Method and device for analyzing mineral resource exploration data based on three-dimensional space modeling

Abstract

The embodiment of the application discloses a kind of mineral resources exploration data analysis method and equipment based on three-dimensional space modeling, method includes: by constructing interface occurrence measurement, lithology combination analysis and structural movement trace identification extraction thrust nappe structural feature information and convert into geological structure feature parameter;Integrate stratum age data and lithology distribution information, with stratum superimposed relationship as constraint to build three-dimensional stratum model;Import geophysical data to extract different depth stratum geophysical response characteristics and combined with lithology boundary correction to obtain deep fracture feature parameter;Sedimentary system division is carried out to lithofacies paleogeographic information, and the three-dimensional model of fracture system is established by integrating deep fracture feature parameters;Identify multiple feature correlation and establish ore body prediction model, input spatial data to obtain the three-dimensional visualization prediction result of the spatial distribution of concealed ore body, improve the exploration efficiency and accuracy in the process of deep phosphorite exploration.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the technical field of computer data analysis, in particular to a mineral resource exploration data analysis method and device based on three-dimensional space modeling. BACKGROUND

[0002] In the field of mineral resource exploration, the extraction of geological structure characteristics is an important basis for analyzing regional geological evolution and mineral resource distribution. As a kind of geological structure, the extraction of characteristic information of thrust nappe structure is usually realized by means of structure interface occurrence measurement, lithological combination analysis and structure movement trace identification, and the characteristic information is crucial for understanding the formation and evolution of regional geological structure. The construction of stratigraphic model is a key link in mineral resource exploration. In the prior art, stratigraphic age data and lithological distribution information are usually integrated based on geological structure characteristic parameters, and a stratigraphic model is constructed based on the constraint condition of stratigraphic superimposition relationship, which is used to reflect the spatial distribution of strata in the target exploration area. Geophysical data are widely used in mineral resource exploration. By processing and analyzing geophysical data, the geophysical response characteristics of different depth strata can be extracted, which can be used to infer deep geological structure and mineral resource distribution. The analysis of lithofacies paleogeographic information plays an important role in understanding the depositional environment and the formation of mineral resources. By dividing the sedimentary system based on lithofacies paleogeographic information, the spatial correlation between faults and sedimentary facies belts is analyzed based on fault characteristic parameters, and a fault system model is established. The establishment of ore body prediction model is one of the core goals of mineral resource exploration. Based on geological structure, strata, geophysical and lithofacies paleogeographic data, the correlation between multiple characteristics is identified, and an ore body prediction model is established to predict the spatial distribution of concealed ore bodies.

[0003] However, the prior art has problems of unclear relationship between sedimentary microfacies and phosphorus enrichment mineralization, unclear spatial distribution rule of deep phosphorus mine, and no systematic establishment of deep phosphorus mine exploration technology, which makes it difficult to ensure the exploration efficiency and accuracy in the process of deep phosphorus mine exploration. SUMMARY

[0004] The embodiment of the present application provides a mineral resource exploration data analysis method and device based on three-dimensional space modeling.

[0005] The embodiment of the present application provides a mineral resource exploration data analysis method based on three-dimensional space modeling, which comprises:

[0006] The characteristic information of the thrust nappe structure in the target exploration area is extracted by means of structure interface occurrence measurement, lithological combination analysis and structure movement trace identification, and the characteristic information is converted into geological structure characteristic parameters;

[0007] Integrate stratum age data and lithology distribution information of the target exploration area based on the geological structure feature parameter, to construct a three-dimensional stratum model reflecting stratum spatial distribution of the target exploration area by taking the determined stratum superimposed relationship of the tectonic sequence division as a constraint condition;

[0008] Based on the three-dimensional stratum model, import geophysical data of the target exploration area, perform geophysical response feature extraction of strata at different depths on the geophysical data, and correct the extraction result in combination with the lithology boundary in the three-dimensional stratum model, to obtain deep fault feature parameters;

