A prospecting prediction method applicable to the exploration of altered rock type gold deposits

Abstract

The present invention relates to the technical field of mining area exploration, and specifically to a prospecting prediction method applicable to tectonic altered rock type gold deposits, including determining ore-forming conditions based on ore-forming geological bodies and structural features; constructing a three-dimensional spatial model of the mining area based on the fused data; using the three-dimensional spatial model to locate the areas with mineralization; calculating the bulk ore-bearing rate by applying the trend extrapolation method to complete the quantitative estimation of the located mining area; by integrating geological, geophysical, and geochemical data into the three-dimensional spatial model, the present invention improves the accuracy of mineralization area location; by using trend extrapolation and bulk ore-bearing rate calculation, the mineral reserves can be quantitatively estimated, providing a reliable prediction for the ore distribution; identifying rich ore sections and poor ore sections along the plunge direction in segments, effectively determining the exploration target areas, and optimizing the resource allocation.

Description

Technical Field

[0001] The present invention relates to the technical field of mining area exploration, and specifically to a prospecting prediction method applicable to tectonic altered rock type gold deposits. Background Art

[0002] Gold deposits are important mineral resources, and their exploitation is of great significance to the national and local economies. Accurately predicting the location and scale of gold deposits can improve the efficiency and success rate of resource development. By predicting the distribution of gold deposits, mineral resources can be better planned and managed, blind exploitation and resource waste can be reduced. Understanding the location and scale of ore bodies helps to formulate scientific mining plans, reduce the impact on the environment, and lower the risks of environmental pollution and damage;

[0003] At the present stage, the prospecting prediction for tectonic altered rock type gold deposits faces the following problems:

[0004] Data in some areas may be insufficient, especially in remote or inaccessible areas, which will affect the accurate prediction of ore bodies;

[0005] Effectively integrating geological, geophysical, chemical and remote sensing data is a challenge, and data from different technologies may need to be converted and corrected;

[0006] Geological structures may be very complex, such as the overlap of faults and folds, which increases the difficulty of ore body prediction. Tectonic activities and deformations may change the original position and shape of mineralized bodies, and the influence of tectonic evolution needs to be considered. Summary of the Invention

[0007] In view of the above problems, the present invention is proposed.

[0008] To solve the above technical problems, the present invention provides the following technical solution: A prospecting prediction method applicable to tectonic altered rock type gold deposits, including the following steps,

[0009] Determining ore-forming conditions based on ore-forming geological bodies and tectonic characteristics, including,

[0010] Using a multi-source data fusion algorithm to achieve the fusion of ore body data and perform feature analysis on the fused data information, specifically:

[0011] Analyzing the fused data layer by layer, calculating a dimensionality reduction feature matrix, and performing weighted combination according to the calculated dimensionality reduction feature matrix;

[0012] Constructing a three-dimensional space model of the mining area based on the fused data, including,

[0013] Using a geographic transformation algorithm to convert the fused data into three-dimensional geographic coordinates, and generating a spatial network according to the three-dimensional geographic coordinates, specifically:

[0014] Use the clustering algorithm to intelligently identify data density, dynamically adjust the spatial network nodes, calculate the Euclidean distance between nodes to determine the edges of the network space, and finally construct weighted edges based on the node feature differences;

[0015] Use the three-dimensional space model to locate the mineralized areas, including,

[0016] Segment the current mineralized area, and achieve the mining area location by analyzing the extension of the ore body along the plunge direction, specifically:

[0017] Calculate the thickness T and gold grade G of the current mining area, and at the same time use the thickness and strike data of the ore body to analyze the changes of the ore body along the plunge direction. Finally, use the GIS tool to draw the target area map, predict the prospecting location, and at the same time use the actual exploration data to complete the determination of the target area;

[0018] Calculate the ore-bearing rate of the body by applying the trend extrapolation method to complete the quantitative estimation of the located mining area.

