Mine prospecting method, device and equipment based on multi-source remote sensing technology and medium

Abstract

The invention discloses a prospecting method, device and equipment based on a multi-source remote sensing technology and a medium, and relates to the technical field of remote sensing geological survey, and the prospecting method based on the multi-source remote sensing technology comprises the steps: determining the distribution data of ore indicating factors of a research area through the multi-source remote sensing technology; the ore indicating factors comprise geological data, geophysical data and geochemical element content data of a research area extracted by a multi-source remote sensing technology; carrying out mineral content analysis on the known ore deposits in the research area, and determining the ore grades of the known ore deposits; training a pre-constructed prospecting model by taking the ore grade of the known ore deposit and the ore indicating factor of the known ore deposit as sample data to obtain a trained prospecting model; and inputting the distribution data of the ore-showing factors of the research area into the trained prospecting model so as to predict the mineral content of the research area through the prospecting model, and determining a prospecting potential area in the research area.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing geological survey, and in particular to a prospecting method, device, equipment and medium based on multi-source remote sensing technology. Background Art

[0002] Geological prospecting, geophysical prospecting, geochemical prospecting and remote sensing prospecting are the most mature prospecting and exploration techniques currently used. Among them, geological prospecting is the most basic technology in prospecting methods and is also the most widely used. It starts with studying the geological background of mineralization, and comprehensively applies knowledge of mineralogy, petrology, structural geology, ore deposits, etc. to study the geological conditions of mineralization and geological criteria for prospecting, so as to find minerals; geophysical prospecting is a method based on the changes in the geophysical field, using the principles of physics and instruments to obtain electromagnetic, gravity, density and other information of the ore body, and then determine the potential location and distribution law of mineral resources; geochemical prospecting is a method based on mineralogy and geochemical theory, which grasps the distribution law of geochemical elements and guides prospecting; remote sensing prospecting has developed rapidly in recent years. It is a method that uses remote sensing technology to obtain surface and underground information to predict and locate mineral resources.

[0003] Traditional geological, geophysical, and geochemical prospecting methods usually rely on intensive ground surveys and drilling methods, which are not only costly but also have a great impact on the environment. Conventional remote sensing prospecting methods mainly focus on the acquisition of geological information such as lithology, structure, and altered minerals, while remote sensing extraction of important geological, geophysical, and geochemical information such as geothermal anomalies, gravity and magnetic anomalies, and geochemical element content has not yet been included in the scope of comprehensive prospecting. Although the use of remote sensing data to extract ore-indicating factors such as geology (lithology, structure, altered minerals, geothermal), geophysics (gravity anomalies, magnetic anomalies), and geochemistry (geochemical element content) has become a reality, how to combine the geological, geophysical, and geochemical ore-indicating factors obtained by remote sensing to complete prospecting and exploration needs in-depth research. Summary of the invention

[0004] In view of the above-mentioned technical status, the present invention provides a prospecting method, device, equipment and medium based on multi-source remote sensing technology.

[0005] In order to achieve the above object, the present invention provides the following technical solutions: A prospecting method based on multi-source remote sensing technology, comprising: Using multi-source remote sensing technology to determine the distribution data of the ore-indicating factors in the study area; wherein the ore-indicating factors include: geological data, geophysical data and geochemical element content data of the study area extracted by multi-source remote sensing technology; Conducting mineral content analysis on known mineral points in the study area to determine the ore grade of the known mineral points; wherein the ore grade represents the mineral content of the known mineral points; Using the ore grade of the known mineral point and the ore-indicating factor of the known mineral point as sample data, training a pre-constructed mineral prospecting model to obtain a trained mineral prospecting model; The distribution data of the ore-indicating factors of the study area are input into the trained prospecting model, so as to predict the mineral content of the study area and determine the prospecting potential area in the study area by combining the prospecting model with the distribution data of the ore-indicating factors.

[0006] In an optional implementation manner of the present application, the performing of mineral content analysis on the known mineral points in the study area to determine the ore grade of the known mineral points includes: Performing mineral content analysis on a plurality of ore samples collected from the known mining sites to obtain the mineral content of each of the ore samples; The mineral content of each of the ore samples is averaged to determine the ore grade of the known ore point.

[0007] In an optional implementation of the present application, the geological data includes: altered mineral data, lithology data, structural data and geothermal data; The geological data are obtained by the following method: Obtaining ground spectrum data of altered minerals in the study area measured by a ground object spectrometer, and obtaining high-spectral satellite remote sensing data of the study area collected by a satellite remote sensing platform; obtaining altered mineral data in the study area based on the ground spectrum data of altered minerals and the high-spectral satellite remote sensing data; Acquire Landsat-8 multispectral remote sensing data, ASTER multispectral remote sensing data and Sentinel-1 radar remote sensing data of the study area collected by the satellite remote sensing platform; acquire lithology data of the study area according to the color, shape and texture characteristics of different lithologies in the visible light-shortwave infrared band of the Landsat-8 multispectral remote sensing data and the ASTER multispectral remote sensing data; Acquire structural data of the study area based on the color, shape and texture characteristics of different lithologies in the visible light-shortwave infrared band of the Landsat-8 multispectral remote sensing data and the ASTER multispectral remote sensing data, and the radar remote sensing characteristics of the Sentinel-1 radar remote sensing data; The surface temperature is inverted on the thermal infrared bands of the Landsat-8 multispectral remote sensing data and the ASTER multispectral remote sensing data through a single window algorithm to obtain the geothermal data of the study area.

[0008] In an optional implementation manner of the present application, the geophysical data includes: gravity anomaly data and magnetic anomaly data; The geophysical data are obtained by the following method: Obtaining distance change data between two satellites in the GRACE satellites; combining the geophysical model and the distance change data, analyzing the gravity field change of the study area, and determining the gravity anomaly data of the study area; The strength and direction of the earth's magnetic field measured by the Zhangheng-1 satellite are obtained; a magnetic anomaly analysis is performed on the strength and direction of the earth's magnetic field to determine the magnetic anomaly data of the study area.

