Mineral resource prediction method and system for shallow-hidden fluorite mine

Abstract

The invention relates to a mineral resource prediction method and system for a shallow-hidden fluorite mine, and the method comprises the steps: carrying out the metallogenic characteristic analysis of a typical fluorite deposit, and constructing a prospecting prediction conceptual model according to an analysis result; performing evaluation analysis on the resource potential, the mineralization favorable degree and the prospecting prediction matching degree of the prospecting area to obtain an optimal mineralization prediction geological unit; on the basis of the prospecting prediction conceptual model, a prospecting prediction quantitative model is obtained in combination with geological information of the typical fluorite deposit, and fluorite mine quantitative prediction is conducted on the optimal metallogenic prediction geological unit through the prospecting prediction quantitative model, so that fluorite mine prediction quantity is obtained; and under the condition that the fluorite mine prediction amount reaches a set threshold value, fluorite mine resource evaluation analysis is carried out according to the fluorite mine prediction amount in combination with the obtained geological exploration data of the ore searching area, and fluorite mine resource distribution data is obtained. From the perspective of green prospecting, the method can quickly and accurately carry out prospecting prediction on shallow hidden or deep fluorite mines.

Description

Technical Field

[0001] This invention relates to the field of mineral resource exploration technology, and in particular to a method and system for predicting shallowly concealed fluorite deposits. Background Technology

[0002] Fluorite is a key raw material for the fluorochemical industry. High-end fluorine-containing materials are increasingly important in next-generation information technology, new energy, new materials, new medicine, and aerospace, making it a crucial raw material for emerging industries and an important strategic non-metallic mineral resource. The world's fluorite deposits are mainly concentrated in South Africa, China, Mexico, and Mongolia, with total global reserves of approximately 3.2 billion tons. The largest reserves are found in the Circum-Pacific metallogenic belt. China is one of the world's largest fluorite resource countries, with proven reserves of approximately 420 million tons, ranking second globally and accounting for about one-eighth of the world's total. Fluorite is widely distributed, with large and medium-sized mines concentrated in the eastern coastal areas, central China, and central and eastern Inner Mongolia. In 2021, global fluorite production was approximately 86 million tons, with China producing approximately 54 million tons, ranking first globally and accounting for over 60% of the world's total production. However, as global demand for fluorite resources continues to increase, the global fluorite resource landscape is quietly changing, and ensuring fluorite resource security has become a strategic necessity for nations.

[0003] Currently, with shallow fluorite resources gradually depleted, shallow concealed and deep fluorite deposits will become important targets for future fluorite resource replenishment. As exploration work in shallow concealed and deep fluorite deposits deepens, the difficulty of prospecting continues to increase. The direction of solid mineral prospecting has shifted from shallow to deep, from outcrop prospecting to overburden prospecting, from low mountain prospecting to high mountain-deeply dissected areas, and from extensive prospecting to green and intensive prospecting.

[0004] Therefore, those skilled in the art urgently need to develop a green, fast, and effective prospecting and prediction scheme for shallowly concealed or deep fluorite deposits. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a mineral resource prediction method and system for shallow concealed fluorite deposits, which solves the technical problem of the difficulty in prospecting shallow concealed fluorite deposits.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the main technical solutions adopted by the present invention include:

[0009] In a first aspect, embodiments of the present invention provide a method for predicting mineral resources of shallowly concealed fluorite deposits, comprising:

[0010] Metallogenic characteristics of typical fluorite deposits in the identified prospecting areas were analyzed, and a prospecting prediction conceptual model for typical fluorite deposits was constructed based on the analysis results.

[0011] The resource potential, mineralization favorability, and mineralization prediction matching degree of the prospecting area are evaluated and analyzed to obtain the optimal mineralization prediction geological unit of the prospecting area.

[0012] Based on the conceptual model of mineral exploration prediction, combined with the geological information of typical fluorite deposits, a quantitative model for mineral exploration prediction is obtained. The quantitative model for mineral exploration prediction is then used to quantitatively predict fluorite deposits in the optimal mineralization geological unit, thereby obtaining the predicted amount of fluorite deposits.

[0013] When the predicted amount of fluorite ore reaches the set threshold, based on the predicted amount of fluorite ore and combined with the geological exploration data of the prospecting area, a fluorite ore resource evaluation and analysis is conducted to obtain the distribution data of fluorite ore resources in the prospecting area.

[0014] Optionally, mineralization characteristics analysis is performed on typical fluorite deposits in the identified prospecting areas, and a prospecting prediction conceptual model for typical fluorite deposits is constructed based on the analysis results, including:

[0015] Obtain mineral resource assessment information for the prospecting area. The preliminary assessment information includes the scale of resource reserves, the degree of geological exploration, and the degree of geological research.

[0016] When the mineral resource assessment information meets the set target requirements, a typical fluorite deposit is selected from the prospecting area based on the geological exploration data of the prospecting area.

[0017] When the mineral resource assessment information does not meet the set target requirements, typical fluorite deposits are selected from geographical areas that match the geological background of the prospecting area.

[0018] Metallogenic characteristics analysis was conducted on the metallogenic geological background, metallogenic geological bodies, metallogenic structures and metallogenic structural surfaces, metallogenic features and prospecting indicators of typical fluorite deposits. Based on the analysis results, the prediction element level of each prediction item in the prospecting prediction conceptual model was determined.

[0019] By iterating through all prediction element levels and combining them with the set model output format, a conceptual model for prospecting prediction of typical fluorite deposits is constructed.

[0020] Optionally, an evaluation and analysis of the resource potential, mineralization favorability, and mineralization prediction matching degree of the prospecting area is conducted to obtain the optimal mineralization prediction geological units of the prospecting area, including:

[0021] Obtain mineral geological maps, information on favorable mineral-forming geological bodies, geophysical information, geochemical information, and remote sensing alteration information of the prospecting area;

[0022] Using a mineral geological map as a base map, geological body information, geophysical information, geochemical information, and remote sensing alteration information are overlaid on the base map to obtain a comprehensive geological information map;

[0023] The geological comprehensive information map is evaluated and analyzed for its resource potential, mineralization favorability, and mineral exploration prediction matching degree to obtain the optimal mineralization prediction geological unit of the mineral exploration area.

[0024] Optionally, based on the conceptual model for mineral exploration prediction, and combined with the geological information of typical fluorite deposits, a quantitative model for mineral exploration prediction is obtained. This quantitative model is then used to quantitatively predict fluorite deposits in the optimal mineralization geological unit, yielding the predicted fluorite deposit quantity, including:

[0025] Obtain geological information of typical fluorite deposits, including the horizontal area, dip depth, and dip angle of the ore-forming geological bodies.

[0026] The mineralization influencing factors output by the mineralization prediction conceptual model are compared with the mineralization influencing factors of typical fluorite deposits. Based on the comparison results, the correlation coefficient between the optimal mineralization prediction geological unit and the typical fluorite deposit is determined.

[0027] Based on the geological information of typical fluorite deposits, the geological information output by the prospecting prediction conceptual model, and the correlation coefficient between the predicted ore-forming geological bodies and typical fluorite deposits, a quantitative prospecting prediction model is constructed.

[0028] A quantitative model for mineral exploration prediction was used to quantitatively predict fluorite deposits in the optimal mineralized geological unit, and the predicted amount of fluorite deposits was obtained.