[0019] As a preferred solution of the prospecting prediction method for tectonic altered rock type gold deposits described in the present invention, wherein: the use of the multi-source data fusion algorithm to achieve the fusion of ore body data is as follows:

[0020] Fuse the remote sensing data, seismic data and geological data, then there is,

[0021] F = w1·R + w2·S + w3·G

[0022] Wherein, w1, w2, and w3 respectively represent the weight coefficients of the remote sensing data, seismic data and geological data, and satisfy the formula w1 + w2 + w3 = 1. R represents the collected remote sensing data, which is collected by satellite remote sensing technology. S represents the seismic data, which is collected based on the database. G represents the geological data, which is collected by geological personnel through on-site rock and mineral sampling.

[0023] As a preferred solution of the prospecting prediction method for tectonic altered rock type gold deposits described in the present invention, wherein: the feature analysis of the fused data information is as follows:

[0024] Based on the fused data F, divide it into three layers according to the remote sensing data, seismic data and geological data, and perform principal component analysis on each layer of data, then there is,

[0025] Z i = F i ′·W i

[0026] F i ′ = F i -μ

[0027] Among them, F i ′ represents the data after the i-th layer of data is centralized, and W i represents the feature vector matrix of the i-th layer, and F i represents the i-th layer data of the fused data F. i = 1, 2,..., n represents the index layer of the fused data, which respectively represent remote sensing data, seismic data, and geological data. μ represents the mean of the fused data, and Z i represents the dimensionality reduction feature matrix of the i-th layer;

[0028] According to the calculated dimensionality reduction feature matrix, the data is weighted and combined, and then the feature analysis of the data information is completed. Then,

[0029]

[0030] Among them, w i represents the weight coefficient of the i-th layer data, and satisfies the formula w1 + w2 + w3 +.... + w n = 1, and Z i represents the dimensionality reduction feature matrix of the i-th layer. Z represents the feature matrix after weighted combination, which is used to complete the feature analysis of the data information and then determine the metallogenic conditions.

[0031] As a preferred scheme of the prospecting prediction method for tectonic altered rock type gold deposits described in the present invention, wherein: the specific implementation of the geographic transformation algorithm is as follows:

[0032] Use the geographic transformation algorithm to convert the fused data F into the corresponding three-dimensional geographic coordinates F(x, y, z), and then generate a spatial network according to the converted three-dimensional geographic coordinates. Then,

[0033] Use the data points in the three-dimensional geographic coordinates as the network nodes of the spatial network (x i , y i , z i );

[0034] Before generating the spatial network nodes, first use the clustering algorithm to automatically identify the potential threats in the dense area of the data. Then,

[0035] C k = {F(x i , y i , z i )|argmin j ||F(x i , y i , z i ) - μ j}

[0036] Among them, F(x i , y i , zi ) represents the three-dimensional geographical coordinates of the i-th data point, μ j represents the j-th cluster center, C k represents the k-th cluster center. By calculating the cluster center, the dense area of the data is automatically identified, and the node connection is dynamically adjusted.

[0037] As a preferred solution of the prospecting prediction method for constructing altered rock type gold deposits according to the present invention, wherein: the generation of the spatial network according to the three-dimensional geographical coordinates is specifically constructed as follows:

[0038] After the network nodes are determined, calculate the Euclidean geometric distance between the nodes, then there is,

[0039]

[0040] Among them, (x i , y i , z i ), (x j , y j , z j ) respectively represent the i-th and j-th node coordinates, and d ij represents the Euclidean distance between the two nodes;

[0041] Determine the edges of the spatial network according to the calculated Euclidean distance, then there is,

[0042] When the calculated Euclidean distance satisfies the formula d ij ≥ α·std(d ij )), it means that an edge needs to be established between the current two nodes;

[0043] Among them, α represents the adjustment coefficient, which is set by the implementer according to the actual application conditions, and std(d ij ) represents the standard deviation of the Euclidean distance between the two nodes;

[0044] Construct weighted edges using node feature differences, then there is,

[0045]

[0046] w ij (t) = w ij × (1 + f(t))

[0047] d = d ij · w ij (t)

[0048] Among them, f i , f jrespectively represent the node eigenvalue, which is determined by the feature matrix in the fused data, β represents the feature coefficient, which is set by the implementer according to the actual application scenario, f(t) represents the time function, which is used to reflect the change trend of the data over time series, w ij represents the edge weight between two nodes, w ij (t) represents the edge weight between two nodes under the action of the time function, d ij represents the Euclidean distance between two nodes, d represents the side length between two nodes in the spatial network. When the edges between all nodes are constructed, it represents the generation of the spatial network of the current mining area, and then the generated spatial networks are combined to form a three-dimensional spatial model.

