Mineral resource reserve assessment method based on multi-source data fusion

Abstract

The invention relates to the field of mineral resource assessment, in particular to a mineral resource reserve assessment method based on multi-source data fusion. Comprising the following steps of data collection and analysis, representative drill hole selection, rapid detection data acquisition, key segment chemical analysis, data verification model construction, historical data reliability evaluation, ore body spatial feature analysis, resource quantity re-verification and grading and economic value dynamic prediction. Through a multi-source data fusion and intelligent analysis technology, existing exploration and production data are fully utilized, and dependence on newly added field exploration is reduced; a representative sample grading collection and intelligent space modeling method is adopted, the precision of resource reserve assessment is effectively improved, the assessment process is accelerated through an automatic data processing platform and a dynamic feedback mechanism, the problem of data reliability and cost-benefit balance in undeveloped mine right assessment is solved, and the method is suitable for popularization and application. And a scientific basis is provided for mine right circulation and resource development.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of mineral resources evaluation, and particularly relates to a mineral resources reserve evaluation method based on multi-source data fusion. BACKGROUND

[0002] The accuracy of mineral resources reserve evaluation and the scientificity of economic benefit prediction are key factors for investment decision-making in the mining industry. With the rapid development of the mining market and the increasing frequency of mineral rights transactions, investors have higher requirements for the accuracy, timeliness and reliability of mineral resources reserve evaluation and value prediction. In particular, for mineral rights that have completed exploration and are ready for development or have been partially developed and need to be transferred, how to quickly and accurately evaluate their resource reserves and predict their economic value under limited investment conditions has become a technical problem that needs to be solved in the current mining investment field.

[0003] Currently, there are mainly three methods for mineral resources reserve evaluation: 1) traditional geostatistical methods, such as block segment method and section method; 2) geological mathematical methods, such as Kriging method and inverse distance weighting method; and 3) computer 3D modeling method. Although these methods have been widely used in practical production, there are still the following outstanding problems in the actual application process:

[0004] Improving the evaluation accuracy requires high cost and long cycle. In the application process of traditional evaluation methods, on the one hand, in order to ensure the evaluation accuracy, a large amount of data chemical analysis is required, which has the problems of high cost and long time; on the other hand, a large amount of supplementary exploration work is required, including new drilling engineering and surface engineering, which leads to high evaluation cost and long cycle. For example, in some mineral right evaluation projects, the cost of supplementary drilling engineering accounts for more than 65% of the total evaluation cost, and the evaluation cycle is as long as 6-8 months, which is difficult to meet the demand of investors for quick decision-making. If no supplementary exploration work is carried out, the evaluation accuracy will be low.

[0005] The traditional method also has the problem of static solidification of the evaluation model. The existing evaluation model is mostly of static structure, which cannot be dynamically adjusted according to the changes of resource properties, resulting in a large deviation between the evaluation results and the actual mining situation. Especially in the case of uneven quality of historical data, insufficient understanding of ore body characteristics, and frequent fluctuations in market environment, the traditional model is difficult to reflect the changes of resource value in time, which easily restricts the scientific decision-making of mineral right transfer and resource development. In addition, in the traditional method, economic benefit analysis is usually carried out as a subsequent link of resource evaluation, and the two lack organic combination, making it difficult to realize dynamic prediction and linkage analysis of resource value. This fragmented evaluation system leads to a lack of scientific basis for investment decision-making, increasing the investment risk.

[0006] The current research on mineral resource reserve evaluation and value prediction mainly focuses on the improvement of single technical method, such as Tang Pan (Tang Pan, Tang Juxing, Lin Bin, et al. Comparative analysis of traditional geometric method and geostatistical method in mineral resource reserve estimation, 2016), Li Huan (Li Huan, Yan Tingting, Liu Xiaoli. Application of three-dimensional visualization technology in mineral resource evaluation, 2011), Wei Wei (Wei Wei. Comparative study on mineral right value evaluation method, 2016) and the like. These studies focus on the improvement of a specific method or technical link, and have not formed a complete method system capable of dynamic adjustment, linkage analysis, improvement of evaluation efficiency and result accuracy, resulting in limited application effect in actual production practice.

[0007] In order to improve the accuracy of mineral resource reserve evaluation and the scientificity of economic value prediction, reduce the evaluation cost and shorten the evaluation period, it is urgent to carry out innovative research on the dynamic prediction method of mineral resource reserve evaluation and economic value. SUMMARY

[0008] The purpose of the present application is to overcome the shortcomings of the prior art, and to provide a mineral resource reserve evaluation method based on multi-source data fusion, which is based on multi-source data fusion, combined with artificial intelligence algorithm and economic model, and uses a systematic and intelligent research method to solve the above technical problems.

