A method and system for predicting mineral resource distribution based on machine learning

Abstract

The present invention discloses a method and system for predicting mineral resource distribution based on machine learning, comprising the following steps: using data obtained from a preset mineral resource database as data to be analyzed; extracting exploration features of the data to be analyzed, performing feature comparison analysis on the exploration features and the reference database to obtain a matching degree, outputting the exploration features with a matching degree higher than a matching degree threshold as first data, and outputting the exploration features with a matching degree lower than a matching degree threshold as fuzzy data; calculating the fuzzy data and the reference data in the matching results to perform uncertainty assessment, and outputting the fuzzy data with a dissimilarity lower than a dissimilarity threshold as second data; adding the second data to the first data to optimize the matching degree; constructing a mineral resource distribution prediction model based on the first data, the matching degree and the environmental data, adding the second data to the reference database to obtain an updated reference database, and outputting a target prediction model.

Description

Technical Field

[0001] The present invention relates to the field of prediction, and in particular to a method and system for predicting mineral resource distribution based on machine learning. Background Art

[0002] Mineral resource distribution prediction is a core link in geological exploration and resource development. Traditional methods mainly rely on expert experience or static statistical models, and have the following limitations: existing methods usually directly integrate multi-source heterogeneous data, but do not distinguish the certainty level of the data. The differentiated application of reference databases and pending databases is insufficient, resulting in low-quality data interfering with modeling accuracy; traditional deterministic databases are updated laggingly and cannot promptly incorporate new observation data generated during the exploration process, limiting the model's adaptability to dynamic geological environments; low-matching data are simply discarded without quantifying their differences from the reference data, resulting in a waste of potentially effective information; mineral distribution is affected by the combined influence of geological structure and environmental factors, but existing models mostly focus on single geological features and lack collaborative modeling of multi-dimensional environmental data.

[0003] In recent years, machine learning has demonstrated advantages in feature fusion and uncertainty modeling. However, its application in mineral resource prediction still faces the following challenges: how to construct a dynamically updated reference database that balances the contributions of historical, high-confidence data with newly added, low-confidence data; how to design feature comparison and dissimilarity quantification rules to improve the reuse of fuzzy data; and how to couple environmental data with geological characteristics to construct temporally and spatially sensitive distribution probability models. Therefore, a prediction method that can dynamically integrate multi-source data, hierarchically screen effective features, and quantify uncertainty is urgently needed. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for predicting mineral resource distribution based on machine learning.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0006] The present invention comprises the following steps:

[0007] The data obtained from a preset mineral resource database within a specified time period is used as the data to be analyzed; wherein the mineral resource database includes a reference database and a pending database; the reference database is a database with a higher level of certainty than the pending database; the data includes multiple data types, and different data types are used to characterize mineral distribution from different types; the data to be analyzed includes observation data and environmental data; the observation data is real-time exploration data of a preset area; the observation data includes geological data, geophysical data, remote sensing data, mineral exploration data, climate data, vegetation data, and hydrological data;

[0008] Extracting exploration features of the data to be analyzed, performing feature comparison analysis on the exploration features and the reference database to obtain a matching degree, and outputting the exploration features having a matching degree higher than a matching degree threshold as first data, otherwise outputting the exploration features as fuzzy data;

[0009] Calculating the dissimilarity between the fuzzy data and the reference data in the matching result, performing uncertainty assessment based on the dissimilarity, outputting the fuzzy data with a dissimilarity lower than a dissimilarity threshold as second data; and adding the second data to the first data to optimize the matching degree;

[0010] A distribution probability prediction is performed based on the first data, the matching degree and the environmental data, a mineral resource distribution prediction model is constructed based on the distribution probability, the second data is added to the reference database to obtain an updated reference database, and a target prediction model is output.

[0011] Furthermore, the data types include pictures, videos, and text.