[0009] Perform sedimentary system division on lithofacies paleogeographic information of the target exploration area to obtain a sedimentary system division result, integrate the deep fault feature parameters into the sedimentary system division result, and establish a fault system three-dimensional model reflecting spatial distribution of faults and sedimentary relationship through spatial correlation analysis of faults and sedimentary facies belts;

[0010] According to the three-dimensional stratum model and the fault system three-dimensional model, identify the correlation relationship among lithology combination features corresponding to ore body deposition environment, tectonic ore-controlling regularity features corresponding to geological structure feature parameters, and physical property anomaly features corresponding to geophysical data, establish an ore body prediction model by taking the correlation relationship as a constraint, input spatial data of the target exploration area into the ore body prediction model, and obtain a three-dimensional visual prediction result for spatial distribution of concealed ore bodies.

[0011] Embodiments of the present application also provide a computer device, comprising:

[0012] a processor;

[0013] a storage device having a computer program stored thereon,

[0014] When the computer program is executed by the processor, the processor implements any of the mineral resource exploration data analysis methods based on three-dimensional space modeling.

[0015] Embodiments of the present application also provide a readable storage medium having a program or instruction stored thereon, which is executed by a processor to implement steps of the mineral resource exploration data analysis method based on three-dimensional space modeling.

[0016] Therefore, the embodiment of the present application has the following beneficial effects: the embodiment of the present application extracts feature information of the thrust nappe structure of the target exploration area and converts the feature information into geological structure feature parameters, integrates stratum age data and lithology distribution information to construct a three-dimensional stratum model, imports geophysical data to extract geophysical response characteristics of strata at different depths and corrects the geophysical response characteristics in combination with lithology boundaries to obtain deep fault feature parameters, divides a sedimentary system according to facies paleogeographic information and integrates the deep fault feature parameters to establish a three-dimensional model of a fault system, finally identifies a multi-feature correlation and establishes a ore body prediction model to obtain a three-dimensional visual prediction result of spatial distribution of a concealed ore body.

[0017] In this way, multi-dimensional data fusion and analysis of geological structures, spatial distribution of strata, geophysical responses, facies paleogeography and ore body prediction are realized as a whole, scientific problems that sedimentary microfacies and phosphorus enrichment mineralization relationship are not clear and spatial distribution rules are not clear due to large changes in spatial distribution of deep phosphorus ore are effectively solved, deep phosphorus ore exploration technology is established, fine reconstruction technology of facies paleogeography based on big data, three-dimensional prediction technology of deep phosphorus ore in a mineralization area based on multi-source data, green and efficient ore prediction technology based on fusion of geological, geophysical, geochemical and remote sensing big data and artificial intelligence, and deep phosphorus ore spatial accurate positioning technology are broken through, comprehensive and accurate technical support is provided for exploration and prediction of deep phosphorus ore, and efficiency and accuracy of deep phosphorus ore exploration are improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A flowchart of a mineral resource exploration data analysis method based on three-dimensional space modeling provided by the embodiment of the present application.

[0019] Figure 2 A schematic diagram of a basic structure of a computer device provided by the embodiment of the present application.

[0020] Figure 3 A function module block diagram of a mineral resource exploration data analysis device based on three-dimensional space modeling provided by the embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the embodiment of the present application will be further described in detail below in combination with the drawings and specific implementation manners.

[0022] Reference Figure 1 As shown in the figure, the figure is a flowchart of a mineral resource exploration data analysis method based on three-dimensional space modeling provided by the embodiment of the present application, and the method is executed by a computer device. As shown in the figure, Figure 1 As shown in the figure, the method includes steps 110-150.

[0023] This application embodiment is applied to a mineral resource exploration data analysis scenario based on three-dimensional spatial modeling. The target exploration area is a phosphate mine exploration area, aiming to achieve accurate prediction of concealed phosphate ore bodies through multi-dimensional geological data integration and analysis.

