Multi-factor prospecting prediction method suitable for tin polymetallic ore

Abstract

The invention discloses a multi-factor prospecting prediction method suitable for tin polymetallic ore, and relates to the technical field of geological science. The prospecting prediction method comprises the following steps: collecting data; fusing multi-source data; extracting and analyzing mineralization information; constructing a comprehensive prediction model; and predicting mineralization. According to the method, a potential tin polymetallic ore deposit area is preliminarily selected by comprehensively considering multi-source information such as geology, geophysics, geochemistry and remote sensing, drilling data and profile data are collected in the area, the drilling data and the profile data are utilized to construct a comprehensive prediction model, and the model is combined with advanced algorithms such as geostatistics and machine learning, so that the prediction accuracy of the tin polymetallic ore deposit area is improved. The multi-source data is efficiently integrated, and a three-dimensional visualization result is provided, so that the position and the scale of a potential ore body can be more accurately predicted, and the success rate of prospecting is improved.

Description

Technical Field

[0001] The present invention relates to the field of geological science and technology, and in particular to a multi-factor prospecting prediction method applicable to tin polymetallic ores. Background Art

[0002] Tin-polymetallic deposits are located in areas with complex geological structures. Most of the deposits are magmatic and hydrothermal, with the main mineralization periods being the Variscan and Yanshan periods. The deposits are of hydrothermal and contact-replacement types. The distribution of tin-polymetallic deposits is strictly controlled by geological structures, rock masses, and strata, with a lateral distribution that appears as a north-northeast (NE)-trending belt. Currently, tin-polymetallic prospecting and prediction are primarily based on geological surveys of typical deposits, geological anomalies, and mineralization system theories. However, due to the complex mineralization of tin-polymetallic deposits, the weak existing scientific research foundation, insufficient geological surveys, and relatively limited geological anomaly information, prospecting accuracy is limited. Summary of the Invention

[0003] In response to the above shortcomings, the present invention provides a multi-factor prospecting prediction method applicable to tin-polymetallic ores. This method constructs a comprehensive prediction model by comprehensively considering multiple sources of information such as geology, geophysics, geochemistry, and remote sensing, thereby solving the current problem of low accuracy in prospecting prediction for tin-polymetallic ores. The specific technical solution is as follows:

[0004] A multi-factor prospecting prediction method applicable to tin polymetallic ores comprises the following steps:

[0005] Step 1. Data collection: Collect geological data, geophysical data, geochemical data and remote sensing data of the mining area;

[0006] Step 2: Multi-source data fusion: spatially superimpose the data from step 1, analyze their spatial correlation, assign different weights based on the contribution of each factor to mineralization, and standardize different types of data;

[0007] Step 3: Extraction and analysis of mineralization information: Based on the data fused in step 2, metallogenic geological conditions analysis, geochemical anomaly analysis, geophysical anomaly interpretation, and remote sensing information extraction are performed to preliminarily select potential tin-polymetallic ore deposit areas;

[0008] Step 4: Constructing a comprehensive prediction model: collecting drill hole data and profile data in the potential tin polymetallic ore deposit area selected in step 3, and constructing a comprehensive prediction model using the drill hole data and profile data;

[0009] Step 5. Mineralization prediction: Calculate the mineralization favorableness of each area based on the comprehensive prediction model in step 4, divide the area into high, medium and low potential areas, verify the predicted target areas through field surveys and drilling, and adjust the prediction model based on the verification results to generate a mineralization prediction map for tin polymetallic deposits and mark the high-potential target areas.

[0010] Furthermore, in step 1, the geological data includes geological maps, structural maps, lithology distribution and deposit types.

[0011] Furthermore, in step 1, the geophysical data includes gravity data, magnetic data, electrical data and seismic data.

[0012] Furthermore, in step 1, the geochemical data is obtained by collecting soil, rock, and stream sediment samples and analyzing the content of each element, such as tin, tungsten, copper, lead, and zinc.

[0013] Furthermore, in step 1, the remote sensing data is obtained by using satellite remote sensing images to extract information on linear structures, annular structures and alteration zones.

[0014] Furthermore, in step 3, the analysis of mineralization geological conditions includes structural mineralization control analysis, lithologic mineralization control analysis and alteration zone analysis; the structural mineralization control analysis is to analyze the control of faults, folds and fissures on mineralization; the lithologic mineralization control analysis is to study the relationship between different lithologies and mineralization, especially granite related to tin polymetallic ores; the alteration zone analysis is to identify alteration zones related to mineralization, such as greisenization, silicification, chloritization, etc., through remote sensing or field surveys.