[0012] Furthermore, the dissimilarity includes:

[0013] Extract the feature vector of the reference data and calculate the dissimilarity between the exploration feature in the fuzzy data and the reference data in the matching result:

[0014]

[0015]

[0016] The a-th exploration feature vector of the k-th dimension at the key quantile p is The cth reference data feature vector of the kth dimension at the key quantile p is The dissimilarity between the fuzzy data and the reference data is The number of dimensions is N k , the weight of the kth dimension is The normalization factor of the kth dimension is ρ, the bandwidth parameter is U, and the spatial distance between the kth dimension environmental factor and the target area is d k , the key quantile is p.

[0017] Furthermore, the method further includes adjusting the matching threshold and the dissimilarity threshold:

[0018] According to the matching degree between the fuzzy data and the reference data, a graph attention network A is constructed and the confidence evaluation of the matching degree is performed. The expression is:

[0019]

[0020] The matching degree between the ath exploration feature vector and the cth reference data feature vector is The confidence level of the match is The modulation coefficient of the matching degree is β, and the number of exploration feature vectors is N u , the attention weights of the a-th exploration feature vector and the c-th reference data feature vector are

[0021] The exploration feature vector and the reference data feature vector are used as nodes, and a graph attention network B is constructed based on the dissimilarity. The confidence evaluation of the dissimilarity is performed, and the expression is:

[0022]

[0023] The dissimilarity between the ath fuzzy data and the cth reference data is The confidence level of dissimilarity is The amplitude modulation coefficient of dissimilarity is The feature distribution of the a-th node is E a , the feature distribution of the cth node is E c , the maximum divergence is HX max , characteristic distribution E a and characteristic distribution E c The divergence is HX(E a ‖E c );

[0024] The matching threshold and dissimilarity threshold are dynamically adjusted according to the confidence level. The expression is:

[0025]

[0026] The dynamically adjusted i-th target threshold is D i , the initial value of the i-th target threshold is The feature distribution density corresponding to the target threshold is V, and the average confidence value of the i-th target threshold is The adaptive adjustment function of feature distribution density V and confidence average is:

[0027] Furthermore, the method further includes updating the distribution probability:

[0028] According to the exploration characteristics of the first data, a deep reinforcement neural network learning is built and a probability update strategy is given. The expression is:

[0029]

[0030] The j-th probability update strategy is T j, the index variable is j, the discount factor is ζ; update the distribution probability according to the probability update strategy.

[0031] Furthermore, the method for obtaining the distribution probability includes:

[0032] Taking environmental data as the background, the degree of influence of environmental data on the distribution of mines is obtained through a time-varying impact algorithm. The first data is dynamically adjusted according to the degree of influence, and the matching degree is recalculated using the adjusted first data to obtain reference data with a matching degree higher than the matching threshold. The regional matching degree of the reference data is calculated, and the area with the highest regional matching degree is used as the regional prediction result. If the matching degree error is lower than 0.12, multimodal fusion is performed on the region to obtain the regional prediction result. The spliced ​​regional prediction results are output as distribution probabilities.

[0033] Furthermore, a method for constructing a mineral resource distribution prediction model based on the distribution probability and environmental data includes:

[0034] The weighted sum of the variance and loss function of the distribution probability prediction value and the actual distribution probability value is used as the objective function of the mineral resource distribution prediction model;

[0035] Mineral resource distribution prediction models include random forest algorithms, support vector machine algorithms, and deep learning algorithms;

[0036] The random forest algorithm divides the input data into training data and test data in a ratio of 7:2;

[0037] The support vector machine algorithm separates the training data of different categories by finding an optimal hyperplane, and iterates continuously until the interval between the two categories is maximized to obtain classified data;

[0038] Deep learning algorithms automatically learn and extract features and patterns related to the presence of mineral resources by training classified data, and use the objective function as an optimization guide to predict the distribution of mineral resources in unknown areas;

[0039] The test data is input into the mineral resource distribution prediction model to obtain the predicted mineral resource distribution. According to the mean square error between the actual mineral resource distribution and the predicted mineral resource distribution, the learning rate is continuously adjusted until the mean square error is minimized.

