Mineral resource exploration method and device for wide-area electromagnetic measurement

Abstract

The invention provides a mineral resource exploration method and device for wide-area electromagnetic measurement, and relates to the technical field of geological exploration, and the method comprises the steps: obtaining geological survey data, carrying out the recognition of mineral types, and constructing a related mineral type database; carrying out centralized pre-judgment on physical properties and chemical properties of associated minerals; carrying out electromagnetic measurement scheme identification; laying measurement equipment to obtain L measurement data sets; performing abnormity verification to obtain M abnormal measuring points; collecting a surface sample, and analyzing the chemical characteristics of the sample; and verifying the chemical characteristic set of the M abnormal measuring points, and obtaining a mineral resource exploration result according to a verification result. According to the mineral resource exploration method and device, the technical problem that in the prior art, due to the fact that a traditional mineral resource exploration method depends on a single data source, the exploration result is not accurate enough can be solved, and the mineral resource exploration accuracy is improved by comprehensively analyzing the physical property and the chemical property of the mineral and combining electromagnetic measurement data.

Description

Technical Field

[0001] The present application relates to the field of geological exploration technology, and in particular to a method and device for mineral resource exploration using wide-area electromagnetic measurement. Background Art

[0002] With the growing demand for mineral resources, the development of mineral resource exploration technology has become increasingly important. In the field of mineral resource exploration, traditional exploration methods are usually limited to a single data source, such as relying solely on geological surveys or geophysical measurement techniques such as seismic exploration, gravity measurement, magnetic measurement, and electrical measurement, which have limitations in describing the distribution and properties of minerals, which may lead to inaccurate and inefficient exploration results. Wide-area electromagnetic survey is a very effective technology that measures the response of underground media to natural electromagnetic fields (such as thunderstorm activities in the atmosphere or electromagnetic waves caused by solar wind) to infer the distribution of underground structures and ore bodies, providing information about underground conductivity anomalies, thereby helping to locate potential ore bodies. However, traditional wide-area electromagnetic survey technology usually only considers the electromagnetic response itself, without fully considering the specific physical and chemical properties of minerals.

[0003] In summary, there is a technical problem in the prior art that the exploration results are not accurate enough because the traditional exploration methods are usually limited to a single data source, such as relying solely on geological surveys or electromagnetic measurement technology. Summary of the invention

[0004] The purpose of this application is to provide a method and device for mineral resource exploration by wide-area electromagnetic measurement, so as to solve the technical problem in the prior art that the exploration results are not accurate enough because the traditional exploration methods are usually limited to a single data source, such as relying only on geological surveys or electromagnetic measurement technology.

[0005] In view of the above problems, the present application provides a method and device for mineral resource exploration using wide-area electromagnetic measurement.

[0006] In a first aspect, the present application provides a method for mineral resource exploration by wide-area electromagnetic measurement, wherein the method for mineral resource exploration by wide-area electromagnetic measurement is implemented by a mineral resource exploration device by wide-area electromagnetic measurement, wherein the method for mineral resource exploration by wide-area electromagnetic measurement comprises: acquiring geological survey data of a target area, identifying mineral types in combination with the geological survey data, and constructing an associated mineral type database; traversing the associated mineral type database and the geological survey data to perform centralized prediction of the physical properties and chemical properties of associated minerals, and obtaining K sets of associated mineral centralized physical properties and K sets of associated mineral centralized chemical properties, where K is an integer greater than or equal to 1, wherein the K sets of associated mineral centralized physical properties correspond to the K sets of associated mineral centralized chemical properties in one-to-one correspondence; combining the K sets of associated minerals The method comprises the following steps: identifying an electromagnetic measurement scheme based on the K sets of physical properties of the associated minerals, the K sets of concentrated chemical properties of the associated minerals, and the geological survey data to obtain a target electromagnetic measurement scheme; deploying measurement equipment based on the target electromagnetic measurement scheme to obtain L sets of measurement data of L measuring points, where L is an integer greater than or equal to 1; performing anomaly verification based on the K sets of concentrated physical properties of the associated minerals and the L sets of measurement data to obtain M abnormal measuring points, where M≤L and M is a positive integer; collecting surface samples from the M abnormal measuring points, and performing chemical property analysis on the collected samples to obtain chemical property sets of the M abnormal measuring points; performing chemical property verification on the chemical property sets of the M abnormal measuring points using the K sets of concentrated chemical properties of the associated minerals, and obtaining mineral resource exploration results of the target area based on the verification results.

[0007] In a second aspect, the present application further provides a mineral resource exploration device for wide-area electromagnetic measurement, which is used to execute a mineral resource exploration method for wide-area electromagnetic measurement as described in the first aspect, wherein the mineral resource exploration device for wide-area electromagnetic measurement includes: a database construction module, the database construction module is used to obtain geological survey data of the target area, identify mineral types in combination with the geological survey data, and construct an associated mineral type database; a centralized prediction module, the centralized prediction module is used to traverse the associated mineral type database and the geological survey data to perform centralized prediction of the physical properties and chemical properties of associated minerals, and obtain K associated mineral centralized physical property sets and K associated mineral centralized chemical property sets, K is an integer greater than or equal to 1, wherein the K associated mineral centralized physical property sets and the K associated mineral centralized chemical property sets have a one-to-one correspondence; a scheme determination module, the scheme determination module is used to combine the K associated mineral centralized physical property sets, the K associated mineral centralized chemical property sets, and the associated mineral centralized physical property sets. an electromagnetic measurement scheme is identified based on the concentrated chemical property sets of associated minerals and the geological survey data to obtain a target electromagnetic measurement scheme; an equipment deployment module, the equipment deployment module is used to deploy measurement equipment based on the target electromagnetic measurement scheme to obtain L measurement data sets of L measurement points, where L is an integer greater than or equal to 1; an anomaly verification module, the anomaly verification module is used to perform anomaly verification based on the K concentrated physical property sets of associated minerals and the L measurement data sets to obtain M abnormal measurement points, where M≤L and M is a positive integer; a chemical analysis module, the chemical analysis module is used to collect surface samples from the M abnormal measurement points, and perform chemical property analysis on the collected samples to obtain chemical property sets of the M abnormal measurement points; a result verification module, the result verification module is used to perform chemical property verification on the chemical property sets of the M abnormal measurement points using the K concentrated chemical property sets of associated minerals, and obtain mineral resource exploration results of the target area based on the verification results.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By acquiring geological survey data of the target area, combining the geological survey data to identify mineral types, and constructing an associated mineral type database; traversing the associated mineral type database and the geological survey data to perform centralized prediction of the physical properties and chemical properties of associated minerals, and obtaining K associated mineral centralized physical property sets and K associated mineral centralized chemical property sets, K is an integer greater than or equal to 1, wherein the K associated mineral centralized physical property sets and the K associated mineral centralized chemical property sets have a one-to-one correspondence; combining the K associated mineral centralized physical property sets, the K associated mineral centralized chemical property sets and the geological survey data to perform electromagnetic measurement scheme identification, and obtain Obtain a target electromagnetic measurement plan; arrange measurement equipment based on the target electromagnetic measurement plan to obtain L measurement data sets of L measurement points, where L is an integer greater than or equal to 1; perform anomaly verification based on the K associated mineral concentrated physical property sets and the L measurement data sets to obtain M abnormal measurement points, where M≤L and M is a positive integer; collect surface samples from the M abnormal measurement points, and perform chemical property analysis on the collected samples to obtain chemical property sets of M abnormal measurement points; perform chemical property verification on the chemical property sets of the M abnormal measurement points using the K associated mineral concentrated chemical property sets, and obtain the mineral resource exploration results of the target area based on the verification results. In other words, by combining the centralized prediction of physical and chemical properties, identifying abnormal points in electromagnetic measurement data, and combining geological survey data, physical and chemical properties of minerals, and wide-area electromagnetic measurements, a multi-dimensional comprehensive method is provided to further confirm abnormal points and significantly improve the accuracy of exploration results.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 A schematic flow chart of a method for mineral resource exploration using wide-area electromagnetic surveying for this application;

