A geological and mineral exploration device and method

Abstract

This invention discloses a geological and mineral exploration device and method, specifically relating to the field of geological exploration technology. It involves collecting data from a mining area over multiple preset time periods and analyzing the data within each period to determine if there are early signs of insufficient data quality. If quality issues are found, further feature analysis is performed to assess the accuracy and coverage of the data. Based on the assessment results, the data collection period is divided into high-quality and low-quality periods. For low-quality collection periods, resampling or supplementary collection is performed to improve data accuracy and coverage, ensuring the integrity and accuracy of the exploration data. Through multi-level assessment of accuracy and coverage indices, this invention can promptly identify quality problems in data collection and perform multiple correction processes, significantly improving the accuracy and coverage of the collected data and ensuring that the collected data accurately reflects the true geological conditions of the mining area.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, and more specifically, to a geological and mineral exploration device and method. Background Technology

[0002] Geological and mineral exploration refers to the process of investigating the existence and distribution patterns of underground mineral resources through geological surveys, analysis, and research, in order to determine the scale, quality, grade, and economic value of these resources. The exploration process typically includes stages such as preliminary geological surveys, detailed surveys, and prospecting. Through sampling, analysis, and other technical means, the types, distribution locations, and reserves of minerals in the mining area are scientifically assessed.

[0003] The first step in geological and mineral exploration is geological survey, which involves understanding the surface and shallow geological conditions of a specific area to determine whether the geological conditions for mineral resource formation are present. This stage, through observation, sampling, and basic analysis, preliminarily identifies potential mineral-rich areas, providing a foundation for further exploration.

[0004] In geological and mineral exploration, the quality of acquired geological data directly affects the accuracy of ore body models and the scientific validity of mineral resource assessments. However, traditional geological exploration methods face many challenges during data acquisition. First, the accuracy and spatial coverage of acquired data are often difficult to guarantee, especially in environments with complex terrain or harsh mining conditions. Acquired data may deviate from reality, leading to errors in resource reserve assessments. Second, traditional exploration typically relies on manual data quality checks, making it difficult to identify and correct errors in the acquisition process in a timely manner. Abnormal data fluctuations and insufficient coverage may be overlooked or improperly handled, rendering the exploration results unreliable. Therefore, this paper proposes a geological and mineral exploration device and method. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A geological and mineral exploration method includes the following steps:

[0007] Data is collected from geological and mining areas within multiple preset time periods;

[0008] Data analysis is performed on the collected data at each time period, and the results of the data analysis are used to determine whether there are any early signs of insufficient quality of geological and mineral exploration data.

[0009] In the presence of early signs of insufficient quality in geological and mineral exploration data, feature analysis is performed on the data for this time period, and the accuracy and coverage of the data collected within this time period are evaluated respectively.

[0010] The data collection quality for that time period is determined based on the evaluation results, and the data collection period is divided into high-quality collection period and low-quality collection period.

[0011] For low-quality acquisition periods, the data for that time period is resampled or supplemented.

[0012] In a preferred embodiment, a correction process is performed after resampling or supplementary acquisition to reduce biases generated during data acquisition.

[0013] In a preferred embodiment, data analysis refers to:

[0014] Within each time period, statistical processing is performed on the data from all sampling points, including the mean. Then, calculate the standard deviation σM and perform outlier detection:

[0015] Mi is the data value of the i-th sampling point, Di is the deviation value of the i-th sampling point, and k is the preset threshold coefficient for anomaly detection. If Di>k*σM, then the sampling point is marked as an anomaly.

[0016] The data coverage rate is calculated within the time period based on the actual number of data points collected in the exploration area. The data coverage rate is obtained by dividing the actual number of data points collected by the preset theoretical number of data points.

[0017] The mean change rate between adjacent data collection periods is calculated to detect any abnormal fluctuations in the data. The formula for calculating the mean change rate is as follows: t represents the index number of the current acquisition period. Rt represents the mean value of the current acquisition period t, and Rt represents the rate of change of the mean value of the current acquisition period t.

[0018] In a preferred embodiment, determining whether there are early signs of insufficient geological and mineral exploration data quality based on data analysis results refers to:

[0019] The number of outliers is counted. If the number of outliers exceeds a preset first threshold, an anomaly signal is generated.

[0020] If the actual data coverage in the exploration area is greater than the preset second threshold, an abnormal signal is generated;

[0021] If the absolute value of the mean change rate of the current acquisition period t is greater than the preset third threshold, an abnormal signal is generated.