[0009] The purpose of the present application is achieved by the following technical scheme: a mineral resource reserve evaluation method based on multi-source data fusion, comprising the following steps:

[0010] S1, data collection and analysis: collecting and sorting the historical exploration data of the mineral resource belonging to the mining area, including geological report, drilling record, analysis data, resource estimation result, resource development and utilization scheme or three or more data in technical and economic evaluation, checking or establishing standardized database;

[0011] S2, representative drill hole selection: selecting representative drill holes with an area of 70% to 80% of the total area of the ore body in the area covering the ore body enrichment zone, edge zone and different grade interval, and with high core preservation integrity and complete historical exploration data as evaluation objects;

[0012] S3, rapid detection data acquisition: using a handheld X-ray fluorescence spectrometer to scan the selected drill core at an interval of 10 to 40 cm to obtain high-density continuous element content rapid detection data;

[0013] S4, key section chemical analysis: according to the rapid detection result, selecting core samples of mineralization abnormal section, grade change obvious section and representative stable section, using standardized chemical analysis method to determine the content of main useful elements, and recording the analysis result data;

[0014] Definition of terms: 1) Mineralization anomaly: refers to the abnormal section of the ore body in the process of geological exploration, which is significantly higher than the regional background value in terms of parameters such as grade and thickness.

[0015] 2) Obvious change in grade: refers to the significant change in ore grade between adjacent sampling points or sections in the spatial distribution of the ore body. Generally, when the grade change rate exceeds 10%, it is considered that the grade change is obvious.

[0016] 3) Representative stable section: refers to a section of the ore body with small fluctuations in parameters such as grade and thickness, high uniformity, and typical characteristics of the ore body, which is usually determined by statistical methods.

[0017] S5, data verification model construction: using Python programming language and machine learning algorithm to establish a correction relationship model between rapid detection data and chemical analysis data, and calculate the correlation coefficient and error rate;

[0018] S6, reliability evaluation of historical data: based on the established data verification model, the historical exploration data is systematically evaluated, and the data is classified according to reliability level;

[0019] S7, analysis of spatial characteristics of ore body: combined with the chemical analysis results of the key section core samples in step S4, the ore body is studied in three vertical zoning, the mineralization continuity is evaluated, and the spatial distribution pattern of grade is analyzed;

[0020] S8, re-verification and classification of resource quantity: based on the results of historical data reliability evaluation and spatial characteristics analysis of ore body, the resource quantity is re-verified and classified, and the proven, controlled and inferred resource quantity is determined.

[0021] Further, in step S1, the mining area is a mining area that has completed exploration, is ready for development, or has been partially developed.

[0022] Preferably, the types of mineral resources include metal and non-metallic minerals. Metal minerals such as metal iron ore, copper ore, gold ore, molybdenum ore, lead-zinc ore, etc.; non-metallic minerals such as phosphate ore, graphite ore, limestone ore, etc. Different minerals have differences in data types and parameter selection, but the technical principles and implementation processes of the method remain the same, and those skilled in the art can make appropriate adjustments according to the actual situation of the specific mineral, so the present application is not limited to molybdenum and gold ore.

[0023] Further, in step S5, the construction of the data verification model includes the following steps:

[0024] a) Data preprocessing: using Isolation Forest algorithm for outlier detection, and using StandardScaler for data standardization;

[0025] b) Correlation analysis: Calculate the Pearson correlation coefficient between the rapid detection data and the chemical analysis data;

[0026] c) Regression model establishment: Establish a prediction model using linear regression, polynomial regression or random forest regression, etc.

[0027] d) Model validation: Evaluate the model performance by cross-validation method, calculate the root mean square error (RMSE) and the determination coefficient (R 2 ) of the model;

[0028] e) Correction relationship establishment: Establish the correction relationship between the rapid detection data and the chemical analysis data based on the optimal model.

[0029] Further, in step S6, the evaluation criteria for the reliability level classification of the data include the size of the correlation coefficient, the relative error and the coefficient of variation.

[0030] Further, in step S6, the reliability level classification includes A, B and C three-level standards; the A-class data is representative and stable, which is directly used for main model modeling of resource reserves and economic value prediction; the B-class data is generally representative, which is used as auxiliary samples for model correction and spatial supplement; the C-class data is relatively weak in representativeness and stability, which has certain practical significance in the field of geology, but is mainly used for anomaly detection and risk warning due to the influence of geological anomalies, data sources, etc., and is not directly included in the main model calculation.

[0031] Further, in step S6, the method for the reliability level classification of the data is:

[0032] a) A-class data: two or more of the following conditions are met: the correlation coefficient is greater than 0.90, the relative error is between 0-15%, and the coefficient of variation is less than 20%;

[0033] b) B-class data: two or more of the following conditions are met: the correlation coefficient is between 0.70-0.90, the relative error is between 15%-30%, and the coefficient of variation is between 20%-40%;

[0034] c) C-class data: two or more of the following conditions are met: the correlation coefficient is between 0.50-0.70, the relative error is between 30%-40%, and the coefficient of variation is between 40%-70%;

[0035] d) Unreliable data: two or more of the following conditions are met: the correlation coefficient is less than 0.50, the relative error is greater than 40%, and the coefficient of variation is greater than 70%.