[0013] Figure 2 This is a schematic structural diagram of a mineral resource exploration device for wide-area electromagnetic measurement in the present application.

[0014] Explanation of the accompanying drawings: database construction module 11, centralized prediction module 12, plan determination module 13, equipment layout module 14, abnormality verification module 15, chemical analysis module 16, result verification module 17. DETAILED DESCRIPTION

[0015] This application provides a method and device for mineral resource exploration using wide-area electromagnetic measurement, which solves the technical problem in the prior art that the exploration results are not accurate enough because the traditional exploration methods are usually limited to a single data source, such as relying solely on geological surveys or electromagnetic measurement technology. By combining the centralized prediction of physical and chemical properties and identifying anomalies in electromagnetic measurement data, a multi-dimensional comprehensive method is provided to further confirm anomalies and significantly improve the accuracy of exploration results by combining geological survey data, physical and chemical properties of minerals and wide-area electromagnetic measurement.

[0016] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0017] For example, please refer to the attached Figure 1 The present application provides a method for mineral resource exploration by wide-area electromagnetic measurement, wherein the method for mineral resource exploration by wide-area electromagnetic measurement is applied to a mineral resource exploration device by wide-area electromagnetic measurement, and the method for mineral resource exploration by wide-area electromagnetic measurement specifically comprises the following steps:

[0018] Step 1: Obtain geological survey data of the target area, identify mineral types based on the geological survey data, and build a database of associated mineral types.

[0019] Specifically, geological survey data refers to geological information of the target exploration area obtained through geological surveys, historical data or other geophysical means, such as topography, rock type, fault distribution, existing mineral deposit information, etc. Collect geological survey data in the target area, including regional geological maps, geological structure maps, mineral deposit distribution maps, etc.; obtain historical mineral exploration reports in the target area, analyze the types of mineral deposits, distribution of mineralized zones, mineral types and ore body scales; combine satellite remote sensing data, gravity data, magnetic data and other geophysical data to analyze surface and underground geological characteristics. Conduct field surveys of the target area, collect surface samples (such as rocks, soil, ores, etc.), and record geological characteristics, such as the distribution, thickness, inclination and lithology changes of rock layers.

[0020] Conduct laboratory analysis on the collected samples, such as microscopic observation, X-ray diffraction (XRD), scanning electron microscopy (SEM), etc., to identify the types of minerals in the samples. Use hyperspectral remote sensing technology to identify the spectral characteristics of surface minerals by analyzing satellite or drone data. Different minerals have unique reflectance spectra, which are used to identify the distribution of surface minerals, such as iron oxide minerals, carbonate minerals, etc. Analyze soil and water samples in the target area, detect the elemental composition of minerals through geochemical analysis, and identify geochemical anomaly areas.

[0021] Analyze regional geological structures, such as faults, folds, volcanic structures, and magmatic intrusions, which control the deposition and mineralization of minerals. Combine mineral distribution with geological structures to identify possible ore body distribution locations. Use gravity and magnetometry results to analyze underground density and magnetic anomalies, and infer the spatial distribution and morphology of underground ore bodies by comparing them with the physical properties of minerals. Construct database entries for each mineral and its associated minerals, including mineral name, chemical composition, physical properties, mineralization environment, common associated minerals, and typical deposit types. Enter the mineral types and distribution information identified by geological surveys, mineralogical analysis, geochemical analysis, etc. into the database to ensure that each mineral entry is associated with detailed information on its mineralization environment, physical and chemical properties. Construct data associations for each geological survey area to record the mineral types, ore body distribution, associated mineral characteristics, etc. found in the area. As new exploration data is obtained, the database is continuously updated and improved, especially to add new mineral types and their mineralization mechanisms. By acquiring and analyzing geological survey data in the target area, the types of minerals are identified and a comprehensive database of associated mineral types is constructed.

[0022] Step 2: Traverse the associated mineral type database and the geological survey data to perform concentrated prediction of the physical properties and chemical properties of associated minerals, and obtain K sets of associated mineral concentrated physical properties and K sets of associated mineral concentrated chemical properties, where K is an integer greater than or equal to 1, and the K sets of associated mineral concentrated physical properties and the K sets of associated mineral concentrated chemical properties correspond one to one.

[0023] Specifically, the mineral types related to the mineral deposits in the target area are extracted from the geological survey data and the mineral type database. The physical properties (such as density, magnetism, conductivity, acoustic velocity, etc.) and chemical properties (such as elemental composition, redox reactivity, solubility, etc.) of the target minerals are analyzed to obtain K sets of physical properties of associated minerals and K sets of chemical properties of associated minerals. K represents the number of mineral types, that is, the number of different mineral types identified by the system, which needs to be greater than or equal to 1. The physical and chemical properties of each mineral type are compared with the geological survey data to find similarities or consistency. For each associated mineral type, the central trend of its physical and chemical properties is recorded to ensure that the K sets of physical properties of associated minerals correspond to the K sets of chemical properties of associated minerals, that is, each set of physical properties corresponds to a set of chemical properties. Ensure that the K sets of physical properties of associated minerals correspond to the K sets of chemical properties of associated minerals, that is, each set of physical properties corresponds to a set of chemical properties. By combining geological survey data and the associated mineral type database, the mineral types and their physical and chemical properties that may exist in the target area can be more accurately predicted.