[0049] As a preferred scheme of the prospecting prediction method for tectonic altered rock type gold deposit described in the present invention, wherein: the regional segmentation of the current mineralization area is specifically implemented as follows:

[0050] Apply the mining area data to the three-dimensional spatial model, calculate the thickness T, gold grade G of the current mining area, and the product T×G between the two, and at the same time set the rich ore section threshold K F and the poor ore section threshold K P and the mineralization section threshold K, specifically as follows:

[0051] When the product of the thickness and gold grade of the mining area satisfies the formula T×G≥K F it indicates that the current mineralization area is a rich ore section area;

[0052] When the product of the thickness and gold grade of the mining area satisfies the formula T×G≤K P it indicates that the current mineralization area is a poor ore section area

[0053] When the product of the thickness and gold grade of the mining area satisfies the formula K P <T×G<K F it indicates that the current mineralization area is a mineralized area.

[0054] As a preferred scheme of the prospecting prediction method for tectonic altered rock type gold deposit described in the present invention, wherein: calculating the bulk ore-bearing rate by applying the trend extrapolation method is to calculate the bulk ore-bearing rate by constructing a trend line equation, and the specific calculation is as follows:

[0055] Using the thickness T and gold grade G data of the current mining area, construct a trend line equation, then there is,

[0056] y=k·X+b

[0057] wherein, X represents the position variable of the current mining area, k and b respectively represent the slope and intercept of the trend line equation, and y represents the predicted mineralization parameters, including the thickness and gold grade of the mining area;

[0058] Using the predicted mineralization parameters, calculate the bulk ore content rate of the mining area, then there is,

[0059]

[0060] wherein, R represents the calculated bulk ore content rate, T i represents the thickness of the i-th block, G i represents the gold grade of the i-th block, V i represents the volume of the i-th block.

[0061] Advantages of the present invention: By integrating geological, geophysical and geochemical data into a three-dimensional space model, the present invention improves the accuracy of mineralization area positioning; using trend extrapolation and bulk ore content rate calculation, it can quantitatively estimate the mineral reserves and provide a reliable prediction for the ore distribution; by segmentally identifying rich ore sections and poor ore sections along the plunge direction, it effectively determines the exploration target area and optimizes the resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Among them:

[0063] Figure 1 It is a schematic structural diagram of the overall method steps of a prospecting prediction method for tectonic altered rock type gold deposits of the present invention.

[0064] Figure 2 It is a schematic structural diagram of the overall composition of a prospecting prediction system for tectonic altered rock type gold deposits of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0065] To make the above objects, features and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0066] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0067] Secondly, the so-called "one embodiment" or "embodiment" herein refers to specific features, structures or characteristics that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other with other embodiments.

[0068] The present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally out of the general scale, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width and depth should be included.

[0069] Meanwhile, in the description of the present invention, it should be noted that the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0070] Unless otherwise clearly defined and limited in the present invention, the terms "installed, connected, connected" should be understood in a broad sense. For example: it may be a fixed connection, a detachable connection or an integral connection; it may also be a mechanical connection, an electrical connection or a direct connection, and may also be indirectly connected through an intermediate medium, or may be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0071] Embodiment 1

[0072] Referring to Figure 1 , as the first embodiment of the present invention, a prospecting prediction method applicable to constructing altered rock type gold deposits is provided, including the following steps.

[0073] S1: Determine the metallogenic conditions based on the ore-forming geological bodies and structural features.

[0074] Specifically, determining the metallogenic conditions based on the ore-forming geological bodies and structural features is to integrate multi-source data information, analyze the ore-forming geological bodies and structural features, and then complete the determination of the metallogenic conditions. Integrating multi-source data information is to integrate remote sensing data, seismic data and geological data, and realize the determination of the metallogenic conditions according to the analysis of the data characteristics after integration.