[0040] Second, a mineral resource distribution prediction system based on machine learning, comprising:

[0041] Data acquisition module: used to use data acquired from a preset mineral resource database within a specified time period as data to be analyzed; wherein the mineral resource database includes a reference database and a pending database; the reference database is a database with a higher level of certainty than the pending database; the data includes multiple data types, and different data types are used to characterize mineral distribution from different types; the data to be analyzed includes observation data and environmental data; the observation data is real-time exploration data of a preset area; the observation data includes geological data, geophysical data, remote sensing data, mineral exploration data, climate data, vegetation data, and hydrological data;

[0042] Matching degree comparison and analysis module: used for extracting exploration features of the data to be analyzed, performing feature comparison analysis on the exploration features and the reference database to obtain a matching degree, and outputting the exploration features with a matching degree higher than a matching degree threshold as first data, otherwise outputting them as fuzzy data;

[0043] Uncertainty evaluation and optimization module: used to calculate the dissimilarity between the fuzzy data and the reference data in the matching result, perform uncertainty evaluation based on the dissimilarity, output the fuzzy data with a dissimilarity lower than a dissimilarity threshold as second data; and add the second data to the first data to optimize the matching degree;

[0044] Model construction module: used to predict the distribution probability based on the first data, the matching degree and the environmental data, build a mineral resource distribution prediction model based on the distribution probability, add the second data to the reference database to obtain an updated reference database, and output a target prediction model.

[0045] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0046] A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0047] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0048] The beneficial effects of the present invention are:

[0049] The present invention is a method and system for predicting mineral resource distribution based on machine learning. Compared with the existing technology, the present invention has the following technical effects:

[0050] The present invention can improve the accuracy of mineral resource distribution prediction based on machine learning through preprocessing, obtaining first data and fuzzy data, obtaining dissimilarity, obtaining second data, updating distribution prediction and model construction steps, thereby improving the precision of mineral resource distribution prediction based on machine learning, optimizing mineral resource distribution prediction based on machine learning, greatly saving resources, improving work efficiency, realizing intelligent prediction and identification of mineral resource distribution, performing data correction and real-time updating of mineral resource distribution prediction in real time, which is of great significance to mineral resource distribution prediction, can adapt to mineral resource distribution prediction of different standards and different mineral resource distribution prediction requirements, and has a certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flowchart of the steps of a method for predicting mineral resource distribution based on machine learning of the present invention;

[0052] Figure 2 This is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0053] The present invention will be further described below through specific examples. The illustrative examples and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0054] The present invention provides a method and system for predicting mineral resource distribution based on machine learning, comprising the following steps:

[0055] like Figure 1 As shown, in this embodiment, the following steps are included:

[0056] The data obtained from a preset mineral resource database within a specified time period is used as the data to be analyzed; wherein the mineral resource database includes a reference database and a pending database; the reference database is a database with a higher level of certainty than the pending database; the data to be analyzed includes multiple data types, and different data types are used to characterize mineral distribution from different types; the data to be analyzed includes observation data and environmental data; the observation data is real-time exploration data of a preset area; the observation data includes geological data, geophysical data, remote sensing data, mineral exploration data, climate data, vegetation data, and hydrological data;

[0057] In the actual assessment, the XXX copper mine was selected as the research object. The reference database contained a large amount of long-term verified, high-reliability data, including detailed stratigraphic distribution and fault trend data for the target area over the past 30 years. The copper mine reserves were 5.36 million tons, with a grade of 2%. The pending database included preliminary geological survey data for newly discovered suspected mineralized areas, and preliminary detection of weak metal anomaly signals in rock samples from the target area.

[0058] The observational data to be analyzed include: the lithologic distribution of a specific region, as determined by the latest field survey, showing that the target region at a depth of 500-1000 meters is primarily composed of alternating layers of granite and sandstone; gravity anomaly data showing a range of -50 to +30 milligal in a specific area; remote sensing data showing that the target region has a surface vegetation coverage of approximately 40%, with linear texture features in some areas; small amounts of lead and zinc mineralization were detected in samples collected at a depth of 200 meters at a certain location; the average temperature in the target region over the past 10 years has been 15°C, with annual precipitation of 800 mm; the target region is primarily composed of temperate broadleaf forest and grassland, with coverage of 31% and 9.81%, respectively; and a river with a dry season flow of 5 cubic meters per second and a wet season flow of 20 cubic meters per second.

