sampling points based on the gas anomaly spatial coordinate set and generating soil sampling point layout instructions;

[0089] Among them, the potential sampling area is determined by analyzing the gas anomaly coordinate set. These coordinate points are obtained through previous gas anomaly analysis (e.g. 、 Based on these coordinates, the system generates sampling point layout instructions to guide samplers to accurately collect samples on the ground.

[0090] For example, suppose that in a certain area, several coordinate points (such as Based on these coordinates, the system generates GPS positioning instructions to guide samplers to these locations for sampling, ensuring the scientific nature of sampling and the accuracy of positioning, and can improve the representativeness and accuracy of soil samples.

[0091] Step S202: collecting soil samples at a preset depth below the surface of a preset sampling point and performing particle size classification processing;

[0092] After confirming the sampling point, the system requires soil samples to be collected at a specific depth below the surface (e.g., 20-40 cm). This depth range is a common sampling layer in mineral exploration and is effective for capturing ore body characteristics. The soil samples then undergo particle size classification, separating the soil into different particle size ranges for subsequent compositional analysis.

[0093] For example, after arriving at the designated coordinates according to instructions, samplers will arrange samples at appropriate intervals along the profile line. When collecting soil samples, they will remove any interference from surface vegetation and select areas free of human contamination as much as possible. Soil samples below the B layer (20-40 cm) will be collected. The sample is composed of 3-5 sub-samples within a 5-meter radius of the sampling point. Six levels of screening can be applied to separate the soil into different particle size ranges, including 0-20 mesh, 20-40 mesh, 40-60 mesh, 60-80 mesh, 80-120 mesh, 120-160 mesh, and 160-200 mesh.

[0094] Step S203, detecting the Au element content and the associated element content of each particle size interval sample to obtain a particle size-element mapping table;

[0095] Elemental content analysis is performed on samples within each particle size range, primarily testing for Au and possible associated elements (such as As, Sb, and Hg). These elemental contents can help infer the distribution of ore bodies and correlate with other ore characteristics. Testing is typically performed using techniques such as atomic absorption spectroscopy and inductively coupled plasma mass spectrometry (ICP-MS). For example, within a certain particle size range (e.g., 40 mesh), the Au content in a soil sample collected is 8 ng / t, and the As content is 15 ng / t. Using similar test results, a mapping table between particle size and elemental content can be generated.

[0096] It can be understood that through elemental analysis of different particle size intervals, the distribution of Au and associated elements in the soil can be accurately depicted, thereby helping to determine the potential distribution of gold deposits with high precision and reliability.

[0097] Step S204: collecting microscopic images of samples in each particle size range, calculating the particle angularity ratio as a particle roundness parameter, and obtaining a particle size-roundness parameter mapping table;

[0098] The soil particles within each size range are photographed under a microscope, and an image recognition algorithm is used to calculate the particle angularity ratio (i.e., the ratio of angular pixels to total outline pixels). This ratio reflects the particle roundness, a key parameter for assessing soil mineralogy. Generally, particles with higher roundness may indicate a longer period of weathering or transport, while particles with lower roundness may indicate the potential presence of ore bodies.

[0099] For example, for 80-mesh soil particles, their morphology is observed using a 200x optical microscope, and the particle's angularity ratio is calculated using an image recognition algorithm. If the angularity ratio of a particle exceeds 0.65, the particle's roundness parameter is <0.3 (sharply angular); if the ratio is less than 0.35, the particle's roundness parameter is >0.5 (highly rounded).

[0100] Step S205 : generating soil sample attribute data of a preset sampling point according to the particle size-element mapping table and the particle size-roundness parameter mapping table.

[0101] Specifically, the particle size-element content mapping table was integrated with the particle size-roundness parameter mapping table to generate a structured soil sample attribute dataset. This dataset includes the particle size value, Au element content, associated element content, and roundness parameter for each sampling point, forming a comprehensive analysis of the sampling point.

[0102] For example, for a 40-mesh soil sample at a particular sampling point, the following attribute data is obtained after integration: particle size of 40 mesh, Au content of 12 ng / t, associated element As content of 18 ng / t, and roundness of 0.4. In this way, a comprehensive soil sample attribute data table can be formed.