[0024] First, existing technologies for mineral resource exploration data analysis often rely on single-dimensional data or experience-driven approaches. However, this application innovatively constructs a multi-dimensional technical system comprising a "three-dimensional stratigraphic model, a three-dimensional fault system model, and an orebody prediction model," achieving deep integration of multi-source data such as geological structure, stratigraphic spatial distribution, geophysical response, and lithofacies paleogeography. Traditional methods for extracting features from thrust-nappe structures only provide qualitative descriptions. This application, by converting features such as the occurrence of tectonic interfaces, lithological combinations, and traces of tectonic movement into quantitative parameters, and constructing a three-dimensional stratigraphic model constrained by the stratigraphic sequence division, solves the problem of quantitative characterization of stratigraphic spatial distribution.

[0025] Secondly, in terms of deep fault feature extraction, existing technologies often analyze geophysical data separately, which is easily affected by lithological boundaries, leading to inaccurate anomaly identification. The embodiments of this application divide geophysical data into data subsets according to depth gradients, correct them by combining the lithological boundaries of the three-dimensional stratigraphic model, and then extract deep fault feature parameters through signal feature decomposition and correlation comparison, thereby achieving accurate identification of fault features. This process breaks through the technical bottleneck of lithology and tectonic anomaly confusion in traditional geophysical data processing.

[0026] Furthermore, in the construction of three-dimensional models of fracture systems, existing technologies rarely consider the spatial correlation between fractures and sedimentary facies zones. In this application, the characteristic parameters of deep fractures are incorporated into the sedimentary system division, the spatial correlation rules between fractures and sedimentary facies zones are analyzed, and a three-dimensional model containing fracture spatial coordinates, extension morphology, and sedimentary facies zone correlation attributes is established. This reveals the control effect of fractures on sedimentary facies zones. This innovative approach provides a new technical way to understand the coupling relationship between tectonics and sedimentation.

[0027] Finally, in the construction of orebody prediction models, traditional methods often rely on single features or simple superposition. The embodiments of this application construct a prediction model that includes a feature input layer, a correlation analysis layer and a result output layer by identifying the correlation between lithological combination features, structural ore-controlling features and physical property anomaly features. This realizes the collaborative analysis and quantitative calculation of multiple features and solves the problem of multi-factor coupling analysis in the prediction of concealed ore bodies.

[0028] Step 110: By measuring the occurrence of structural interfaces, analyzing lithological combinations, and identifying traces of tectonic movement, the characteristic information of the thrust-nappe structure in the target exploration area is extracted, and the characteristic information is converted into geological structural characteristic parameters.

[0029] The computer device firstly calls geological survey system data of a target exploration area, measures an interface occurrence of a thrust nappe structure, and obtains strike, tendency and dip angle data of the interface; then performs lithology combination analysis on core sample data of the target exploration area, identifies a lithology combination mode of a phosphorus-bearing rock system, such as an interactive combination of carbonate rock and phosphate rock; then identifies structural movement traces, including fault scratches and fold axial surfaces; and then converts the above feature information into geological structure feature parameters, including interface occurrence parameters, lithology combination parameters and structural movement trace parameters, wherein the interface occurrence parameters include strike angle range, tendency angle range and dip angle range, the lithology combination parameters include thickness proportion and combination sequence of different lithologies, and the structural movement trace parameters include length range of fault scratches and morphological parameters of fold axial surfaces.

[0030] Step 120: Integrating stratum age data and lithology distribution information of the target exploration area based on the geological structure feature parameters, to construct a three-dimensional stratum model reflecting spatial distribution of strata in the target exploration area as a constraint condition of the determined stratum superimposition relationship by structural sequence division.

[0031] The computer device firstly obtains stratum age data of the target exploration area, including absolute age range and relative age sequence of each era stratum, and then integrates lithology distribution information, including distribution range of different lithologies in the plane and in the vertical direction; then performs structural sequence division according to interface occurrence and structural movement trace parameters in the geological structure feature parameters to determine the superimposition relationship of the strata, such as integrated contact and unconformable contact; then constructs a three-dimensional stratum model by taking the stratum superimposition relationship as a constraint condition, using a three-dimensional modeling software, and the spatial coordinate system of the model uses a local coordinate system of the target exploration area, and the model includes spatial coordinate range, lithology attribute and age attribute of a stratum unit, wherein the spatial coordinate range of the stratum unit is represented by a three-dimensional coordinate point set, the lithology attribute includes lithology type and physical and chemical properties, and the age attribute includes absolute age and relative age of the stratum.