[0024] Step 3: Identify the electromagnetic survey scheme by combining the K sets of physical properties of the associated minerals, the K sets of chemical properties of the associated minerals and the geological survey data to obtain a target electromagnetic survey scheme.

[0025] Specifically, by combining a set of physical properties of K related minerals, a set of chemical properties of K related minerals, and geological survey data, more comprehensive information on mineral properties and detailed geological information of the target area can be obtained, and the most suitable electromagnetic measurement scheme for the target area can be identified, including measurement frequency, measurement layout points, and measurement equipment type. Electromagnetic measurement is a geophysical exploration technology that identifies mineral deposits by measuring the electromagnetic response of underground materials. The design of the measurement scheme needs to take into account the physical properties of minerals, such as conductivity, magnetism, etc., as well as chemical properties, such as elemental composition, oxidation state, etc.

[0026] Select the appropriate measurement frequency based on the physical and chemical properties of the minerals. For example, highly conductive metallic minerals may require a lower frequency wide-area electromagnetic measurement, while metallic minerals or minerals containing non-metallic components may require higher frequency measurements to enhance their abnormal signals. Determine the location of the measurement points based on the geological survey data and the distribution of the minerals. Based on the geological survey data, determine the potential mineralized areas and geological features, and place more measurement points in faults, folds, and high sulfide content areas, which are associated with mineralization. Select the appropriate measurement equipment based on the physical properties of the minerals and the requirements of the electromagnetic measurement plan. Consider the sensitivity, resolution, dynamic range, and ease of operation of the equipment, and select equipment with an appropriate frequency range and power output to ensure that data can be collected effectively. By designing an electromagnetic measurement plan suitable for the target area, the accuracy of the measurement can be improved, thereby more accurately identifying the abnormal areas of underground conductivity.

[0027] Step 4: Deploy measurement equipment based on the target electromagnetic measurement scheme to obtain L measurement data sets of L measurement points, where L is an integer greater than or equal to 1.

[0028] Specifically, according to the determined target electromagnetic measurement plan, the layout of the measuring points is planned. Considering factors such as geological structure, distribution of mineralized zones and geological characteristics, the location of the measuring points is determined to ensure that the measuring points cover the key geological features of the target area, and the measuring equipment is laid out. L measuring points are set up in the target area for data collection. Each measuring point will record the response of the electromagnetic signal, including electric and magnetic field data, calculate parameters such as apparent resistivity and impedance phase, and form a set of measurement data. For example, apparent resistivity reflects the conductivity of underground materials, and impedance phase may be related to the magnetism and electrical properties of minerals. The number of measuring points L represents the total number of measuring points laid out in the target area. L is an integer greater than or equal to 1, and the specific number depends on the scale of the target area, the geological complexity and the exploration needs. By laying out measuring equipment and setting up multiple measuring points, the underground material property data of the target area is comprehensively collected, which helps to effectively utilize geological survey data and mineral property information and improve data utilization efficiency.

[0029] Step 5: Perform anomaly verification based on the K associated mineral concentrated physical property sets and the L measurement data sets to obtain M abnormal measurement points, where M≤L and M is a positive integer.

[0030] Specifically, the physical property sets of K associated minerals are compared and analyzed with the L measurement data sets to identify abnormal areas in the measurement data and locate those measurement points that show significant anomalies in conductivity, apparent resistivity, etc. For example, in electromagnetic measurement data, if the apparent resistivity of a certain measurement point does not match the conductivity of the expected mineral, then the point is an abnormal measurement point. Based on the results of anomaly verification, M abnormal measurement points are identified. The M abnormal measurement points are part of the L measurement points, which means that not all measurement points will show anomalies. Through anomaly verification, abnormal areas related to associated mineral types are identified, and reducing invalid exploration helps optimize exploration strategies and improve exploration efficiency.

[0031] Step 6: Collect surface samples from the M abnormal measuring points, and perform chemical property analysis on the collected samples to obtain a set of chemical properties of the M abnormal measuring points.

[0032] Specifically, samples (such as rock, soil, water samples, etc.) are collected on the surface or underground of M abnormal measurement points, including excavation or drilling, to obtain samples of underground rocks or minerals. The collected samples are then subjected to chemical property analysis, including elemental analysis, mineral identification, chemical composition testing, etc., to determine the chemical composition and mineral type of the samples. Based on the results of the chemical analysis, a chemical property set is created for each abnormal measurement point, recording information such as its mineral composition and content, such as the element content, isotopic characteristics, chemical reactivity, etc. in the mineral. Using this chemical property information, it is possible to verify whether the abnormal measurement point does contain the target mineral, and further confirm the existence and scale of the mineral deposit. Through chemical property analysis, it is possible to more accurately verify whether the abnormal measurement point does contain the expected mineral.

[0033] Step 7: Use the K associated mineral concentrated chemical property sets to verify the chemical property sets of the M abnormal measurement points, and obtain the mineral resource exploration results of the target area according to the verification results.

[0034] Specifically, based on the chemical property set of the associated minerals and the expected mineral type, the standard for chemical property verification is determined. A threshold for chemical property matching is set to determine whether the chemical properties of the abnormal measuring point match the associated mineral type. The chemical property set of each abnormal measuring point is compared with the chemical property set of the associated mineral to determine whether they match. For example, if the chemical property set of an abnormal measuring point is highly similar to the chemical property set of the expected mineral, it indicates that the measuring point does contain the expected mineral. Based on the results of chemical property verification, the mineral resource exploration results in the target area are determined, including information such as the type of mineral, distribution range, and scale of existence. Chemical property verification provides clear information on whether the abnormal measuring point contains the target mineral, which helps to reduce exploration uncertainty, accurately identify and evaluate the mineral resources in the target area, and thus improve the success rate and efficiency of exploration.

[0035] Furthermore, step one of this application includes:

[0036] The geological survey data is retrieved according to a preset survey indicator set to obtain retrieved geological structure data, retrieved mineralized belt distribution data and retrieved geological characteristics, wherein the preset survey indicator set includes geological structure indicators, mineralized belt distribution indicators and geological characteristic indicators; mineral data mining is performed using the retrieved geological structure data, retrieved mineralized belt distribution data and retrieved geological characteristics as indexes to obtain a mineral mining database; associated mineral types are identified based on the mineral mining database to obtain the associated mineral type database.