[0075] Furthermore, integrating multi-source data information is to fuse remote sensing data, seismic data and geological data using a multi-source data fusion algorithm, and realize the determination of the metallogenic conditions by analyzing the characteristics of the data information after fusion, specifically as follows:

[0076] Fuse the remote sensing data, seismic data and geological data, then there is

[0077] F = w1·R + w2·S + w3·G

[0078] Among them, w1, w2, and w3 respectively represent the weight coefficients of remote sensing data, seismic data, and geological data, and satisfy the formula w1 + w2 + w3 = 1. R represents the collected remote sensing data, which is collected through satellite remote sensing technology. S represents the seismic data, which is collected based on the database. G represents the geological data, which is collected by geological personnel through on-site rock and mineral sampling;

[0079] Perform feature analysis on the fused data information, then there is,

[0080] According to the fused data F, it is divided into three layers based on remote sensing data, seismic data, and geological data, and principal component analysis is performed on each layer of data, then there is,

[0081] Z i = F i ′·W i

[0082] F i ′ = F i - μ

[0083] Among them, F i ′ represents the data after centering of the i-th layer of data, W i represents the eigenvector matrix of the i-th layer, F i represents the i-th layer of data of the fused data F, i = 1, 2,..., n represents the index layer of the fused data, respectively representing remote sensing data, seismic data, and geological data, μ represents the mean of the fused data, and Z i represents the dimensionality-reduced feature matrix of the i-th layer;

[0084] According to the calculated dimensionality-reduced feature matrix, perform weighted combination on the data, and then complete the feature analysis of the data information, then there is,

[0085]

[0086] Among them, w i represents the weight coefficient of the i-th layer of data, and satisfies the formula w1 + w2 + w3 +.... + w n = 1, Z i represents the dimensionality-reduced feature matrix of the i-th layer, Z represents the weighted combined feature matrix, which is used to complete the feature analysis of the data information, and then determine the metallogenic conditions.

[0087] S2: Build a three-dimensional spatial model of the mining area based on the fused data.

[0088] Specifically, constructing a three-dimensional spatial model of the mining area based on the fused data is to convert the data into the data format of the three-dimensional spatial model, generate a spatial network, and construct a three-dimensional spatial model according to the generated spatial network to determine the spatial distribution and shape of the ore body, providing a data basis for subsequent positioning and extraction of the mining area.

[0089] Further, converting the data into the data format of the three-dimensional spatial model is to use a geographic transformation algorithm to convert the fused data F into the corresponding three-dimensional geographic coordinates F(x, y, z), and then generate a spatial network according to the converted three-dimensional geographic coordinates. That is,

[0090] Using the data points in the three-dimensional geographic coordinates as the network nodes of the spatial network (x i , y i , z i );

[0091] Before generating the spatial network nodes, first use a clustering algorithm to automatically identify potential threats in the dense areas of the data to improve the intelligence of the network structure. That is,

[0092] C k ={F(x i , y i , z i )|argmin j ||F(x i , y i , z i ) - μ j}

[0093] Among them, F(x i , y i , z i ) represents the three-dimensional geographic coordinates of the i-th data point, μ j represents the j-th clustering center, and C k represents the k-th clustering center. By calculating the clustering centers, automatically identify the dense areas of the data and dynamically adjust the node connections;

[0094] After the network nodes are determined, calculate the Euclidean geometric distance between the nodes. That is,

[0095]

[0096] Among them, (x i , y i , z i ), (x j , y j , z j ) respectively represent the coordinates of the i-th and j-th nodes, and d ij represents the Euclidean distance between the two nodes;

[0097] Determine the edges of the spatial network based on the calculated Euclidean distance. Then,

[0098] When the calculated Euclidean distance satisfies the formula d ij ≥α·std(d ij ), it indicates that an edge needs to be established between the current two nodes;

[0099] Among them, α represents the adjustment coefficient, which is set by the implementer according to the actual application conditions, and std(d ij ) represents the standard deviation of the Euclidean distance between the two nodes;

[0100] Construct weighted edges using the node feature differences. Then,

[0101]

[0102] w ij (t) = w ij ×(1 + f(t))

[0103] d = d ij ·w ij (t)

[0104] Among them, f i and f j respectively represent the node feature values, which are determined by the feature matrix in the fused data. β represents the feature coefficient, which is set by the implementer according to the actual application scenario. f(t) represents the time function, which is used to reflect the change trend of the data over time series. w ij represents the edge weight between the two nodes, w ij (t) represents the edge weight between the two nodes under the action of the time function. d ij represents the Euclidean distance between the two nodes, and d represents the side length between the two nodes of the spatial network. When the edges between all nodes are constructed, it indicates that the spatial network of the current mining area is generated, and then the generated spatial networks are combined to form a three-dimensional space model.

