A multi-scale progressive prospecting prediction method for solid minerals based on geological big data

Abstract

The present invention belongs to the field of geological mineral resource prediction, and particularly relates to a multi-scale progressive prospecting prediction method for solid minerals based on geological big data. The present invention collects multi-scale geological, geophysical exploration data, geochemical exploration data, remote sensing and other data in the study area, and uses data mining technology and machine learning methods to achieve prospecting prediction and target area delineation. This method aims to combine three-dimensional geological models, machine learning methods, and data mining technologies to process geological big data in the prediction area, determine the target area progressively at multiple scales, and provide a prospecting prediction method for mineral resource prediction; based on prospecting case review, a prospecting knowledge map is constructed, and a prospecting process knowledge association map is drawn. According to the working scale, the geological, geophysical, geochemical and remote sensing data are divided into 1:500,000, 1:250,000, 1:50,000, 1:25,000 and 1:5,000, a three-dimensional geological model and attribute data mart are constructed, and the laws between mineralization conditions and mineralization controlling elements are analyzed to form a mineralization prediction model to achieve prospecting prediction.

Description

Technical Field

[0001] The present invention belongs to the field of geological mineral resource prediction, and in particular relates to a multi-scale progressive prospecting prediction method for solid minerals based on geological big data. Background Art

[0002] Mineral prospecting prediction is an estimation or inference of unknown characteristics of mineralization events that occurred in the past. The prediction process is essentially a rigorous scientific logical thinking process, including observation, analysis, induction, deduction and reasoning. Mineralization prediction based on geological spatiotemporal big data involves a series of theories, methods and key technologies of the fourth paradigm of scientific research and geological data science. Establishing a workflow for the aggregation, integration, fusion, mining and evaluation of geological big data that is suitable for prediction work is of great help to the development of solid mineral prospecting prediction work.

[0003] Geological big data is a kind of spatiotemporal big data, which is mainly generated in the investigation, exploration and corresponding geological science research process of basic geology, mineral geology, hydrogeology, engineering geology and environmental geology. The data obtained for the purpose of prospecting through outcrop observation records, core compilation, geophysical instrument records, remote sensing records, sample chemical analysis and physical testing is a mapping of the real geological world, which contains mineral information. Mineralization prediction is to solve how to discover mineralization information from the collected multi-scale heterogeneous and heterogeneous data, explore mineralization laws, establish mineralization prediction models, and provide services for the delineation of target areas for prospecting. Summary of the invention

[0004] The purpose of the present invention is to combine three-dimensional geological models, machine learning methods, and data mining technology to process geological big data in the prediction area, determine the target area in a multi-scale progressive manner, and provide a prospecting prediction method for mineral resource prediction.

[0005] The specific technical solution adopted is: a multi-scale progressive prospecting prediction method for solid minerals based on geological big data, which forms a mineralization prediction model through coupling model data and model-free data, multi-source data fusion, construction of prospecting knowledge graph, data mining and construction of three-dimensional attribute model. The specific steps are as follows:

[0006] Step 1: According to the purpose of prospecting and prediction, the data of the area to be studied are sorted and collected. According to whether there is an existing mineralization model in the area to be studied, the collected data of the area to be studied are divided into model data and non-model data. The model data at least include the existing mineralization system, the existing mineralization model and prospecting cases, and the non-model data at least include geological, geophysical, geochemical and remote sensing data; and the non-model data are divided into geological, geophysical, geochemical and remote sensing data of 1:500,000, 1:250,000, 1:50,000, 1:25,000 and 1:5,000 according to the working scale;

[0007] Step 2: Analyze the important nodes in the prospecting process through the existing mineralization system and existing mineralization mode with model data in step 1, and draw a knowledge association diagram of the prospecting process based on the relationship between each important node; then analyze the prospecting knowledge between the important nodes based on the Bayesian network, and combine the prospecting knowledge with the prospecting cases with model data to construct a prospecting knowledge map, and the constructed prospecting knowledge map includes at least the structure, stratum, lithology data of the area to be studied, the spatial distribution range data of mineral resources, the spatial distribution range data of ore deposits, the location, stratigraphic and scale data of ore deposits, the location of ore bodies, ore body morphology, ore body thickness and ore body grade data;