[0029] The mathematical expression of the quantitative model for mineral exploration prediction is as follows:

[0030]

[0031] In the formula, M represents the predicted amount of fluorite deposit, S* represents the horizontal area of ​​the ore-forming geological body in the prospecting area, H* represents the dip extension of the ore-forming geological body in the prospecting area, α represents the dip angle of the ore-forming geological body in the prospecting area, i represents the correlation coefficient between the predicted ore-forming geological body and the typical fluorite deposit, S represents the horizontal area of ​​the ore-forming geological body of the typical fluorite deposit, H represents the dip extension of the ore-forming geological body of the typical fluorite deposit, and β represents the dip angle of the ore-forming geological body of the typical fluorite deposit.

[0032] Optionally, the mineralization influencing factors output by the mineralization prediction conceptual model are compared with those of typical fluorite deposits. Based on the comparison results, the correlation coefficient between the optimal mineralization prediction geological unit and the typical fluorite deposit is determined, including:

[0033] Obtain the metallogenic influencing factors and typical fluorite deposits output from the mineral exploration prediction conceptual model. The metallogenic influencing factors include metallogenic geological background, metallogenic geological bodies, metallogenic structures and metallogenic structural surfaces, mineralization characteristics, and mineral exploration indicator characteristics.

[0034] The similarity of each group of mineralization influencing factors is compared to obtain the similarity of each group of mineralization influencing factors. Based on the set weight allocation ratio, a weight is assigned to each similarity.

[0035] The correlation coefficient between the predicted ore-forming geological bodies and typical fluorite deposits is obtained by summing the weighted values ​​of the similarity of all ore-forming influencing factors.

[0036] Optionally, if the predicted fluorite deposit amount reaches a set threshold, a fluorite resource evaluation and analysis is conducted based on the predicted fluorite deposit amount and the obtained geological exploration data of the prospecting area. The resulting fluorite resource distribution data in the prospecting area includes:

[0037] Obtain the geological base map of the prospecting area;

[0038] When the predicted amount of fluorite ore reaches the set threshold, the geological base map is processed by mineral geological mapping based on the predicted amount of fluorite ore to obtain a mineral geological map of the prospecting area.

[0039] Based on the magnetic anomaly exploration data and resistivity anomaly exploration data of the mineral exploration area, the favorable areas for fluorite mineralization are marked on the mineral geological map to obtain the marked mineral geological map.

[0040] Based on the marked mineral geological map and the obtained exploration data on the ore-bearing potential of the prospecting area, the mineralization of the fluorite deposit location is evaluated.

[0041] Based on the mineralization evaluation results, combined with the deep exploration data and drilling parameters of the prospecting area, a comprehensive profile of the fluorite resource distribution in the prospecting area was obtained.

[0042] Optionally, based on the obtained magnetic anomaly exploration data and resistivity anomaly exploration data of the prospecting area, favorable areas for fluorite mineralization are marked on the mineral geological map, resulting in a marked mineral geological map including:

[0043] Magnetic anomaly exploration data and resistivity anomaly exploration data of the mineral exploration area were obtained;

[0044] The magnetic anomaly exploration data and resistivity anomaly exploration data are processed to obtain magnetic anomaly thematic layers and resistivity anomaly thematic layers.

[0045] Both the magnetic anomaly thematic layer and the resistivity anomaly thematic layer are overlaid with the mineral geological map. Based on the marking rules for favorable areas of fluorite deposits, favorable areas of fluorite deposits are marked on the overlaid mineral geological map to obtain the marked mineral geological map.

[0046] The rules for marking favorable areas for fluorite deposits are as follows:

[0047] When the similarity between the ore-forming geological body and the typical fluorite deposit in terms of ore-forming structure and mineralization alteration reaches a set value, the area to which the ore-forming geological body belongs is marked as a favorable area for fluorite ore-bearing.

[0048] The linear transition boundary region of magnetic anomaly is marked as a favorable area for fluorite mineralization.

[0049] Linear resistivity anomaly areas and banded low resistivity anomaly areas are marked as favorable areas for fluorite mineralization.

[0050] Optionally, based on the marked mineral geological map and the obtained exploration data on the ore-bearing potential of the prospecting area, an evaluation of the mineralization of the fluorite deposit location is conducted, including:

[0051] Based on the marked mineral geological map, the radioactivity anomaly information of the favorable fluorite-bearing area was analyzed by profile measurement to obtain a line graph of total radioactivity.

[0052] Based on the marked mineral geological map, profile measurement and analysis were conducted on the abnormal information of calcium content in the favorable fluorite ore-bearing area to obtain a calcium content polygon map.

[0053] Based on the line graphs of total radioactivity and calcium content, trenching areas were selected from the marked mineral geological map. The mineralization of fluorite deposits in the area was evaluated based on the trenching data, and the mineralization evaluation value was obtained.

[0054] Optionally, based on the mineralization assessment results, and combined with the obtained deep exploration data and drilling parameters of the prospecting area, a comprehensive profile of the fluorite resource distribution in the prospecting area is obtained, including:

[0055] When the mineralization evaluation value meets the set evaluation index threshold, audio magnetotelluric sounding data of the trenching project area is obtained, and a deep resistivity anomaly profile of the trenching project area is constructed based on the audio magnetotelluric sounding data.

[0056] By overlaying the total radioactivity line graph, the calcium content line graph, and the deep resistivity anomaly profile, an initial comprehensive profile of the fluorite mineral resource distribution in the prospecting area is obtained.

[0057] Based on the obtained drilling engineering parameter information, the deep mineralization of the mineralized zone to which the initial comprehensive profile belongs is verified, and a comprehensive profile of the distribution of fluorite resources in the prospecting area is obtained.

[0058] In a first aspect, embodiments of the present invention provide a mineral resource prediction system for shallowly concealed fluorite deposits, comprising:

[0059] The mineral exploration prediction conceptual model construction module is used to analyze the mineralization characteristics of typical fluorite deposits in the obtained mineral exploration area, and construct a mineral exploration prediction conceptual model for typical fluorite deposits based on the analysis results.

[0060] The mineralization prediction geological unit selection module is used to evaluate and analyze the resource potential, mineralization favorability, and mineralization prediction matching degree of the prospecting area, and obtain the optimal mineralization prediction geological unit of the prospecting area.

[0061] The fluorite ore quantitative prediction module is used to obtain a fluorite ore quantitative prediction model based on the mineral exploration prediction conceptual model and combined with the geological information of typical fluorite deposits. The fluorite ore quantitative prediction model is then used to make quantitative predictions of fluorite ore in the optimal mineralization geological unit to obtain the predicted amount of fluorite ore.

[0062] The mineral resource analysis module is used to evaluate and analyze fluorite resources based on the predicted fluorite amount and the geological exploration data of the prospecting area when the predicted fluorite amount reaches a set threshold, thereby obtaining the distribution data of fluorite resources in the prospecting area.