[0015] Furthermore, in step 3, the geochemical anomaly analysis includes element combination analysis, anomaly delineation and anomaly evaluation; the element combination analysis is to analyze the combination characteristics of each element of tin, tungsten, copper, lead and zinc to identify geochemical anomalies related to mineralization; the anomaly delineation is to use statistical methods to delineate geochemical anomaly areas; the anomaly evaluation is to evaluate the mineralization potential of the anomaly area in combination with the geological background.

[0016] Furthermore, in step 3, the geophysical anomaly interpretation includes gravity anomaly interpretation, magnetic anomaly interpretation and electrical anomaly interpretation; the gravity anomaly interpretation is to identify density anomalies related to tin polymetallic ores, such as the distribution of granite bodies; the magnetic anomaly interpretation is to analyze the distribution of magnetic minerals and identify magnetic anomalies related to mineralization; the electrical anomaly interpretation is to identify mineralized zones or alteration zones through resistivity and polarizability.

[0017] Furthermore, in step 3, the remote sensing information extraction includes linear structure extraction, annular structure extraction and alteration information extraction; the linear structure extraction is to extract fractures and fissures using remote sensing images and analyze their relationship with mineralization; the annular structure extraction is to identify annular structures related to magma activity; the alteration information extraction is to extract alteration mineral information related to mineralization through multispectral or hyperspectral remote sensing data.

[0018] Furthermore, in step 4, the method for constructing the comprehensive prediction model is:

[0019] a. Data preparation: Collect borehole data, geological profiles, topographic data, geophysical data, and geochemical data, digitize the geological profiles, and extract the coordinates and attributes of key geological boundaries;

[0020] b. Data preprocessing: data cleaning, unified coordinates, drilling trajectory correction and data interpolation processing of the data;

[0021] c. Geological interface extraction: extracting stratum interfaces, rock mass boundaries and structural interfaces;

[0022] d. 3D geological modeling: Use professional 3D geological modeling software to build a geological framework model, generate a geological interface, construct a geological body, and generate a 3D geological body model;

[0023] e. Model optimization and verification: optimize and verify the model;

[0024] f. Attribute modeling: Generate three-dimensional distribution models of lithology, mineralization zones, and physical properties;

[0025] g. Three-dimensional visualization: Visualize the geological body and attributes and generate a geological profile to obtain the comprehensive prediction model.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention preliminarily selects a potential tin-polymetallic ore deposit area by comprehensively considering multiple sources of information such as geology, geophysics, geochemistry, and remote sensing, and collects drilling data and profile data in the area. The drilling data and profile data are used to build a comprehensive prediction model. The model combines advanced algorithms such as geostatistics and machine learning to efficiently integrate multiple source data and provide three-dimensional visualization results. It can more accurately predict the location and scale of potential ore bodies and improve the success rate of mineral exploration. At the same time, the model has good adaptability and flexibility and can be continuously updated and adjusted according to the latest exploration data to reflect the latest exploration results and understanding. The method of the present invention can efficiently predict the prospecting of tin-polymetallic ore with high accuracy, improve the efficiency of mineral exploration and the success rate of mineral exploration, reduce exploration risks, and reduce exploration time and capital costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments.

[0029] Figure 1 is a three-dimensional geological map of the study area in the embodiment;

[0030] Figure 2 2 is a cross-sectional view of the study area in the embodiment. DETAILED DESCRIPTION

[0031] The specific embodiments of the present invention are described in detail below, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

[0032] Example

[0033] The Dafulou mining area of the Wuyi Mine in Nandan County, Guangxi Province was selected as the research area. The following method was used to conduct prospecting and prediction of tin polymetallic deposits in the research area. The research area is located in Dawan Village, Chehe Town, Nandan County, Guangxi Province. The former Guizhou-Guangxi Highway (County Road 851) passes through the mining area. The Nanning-Guiyang G210 National Highway passes through the east side of the mining area, about 1 km away from the mining area. The mining area is 17 km north of Nandan County and 62 km south of Jinchengjiang City. The specific steps are as follows:

[0034] Step 1. Data collection: Collect geological data, geophysical data, geochemical data and remote sensing data of the study area;

[0035] Geological data include geological maps, structural maps, lithology distribution and deposit types.