[0032] Step 130: Based on the three-dimensional stratum model, importing geophysical data of the target exploration area, performing geophysical response feature extraction of strata at different depths on the geophysical data and combining lithology boundary correction of the extraction result in the three-dimensional stratum model to obtain deep fracture feature parameters.

[0033] The computer device imports geophysical data of a target exploration area first, including gravity data, magnetic data and seismic data; then delimits a processing interval of the geophysical data with reference to the spatial distribution range and lithological interface coordinates of the strata in the three-dimensional strata model; then performs geophysical response feature extraction of different depth strata on the geophysical data, and obtains response features of different depth strata, such as gravity anomaly features, magnetic anomaly features and seismic reflection features, through a signal processing algorithm; then corrects the extraction results in combination with the lithological boundaries in the three-dimensional strata model, and removes false anomalies caused by the lithological boundaries; finally, deep fracture feature parameters are obtained, which include the spatial position coordinates, extension direction, dip angle and fracture zone width of the fracture.

[0034] Step 131: delimiting a processing interval of the geophysical data with reference to the spatial distribution range and lithological interface coordinates of the strata in the three-dimensional strata model, and dividing the geophysical data into a plurality of data subsets according to depth gradients, each data subset corresponding to a stratum unit in the three-dimensional strata model.

[0035] In the method, the computer device extracts the spatial distribution range of the strata from the three-dimensional strata model first, including the maximum and minimum plane coordinates and the vertical depth range of the strata, and then extracts the lithological interface coordinates, including the three-dimensional coordinate point set of each lithological interface; then delimits the processing interval of the geophysical data according to the spatial distribution range and the lithological interface coordinates of the strata, the plane range of the processing interval being consistent with the plane distribution range of the strata, and the vertical range being consistent with the vertical depth range of the strata; then divides the geophysical data into a plurality of data subsets according to depth gradients, the depth gradients being determined according to the thickness of the strata, each data subset corresponding to a stratum unit in the three-dimensional strata model, such as dividing the vertical depth range into a plurality of depth segments, each depth segment corresponding to a stratum unit, and each data subset containing the geophysical data in the depth segment.

[0036] Step 132: performing geophysical response feature extraction on each data subset, and obtaining an initial response feature set containing amplitude features, phase features and continuity features through signal feature decomposition, and synchronously extracting the lithological boundary coordinates and lithological change attributes of the corresponding stratum unit in the three-dimensional strata model.

[0037] In the method, the computer device performs geophysical response feature extraction on each data subset by using a signal decomposition algorithm, the signal decomposition algorithm including Fourier transform, wavelet transform and the like, and obtains the initial response feature set through signal decomposition, wherein the amplitude features represent the intensity of the geophysical signal, the phase features represent the phase change of the signal, and the continuity features represent the continuity of the signal in space; and the lithological boundary coordinates of the corresponding stratum unit are extracted from the three-dimensional strata model, including the three-dimensional coordinate point set of the lithological boundary, and the lithological change attributes, including the conversion mode and conversion frequency of the lithological type.

[0038] Step 133: Spatially match the initial response feature set with the lithological boundary coordinates, determine whether there is an abnormal feature point in the initial response feature set based on the lithological change attribute, and if so, mark the spatial position corresponding to the abnormal feature point and analyze its spatial relationship with the lithological boundary.

[0039] Wherein, the computer device matches the initial response feature set with the lithological boundary coordinates by using a spatial matching algorithm, the spatial matching algorithm includes nearest neighbor matching, regional matching, etc.; then determines whether there is an abnormal feature point in the initial response feature set based on the lithological change attribute, such as sudden conversion of lithological type, abnormal change of lithological thickness, etc., the judgment basis of the abnormal feature point is that the feature value exceeds the normal range; if there is an abnormal feature point, mark the spatial position corresponding to the abnormal feature point, including three-dimensional coordinates, and then analyze its spatial relationship with the lithological boundary, such as the abnormal feature point being above, below or at the lithological boundary.