[0022] If abnormal signals are generated within the same acquisition period t, it indicates that there are early signs of insufficient geological and mineral exploration data quality in that acquisition period t.

[0023] In a preferred embodiment, feature analysis refers to generating an accuracy index corresponding to the data acquisition accuracy based on data acquisition accuracy information, and generating a coverage index corresponding to the data coverage based on data coverage information.

[0024] In a preferred embodiment, the logic for obtaining the accuracy index is as follows:

[0025] For each sampling point i within a time period, the acquisition error value is calculated using the following formula:

[0026] Ei = |Bi - Mi|; Mi is the data value of the i-th sampling point, Bi is the reference data value of the i-th sampling point, and Ei is the acquisition error value of the i-th sampling point;

[0027] Based on the error value Ei of all sampling points, the mean square error MSE of the collected data is calculated in order to quantify the acquisition accuracy.

[0028] The precision index is calculated using inverse proportional standardization, and the formula is as follows:

[0029] JDI is the accuracy index, and α is the standardization coefficient.

[0030] In a preferred embodiment, the logic for obtaining the coverage index is as follows:

[0031] The exploration area is divided into multiple grid cells, and the actual number of data collection points in each grid cell within a time period is obtained. Let nj be the actual number of data collection points in grid cell j, and Nj be the theoretical number of data collection points in grid cell j. The coverage ratio Cj for each grid cell is then calculated.

[0032] To reflect the contribution of each grid cell to the overall coverage, a weighted average coverage is calculated.

[0033] m is the total number of grid cells, and wj is the scaling factor corresponding to grid cell j;

[0034] To assess the stability of coverage, the standard deviation σC of the coverage proportion Cj of all grid cells is calculated to quantify the non-uniformity of coverage.

[0035] The formula for calculating the coverage index is:

[0036] β is the preset adjustment coefficient, and ZFI is the coverage index.

[0037] In a preferred embodiment, dividing the data acquisition cycle into a high-quality acquisition cycle and a low-quality acquisition cycle means:

[0038] The coverage index and accuracy index within the same time period are input into a pre-trained machine learning model. The output is a period division value. If the period division value is greater than or equal to the preset division threshold, the time period is divided into a high-quality acquisition period. If the period division value is less than the preset division threshold, the time period is divided into a low-quality acquisition period.

[0039] In a preferred embodiment, the correction process refers to:

[0040] The acquisition error value Ei is compared with the preset error threshold. If the acquisition error value Ei is greater than the preset error threshold, it is marked as a correction data point. Linear interpolation correction is performed using the data of adjacent high-quality acquisition points. The average value of the high-quality acquisition point data before and after the correction data point is calculated to obtain the data Mi′ after one correction.

[0041] Then, a moving average is used for smoothing. A sliding window W is set, and the formula for calculating the secondary corrected data Mi within the sliding window is:

[0042]

[0043] Next, spatial bias correction is performed. The formula for calculating the data Mi”' after three corrections is as follows:

[0044] Mi”′=Mi”*f(Δh,Δg); f(Δh,Δg) represents a correction factor based on the topographic height difference Δh and the geological feature difference Δg, in order to correct errors caused by topographic or geological characteristics;

[0045] After calibration, the data Mi”' after three calibrations is stored and updated to the database, and then re-evaluated until the time period is classified as a high-quality acquisition period.

[0046] In a preferred embodiment, a geological and mineral exploration apparatus includes:

[0047] The data acquisition module collects data from the geological and mining areas within multiple preset time periods and stores the collected data for subsequent analysis.

[0048] The data analysis module analyzes the collected data in each time period. Based on the analysis results, it determines whether there are early signs of insufficient geological and mineral exploration data quality. If there are early signs of insufficient data quality, the feature analysis module is triggered.

[0049] The feature analysis module further performs feature analysis on the data in the time period where signs of insufficient data quality are detected, generates an accuracy index corresponding to the data acquisition accuracy, evaluates the accuracy level of the acquired data, and generates a coverage index corresponding to the data coverage range, evaluates the coverage of the acquired data.

[0050] The data quality assessment module judges the data collection quality of the time period based on the accuracy index and coverage index of the feature analysis module, and divides the data collection period into high-quality collection period and low-quality collection period. If the data quality meets the standard, it is marked as a high-quality collection period; otherwise, it is marked as a low-quality collection period.