[0036] Geophysical data includes gravity data, magnetic data, electrical data and seismic data.

[0037] Geochemical data are collected from soil, rock, and stream sediment samples and analyzed for the contents of tin, tungsten, copper, lead, and zinc.

[0038] Remote sensing data uses satellite remote sensing images to extract information on linear structures, annular structures and alteration zones.

[0039] Step 2: Multi-source data fusion: spatially superimpose the data from step 1, analyze their spatial correlation, assign different weights based on the contribution of each factor to mineralization, and standardize different types of data;

[0040] Step 3: Extraction and analysis of mineralization information: Based on the data fused in step 2, metallogenic geological conditions analysis, geochemical anomaly analysis, geophysical anomaly interpretation, and remote sensing information extraction are performed to preliminarily select potential tin-polymetallic ore deposit areas;

[0041] The analysis of mineralization geological conditions includes structural mineralization control analysis, lithologic mineralization control analysis and alteration zone analysis; the structural mineralization control analysis is to analyze the control of faults, folds and fissures on mineralization; the lithologic mineralization control analysis is to study the relationship between different lithologies and mineralization, especially granite related to tin polymetallic ores; the alteration zone analysis is to identify alteration zones related to mineralization, such as greisenization, silicification, chloritization, etc., through remote sensing or field surveys.

[0042] Geochemical anomaly analysis includes element combination analysis, anomaly delineation and anomaly evaluation; the element combination analysis is to analyze the combination characteristics of tin, tungsten, copper, lead and zinc, and identify geochemical anomalies related to mineralization; the anomaly delineation is to use statistical methods to delineate geochemical anomaly areas; the anomaly evaluation is to evaluate the mineralization potential of the anomaly area in combination with the geological background.

[0043] Geophysical anomaly interpretation includes gravity anomaly interpretation, magnetic anomaly interpretation and electrical anomaly interpretation; the gravity anomaly interpretation is to identify density anomalies related to tin polymetallic minerals, such as the distribution of granite bodies; the magnetic anomaly interpretation is to analyze the distribution of magnetic minerals and identify magnetic anomalies related to mineralization; the electrical anomaly interpretation is to identify mineralized zones or alteration zones through resistivity and polarizability.

[0044] Remote sensing information extraction includes linear structure extraction, annular structure extraction and alteration information extraction; the linear structure extraction is to extract fractures and fissures using remote sensing images and analyze their relationship with mineralization; the annular structure extraction is to identify annular structures related to magma activity; the alteration information extraction is to extract alteration mineral information related to mineralization through multispectral or hyperspectral remote sensing data.

[0045] Step 4: Build a comprehensive prediction model: Collect drill hole data and profile data in the potential tin-polymetallic ore deposit area selected in step 3, and use the drill hole data and profile data to build a comprehensive prediction model:

[0046] a. Data preparation:

[0047] Drilling data: Collect the borehole coordinates, depth, lithology description, stratigraphic division, mineralization information, etc. Ensure the integrity and accuracy of drilling data, including inclination data (used to correct drilling trajectory).

[0048] Profile data: Collect geological profiles, including information such as stratigraphic boundaries, structural lines, and rock mass boundaries.

[0049] Digitize the cross-sections and extract the coordinates and attributes of key geological boundaries.

[0050] Other data: topographic data (DEM), geophysical data, geochemical data, etc., used to assist modeling.

[0051] b. Data preprocessing

[0052] Data cleaning: Check the integrity of drill hole and profile data, fill in missing values, and remove outliers.

[0053] Coordinate unification: Convert all data to a unified coordinate system (such as WGS84 or local coordinate system).

[0054] Drilling trajectory correction: Correct the drilling trajectory based on the inclination data to generate the true three-dimensional spatial distribution of the drilling holes.

[0055] Data interpolation: Interpolate sparse drillhole data to generate continuous stratigraphic or lithologic distribution.

[0056] c. Geological interface extraction

[0057] Stratum interface extraction: Extract the top and bottom depths of each stratum from the borehole data to generate stratigraphic interface point data. Extract stratigraphic boundaries from the profile data to generate stratigraphic interface line data.

[0058] Rock mass boundary extraction: Extract rock mass boundary information based on drilling and profile data.