[0040] Step 134: For the marked abnormal feature point, backtrack to the corresponding geophysical data subset, re-execute signal feature decomposition and adjust the decomposition parameters to obtain a corrected response feature set, and associate and compare the corrected response feature set with the response feature set of the adjacent depth stratum unit.

[0041] Wherein, the computer device determines the geophysical data subset corresponding to the marked abnormal feature point; then re-executes signal feature decomposition and adjusts the decomposition parameters, such as the decomposition frequency range, wavelet basis function, etc., to obtain a corrected response feature set; then associate and compare the corrected response feature set with the response feature set of the adjacent depth stratum unit, the contents of association and comparison include the change trend of amplitude feature, the consistency of phase feature and the spatial extension of continuity feature.

[0042] Step 135: Extract the region with feature mutation in the association and comparison process, determine the spatial range of the region in combination with the lithological boundary coordinates, analyze the feature mutation law and spatial form of the region, and generate deep fracture feature parameters including mutation position, extension direction and feature change amplitude.

[0043] Wherein, the computer device extracts the region with feature mutation in the association and comparison process by using a feature mutation detection algorithm, the feature mutation detection algorithm includes gradient detection, difference detection, etc.; then determines the spatial range of the region in combination with the lithological boundary coordinates, including the planar range and the vertical depth range; then analyzes the feature mutation law of the region, such as mutation frequency, intensity change, etc., and the spatial form, such as linearity, arc shape, etc.; finally generates deep fracture feature parameters, the parameters include three-dimensional coordinates of mutation position, angle range of extension direction and numerical range of feature change amplitude.

[0044] Step 140: sedimentary system division is performed on the lithofacies paleogeographic information of the target exploration area to obtain a sedimentary system division result, the deep fault feature parameter is integrated into the sedimentary system division result, and a fault system three-dimensional model reflecting the spatial distribution of faults and the relationship with deposition is established through spatial correlation analysis of faults and sedimentary facies belts.

[0045] The computer device first acquires lithofacies paleogeographic information of a target exploration area, including sedimentary facies types, paleocurrent directions, and sedimentary environments; then performs sedimentary system division on the lithofacies paleogeographic information, the division basis including the combination of sedimentary facies types, the consistency of paleocurrent directions, and the similarity of sedimentary environments, to obtain a sedimentary system division result, which contains multiple sedimentary system units, each of which contains sedimentary facies types, distribution ranges, and sedimentary characteristics; then the deep fault feature parameter is integrated into the sedimentary system division result, including superimposing the spatial position of the fault on the distribution range of the sedimentary system unit; then the control of the fault on the sedimentary facies belt is analyzed through spatial correlation analysis of the fault and the sedimentary facies belt, such as the boundary limitation of the fault on the sedimentary facies belt, the consistency of the extension direction of the fault and the distribution direction of the sedimentary facies belt, etc.; finally, a fault system three-dimensional model is established, which contains the spatial distribution, extension direction, and correlation relationship with the sedimentary facies belt of the fault.

[0046] Step 141: collect lithofacies paleogeographic information covering sedimentary facies types, paleocurrent directions, and lithological facies change characteristics of the target exploration area, and perform sedimentary system division on the target exploration area based on the spatial distribution rule of the sedimentary facies types and the gradient change of the lithological facies change characteristics to obtain multiple initial sedimentary system units.

[0047] Among them, the computer device collects lithofacies paleogeographic information of the target exploration area, including sedimentary facies type data obtained through field geological investigation, such as shallow marine facies, coastal facies, and delta facies, paleocurrent direction data obtained through paleocurrent measurement, such as the flow direction angle range of the water flow, and lithological facies change characteristic data obtained through core observation, such as the conversion of lithological types and the change of thickness; then analyze the spatial distribution rule of the sedimentary facies types, such as the shallow marine facies mainly distributed in the middle of the target exploration area and the coastal facies distributed on the edge; then analyze the gradient change of the lithological facies change characteristics, such as the gradual change of the lithological types from carbonate rock to clastic rock and the gradual thinning of the thickness from the shallow marine facies to the coastal facies; then perform sedimentary system division on the target exploration area based on the spatial distribution rule of the sedimentary facies types and the gradient change of the lithological facies change characteristics, the division method including cluster analysis, regional division, etc., to obtain multiple initial sedimentary system units, each of which contains sedimentary facies types, distribution ranges, and lithological facies change characteristics.