[0008] Step 3: Extract geological, geophysical and remote sensing anomalies from the 1:500,000 geological, geophysical, geochemical and remote sensing data, combine the model data from step 1 and the structural, stratigraphic, lithological data and spatial distribution range data of the area to be studied in the prospecting knowledge map from step 2, and use GIS software to analyze the law between the remote sensing, geophysical and geochemical anomalies and the spatial distribution range data of the mineral resources from the perspective of the structural framework and the metallogenic belt;

[0009] Step 4: Extract geological, geophysical, geochemical and remote sensing anomalies in the 1:250,000 geological, geophysical, geochemical and remote sensing data from the spatial distribution range of mineral resources determined in step 3, combine the model data in step 1 and the spatial distribution range data of the ore deposit in the prospecting knowledge map in step 2, and use GIS software to analyze the rules between the remote sensing, geophysical and geochemical anomalies and the spatial distribution range data of the ore deposit from the perspectives of mineral source, bottom splitting channel and mineralization environment;

[0010] Step 5: Extract geological, geophysical, geochemical and remote sensing anomalies in the 1:50,000 geological, geophysical, geochemical and remote sensing data within the spatial distribution range of the ore deposit determined in step 4, combine the model data in step 1 and the ore deposit location, horizon and scale data in the ore prospecting knowledge map in step 2, and use GIS software to analyze the regularity between the geological, geophysical and geochemical anomalies and the ore deposit location, horizon and scale data from the perspective of metallogenic environment, ore guiding structure and ore matching structure;

[0011] Step 6: extracting magnetotelluric, seismic, drilling, induced polarization, high-precision magnetic survey data and geochemical anomalies from the 1:25,000 geological, geophysical, geochemical and remote sensing data from the ore deposit location, layer and scale determined in step 5, combining the model data of step 1 and the ore body location and ore body morphology data of step 2, using GIS software to analyze the law between the magnetotelluric, seismic, drilling, induced polarization, high-precision magnetic survey data and geochemical anomalies and the ore body location and ore body morphology data from the perspective of deep channels, shallow ore-guiding faults, ore-matching faults and ore-bearing geological bodies, and constructing a three-dimensional attribute model to predict three-dimensional potential resource areas;

[0012] Step 7: Extract the geomagnetic, drilling and induced polarization anomalies in the 1:50000 geological, geophysical, geochemical and remote sensing data from the ore body location, ore body morphology, ore body thickness and ore body grade data determined in step 6; combine the model data in step 1 and the ore body location, ore body morphology, ore body thickness and ore body grade data in step 2; use GIS software to analyze the law between the geomagnetic, drilling and induced polarization anomalies and the ore body location, ore body morphology, ore body thickness and ore body grade data from the perspective of shallow ore-guiding faults, ore-bearing faults and ore-bearing geological bodies; carry out mineralization system structure mining on the constructed three-dimensional attribute model to predict the prospective ore body areas.

[0013] Furthermore, the three-dimensional geological model is constructed in GIS software using the modeled data and the non-modeled data in step 1; the GIS software at least includes QuantyView software.

[0014] Moreover, GIS software is used to produce the geological, geophysical, geochemical and remote sensing data of 1:500,000, 1:250,000, 1:50,000, 1:25,000 and 1:5,000 in step 1 respectively; the GIS software includes at least QuantyView software, and the formed geological map database is imported into the GIS database.

[0015] Moreover, the GIS software in steps 3 to 7 at least includes QuantyView software.

[0016] Moreover, among the 1:500,000 geological, geophysical, geochemical and remote sensing data, the geological data at least include 1:500,000 geological maps, the geophysical data at least include 1:500,000 aeromagnetic maps, and the remote sensing data at least include hyperspectral data, high-resolution satellite images, and radar satellite image data.