[0036] Further, in step S8, the resource quantity grading standard is as follows:

[0037] a) proven resources: data reliability is A level, ore body continuity is good, and grade variation is clear;

[0038] b) controlled resources: data reliability is B level, ore body continuity is better, and grade variation is basically clear; and 0-2 of the data reliability, ore body continuity and grade variation are better;

[0039] c) inferred resources: data reliability is C level, ore body continuity is general, and grade variation is not clear enough; and 0-2 of the data reliability, ore body continuity and grade variation are better.

[0040] Further, before step S8 and after step S7, the following steps are further included:

[0041] Multi-source geological sample system collection and comprehensive chemical analysis: through field reconnaissance and recording, the range and position of the mined-out area formed by historical mining activities are determined, and representative samples are collected for analysis in the laboratory.

[0042] The beneficial effects of the present application are: the present application fully utilizes the existing exploration and production data through multi-source data fusion and intelligent analysis technology, reduces the dependence on new field exploration; adopts representative sample hierarchical collection and intelligent space modeling method, effectively improves the accuracy of resource reserve evaluation, and through the automatic data processing platform and dynamic feedback mechanism, accelerates the evaluation process and shortens the evaluation period, thereby saving the exploration investment while improving the evaluation accuracy and reducing the overall evaluation cost. The data reliability and cost-benefit balance problem in the evaluation of undeveloped mining rights is solved, which provides a scientific basis for the transfer of mining rights and resource development. Practice has proved that the application of the present application method in the Luanchuan molybdenum mine area reduces the evaluation deviation from 10-15% to less than 5%, saves 73% of the exploration investment, shortens the evaluation period by 62%, and effectively solves the data reliability and cost-benefit balance problem in the evaluation of old mine mining rights. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is the overall flowchart of the method of the present application;

[0044] Figure 2 is the distribution map of the selected main drill hole;

[0045] Figure 3 is the reserve evaluation data analysis flowchart based on Python and AI data model;

[0046] Figure 4 is the comprehensive comparison graph of ZK4922 drill hole gold (Au) content self-test and reference data;

[0047] Figure 5 is the comprehensive comparison graph of ZK526 drill hole gold (Au) content self-test and reference data;

[0048] Figure 6 ZK5208 borehole self-test and reference test data comparison chart;

[0049] Figure 7 ZK5713 borehole self-test and reference test data comparison chart;

[0050] Figure 8 ZK5709 borehole self-test and reference test data comparison chart;

[0051] Figure 9 ZK499 borehole self-test and reference test data comparison chart;

[0052] Figure 10 ZK5311 borehole self-test and reference test data comparison chart;

[0053] Figure 11 Net present value and discount rate relationship chart;

[0054] Figure 12 Investment rate of return trend chart;

[0055] Figure 13 Gold mine break-even analysis chart. DETAILED DESCRIPTION

[0056] The technical solutions of the present application will be described in further detail below in conjunction with the accompanying drawings, but the protection scope of the present application is not limited to the following description.

[0057] Example: Feasibility evaluation of development and utilization of a certain gold-molybdenum mine in Henan

[0058] A mineral resource reserve evaluation and economic value dynamic prediction method based on multi-source data fusion, as shown in Figure 1 , includes the following steps:

[0059] Step 1: Historical data collection and analysis

[0060] The system collects and organizes historical exploration data of the mining area, including geological reports, drilling records, assay data, resource estimation results, etc. In this embodiment, the focus is on collecting data such as "XX Gold and Molybdenum Mine Exploration Report in Luanchuan County, Henan Province" and "Gold Mine Detailed Investigation Report in XX Mining Area in Luanchuan County, Henan Province", as well as mining development and utilization plans, adjacent mining engineering data, design plans, and metallurgical test reports. At the same time, the system organizes the mining production records, grade detection data, and annual reserve dynamic monitoring reports of the adjacent typical gold mine (XXX Gold Mine) from 2008 to 2024, and establishes a comprehensive database of typical molybdenum and gold mines in Luanchuan County, including geology, mining, and beneficiation. (If the original data lacks the area, it needs to re-lay drilling and trenching engineering for sampling and analysis verification. There is no area lacking original data in this embodiment)

[0061] Step 2: Representative Drilling Selection

[0062] Based on the spatial distribution characteristics of the ore body and the completeness of historical data, representative drill holes are selected for verification analysis. In this embodiment ( Figure 2 ), ZK4922, ZK526, and ZK685 gold drill holes, as well as ZK499, ZK5208, ZK5307, ZK5309, ZK5311, ZK5709, and ZK5713 molybdenum drill holes are selected as verification objects. The selection criteria include: covering the main enrichment area, edge area, and different grade intervals of the ore body; high core preservation integrity; complete historical analysis data; and representative spatial position.