[0037] Specifically, a preset survey indicator set is established, including geological structure indicators, mineralized belt distribution indicators and geological characteristic indicators. Among them, geological structure indicators include geological structure information such as faults, folds, magmatic intrusions, and stratigraphic contact zones; mineralized belt distribution indicators are used to describe the distribution of mineralized belts, indicating the mineralized areas that may contain mineral deposits in the region, including the distribution, scale, and mineralization type of known mineralized belts; geological characteristic indicators include geological characteristics such as rock type, mineral composition, and geochemical anomalies. Retrieve geological survey data in the target area. Geological survey data refers to geological information of the target exploration area obtained through geological surveys, historical data or other geophysical means, such as topography, rock type, fault distribution, and existing mineral deposit information. Use geological structure indicators to retrieve geological survey data, extract data related to regional faults, folds, magmatic activities and other geological structures, and identify potential mineralization structures. According to the mineralized belt distribution indicators, the distribution information of the mineralized belt is screened out from the geological survey data, including the geographical location, mineralization type and scale of the mineralized belt in the region, and identify areas related to mineral resources. Using geological characteristic indicators, specific geological features can be identified from the data, including regional lithology, stratigraphic distribution, sedimentary environment and other information.

[0038] Using the retrieval of geological structure data, the retrieval of mineralized belt distribution data and the retrieval of geological characteristics as indexes, the mineral data is mined in depth to determine the types of minerals that exist and their distribution in the region. Specifically, the distribution of minerals related to structures such as faults, folds and intrusions is analyzed, combined with known mineralization laws (such as the possibility of hydrothermal deposits near faults); typical mineral types and associated minerals in mineralized belts are identified to infer possible mineral resources in mineralized belts; based on characteristics such as lithology and sedimentary environment, the types of minerals that may be contained in the area are inferred (such as lead-zinc ores in carbonate rocks, gold ores in siliceous rocks, etc.). Through mineral data mining, a mineral mining database is obtained, which contains mineral-related information extracted from geological survey data.

[0039] Analyze the types of minerals and their distribution in the mineral mining database, combine the data of geological structure, mineralized belt and geological characteristics, identify the mineral types associated with the target area, and build a database of associated mineral types. Analyze the types of minerals and their distribution in the mineral mining database, combine the data of geological structure, mineralized belt and geological characteristics, identify the mineral types associated with the target area, and build a database of associated mineral types. According to the information in the mineral mining database, identify the mineral types associated with different geological backgrounds (such as specific geological structures, distribution of mineralized belts, lithology, etc.) to form a classification of mineral types. Through statistical analysis or machine learning methods, analyze the correlation between various types of minerals and regional geological characteristics, structures, etc., and screen out the most likely mineral-forming mineral types. Through the retrieval, data mining and analysis of geological survey data, a database of associated mineral types is constructed to accurately identify potential mineral types and their distribution.

[0040] Furthermore, the present application also includes the following steps:

[0041] The mineral mining database is integrated to construct a three-dimensional recognition space, wherein the x-axis of the three-dimensional recognition space is geological structure data, the y-axis is mineralized belt distribution data, and the z-axis is geological features; a plane passing through the y-axis and the z-axis and perpendicular to the x-axis is used as a first plane, a plane passing through the y-axis and the x-axis and perpendicular to the z-axis is used as a second plane, and a plane passing through the x-axis and the z-axis and perpendicular to the y-axis is used as a third plane; a plane passing through the retrieved geological structure data and parallel to the first plane is used as a fourth plane; a plane passing through the retrieved mineralized belt distribution data and parallel to the third plane is used as a fifth plane; a plane passing through the retrieved geological features and parallel to the second plane is used as a sixth plane; based on the internal space surrounded by the first plane, the second plane, the third plane, the fourth plane, the fifth plane and the sixth plane as a type recognition subspace, wherein the type recognition subspace includes multiple recognition particles, each recognition particle corresponds to a mineral mining data; associated mineral type recognition is performed in the type recognition subspace to obtain the associated mineral type database.

[0042] Specifically, the information in the mineral mining database is integrated into a three-dimensional recognition space, in which each axis represents a different dimension of geological information. The three-dimensional recognition space is a multi-dimensional space used to represent geological information. In this space, each dimension represents a different aspect of geological information, such as geological structure, distribution of mineralized zones, and geological characteristics. The x-axis represents geological structure information, such as faults, folds, magmatic intrusions, etc., the y-axis represents the information of mineralized zones, such as metal mineralized zones and non-metallic mineralized zones, and the z-axis represents geological characteristic information, such as rock type, lithology, etc.

[0043] The three-dimensional space is subdivided into smaller subspaces using multiple planes. The first plane is perpendicular to the x-axis, defined by the y-axis and the z-axis, and is used to show the relationship between the mineralized zone distribution data and the geological characteristics; the second plane is perpendicular to the z-axis, defined by the y-axis and the x-axis, and is used to show the relationship between the geological structure data and the mineralized zone distribution data; the third plane is perpendicular to the y-axis, defined by the x-axis and the z-axis, and is used to show the relationship between the geological structure data and the geological characteristics; the fourth plane is parallel to the first plane, and is used to represent the distribution of mineralized zones and the combination of geological characteristics perpendicular to the x-axis, with special attention paid to the geological structure data, which helps to identify the interaction between the mineralized zones and the geological structures; the fifth plane is parallel to the third plane, and is used to represent the distribution of mineralized zones and geological characteristics, with special attention paid to the mineralized zone data, which helps to identify the interaction between the mineralized zones and the geological characteristics; the sixth plane is parallel to the second plane, and is used to represent the combination of geological structures and geological characteristics, with special attention paid to the geological feature data, which helps to identify the interaction between geological structures and geological characteristics.

[0044] The inner space surrounded by the first plane, the second plane, the third plane, the fourth plane, the fifth plane and the sixth plane is taken as the type identification subspace, which is a multidimensional space surrounded by six planes. Each plane represents a specific dimension of geological information and is used to finely analyze and identify different types of minerals. The type identification subspace includes multiple identification particles, each of which corresponds to a mineral mining data point. The identification particle is a point in the type identification subspace, representing a specific mineral mining data. Each particle contains geological information related to a specific mineral, such as the physical properties, chemical composition, and mineralization environment of the mineral.

[0045] In the type identification subspace, the central identification particle is determined, and the iterative identification particle is obtained by taking the central identification particle as the starting point and the preset identification bandwidth as the radius. The identification density of the iterative identification particle and the central identification particle is determined, and the identification particle with a larger density is taken as the iteration starting point, and the iterative identification particle is iterated according to the preset identification bandwidth. The identification particle corresponding to the maximum identification density in the iteration is taken as the target identification particle, and all identification particles within the preset identification bandwidth range from the target identification particle are obtained. According to the physical properties, chemical properties and subspace attributes of each identification particle, it is analyzed whether it meets the characteristics of the associated mineral type. By analyzing each identification particle, the possible mineral type is identified, and the mineral type is classified by statistical or machine learning methods, and finally the associated mineral type database is constructed. By integrating the three important geological dimension data of geological structure, mineralized belt distribution and geological characteristics, the mineral type is comprehensively identified and classified. Through the division of multiple planes, multiple detailed type identification subspaces are formed, which makes the identification process more refined and improves the accuracy of mineral type identification. By constructing a three-dimensional identification space and a type identification subspace, mineral resources are identified based on multi-dimensional data such as geological structure, mineralized belt distribution and geological characteristics. The identified particles are analyzed, the associated mineral types are identified and a mineral type database is constructed, which improves the efficiency and accuracy of mineral resource exploration.