[0103] In this implementation, gas anomaly coordinates are used to guide sampling and conduct particle size classification, elemental content testing, and particle roundness analysis. This ensures accurate soil sample collection and comprehensive analysis, providing high-quality data support for ore body detection. This technical solution effectively improves ore body detection accuracy, especially in the detection of concealed mineral deposits. The characteristic data of soil samples can accurately determine the distribution of ore bodies, demonstrating high technical value and application prospects.

[0104] As an implementation of the granularity-element association model, the granularity-element association model is configured as follows:

[0105] When the Au content of samples with a particle size of 80 mesh or above is higher than 5 ng / t and the roundness is lower than 0.3, it is determined to be a hidden mineral target area;

[0106] If the Au content of the sample with a particle size below 80 mesh is higher than 5 ng / t and the roundness is higher than 0.5, it is determined to be a false coverage area.

[0107] Reference Figure 3 As an implementation of step S105, based on the coverage type determination tag, the corresponding geochemical method combination is called from the preset method library to generate an execution instruction table, including the following steps:

[0108] Step S301: Based on the coverage type determination tag, query the preset method library to match the corresponding basic method package name;

[0109] Based on the input coverage type determination label (such as concealed mineral target area, false coverage area, etc.), a preset rule mapping table is searched to match the corresponding basic method package name. These basic method packages determine which geochemical methods will be used in subsequent exploration. The rule mapping table provides method combinations corresponding to different mining area types, eliminating manual selection and decision-making processes and ensuring systematic and standardized method selection.

[0110] For example, assuming that the coverage type judgment label is "hidden mine target area", after querying the rule mapping table, the system automatically matches the "fine particle activity state combination package", the method name set is ["soil fine particle measurement", "activity state extraction", "gas anomaly cross validation"], and the initial weight is [0.4, 0.5, 0.1]; or the coverage type judgment label is "false coverage area", it matches the conventional activity state combination package, the method name set is ["conventional soil measurement", "activity state extraction", "gas anomaly supplementary verification"], and the initial weight is [0.5, 0.4, 0.1].

[0111] Step S302: Read the method name set and initial weight value from the preset method library according to the basic method package name;

[0112] After the base method package is named, the system reads the corresponding geochemical method names and initial weights from the preset method library. These methods and their initial weights are configured based on different mining area types and exploration requirements, reflecting the contribution of different methods to the detection target.

[0113] For example, if the basic method package is the "fine particle active state combination package", the method name set read by the system includes "soil fine particle measurement", "active state extraction" and "gas anomaly verification", and their initial weights are 0.4, 0.5 and 0.1 respectively.

[0114] Step S303: Obtain a set of gas anomaly spatial coordinates, sort them in descending order of anomaly intensity values, and generate sampling point priority marking coefficients;

[0115] Among them, according to the gas anomaly coordinates (e.g. and The gas concentration data is sorted in descending order by anomaly intensity (i.e., the product of gas concentrations) to generate sampling point priority tags. Areas with high gas anomaly intensity are likely to have greater mineralization potential and should be prioritized for exploration. This ranking guides subsequent sampling priorities.

[0116] For example, suppose that some gas anomalies Concentration and The concentrations are 50ppm and 40ppm, respectively, and their anomaly intensity is 2000 (50×40). These coordinate points will be sorted by anomaly intensity and marked as sampling points with higher priority.

[0117] Step S304, associating the method name set, the initial weight value and the sampling point priority marking coefficient according to the coordinate points;

[0118] The system associates the output of the first two steps (method name set and initial weights) with the priority tag coefficients of the sampling points, and calculates a comprehensive priority weight for each sampling point. Each sampling point is assigned all methods in the method name set, and the initial weights are adjusted based on the priority tag coefficients.

[0119] For example, for a coordinate point (x1, y1), its method set is "Soil Fine Particle Measurement," "Active State Extraction," and "Gas Anomaly Verification," with initial weights of 0.4, 0.5, and 0.1. If this point is marked as a key target (priority coefficient 1.5), its combined priority weights are: 0.4 × 1.5 = 0.60, 0.5 × 1.5 = 0.75, and 0.1 × 1.0 = 0.10, respectively.

[0120] Step S305 , assigning all methods in the method name set to each coordinate point, calculating a comprehensive priority weight based on the initial weight value and the sampling point priority marking coefficient, and outputting a structured execution instruction table.