[0063] Step 3: Rapid Detection Data Collection

[0064] Using a handheld X-ray fluorescence spectrometer (Niton XL3t 980), the selected drill core is scanned at 40cm intervals to obtain high-density continuous element content data. The specific operation steps are as follows:

[0065] a) Preparation: Install the battery and turn on the power of the handheld spectrometer;

[0066] b) Calibration: Adjust the handheld spectrometer to calibration mode, align the standard light source, and calibrate once every 100 samples;

[0067] c) Sample selection: According to the depth of the drill core sample, measure every 40cm from shallow to deep;

[0068] d) Measurement: Align the sample with the spectrometer light lens and wait for 30 seconds to complete data collection;

[0069] e) Data export and analysis: Use the dedicated software to export the measurement data to the computer for data analysis and processing.

[0070] In this embodiment, a total of 7 drill holes with a total length of 5004 meters were measured, and a total of 11856 points were measured.

[0071] Step 4: Key segment chemical analysis

[0072] According to the rapid detection results, core samples of mineralization anomaly segments, grade change obvious segments and representative stable segments were selected and sent to the laboratory for standard chemical analysis. In this embodiment, a total of 70 samples were collected, including 23 samples from ZK4922 drill hole, 37 samples from ZK526 drill hole, 4 samples from ZK685 drill hole, 3 samples from xx gold mine old tunnel (LD series) and 3 samples from xxx gold molybdenum mine (NP series). The sample collection work adopts 1 / 4 core system sampling method, and the sample length standard is 1m. The interval of 0.5-1m is used for encryption sampling in the ore-bearing segment. The analysis work is completed by the Institute of Mineral Resources Comprehensive Utilization of Chinese Academy of Geological Sciences. The analysis projects include S, Pb, Zn (10 -2 order of magnitude) and Mo, Au (10 -6 order of magnitude). Among them, S, Pb, Zn (10 -2 order of magnitude) as the main associated elements of gold molybdenum mine can be comprehensively utilized according to its grade and scale under the condition of economic feasibility, and the overall value of the mining right is improved.

[0073] Note: For gold mine old tunnel samples and gold molybdenum mine samples (see step 8), in the process of mine resource reserve evaluation, in addition to the conventional representative drill hole samples, samples from old tunnels (i.e. historical mining tunnels) or outcrops and other special parts may also be collected. Such samples can reflect the actual geological and mineralization characteristics of the ore body at a specific spatial location (such as the mined area, boundary zone, etc.), which is an important supplement to the drill hole samples.

[0074] 1. 3 samples of xx gold mine old tunnel (LD series)

[0075] Collection location and purpose:

[0076] LD series samples are collected from the old tunnel (tunnel) inside the historical mining of xx gold mine, which is distributed in different paragraphs of the ore body. This series of samples are mainly used to reveal the residual mineralization characteristics of the ore in the old tunnel area, the grade change after mining disturbance, and the applicability of drill hole data in the mined area.

[0077] Technical significance:

[0078] Old tunnel samples can directly reflect the actual situation of the ore body exposure surface, make up for the lack of representativeness of drill hole samples in the mined area, and provide direct evidence for fine evaluation of resource reserves and reuse of remaining resources.

[0079] 2. 3 samples of xxx gold molybdenum mine (NP series)

[0080] Collection location and purpose:

[0081] NP Mine is adjacent to the mining right of this survey. Since it is in the same metallogenic belt and the ore body is adjacent, the characteristics of the ore body can be observed inside the NP Mine tunnel, and samples can be collected to supplement the spatial data of the region with sparse drill hole layout and insufficient geological information. Such samples help to identify ore body boundaries and reveal abnormal enrichment or depletion zones.

[0082] Collection location and purpose:

[0083] NP Mine is adjacent to the mining right of this survey. Since it is in the same metallogenic belt and the ore body is adjacent, the characteristics of the ore body can be observed inside the NP Mine tunnel, and samples can be collected to supplement the spatial data of the region with sparse drill hole layout and insufficient geological information. Such samples help to identify ore body boundaries and reveal abnormal enrichment or depletion zones.

[0084] Step 5: Data verification model construction

[0085] Using Python programming language and machine learning algorithms, a correction relationship model between rapid detection data and chemical analysis data is established, and the correlation coefficient and error rate are calculated. The specific steps are as follows:

[0086] a) Data preprocessing: use Isolation Forest algorithm for outlier detection, and use StandardScaler for data standardization;

[0087] b) Correlation analysis: calculate the Pearson correlation coefficient between rapid detection data and chemical analysis data;

[0088] c) Regression model establishment: use linear regression, polynomial regression or random forest regression method to establish prediction model;

[0089] d) Model verification: evaluate the model performance by cross-validation method, calculate the root mean square error (RMSE) and the determination coefficient (R 2 );

[0090] e) Correction relationship establishment: based on the optimal model, establish the correction relationship between rapid detection data and chemical analysis data.