[0046] Furthermore, the present application also includes the following steps:

[0047] Determine the central identification particle of the type identification subspace, wherein the central identification particle is located at the center of the type identification subspace; take the central identification particle as the identification starting point, iterate in the type identification subspace according to the preset identification bandwidth, and obtain an iterative identification particle, wherein the direction from the central identification particle to the iterative identification particle is the first identification direction; determine whether the identification density of the iterative identification particle is greater than or equal to the identification density of the central identification particle, if so, update the iterative identification particle to the iteration starting point, and iterate in the type identification subspace according to the preset identification bandwidth; if not, add the first identification direction to the disabled list, and continue to iterate based on the central identification particle until the preset number of iterations is met, and take the identification particle corresponding to the maximum value of the identification density in the iteration as the target identification particle, wherein the disabled list includes the number of disabled times in the iterative process; obtain the target identification neighborhood of the target identification particle, wherein the distance from the identification particle in the target identification neighborhood to the target identification particle is the preset identification bandwidth; add the mineral types of multiple mineral mining data in the target identification neighborhood into the associated mineral type database.

[0048] Furthermore, the present application also includes the following steps:

[0049] When the identification density of the iterative identification particle is less than the identification density of the central identification particle, the opposite direction of the first identification direction is used as the second identification direction; the central identification particle is used as the identification starting point, and the second identification direction is used as the iterative direction. Iteration is performed in the type identification subspace according to the preset identification bandwidth until the preset number of iterations is met, and the identification particle corresponding to the maximum value of the identification density in the iteration is used as the target identification particle, and the associated mineral type database is constructed based on the target identification particle.

[0050] Specifically, in the type identification subspace, a central identification particle is determined, which is located at the center of the type identification subspace. The central identification particle is used as the identification starting point, and it is iteratively moved according to the preset identification bandwidth to obtain a new particle. The preset identification bandwidth is the distance between two identification particles in a single iteration that is preset in advance. The first identification direction is the direction from the central identification particle to the iterative identification particle. In this direction, the iterative identification particle represents the geological information within a specific distance range around the central identification particle.

[0051] Determine whether the identification density of the iterative identification particle is greater than or equal to the identification density of the central identification particle. The identification density is evaluated by calculating the ratio of the number of identification particles in the area constructed with the iterative identification particle as the center and the preset identification bandwidth as the radius to the regional density. This ratio reflects the density of particles around the iterative center particle. The higher the density, the more representative the mineral distribution of this particle is. If the identification density of the iterative identification particle is greater than or equal to the identification density of the central identification particle, the iterative identification particle is updated to the new iteration starting point, and iterates in the type identification subspace according to the preset identification bandwidth.

[0052] If the recognition density of the iterative recognition particle is less than that of the central recognition particle, the current recognition direction is added to the disabled list, and the iteration is continued based on the central recognition particle. The opposite direction of the first recognition direction is used as the second recognition direction to explore the space area opposite to the original direction. Use the central recognition particle as the iteration starting point, and the second recognition direction as the iteration direction, and iterate in the type recognition subspace according to the preset recognition bandwidth. Starting from the central recognition particle, move the preset recognition bandwidth along the second recognition direction to generate a new iterative recognition particle. The disabled list is a list that records the number of disabled times during the iteration process, which is used to record those directions that have been iteratively recognized and are no longer used due to low recognition density. Even if the first recognition direction is disabled, the iterative recognition process continues, but it will avoid the first recognition direction and try other directions. The disabled list includes the number of disabled times during the iteration process, that is, the number of times a direction is disabled during the iteration process. When a direction is disabled, its disabled number increases.

[0053] The iterative process continues until the preset number of iterations is met. During the iteration process, the identified particle corresponding to the maximum value of the identification density is recorded and used as the target identification particle. The preset number of iterations is a limit on the number of iterative identifications that is pre-set according to the geological complexity and exploration requirements of the target area, which determines how many times the iterative identification process will be performed. The number of iterations is closely related to the recognition effect. An appropriate number of iterations can improve the accuracy of recognition, while too high a number of iterations may lead to inefficiency and waste of computing resources. If the number of iterations is set too low, the geological information may not be fully covered, resulting in inaccurate recognition; if it is set too high, it may lead to a waste of computing resources.

[0054] Get the target recognition neighborhood of the target recognition particle, including all the recognition particles within the preset recognition bandwidth of the target recognition particle. The target recognition particle is the recognition particle corresponding to the maximum recognition density in the iterative recognition process, representing the area with the highest recognition density in the current iteration. The target recognition neighborhood is an area with the target recognition particle as the center and the preset recognition bandwidth as the radius. All recognition particles in this area have the same recognition bandwidth distance with the target recognition particle. Identify the mineral types of multiple mineral mining data in the target recognition neighborhood, add these mineral types to the associated mineral type database, and update the mineral type information in the database.

[0055] In a specific example, assuming that the coordinates of the central identification particle are (5, 3, 7), the central identification particle is used as the starting point, and iteration is performed in the type identification subspace according to the preset identification bandwidth (bandwidth is 1). Starting from the central identification particle, the preset identification bandwidth is moved along the first identification direction to obtain the iterative identification particle P2 (assuming the coordinates are x=6, y=3, z=7). The identification density of the iterative identification particle P2 is calculated to be 10, and the identification density of the central identification particle is 8. Since the identification density of P2 is greater than that of the central identification particle, P2 is updated as the new iteration starting point. Continue to iterate until the preset number of iterations (100 times) is met, and record the identification particle P5 (assuming the coordinates are x=6, y=4, z=8) corresponding to the maximum identification density 12 during the iteration process as the target identification particle. With the target identification particle P5 as the center and the preset identification bandwidth as the radius, the target identification neighborhood is obtained. Through iteration and spatial analysis technology, the most likely mineral type is identified from a large amount of geological data. Improve the accuracy and efficiency of mineral resource exploration by identifying density and iterative processes to focus on the most promising areas.

[0056] Further, step three of this application includes:

[0057] The electromagnetic measurement scheme includes measurement frequency, measurement layout points and measurement equipment type.