[0059] The environmental data to be analyzed include topographic data of the selected area. The simulated target area has an altitude of 500-1500 meters and is mainly mountainous and hilly. The plate motion-related data also include data related to the plate movement. The simulated target area is located at the edge of the plate, and the relative plate movement speed is 2-3 centimeters per year.

[0060] Extracting exploration features of the data to be analyzed, performing feature comparison analysis on the exploration features and the reference database to obtain a matching degree, and outputting the exploration features having a matching degree higher than a matching degree threshold as first data, otherwise outputting the exploration features as fuzzy data;

[0061] In the actual assessment, the exploration characteristics are stratum lithology, geological structure, surface linear texture, number of exploration boreholes, air temperature, extreme weather events, vegetation cover, color tonal anomalies, precipitation, river flow, water level changes, groundwater level and quality, preliminary characteristics;

[0062] Fuzzy data include gravity anomalies, magnetic anomalies, underground electrical structure, and mineralization location;

[0063] The matching threshold is 0.713; the matching degrees of the target area with copper mine C1, copper mine C3, and copper mine C7 are 0.715, 0.809, and 0.785, respectively;

[0064] Calculating the dissimilarity between the fuzzy data and the reference data in the matching result, performing uncertainty assessment based on the dissimilarity, outputting the fuzzy data with a dissimilarity lower than a dissimilarity threshold as second data; and adding the second data to the first data to optimize the matching degree;

[0065] In the actual evaluation, the dissimilarity threshold is 0.257, and the second data are gravity anomalies and mineralization point locations;

[0066] Performing a distribution probability prediction based on the first data, the matching degree, and the environmental data, constructing a mineral resource distribution prediction model based on the distribution probability, adding the second data to the reference database to obtain an updated reference database, and outputting a target prediction model;

[0067] In this embodiment, the data types include pictures, videos, and text.

[0068] In this embodiment, the dissimilarity includes:

[0069] Extract the feature vector of the reference data and calculate the dissimilarity between the exploration feature in the fuzzy data and the reference data in the matching result:

[0070]

[0071] The a-th exploration feature vector of the k-th dimension at the key quantile p is The cth reference data feature vector of the kth dimension at the key quantile p is The dissimilarity between the fuzzy data and the reference data is The number of dimensions is N k , the weight of the kth dimension is The normalization factor of the kth dimension is ρ, the bandwidth parameter is U, and the spatial distance between the kth dimension environmental factor and the target area is d k , the key quantile is p;

[0072] In the actual evaluation, the key quantiles are 0.12, 0.49, and 0.93 respectively.

[0073] In this embodiment, the method further includes adjusting the matching threshold and the dissimilarity threshold:

[0074] According to the matching degree between the fuzzy data and the reference data, a graph attention network A is constructed and the confidence evaluation of the matching degree is performed. The expression is:

[0075]

[0076] The matching degree between the ath exploration feature vector and the cth reference data feature vector is The confidence level of the match is The modulation coefficient of the matching degree is β, and the number of exploration feature vectors is N u , the attention weights of the a-th exploration feature vector and the c-th reference data feature vector are

[0077] The exploration feature vector and the reference data feature vector are used as nodes, and a graph attention network B is constructed based on the dissimilarity. The confidence evaluation of the dissimilarity is performed, and the expression is:

[0078]

[0079] The dissimilarity between the ath fuzzy data and the cth reference data is The confidence level of dissimilarity is The amplitude modulation coefficient of dissimilarity is The feature distribution of the a-th node is E a , the feature distribution of the cth node is E c , the maximum divergence is HX max , characteristic distribution E a and characteristic distribution E c The divergence is HX(E a ‖E c );