[0091] In this example, as Figure 3As shown, the reserve evaluation data analysis process based on Python and AI data model is established, including data preprocessing, statistical analysis and quality evaluation three main links. Through the model analysis, the correlation coefficient of ZK5208 drilling is as high as 0.85, the correlation coefficient of ZK5713 drilling is 0.814, the correlation coefficient of ZK5311 drilling is 0.676, the correlation coefficient of ZK5709 drilling is 0.542, and the correlation coefficient of ZK499 drilling is as high as 0.939, indicating that the rapid detection data and chemical analysis data have good correlation.(Note: In the field of geological, mining and other natural resources, due to the complex geological conditions and limited data samples, a correlation coefficient of more than 0.5 is usually considered to have certain practical significance, belonging to the level of "medium to good", especially in the application of multi-source data fusion, spatial interpolation, etc.) The relative error of ZK499 is 10.52%, the average relative error of ZK5208 is 15.2%, the average relative error of ZK5713 is 30.71%, the average relative error of ZK5709 is 35.04%, and the relative error of ZK5311 is 34.6%. The coefficient of variation of ZK5208 is 16.6%, the coefficient of variation of ZK499 is 18.70%, the coefficient of variation of ZK5713 is 37.66%, the average difference of ZK5709 data is 53.09%, and the coefficient of variation of ZK5311 is 51.24%.

[0092] Step 6: Reliability evaluation of historical data

[0093] Based on the established data verification model, the historical exploration data is systematically evaluated to determine the data reliability level, and classified according to A, B, C three-level standards:

[0094] a) A-level data: correlation coefficient greater than 0.90, relative error between 0-15%, coefficient of variation less than 20%;

[0095] b) B-level data: correlation coefficient between 0.70-0.90, relative error between 15%-30%, coefficient of variation between 20%-40%;

[0096] c) C-level data: correlation coefficient between 0.50-0.70, relative error between 30%-40%, coefficient of variation between 40%-70%;

[0097] d) Unreliable data: correlation coefficient less than 0.50, relative error greater than 40%, coefficient of variation greater than 70%.

[0098] Note: In the classification standard, the "comprehensive judgment" principle is added, that is, when each index does not completely fall into the same level, all three meet, or meet two of them, combined with actual application to determine its level.

[0099] Simultaneously, considering factors such as statistical test results, geological characteristic conformity, and spatial continuity, a comprehensive evaluation of data reliability was conducted. In this embodiment, borehole data ZK499 was rated as Grade A, borehole data ZK5208 was rated as Grade B, borehole data ZK5713 was rated as Grade B, and borehole data ZK5709 and ZK5311 were rated as Grade C.

[0100] Furthermore, Grade A data can serve as the most crucial basis for reserve calculation and is applicable to the control sections of important ore bodies;

[0101] Level B data can be used for reserve calculations of minor ore bodies or inferred sections, but it is recommended to use it in conjunction with encrypted verification.

[0102] Level C data is only used for prospective resource quantities, inferential estimates, or preliminary economic evaluations;

[0103] Unreliable data should only be used as clues for mineral exploration or for supplementary sampling and verification. It is not recommended to use it directly for reserve or economic value assessment.

[0104] In geological exploration, resource estimation involves dividing the ore body into multiple sections based on the engineering control area. The resource quantity, boundaries, and grade of different ore bodies are controlled by different boreholes or trenches during reserve calculations. Therefore, we can use data grading to determine which borehole data-controlled ore bodies are reliable and which are not, and verify reserves according to grading standards. This is a dynamic process; for example, we can compare data from other boreholes within the same ore body with self-tested data, or, while saving money and time, redeploy a small number of boreholes in the core area to collect samples for verification. With increased comparison engineering and data, a grade B ore body may be upgraded to grade A, and a grade C ore body may be upgraded to grade B.

[0105] Step 7: Spatial Feature Analysis of Ore Body

[0106] Based on the verification analysis results, a three-section vertical zoning study was conducted on the ore body to assess mineralization continuity and analyze the spatial distribution pattern of grades. In this embodiment, as... Figures 5 to 10 As shown, through systematic analysis of multiple boreholes, the molybdenum orebody exhibits distinct vertical zoning characteristics: In the shallow section (0-200m), the average Mo grade ranges from 0.03% to 0.05%, with relatively stable grade variations; in the middle section (200-600m), the grade distribution shows a clear enrichment pattern, with the highest Mo grade reaching 0.871% (at 146.28m), representing the main enrichment zone; in the deep section (600-800m), the grade generally shows a fluctuating downward trend, but local enrichment phenomena still exist. The gold orebody also exhibits similar vertical zoning characteristics, with different grade variation patterns and continuity characteristics at different depths.

[0107] The collected data is distributed according to the hole depth of the drill hole, such as data collection by a handheld fluorescence instrument at intervals of 40 cm, or data collection by laboratory chemical analysis for a gold mine. Finally, the samples at different core depths have corresponding data, and when the data samples are sufficient, they can be compared with reference data (drill hole sample data obtained in the history of the mining area through laboratory chemical analysis. These data come from previous geological exploration work and are an important reference basis for reserve evaluation in the mining area) for comparative analysis. Through comparison, the following can be achieved:

[0108] 1. Calibration and correction of systematic deviation of rapid detection data, improving its accuracy and reliability;

[0109] 2. Verify whether the grade change trend of new data and historical data at the same depth section is consistent to ensure that the new data can truly reflect the spatial distribution characteristics of the ore body;

[0110] 3. Through data fusion and comparison, reveal the grade change law and continuity characteristics of the ore body in the vertical and horizontal directions, and provide a scientific basis for subsequent reserve and economic value evaluation.