[0051] The data optimization processing module resamples or supplements the data for the collection period marked as low quality. After resampling or supplementing the data, the correction processing module is executed to reduce the deviation generated during the data collection process.

[0052] The correction processing module performs error correction on the re-acquired data to reduce errors caused by acquisition time or spatial deviations, ensuring that the accuracy and coverage of the acquired data meet preset standards, thereby optimizing the overall data quality.

[0053] The technical effects and advantages of this invention are as follows:

[0054] This invention, through multi-level evaluation of accuracy index and coverage index, can promptly identify quality problems in data acquisition and perform multiple correction processes based on the topography and geological characteristics of the acquisition area, significantly improving the accuracy and coverage of the acquired data and ensuring that the acquired data can accurately reflect the true geological conditions of the mining area.

[0055] This invention employs a machine learning model to automatically assess data quality within each time period, dividing the acquisition cycle into high-quality and low-quality periods in real time. This automated quality detection method reduces reliance on manual judgment, significantly improving exploration efficiency and data processing accuracy. By selectively resampling or supplementing data from low-quality acquisition cycles, this invention effectively avoids unnecessary duplicate data collection, optimizes the use of exploration resources, and reduces manpower, equipment, and time costs during the exploration process.

[0056] Multi-level correction processes, including outlier correction, smoothing, and spatial bias correction, ensure the continuity and stability of the collected data. This reduces data fluctuations caused by environmental changes or equipment malfunctions, maintaining data consistency across different times and spaces, and providing a reliable data foundation for subsequent analysis.

[0057] This invention specifically considers the impact of terrain elevation differences and geological characteristics on the collected data, providing an effective correction method even under complex geological conditions. This enhances the adaptability of exploration methods, enabling the acquisition of high-quality data in various geological environments. By ensuring high-quality and consistent data, this invention provides more reliable data support for mineral resource assessment, ore body modeling, and development decisions, thereby improving the accuracy of resource assessment and reducing the risks of exploration and development. Attached Figure Description

[0058] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0059] Figure 1 This is a schematic diagram of a geological and mineral exploration method according to the present invention.

[0060] Figure 2 This is a schematic diagram of a geological and mineral exploration device according to the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Reference Figure 1 - Figure 2 The following examples were obtained:

[0063] Example 1: In the process of geological and mineral exploration, incomplete or insufficient data collection can lead to the following problems:

[0064] Inaccurate resource reserve estimation: Incomplete or low-precision data collection can lead to deviations in the estimation of the size, distribution, and reserves of ore bodies. Insufficient data collection may cause explorers to underestimate or overestimate mineral resource reserves, thereby affecting the mining value and economic benefits of the minerals and leading to unforeseen economic risks during resource development.

[0065] Incomplete information on ore body morphology and grade: Insufficient exploration data can affect the accurate understanding of the ore body's three-dimensional structure and ore grade. For example, in complex deposits, insufficiently precise data acquisition may fail to reveal the true morphology and internal grade distribution of the ore body. This can affect the scientific nature of mining plans, leading to insufficient mining or resource waste, and even impacting safe mining operations.

[0066] Inaccurate mining design and cost estimates: Insufficient data can lead to inaccurate deposit models, and problems may arise in mining design and equipment allocation. For example, vague information about the strike, thickness, and geological structure of the ore body may result in unreasonable equipment placement, leading to geological difficulties or unnecessary operating costs during mining. Such deviations not only increase exploration and mining costs but may also prolong the construction period.

[0067] Therefore, comprehensive and high-precision data acquisition is crucial for geological and mineral exploration, helping to more scientifically assess mineral resources, design reasonable mining plans, and reduce mining risks. This invention proposes a geological and mineral exploration method, including the following steps:

[0068] Data is collected from the geological and mining areas within multiple pre-set time periods. By setting multiple time periods, data collection from the mining areas is carried out regularly to ensure the temporal continuity and coverage integrity of the data. This operation provides basic data, laying a reliable foundation for subsequent quality analysis, feature analysis, and data evaluation.

[0069] Data analysis is performed on the collected data within each time period. Based on the data analysis results, it is determined whether there are any early signs of insufficient geological and mineral exploration data quality. Analyzing the data within each time period can identify potential deficiencies in the quality of the collected data in a timely manner. This operation helps to discover data quality problems in advance, reduce misjudgments or analytical biases caused by insufficient data quality, and thus ensure the accuracy and effectiveness of subsequent analyses.