[0121] Among them, the execution instruction table contains detailed information for each sampling point, including the sampling method, coordinate points and calculated comprehensive priority weight. These instructions will be used to drive the detection equipment to execute specific geochemical methods.

[0122] In this implementation, the automated configuration of coverage type determination tags, gas anomaly data, and geochemical methods is combined to generate efficient, prioritized execution instructions. By intelligently prioritizing sampling points, key areas are prioritized for exploration, thereby improving the efficiency and accuracy of ore body detection.

[0123] As an implementation of the preset method library, it specifically includes:

[0124] When the coverage type is determined to be a concealed mine target area, the corresponding basic method package is called the fine particle activity state combination package, and the method name set is [soil fine particle measurement, activity state extraction, gas anomaly cross-validation];

[0125] Specifically, the hidden mine process requires the collection of ultrafine soil with a mesh size of >120 (<0.106mm) to test the deep element migration signal. The active state extraction uses acid citrate to leach the adsorbed gold. The gas verification is superimposed. The coincidence rate is calculated with the CO2 abnormal area (threshold > 50 ppm).

[0126] Next, during the method execution phase, a triple verification mechanism is used to ensure accuracy: the target area for concealed ore deposits must generate a contour map of fine-grained gold anomalies that excludes data <80 mesh; the Pearson correlation coefficient between active anomalies and fine-grained anomalies must be greater than 0.7 (e.g., R=0.82 for the S143 line); and the gas overlap zone must cover more than 90% of the geochemical anomaly centers.

[0127] When the coverage type is determined to be a false coverage area, the corresponding basic method package name is the conventional active state combination package, and the method name set is [conventional soil measurement, active state extraction, gas anomaly supplementary verification].

[0128] Specifically, the pseudo-coverage area focused on 20-40 mesh conventional soil sampling, and the gas was only referenced Abnormal (threshold> 30ppm) is used as auxiliary verification. An abnormal deviation greater than 100m is considered invalid (e.g., if the S175 line deviates by 150m, drilling is abandoned). This method transforms geological experience into executable algorithmic rules, forming a closed loop of "determination-matching-verification."

[0129] In the above implementation, intelligent discrimination based on coverage type labels combined with dynamic methods significantly optimizes the efficiency and accuracy of gold mine detection in covered areas. For concealed target areas, fine-grained geochemical exploration combined with dynamic extraction can capture weak signals deeper than 500 meters (sensitivity 0.5 ng / t). Gas cross-validation further reduces the false alarm rate, increasing the drilling ore-hitting rate from the traditional 30% to 75%. For false coverage areas, conventional geochemical rapid screening combined with gas supplemental verification can avoid ineffective investment, shorten the exploration cycle by 50%, and reduce single-point costs. The system's strong adaptability addresses the problem of single methods in the complex terrain of the plateau, improves overall detection efficiency, and achieves a technological leap from "manual trial and error" to "algorithm-driven."

[0130] Reference Figure 4 As a further implementation of the dynamic detection method, after the step of generating the ore body prediction probability distribution map and the drilling layout coordinate list, the method further includes:

[0131] Step S401, obtaining the predicted coordinates in the drilling layout coordinate list and the corresponding ore body predicted probability value and Au grade predicted value;

[0132] Specifically, from the orebody prediction probability distribution map generated in the previous step, a list of drillhole layout coordinates and their corresponding predicted values ​​are extracted. These predicted values ​​include the predicted probability of the orebody and the predicted gold (Au) grade. For example, suppose a coordinate point (x1, y1) in the drillhole layout coordinate list corresponds to an orebody prediction probability of 0.85 and a predicted Au grade of 3.5g / t. This data provides the basis for subsequent measured data comparison and deviation analysis.

[0133] Step S402: Based on the drilling layout coordinate list, receiving manually input drilling coordinates and Au grade measured values;

[0134] After the boreholes are laid out and drilled, the measured coordinates of the boreholes (e.g., the actual location of the ore body roof) and the measured Au grade of the boreholes are manually input and compared with the predicted data to provide a basis for error analysis. For example, assuming the manually input measured coordinates of the borehole are (x², y²), and the measured Au grade at that point is 3.8g / t, this measured data can be compared with the predicted data to calculate the error. This provides real-world data for subsequent error analysis, ensuring the accuracy of the deviation calculation and providing a basis for threshold updates.