[0009] In an optional embodiment of the present application, the geochemical element content is obtained by the following method: Obtaining the geochemical element contents of the sample points in the study area, and the drone hyperspectral remote sensing data of the study area collected by the drone remote sensing platform; Based on the UAV hyperspectral remote sensing data of the study area, determining the pixel values ​​of the sample points in different bands; Performing correlation analysis on the geochemical element content of the sample point and the pixel value of the sample point in different bands to determine the characteristic bands in the UAV hyperspectral remote sensing data; Based on the geochemical element content of the sample point and the pixel value of the sample point in the characteristic band, a pre-constructed geochemical element prediction model is trained to obtain a trained geochemical element prediction model; The UAV hyperspectral remote sensing data is input into the trained geochemical element prediction model, so as to predict the geochemical element content of the study area based on the UAV hyperspectral remote sensing data through the geochemical element prediction model to obtain the geochemical element content of the study area.

[0010] In an optional implementation of the present application, the sample data includes training sample data and test sample data; the training sample data is used to train the pre-constructed prospecting model, and the test sample data is used to verify the accuracy of the prospecting model.

[0011] In an optional implementation of the present application, it also includes: Determining the acceptance rate of each hidden layer of the prospecting model; Training the prospecting model using the sample data to determine a first training loss of the prospecting model; When the mth hidden layer of the prospecting model performs data enhancement on the sample data input into the prospecting model, determining the second training loss of the mth hidden layer; According to the second training loss and the first training loss, optimizing the reception rate of the mth hidden layer by using a gradient descent method; Based on the optimized acceptance rate of the mth hidden layer, optimizing the acceptance rates of other hidden layers other than the mth hidden layer of the prospecting model; Wherein, m is a positive integer, and m is less than or equal to the number of hidden layers of the prospecting model.

[0012] Compared with the prior art, the prospecting method based on multi-source remote sensing technology provided by the present invention uses multi-source remote sensing technology to extract the distribution data of different types of ore-indicating factors in the study area, and at the same time trains the pre-constructed prospecting model in combination with the ore-indicating factors and ore grades of known mineral points, so that the prospecting model can learn the relationship between the ore-indicating factors and the ore grades, and then predict the mineral content of the distribution data of the ore-indicating factors of the study area collected by multi-source remote sensing technology based on the prospecting model, and determine the prospecting potential area in the study area. This method realizes the combined prediction and positioning of mineral resources based on geological, geophysical, and geochemical ore-indicating factors, which is conducive to improving the accuracy of prediction and positioning of mineral resources.

[0013] The present invention also provides a prospecting device based on multi-source remote sensing technology, comprising: The ore-indicating factor acquisition unit is used to determine the distribution data of the ore-indicating factors of the study area by using multi-source remote sensing technology; wherein the ore-indicating factors include: geological data, geophysical data and geochemical element content data of the study area extracted by multi-source remote sensing technology; An ore grade determination unit, used to analyze the mineral content of the known mineral points in the study area to determine the ore grade of the known mineral points; wherein the ore grade represents the mineral content of the known mineral points; A prospecting model training unit is used to train a pre-built prospecting model using the ore grade of the known mineral point and the ore-indicating factor of the known mineral point as sample data to obtain a trained prospecting model; The prospecting model prediction unit is used to input the distribution data of the ore-indicating factors of the study area into the trained prospecting model, so as to predict the mineral content of the study area through the prospecting model combined with the distribution data of the ore-indicating factors, and determine the prospecting potential area in the study area.

[0014] Compared with the prior art, the beneficial effects of the prospecting device based on multi-source remote sensing technology provided by the present invention are the same as the beneficial effects of the prospecting method based on multi-source remote sensing technology described in the above technical solution, which will not be repeated here.

[0015] The present invention also provides an electronic device, comprising: processor; a memory for storing instructions executable by the processor; The processor is used to execute the above-mentioned prospecting method based on multi-source remote sensing technology by running the instructions in the memory.

[0016] Compared with the prior art, the beneficial effects of the electronic device provided by the present invention are the same as the beneficial effects of the prospecting method based on multi-source remote sensing technology described in the above technical solution, and will not be elaborated here.

[0017] The present invention also provides a computer storage medium, in which instructions are stored. When the instructions are executed, the above-mentioned prospecting method based on multi-source remote sensing technology is implemented.

[0018] Compared with the prior art, the beneficial effects of the computer storage medium provided by the present invention are the same as the beneficial effects of the prospecting method based on multi-source remote sensing technology described in the above technical solution, and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A flow chart of a prospecting method based on multi-source remote sensing technology provided in an embodiment of the present application.

[0020] Figure 2 Schematic diagram of extraction of mineral-indicating factors provided in an embodiment of the present application.

[0021] Figure 3 A flowchart of the application and training of a prospecting model provided in an embodiment of the present application.

[0022] Figure 4 This is a mineralization prediction potential map provided in the embodiments of the present application.

[0023] Figure 5 Structural diagram of a prospecting device based on multi-source remote sensing technology provided in an embodiment of the present application.

[0024] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and effects. For example, the first threshold and the second threshold are only used to distinguish different thresholds, and their order is not limited. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0026] It should be noted that, in the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0027] In the present invention, "at least one" means one or more, and "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, the combination of a and b, the combination of a and c, the combination of b and c, or the combination of a, b and c, where a, b, c can be single or multiple.