[0017] Moreover, among the 1:250,000 geological, geophysical, geochemical and remote sensing data, the geological data at least include 1:250,000 DEM data and 1:250,000 geological maps, the geophysical data at least include 1:200,000 regional gravity and 1:200,000 aeromagnetic, the geochemical data at least include 1:200,000 regional geochemical scanning data, and the remote sensing data at least include hyperspectral data, high-resolution satellite images, and radar satellite image data.

[0018] Moreover, among the 1:50,000 geological, geophysical, geochemical and remote sensing data, the geological data include at least 1:50,000 DEM data and 1:50,000 geological maps, the geophysical data include at least 1:50,000 gravity and 1:50,000 ground high-precision magnetic surveys, the geochemical data include at least 1:50,000 stream sediments, soil geochemical measurements and rock measurement data, and the remote sensing data include at least hyperspectral data, high-resolution satellite images and radar satellite image data.

[0019] Moreover, among the 1:25,000 geological, geophysical, geochemical and remote sensing data, the geological data at least include drilling information, drilling column charts, and 1:10,000 exploration line profiles; the geophysical data at least include magnetotelluric sounding and induced polarization sounding profiles; the geochemical data at least include drilling sample analysis data; and the remote sensing data at least include hyperspectral data, high-resolution satellite images, and radar satellite image data.

[0020] Moreover, among the 1:50,000 geological, geophysical, geochemical and remote sensing data, the geological data at least include drilling information, drilling column charts, 1:10,000 DEM data, 1:50,000 geological maps or 1:10,000 geological maps, 1:2,000 exploration line profiles, mid-section plane geological maps, and ore body resource reserve estimation maps; the geophysical data at least include magnetotelluric sounding and induced polarization sounding profiles; the geochemical data at least include drilling sample analysis data; and the remote sensing data at least include hyperspectral data, high-resolution satellite images, and radar satellite image data.

[0021] Compared with the prior art, the beneficial effects of this solution are:

[0022] 1. The collected data of the area to be studied are divided into modeled data and model-free data, which bring together multi-scale (1:500,000, 1:250,000, 1:50,000, 1:25,000, 1:5,000), multi-variable, multi-temporal and multi-correlated spatiotemporal structure and geological attribute data with ultra-high dimension, ultra-high computational complexity and ultra-high uncertainty; for the existing mineralization system, existing mineralization model and prospecting cases, the "re-examination" method is adopted to have an in-depth understanding and understanding of the historical prospecting experience and mineralization model of the prediction area, and through consulting geological reports, literature and consultation, the basic geological characteristics of mineralization in the prediction area are deeply understood. Through the comprehensive analysis of comprehensive data and information, the mineralization mechanism, mineralization conditions and ore-controlling factors are deeply understood, which is convenient for constructing a prospecting knowledge map and drawing a knowledge association map of the prospecting process.

[0023] 2. Constructing a prospecting knowledge graph and drawing a knowledge association graph of the prospecting process can standardize and formalize the original data and build a prediction data mart to better mine algorithms, prediction methods and various potential data patterns.