[0105] Furthermore, to determine the spatial distribution and shape of the ore body is to use the generated three-dimensional space model to determine the spatial distribution and shape of the ore body, specifically as follows:

[0106] Use the generated three-dimensional space model to display the three-dimensional shape of the ore body in the terminal device through volume rendering technology;

[0107] At the same time, use the isosurface technology to identify the boundary of the current ore body;

[0108] Based on the determined boundary and the shape of the ore body, and according to the current application scenario, the implementer selects the cross-section of the ore body for feature analysis, and then completes the distribution of the ore body at different levels;

[0109] Finally, adjust the model parameters to improve the accuracy, and at the same time use historical data for data verification of the model to ensure the accuracy of the analyzed ore body distribution.

[0110] S3: Use the three-dimensional space model to locate the areas with mineralization.

[0111] Specifically, using the three-dimensional space model to locate the areas with mineralization is to analyze the geological features related to mineralization in the model using the three-dimensional space model, and based on the comparison between the geological features and the mineralization threshold, accurately locate the mineralized areas.

[0112] Furthermore, analyzing the geological features related to mineralization in the model using the three-dimensional space model is to apply the mining area data to the three-dimensional space model, calculate the thickness T, gold grade G of the current mining area, and the product T×G between the two, and at the same time set the rich ore section threshold K F , poor ore section threshold K P and mineralized section threshold K, specifically as follows:

[0113] When the product of the thickness and gold grade of the mining area satisfies the formula T×G≥K F , it indicates that the current mineralized area is a rich ore section area;

[0114] When the product of the thickness and gold grade of the mining area satisfies the formula T×G≤K P , it indicates that the current mineralized area is a poor ore section area

[0115] When the product of the thickness and gold grade of the mining area satisfies the formula K P <T×G<K F , it indicates that the current mineralized area is a mineralized area.

[0116] It should be noted that the thickness of the current mining area is calculated through the borehole spacing and core length in the exploration data, specifically as follows:

[0117]

[0118] Among them, L represents the vertical length of the core, and θ represents the dip angle;

[0119] The gold grade of the current mining area is calculated based on the gold content analysis of ore samples, specifically as follows:

[0120]

[0121] Among them, W ore represents the total weight of the ore sample, and W gold represents the weight of gold in the sample.

[0122] Furthermore, precise positioning of the mineralized area is achieved by analyzing the changes in the ore body along the plunge direction, while using a geological model for dip analysis to determine the extension of the ore body along the dip direction. Based on the mineralized area and the plunge direction, the prospecting target area is determined. Meanwhile, combined with exploration data, the positioning of the mining area is optimized.

[0123] Specifically, determining the extension of the ore body along the dip direction is to utilize the thickness and strike data of the ore body to analyze the changes in the ore body along the plunge direction, as follows:

[0124] D=(x,y)=(L·cos(φ),L·sin(φ))

[0125] L = T·cos(θ)

[0126] Among them, (x,y) represents the horizontal coordinates, D represents the displacement of the ore body on the horizontal plane, L represents the vertical length of the core, θ represents the dip angle, φ represents the strike data, and T represents the thickness of the current mining area;

[0127] The positioning of the target area is based on the calculated extension of the ore body along the dip direction, and using the mineralized area and the plunge direction, a target area map is drawn using GIS tools to predict the prospecting location. Meanwhile, using the actual exploration data, the determination of the target area is completed, and thus the precise positioning of the mining area is achieved.

[0128] S4: Calculate the ore-bearing rate of the ore body by applying the trend extrapolation method to complete the quantitative estimation of the positioned mining area.