[0048] Step 1411: Collect lithofacies paleogeographic information of the target exploration area, and classify the lithofacies paleogeographic information to determine sedimentary facies type, paleocurrent direction data and lithology facies change characteristic description.

[0049] Among them, the computer device collects the lithofacies paleogeographic information of the target exploration area, and the information sources include field geological report, core analysis data, logging data, etc.; then the lithofacies paleogeographic information is classified and sorted, and the information is divided into sedimentary facies type data, paleocurrent direction data and lithology facies change characteristic description, wherein the sedimentary facies type data includes the names and distribution positions of shallow marine facies, coastal facies and delta facies, the paleocurrent direction data includes the flow direction angle and measurement position of the water flow, and the lithology facies change characteristic description includes the conversion process of lithology type, the change of thickness, etc.

[0050] Step 1412: Classify the sedimentary facies type, determine the main sedimentary facies type and the typical lithology marker of each type, convert the paleocurrent direction data into spatial vector form, and quantitatively process the lithology facies change characteristic description to obtain lithology facies change gradient data.

[0051] Among them, the computer device classifies the sedimentary facies type, classifies similar sedimentary facies types into one category, determines the main sedimentary facies type, such as shallow marine facies, coastal facies and delta facies, and the typical lithology marker of each main sedimentary facies type includes carbonate rock of shallow marine facies, clastic rock of coastal facies and sand-shale interbedding of delta facies; then the paleocurrent direction data is converted into spatial vector form, the direction of the spatial vector is consistent with the paleocurrent direction, and the length represents the intensity of the water flow; then the lithology facies change characteristic description is quantitatively processed, and the conversion frequency of lithology type and the change amplitude of thickness are converted into numerical form to obtain lithology facies change gradient data.

[0052] Step 1413: Based on the plane coordinates of the target exploration area, a two-dimensional analysis grid is constructed, and the classified sedimentary facies type, the spatial vector form of the paleocurrent direction and the quantified lithology facies change gradient data are respectively mapped to the corresponding nodes of the two-dimensional analysis grid.

[0053] Among them, the computer device sets the size of the grid based on the plane coordinates of the target exploration area, constructs a two-dimensional analysis grid, and the node coordinates of the grid correspond one-to-one to the plane coordinates of the target exploration area; then the classified sedimentary facies type is mapped to the corresponding nodes of the two-dimensional analysis grid, such as shallow marine facies mapped to the middle nodes of the grid and coastal facies mapped to the edge nodes; then the spatial vector form of the paleocurrent direction is mapped to the corresponding nodes, the starting point of the vector is the node coordinate, and the direction is the paleocurrent direction; then the quantified lithology facies change gradient data is mapped to the corresponding nodes, and the data value represents the lithology facies change gradient at the node.

[0054] Step 1414: Calculate the similarity of sedimentary facies types, the angle between paleocurrent direction vectors, and the gradient difference of lithofacies change of adjacent nodes in the two-dimensional analysis grid, set the similarity threshold, the angle threshold, and the difference threshold, and classify the adjacent nodes that meet the conditions of the similarity of sedimentary facies types being higher than the similarity threshold, the angle between paleocurrent direction vectors being smaller than the angle threshold, and the gradient difference of lithofacies change being smaller than the difference threshold into the same unit; wherein the similarity of sedimentary facies types is calculated based on type matching degree, the angle between paleocurrent direction vectors is calculated based on spatial vector angle measurement, and the gradient difference of lithofacies change is quantified based on lithofacies change intensity.