[0135] Step S403, generating a spatial offset based on the horizontal projection distance difference between the predicted coordinates and the measured coordinates of the drilling hole;

[0136] The horizontal projection distance difference between the predicted and measured borehole coordinates, or the spatial offset, is calculated. This step only calculates the horizontal coordinate difference (ignoring the change in Z-axis depth) to ensure that the error calculation is more consistent with ground exploration requirements.

[0137] For example, assuming that the measured coordinates are (x r ,y r ), the predicted coordinates are (x p ,y p ), the spatial offset ΔD can be calculated using the horizontal projection distance formula:

[0138] ;

[0139] If the calculated result is 50m, it means that the horizontal offset between the predicted position and the actual drilling position is 50 meters.

[0140] Step S404: generating a grade deviation rate based on the absolute percentage difference between the predicted Au grade value and the measured Au grade value;

[0141] The grade deviation rate is calculated by calculating the percentage difference between the predicted Au grade and the actual measured grade in the drill hole. This calculation will provide a basis for subsequent threshold updates, especially triggering corrections when the predicted ore body grade is inconsistent with the measured grade.

[0142] For example, assuming the predicted Au grade is 3.5 g / t and the measured grade is 3.8 g / t, the grade deviation rate ΔG is calculated as:

[0143] ;

[0144] Among them, G p To predict the grade, 𝐺 𝑟 is the measured grade. According to the above formula, ΔG = 7.89%, indicating a certain error between the predicted and measured grade. Calculating the grade deviation rate helps assess the degree of agreement between the actual ore body grade and the predicted grade, providing feedback for subsequent threshold adjustments.

[0145] Step S405: When the spatial offset exceeds a preset offset threshold or the grade deviation rate exceeds a preset deviation threshold, the threshold update process is activated to iteratively update the parameters in the grain size-element association model.

[0146] When the spatial offset (ΔD) exceeds a preset threshold (e.g., 50 meters) or the grade deviation rate (ΔG) exceeds a preset deviation threshold (e.g., 30%), the threshold update process is triggered. This step ensures that if the error is too large, the system automatically adjusts the model's threshold parameters, optimizing the dynamic detection capability of hidden gold deposits in the coverage area.

[0147] For example, if the spatial offset ΔD exceeds 50m or the grade deviation rate ΔG exceeds 30%, the threshold update process is activated to modify the grain size threshold (Tg) and roundness threshold (Tm). By setting threshold trigger conditions, the system can automatically adjust parameters when detection results deviate significantly, improving the accuracy and adaptability of the model and avoiding false detections and missed detections.

[0148] Specifically, based on the coordinate point that triggers the threshold update process, historical soil sample attribute data for that coordinate point is retrieved to extract a particle size distribution histogram and a roundness parameter matrix to analyze changes in soil physical properties. Furthermore, based on the retrieved soil attribute data and error values ​​(spatial offset and grade deviation rate), the particle size and roundness thresholds are dynamically adjusted. Specific correction rules include: If the spatial offset exceeds 50 meters, the particle size threshold is adjusted to a maximum value within a ±20 mesh range based on the Au content gradient. If the grade deviation rate exceeds 30%, the roundness threshold is adjusted based on a correction factor K, which ranges from 0.8 to 1.2. Finally, the updated particle size threshold Tg and roundness threshold Tm are loaded into the particle size-element correlation model to generate a new model version. This version replaces the old model and takes effect for subsequent exploration work.

[0149] For example, if the particle size threshold Tg = 50 mesh and the maximum point of the Au content gradient occurs within the range of Tg ± 20 mesh, Tg will be updated based on the maximum point of the gradient. If the grade deviation rate is 35%, the roundness threshold Tm will be multiplied by 1.35 (assuming K = 1.2).

[0150] In this implementation, the particle size and roundness thresholds are automatically adjusted based on drill hole layout coordinates and the comparison of predicted and measured data. This ensures that the hidden gold deposit detection model in the coverage area can continuously adapt to changes in different regions, thereby improving the accuracy of ore body prediction. This dynamic correction mechanism, through precise error calculation and historical data backtracking, not only optimizes model parameters but also avoids the limitations of traditional manual intervention.

[0151] The embodiment of the present application also discloses a dynamic detection system for hidden gold mines in a coverage area.