[0024] 3. Extract geological, geophysical and remote sensing anomalies from geological, geophysical, geochemical and remote sensing data at a scale of 1:500,000, and use GIS software to analyze the regularity between the geological, geophysical and remote sensing anomalies and the spatial distribution range data of the mineral resources from the perspective of structural framework and mineralization belt; further extract geological, geophysical, geochemical and remote sensing anomalies from geological, geophysical, geochemical and remote sensing data at a scale of 1:250,000 within the determined spatial distribution range of mineral resources, and use GIS software to analyze the regularity between the geological, geophysical, geochemical and remote sensing anomalies and the spatial distribution range data of the ore deposit from the perspective of mineral source, bottom splitting channel and mineralization environment; further extract geological, geophysical, geochemical and remote sensing anomalies from geological, geophysical, geochemical and remote sensing data at a scale of 1:50,000 within the determined spatial distribution range of the ore deposit, and use GIS software to analyze the regularity between the geological, geophysical, geochemical and remote sensing anomalies and the location of the ore deposit from the perspective of mineralization environment, ore-guiding structure and ore-matching structure , stratigraphic and scale data; further extract the magnetotelluric, seismic, drilling, induced polarization, high-precision magnetic survey data and geochemical anomalies in the 1:25,000 geological, geophysical, geochemical and remote sensing data in the determined ore deposit location, stratigraphic and scale, and use GIS software to analyze the laws between the magnetotelluric, seismic, drilling, induced polarization, high-precision magnetic survey data and geochemical anomalies and the ore body occurrence location and ore body morphology data from the perspectives of deep channels, shallow ore-guiding faults, ore-bearing faults and ore-bearing geological bodies; further extract the geo-audio-frequency electromagnetic, drilling and induced polarization anomalies in the 1:5,000 geological, geophysical, geochemical and remote sensing data in the determined ore deposit location, stratigraphic and scale, and use GIS software to analyze the laws between the geo-audio-frequency electromagnetic, drilling and induced polarization anomalies and the ore body occurrence location, ore body morphology, ore body thickness and ore body grade data from the perspectives of deep channels, shallow ore-guiding faults, ore-bearing faults and ore-bearing geological bodies. Realize multi-scale progressive analysis of the spatial anomaly rules, spatial association rules, spatial distribution laws and spatial prediction rules between mineralization conditions and ore-controlling elements.

[0025] 4. Use the above-mentioned laws to form a mineralization prediction model, combine it with three-dimensional geological models, machine learning methods, and data mining technology to process the geological big data of the prediction area, so as to realize the progressive discovery of mineralization possibility, feasible prospecting, favorable prospecting, potential resources, and prospective ore bodies, so as to determine the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flow chart of the method of the present invention;

[0027] Figure 2 Negative ore-controlling structures based on Bouguer gravity and aeromagnetic anomaly data mining in 1:500,000 geological, geophysical, geochemical and remote sensing data;

[0028] Figure 3The Devonian-Carboniferous Wei-Shui Northwest Small Rift Valley was mined based on the geophysical, geochemical and Bouguer gravity anomaly data in the 1:250,000 geological, geophysical, geochemical and remote sensing data, and the superposition with the east-west rift belt caused hydrothermal enrichment;

[0029] Figure 4 To make preliminary prospecting predictions within the determined spatial distribution range of the mineral deposits based on the regularity between the geological, geophysical and geochemical anomalies in the 1:50,000 geological, geophysical, geochemical and remote sensing data and the mineral deposit location, stratigraphic and scale data;

[0030] Figure 5 To combine 1:25,000 geological, geophysical, geochemical and remote sensing data and geological background, a 3D attribute model and 3D potential resource location prediction were constructed;

[0031] Figure 6 In order to carry out structural excavation of the mineralization system on the geological profile, a 1:2000 profile was designed using the exploration line, and the data of the area to be studied was processed to delineate the prospective ore body area. DETAILED DESCRIPTION

[0032] The present invention is described in detail below with reference to the accompanying drawings and embodiments, but the content of the present invention is not limited to the following embodiments.

[0033] The implementation process of using the method of the present invention to predict the lead-zinc ore concentration area in a certain test area is as follows:

[0034] The data collected in the test area are divided into modeled data and non-modeled data. The modeled data at least include the existing mineralization system, existing mineralization model and prospecting cases, and the non-modeled data at least include geological, geophysical, geochemical and remote sensing data; and the non-modeled data are divided into geological, geophysical, geochemical and remote sensing data of 1:500,000, 1:250,000, 1:50,000, 1:25,000 and 1:5,000 according to the working scale, and are produced separately using QuantyView software.