[0070] In cases where there are early signs of insufficient geological and mineral exploration data quality, characteristic analysis of the data for that time period is conducted to assess the accuracy and coverage of the collected data. Characteristic analysis of the time period with insufficient data quality allows for a more in-depth assessment of the accuracy and coverage of the collected data. By separately evaluating accuracy and coverage, the specific reasons for the data deficiency can be clearly identified (e.g., low accuracy or insufficient coverage), providing a clear direction for subsequent data supplementation or re-collection.

[0071] The data collection quality for each time period is assessed based on the evaluation results, and the data collection period is divided into high-quality and low-quality collection periods. Based on the results of feature analysis, data collection periods are further divided into high-quality and low-quality periods to ensure reasonable hierarchical management of the data. Data from high-quality periods is directly used for subsequent analysis or processing, while data from low-quality periods requires further supplementary collection or correction. This division helps improve the overall reliability and accuracy of the entire dataset.

[0072] For low-quality acquisition periods, data from those periods is resampled or supplemented. Resampling or supplementing data from low-quality acquisition periods helps compensate for deficiencies in data quality, ensuring that data coverage and acquisition accuracy meet standard requirements. This operation reduces analytical errors caused by incomplete or inaccurate data, providing higher-quality data support for subsequent processing and interpretation.

[0073] Correction processing is performed after resampling or supplementary data collection to reduce biases introduced during the data acquisition process. After resampling or supplementary data collection, correction processing reduces biases caused by temporal or spatial differences during the collection process. Correction processing effectively improves data consistency and accuracy, allowing supplementary data to be seamlessly integrated into the original dataset, improving the overall data integrity and accuracy, and ensuring the reliability of subsequent analysis and applications.

[0074] The purpose of resampling is to obtain more reliable data within an existing collection area to address data quality issues caused by insufficient accuracy. Supplementary collection aims to add data collection points to sub-areas with insufficient density within the original collection area to address data quality issues caused by insufficient coverage or sampling density. In practice, the user can determine which sampling method to use.

[0075] Data analysis refers to:

[0076] Within each time period, statistical processing is performed on the data from all sampling points, including the mean. The sum and standard deviation σM help assess the overall trend and distribution of the data. These statistics are an important basis for judging data quality, helping to identify potential outliers, and then perform outlier detection. Mi is the data value of the i-th sampling point, Di is the deviation value of the i-th sampling point, and k is the preset threshold coefficient for anomaly detection. If Di > k * σM, then the sampling point is marked as an anomaly. The actual data coverage rate within the exploration area is calculated within the time period. The data coverage rate is obtained by dividing the actual number of data points by the preset theoretical number of data points. The mean change rate between adjacent sampling periods is calculated to detect any abnormal fluctuations in the data. The formula for calculating the mean change rate is as follows: t represents the index number of the current acquisition period. Rt represents the mean value of the current data collection period t, and Rt represents the rate of change of the mean value of the current data collection period t. Calculating the rate of change of the mean value between adjacent data collection periods is used to detect whether there are abnormal fluctuations in the data. The rate of change of the mean value can quantify the consistency of the data over time. If the absolute value of the rate of change is too large, there may be inconsistencies in data collection or data quality problems, thus issuing an early warning in a timely manner to avoid further geological and mineral exploration and analysis of unstable data.

[0077] Judging from the data analysis results whether there are early signs of insufficient quality in geological and mineral exploration data refers to:

[0078] The system counts the number of outliers. If the number of outliers exceeds a preset first threshold, an anomaly signal is generated. Similarly, if the actual data coverage within the exploration area exceeds a preset second threshold, an anomaly signal is generated. If the absolute value of the mean change rate of the current acquisition period t exceeds a preset third threshold, an anomaly signal is generated. The presence of anomaly signals within the same acquisition period t indicates early signs of insufficient geological and mineral exploration data quality. The system counts the number of outliers in the sampled data and compares it to a preset threshold (first threshold). When the number of outliers exceeds the first threshold, an anomaly signal is generated, indicating potential data anomalies. This indicator is used to assess data accuracy, ensuring that the acquired data is not affected by noise or errors. The system calculates the actual data coverage within the exploration area and compares it to a preset coverage threshold (second threshold). If the coverage is insufficient, an anomaly signal is generated, indicating that there may be uncovered areas in the data. This indicator is used to assess the spatial integrity of the data, ensuring that the acquired data fully covers the target area. The system calculates the absolute value of the mean change rate of the current acquisition period and compares it to a preset change rate threshold (third threshold). When the absolute value of the rate of change of the mean exceeds the third threshold, an anomaly signal is generated, indicating that the data fluctuates excessively over time, potentially indicating instability or inconsistency. This indicator is used to assess the temporal stability of the data, ensuring that the data changes smoothly within a continuous period. If any of the above indicators generates an anomaly signal within the same acquisition period, it is determined that there are early signs of insufficient geological and mineral exploration data quality in that acquisition period. By comprehensively judging multiple indicators, data quality problems can be identified more comprehensively, ensuring that the data meets exploration requirements and reducing biases in subsequent analyses. These judgment criteria help to promptly identify data quality problems, ensuring the accuracy, coverage, and stability of geological and mineral exploration data, and providing a reliable data foundation for subsequent analysis.