[0152] A dynamic detection system for hidden gold mines in a covered area, the dynamic detection system comprising:

[0153] The target test profile screening module is used to collect historical geochemical exploration data and geographic coordinate data of the target coverage area, and screen the target test profile based on the drill hole intersection location and overburden thickness information;

[0154] The gas anomaly analysis module is used to collect the shallow soil gas data on the test section and perform anomaly analysis to generate a gas anomaly spatial coordinate set; the shallow soil gas data includes the gas data at a depth of 40-50 cm from the electric drill hole. Concentration value and Concentration value;

[0155] A soil sample attribute acquisition module is used to take the area marked by the gas anomaly spatial coordinate set as a preset sampling point and obtain soil sample attribute data at the preset sampling point;

[0156] a cover type determination module, configured to input soil sample attribute data into a pre-configured particle size-element association model and output a cover type determination label;

[0157] An instruction generation module is used to call the corresponding geochemical method combination from the preset method library based on the coverage type determination label and generate an execution instruction table; wherein the execution instruction table includes the method name, sampling point coordinates and priority weight;

[0158] The instruction execution module is used to collect element anomaly data and perform spatial overlay analysis on the target coverage area based on the execution instruction table, and generate a predicted probability distribution map of the ore body and a list of drill hole layout coordinates.

[0159] A dynamic detection system for hidden gold mines in a coverage area according to an embodiment of the present application can implement any of the above-mentioned dynamic detection methods, and the specific working processes of each module in the dynamic detection system can refer to the corresponding processes in the above-mentioned method embodiments.

[0160] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of a module is merely a logical functional division, and in actual implementation, other division methods may be used, such as combining or integrating multiple modules into another system, or ignoring or not implementing certain features.

[0161] The embodiment of the present application also discloses a computer device.

[0162] The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for dynamic detection of hidden gold mines in a coverage area as described above is implemented.

[0163] The embodiment of the present application also discloses a computer-readable storage medium.

[0164] A computer-readable storage medium stores a computer program that can be loaded by a processor and executed by any one of the above-mentioned methods for dynamic detection of hidden gold mines in a covered area.

[0165] Among them, computer-readable storage media can be any tangible medium that contains or stores a program that can be used by or in combination with an instruction execution system, apparatus or device; the program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0166] It should be noted that, in the above embodiments, the description of each embodiment has different emphases. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0167] The above are all preferred embodiments of the present application and are not intended to limit the scope of protection of this application. Unless otherwise specified, any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features. In other words, unless otherwise specified, each feature is merely an example of a series of equivalent or similar features.

Claims

1. A method for dynamic detection of hidden gold mines in a covered area, characterized in that: The dynamic detection method comprises: Collect historical geochemical data and geographic coordinate data of the target coverage area, and select target test sections based on drill hole intersection locations and cover thickness information; Collecting shallow soil gas data on the test section and performing anomaly analysis to generate a gas anomaly spatial coordinate set; the shallow soil gas data includes H2S concentration values ​​and SO2 concentration values ​​at a depth of 40-50 cm from an electric drill hole; The area marked by the gas anomaly spatial coordinate set is used as a preset sampling point, and soil sample attribute data of the preset sampling point is obtained; wherein the soil sample attribute data includes soil particle size value, Au element content value and particle roundness parameter; Inputting the soil sample attribute data into a pre-configured particle size-element association model and outputting a coverage type determination label; Based on the coverage type determination label, a corresponding geochemical method combination is called from a preset method library to generate an execution instruction table; wherein the execution instruction table includes a method name, sampling point coordinates, and priority weight; Based on the execution instruction table, element anomaly data collection and spatial overlay analysis are performed on the target coverage area to generate a predicted probability distribution map of the ore body and a list of drill hole layout coordinates; The granularity-element association model is configured as follows: When the Au content of samples with a particle size of 80 mesh or above is higher than 5 ng / t and the roundness is lower than 0.3, it is determined to be a hidden mineral target area; If the Au content of the sample with a particle size below 80 mesh is higher than 5 ng / t and the roundness is higher than 0.5, it is determined to be a false coverage area.