[0111] The comparative analysis of the two types of data is not only the need for data correction and quality control, but also an important step to establish the spatial distribution model of the ore body and reveal the grade change law. This will form the grade change law and continuity characteristics at different depth sections. This will obtain the distribution characteristics of the ore body in the vertical space, and combined with the verification data of all drill holes, the ore body distribution characteristics in the plane will be formed. This can well evaluate the authenticity of the reserves and economic value within the mining right range.

[0112] "Combined with the verification analysis results" means that by the following steps, the newly collected rapid detection data and the historical reference data are systematically compared, analyzed and corrected to ensure the scientificity and reliability of the final evaluation results:

[0113] (1) Data pairing and arrangement: pair and arrange the newly collected data with the historical data at the corresponding depth section of the corresponding drill hole.

[0114] (2) Comparative analysis: use correlation analysis, difference analysis and other methods to compare the new and old data at the same depth section, and evaluate the accuracy and consistency of the rapid detection data.

[0115] (3) Data correction and fusion: use statistical modeling methods (such as regression analysis) to correct the rapid detection data to make it consistent with the historical data, forming a unified data set.

[0116] (4) Grade variation law determination: Based on the corrected data set, analyze the grade variation trend of the ore body at different depth sections and between different drill holes, and determine its spatial continuity characteristics.

[0117] (5) Reserve and economic value evaluation: Use the above analysis results, combined with spatial modeling methods, to scientifically evaluate the resource reserves and economic value within the mining right range.

[0118] Step 8: Multi-source geological sample system collection and comprehensive chemical analysis

[0119] This step integrates drill core samples and historical mined-out area samples to build a multi-source integrated geological chemical data system, which includes:

[0120] (1) Drill core sample collection According to the rapid detection results of step 3, select core samples from mineralized abnormal sections, sections with obvious grade variation, and representative stable sections. Use the 1 / 4 core system sampling method, with a sample length standard of 1m, and use 0.5-1m interval encryption sampling for ore-bearing sections. In this embodiment, a total of 64 drill core samples were collected, including 23 from ZK4922 drill, 37 from ZK526 drill, and 4 from ZK685 drill.

[0121] (2) Historical mined-out area exploration and supplementary sampling Through field exploration and documentation, determine the range and location of mined-out areas formed by historical mining activities. In this embodiment, the exploration of the old tunnel of the xx gold mine and the documentation of the adjacent xxx gold-molybdenum mine were completed, with a total tunnel length of about 1360m. The ore body occurs in and is controlled by the gold-bearing alteration zone, and more than 10 gold-bearing alteration zones are found in the cave.

[0122] Limited by the safety conditions of the mined-out area (fractured roof, missing support, serious water accumulation, etc.), only 6 representative samples were collected in relatively stable tunnels:

[0123] 3 samples from the old tunnel of the xx gold mine (LD series): used to reveal the residual mineralization characteristics of the ore in the old tunnel area, the grade variation after mining disturbance, and to verify the applicability of drill data in the mined area;

[0124] 3 samples from the xxx gold-molybdenum mine (NP series): this mine is located in the adjacent area of the mining right investigated this time and is in the same ore-forming belt, and its samples can supplement the spatial data of the area with sparse drill holes and insufficient geological information.

[0125] (3) Unified chemical analysis and data integration The above 70 samples (64 drill hole samples + 6 old tunnel samples) were sent to the Institute of Mineral Resources Utilization, Chinese Academy of Geological Sciences, for standard chemical analysis. Analysis items include S, Pb, Zn (10 -6The order of magnitude), wherein S, Pb, Zn are main associated elements of gold molybdenum ore, and can be comprehensively utilized according to the grade and scale under the economic feasibility condition, so as to improve the overall value of the mining right.

[0126] Through the comprehensive comparison and analysis of the drilling samples and the old tunnel samples, the following is achieved:

[0127] 1. Mutual verification of new and old data, improving the accuracy of resource evaluation;

[0128] 2. Quantitative evaluation of the influence of goaf, providing basis for the calculation of remaining resources;

[0129] 3. System integration of multi-source data, enhancing the reliability of economic value prediction.

[0130] Step 9: Re-verification and classification of resource quantity

[0131] Based on the data reliability evaluation results and the spatial feature analysis of the ore body, the resource quantity is re-verified and classified, and the proven, controlled and inferred resource quantities are determined. The resource quantity classification standards are as follows:

[0132] a) Proven resource quantity: data reliability is A level, ore body continuity is good, and grade variation rule is clear;

[0133] b) Controlled resource quantity: data reliability is B level, ore body continuity is better, and grade variation rule is basically clear;

[0134] c) Inferred resource quantity: data reliability is C level, ore body continuity is general, and grade variation rule is not clear enough.