[0079] Feature analysis refers to generating a precision index corresponding to the data acquisition precision based on data acquisition precision information, and a coverage index corresponding to the data coverage area based on data coverage area information. The logic for obtaining the precision index is as follows:

[0080] For each sampling point i within a time period, the acquisition error value is calculated using the following formula:

[0081] Ei = |Bi - Mi|; Mi is the data value of the i-th sampling point, Bi is the reference data value of the i-th sampling point (such as the mean or standard value of neighboring high-quality sampling points), and Ei is the sampling error value of the i-th sampling point, which reflects the accuracy deviation of each sampling point and quantifies the difference between the collected data and the standard data. The larger the error value, the lower the data accuracy of the sampling point.

[0082] Based on the error values ​​Ei of all sampling points, the mean square error (MSE) of the collected data is calculated to quantify the acquisition accuracy. This MSE is used to quantify the overall data acquisition accuracy within the time period. The smaller the MSE value, the higher the overall accuracy of the collected data, because the error values ​​of each sampling point are lower.

[0083] The precision index is calculated using inverse proportional standardization, and the formula is as follows:

[0084] JDI stands for Accuracy Index, and α is the standardization coefficient, adjusting the magnitude of the index. The accuracy index JDI ranges from 0 to 1, with a higher value indicating higher accuracy of the acquired data. When JDI is close to 1, it indicates that the error in the acquired data is very small and the accuracy is high; when the JDI value is low, it indicates that the error in the acquired data is large and the accuracy is insufficient. The accuracy index JDI is an important indicator for measuring the accuracy of acquired data and is used to determine whether the acquired data meets quality requirements.

[0085] The logic for obtaining the coverage index is as follows:

[0086] The exploration area is divided into multiple grid cells, and the actual number of data collection points in each grid cell within a time period is obtained. Let nj be the actual number of data collection points in grid cell j, and Nj be the theoretical number of data collection points in grid cell j. The coverage ratio Cj for each grid cell is then calculated. Assess coverage within each grid cell to ensure that data collection achieves the expected spatial coverage.

[0087] To reflect the contribution of each grid cell to the overall coverage, a weighted average coverage is calculated.

[0088] m represents the total number of grid cells. The weighted average coverage rate reflects the impact of different grid cells on the overall coverage rate. wj is the proportional coefficient corresponding to grid cell j, which can be defined as the ratio of the grid area or the ratio of the theoretical collection points of the grid cell to the theoretical collection points of the survey area.

[0089] To assess the stability of coverage, the standard deviation σC of the coverage ratio Cj of all grid cells is calculated to quantify the non-uniformity of coverage and evaluate the stability of coverage. The smaller the standard deviation, the more uniform the coverage is among the grid cells.

[0090] The formula for calculating the coverage index is:

[0091] β is a preset adjustment coefficient used to adjust the sensitivity to non-uniformity (standard deviation). ZFI is the coverage index; a higher ZFI indicates higher and more uniform coverage. A high ZFI value indicates that the collected data in the exploration area has achieved high spatial coverage, and the coverage distribution is also relatively uniform across different grid cells. A lower ZFI value indicates insufficient coverage or uneven coverage distribution, which may require further supplementary collection to improve the spatial integrity of the data.

[0092] Dividing data acquisition cycles into high-quality acquisition cycles and low-quality acquisition cycles means:

[0093] The coverage index and accuracy index within the same time period are input into a pre-trained machine learning model. The machine learning model is a multinomial regression model, which will not be elaborated on here. The output is the period division value. If the period division value is greater than or equal to the preset division threshold, the time period is divided into a high-quality acquisition period. If the period division value is less than the preset division threshold, the time period is divided into a low-quality acquisition period.