[0028] Geological prospecting, geophysical prospecting, geochemical prospecting and remote sensing prospecting are the most mature prospecting and exploration techniques currently used. Among them, geological prospecting is the most basic technology in prospecting methods and is also the most widely used. It starts with studying the geological background of mineralization, and comprehensively applies knowledge of mineralogy, petrology, structural geology, ore deposits, etc. to study the geological conditions of mineralization and geological criteria for prospecting, so as to find minerals; geophysical prospecting is a method based on the changes in the geophysical field, using the principles of physics and instruments to obtain electromagnetic, gravity, density and other information of the ore body, and then determine the potential location and distribution law of mineral resources; geochemical prospecting is a method based on mineralogy and geochemical theory, which grasps the distribution law of geochemical elements and guides prospecting; remote sensing prospecting has developed rapidly in recent years. It is a method that uses remote sensing technology to obtain surface and underground information to predict and locate mineral resources.

[0029] Traditional geological, geophysical, and geochemical prospecting methods usually rely on intensive ground surveys and drilling methods, which are not only costly but also have a great impact on the environment. Conventional remote sensing prospecting methods mainly focus on the acquisition of geological information such as lithology, structure, and altered minerals, while remote sensing extraction of important geological, geophysical, and geochemical information such as geothermal anomalies, gravity and magnetic anomalies, and geochemical element content has not yet been included in the scope of comprehensive prospecting. Although the use of remote sensing data to extract ore-indicating factors such as geology (lithology, structure, altered minerals, geothermal), geophysics (gravity anomalies, magnetic anomalies), and geochemistry (geochemical element content) has become a reality, how to combine the geological, geophysical, and geochemical ore-indicating factors obtained by remote sensing to complete prospecting and exploration needs in-depth research.

[0030] In view of the above-mentioned technical status, the embodiments of the present application provide a prospecting method, device, equipment and medium based on multi-source remote sensing technology, which are described in detail one by one in the following embodiments.

[0031] The present application embodiment first provides a prospecting method based on multi-source remote sensing technology, please refer to Figure 1 , Figure 1 A flow chart of a prospecting method based on multi-source remote sensing technology provided in an embodiment of the present application.

[0032] like Figure 1 As shown, the prospecting method based on multi-source remote sensing technology includes the following S101 to S104: S101, using multi-source remote sensing technology to determine the distribution data of ore-indicating factors in the study area; wherein the ore-indicating factors include: geological data, geophysical data and geochemical element content data of the study area extracted by multi-source remote sensing technology.

[0033] The multi-source remote sensing technology refers to a technology that comprehensively utilizes remote sensing data from different sensors and platforms to obtain remote sensing information of the same area. In the embodiment of the present application, the remote sensing data collected by the multi-source remote sensing technology includes: remote sensing data collected by satellite, remote sensing data collected by a ground object spectrometer, and remote sensing data collected by an unmanned aerial vehicle.

[0034] The ore-indicating factors can be understood as geological, geochemical, geophysical, etc. characteristics or signs that can indicate the existence of a ore deposit or a potential mineralized area. In the embodiment of the present application, the ore-indicating factors include: geological data, geophysical data, and chemical element content data.

[0035] Please refer to Figure 2 , Figure 2 Schematic diagram of extraction of mineral-indicating factors provided in an embodiment of the present application.

[0036] The type of geological data and the content of geochemical elements in the ore-indicating factors can be obtained based on the analysis of typical ore deposits in the study area and the collection and summary of geochemical remote sensing data of the mining area in the study area.

[0037] The geological data include: altered mineral data, lithology data, structural data and geothermal data.

[0038] Altered mineral data are used to represent the data of secondary minerals formed in the study area due to chemical changes in minerals in the original rocks caused by natural processes such as hydrothermal action and weathering. The rocks around the deposits are often altered to form specific alteration zones. For example, altered mineral data such as skarnization, sericitization and chloritization can indicate the presence of hydrothermal deposits.

[0039] Specifically, the altered mineral data can be obtained by the following method: Obtaining ground spectrum data of altered minerals in the study area measured by a ground object spectrometer, and obtaining high-spectral satellite remote sensing data of the study area collected by a satellite remote sensing platform; The altered mineral data of the study area are acquired according to the altered mineral ground spectrum data and the hyperspectral satellite remote sensing data.

[0040] like Figure 2 As shown, the ground feature spectrometer can adopt the CSD350 ground feature spectrometer to perform spectral measurement on the altered minerals in the study area, and then construct ground spectral data of the altered minerals in the study area, so as to construct a spectral data set that conforms to the actual environment of the study area for different types of altered minerals in the study area.

[0041] The hyperspectral satellite remote sensing data is measured by the GF-5 hyperspectral satellite or the Resource 1 02D satellite. After obtaining the hyperspectral satellite remote sensing data, the hyperspectral satellite remote sensing data is subjected to altered mineral analysis with reference to the ground spectrum data of the altered minerals, thereby obtaining the altered mineral data.

[0042] Lithological data mainly include rock types and mineral combinations in the study area. Different rock types are related to characteristic deposit types, for example, sulfide deposits are usually related to igneous rocks, and sedimentary deposits are related to sedimentary rocks. In addition, specific mineral combinations can also indicate the existence of deposits, for example, the combination of pyrite, galena and sphalerite often indicates sulfide deposits.

[0043] Structural data is used to represent the geological structure of the study area, such as faults, fissures, folds, etc. These geological structures are important channels for the migration and enrichment of ore fluids. The collection of structural data related to the geological structure of the study area is conducive to the exploration of ore fluids in the study area.

[0044] Geothermal data is used to indicate the presence of hydrothermal activity in the study area, and thus potential mineral deposits; many mineral deposits, especially metal deposits such as gold, silver, copper, lead, zinc, etc., are formed during hydrothermal activity. Hydrothermal activity refers to the process in which high-temperature fluids (hydrothermal fluids) deep underground rise along geological structures to the surface or near the surface, carrying and precipitating minerals. Hydrothermal activity can cause the temperature of the surface or shallow underground to rise, forming geothermal anomalies.