[0063] (III) Beneficial Effects

[0064] The beneficial effects of this invention are as follows: This invention provides a method for predicting mineral resources in shallowly concealed fluorite deposits. First, it analyzes the metallogenic characteristics of typical fluorite deposits and constructs a prospecting prediction conceptual model. Then, based on the prospecting prediction conceptual model and the geological information of typical fluorite deposits, it constructs a quantitative prospecting model. This model is then used to quantitatively predict fluorite deposits in the selected optimal metallogenic geological units. Finally, the prediction results and geological exploration data are analyzed to obtain fluorite resource distribution data in the prospecting area. Compared to existing technologies, this invention, from a green prospecting perspective, enables rapid and accurate prospecting prediction for shallowly concealed or deep fluorite deposits. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating a method for predicting mineral resources of shallowly concealed fluorite deposits according to an embodiment of the present invention.

[0066] Figure 2 A conceptual model diagram for prospecting and predicting a typical fluorite deposit, provided for one embodiment of the invention;

[0067] Figure 3A 1:10000 mineral geological map is provided for one embodiment of the invention.

[0068] Figure 4 A mineral geological map with a magnetic anomaly thematic layer overlaid, provided as an embodiment of the invention;

[0069] Figure 5 A mineral geological map with a resistivity anomaly thematic layer superimposed is provided as an embodiment of the invention;

[0070] Figure 6 This invention provides an embodiment of a method for creating a composite profile by overlaying a line graph of total radioactivity, a line graph of calcium content, and a profile of deep resistivity anomalies.

[0071] Figure 7 This is a verification cross-sectional view for verifying borehole parameters, provided as an embodiment of the present invention. Detailed Implementation

[0072] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0073] refer to Figure 1 As shown in the embodiment of the present invention, a mineral resource prediction method for shallowly concealed fluorite deposits is proposed, which includes: analyzing the metallogenic characteristics of typical fluorite deposits in the exploration area, and constructing a prospecting prediction conceptual model for typical fluorite deposits based on the analysis results; evaluating and analyzing the resource potential, metallogenic favorability, and prospecting prediction matching degree of the exploration area to obtain the optimal metallogenic prediction geological unit of the exploration area; obtaining a prospecting prediction quantitative model based on the prospecting prediction conceptual model and combined with the geological information of typical fluorite deposits, and using the prospecting prediction quantitative model to quantitatively predict fluorite deposits in the optimal metallogenic prediction geological unit to obtain the predicted amount of fluorite deposits; when the predicted amount of fluorite deposits reaches a set threshold, conducting a fluorite resource evaluation analysis based on the predicted amount of fluorite deposits and combined with the geological exploration data of the exploration area to obtain the fluorite resource distribution data in the exploration area.

[0074] This embodiment solves the technical problem of high difficulty in finding shallow concealed fluorite deposits, and from the perspective of green mineral exploration, it can quickly and accurately predict mineral deposits in both shallow and deep concealed fluorite deposits.

[0075] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present invention can be understood more clearly and thoroughly, and that the scope of the present invention can be fully conveyed to those skilled in the art.

[0076] Specifically, refer to Figure 1 As shown in the embodiment of the present invention, a method for predicting mineral resources of shallowly concealed fluorite deposits is proposed, the method comprising:

[0077] S100. Analyze the mineralization characteristics of typical fluorite deposits in the obtained prospecting areas, and construct a prospecting prediction conceptual model for typical fluorite deposits based on the analysis results.

[0078] In this embodiment, step S100 may include the following sub-steps S110 to S140:

[0079] S110. Obtain mineral resource assessment information for the prospecting area. This information includes: resource reserve size, degree of geological exploration, and degree of geological research.

[0080] Resource reserves are categorized into small, medium, and large scales based on established resource reserve standards. Geological exploration is categorized into preliminary survey, general survey, detailed survey, and exploration. Preliminary survey involves initial understanding and assessment of mineral resources to determine their existence and approximate distribution. General survey, building upon preliminary survey, involves more detailed exploration of mineral resources to understand their basic geological characteristics and reserves. Detailed survey involves in-depth exploration of mineral resources with development potential discovered during general survey, clarifying their geological characteristics, reserves, and mining conditions. Exploration, based on detailed survey, involves more refined exploration of industrially valuable mineral resources, providing detailed geological data for mine design and mining. Geological research can be categorized into preliminary research, detailed research, and in-depth research. Preliminary research involves initial investigations of the geological background, topography, and geological structure to understand the general geological conditions of the region. Detailed research builds upon preliminary research by conducting more detailed geological surveys and studies, such as estimating reserves and evaluating economic value. In-depth research, based on detailed research, involves more refined exploration of mineral resources with industrial value, such as identifying the spatial distribution, morphology, occurrence, and continuity of ore bodies.

[0081] S120a. When the mineral resource assessment information meets the set target requirements, select typical fluorite deposits from the prospecting area based on the geological exploration data of the prospecting area.

[0082] S120b. When the mineral resource assessment information does not meet the set target requirements, select typical fluorite deposits from geographical areas that match the geological background of the prospecting area.

[0083] In this embodiment, the preferred target requirements for mineral resource assessment information are: the resource reserve scale reaches the medium-sized resource reserve scale, the geological exploration level reaches the general survey level, and the geological research level reaches the detailed study level.

[0084] S130. Analyze the metallogenic geological background, metallogenic geological bodies, metallogenic structures and metallogenic structural surfaces, metallogenic characteristics, and prospecting indicators of typical fluorite deposits. Based on the analysis results, determine the prediction element level of each prediction item in the prospecting prediction conceptual model.

[0085] When analyzing the metallogenic geological background of typical fluorite deposits, the analysis includes the tectonic location, regional metallogenic belt, tectonic environment, and metallogenic epoch. The degree of constraint L of the metallogenic environment is obtained. When 100% ≥ L ≥ 80%, the prediction element level of the metallogenic geological background prediction item is necessary; when 80% > L ≥ 60%, the prediction element level of the metallogenic geological background prediction item is important; and when 60% > L ≥ 0%, the prediction element level of the metallogenic geological background prediction item is secondary.

[0086] When analyzing the ore-forming geological bodies of typical fluorite deposits, the analysis includes the morphology, formation age, rock geochemistry, spatial location, and surrounding rock characteristics of the ore-forming geological bodies. The contribution degree (C) of the ore-forming geological bodies to the ore body relationship and mineralization is obtained. When 100% ≥ C ≥ 80%, the prediction element level of the ore-forming geological body prediction item is necessary; when 80% > C ≥ 50%, the prediction element level of the ore-forming geological body prediction item is important; and when 50% > C ≥ 0%, the prediction element level of the ore-forming geological body prediction item is secondary.

[0087] When analyzing the ore-forming structures and mineralization structural planes of typical fluorite deposits, the analysis includes the types, characteristics, and distribution of ore-forming structures, as well as the types and characteristics of mineralization structural planes, to obtain the degree of influence (E) of ore-forming structures and mineralization structural planes on the spatial distribution, ore body morphology, and ore body destruction of the ore body. s When 100% ≥ E s When ≥80%, the prediction element level of the metallogenic tectonics and metallogenic structural plane prediction items is necessary; when 80% > E s When ≥60%, the prediction element level of the metallogenic tectonics and metallogenic structural plane prediction items is important; when 60% > E s When the percentage is ≥0%, the prediction element level of the metallogenic structure and metallogenic structural surface prediction item is secondary.