[0094] Coverage and accuracy indices are input into a pre-trained machine learning model, and the quality level of the collected data is automatically determined by the output period segmentation value. This method reduces the subjectivity of human judgment, improves the efficiency and consistency of evaluation, and ensures that the classification process of collection periods is objective and rapid. The purpose of period segmentation is to identify whether the data quality meets exploration requirements. High-quality collection periods mean that the data in that time period has sufficient coverage and collection accuracy and can be used for subsequent analysis. Low-quality collection periods indicate that the data is insufficient or biased and needs further supplementation or resampling. This segmentation ensures that the data used for subsequent analysis has high accuracy and completeness. By segmenting periods, time and resources can be concentrated on the processing of data from low-quality collection periods, such as re-collection or supplementary collection, without requiring additional processing of data from high-quality collection periods. This prioritization improves the utilization efficiency of exploration resources, avoids unnecessary duplicate collection, and reduces exploration costs. In subsequent geological analysis, model building, or prediction, data from high-quality collection periods can provide reliable basic support, while low-quality data may introduce bias or instability. By segmenting periods, data that meets quality requirements can be effectively screened, providing more stable and reliable data support for subsequent analysis.

[0095] Correction processing refers to:

[0096] The acquisition error value Ei is compared with a preset error threshold. If the acquisition error value Ei is greater than the preset error threshold, it is marked as a correction data point. Linear interpolation correction is performed using data from adjacent high-quality acquisition points. The average value of the high-quality acquisition point data before and after the correction data point is calculated to obtain the data Mi′ after the first correction. This process is mainly to eliminate abnormal data from individual sampling points and make them consistent with the surrounding high-quality data. The first correction ensures that each sampling point does not deviate too far from the adjacent data, thereby initially improving the consistency and accuracy of the data as a whole.

[0097] Then, a moving average is used for smoothing. A sliding window W is set, and the formula for calculating the secondary corrected data Mi within the sliding window is:

[0098] This process eliminates short-term fluctuations, smooths out subtle differences between sampling points, and reduces accidental sampling errors. Secondary correction improves the continuity and stability of the data, making it spatially smoother and suitable for further analysis.

[0099] Next, spatial bias correction is performed. The formula for calculating the data Mi”' after three corrections is as follows:

[0100] Mi”′=Mi”*f(Δh,Δg); f(Δh,Δg) represents a correction factor based on the topographic height difference Δh and the geological feature difference Δg, to correct errors caused by topography or geological characteristics; the third correction is a spatial correction for the influence of geological characteristics and topographic features, further improving the accuracy of data in geologically complex areas and making the data more consistent with the actual geological conditions.

[0101] After calibration, the data Mi”' after three calibrations is stored and updated to the database, and then re-evaluated until the time period is divided into a high-quality acquisition period.

[0102] The first correction removes outliers by linear interpolating with high-quality data to eliminate obvious anomalies and ensure initial data quality. The second correction smooths the data, reducing fluctuations and making it more continuous and stable, laying the foundation for spatial correction. The third correction considers geological and topographical influences, further correcting for biases caused by terrain or geological characteristics, improving spatial consistency and accuracy, based on the already smoothed and stable data. This gradual improvement in data quality, from simple outlier handling to complex geological correction, ensures accuracy and stability at each step.

[0103] Data collection at different locations and times may be affected by environmental, equipment, or human factors, resulting in errors and biases. Correction can eliminate these errors and improve data accuracy. Primary, secondary, and tertiary corrections successively eliminate outliers, smooth fluctuations, and mitigate geological influences, making the data more consistent and continuous, facilitating subsequent analysis and interpretation. Tertiary correction specifically considers topographic and geological characteristics, ensuring the data more accurately reflects the geological conditions of the exploration area, which is helpful for subsequent geological modeling and prediction. The data correction process is essential to ensuring the quality and accuracy of geological and mineral exploration data.

[0104] Example 2: A geological and mineral exploration device, comprising:

[0105] The data acquisition module collects data from the geological and mining areas within multiple preset time periods and stores the collected data for subsequent analysis.

[0106] The data analysis module analyzes the collected data in each time period. Based on the analysis results, it determines whether there are early signs of insufficient geological and mineral exploration data quality. If there are early signs of insufficient data quality, the feature analysis module is triggered.

[0107] The feature analysis module further performs feature analysis on the data in the time period where signs of insufficient data quality are detected, generates an accuracy index corresponding to the data acquisition accuracy, evaluates the accuracy level of the acquired data, and generates a coverage index corresponding to the data coverage range, evaluates the coverage of the acquired data.