[0059] Structural interface extraction: Extract interface information of structures such as faults and folds, and generate structural interface point or line data.

[0060] d. 3D geological modeling

[0061] Choose modeling software: Use professional 3D geological modeling software such as GOCAD, Leapfrog, Surpac, Micromine, etc.

[0062] Create geological framework: Based on the drill hole and profile data, construct a geological framework model, including the basic geometric forms of strata, rock masses, structures, etc.

[0063] Generate geological interfaces: Generate interface surfaces of strata, rock masses, and structures using borehole and profile data. Use interpolation algorithms (such as Kriging interpolation and inverse distance weighted interpolation) to generate smooth geological interfaces.

[0064] Constructing geological bodies: Connecting geological interfaces to generate a 3D geological model. Classifying and assigning attributes to strata, rock masses, structures, etc.

[0065] e. Model optimization and verification

[0066] Model optimization: Check the geometric rationality of the model and adjust the smoothness and continuity of the interface.

[0067] Update the model based on new data (such as new drill holes or sections).

[0068] Model Validation:

[0069] Compare the model with known geological profiles and drill hole data to verify the accuracy of the model.

[0070] Use geophysical data (e.g. gravity, magnetics) for model validation.

[0071] f. Attribute Modeling

[0072] Lithology modeling: Generate a three-dimensional distribution model of lithology based on the lithology description of the drill hole.

[0073] Mineralization modeling: Generate a three-dimensional distribution model of the mineralized zone based on the mineralization information of the drill hole.

[0074] Physical property modeling: Generate a three-dimensional distribution model of physical property parameters such as density and magnetism based on geophysical data.

[0075] g. 3D visualization

[0076] Geological body visualization: Use the three-dimensional visualization function of the modeling software to display the spatial distribution of geological bodies such as strata, rock masses, and structures.

[0077] Attribute visualization: color rendering of lithology, mineralization, physical properties and other attributes to enhance the readability of the model.

[0078] Section Cutting: Generate geological sections in any direction to examine the internal structure of the model.

[0079] h. Output

[0080] 3D model export: Export 3D geological models to common formats (such as DXF, OBJ, STL, etc.) for use in other software.

[0081] Map output: Generate three-dimensional geological map (see Figure 1 ) and cross-sections (see Figure 2 ).

[0082] Step 5. Mineralization prediction: Calculate the mineralization favorableness of each area based on the comprehensive prediction model in step 4, divide the area into high, medium and low potential areas, verify the predicted target areas through field surveys and drilling, and adjust the prediction model based on the verification results to generate a mineralization prediction map for tin polymetallic deposits and mark the high-potential target areas.

[0083] In summary, the present invention preliminarily selects potential tin-polymetallic mineral deposit areas by comprehensively considering multi-source information such as geology, geophysics, geochemistry, and remote sensing, and collects drilling data and profile data in this area. The drilling data and profile data are used to construct a comprehensive prediction model. This model combines advanced algorithms such as geostatistics and machine learning to efficiently integrate multi-source data and provide three-dimensional visualization results, which can more accurately predict the location and scale of potential ore bodies and improve the success rate of mineral exploration.

[0084] The foregoing descriptions of specific exemplary embodiments of the present invention are for purposes of illustration and description. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is apparent that many variations and modifications are possible in light of the foregoing teachings. The exemplary embodiments have been selected and described for the purpose of explaining the specific principles of the invention and their practical application, thereby enabling those skilled in the art to realize and utilize a variety of exemplary embodiments of the invention and various options and modifications. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A multi-factor prospecting prediction method applicable to tin polymetallic ores, characterized in that: The following steps are involved: Step 1: Data collection: Collect geological data, geophysical data, geochemical data and remote sensing data of the mining area; Step 2: Multi-source data fusion: spatially superimpose the data from step 1, analyze their spatial correlation, assign different weights based on the contribution of each factor to mineralization, and standardize different types of data; Step 3: Extraction and analysis of mineralization information: Based on the data fused in step 2, metallogenic geological conditions analysis, geochemical anomaly analysis, geophysical anomaly interpretation, and remote sensing information extraction are performed to preliminarily select potential tin-polymetallic ore deposit areas; Step 4: Constructing a comprehensive prediction model: collecting drill hole data and profile data in the potential tin polymetallic ore deposit area selected in step 3, and constructing a comprehensive prediction model using the drill hole data and profile data; Step 5. Mineralization prediction: Calculate the mineralization favorableness of each area based on the comprehensive prediction model in step 4, divide the area into high, medium and low potential areas, verify the predicted target areas through field surveys and drilling, and adjust the prediction model based on the verification results to generate a mineralization prediction map for tin polymetallic deposits and mark the high-potential target areas.