[0035] The existing mineralization system and existing mineralization mode with model data analyze the important nodes in the prospecting process, and draw a knowledge association diagram of the prospecting process based on the relationship between each important node; then based on the Bayesian network, analyze the prospecting knowledge between the important nodes, and combine the prospecting knowledge with the prospecting cases with model data to construct a prospecting knowledge map. The constructed prospecting knowledge map includes at least the structure, stratigraphy, lithology data of the study area, spatial distribution range data of mineral resources, spatial distribution range data of ore deposits, ore deposit location, stratigraphic and scale data, ore body occurrence location, ore body morphology, ore body thickness and ore body grade data.

[0036] Find out the geological, geophysical and remote sensing anomalies in the 1:500,000 geological, geophysical, geochemical and remote sensing data, combine the structural, stratigraphic, lithological data and spatial distribution range data of the study area in the model data and the prospecting knowledge map, and use GIS software to analyze the regularity between the geological, geophysical and remote sensing anomalies and the spatial distribution range data of the mineral resources from the perspective of structural framework and metallogenic belt; among them, the 1:500,000 anomaly data of the test area has the strongest regularity with the Bouguer gravity and aeromagnetic anomaly data, thus discovering the hidden east-west Sinian-Cambrian rift zone, and determining the east-west rift zone as a possible section of lead-zinc deposits, such as Figure 2 shown.

[0037] Within the scope determined in the previous step, according to the geological, geophysical, geochemical and remote sensing anomalies in the 1:250,000 geological, geophysical, geochemical and remote sensing data, combined with the model data and the spatial distribution range data of the ore deposit in the prospecting knowledge map, GIS software is used to analyze the regularity between the geological, geophysical, geochemical and remote sensing anomalies and the spatial distribution range data of the ore deposit from the perspective of mineral source, bottom splitting channel and mineralization environment; among them, the 1:250,000 anomaly data has the strongest regularity in the geophysical, geochemical and Bouguer gravity anomaly data, and the Devonian-Carboniferous Wei-Shuibeixi Small Rift was excavated, and the Devonian-Carboniferous Wei-Shuibeixi Small Rift was superimposed on the east-west rift belt discovered in the previous step to cause hydrothermal enrichment. Since the basement jets of the two rifts provide deep-source lead and zinc and iron respectively, it is speculated that the intersection (Zhugongtang, Caiyuanzi, Wuzhishan working area) can provide later hydrothermal channels, thereby determining the spatial distribution range of the ore deposit, such as Figure 3 shown.

[0038] In the determined spatial distribution range of the ore deposit (Zhugongtang, Caiyuanzi, Wuzhishan working area), the geological, geophysical, geochemical and remote sensing anomalies in the 1:50,000 geological, geophysical, geochemical and remote sensing data are extracted, and the ore deposit location, horizon and scale data in the model data and the prospecting knowledge map are combined. The GIS software is used to analyze the laws between the geological, geophysical, geochemical and remote sensing anomalies and the ore deposit location, horizon and scale data from the perspective of metallogenic environment, ore-guiding structure and ore-matching structure, and a convolutional neural network prediction model is constructed and trained for preliminary prospecting prediction. The prediction results are as follows: Figure 4 shown.

[0039] Furthermore, based on the geological background including the location and morphology of ore bodies in the 1:25,000 geological, geophysical, geochemical and remote sensing data, magnetotelluric, seismic, drilling, induced polarization, high-precision magnetic survey and geochemical anomalies, and the induction of the mineralization system, mineralization conditions and ore-controlling factors, a 1:25,000 three-dimensional attribute model and attribute data mart of the test area were constructed, and a three-dimensional potential resource area prediction was carried out for the survey area, such as Figure 5 shown.

[0040] On the basis of the constructed three-dimensional attribute model of the potential resource area containing lead and zinc ore, the geomagnetic frequency electromagnetic (AMT), drilling and induced polarization anomalies in the 1:0.5000 geological, geophysical, geochemical and remote sensing data are combined with the data on the location, morphology, thickness and grade of the ore body. GIS software is used to analyze the geomagnetic, seismic, drilling, induced polarization, high-precision magnetic survey and geochemical anomalies and the data on the location, morphology, thickness and grade of the ore body from the perspectives of deep channels, shallow ore-guiding faults, ore-bearing faults and ore-bearing geological bodies, and the mineralization system structure is mined on the geological profile. Then, all the above-mentioned laws are used to form a mineralization prediction model to process the data of the area to be studied. Figure 6 In order to design a 1:2000 profile using the exploration line and process the data of the area to be studied, the prospective ore body area is delineated.