[0088] When analyzing the mineralization characteristics of typical fluorite deposits, the analysis includes ore body characteristics, ore characteristics, mineral composition, mineralization stage, wall rock alteration, geochemical characteristics of ore and wall rock, and ore-forming fluid characteristics. This analysis aims to determine the degree of influence of these mineralization characteristics on ore structure, mineral composition, content of useful components, content of beneficial components, content of harmful components, and physicochemical properties of the ore. c When 100% ≥ E cWhen ≥70%, the prediction element level of the mineralization characteristic prediction item is necessary; when 70% > E c When ≥50%, the prediction element level of the mineralization characteristic prediction item is important; when 50% > E c When the percentage is ≥0%, the prediction element level of the mineralization characteristic prediction item is minor.

[0089] When analyzing the prospecting indicators of typical fluorite deposits, the analysis includes geological indicators, wall rock alteration indicators, geochemical indicators, geophysical indicators, and remote sensing prospecting indicators. The strength of the indicative effect (S) of the prospecting indicators on the search for similar deposits is obtained. When 100% ≥ S ≥ 80%, the predictive element level of the prospecting indicator is necessary; when 80% > S ≥ 60%, the predictive element level of the prospecting indicator is important; and when 60% > S ≥ 0%, the predictive element level of the prospecting indicator is secondary.

[0090] S140. Traverse all prediction element levels of all prediction items, and construct a prospecting prediction concept model for typical fluorite deposits by combining the set model output format.

[0091] S200: Evaluate and analyze the resource potential, mineralization favorability, and mineralization prediction matching degree of the prospecting area to obtain the optimal mineralization prediction geological unit of the prospecting area.

[0092] In this embodiment, step S200 may include the following sub-steps S210 to S230:

[0093] S210. Obtain mineral geological maps, information on favorable mineral-forming geological bodies, geophysical information, geochemical information, and remote sensing alteration information of the prospecting area.

[0094] S220. Using the mineral geological map as the base map, geological body information, geophysical information, geochemical information, and remote sensing alteration information are overlaid on the base map to obtain a comprehensive geological information map.

[0095] For example, using a 1:50,000 mineral geological map as the base map, information on favorable mineralized geological bodies, geophysical anomalies such as gravity and magnetic methods, geochemical anomalies such as F element, and remote sensing alteration anomalies are overlaid on the base map to obtain a 1:50,000 comprehensive geological information map.

[0096] S230. Evaluate and analyze the resource potential, mineralization favorability, and mineral exploration prediction matching degree of the geological comprehensive information map to obtain the optimal mineralization prediction geological unit of the mineral exploration area.

[0097] For example, the evidence weighting model module of the MRAS (Mineral Resources Assessment System) software is used to initially delineate the prospecting area by computer, and the selected results are overlaid on a 1:50,000 geological comprehensive information map to form a 1:50,000 prediction map. Then, the resource potential, mineralization favorability, and mineralization prediction matching degree of the machine-selected prediction units delineated by the MRAS software are evaluated and analyzed. Based on the analysis results, the optimal mineralization prediction geological units are delineated on the 1:50,000 prediction map.

[0098] S300. Based on the concept model of mineral exploration prediction, combined with the geological information of typical fluorite deposits, a quantitative model for mineral exploration prediction is obtained. The quantitative model for mineral exploration prediction is then used to quantitatively predict fluorite deposits in the optimal mineralization geological unit, thereby obtaining the predicted amount of fluorite deposits.

[0099] In this embodiment, step S300 may include the following sub-steps S310 to S340:

[0100] S310. Obtain geological information of typical fluorite deposits. This geological information includes the horizontal area of ​​the ore-forming geological body, its dip depth, and its dip angle.

[0101] S320. Compare the mineralization influencing factors output by the mineral exploration prediction conceptual model with the mineralization influencing factors of typical fluorite deposits, and determine the correlation coefficient between the optimal mineralization prediction geological unit and the typical fluorite deposit based on the comparison results.

[0102] Further, step S320 may include the following sub-steps S321 to S323:

[0103] S321. Obtain the metallogenic influencing factors and typical fluorite deposits output from the mineral exploration prediction conceptual model. Among them, the metallogenic influencing factors include: metallogenic geological background, metallogenic geological bodies, metallogenic structures and metallogenic structural surfaces, mineralization characteristics, and mineral exploration indicator characteristics.

[0104] S322. Iterate through the influencing factors of each group of mineralization and compare their similarity to obtain the similarity of the influencing factors of each group of mineralization. Then, assign a weight to each similarity according to the set weight allocation ratio.

[0105] In this embodiment, in predicting the correlation coefficient between ore-forming geological bodies and typical fluorite deposits, the weighting of similarity between ore-forming geological backgrounds is 10%, similarity between ore-forming geological bodies is 25%, similarity between ore-forming structures and ore-forming structural planes is 20%, similarity between mineralization characteristics is 25%, and similarity between prospecting indicator characteristics is 20%. That is, the weighted correlation coefficient factor values ​​are: i1 = 0–0.10, i2 = 0–0.25, i3 = 0–0.20, i4 = 0–0.25, and i5 = 0–0.20.

[0106] The value of the metallogenic geological body influence factor i1 is determined based on the similarity S between the prospecting area and the typical fluorite deposit in terms of tectonic location, metallogenic belt, tectonic environment, and other metallogenic geological background. g When 100% ≥ S g When the percentage is greater than 80%, the value of i1 ranges from 0.10 to 0.07; when 80% ≥ S g When ≥60%, i1 ranges from 0.06 to 0.03; when 60% ≥ S g When ≥40%, the value range of i1 is 0.03 to 0.00.

[0107] The value of the ore-forming geological body influence factor i2 is determined based on the similarity S between the prospecting area and the typical fluorite deposit's ore-forming geological body in terms of lithology, structure, texture, mineral composition, geochemical properties, etc. b When 100% ≥ S b When ≥80%, i2 ranges from 0.25 to 0.20; when 80% ≥ S b When ≥60%, i2 ranges from 0.19 to 0.13; when 60% ≥ S b When the percentage is greater than 40%, the value of i2 ranges from 0.12 to 0.07; when 40% ≥ S b When the percentage is ≥20%, the value of i2 ranges from 0.06 to 0.00.

[0108] The value of the metallogenic tectonics and metallogenic structural surface influence factor i3 is determined based on the similarity S between the mineral exploration area and typical fluorite deposits in terms of metallogenic tectonics type, nature, scale, and phase, and metallogenic structural surface type and nature. s When 100% ≥ S s When the percentage is greater than 80%, the value of i3 ranges from 0.20 to 0.16; when 80% ≥ S s When the percentage is greater than 60%, the value of i3 ranges from 0.15 to 0.11; when 60% ≥ S s When the percentage is greater than 40%, the value of i3 ranges from 0.10 to 0.06; when 40% ≥ S s When the percentage is ≥20%, the value of i3 ranges from 0.05 to 0.00.

[0109] The value of the mineralization characteristic influence factor i4 is determined based on the similarity between the prospecting area and typical fluorite deposits in terms of ore body morphology, occurrence, scale, ore structure, texture, mineral composition, and the type, intensity, and alteration mineral assemblage of the surrounding rocks, as well as other mineralization characteristics. c When 100% ≥ S c When the percentage is greater than 80%, the value of i4 ranges from 0.25 to 0.20; when 80% ≥ S c When the percentage is greater than 60%, the value of i4 ranges from 0.19 to 0.13; when 60% ≥ S c When the percentage is greater than 40%, the value of i4 ranges from 0.12 to 0.07; when 40% ≥ S c When the percentage is ≥20%, the value of i4 ranges from 0.06 to 0.00.