[0129] Specifically, calculating the ore-bearing rate of the ore body by applying the trend extrapolation method is to calculate the ore-bearing rate of the ore body by constructing a trend line equation, and the specific calculation is as follows:

[0130] Using the thickness T and gold grade G data of the current mining area to construct a trend line equation, then

[0131] y = k·X + b

[0132] Among them, X represents the position variable of the current mining area, k and b respectively represent the slope and intercept of the trend line equation, and y represents the predicted mineralization parameters, including the thickness and gold grade of the mining area;

[0133] Using the predicted mineralization parameters to calculate the ore-bearing rate of the mining area, then

[0134]

[0135] Among them, R represents the calculated ore-bearing rate of the ore body, T i represents the thickness of the i-th block, G i represents the gold grade of the i-th block, V i represents the volume of the i-th block.

[0136] It should be noted that in order to further improve the accuracy of the trend line equation, the trend line equation is optimized and verified by using the historical data of the mining area. Through the input historical data, the slope and intercept of the linear equation are adjusted to ensure the accuracy of the calculated bulk ore-bearing rate of the mining area.

[0137] Example 2

[0138] Refer to Figure 2 , the second embodiment of the present invention provides a prospecting prediction system applicable to tectonic altered rock type gold deposits, including an ore-forming condition determination module, a three-dimensional space model construction module, a mineralized area location module, and a mining area quantitative estimation module;

[0139] Specifically, the ore-forming condition determination module is used to determine the ore-forming conditions based on ore-forming geological bodies and structural features; the three-dimensional space model construction module is used to construct a three-dimensional space model of the mining area based on the fused data; the mineralized area location module is used to locate the mineralized area by using the three-dimensional space model; the mining area quantitative estimation module calculates the bulk ore-bearing rate by applying the trend extrapolation method to complete the quantitative estimation of the located mining area.

[0140] Furthermore, the ore-forming condition determination module is the basis of the system. It first collects and analyzes multi-source information such as geological exploration data, remote sensing images, geophysical exploration data, and historical mining records of the target area. These information are comprehensively analyzed through advanced data processing algorithms (such as machine learning, data mining, etc.) to identify potential ore-forming geological bodies (such as lithology, tectonic belts, magmatic activities, etc.) and ore-forming structural features (such as faults, folds, alteration zones, etc.). By comparing the geological characteristics of known gold mining areas, this module can determine whether the target area has similar ore-forming conditions, and then screen out high-potential ore-forming areas;

[0141] Based on the output results of the ore-forming condition determination module, the three-dimensional space model construction module will fuse multi-dimensional information such as geological exploration data, topographic and geomorphic data, and groundwater level data, and use three-dimensional modeling software (such as ArcGIS, GOCAD, etc.) to construct a fine three-dimensional space model of the mining area. This model not only includes the morphological characteristics of the surface, but also goes deep underground to show the three-dimensional distribution of key geological elements such as rock structures, fault systems, and alteration zones. Through visualization technology, researchers can intuitively view and analyze the geological structure of the mining area, providing an accurate spatial framework for subsequent mineralized area location;

[0142] The mineralized area positioning module uses a three-dimensional spatial model, combines geostatistical methods and geophysical inversion techniques to accurately locate potential mineralized areas. By simulating the processes of ore fluid migration, precipitation, and enrichment, this module can predict the spatial distribution pattern of ore bodies. At the same time, using remote sensing technology and ground survey data, it identifies information such as alteration mineral assemblages and geochemical anomalies related to mineralization, further narrowing down the scope of the mineralized area. Finally, through comprehensive analysis and multi-source information fusion, it determines the specific location and scale of the mineralized area;

[0143] The mining area quantitative estimation module, based on the positioning of the mineralized area, uses the trend extrapolation method and other statistical prediction methods (such as Kriging interpolation, geostatistical simulation, etc.) to quantitatively estimate the located mining area. This module first collects parameter data such as the grade, thickness, and shape of known ore bodies in the mining area and establishes a mathematical model of ore body characteristics. Then, by extrapolating the model to the entire mineralized area, it calculates the ore-bearing rate of the ore body (i.e., the content of useful minerals in the rock per unit volume) and estimates the total resource volume of the mining area accordingly. In addition, this module also considers geological uncertainty factors and conducts a probability assessment of the resource volume to provide a reliable resource assessment basis for mine development

[0144] It should be noted that through the close cooperation and interaction of the above four modules, this system can achieve a comprehensive prospecting prediction for altered rock type gold deposits and provide strong technical support for gold exploration and development

[0145] Furthermore, if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. And the aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0146] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definitional sequence of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. As used in this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device or in connection with these instruction execution systems, apparatus, or devices.