[0135] In this embodiment, the resource quantity of gold and molybdenum mines is re-verified and classified, and the results are as follows:

[0136] Gold mine resource quantity: proven resource quantity (TM) 11.7x10 4 tons, gold metal quantity 439 kg, average grade 3.74 g / t; controlled resource quantity (KZ) 95.3x10 4 tons, gold metal quantity 5123 kg, average grade 5.38 g / t; inferred resource quantity (TD) 74.8x10 4 tons, gold metal quantity 3670 kg, average grade 4.91 g / t.

[0137] Molybdenum mine resource quantity: proven resource quantity (TM) 2866.6x10 4 tons, molybdenum metal quantity 21661 tons, average grade 0.076%; controlled resource quantity (KZ) 13000.8x10 4 tons, molybdenum metal quantity 107603 tons, average grade 0.083%; inferred resource quantity (TD) 7853.9x10 4Tons, molybdenum metal amount 64274 tons, average grade 0.082%.

[0138] The above project collects and utilizes the chemical analysis data of the previous drilling under the condition of only 450,000 yuan of fund and no new construction drilling, and extracts 7 representative drillings in the core library to carry out part of chemical analysis and all hand-held fluorescence instrument detection. Through the data fusion and correction modeling method proposed in the invention, the self-sampling data and the previous data are systematically compared and reliability evaluated. The results show that the evaluation deviation is reduced from 10-15% of the traditional method to less than 5%, the evaluation period is shortened from more than half a year to about 2 months, and the input saving rate reaches 73%. The method effectively solves the problem of data reliability and cost benefit balance in the evaluation of old mine mineral rights, and has significant practical application value and popularization prospect.

[0139] Step 10: Dynamic prediction of economic value

[0140] The dynamic mapping relationship between resource endowment and economic value is established, and the mineral right value is scientifically evaluated by combining the discount cash flow method (DCF) with the risk adjustment coefficient.

[0141] The core formula of the discount cash flow method is:

[0142] NPV = -I0 + ∑(CF t / (1 + r) t )

[0143] Wherein, NPV is the net present value, I0 is the initial investment, CF t is the cash flow of the t period, r is the discount rate, and t is the time period.

[0144] In this embodiment, the risk adjustment coefficient is introduced, including the price risk coefficient (α), the cost risk coefficient (β) and the yield risk coefficient (γ), and the comprehensive risk coefficient K = α × β × γ. The determination method of each risk coefficient is as follows:

[0145] a) Price risk coefficient (α): calculated based on the difference between market price and break-even price, when the market price is higher than the break-even price, the value of α is greater than 1, and vice versa. In this embodiment, according to the price fluctuation of metal molybdenum and gold from 2020 to 2024, combined with the supply and demand relationship analysis, it is determined that the price risk coefficient of molybdenum is 1.15, and the price risk coefficient of gold is 1.22;

[0146] b) Cost risk coefficient (β): based on the risk assessment of rising operating costs, considering the change trend of labor cost, energy cost and material cost, etc. In this embodiment, by analyzing the mining cost change in Luanchuan area in the past 5 years, the cost risk coefficient is determined to be 0.92;

[0147] c) Production risk coefficient (γ): Based on the comprehensive evaluation of mining difficulty and production decline factors. In this embodiment, considering factors such as ore body burial conditions, mining technical level and resource grade changes, the production risk coefficient is determined to be 0.88.

[0148] The comprehensive risk coefficient K = 1.15 x 0.92 x 0.88 = 0.93 (molybdenum) and K = 1.22 x 0.92 x 0.88 = 0.99 (gold).

[0149] After determining each parameter, the DCF model is programmed using Python programming language to evaluate the value of the mining right. As shown in FIG. 4, the change of the net present value of the project under different discount rates is analyzed; as shown in FIG. 5, the change trend of the investment return rate is shown; as shown in FIG. 6, the break-even analysis is performed. Figure 11 Figure 12 Figure 13

[0150] Through comprehensive analysis, the embodiment determines that the recommended price range of the mining right acquisition is 618-868 million yuan, the recommended acquisition price is 652 million yuan, and the negotiation range is ±10%. At the same time, the price fluctuation risk, cost increase risk and production expectation risk are prompted and countermeasures are provided.