2. A multi-factor prospecting prediction method applicable to tin polymetallic ores according to claim 1, characterized in that: In step 1, the geological data includes geological maps, structural maps, lithology distribution and mineral deposit types.

3. A multi-factor prospecting prediction method applicable to tin polymetallic ores according to claim 1, characterized in that: In step 1, the geophysical data includes gravity data, magnetic data, electrical data and seismic data.

4. The multi-factor prospecting prediction method applicable to tin polymetallic ores according to claim 1, characterized in that: In step 1, the geochemical data is collected from soil, rock, and stream sediment samples and analyzed for the contents of tin, tungsten, copper, lead, and zinc.

5. The multi-factor prospecting prediction method applicable to tin polymetallic ores according to claim 1, characterized in that: In step 1, the remote sensing data is obtained by using satellite remote sensing images to extract information on linear structures, annular structures and alteration zones.

6. A multi-factor prospecting prediction method applicable to tin polymetallic ores according to claim 1, characterized in that: In step 3, the analysis of mineralization geological conditions includes structural mineralization control analysis, lithologic mineralization control analysis and alteration zone analysis; the structural mineralization control analysis is to analyze the control of faults, folds and fissures on mineralization; the lithologic mineralization control analysis is to study the relationship between different lithologies and mineralization; the alteration zone analysis is to identify alteration zones related to mineralization through remote sensing or field surveys.

7. The multi-factor prospecting prediction method applicable to tin polymetallic ores according to claim 1, characterized in that: In step 3, the geochemical anomaly analysis includes element combination analysis, anomaly delineation and anomaly evaluation; the element combination analysis is to analyze the combination characteristics of each element of tin, tungsten, copper, lead and zinc to identify geochemical anomalies related to mineralization; the anomaly delineation is to use statistical methods to delineate geochemical anomaly areas; the anomaly evaluation is to evaluate the mineralization potential of the anomaly area in combination with the geological background.

8. The multi-factor prospecting prediction method applicable to tin polymetallic ores according to claim 1, characterized in that: In step 3, the geophysical anomaly interpretation includes gravity anomaly interpretation, magnetic anomaly interpretation and electrical anomaly interpretation; the gravity anomaly interpretation is to identify density anomalies related to tin polymetallic deposits; The magnetic anomaly interpretation is to analyze the distribution of magnetic minerals and identify magnetic anomalies related to mineralization; the electrical anomaly interpretation is to identify mineralized zones or alteration zones through resistivity and polarizability.

9. The multi-factor prospecting prediction method applicable to tin polymetallic ores according to claim 1, characterized in that: In step 3, the remote sensing information extraction includes linear structure extraction, annular structure extraction and alteration information extraction; the linear structure extraction is to extract fractures and fissures using remote sensing images and analyze their relationship with mineralization; the annular structure extraction is to identify annular structures related to magma activity; the alteration information extraction is to extract alteration mineral information related to mineralization through multispectral or hyperspectral remote sensing data.

10. The multi-factor prospecting prediction method applicable to tin polymetallic ores according to claim 1, characterized in that: In step 4, the method for constructing the comprehensive prediction model is: a. Data preparation: Collect borehole data, geological profiles, topographic data, geophysical data, and geochemical data, digitize the geological profiles, and extract the coordinates and attributes of key geological boundaries; b. Data preprocessing: data cleaning, unified coordinates, drilling trajectory correction and data interpolation processing of the data; c. Geological interface extraction: extracting stratum interfaces, rock mass boundaries and structural interfaces; d. 3D geological modeling: Use professional 3D geological modeling software to build a geological framework model, generate a geological interface, construct a geological body, and generate a 3D geological body model; e. Model optimization and verification: optimize and verify the model; f. Attribute modeling: Generate three-dimensional distribution models of lithology, mineralization zones, and physical properties; g. Three-dimensional visualization: Visualize the geological body and attributes and generate a geological profile to obtain the comprehensive prediction model.

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