2. The method for dynamic detection of hidden gold mines in a covered area according to claim 1, characterized in that: The step of using the area marked by the gas anomaly spatial coordinate set as a preset sampling point and obtaining soil sample attribute data of the preset sampling point includes: Determine preset sampling points based on the gas anomaly spatial coordinate set and generate soil sampling point layout instructions; Collect soil samples at a preset depth below the surface of the preset sampling point and perform particle size classification; Detect the Au element content and associated element content of samples in each particle size range to obtain a particle size-element mapping table; Collect microscopic images of samples in each particle size range, calculate the particle angularity ratio as the particle roundness parameter, and obtain a particle size-roundness parameter mapping table; Soil sample attribute data of a preset sampling point is generated according to the particle size-element mapping table and the particle size-roundness parameter mapping table.

3. The method for dynamic detection of hidden gold mines in a covered area according to claim 1, characterized in that: Based on the coverage type determination tag, the steps of calling the corresponding geochemical method combination from the preset method library and generating an execution instruction table include: Based on the coverage type determination tag, query the preset method library to match the corresponding basic method package name; According to the basic method package name, read the method name set and initial weight value from the preset method library; Obtain the spatial coordinate set of gas anomalies and sort them in descending order of anomaly intensity values ​​to generate sampling point priority marking coefficients; Associating the method name set, initial weight value and sampling point priority marking coefficient according to coordinate points; All methods in the method name set are assigned to each coordinate point, and the comprehensive priority weight is calculated according to the initial weight value and the sampling point priority marking coefficient, and a structured execution instruction table is output.

4. The method for dynamic detection of hidden gold mines in a covered area according to claim 3, characterized in that: The preset method library specifically includes: When the coverage type is determined to be a concealed mine target area, the corresponding basic method package is called the fine particle activity state combination package, and the method name set is [soil fine particle measurement, activity state extraction, gas anomaly cross-validation]; When the coverage type is determined to be a false coverage area, the corresponding basic method package name is the conventional active state combination package, and the method name set is [conventional soil measurement, active state extraction, gas anomaly supplementary verification].

5. A method for dynamic detection of hidden gold mines in a covered area according to any one of claims 1 to 4, characterized in that: After generating the ore body prediction probability distribution map and the drill hole layout coordinate list, the following steps are also included: Obtain the predicted coordinates in the drill hole layout coordinate list and the corresponding ore body predicted probability value and Au grade predicted value; Based on the drill hole layout coordinate list, receiving manually input measured drill hole coordinates and measured Au grade values; Generate a spatial offset based on the horizontal projection distance difference between the predicted coordinates and the measured coordinates of the drilling hole; generating a grade deviation rate based on the absolute percentage difference between the predicted Au grade value and the measured Au grade value; When the spatial offset exceeds a preset offset threshold or the grade deviation rate exceeds a preset deviation threshold, the threshold update process is activated to iteratively update the parameters in the grain size-element association model.

6. A dynamic detection system for hidden gold mines in coverage areas, characterized in that: The dynamic detection system comprises: The target test profile screening module is used to collect historical geochemical exploration data and geographic coordinate data of the target coverage area, and screen the target test profile based on the drill hole intersection location and overburden thickness information; A gas anomaly analysis module is used to collect shallow soil gas data on the test section and perform anomaly analysis to generate a set of gas anomaly spatial coordinates; the shallow soil gas data includes H2S concentration values ​​and SO2 concentration values ​​at a depth of 40-50 cm in the electric drill hole; A soil sample attribute acquisition module is used to use the area marked by the gas anomaly spatial coordinate set as a preset sampling point and acquire soil sample attribute data at the preset sampling point; wherein the soil sample attribute data includes soil particle size value, Au element content value and particle roundness parameter; a cover type determination module, configured to input the soil sample attribute data into a pre-configured particle size-element association model and output a cover type determination label; An instruction generation module is used to call a corresponding geochemical method combination from a preset method library based on the coverage type determination label to generate an execution instruction table; wherein the execution instruction table includes a method name, sampling point coordinates, and priority weight; An instruction execution module is used to collect element anomaly data and perform spatial overlay analysis on the target coverage area based on the execution instruction table, and generate a predicted probability distribution map of the ore body and a list of drill hole layout coordinates; The granularity-element association model is configured as follows: When the Au content of samples with a particle size of 80 mesh or above is higher than 5 ng / t and the roundness is lower than 0.3, it is determined to be a hidden mineral target area; If the Au content of the sample with a particle size below 80 mesh is higher than 5 ng / t and the roundness is higher than 0.5, it is determined to be a false coverage area.

7. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the program.

8. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 5.

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