[0108] The data quality assessment module judges the data collection quality of the time period based on the accuracy index and coverage index of the feature analysis module, and divides the data collection period into high-quality collection period and low-quality collection period. If the data quality meets the standard, it is marked as a high-quality collection period; otherwise, it is marked as a low-quality collection period.

[0109] The data optimization processing module resamples or supplements the data for the collection period marked as low quality. After resampling or supplementing the data, the correction processing module is executed to reduce the deviation generated during the data collection process.

[0110] The correction processing module performs error correction on the re-acquired data to reduce errors caused by acquisition time or spatial deviations, ensuring that the accuracy and coverage of the acquired data meet preset standards, thereby optimizing the overall data quality.

[0111] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0112] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0113] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0114] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0115] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for geological and mineral exploration, characterized in that, Includes the following steps: Data is collected from geological and mining areas within multiple preset time periods; Data analysis is performed on the collected data at each time period, and the results of the data analysis are used to determine whether there are any early signs of insufficient quality of geological and mineral exploration data. In the presence of early signs of insufficient quality in geological and mineral exploration data, feature analysis is performed on the data for this time period, and the accuracy and coverage of the data collected within this time period are evaluated respectively. The data collection quality for that time period is determined based on the evaluation results, and the data collection period is divided into high-quality collection period and low-quality collection period. For low-quality acquisition periods, the data for that time period is resampled or supplemented. Feature analysis refers to generating an accuracy index corresponding to the data acquisition accuracy based on data acquisition accuracy information, and generating a coverage index corresponding to the data coverage area based on data coverage area information. The logic for obtaining the precision index is as follows: For each sampling point i within a time period, the acquisition error value is calculated using the following formula: Ei = |Bi - Mi|; Mi is the data value of the i-th sampling point, Bi is the reference data value of the i-th sampling point, and Ei is the acquisition error value of the i-th sampling point; Based on the error value Ei of all sampling points, the mean square error MSE of the collected data is calculated in order to quantify the acquisition accuracy. The precision index is calculated using inverse proportional standardization, and the formula is as follows: JDI is the accuracy index, and α is the standardization coefficient. The logic for obtaining the coverage index is as follows: The exploration area is divided into multiple grid cells, and the actual number of data collection points in each grid cell within a time period is obtained. Let nj be the actual number of data collection points in grid cell j, and Nj be the theoretical number of data collection points in grid cell j. The coverage ratio Cj for each grid cell is then calculated. To reflect the contribution of each grid cell to the overall coverage, a weighted average coverage is calculated. m is the total number of grid cells, and wj is the scaling factor corresponding to grid cell j; To assess the stability of coverage, the standard deviation σC of the coverage proportion Cj of all grid cells is calculated to quantify the non-uniformity of coverage. The formula for calculating the coverage index is: β is the preset adjustment coefficient, and ZFI is the coverage index.

2. The geological and mineral exploration method according to claim 1, characterized in that, Correction processing is performed after resampling or supplementary data acquisition to reduce biases generated during the data acquisition process.

3. The geological and mineral exploration method according to claim 2, characterized in that, Data analysis refers to: Within each time period, statistical processing is performed on the data from all sampling points, including the mean. Then, calculate the standard deviation σM and perform outlier detection: Mi is the data value of the i-th sampling point, Di is the deviation value of the i-th sampling point, and k is the preset threshold coefficient for anomaly detection. If Di>k*σM, then the sampling point is marked as an anomaly. The data coverage rate is calculated within the time period based on the actual number of data points collected in the exploration area. The data coverage rate is obtained by dividing the actual number of data points collected by the preset theoretical number of data points. The mean change rate between adjacent data collection periods is calculated to detect any abnormal fluctuations in the data. The formula for calculating the mean change rate is as follows: t represents the index number of the current acquisition period. Rt represents the mean value of the current acquisition period t, and Rt represents the rate of change of the mean value of the current acquisition period t.

4. The geological and mineral exploration method according to claim 3, characterized in that, Judging from the data analysis results whether there are early signs of insufficient quality in geological and mineral exploration data refers to: The number of outliers is counted. If the number of outliers exceeds a preset first threshold, an anomaly signal is generated. If the actual data coverage in the exploration area is greater than the preset second threshold, an abnormal signal is generated; If the absolute value of the mean change rate of the current acquisition period t is greater than the preset third threshold, an abnormal signal is generated. If abnormal signals are generated within the same acquisition period t, it indicates that there are early signs of insufficient geological and mineral exploration data quality in that acquisition period t.