[0110] The value of the mineral exploration indicator characteristic influence factor i5 is determined based on the similarity S between the mineral exploration area and typical fluorite deposits in terms of lithological, structural, wall rock alteration, vein, geochemical, geophysical, and remote sensing anomaly characteristics. i When 100% ≥ S i When the percentage is greater than 80%, the value of i5 ranges from 0.20 to 0.16; when 80% ≥ S i When the percentage is greater than 60%, the value of i5 ranges from 0.15 to 0.11; when 60% ≥ S i When the percentage is greater than 40%, the value of i5 ranges from 0.10 to 0.06; when 40% ≥ S i When the percentage is ≥20%, the value of i5 ranges from 0.05 to 0.00.

[0111] S323. Sum the weighted values ​​of the similarity of all mineralization influencing factors to obtain the correlation coefficient between the predicted mineralization geological body and the typical fluorite deposit.

[0112] S330. Based on the geological information of typical fluorite deposits, the geological information output by the prospecting prediction conceptual model, and the correlation coefficient between the predicted ore-forming geological bodies and typical fluorite deposits, a quantitative prospecting prediction model is constructed.

[0113] The mathematical expression of the quantitative model for mineral prediction is as follows:

[0114]

[0115] In the formula, M represents the predicted amount of fluorite deposit, S* represents the horizontal area of ​​the ore-forming geological body in the prospecting area, H* represents the dip extension of the ore-forming geological body in the prospecting area, α represents the dip angle of the ore-forming geological body in the prospecting area, i represents the correlation coefficient between the predicted ore-forming geological body and the typical fluorite deposit, S represents the horizontal area of ​​the ore-forming geological body of the typical fluorite deposit, H represents the dip extension of the ore-forming geological body of the typical fluorite deposit, and β represents the dip angle of the ore-forming geological body of the typical fluorite deposit.

[0116] S340. Quantitatively predict fluorite deposits in the optimal mineralization geological unit using a mineralization prediction quantitative model to obtain the predicted amount of fluorite deposits.

[0117] S400. When the predicted amount of fluorite ore reaches the set threshold, based on the predicted amount of fluorite ore and combined with the geological exploration data of the prospecting area, a fluorite ore resource evaluation and analysis is conducted to obtain the distribution data of fluorite ore resources in the prospecting area.

[0118] In this embodiment, step S400 may include the following sub-steps S410 to S450:

[0119] S410. Obtain the geological base map of the prospecting area.

[0120] Geological base maps can be selected at various scales, such as 1:10000, 1:5000, and 1:2000, depending on the area of ​​the prospecting region. Based on the characteristics of the mineralized geological bodies, mineralized structures, mineralization, and alteration of the prospecting region, corresponding point, line, and area geological thematic layers are drawn using MapGIS software (Geographic Information System software platform), and the data are compiled to form the geological base map of the prospecting region.

[0121] S420. When the predicted amount of fluorite ore reaches the set threshold, the geological base map is processed by mineral geological mapping based on the predicted amount of fluorite ore to obtain a mineral geological map of the prospecting area.

[0122] For example, when the predicted amount of fluorite ore reaches 200,000 tons and the threshold for predicted fluorite ore is 150,000 tons, the geological base map is processed by mineral geological mapping based on the predicted amount of fluorite ore to obtain a mineral geological map of the prospecting area.

[0123] S430. Based on the magnetic anomaly exploration data and resistivity anomaly exploration data of the mineral exploration area, mark the favorable areas for fluorite mineralization on the mineral geological map to obtain the marked mineral geological map.

[0124] Further, step S430 may include the following sub-steps S431 to S433:

[0125] S431. Magnetic anomaly exploration data and resistivity anomaly exploration data of the mineral exploration area.

[0126] Magnetic anomaly exploration data of the prospecting area were obtained through high-precision ground magnetic surveys. Resistivity anomaly exploration data of the prospecting area were obtained through induced polarization gradient measurements.

[0127] S432. Process the magnetic anomaly exploration data and resistivity anomaly exploration data to obtain magnetic anomaly thematic layers and resistivity anomaly thematic layers.

[0128] For example, by controlling MapGIS software, magnetic anomaly exploration data can be plotted into a thematic layer of magnetic anomalies in MapGIS format, using points, lines, and polygons. Similarly, resistivity anomaly exploration data can be plotted into a thematic layer of resistivity anomalies in MapGIS format, also using points, lines, and polygons.

[0129] S433. Overlay the magnetic anomaly thematic layer and the resistivity anomaly thematic layer with the mineral geological map, and mark the favorable areas of fluorite mineralization on the overlaid mineral geological map according to the marking rules for favorable areas of fluorite mineralization, so as to obtain the marked mineral geological map.

[0130] The rules for marking favorable areas for fluorite deposits are as follows: when the similarity between the ore-forming geological body and the typical fluorite deposit in terms of ore-forming structure and mineralization alteration reaches a set value, the area to which the ore-forming geological body belongs is marked as a favorable area for fluorite deposits; the linear transition boundary area of ​​magnetic anomalies is marked as a favorable area for fluorite deposits; and the linear resistivity anomaly area and the banded low resistivity anomaly area are marked as favorable areas for fluorite deposits.

[0131] S440. Based on the marked mineral geological map and the obtained mineral potential exploration data of the prospecting area, evaluate the mineralization of the fluorite deposit location.

[0132] Further, step S440 may include the following sub-steps S441 to S443:

[0133] S441. Based on the marked mineral geological map, conduct profile measurement and analysis of radioactive anomaly information in the favorable fluorite-bearing areas to obtain a line graph of total radioactivity.

[0134] For example, when determining a favorable area for fluorite ore, an environmental gamma total profile is measured using a gamma spectroscopy instrument to obtain radioactive anomaly information of the favorable area for fluorite ore. Then, the radioactive anomaly information is plotted into a line graph of total radioactivity using Excel software (spreadsheet software).

[0135] S442. Based on the marked mineral geological map, profile measurement and analysis were performed on the abnormal information of calcium content in the favorable areas of fluorite ore-bearing region to obtain a line graph of calcium content.

[0136] For example, in identifying favorable areas for fluorite ore deposits, rapid geochemical analysis profile measurements are performed using an X-ray fluorescence analyzer to obtain information on calcium content anomalies in these areas. Then, Excel software is used to plot the calcium content anomalies into a line graph.

[0137] S443. Based on the line graph of total radioactivity and the line graph of calcium content, select trenching areas from the marked mineral geological map, and evaluate the mineralization of fluorite deposits in the area based on the trenching data, and obtain the mineralization evaluation value.

[0138] By using line graphs of total radioactivity and calcium content, areas with overlapping high radioactivity and high calcium content (i.e., trenching areas) are selected from the marked mineral geological map. Then, based on the trenching data revealed in these areas, the mineralization of fluorite deposits in these areas is evaluated (the exposure of fluorite ore bodies and mineralized bodies), and the mineralization evaluation value is obtained.

[0139] S450. Based on the mineralization evaluation results, combined with the deep exploration data and drilling engineering parameter information of the exploration area, a comprehensive profile map of the distribution of fluorite resources in the exploration area is obtained.