[0045] Specifically, the lithology data, structural data and geothermal data can be obtained by the following method: First, regarding the lithology data and structural data.

[0046] The lithology data can be obtained through Landsat-8 multispectral remote sensing data and ASTER multispectral remote sensing data of the study area collected by satellite remote sensing platforms.

[0047] Specifically, the lithology data of the study area are obtained according to the color, shape and texture characteristics of different lithologies in the visible light-shortwave infrared band of the Landsat-8 multispectral remote sensing data and the ASTER multispectral remote sensing data.

[0048] The structural data can be obtained through Landsat-8 multispectral remote sensing data, ASTER multispectral remote sensing data and Sentinel-1 radar remote sensing data of the study area collected by satellite remote sensing platforms.

[0049] Specifically, the structural data of the study area is obtained based on the color, shape and texture characteristics of different lithologies in the visible light-shortwave infrared bands of the Landsat-8 multispectral remote sensing data and the ASTER multispectral remote sensing data, and the radar remote sensing characteristics of the Sentinel-1 radar remote sensing data.

[0050] Furthermore, the geothermal data can be obtained by performing surface temperature inversion on the thermal infrared bands of the Landsat-8 multispectral remote sensing data and the ASTER multispectral remote sensing data through a single window algorithm to obtain the geothermal data of the study area.

[0051] Geophysical data include gravity anomaly data and magnetic anomaly data. The gravity anomaly data can indicate underground density changes, so as to identify mineral deposits or mineralized areas; the magnetic anomaly data can indicate the distribution of underground magnetic materials, so as to identify magnetic mineral deposits and geological structures.

[0052] like Figure 2As shown, the gravity anomaly data can be obtained based on GRACE satellite data. GRACE satellites include two satellites flying along the same orbit. The distance change between the two satellites can be accurately measured by a microwave rangefinder. The change in distance reflects the inhomogeneity of the earth's gravity field. Combining the distance change data with the geophysical model, the change in the earth's gravity field can be inferred. Based on this principle, the gravity anomaly data of the study area can be inferred by identifying the distance change information of the GRACE satellite.

[0053] The magnetic anomaly data can be obtained based on the strength and direction of the earth's magnetic field, which can be obtained through the Zhang Heng-1 satellite data collected by the Zhang Heng-1 satellite equipped with a high-precision magnetometer. After that, the magnetic anomaly data can be identified by correcting the strength and direction of the earth's magnetic field in combination with solar activity data and an atmospheric model.

[0054] Geochemical element content data refers to the element content data of geochemical elements such as lead and zinc in the study area. In practical applications, the content of geochemical elements can directly reflect the existence of mineral deposits. For example, the analysis of anomalies of trace elements such as selenium, tellurium, and rhenium can indicate the existence of copper deposits; the analysis of sulfur isotope ratios can indicate the hydrothermal source of mineral deposits.

[0055] Specifically, the geochemical element content can be obtained by the following method: The geochemical element contents of the sample points in the study area and the UAV hyperspectral remote sensing data of the study area collected by the UAV remote sensing platform are obtained.

[0056] Based on the UAV hyperspectral remote sensing data of the study area, the pixel values ​​of the sample points in different bands are determined.

[0057] A correlation analysis is performed on the geochemical element content of the sample point and the pixel value of the sample point in different bands to determine the characteristic bands in the UAV hyperspectral remote sensing data.

[0058] Based on the geochemical element content of the sample point and the pixel value of the sample point in the characteristic band, a pre-constructed geochemical element prediction model is trained to obtain a trained geochemical element prediction model.

[0059] The UAV hyperspectral remote sensing data is input into the trained geochemical element prediction model, so as to predict the geochemical element content of the study area based on the UAV hyperspectral remote sensing data through the geochemical element prediction model to obtain the geochemical element content of the study area.

[0060] In actual application, hyperspectral satellites usually provide a spatial resolution of tens to hundreds of meters, and the satellite's remote sensing observation data is easily affected by cloud cover. For drones, due to their low flight altitude, drones can capture tiny details on the ground and can provide higher spatial resolution, usually between a few centimeters and a few meters. Therefore, in order to predict the content of geochemical elements in the study area and improve the prediction accuracy of the content of chemical elements, the embodiment of the present application uses drones to collect and obtain drone hyperspectral remote sensing data, and in the drone hyperspectral remote sensing data collected by the drone, the pixel values ​​of the drone hyperspectral remote sensing data of the sample point in different bands are determined.

[0061] like Figure 2 As shown, the geochemical element contents of the sample points in the study area can be obtained based on the geochemical remote sensing data of the study area collected in advance.

[0062] Furthermore, each pixel of the UAV hyperspectral remote sensing data corresponds to a small area on the ground, and each pixel contains a series of numerical values, which are pixel values. These numerical values ​​represent the reflectivity or radiation intensity of the small area in different bands. In the embodiment of the present application, these numerical values ​​are related to the geochemical element content of the small area.

[0063] In practical applications, the relationship between the pixel value and the content of geochemical elements is mainly established through the spectral characteristics of the earth's surface material. Different chemical elements and compounds have different absorption and reflection characteristics in a specific wavelength range, and these absorption and reflection characteristics can be reflected by the pixel value.

[0064] Furthermore, the correlation analysis between the geochemical element content of the sample point and the pixel value of the sample point in different bands is performed, with the purpose of finding a characteristic band that can reflect the geochemical element content of the sample point from the pixel values ​​of the sample point in different bands. In the actual application process, statistical methods (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.) can be used for calculation.

[0065] The geochemical element prediction model can be understood as a machine learning model. Specifically, the geochemical element prediction model can be an AlexNet convolutional neural network model (The adaptive layer selection, AdaLASE), and this application does not impose any limitation on this.