[0080] The matching threshold and dissimilarity threshold are dynamically adjusted according to the confidence level. The expression is:

[0081]

[0082] The dynamically adjusted i-th target threshold is D i , the initial value of the i-th target threshold is The feature distribution density corresponding to the target threshold is V, and the average confidence value of the i-th target threshold is The adaptive adjustment function of feature distribution density V and confidence average is:

[0083] In this embodiment, the method further includes updating the distribution probability:

[0084] According to the exploration characteristics of the first data, a deep reinforcement neural network learning is built and a probability update strategy is given. The expression is:

[0085]

[0086] The j-th probability update strategy is T j , the index variable is j, the discount factor is ζ; update the distribution probability according to the probability update strategy.

[0087] In this embodiment, the method for obtaining the distribution probability includes:

[0088] Taking environmental data as the background, the degree of influence of environmental data on the distribution of mines is obtained through a time-varying impact algorithm. The first data is dynamically adjusted according to the degree of influence, and the matching degree is recalculated using the adjusted first data to obtain reference data with a matching degree higher than the matching threshold. The regional matching degree of the reference data is calculated, and the area with the highest regional matching degree is used as the regional prediction result. If the matching degree error is lower than 0.12, multimodal fusion is performed on the region to obtain the regional prediction result. The spliced ​​regional prediction results are output as distribution probabilities.

[0089] In this embodiment, the method for constructing a mineral resource distribution prediction model based on the distribution probability and environmental data includes:

[0090] The weighted sum of the variance and loss function of the distribution probability prediction value and the actual distribution probability value is used as the objective function of the mineral resource distribution prediction model;

[0091] Mineral resource distribution prediction models include random forest algorithms, support vector machine algorithms, and deep learning algorithms;

[0092] The random forest algorithm divides the input data into training data and test data in a ratio of 7:2;

[0093] The support vector machine algorithm separates the training data of different categories by finding an optimal hyperplane, and iterates continuously until the interval between the two categories is maximized to obtain classified data;

[0094] Deep learning algorithms automatically learn and extract features and patterns related to the presence of mineral resources by training classified data, and use the objective function as an optimization guide to predict the distribution of mineral resources in unknown areas;

[0095] The test data is input into the mineral resource distribution prediction model to obtain the predicted mineral resource distribution. According to the mean square error between the actual mineral resource distribution and the predicted mineral resource distribution, the learning rate is continuously adjusted until the mean square error is minimized.

[0096] In the actual evaluation, the learning rate is 0.001.

[0097] Second, a mineral resource distribution prediction system based on machine learning, comprising:

[0098] Data acquisition module: used to use data acquired from a preset mineral resource database within a specified time period as data to be analyzed; wherein the mineral resource database includes a reference database and a pending database; the reference database is a database with a higher level of certainty than the pending database; the data includes multiple data types, and different data types are used to characterize mineral distribution from different types; the data to be analyzed includes observation data and environmental data; the observation data is real-time exploration data of a preset area; the observation data includes geological data, geophysical data, remote sensing data, mineral exploration data, climate data, vegetation data, and hydrological data;

[0099] Matching degree comparison and analysis module: used for extracting exploration features of the data to be analyzed, performing feature comparison analysis on the exploration features and the reference database to obtain a matching degree, and outputting the exploration features with a matching degree higher than a matching degree threshold as first data, otherwise outputting them as fuzzy data;

[0100] Uncertainty evaluation and optimization module: used to calculate the dissimilarity between the fuzzy data and the reference data in the matching result, perform uncertainty evaluation based on the dissimilarity, output the fuzzy data with a dissimilarity lower than a dissimilarity threshold as second data; and add the second data to the first data to optimize the matching degree;

[0101] Model construction module: used to predict the distribution probability based on the first data, the matching degree and the environmental data, build a mineral resource distribution prediction model based on the distribution probability, add the second data to the reference database to obtain an updated reference database, and output a target prediction model.