[0041] The detailed data list used in this implementation process is as follows:

[0042]

[0043]

Claims

1. A multi-scale progressive prospecting prediction method for solid minerals based on geological big data, characterized by: The mineralization prediction model is formed by coupling modeled data with model-free data, fusing multi-source data, building a prospecting knowledge graph, data mining, and building a three-dimensional attribute model. The specific steps are as follows: Step 1: According to the purpose of prospecting and prediction, the data of the area to be studied are sorted and collected. According to whether there is an existing mineralization model in the area to be studied, the collected data of the area to be studied are divided into model data and non-model data. The model data at least include the existing mineralization system, the existing mineralization model and prospecting cases, and the non-model data at least include geological, geophysical, geochemical and remote sensing data; and the non-model data are divided into geological, geophysical, geochemical and remote sensing data of 1:500,000, 1:250,000, 1:50,000, 1:25,000 and 1:5,000 according to the working scale; Step 2: Analyze the important nodes in the prospecting process through the existing mineralization system and existing mineralization mode with model data in step 1, and draw a knowledge association diagram of the prospecting process based on the relationship between each important node; then analyze the prospecting knowledge between the important nodes based on the Bayesian network, and combine the prospecting knowledge with the prospecting cases with model data to construct a prospecting knowledge map, and the constructed prospecting knowledge map includes at least the structure, stratum, lithology data of the area to be studied, the spatial distribution range data of mineral resources, the spatial distribution range data of ore deposits, the location, stratigraphic and scale data of ore deposits, the location of ore bodies, ore body morphology, ore body thickness and ore body grade data; Step 3: extract geological, geophysical and remote sensing anomalies from the geological, geophysical, geochemical and remote sensing data at a scale of 1:500,000, combine the model data in step 1 and the structural, stratigraphic, lithological data and spatial distribution range data of the area to be studied in the prospecting knowledge map in step 2, and use GIS software to analyze the regularity between the remote sensing, geophysical and geochemical anomalies and the spatial distribution range data of the mineral resources from the perspective of the structural framework and the metallogenic belt; among the geological, geophysical, geochemical and remote sensing data at a scale of 1:500,000, the geological data shall at least include a 1:500,000 geological map, the geophysical data shall at least include a 1:500,000 aeromagnetic map, and the remote sensing data shall at least include hyperspectral data, high-resolution satellite images and radar satellite image data; Step 4: extract geological, geophysical, geochemical and remote sensing anomalies in the 1:250,000 geological, geophysical, geochemical and remote sensing data within the spatial distribution range of mineral resources determined in step 3, combine the model data in step 1 and the spatial distribution range data of the ore deposit in the prospecting knowledge map in step 2, and use GIS software to analyze the law between the remote sensing, geophysical and geochemical anomalies and the spatial distribution range data of the ore deposit from the perspectives of mineral source, bottom splitting channel and mineralization environment; among the 1:250,000 geological, geophysical, geochemical and remote sensing data, the geological data at least include 1:250,000 DEM data and 1:250,000 geological map, the geophysical data at least include 1:200,000 regional gravity and 1:200,000 aeromagnetic, the geochemical data at least include 1:200,000 regional geochemical scanning data, and the remote sensing data at least include hyperspectral data, high-resolution satellite imagery and radar satellite imagery data; Step 5: Extract geological, geophysical, geochemical and remote sensing anomalies in the 1:50,000 geological, geophysical, geochemical and remote sensing data within the spatial distribution range of the ore deposit determined in step 4, combine the model data in step 1 and the ore deposit location, horizon and scale data in the ore prospecting knowledge map in step 2, and use GIS software to analyze the regularity between the geological, geophysical and geochemical anomalies and the ore deposit location, horizon and scale data from the perspective of metallogenic environment, ore guiding structure and ore matching structure; Step 6: extracting magnetotelluric, seismic, drilling, induced polarization, high-precision magnetic survey data and geochemical anomalies from the 1:25,000 geological, geophysical, geochemical and remote sensing data from the ore deposit location, layer and scale determined in step 5, combining the model data of step 1 and the ore body location and ore body morphology data of step 2, using GIS software to analyze the law between the magnetotelluric, seismic, drilling, induced polarization, high-precision magnetic survey data and geochemical anomalies and the ore body location and ore body morphology data from the perspective of deep channels, shallow ore-guiding faults, ore-matching faults and ore-bearing geological bodies, and constructing a three-dimensional attribute model to predict three-dimensional potential resource areas; Step 7: Extract the geomagnetic, drilling and induced polarization anomalies in the 1:50000 geological, geophysical, geochemical and remote sensing data from the ore body location, ore body morphology, ore body thickness and ore body grade data determined in step 6; combine the model data in step 1 and the ore body location, ore body morphology, ore body thickness and ore body grade data in step 2; use GIS software to analyze the law between the geomagnetic, drilling and induced polarization anomalies and the ore body location, ore body morphology, ore body thickness and ore body grade data from the perspective of shallow ore-guiding faults, ore-bearing faults and ore-bearing geological bodies; carry out mineralization system structure mining on the constructed three-dimensional attribute model to predict the prospective ore body areas.