[0140] Further, step S450 may include the following sub-steps S451 to S453:

[0141] S451. When the mineralization evaluation value meets the set evaluation index threshold, acquire the audio magnetotelluric sounding data of the trenching project area, and construct a deep resistivity anomaly profile of the trenching project area based on the audio magnetotelluric sounding data.

[0142] For example, when the mineralization evaluation value meets the set evaluation index threshold, that is, when the fluorite ore body and mineralized body exposed in the trenching project area are well exposed, audio-frequency magnetotelluric sounding profile measurements are carried out in the trenching project area to obtain high-precision deep resistivity anomaly information. Finally, the high-precision deep resistivity anomaly information is plotted into a deep resistivity anomaly profile map by controlling GeoElec software (a comprehensive electrical exploration numerical simulation and inversion imaging software).

[0143] S452. By overlaying the total radioactivity line graph, the calcium content line graph, and the deep resistivity anomaly profile, an initial comprehensive profile of the fluorite resource distribution in the prospecting area is obtained.

[0144] For example, CorelDRAW software (graphic design and vector drawing software) can be used to overlay the total radioactivity line graph, the calcium content line graph, and the deep resistivity anomaly profile to create an initial comprehensive profile map of gamma measurement, X-ray fluorescence analysis, and audio-frequency magnetotelluric sounding, which is the initial comprehensive profile map of fluorite resource distribution in the prospecting area.

[0145] S453. Based on the obtained drilling engineering parameter information, the deep mineralization of the mineralized zone to which the initial comprehensive profile belongs is verified to obtain a comprehensive profile of the distribution of fluorite resources in the prospecting area.

[0146] Based on the occurrence of fluorite ore bodies and mineralization bodies revealed by trenching in the trenching area, and the dip of low resistivity anomaly zones in the deep resistivity anomaly profile, drilling engineering parameter information, including drilling azimuth, dip angle, and depth, is obtained. Then, based on the drilling engineering parameter information, the deep mineralization of the mineralization zone to which the initial comprehensive profile belongs is verified, resulting in a comprehensive profile of the fluorite resource distribution in the prospecting area, thus achieving the objective of Zhaozhuang.

[0147] In one specific embodiment, the mineral resource prediction method for shallowly concealed fluorite deposits described in this embodiment is used to predict the mineral resources of shallowly concealed fluorite deposits in the Huashitoushan area of ​​the Beishan metallogenic belt in Inner Mongolia. The specific process is as follows:

[0148] Step 1: Dongqiyishan in Ejin Banner, Alxa League, Inner Mongolia Autonomous Region was selected as a typical fluorite deposit. The deposit has a large-scale resource reserve, and the geological exploration level is exploration level. Studies have been carried out on the mineralization age, ore-forming fluids, and sources of ore-forming materials, and the research level is in-depth research.

[0149] Step 2: A metallogenic characteristic analysis is conducted on the metallogenic geological background, metallogenic geological bodies, metallogenic structures and mineralization surfaces, metallogenic features, and prospecting indicators of the typical fluorite deposit in Dongqiyishan. Based on the analysis results, a prospecting prediction conceptual model for the typical fluorite deposit in Dongqiyishan is constructed, such as... Figure 2 As shown.

[0150] Step 3: An evaluation and analysis of the resource potential, mineralization favorability, and mineral exploration prediction matching degree of the Huashitoushan area in the Beishan metallogenic belt of Inner Mongolia was conducted, and three optimal mineralization prediction geological units were obtained, namely the Huashitoushan area of ​​1.203 square kilometers, the Gudongjing area of ​​0.607 square kilometers, and the Badouwulannan area of ​​0.815 square kilometers.

[0151] Step 4: Based on the geological information of the typical fluorite deposit in Dongqiyishan, the geological information output by the prospecting prediction conceptual model, and the correlation coefficient between the predicted ore-forming geological body and the typical fluorite deposit in Dongqiyishan, construct a quantitative prospecting prediction model.

[0152] Step 5: Quantitatively predict fluorite deposits in the optimal metallogenic geological unit using a mineral exploration prediction model to obtain the predicted fluorite deposit quantity. Taking the Huashitoushan area, the optimal metallogenic geological unit, as an example, the predicted fluorite deposit quantity in this area is calculated as follows:

[0153] First, we obtained the mineral resources and geological data of Dongqiyishan: the fluorite resources of Dongqiyishan are 3.0416 million tons, the horizontal area of ​​the ore-forming geological body of Dianxing fluorite deposit is 6.23 square kilometers, the geological body extends 625 meters, and the average dip angle of the ore-forming geological body is 71.63°.

[0154] Secondly, geological data of the Huashitoushan area were obtained: the area of ​​the Huashitoushan area is 1.203 square kilometers, the average dip angle of the ore-forming geological bodies is 70.5°, and the dip extension of the typical ore deposit geological body is selected as the dip extension of the ore body in the Huashitoushan area, which extends by 625 meters.

[0155] Then, the correlation coefficient i = i1 + i2 + i3 + i4 + i5 = 0.92:1 between the metallogenic geological bodies in the Huashitoushan area and typical fluorite deposits was obtained. The similarity S between the Huashitoushan area and the typical fluorite deposits in Dongqiyishan, including their tectonic location, metallogenic belt, and tectonic environment, was also obtained. g If the similarity is 97%, then the influence factor i1 of the ore-forming geological body is 0.1. 2. Obtain the similarity S between the ore-forming geological bodies of the Huashitoushan area and the typical fluorite deposit in Dongqiyishan, including lithology, structure, texture, mineral composition, and geochemical properties. b If the ore-forming geological body influence factor i2 is 83%, then the value is 0.21. 3. Obtain the similarity S between the ore-forming tectonic type, nature, scale, and phase of the typical fluorite deposits in the Huashitoushan area and Dongqiyishan, and the similarity S between the ore-forming structural type and nature of the ore-forming structural planes. s The similarity score (S) is 99%, and the influence factor i3 of the ore-forming structure and ore-forming structural surface is 0.20. 4. Obtain the similarity S between the typical fluorite deposits in the Huashitoushan area and the Dongqiyishan area in terms of ore body morphology, occurrence, scale, ore structure, texture, mineral composition, and the type, intensity, and alteration mineral assemblage of the surrounding rocks, as well as other mineralization characteristics. c The similarity score (S) was 95%, and the influencing factor i4 of mineralization characteristics was 0.24. 5. The similarity score (S) was obtained between the typical fluorite deposits in the Huashitoushan area and the Dongqiyishan area, considering lithological, structural, wall-rock alteration, vein, geochemical, geophysical, and remote sensing anomaly characteristics of the geological formations. i The value was 84%, and the influence factor i4 of the mineral exploration indicator characteristics was 0.17.

[0156] Finally, based on the quantitative model for mineral prediction, the predicted amount of fluorite deposits in the Huashitoushan area is 509,200 tons.

[0157] Step 6: Based on the predicted fluorite deposits, perform mineral geological mapping on the geological base map to obtain a mineral geological map of the prospecting area. Taking the Huashitoushan area as an example, a 1:10000 geological base map is selected. After mineral geological mapping, the final result is as follows: Figure 3 The map shown is a geological map of mineral deposits.