[0102] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor and, optionally, an internal bus, a network interface, and memory. The memory may include internal memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for its services.

[0103] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0104] The memory is used to store programs. Specifically, the program may include program code, which includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0105] The processor reads the corresponding computer program from the non-volatile memory into the internal memory and then runs it, logically forming a device for predicting mineral resource distribution based on machine learning. The processor executes the program stored in the memory and is specifically configured to perform any of the aforementioned methods for predicting mineral resource distribution based on machine learning.

[0106] The above application Figure 1The machine learning-based mineral resource distribution prediction method disclosed in the illustrated embodiment can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above method can be performed by hardware integrated logic circuits in the processor or by software instructions. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can 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, or discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of this application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0107] The electronic device may also perform Figure 1 A method for predicting mineral resource distribution based on machine learning and implementing Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0108] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple applications, any one of the aforementioned mineral resource distribution prediction methods based on machine learning is executed.

[0109] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0110] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0113] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0114] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0115] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0116] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0117] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting mineral resource distribution based on machine learning, characterized in that: The following steps are involved: The data obtained from a preset mineral resource database within a specified time period is used as the data to be analyzed; wherein the mineral resource database includes a reference database and a pending database; the reference database is a database with a higher level of certainty than the pending database; the data to be analyzed includes multiple data types, and different data types are used to characterize mineral distribution from different types; the data to be analyzed includes observation data and environmental data; the observation data is real-time exploration data of a preset area; the observation data includes geological data, geophysical data, remote sensing data, mineral exploration data, climate data, vegetation data, and hydrological data; Extracting exploration features of the data to be analyzed, performing feature comparison analysis on the exploration features and the reference database to obtain a matching degree, and outputting the exploration features having a matching degree higher than a matching degree threshold as first data, otherwise outputting the exploration features as fuzzy data; Calculating the dissimilarity between the fuzzy data and the reference data in the matching result, performing uncertainty assessment based on the dissimilarity, outputting the fuzzy data with a dissimilarity lower than a dissimilarity threshold as second data; and adding the second data to the first data to optimize the matching degree; A distribution probability prediction is performed based on the first data, the matching degree and the environmental data, a mineral resource distribution prediction model is constructed based on the distribution probability, the second data is added to the reference database to obtain an updated reference database, and a target prediction model is output.

2. The method for predicting mineral resource distribution based on machine learning according to claim 1, characterized in that: The data types include pictures, videos, and text.

3. The method for predicting mineral resource distribution based on machine learning according to claim 1, characterized in that: The dissimilarity includes: Extract the feature vector of the reference data and calculate the dissimilarity between the exploration feature in the fuzzy data and the reference data in the matching result: The a-th exploration feature vector of the k-th dimension at the key quantile p is , the cth reference data feature vector of the kth dimension at the key quantile p is , the dissimilarity between the fuzzy data and the reference data is , the number of dimensions is , the weight of the kth dimension is , the normalization factor of the kth dimension is , the bandwidth parameter is U, and the spatial distance between the k-th dimension environmental factor and the target area is , the key quantile is p.

4. The method for predicting mineral resource distribution based on machine learning according to claim 1, characterized in that: The method further includes adjusting the matching threshold and the dissimilarity threshold: According to the matching degree between the fuzzy data and the reference data, a graph attention network A is constructed and the confidence evaluation of the matching degree is performed. The expression is: The matching degree between the ath exploration feature vector and the cth reference data feature vector is , the confidence level of the match is , the amplitude modulation coefficient of the matching degree is , the number of exploration feature vectors is , the attention weights of the a-th exploration feature vector and the c-th reference data feature vector are ; The exploration feature vector and the reference data feature vector are used as nodes, and a graph attention network B is constructed based on the dissimilarity. The confidence evaluation of the dissimilarity is performed, and the expression is: The dissimilarity between the ath fuzzy data and the cth reference data is , the confidence level of dissimilarity is , the amplitude modulation coefficient of dissimilarity is , the feature distribution of the a-th node is , the feature distribution of the cth node is , the maximum divergence is , feature distribution and feature distribution The divergence of ; The matching threshold and dissimilarity threshold are dynamically adjusted according to the confidence level. The expression is: The dynamically adjusted i-th target threshold is , the initial value of the i-th target threshold is , the feature distribution density corresponding to the target threshold is , the average confidence value of the i-th target threshold is , feature distribution density The adaptive adjustment function of the average confidence value is .