2. The multi-scale progressive prospecting prediction method for solid minerals based on geological big data according to claim 1 is characterized by: The three-dimensional geological model is constructed in GIS software using the model data and the non-model data in step 1; the GIS software at least includes QuantyView software.

3. The multi-scale progressive prospecting prediction method for solid minerals based on geological big data according to claim 1 is characterized by: Use GIS software to produce the geological, geophysical, geochemical and remote sensing data of 1:500,000, 1:250,000, 1:50,000, 1:25,000 and 1:5,000 in step 1 respectively; the GIS software includes at least QuantyView software, and the formed geological map database is imported into the GIS database.

4. The multi-scale progressive prospecting prediction method for solid minerals based on geological big data according to claim 1 is characterized by: The GIS software described in step 3 to step 7 at least includes QuantyView software.

5. The multi-scale progressive prospecting prediction method for solid minerals based on geological big data according to claim 1 is characterized by: Among the 1:50,000 geological, geophysical, geochemical and remote sensing data, the geological data include at least 1:50,000 DEM data and 1:50,000 geological maps, the geophysical data include at least 1:50,000 gravity and 1:50,000 ground high-precision magnetic surveys, the geochemical data include at least 1:50,000 stream sediments, soil geochemical measurements and rock measurements, and the remote sensing data include at least hyperspectral data, high-resolution satellite images and radar satellite image data.

6. The multi-scale progressive prospecting prediction method for solid minerals based on geological big data according to claim 1 is characterized by: Among the 1:25,000 geological, geophysical, geochemical and remote sensing data, the geological data at least include drilling information, drilling column chart, and 1:10,000 exploration line profile; the geophysical data at least include magnetotelluric sounding and induced polarization sounding profile; the geochemical data at least include drilling sample analysis data; and the remote sensing data at least include hyperspectral data, high-resolution satellite images, and radar satellite image data.

7. The multi-scale progressive prospecting prediction method for solid minerals based on geological big data according to claim 1 is characterized by: Among the 1:50,000 geological, geophysical, geochemical and remote sensing data, the geological data at least include drilling information, drilling column chart, 1:10,000 DEM data, 1:50,000 geological map or 1:10,000 geological map, 1:2,000 exploration line profile, mid-section plane geological map, ore body resource reserve estimation map, the geophysical data at least include magnetotelluric sounding and induced polarization sounding profile, the geochemical data at least include drilling sample analysis data, and the remote sensing data at least include hyperspectral data, high-resolution satellite imagery, and radar satellite imagery data.

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