[0158] Step 7: Based on the obtained magnetic anomaly and resistivity anomaly exploration data of the prospecting area, mark the favorable fluorite-bearing areas on the mineral geological map to obtain the marked mineral geological map. Taking the Huashitoushan area as an example, the 1:5000 magnetic anomaly thematic layer and the 1:10000 resistivity anomaly thematic layer are compared with... Figure 3 By overlaying the shown mineral geological maps, a mineral geological map is obtained that marks favorable areas for fluorite mineralization, such as... Figure 4 and Figure 5 As shown.

[0159] Step 8: Based on the marked mineral geological map, conduct profile measurement and analysis of radioactive anomalies and calcium content anomalies in the favorable areas of fluorite deposits to obtain a line graph of total radioactivity and a line graph of calcium content.

[0160] Step 9: Construct a deep resistivity anomaly profile of the trenching project area based on audio magnetotelluric sounding data.

[0161] Step 10: Overlay the total radioactivity line graph, the calcium content line graph, and the deep resistivity anomaly profile to create a composite profile. Taking the Huashitoushan area as an example, the composite profile is as follows: Figure 6 As shown.

[0162] Step 11: Verify the deep mineralization of the mineralized zone corresponding to the comprehensive profile based on the obtained drilling parameters. Taking the Huashitoushan area as an example, the profile for verifying the borehole parameters is as follows: Figure 7 As shown.

[0163] In addition, this embodiment also provides a mineral resource prediction system for shallowly concealed fluorite deposits, which includes:

[0164] The mineral exploration prediction conceptual model construction module is used to analyze the mineralization characteristics of typical fluorite deposits in the acquired mineral exploration area and construct a mineral exploration prediction conceptual model for typical fluorite deposits based on the analysis results.

[0165] The mineralization prediction geological unit selection module is used to evaluate and analyze the resource potential, mineralization favorability, and mineralization prediction matching degree of the prospecting area, and obtain the optimal mineralization prediction geological unit of the prospecting area.

[0166] The fluorite ore quantitative prediction module is used to obtain a quantitative prediction model for mineral exploration based on a conceptual model of mineral exploration prediction and combined with geological information of typical fluorite deposits. The quantitative prediction model is then used to quantitatively predict fluorite ore in the optimal mineralized geological unit to obtain the predicted amount of fluorite ore.

[0167] The mineral resource analysis module is used to evaluate and analyze fluorite resources based on the predicted fluorite amount and the geological exploration data of the prospecting area when the predicted fluorite amount reaches a set threshold, thereby obtaining the distribution data of fluorite resources in the prospecting area.

[0168] In summary, this invention proposes a method and system for predicting mineral resources in shallowly concealed fluorite deposits. First, it analyzes the metallogenic characteristics of typical fluorite deposits and constructs a prospecting prediction conceptual model. Then, based on the prospecting prediction conceptual model and the geological information of typical fluorite deposits, it constructs a quantitative prospecting model. This model is then used to quantitatively predict fluorite deposits in selected optimal metallogenic geological units. Finally, the prediction results and geological exploration data are analyzed to obtain fluorite resource distribution data in the prospecting area. This invention solves the technical problem of high difficulty in prospecting shallowly concealed fluorite deposits. Furthermore, from a green prospecting perspective, this invention enables rapid and accurate prospecting prediction for both shallowly concealed and deep fluorite deposits.

[0169] Since the systems / devices described in the above embodiments of the present invention are systems / devices used to implement the methods of the above embodiments of the present invention, those skilled in the art can understand the specific structure and modifications of the systems / devices based on the methods described in the above embodiments of the present invention, and therefore will not be repeated here. All systems / devices used in the methods of the above embodiments of the present invention fall within the scope of protection of the present invention.

[0170] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0171] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions.

[0172] It should be noted that in the description of this invention, the word "a" or "an" preceding a component does not exclude the existence of multiple such components. This invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. The use of terms such as first, second, third, etc., is merely for convenience and does not indicate any order. These terms can be understood as part of the component names.

[0173] Furthermore, it should be noted that in the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0174] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning of the basic inventive concept, can make other changes and modifications to these embodiments.

[0175] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from the spirit and scope of the invention.

Claims

1. A method for predicting mineral resources of shallowly concealed fluorite deposits, characterized in that, include: Metallogenic characteristics of typical fluorite deposits in the identified prospecting areas were analyzed, and a prospecting prediction conceptual model for typical fluorite deposits was constructed based on the analysis results. The resource potential, mineralization favorability, and mineralization prediction matching degree of the prospecting area are evaluated and analyzed to obtain the optimal mineralization prediction geological unit of the prospecting area. Based on the conceptual model of mineral exploration prediction, combined with the geological information of typical fluorite deposits, a quantitative model for mineral exploration prediction is obtained. The quantitative model for mineral exploration prediction is then used to quantitatively predict fluorite deposits in the optimal mineralization geological unit, thereby obtaining the predicted amount of fluorite deposits. When the predicted amount of fluorite ore reaches the set threshold, based on the predicted amount of fluorite ore and combined with the geological exploration data of the prospecting area, a fluorite ore resource evaluation and analysis is conducted to obtain the distribution data of fluorite ore resources in the prospecting area.

2. The method as described in claim 1, characterized in that, Metallogenic characteristics of typical fluorite deposits in the identified prospecting areas were analyzed, and a prospecting prediction conceptual model for typical fluorite deposits was constructed based on the analysis results, including: Obtain mineral resource assessment information for the prospecting area. The preliminary assessment information includes the scale of resource reserves, the degree of geological exploration, and the degree of geological research. When the mineral resource assessment information meets the set target requirements, a typical fluorite deposit is selected from the prospecting area based on the geological exploration data of the prospecting area. When the mineral resource assessment information does not meet the set target requirements, typical fluorite deposits are selected from geographical areas that match the geological background of the prospecting area. Metallogenic characteristics analysis was conducted on the metallogenic geological background, metallogenic geological bodies, metallogenic structures and metallogenic structural surfaces, metallogenic features and prospecting indicators of typical fluorite deposits. Based on the analysis results, the prediction element level of each prediction item in the prospecting prediction conceptual model was determined. By iterating through all prediction element levels of all prediction items and combining them with the set model output format, a prospecting prediction conceptual model for typical fluorite deposits is constructed.

3. The method as described in claim 1, characterized in that, An evaluation and analysis of the resource potential, mineralization favorability, and mineralization prediction matching degree of the prospecting area was conducted to obtain the optimal mineralization prediction geological units for the prospecting area, including: Obtain mineral geological maps, information on favorable mineral-forming geological bodies, geophysical information, geochemical information, and remote sensing alteration information of the prospecting area; Using a mineral geological map as a base map, geological body information, geophysical information, geochemical information, and remote sensing alteration information are overlaid on the base map to obtain a comprehensive geological information map; The geological comprehensive information map is evaluated and analyzed for its resource potential, mineralization favorability, and mineral exploration prediction matching degree to obtain the optimal mineralization prediction geological unit of the mineral exploration area.