[0058] Furthermore, the present application also includes the following steps:

[0059] Acquire multiple sample electromagnetic measurement schemes, and construct sample data based on actual physical properties, actual chemical properties and actual geological survey data of the measurement areas corresponding to the multiple sample electromagnetic measurement schemes to obtain a sample data set; use the sample data set to supervise the framework constructed based on the feedforward neural network until the training converges to obtain a trained measurement scheme identifier; use the measurement scheme identifier to identify the K sets of physical properties of the associated minerals, the K sets of chemical properties of the associated minerals and the geological survey data to obtain the target electromagnetic measurement scheme.

[0060] Specifically, the electromagnetic measurement scheme includes measurement frequency, measurement layout points and measurement equipment type. Among them, the measurement frequency refers to the frequency used by the electromagnetic measurement equipment during the measurement process. Different frequencies can penetrate underground media at different depths, so choosing the right frequency is crucial for detecting ore bodies at a specific depth. For example, when detecting deep ore bodies, lower frequencies are used; while when detecting surface ore bodies, higher frequencies are used. The measurement layout points refer to the locations of the measurement points set in the target area, and their selection needs to consider the geological characteristics, the distribution of mineralized belts and the propagation characteristics of the electromagnetic field. For example, in areas where the distribution of mineralized belts is known, it is necessary to densely arrange measurement points near the mineralized belts, while in areas with complex geological structures, they need to be more evenly arranged. The measurement equipment type refers to the instruments and equipment used for electromagnetic measurement. Different equipment has different detection depths, sensitivities and resolutions, so choosing the right equipment is crucial for obtaining high-quality measurement data. For example, for the detection of deep ore bodies, equipment with high sensitivity and deep penetration capabilities, such as time domain electromagnetic method (TEM) or frequency domain electromagnetic method (FDEM) equipment, is used.

[0061] Multiple sample electromagnetic measurement schemes are obtained to test and compare the effects of different schemes. According to the multiple sample electromagnetic measurement schemes obtained, actual measurements are carried out in the target area, and measurement data are collected. The measurement data corresponding to each sample electromagnetic measurement scheme is combined with the actual physical properties, actual chemical properties and actual geological survey data to construct a sample data set. A framework is constructed based on a feedforward neural network, including an input layer, a hidden layer and an output layer. Feedforward neural network is a commonly used deep learning model for processing and identifying complex patterns. Among them, the input layer is the entrance of the neural network, which receives raw data from the outside, including feature vectors of electromagnetic measurement data, geological characteristics, mineralized belt distribution and other information. The hidden layer is located between the input layer and the output layer and is used to process and transform the input data. A typical feedforward neural network may contain several hidden layers, each of which contains multiple neurons for extracting complex features of the data. The output layer is the exit of the neural network, which produces the final prediction or classification result, and contains one or more neurons for predicting the mineral type or category of the electromagnetic measurement scheme in the target area.

[0062] The neural network framework is supervised and trained using a sample data set. The labeled data set is used to train the neural network so that it can recognize and predict unseen data. The network parameters, such as weights and biases, are adjusted to make the network's predictions as consistent as possible with the actual measurement scheme. The training process continues until the network performance converges, that is, the prediction error reaches a preset threshold. Training to convergence means that the performance of the neural network has stabilized and further training will not significantly improve its performance. After supervised training, the neural network becomes a trained measurement scheme identifier that is used to predict the electromagnetic measurement scheme corresponding to given physical properties, chemical properties, and geological survey data.

[0063] The previously trained measurement scheme identifier is used to analyze the physical property set, chemical property set and geological survey data of the minerals. The identifier analyzes the input data, identifies the electromagnetic measurement scheme that best suits the target area, and outputs the target electromagnetic measurement scheme, including the best measurement frequency, measurement point layout, and measurement equipment type. By utilizing the powerful pattern recognition capabilities of neural networks, it is possible to learn and identify the best electromagnetic measurement scheme from complex geological and mineral characteristics, improve exploration efficiency and accuracy, reduce unnecessary measurement work, and more effectively identify and locate mineral resources.

[0064] In summary, the mineral resource exploration method of wide-area electromagnetic measurement provided by this application has the following technical effects:

[0065] By acquiring geological survey data of the target area, combining the geological survey data to identify mineral types, and constructing an associated mineral type database; traversing the associated mineral type database and the geological survey data to perform centralized prediction of the physical properties and chemical properties of associated minerals, and obtaining K associated mineral centralized physical property sets and K associated mineral centralized chemical property sets, K is an integer greater than or equal to 1, wherein the K associated mineral centralized physical property sets and the K associated mineral centralized chemical property sets have a one-to-one correspondence; combining the K associated mineral centralized physical property sets, the K associated mineral centralized chemical property sets and the geological survey data to perform electromagnetic measurement scheme identification, and obtain Obtain a target electromagnetic measurement plan; arrange measurement equipment based on the target electromagnetic measurement plan to obtain L measurement data sets of L measurement points, where L is an integer greater than or equal to 1; perform anomaly verification based on the K associated mineral concentrated physical property sets and the L measurement data sets to obtain M abnormal measurement points, where M≤L and M is a positive integer; collect surface samples from the M abnormal measurement points, and perform chemical property analysis on the collected samples to obtain chemical property sets of M abnormal measurement points; perform chemical property verification on the chemical property sets of the M abnormal measurement points using the K associated mineral concentrated chemical property sets, and obtain the mineral resource exploration results of the target area based on the verification results. In other words, by combining the centralized prediction of physical and chemical properties, identifying abnormal points in electromagnetic measurement data, and combining geological survey data, physical and chemical properties of minerals, and wide-area electromagnetic measurements, a multi-dimensional comprehensive method is provided to further confirm abnormal points and significantly improve the accuracy of exploration results.

[0066] Embodiment 2: Based on the same inventive concept as the method for mineral resource exploration by wide-area electromagnetic measurement in the aforementioned embodiment 1, the present application also provides a mineral resource exploration device by wide-area electromagnetic measurement, see the attached Figure 2 , the mineral resource exploration device for wide-area electromagnetic measurement comprises:

[0067] The database construction module 11 is used to obtain geological survey data of the target area, identify mineral types in combination with the geological survey data, and construct a database of associated mineral types.

[0068] The centralized prediction module 12 is used to traverse the associated mineral type database and the geological survey data to perform centralized prediction of the physical properties and chemical properties of the associated minerals, and obtain K sets of centralized physical properties of associated minerals and K sets of centralized chemical properties of associated minerals, where K is an integer greater than or equal to 1, and the K sets of centralized physical properties of associated minerals and the K sets of centralized chemical properties of associated minerals correspond one to one.

[0069] The scheme determination module 13 is used to identify the electromagnetic measurement scheme by combining the K sets of physical properties of the associated minerals, the K sets of chemical properties of the associated minerals and the geological survey data to obtain a target electromagnetic measurement scheme.