5. A geological and mineral exploration method according to claim 4, characterized in that, Dividing data acquisition cycles into high-quality acquisition cycles and low-quality acquisition cycles means: The coverage index and accuracy index within the same time period are input into a pre-trained machine learning model. The output is a period division value. If the period division value is greater than or equal to the preset division threshold, the time period is divided into a high-quality acquisition period. If the period division value is less than the preset division threshold, the time period is divided into a low-quality acquisition period.

6. The geological and mineral exploration method according to claim 5, characterized in that, Correction processing refers to: The acquisition error value Ei is compared with the preset error threshold. If the acquisition error value Ei is greater than the preset error threshold, it is marked as a correction data point. Linear interpolation correction is performed using the data of adjacent high-quality acquisition points. The average value of the high-quality acquisition point data before and after the correction data point is calculated to obtain the data Mi' after one correction. Then, a moving average is used for smoothing. A sliding window W is set, and the formula for calculating the secondary corrected data Mi within the sliding window is: Next, spatial bias correction is performed. The formula for calculating the data Mi”' after three corrections is as follows: Mi”'=Mi”*f(Δh,Δg); f(Δh,Δg) represents a correction factor based on the topographic height difference Δh and the geological feature difference Δg, to correct errors caused by topographic or geological characteristics; After calibration, the data Mi”' after three calibrations is stored and updated to the database, and then re-evaluated until the time period is divided into a high-quality acquisition period.

7. A geological and mineral exploration device, characterized in that, The geological and mineral exploration equipment includes: The data acquisition module collects data from the geological and mining areas within multiple preset time periods and stores the collected data for subsequent analysis. The data analysis module analyzes the collected data in each time period. Based on the analysis results, it determines whether there are early signs of insufficient geological and mineral exploration data quality. If there are early signs of insufficient data quality, the feature analysis module is triggered. The feature analysis module further performs feature analysis on the data in the time period when signs of insufficient data quality are detected, generates an accuracy index corresponding to the data acquisition accuracy, evaluates the accuracy level of the acquired data, and generates a coverage index corresponding to the data coverage range, evaluates the coverage of the acquired data. The data quality assessment module judges the data collection quality of the time period based on the accuracy index and coverage index of the feature analysis module, and divides the data collection period into high-quality collection period and low-quality collection period. If the data quality meets the standard, it is marked as a high-quality collection period; otherwise, it is marked as a low-quality collection period. The data optimization processing module resamples or supplements the data for the collection period marked as low quality. After resampling or supplementing the data, the correction processing module is executed to reduce the deviation generated during the data collection process. The correction processing module performs error correction on the re-acquired data to reduce errors caused by acquisition time or spatial deviations, ensuring that the accuracy and coverage of the acquired data meet preset standards, thereby optimizing the overall data quality. Feature analysis refers to generating an accuracy index corresponding to the data acquisition accuracy based on data acquisition accuracy information, and generating a coverage index corresponding to the data coverage area based on data coverage area information. The logic for obtaining the precision index is as follows: For each sampling point i within a time period, the acquisition error value is calculated using the following formula: Ei = |Bi - Mi|; Mi is the data value of the i-th sampling point, Bi is the reference data value of the i-th sampling point, and Ei is the acquisition error value of the i-th sampling point; Based on the error value Ei of all sampling points, the mean square error MSE of the collected data is calculated in order to quantify the acquisition accuracy. The precision index is calculated using inverse proportional standardization, and the formula is as follows: JDI is the accuracy index, and α is the standardization coefficient. The logic for obtaining the coverage index is as follows: The exploration area is divided into multiple grid cells, and the actual number of data collection points in each grid cell within a time period is obtained. Let nj be the actual number of data collection points in grid cell j, and Nj be the theoretical number of data collection points in grid cell j. The coverage ratio Cj for each grid cell is then calculated. To reflect the contribution of each grid cell to the overall coverage, a weighted average coverage is calculated. m is the total number of grid cells, and wj is the scaling factor corresponding to grid cell j; To assess the stability of coverage, the standard deviation σC of the coverage proportion Cj of all grid cells is calculated to quantify the non-uniformity of coverage. The formula for calculating the coverage index is: β is the preset adjustment coefficient, and ZFI is the coverage index.

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