4. The method as described in claim 1, characterized in that, Based on a conceptual model for mineral exploration prediction, and combined with geological information of typical fluorite deposits, a quantitative model for mineral exploration prediction is obtained. This quantitative model is then used to quantitatively predict fluorite deposits in the optimal mineralization geological unit, yielding the predicted fluorite deposit quantity, including: Obtain geological information of typical fluorite deposits, including the horizontal area, dip depth, and dip angle of the ore-forming geological bodies. The mineralization influencing factors output by the mineralization prediction conceptual model are compared with the mineralization influencing factors of typical fluorite deposits. Based on the comparison results, the correlation coefficient between the optimal mineralization prediction geological unit and the typical fluorite deposit is determined. Based on the geological information of typical fluorite deposits, the geological information output by the prospecting prediction conceptual model, and the correlation coefficient between the predicted ore-forming geological bodies and typical fluorite deposits, a quantitative prospecting prediction model is constructed. A quantitative model for mineral exploration prediction was used to quantitatively predict fluorite deposits in the optimal mineralized geological unit, and the predicted amount of fluorite deposits was obtained. The mathematical expression of the quantitative model for mineral exploration prediction is as follows: In the formula, M represents the predicted amount of fluorite deposit, S* represents the horizontal area of ​​the ore-forming geological body in the prospecting area, H* represents the dip extension of the ore-forming geological body in the prospecting area, α represents the dip angle of the ore-forming geological body in the prospecting area, i represents the correlation coefficient between the predicted ore-forming geological body and the typical fluorite deposit, S represents the horizontal area of ​​the ore-forming geological body of the typical fluorite deposit, H represents the dip extension of the ore-forming geological body of the typical fluorite deposit, and β represents the dip angle of the ore-forming geological body of the typical fluorite deposit.

5. The method as described in claim 4, characterized in that, The mineralization influencing factors output from the mineralization prediction conceptual model are compared with those of typical fluorite deposits. Based on the comparison results, the correlation coefficient between the optimal mineralization prediction geological unit and typical fluorite deposits is determined, including: Obtain the metallogenic influencing factors and typical fluorite deposits output from the mineral exploration prediction conceptual model. The metallogenic influencing factors include metallogenic geological background, metallogenic geological bodies, metallogenic structures and metallogenic structural surfaces, mineralization characteristics, and mineral exploration indicator characteristics. The similarity of each group of mineralization influencing factors is compared to obtain the similarity of each group of mineralization influencing factors. Based on the set weight allocation ratio, a weight is assigned to each similarity. The correlation coefficient between the predicted ore-forming geological bodies and typical fluorite deposits is obtained by summing the weighted values ​​of the similarity of all ore-forming influencing factors.

6. The method as described in claim 1, characterized in that, When the predicted fluorite deposit amount reaches a set threshold, a fluorite resource evaluation and analysis is conducted based on the predicted fluorite deposit amount and the obtained geological exploration data of the prospecting area. The resulting data on the distribution of fluorite resources in the prospecting area includes: Obtain the geological base map of the prospecting area; When the predicted amount of fluorite ore reaches the set threshold, the geological base map is processed by mineral geological mapping based on the predicted amount of fluorite ore to obtain a mineral geological map of the prospecting area. Based on the magnetic anomaly exploration data and resistivity anomaly exploration data of the mineral exploration area, the favorable areas for fluorite mineralization are marked on the mineral geological map to obtain the marked mineral geological map. Based on the marked mineral geological map and the obtained exploration data on the ore-bearing potential of the prospecting area, the mineralization of the fluorite deposit location is evaluated. Based on the mineralization evaluation results, combined with the deep exploration data and drilling parameters of the prospecting area, a comprehensive profile of the fluorite resource distribution in the prospecting area was obtained.

7. The method as described in claim 6, characterized in that, Based on the obtained magnetic anomaly and resistivity anomaly exploration data of the prospecting area, favorable fluorite-bearing areas are marked on the mineral geological map, resulting in the marked mineral geological map including: Magnetic anomaly exploration data and resistivity anomaly exploration data of the mineral exploration area were obtained; The magnetic anomaly exploration data and resistivity anomaly exploration data are processed to obtain magnetic anomaly thematic layers and resistivity anomaly thematic layers. Both the magnetic anomaly thematic layer and the resistivity anomaly thematic layer are overlaid with the mineral geological map. Based on the marking rules for favorable areas of fluorite deposits, favorable areas of fluorite deposits are marked on the overlaid mineral geological map to obtain the marked mineral geological map. The rules for marking favorable areas for fluorite deposits are as follows: When the similarity between the ore-forming geological body and the typical fluorite deposit in terms of ore-forming structure and mineralization alteration reaches a set value, the area to which the ore-forming geological body belongs is marked as a favorable area for fluorite ore-bearing. The linear transition boundary region of magnetic anomaly is marked as a favorable area for fluorite mineralization. Linear resistivity anomaly areas and banded low resistivity anomaly areas are marked as favorable areas for fluorite mineralization.

8. The method as described in claim 6, characterized in that, Based on the marked mineral geological map and the obtained exploration data on the ore-bearing potential of the prospecting area, the mineralization assessment of the location of fluorite deposits includes: Based on the marked mineral geological map, the radioactivity anomaly information of the favorable fluorite-bearing area was analyzed by profile measurement to obtain a line graph of total radioactivity. Based on the marked mineral geological map, profile measurement and analysis were conducted on the abnormal information of calcium content in the favorable fluorite ore-bearing area to obtain a calcium content polygon map. Based on the line graphs of total radioactivity and calcium content, trenching areas were selected from the marked mineral geological map. The mineralization of fluorite deposits in the area was evaluated based on the trenching data, and the mineralization evaluation value was obtained.

9. The method as described in claim 8, characterized in that, Based on the mineralization assessment results, combined with the obtained deep exploration data and drilling parameters of the prospecting area, a comprehensive profile of the fluorite resource distribution in the prospecting area was obtained, including: When the mineralization evaluation value meets the set evaluation index threshold, audio magnetotelluric sounding data of the trenching project area is obtained, and a deep resistivity anomaly profile of the trenching project area is constructed based on the audio magnetotelluric sounding data. By overlaying the total radioactivity line graph, the calcium content line graph, and the deep resistivity anomaly profile, an initial comprehensive profile of the fluorite mineral resource distribution in the prospecting area is obtained. Based on the obtained drilling engineering parameter information, the deep mineralization of the mineralized zone to which the initial comprehensive profile belongs is verified, and a comprehensive profile of the distribution of fluorite resources in the prospecting area is obtained.

10. A mineral resource prediction system for shallowly concealed fluorite deposits, characterized in that, include: The mineral exploration prediction conceptual model construction module is used to analyze the mineralization characteristics of typical fluorite deposits in the obtained mineral exploration area, and construct a mineral exploration prediction conceptual model for typical fluorite deposits based on the analysis results. The mineralization prediction geological unit selection module is used to evaluate and analyze the resource potential, mineralization favorability, and mineralization prediction matching degree of the prospecting area, and obtain the optimal mineralization prediction geological unit of the prospecting area. The fluorite ore quantitative prediction module is used to obtain a fluorite ore quantitative prediction model based on the mineral exploration prediction conceptual model and combined with the geological information of typical fluorite deposits. The fluorite ore quantitative prediction model is then used to make quantitative predictions of fluorite ore in the optimal mineralization geological unit to obtain the predicted amount of fluorite ore. The mineral resource analysis module is used to evaluate and analyze fluorite resources based on the predicted fluorite amount and the geological exploration data of the prospecting area when the predicted fluorite amount reaches a set threshold, thereby obtaining the distribution data of fluorite resources in the prospecting area.

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