5. The method for predicting mineral resource distribution based on machine learning according to claim 1, characterized in that: The method further includes updating the distribution probability: According to the exploration characteristics of the first data, a deep reinforcement neural network learning is built and a probability update strategy is given. The expression is: The j-th probability update strategy is , the index variable is j, and the discount factor is ; Update the distribution probability according to the probability update strategy.

6. The method for predicting mineral resource distribution based on machine learning according to claim 1, characterized in that: The method for obtaining the distribution probability includes: Taking environmental data as the background, the degree of influence of environmental data on the distribution of mines is obtained through a time-varying impact algorithm. The first data is dynamically adjusted according to the degree of influence, and the matching degree is recalculated using the adjusted first data to obtain reference data with a matching degree higher than the matching threshold. The regional matching degree of the reference data is calculated, and the area with the highest regional matching degree is used as the regional prediction result. If the matching degree error is lower than 0.12, multimodal fusion is performed on the region to obtain the regional prediction result. The spliced ​​regional prediction results are output as distribution probabilities.

7. The method for predicting mineral resource distribution based on machine learning according to claim 1, characterized in that: The method for constructing a mineral resource distribution prediction model based on the distribution probability and environmental data includes: The weighted sum of the variance and loss function of the distribution probability prediction value and the actual distribution probability value is used as the objective function of the mineral resource distribution prediction model; Mineral resource distribution prediction models include random forest algorithms, support vector machine algorithms, and deep learning algorithms; The random forest algorithm divides the input data into training data and test data in a ratio of 7:2; The support vector machine algorithm separates the training data of different categories by finding an optimal hyperplane, and iterates continuously until the interval between the two categories is maximized to obtain classified data; Deep learning algorithms automatically learn and extract features and patterns related to the presence of mineral resources by training classified data, and use the objective function as an optimization guide to predict the distribution of mineral resources in unknown areas; The test data is input into the mineral resource distribution prediction model to obtain the predicted mineral resource distribution. According to the mean square error between the actual mineral resource distribution and the predicted mineral resource distribution, the learning rate is continuously adjusted until the mean square error is minimized.

8. A mineral resource distribution prediction system based on machine learning, used to execute the method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module: used to use data acquired from a preset mineral resource database within a specified time period as data to be analyzed; wherein the mineral resource database includes a reference database and a pending database; the reference database is a database with a higher level of certainty than the pending database; the data includes multiple data types, and different data types are used to characterize mineral distribution from different types; the data to be analyzed includes observation data and environmental data; the observation data is real-time exploration data of a preset area; the observation data includes geological data, geophysical data, remote sensing data, mineral exploration data, climate data, vegetation data, and hydrological data; Matching degree comparison and analysis module: used for extracting exploration features of the data to be analyzed, performing feature comparison analysis on the exploration features and the reference database to obtain a matching degree, and outputting the exploration features with a matching degree higher than a matching degree threshold as first data, otherwise outputting them as fuzzy data; Uncertainty evaluation and optimization module: used to calculate the dissimilarity between the fuzzy data and the reference data in the matching result, perform uncertainty evaluation based on the dissimilarity, output the fuzzy data with a dissimilarity lower than a dissimilarity threshold as second data; and add the second data to the first data to optimize the matching degree; Model construction module: used to predict the distribution probability based on the first data, the matching degree and the environmental data, build a mineral resource distribution prediction model based on the distribution probability, add the second data to the reference database to obtain an updated reference database, and output a target prediction model.

9. An electronic device comprising: processor; as well as A memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, causes the electronic device to execute the method according to any one of claims 1 to 7.

Images