[0066] S102, performing mineral content analysis on known mineral points in the study area to determine the ore grade of the known mineral points; wherein the ore grade represents the mineral content of the known mineral points.

[0067] Ore grade refers to the content of minerals or metals in ore, usually expressed as percentage (%) or grams per ton (g / t).

[0068] Specifically, the above S102 includes: Conduct mineral content analysis on multiple ore samples collected from the known mining site to obtain the mineral content of each ore sample; calculate the average value of the mineral content of each ore sample to determine the ore grade of the known mining site.

[0069] In actual application, the analysis of the ore sample can be achieved by wet chemical analysis, atomic absorption spectroscopy, magnetic testing, radioactive testing and other methods, and this application does not impose any restrictions on this.

[0070] In another optional implementation of the present application, when the data of the known mineral points in the study area are relatively complete, the mineral content analysis of the known mineral points in the study area can also be carried out by searching the data.

[0071] S103, using the ore grade of the known mineral point and the ore-indicating factor of the known mineral point as sample data, training a pre-constructed mineral prospecting model to obtain a trained mineral prospecting model.

[0072] Ore grade is used to directly reflect the actual content of useful minerals or metals in the ore, while the ore-indicating factor is used to reflect the characteristics of the ore point in different dimensions. Therefore, from one perspective, the ore-indicating factor can explain the formation reasons of useful minerals or metals in known ore points.

[0073] In view of this, the present application uses the ore grade of the known mining point and the ore-indicating factor of the known mining point as sample data to train the pre-constructed prospecting model so that the prospecting model learns to have the ability to predict the ore grade of the mining point based on the prospecting factor of the mining point.

[0074] The prospecting model can be understood as a machine learning model. Machine learning (a multi-disciplinary cross-discipline involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines) is specifically used to study how computers reorganize existing knowledge structures to continuously improve their own capabilities. Machine learning usually includes artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning and other technologies. Machine learning is a branch of artificial intelligence (AI) technology.

[0075] In the embodiment of the present application, the prospecting model is an AlexNet convolutional neural network model, which is a classic convolutional neural network (CNN) model. The AlexNet convolutional neural network model consists of five convolutional layers, two fully connected hidden layers and one fully connected output layer, and has strong recognition capabilities. At the same time, the model uses regularization technology to improve the accuracy of the model and reduce overfitting. And using ReLU instead of sigmoid as its activation function greatly speeds up convergence and reduces the time consumption of model training.

[0076] After obtaining the sample data through the above S101 to S103, the sample data can be input into a pre-constructed prospecting model so that the prospecting model can learn the relationship between the ore-indicating factors and the ore grade, so that the prospecting model has the ability to predict the ore grade based on the ore-indicating factors.

[0077] In an optional implementation of the present application, the sample data includes: training sample data and test sample data; the training sample data is used to train a pre-constructed prospecting model, and the test sample data is used to verify the accuracy of the prospecting model.

[0078] Specifically, 70% of the sample data may be used as the training sample data, and 30% of the sample data may be used as the testing sample data.

[0079] Please refer to Figure 3 , Figure 3 A flowchart of the application and training of a prospecting model provided in an embodiment of the present application.

[0080] In the process of optimizing the prospecting model through the training sample data, the prospecting model will iteratively process the training sample set for multiple times, and adjust the prospecting model parameters through an optimization algorithm (such as gradient descent) to minimize the loss.

[0081] Furthermore, the test sample data is mainly used to tune the model to avoid overfitting.

[0082] In neural network models, data enhancement is often an important part of model training, but currently data enhancement often stays at the data input layer of the model, without considering whether applying data enhancement to the hidden layer of the neural network will improve the performance of the model. At the same time, since there are often multiple hidden layers in a neural network, it is difficult to determine which hidden layer to perform data enhancement in to achieve the best effect.

[0083] In order to improve the data processing capability of the prospecting model and improve the data processing accuracy and speed of the prospecting model, the embodiment of the present application combines the adaptive layer selection method (AdaLASE) during the training of the prospecting model to optimize the prospecting model.

[0084] Specifically, the process of optimizing the prospecting model includes: Determining the acceptance rate of each hidden layer of the prospecting model; Training the prospecting model using the sample data to determine a first training loss of the prospecting model; When the mth hidden layer of the prospecting model performs data enhancement on the sample data input into the prospecting model, determining the second training loss of the mth hidden layer; According to the second training loss and the first training loss, optimizing the reception rate of the mth hidden layer by using a gradient descent method; Based on the acceptance rate of the optimized mth hidden layer, the acceptance rates of other hidden layers of the prospecting model other than the mth hidden layer are optimized; wherein m is a positive integer, and m is less than or equal to the number of hidden layers of the prospecting model.

[0085] The acceptance rate of the hidden layer is used to guide the model to dynamically select which hidden layers to perform data augmentation or other operations during training, thereby improving the generalization ability and performance of the model.

[0086] In actual application, the sum of the acceptance rates of each hidden layer is 1, and during the initial iterative optimization, the initial acceptance rates of each hidden layer are the same. For example, when the prospecting model includes 5 hidden layers, the initial acceptance rates of each hidden layer are 0.2.

[0087] The above optimization process of the prospecting model is an iterative process. At the beginning of the iteration, the receiving rate of the i-th hidden layer is the initial receiving rate , the training loss of each hidden layer can be expressed by the following formula (1): (1); in, represents the total loss of the hidden layer of the prospecting model; K represents the number of hidden layers of the prospecting model; Represents the training loss of the i-th hidden layer of the prospecting model.

[0088] In the process of actual application, when the mth hidden layer is selected as the most suitable hidden layer, after the training data input into the prospecting model is propagated forward to the mth hidden layer, the mth hidden layer performs data enhancement, and finally the enhanced data is propagated forward to the output layer to obtain the data result output by the output layer.