[0151] The above only describes the preferred embodiments of the present application, and it should be understood that the present application is not limited to the forms disclosed herein, and should not be considered as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concepts described herein, by the above-mentioned teaching or related technical or knowledge. The modifications and changes made by those skilled in the art without departing from the spirit and scope of the present application shall be within the scope of protection of the appended claims of the present application.​​​

Claims

1. A mineral resource reserve assessment method based on multi-source data fusion, characterized in that, Includes the following steps: S1. Data collection and analysis: Collect and organize historical exploration data of the mining area to which the mineral resources belong, including geological reports, drilling records, laboratory analysis data, resource estimation results, resource development and utilization plans or technical and economic evaluations, and verify or establish a standardized database. S2. Selection of representative boreholes: Select representative boreholes that cover the ore body's enrichment area, edge area and different grade range, and have high core integrity and complete historical exploration data, accounting for 70% to 80% of the total ore body area as evaluation objects. S3. Rapid detection data acquisition: Using a handheld X-ray fluorescence spectrometer, the selected borehole core is scanned at intervals of 10-40 cm to obtain rapid detection data of high-density continuous element content. S4. Key Section Chemical Analysis: Based on the rapid detection results, core samples from mineralized anomaly sections, sections with significant grade changes, and representative stable sections were selected. The contents of their main useful elements were determined using standardized chemical analysis methods, and the analysis results were recorded. S5. Data Validation Model Construction: Using the Python programming language and machine learning algorithms, establish a calibration relationship model between rapid detection data and chemical analysis data, and calculate the correlation coefficient and error rate; S6. Historical Data Reliability Assessment: Based on the established data verification model, a systematic assessment of historical exploration data is conducted, and the data is classified into reliability levels. S7. Spatial Characteristics Analysis of Ore Body: Combining the chemical analysis results of core samples from key sections in step S4, a three-section vertical zoning study of the ore body is conducted to assess mineralization continuity and analyze the spatial distribution pattern of grade. S8. Resource re-verification and classification: Based on the reliability assessment results of historical data and the spatial characteristic analysis of the ore body, the resource quantity is re-verified and classified to determine the proven, controlled and inferred resource quantity.

2. The mineral resource reserve assessment method based on multi-source data fusion according to claim 1, characterized in that, In step S1, the mining area is a mining area that has been explored, is yet to be developed, or has been partially developed.

3. The mineral resource reserve assessment method based on multi-source data fusion according to claim 1, characterized in that, In step S5, the construction of the data validation model includes the following steps: a) Data preprocessing: Outlier detection is performed using the Isolation Forest algorithm, and data standardization is achieved using StandardScaler; b) Correlation analysis: Calculate the Pearson correlation coefficient between rapid detection data and chemical analysis data; c) Regression Model Building: Use methods such as linear regression, multinomial regression, or random forest regression to build a prediction model; d) Model validation: Evaluate model performance using cross-validation methods, and calculate the root mean square error (RMSE) and coefficient of determination (R²). 2 ); e) Establishing the calibration relationship: Based on the optimal model, establish the calibration relationship between rapid detection data and chemical analysis data.

4. The mineral resource reserve assessment method based on multi-source data fusion according to claim 1, characterized in that, In step S6, the evaluation criteria for the data reliability level classification include the magnitude of the correlation coefficient, relative error, and coefficient of variation.

5. The mineral resource reserve assessment method based on multi-source data fusion according to claim 4, characterized in that, In step S6, the reliability level classification includes three levels: A, B, and C. Level A data is highly representative and stable, and is directly used in the main model for resource reserves and economic value prediction. Level B data is moderately representative and serves as an auxiliary sample for model correction and spatial supplementation. Level C data is relatively less representative and stable; although it has some practical significance in the geological field, it is mainly used for anomaly detection and risk warning due to geological anomalies and data sources, and is not directly included in the main model calculation.

6. The mineral resource reserve assessment method based on multi-source data fusion according to claim 5, characterized in that, In step S6, the method for classifying data reliability levels is as follows: a) Level A data: data that meets two or more of the following criteria: correlation coefficient greater than 0.90, relative error between 0-15%, and coefficient of variation less than 20%; b) Level B data: data that meet two or more of the following criteria: correlation coefficient between 0.70 and 0.90, relative error between 15% and 30%, and coefficient of variation between 20% and 40%. c) Level C data: data that meet two or more of the following criteria: correlation coefficient between 0.50 and 0.70, relative error between 30% and 40%, and coefficient of variation between 40% and 70%. d) Unreliable data: data that meet two or more of the following criteria: correlation coefficient less than 0.50, relative error greater than 40%, and coefficient of variation greater than 70%.

7. The mineral resource reserve assessment method based on multi-source data fusion according to claim 1, characterized in that, In step S8, the resource quantity classification criteria are as follows: a) Proven resource quantity: The data reliability is grade A, the ore body has good continuity, and the grade variation pattern is clear; b) Controlling resource quantity: The data reliability is grade B, the ore body continuity is good, and the grade variation pattern is basically clear; among them, the reliability of data items 0 to 2, the ore body continuity, and the grade variation pattern are even better. c) Inferred resource quantity: The data reliability is grade C, the continuity of the ore body is average, and the grade variation pattern is not clear enough; among them, the reliability of 0 to 2 items, the continuity of the ore body, and the grade variation pattern are better.

8. The mineral resource reserve assessment method based on multi-source data fusion according to claim 1, characterized in that, Before step S8 and after step S7, the following steps are also included: Multi-source geological sample collection and comprehensive chemical analysis: Through on-site reconnaissance and logging, the scope and location of mined-out areas formed by historical mining activities are determined, and representative samples are collected for laboratory chemical analysis.

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