[0147] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0148] In addition, to provide a concise description of the exemplary embodiments, all features of the actual embodiments may not be described (i.e., those features that are not relevant to the currently contemplated best mode of carrying out the invention or those features that are not relevant to the implementation of the invention).

[0149] It should be understood that in the development of any actual implementation, as in any engineering or design project, numerous specific implementation decisions may be made. Such development efforts may be complex and time-consuming, but for those of ordinary skill in the art who benefit from this disclosure, without undue experimentation, the development efforts will be a routine task of design, fabrication, and production.

[0150] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A prospecting prediction method applicable to the exploration of altered rock type gold deposits, characterized in that: Including the following steps: Determine the metallogenic conditions based on the ore geology and tectonic characteristics, including: Use the multi-source data fusion algorithm to achieve the fusion of ore body data and perform feature analysis on the fused data information. Specifically: Analyze the fused data layer by layer, calculate the dimensionality reduction feature matrix, and perform weighted combination according to the calculated dimensionality reduction feature matrix; The realization of the fusion of ore body data using the multi-source data fusion algorithm is as follows: Fuse remote sensing data, seismic data, and geological data, then: F = w1·R + w2·S + w3·G Where w1, w2, and w3 respectively represent the weight coefficients of remote sensing data, seismic data, and geological data, and satisfy the formula w1 + w2 + w3 = 1. R represents the collected remote sensing data, which is collected through satellite remote sensing technology. S represents the seismic data, which is collected based on the database. G represents the geological data, which is collected by geological personnel through on-site rock and mineral sampling; For the feature analysis of the fused data information, then: Based on the fused data F, divide it into three layers according to remote sensing data, seismic data, and geological data, and perform principal component analysis on each layer of data, then: Z i = F i ′·W i F i ′ = F i -μ Among them, F i ′ represents the data after centering the data of the i-th layer, and W i represents the feature vector matrix of the i-th layer, and F i represents the i-th layer data of the fused data F. i = 1, 2, 3 represents the index layer number of the fused data. Each layer contains the number of layers of remote sensing data, seismic data, and geological data. μ represents the mean of the fused data, and Z i represents the dimensionality reduction feature matrix of the i-th layer; According to the calculated dimensionality reduction feature matrix, perform weighted combination on the data, and then complete the feature analysis of the data information, then: Among them, w i represents the weight coefficient of the data of the i-th layer, and satisfies the formula w1 + w2 + w3 = 1, Z i represents the dimensionality reduction feature matrix of the i-th layer, Z represents the feature matrix after weighted combination, which is used to complete the feature analysis of data information, and then determine the metallogenic conditions; Construct a three-dimensional space model of the mining area based on the fused data, including: Convert the fused data into three-dimensional geographical coordinates and generate a spatial network according to the three-dimensional geographical coordinates. Specifically: Use the clustering algorithm to intelligently identify the data density, dynamically adjust the spatial network nodes, calculate the Euclidean distance between the nodes to determine the edges of the network space, and finally construct weighted edges according to the node feature differences; Use the three-dimensional space model to achieve the positioning of the mineralized area, including: Perform regional segmentation on the current mineralized area and achieve the positioning of the mining area by analyzing the extension of the ore body along the plunge direction. Specifically: Calculate the thickness T and gold grade G of the current mining area, and at the same time use the thickness and strike data of the ore body to analyze the changes of the ore body along the plunge direction, draw the target area map, predict the prospecting location, and at the same time use the actual exploration data to complete the determination of the target area; Calculate the ore-bearing rate of the body by applying the trend extrapolation method to complete the quantitative estimation of the located mining area.