[0089] However, in order to obtain the most suitable hidden layer for data enhancement, it is necessary to update the acceptance rate of each layer in an iterative manner and determine the most suitable hidden layer based on the acceptance rate.

[0090] Specifically, the gradient descent method is used to optimize the reception rate of the mth hidden layer, which can be implemented by the following formula (2): (2); in, represents the acceptance rate of the optimized mth hidden layer; represents the acceptance rate of the mth hidden layer before optimization; represents the step length; represents the first training loss of the prospecting model; represents the second training loss of the mth hidden layer; .

[0091] S104, inputting the distribution data of the ore-indicating factors of the study area into the trained prospecting model, so as to predict the mineral content of the study area by combining the prospecting model with the distribution data of the ore-indicating factors, and determine the prospecting potential area in the study area.

[0092] Specifically, after obtaining the prospecting model, the distribution data of the ore-indicating factors of the study area can be input into the prospecting model, and then the ore grade of each mining point in the study area can be analyzed by combining the prospecting model with the ore-indicating factors of each mining point in the study area, so as to determine the prospecting potential area in the study area.

[0093] That is, after the prospecting model predicts the ore grade of each mining point, the prospecting potential area in the study area is defined based on the mineral content expressed by the ore grade of each mining point.

[0094] Please refer to Figure 4 , Figure 4 This is a mineralization prediction potential map provided in the embodiments of the present application.

[0095] like Figure 4 As shown, Figure 4 The predicted potential map shown includes the ore grade of each mining point in the study area. The mineral content expressed by the ore grade is different, and the corresponding color in the predicted potential map is also different.

[0096] In the process of actual application, after obtaining the mineralization prediction potential map, relevant staff can calibrate the potential of the study area according to the mineralization potential map, such as classifying the study area into high-potential mineralization areas, medium-potential mineralization areas and low-potential mineralization areas according to the potential map, so as to facilitate relevant staff to carry out exploration work.

[0097] In summary, the prospecting method based on multi-source remote sensing technology provided in the embodiment of the present application utilizes multi-source remote sensing technology to extract the distribution data of different types of ore-indicating factors in the study area, and at the same time, trains the pre-constructed prospecting model in combination with the ore-indicating factors and ore grades of known mineral points, so that the prospecting model can learn the relationship between the ore-indicating factors and the ore grades, and then predict the mineral content of the distribution data of the ore-indicating factors of the study area collected by multi-source remote sensing technology based on the prospecting model, and determine the prospecting potential area in the study area. This method realizes the combined prediction and positioning of mineral resources based on geological, geophysical, and geochemical ore-indicating factors, which is conducive to improving the accuracy of prediction and positioning of mineral resources.

[0098] The present application also provides a prospecting device based on multi-source remote sensing technology. Figure 5 , Figure 5 Structural diagram of a prospecting device based on multi-source remote sensing technology provided in an embodiment of the present application.

[0099] like Figure 5 As shown, the prospecting device based on multi-source remote sensing technology includes: The ore-indicating factor acquisition unit 501 is used to determine the distribution data of the ore-indicating factors of the study area by using multi-source remote sensing technology; wherein the ore-indicating factors include: geological data, geophysical data and geochemical element content data of the study area extracted by multi-source remote sensing technology; The ore grade determination unit 502 is used to analyze the mineral content of the known mineral points in the study area to determine the ore grade of the known mineral points; wherein the ore grade represents the mineral content of the known mineral points; The prospecting model training unit 503 is used to train the pre-built prospecting model by taking the ore grade of the known mineral point and the mineral-indicating factor of the known mineral point as sample data to obtain a trained prospecting model; The prospecting model prediction unit 504 is used to input the distribution data of the ore-indicating factors of the study area into the trained prospecting model, so as to predict the mineral content of the study area through the prospecting model combined with the distribution data of the ore-indicating factors, and determine the prospecting potential area in the study area.

[0100] In an optional implementation manner of the present application, the performing of mineral content analysis on the known mineral points in the study area to determine the ore grade of the known mineral points includes: Performing mineral content analysis on a plurality of ore samples collected from the known mining sites to obtain the mineral content of each of the ore samples; The mineral content of each of the ore samples is averaged to determine the ore grade of the known ore point.

[0101] In an optional implementation of the present application, the geological data includes: altered mineral data, lithology data, structural data and geothermal data; The geological data are obtained by the following method: Obtaining ground spectrum data of altered minerals in the study area measured by a ground object spectrometer, and obtaining high-spectral satellite remote sensing data of the study area collected by a satellite remote sensing platform; obtaining altered mineral data in the study area based on the ground spectrum data of altered minerals and the high-spectral satellite remote sensing data; Acquire Landsat-8 multispectral remote sensing data, ASTER multispectral remote sensing data and Sentinel-1 radar remote sensing data of the study area collected by the satellite remote sensing platform; acquire lithology data of the study area according to the color, shape and texture characteristics of different lithologies in the visible light-shortwave infrared band of the Landsat-8 multispectral remote sensing data and the ASTER multispectral remote sensing data; Acquire structural data of the study area based on the color, shape and texture characteristics of different lithologies in the visible light-shortwave infrared band of the Landsat-8 multispectral remote sensing data and the ASTER multispectral remote sensing data, and the radar remote sensing characteristics of the Sentinel-1 radar remote sensing data; The surface temperature is inverted on the thermal infrared bands of the Landsat-8 multispectral remote sensing data and the ASTER multispectral remote sensing data through a single window algorithm to obtain the geothermal data of the study area.