[0070] The equipment deployment module 14 is used to deploy measurement equipment based on the target electromagnetic measurement scheme to obtain L measurement data sets of L measurement points, where L is an integer greater than or equal to 1.

[0071] The abnormality verification module 15 is used to perform abnormality verification according to the physical property sets of the K associated minerals and the L measurement data sets to obtain M abnormal measurement points, where M≤L and M is a positive integer.

[0072] The chemical analysis module 16 is used to collect surface samples from the M abnormal measuring points and perform chemical property analysis on the collected samples to obtain a set of chemical properties of the M abnormal measuring points.

[0073] The result verification module 17 is used to verify the chemical properties of the M abnormal measurement point chemical property sets using the K associated mineral concentrated chemical property sets, and obtain the mineral resource exploration results of the target area according to the verification results.

[0074] Furthermore, the database construction module 11 in the wide-area electromagnetic measurement mineral resource exploration device is also used for:

[0075] The geological survey data is retrieved according to a preset survey indicator set to obtain retrieved geological structure data, retrieved mineralized belt distribution data and retrieved geological characteristics, wherein the preset survey indicator set includes geological structure indicators, mineralized belt distribution indicators and geological characteristic indicators; mineral data mining is performed using the retrieved geological structure data, retrieved mineralized belt distribution data and retrieved geological characteristics as indexes to obtain a mineral mining database; associated mineral types are identified based on the mineral mining database to obtain the associated mineral type database.

[0076] Furthermore, the database construction module 11 in the wide-area electromagnetic measurement mineral resource exploration device is also used for:

[0077] The mineral mining database is integrated to construct a three-dimensional recognition space, wherein the x-axis of the three-dimensional recognition space is geological structure data, the y-axis is mineralized belt distribution data, and the z-axis is geological features; a plane passing through the y-axis and the z-axis and perpendicular to the x-axis is used as a first plane, a plane passing through the y-axis and the x-axis and perpendicular to the z-axis is used as a second plane, and a plane passing through the x-axis and the z-axis and perpendicular to the y-axis is used as a third plane; a plane passing through the retrieved geological structure data and parallel to the first plane is used as a fourth plane; a plane passing through the retrieved mineralized belt distribution data and parallel to the third plane is used as a fifth plane; a plane passing through the retrieved geological features and parallel to the second plane is used as a sixth plane; based on the internal space surrounded by the first plane, the second plane, the third plane, the fourth plane, the fifth plane and the sixth plane as a type recognition subspace, wherein the type recognition subspace includes multiple recognition particles, each recognition particle corresponds to a mineral mining data; associated mineral type recognition is performed in the type recognition subspace to obtain the associated mineral type database.

[0078] Furthermore, the database construction module 11 in the wide-area electromagnetic measurement mineral resource exploration device is also used for:

[0079] Determine the central identification particle of the type identification subspace, wherein the central identification particle is located at the center of the type identification subspace; take the central identification particle as the identification starting point, iterate in the type identification subspace according to the preset identification bandwidth, and obtain an iterative identification particle, wherein the direction from the central identification particle to the iterative identification particle is the first identification direction; determine whether the identification density of the iterative identification particle is greater than or equal to the identification density of the central identification particle, if so, update the iterative identification particle to the iteration starting point, and iterate in the type identification subspace according to the preset identification bandwidth; if not, add the first identification direction to the disabled list, and continue to iterate based on the central identification particle until the preset number of iterations is met, and take the identification particle corresponding to the maximum value of the identification density in the iteration as the target identification particle, wherein the disabled list includes the number of disabled times in the iterative process; obtain the target identification neighborhood of the target identification particle, wherein the distance from the identification particle in the target identification neighborhood to the target identification particle is the preset identification bandwidth; add the mineral types of multiple mineral mining data in the target identification neighborhood into the associated mineral type database.

[0080] Furthermore, the database construction module 11 in the wide-area electromagnetic measurement mineral resource exploration device is also used for:

[0081] When the identification density of the iterative identification particle is less than the identification density of the central identification particle, the opposite direction of the first identification direction is used as the second identification direction; the central identification particle is used as the identification starting point, and the second identification direction is used as the iterative direction. Iteration is performed in the type identification subspace according to the preset identification bandwidth until the preset number of iterations is met, and the identification particle corresponding to the maximum value of the identification density in the iteration is used as the target identification particle, and the associated mineral type database is constructed based on the target identification particle.

[0082] Furthermore, the scheme determination module 13 in the wide-area electromagnetic measurement mineral resource exploration device is also used for:

[0083] The electromagnetic measurement scheme includes measurement frequency, measurement layout points and measurement equipment type.

[0084] Furthermore, the scheme determination module 13 in the wide-area electromagnetic measurement mineral resource exploration device is also used for:

[0085] Acquire multiple sample electromagnetic measurement schemes, and construct sample data based on actual physical properties, actual chemical properties and actual geological survey data of the measurement areas corresponding to the multiple sample electromagnetic measurement schemes to obtain a sample data set; use the sample data set to supervise the framework constructed based on the feedforward neural network until the training converges to obtain a trained measurement scheme identifier; use the measurement scheme identifier to identify the K sets of physical properties of the associated minerals, the K sets of chemical properties of the associated minerals and the geological survey data to obtain the target electromagnetic measurement scheme.

[0086] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The wide-area electromagnetic measurement method and specific examples of mineral resource exploration in the first embodiment are also applicable to the wide-area electromagnetic measurement device for mineral resource exploration in the present embodiment. Through the above detailed description of the wide-area electromagnetic measurement method for mineral resource exploration, those skilled in the art can clearly know the wide-area electromagnetic measurement device for mineral resource exploration in the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method description.

[0087] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0088] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A method for mineral resource exploration by wide-area electromagnetic measurement, characterized in that: include: Acquire geological survey data of the target area, identify mineral types based on the geological survey data, and build a database of associated mineral types; Traversing the associated mineral type database and the geological survey data to perform concentrated prejudgment of the physical properties and chemical properties of associated minerals, and obtaining K associated mineral concentrated physical property sets and K associated mineral concentrated chemical property sets, where K is an integer greater than or equal to 1, wherein the K associated mineral concentrated physical property sets and the K associated mineral concentrated chemical property sets correspond one to one; Combining the K sets of physical properties of the associated minerals, the K sets of chemical properties of the associated minerals, and the geological survey data, an electromagnetic survey scheme is identified to obtain a target electromagnetic survey scheme; Deploy measurement equipment based on the target electromagnetic measurement scheme to obtain L measurement data sets of L measurement points, where L is an integer greater than or equal to 1; Anomaly verification is performed based on the K associated mineral concentrated physical property sets and the L measurement data sets to obtain M abnormal measurement points, where M≤L, and M is a positive integer; Collecting surface samples from the M abnormal measuring points, and performing chemical property analysis on the collected samples to obtain a set of chemical properties of the M abnormal measuring points; The chemical property sets of the M abnormal measuring points are chemically verified using the K associated mineral concentrated chemical property sets, and the mineral resource exploration results of the target area are obtained according to the verification results.