2. The prospecting prediction method for constructing altered rock type gold deposits as described in claim 1, wherein: The specific realization of converting the fused data into three-dimensional geographical coordinates is as follows: Use the geographical conversion algorithm to convert the fused data F into the corresponding three-dimensional geographical coordinates F(x, y, z), and then generate a spatial network according to the converted three-dimensional geographical coordinates, then: Using data points in three-dimensional geographic coordinates as network nodes of a spatial network (x i , y i , z i ); Before generating the spatial network nodes, first use the clustering algorithm to automatically identify the potential threats in the dense areas of the data, then: C k = {F(x i , y i , z i ) | argmin j ||F(x i , y i , z i ) - μ j} Among them, F(x i , y i , z i ) represents the three-dimensional geographical coordinates of the i-th data point, μ j represents the j-th cluster center, C k represents the k-th cluster center. By calculating the cluster centers, the dense areas of the data are automatically identified, and the node connections are dynamically adjusted.

3. A prospecting prediction method applicable to the exploration of altered rock type gold deposits as described in claim 2, characterized in that: The specific construction of generating the spatial network according to the three-dimensional geographical coordinates is as follows: After the network nodes are determined, calculate the Euclidean geometric distance between the nodes, then: , where (x i , y i , z i ), (x j , y j , z j ) represent the coordinates of the i-th and j-th nodes respectively, and d ij represents the Euclidean distance between the two nodes; Determine the edges of the spatial network according to the calculated Euclidean distance, then: When the calculated Euclidean distance satisfies the formula d ij ≥α·std(d ij ), it means that an edge needs to be established between the current two nodes; Among them, α represents the adjustment coefficient, which is set by the implementer according to the actual application conditions, and std(d ij ) represents the standard deviation of the Euclidean distance between two nodes; Construct weighted edges using the node feature differences, then: w ij y(t) = w ij ×(1 + f(t)) d = d ij ·w ij (t) Among them, f i and f j respectively represent the node eigenvalue, which is determined by the feature matrix in the fused data. β represents the feature coefficient, which is set by the implementer according to the actual application scenario. f(t) represents the time function, which is used to reflect the change trend of the data over the time series. w ij represents the edge weight between two nodes. w ij (t) represents the edge weight between two nodes under the action of the time function. d ij represents the Euclidean distance between two nodes. d represents the side length between two nodes in the spatial network. After all the edges between nodes are constructed, it represents the generation of the spatial network of the current mining area, and then the generated spatial networks are combined to form a three-dimensional spatial model.

4. The prospecting prediction method for constructing altered rock type gold deposits according to claim 3, characterized in that: The specific realization of performing regional segmentation on the current mineralized area is as follows: Apply the mining area data to the three-dimensional space model, calculate the thickness T, gold grade G of the current mining area, and the product T×G between the two. At the same time, set the rich ore section threshold K F , poor ore section threshold K P and mineralized section threshold K, specifically as follows: When the product of the thickness and the gold grade of the mining area satisfies the formula T×G≥K F it indicates that the current mineralized area is a rich ore section area; When the product of the thickness and the gold grade in the mining area satisfies the formula T×G≤K P it indicates that the current mineralized area is a lean ore section area When the product of the thickness and the gold grade in the mining area satisfies the formula K P <T×G<K F it indicates that the current mineralized area is a mineralized area.

5. The prospecting prediction method applicable to the construction of altered rock type gold deposits according to claim 4, wherein: The calculation of the ore-bearing rate of the body by applying the trend extrapolation method is to calculate the ore-bearing rate of the body by constructing a trend line equation. The specific calculation is as follows: Using the thickness T and gold grade G data of the current mining area, a trend line equation is constructed, so there is y = k·X + b where X represents the position variable of the current mining area, k and b represent the slope and intercept of the trend line equation respectively, and y represents the predicted mineralization parameters, including the thickness and gold grade of the mining area; Using the predicted mineralization parameters, calculate the bulk ore-bearing rate of the mining area, so there is Among them, R represents the calculated in-body ore-bearing rate, and T i represents the thickness of the i-th block, and G i represents the gold grade of the i-th block, and V i represents the volume of the i-th block.

Images