[0102] In an optional implementation manner of the present application, the geophysical data includes: gravity anomaly data and magnetic anomaly data; The geophysical data are obtained by the following method: Obtaining distance change data between two satellites in the GRACE satellites; combining the geophysical model and the distance change data, analyzing the gravity field change of the study area, and determining the gravity anomaly data of the study area; The strength and direction of the earth's magnetic field measured by the Zhangheng-1 satellite are obtained; a magnetic anomaly analysis is performed on the strength and direction of the earth's magnetic field to determine the magnetic anomaly data of the study area.

[0103] In an optional embodiment of the present application, the geochemical element content is obtained by the following method: Obtaining the geochemical element contents of the sample points in the study area, and the drone hyperspectral remote sensing data of the study area collected by the drone remote sensing platform; Based on the UAV hyperspectral remote sensing data of the study area, determining the pixel values ​​of the sample points in different bands; Performing correlation analysis on the geochemical element content of the sample point and the pixel value of the sample point in different bands to determine the characteristic bands in the UAV hyperspectral remote sensing data; Based on the geochemical element content of the sample point and the pixel value of the sample point in the characteristic band, a pre-constructed geochemical element prediction model is trained to obtain a trained geochemical element prediction model; The UAV hyperspectral remote sensing data is input into the trained geochemical element prediction model, so as to predict the geochemical element content of the study area based on the UAV hyperspectral remote sensing data through the geochemical element prediction model to obtain the geochemical element content of the study area.

[0104] In an optional implementation of the present application, the sample data includes training sample data and test sample data; the training sample data is used to train the pre-constructed prospecting model, and the test sample data is used to verify the accuracy of the prospecting model.

[0105] In an optional embodiment of the present application, the device is also used for: Determining the acceptance rate of each hidden layer of the prospecting model; Training the prospecting model using the sample data to determine a first training loss of the prospecting model; When the mth hidden layer of the prospecting model performs data enhancement on the sample data input into the prospecting model, determining the second training loss of the mth hidden layer; According to the second training loss and the first training loss, optimizing the reception rate of the mth hidden layer by using a gradient descent method; Based on the optimized acceptance rate of the mth hidden layer, optimizing the acceptance rates of other hidden layers other than the mth hidden layer of the prospecting model; Wherein, m is a positive integer, and m is less than or equal to the number of hidden layers of the prospecting model.

[0106] The above-mentioned device embodiment provided in this embodiment and the method embodiment of this application belong to the same application concept. For the technical details not fully described in this embodiment, please refer to the specific processing content of the prospecting method based on multi-source remote sensing technology provided in the above-mentioned embodiment of this application, which will not be repeated here.

[0107] The present application also provides an electronic device, such as Figure 6 As shown, Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0108] like Figure 6 As shown, the electronic device comprises: Processor 210; A memory 200 for storing instructions executable by the processor 210; The processor 210 is used to execute the prospecting method based on multi-source remote sensing technology disclosed in any of the above embodiments by running the instructions in the memory 200.

[0109] The processor 210, the memory 200, the communication interface 220, the input device 230 and the output device 240 are connected to each other via a bus. A bus may include a pathway that transfers information between components of a computer system.

[0110] The processor 210 may be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the scheme of the present invention. It may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0111] The processor 210 may include a main processor, and may also include a baseband chip, a modem, and the like.

[0112] The memory 200 stores a program for executing the technical solution of the present invention, and may also store an operating system and other key services. Specifically, the program may include a program code, and the program code includes a computer operation instruction. More specifically, the memory 200 may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk storage, a flash, and the like.

[0113] The input device 230 may include a device for receiving data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a touch screen, etc.

[0114] Output device 240 may include devices that allow information to be output to a user, such as a display screen, printer, speaker, etc.

[0115] The communication interface 220 may include any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc.

[0116] The processor 210 executes the program stored in the memory 200 and calls other devices, and can be used to implement each step of any one of the prospecting methods based on multi-source remote sensing technology provided in the above embodiments of the present application.

[0117] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the prospecting method based on multi-source remote sensing technology in various embodiments of the present application.

[0118] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0119] In addition, the embodiments of the present application may also be a storage medium on which a computer program is stored, and the computer program is executed by a processor to execute the steps of the prospecting method based on multi-source remote sensing technology in various embodiments of the present application.

[0120] For the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0121] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0122] The steps in the methods of each embodiment of the present application can be adjusted in order, combined and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0123] The modules and sub-modules in the devices and terminals in the various embodiments of the present application can be combined, divided and deleted according to actual needs.

[0124] In the several embodiments provided in the present application, it should be understood that the disclosed terminals, devices and methods can be implemented in other ways. For example, the terminal embodiments described above are only schematic, for example, the division of modules or submodules is only a logical function division, and there may be other division methods in actual implementation, for example, multiple submodules or modules can be combined or integrated into another module, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0125] The modules or submodules described as separate components may or may not be physically separated, and the components of the modules or submodules may or may not be physical modules or submodules, that is, they may be located in one place, or they may be distributed on multiple network modules or submodules. Some or all of the modules or submodules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0126] In addition, each functional module or submodule in each embodiment of the present application may be integrated into one processing module, or each module or submodule may exist physically separately, or two or more modules or submodules may be integrated into one module. The above-mentioned integrated modules or submodules may be implemented in the form of hardware or in the form of software functional modules or submodules.

[0127] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0128] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly by hardware, software units executed by a processor, or a combination of the two. The software units may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0129] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0130] 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.

Claims

1. A prospecting method based on multi-source remote sensing technology, characterized in that: include: Using multi-source remote sensing technology to determine the distribution data of the ore-indicating factors in the study area; wherein the ore-indicating factors include: geological data, geophysical data and geochemical element content data of the study area extracted by multi-source remote sensing technology; Conducting mineral content analysis on known mineral points in the study area to determine the ore grade of the known mineral points; wherein the ore grade represents the mineral content of the known mineral points; Using the ore grade of the known mineral point and the ore-indicating factor of the known mineral point as sample data, training a pre-constructed mineral prospecting model to obtain a trained mineral prospecting model; The distribution data of the ore-indicating factors of the study area are input into the trained prospecting model, so as to predict the mineral content of the study area and determine the prospecting potential area in the study area by combining the prospecting model with the distribution data of the ore-indicating factors.