2. A method for mineral resource exploration by wide-area electromagnetic measurement as claimed in claim 1, characterized in that: The electromagnetic measurement scheme includes measurement frequency, measurement layout points and measurement equipment type.

3. The method for mineral resource exploration by wide-area electromagnetic measurement according to claim 1, characterized in that: Obtain geological survey data of the target area, identify mineral types based on the geological survey data, and build a database of associated mineral types, including: Retrieving the geological survey data according to a preset survey index set to obtain retrieved geological structure data, retrieved mineralized zone distribution data and retrieved geological characteristics, wherein the preset survey index set includes geological structure indicators, mineralized zone distribution indicators and geological characteristic indicators; Mining mineral data using the retrieved geological structure data, the retrieved mineralized zone distribution data and the retrieved geological features as indexes to obtain a mineral mining database; The associated mineral type database is obtained by identifying the associated mineral type based on the mineral mining database.

4. A method for mineral resource exploration by wide-area electromagnetic measurement as claimed in claim 3, characterized in that: include: Integrate the mineral mining database to construct a three-dimensional identification space, wherein the x-axis of the three-dimensional identification space is geological structure data, the y-axis is mineralized zone distribution data, and the z-axis is geological features; A plane passing through the y-axis and the z-axis and perpendicular to the x-axis is taken as a first plane, a plane passing through the y-axis and the x-axis and perpendicular to the z-axis is taken as a second plane, and a plane passing through the x-axis and the z-axis and perpendicular to the y-axis is taken as a third plane; taking a plane passing through the retrieved geological structure data and parallel to the first plane as a fourth plane; A plane passing through the retrieved mineralized zone distribution data and parallel to the third plane is used as a fifth plane; taking a plane passing through the retrieved geological features and parallel to the second plane as a sixth plane; Based on taking the inner space enclosed by the first plane, the second plane, the third plane, the fourth plane, the fifth plane and the sixth plane as a type identification subspace, wherein the type identification subspace includes a plurality of identification particles, each identification particle corresponds to a mineral mining data; The associated mineral type is identified in the type identification subspace to obtain the associated mineral type database.

5. The method for mineral resource exploration by wide-area electromagnetic measurement according to claim 4, characterized in that: Performing associated mineral type identification in the type identification subspace to obtain the associated mineral type database includes: Determining a central identification particle of the type identification subspace, wherein the central identification particle is located at the center of the type identification subspace; Taking the central identification particle as the identification starting point, iterating in the type identification subspace according to a preset identification bandwidth to obtain an iterative identification particle, wherein the direction from the central identification particle to the iterative identification particle is taken as a first identification direction; Determine whether the identification density of the iterative identification particle is greater than or equal to the identification density of the central identification particle. If so, update the iterative identification particle to the iteration starting point, and iterate in the type identification subspace according to the preset identification bandwidth; If not, the first identification direction is added to the forbidden list, and iterative identification is continued based on the central identification particle until a preset number of iterations is met, and the identification particle corresponding to the maximum value of the identification density in the iteration is used as the target identification particle, wherein the forbidden list includes the number of forbidden times in the iterative process; Acquire a target recognition neighborhood of the target recognition particle, wherein the distance from the recognition particle in the target recognition neighborhood to the target recognition particle is the preset recognition bandwidth; The mineral types of the plurality of mineral mining data in the target identification neighborhood are added into the associated mineral type database.

6. A method for mineral resource exploration by wide-area electromagnetic measurement as claimed in claim 5, characterized in that: include: When the recognition density of the iterative recognition particle is less than the recognition density of the central recognition particle, the opposite direction of the first recognition direction is used as the second recognition direction; Taking the central identification particle as the identification starting point and the second identification direction as the iteration direction, iteration is performed in the type identification subspace according to the preset identification bandwidth until the preset number of iterations is met, and the identification particle corresponding to the maximum value of the identification density in the iteration is taken as the target identification particle, and the associated mineral type database is constructed based on the target identification particle.

7. The method for mineral resource exploration by wide-area electromagnetic measurement according to claim 1, characterized in that: include: Acquire multiple sample electromagnetic measurement schemes, and construct sample data based on actual physical properties, actual chemical properties, and actual geological survey data of the measurement areas corresponding to the multiple sample electromagnetic measurement schemes to obtain a sample data set; Using the sample data set to supervise the training of the framework based on the feedforward neural network until the training reaches convergence, a trained measurement scheme identifier is obtained; The measurement scheme identifier is used to identify the K sets of physical properties of the associated minerals, the K sets of chemical properties of the associated minerals, and the geological survey data to obtain the target electromagnetic measurement scheme.

8. A mineral resource exploration device for wide-area electromagnetic measurement, characterized in that: Steps for implementing a method for mineral resource exploration by wide-area electromagnetic measurement as described in any one of claims 1 to 7, wherein the mineral resource exploration device by wide-area electromagnetic measurement comprises: A database construction module, the database construction module is used to obtain geological survey data of the target area, identify mineral types in combination with the geological survey data, and construct a database of associated mineral types; A centralized prediction module, the centralized prediction module is used to traverse the associated mineral type database and the geological survey data to perform centralized prediction of the physical properties and chemical properties of the associated minerals, and obtain K sets of centralized physical properties of the associated minerals and K sets of centralized chemical properties of the associated minerals, K is an integer greater than or equal to 1, wherein the K sets of centralized physical properties of the associated minerals and the K sets of centralized chemical properties of the associated minerals correspond one to one; A scheme determination module, the scheme determination module is used to identify an electromagnetic measurement scheme by combining the K associated mineral concentrated physical property sets, the K associated mineral concentrated chemical property sets and the geological survey data to obtain a target electromagnetic measurement scheme; An equipment deployment module, the equipment deployment module is used to deploy measurement equipment based on the target electromagnetic measurement scheme to obtain L measurement data sets of L measurement points, where L is an integer greater than or equal to 1; An abnormality verification module, the abnormality verification module is used to perform abnormality verification according to the K associated mineral concentrated physical property sets and the L measurement data sets to obtain M abnormal measurement points, where M≤L, and M is a positive integer; A chemical analysis module, wherein the chemical analysis module is used to collect surface samples from the M abnormal measuring points, and to perform chemical property analysis on the collected samples to obtain a set of chemical properties of the M abnormal measuring points; The result verification module is used to verify the chemical properties of the M abnormal measurement point chemical property sets using the K associated mineral concentrated chemical property sets, and obtain the mineral resource exploration results of the target area according to the verification results.

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