2. The prospecting method based on multi-source remote sensing technology according to claim 1, characterized in that: The method of analyzing the mineral content of the known mineral points in the study area to determine the ore grade of the known mineral points includes: Performing mineral content analysis on a plurality of ore samples collected from the known mining sites to obtain the mineral content of each of the ore samples; The mineral content of each of the ore samples is averaged to determine the ore grade of the known ore point.

3. The prospecting method based on multi-source remote sensing technology according to claim 1, characterized in that: The geological data include: altered mineral data, lithology data, structural data and geothermal data; The geological data are obtained by the following method: Obtaining ground spectrum data of altered minerals in the study area measured by a ground object spectrometer, and obtaining high-spectral satellite remote sensing data of the study area collected by a satellite remote sensing platform; obtaining altered mineral data in the study area based on the ground spectrum data of altered minerals and the high-spectral satellite remote sensing data; Acquire Landsat-8 multispectral remote sensing data, ASTER multispectral remote sensing data and Sentinel-1 radar remote sensing data of the study area collected by the satellite remote sensing platform; acquire lithology data of the study area according to the color, shape and texture characteristics of different lithologies in the visible light-shortwave infrared band of the Landsat-8 multispectral remote sensing data and the ASTER multispectral remote sensing data; Acquire structural data of the study area based on the color, shape and texture characteristics of different lithologies in the visible light-shortwave infrared band of the Landsat-8 multispectral remote sensing data and the ASTER multispectral remote sensing data, and the radar remote sensing characteristics of the Sentinel-1 radar remote sensing data; The surface temperature is inverted on the thermal infrared bands of the Landsat-8 multispectral remote sensing data and the ASTER multispectral remote sensing data through a single window algorithm to obtain the geothermal data of the study area.

4. The prospecting method based on multi-source remote sensing technology according to claim 1, characterized in that: The geophysical data include: gravity anomaly data and magnetic anomaly data; The geophysical data are obtained by the following method: Obtaining distance change data between two satellites in the GRACE satellites; combining the geophysical model and the distance change data, analyzing the gravity field change of the study area, and determining the gravity anomaly data of the study area; The strength and direction of the earth's magnetic field measured by the Zhangheng-1 satellite are obtained; a magnetic anomaly analysis is performed on the strength and direction of the earth's magnetic field to determine the magnetic anomaly data of the study area.

5. The prospecting method based on multi-source remote sensing technology according to claim 1, characterized in that: The geochemical element contents are obtained by the following method: Obtaining the geochemical element contents of the sample points in the study area, and the drone hyperspectral remote sensing data of the study area collected by the drone remote sensing platform; Based on the UAV hyperspectral remote sensing data of the study area, determining the pixel values ​​of the sample points in different bands; Performing correlation analysis on the geochemical element content of the sample point and the pixel value of the sample point in different bands to determine the characteristic bands in the UAV hyperspectral remote sensing data; Based on the geochemical element content of the sample point and the pixel value of the sample point in the characteristic band, a pre-constructed geochemical element prediction model is trained to obtain a trained geochemical element prediction model; The UAV hyperspectral remote sensing data is input into the trained geochemical element prediction model, so as to predict the geochemical element content of the study area based on the UAV hyperspectral remote sensing data through the geochemical element prediction model to obtain the geochemical element content of the study area.

6. The prospecting method based on multi-source remote sensing technology according to claim 1, characterized in that: The sample data includes training sample data and test sample data; the training sample data is used to train the pre-built prospecting model, and the test sample data is used to verify the accuracy of the prospecting model.

7. The prospecting method based on multi-source remote sensing technology according to claim 1, characterized in that: Also includes: Determining the acceptance rate of each hidden layer of the prospecting model; Training the prospecting model using the sample data to determine a first training loss of the prospecting model; When the mth hidden layer of the prospecting model performs data enhancement on the sample data input into the prospecting model, determining the second training loss of the mth hidden layer; According to the second training loss and the first training loss, optimizing the reception rate of the mth hidden layer by using a gradient descent method; Based on the optimized acceptance rate of the mth hidden layer, optimizing the acceptance rates of other hidden layers other than the mth hidden layer of the prospecting model; Wherein, m is a positive integer, and m is less than or equal to the number of hidden layers of the prospecting model.

8. A prospecting device based on multi-source remote sensing technology, characterized in that: include: The ore-indicating factor acquisition unit is used to determine the distribution data of the ore-indicating factors of the study area by using multi-source remote sensing technology; wherein the ore-indicating factors include: geological data, geophysical data and geochemical element content data of the study area extracted by multi-source remote sensing technology; An ore grade determination unit, used to analyze the mineral content of the known mineral points in the study area to determine the ore grade of the known mineral points; wherein the ore grade represents the mineral content of the known mineral points; A prospecting model training unit is used to train a pre-built prospecting model using the ore grade of the known mineral point and the ore-indicating factor of the known mineral point as sample data to obtain a trained prospecting model; The prospecting model prediction unit is used to input the distribution data of the ore-indicating factors of the study area into the trained prospecting model, so as to predict the mineral content of the study area through the prospecting model combined with the distribution data of the ore-indicating factors, and determine the prospecting potential area in the study area.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is used to execute the prospecting method based on multi-source remote sensing technology as described in any one of claims 1 to 7 by running instructions in the memory.

10. A computer storage medium, characterized in that: The computer storage medium stores instructions, and when the instructions are executed, the prospecting method based on multi-source remote sensing technology described in any one of claims 1 to 7 is executed.

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