[0055] In the step, the computer device calculates the similarity of sedimentary facies types of adjacent nodes in the two-dimensional analysis grid, the calculation of the similarity is based on type matching degree, such as the similarity of two nodes is 1 if the sedimentary facies types are the same, and 0 if the sedimentary facies types are different; then calculates the angle between paleocurrent direction vectors, the calculation of the angle is based on spatial vector angle measurement, such as the angle between paleocurrent direction vectors of two nodes is 30 degrees; then calculates the gradient difference of lithofacies change, the calculation of the difference is quantified based on lithofacies change intensity, such as the gradient difference of lithofacies change of two nodes is 0.5; then sets the similarity threshold as 0.8, the angle threshold as 45 degrees, and the difference threshold as 0.6; finally, classifies the adjacent nodes that meet the conditions of the similarity of sedimentary facies types being higher than 0.8, the angle between paleocurrent direction vectors being smaller than 45 degrees, and the gradient difference of lithofacies change being smaller than 0.6 into the same unit.

[0056] Step 1415: Traverse the entire two-dimensional analysis grid, repeat the node merging process, form initial sedimentary units on multiple planes, combine the stratigraphic thickness information and vertical lithofacies change characteristics in the three-dimensional stratigraphic model, extend the initial sedimentary units on the plane along the vertical direction to obtain multiple initial sedimentary system units containing vertical dimension information, extract the vertical boundary coordinates, planar distribution range, and core characteristic parameters of each initial sedimentary system unit, and the core characteristic parameters include the dominant sedimentary facies type, the dominant paleocurrent direction, and the average lithofacies change gradient.

[0057] In the step, the computer device traverses the entire two-dimensional analysis grid, repeats the node merging process, classifies the adjacent nodes that meet the conditions into the same unit to form multiple initial sedimentary units on multiple planes; then extracts the stratigraphic thickness information and vertical lithofacies change characteristics from the three-dimensional stratigraphic model, the stratigraphic thickness information includes the thickness of each stratigraphic unit, and the vertical lithofacies change characteristics include the vertical conversion sequence of lithofacies types; then extends the initial sedimentary units on the plane along the vertical direction, and the extension thickness is determined according to the stratigraphic thickness information to obtain multiple initial sedimentary system units containing vertical dimension information; finally, extracts the vertical boundary coordinates, planar distribution range, and core characteristic parameters of each initial sedimentary system unit, wherein the vertical boundary coordinates include the maximum and minimum vertical depths of the unit, the planar distribution range includes the maximum and minimum planar coordinates of the unit, and the core characteristic parameters include the dominant sedimentary facies type, the dominant paleocurrent direction, and the average lithofacies change gradient.

[0058] The dominant paleocurrent direction is a key indicator reflecting the depositional environment, provenance supply and depositional system distribution rule. From the perspective of reconstructing the depositional environment, the dominant paleocurrent direction can be used as a wind vane. A stable single direction may indicate a directional water power environment such as fluvial facies and delta facies, a two-way alternation may correspond to a tidal facies, and a scattered but concentrated trend may reflect a shallow sea wave dominated environment. As an indicator of the location of the provenance area, the dominant paleocurrent direction usually points to the opposite direction of the provenance area, and in combination with lithological assemblage, the range of the provenance area can be accurately located. At the same time, the dominant paleocurrent direction is also the control axis of the depositional system distribution, which determines the extension direction and spatial distribution of the depositional facies belt, such as fluvial channel sand bodies and delta distributary channel.

[0059] Step 142: Extract the spatial boundary coordinates, core depositional characteristics and lithological composition information of each initial depositional system unit, superimpose the fault position coordinates in the deep fault feature parameters and the spatial boundary coordinates of the initial depositional system unit, and determine the initial depositional system unit and the passing position through which the fault passes.

[0060] The computer device extracts the spatial boundary coordinates of each initial depositional system unit, including the maximum and minimum plane coordinates and the vertical depth range of the unit, the core depositional characteristics including the depositional facies type and the depositional environment, and the lithological composition information including the lithological types contained in the unit and the thickness proportion of each lithology. Then, the fault position coordinates are extracted from the deep fault feature parameters, including a set of three-dimensional coordinate points of the fault. Then, the fault position coordinates are superimposed with the spatial boundary coordinates of the initial depositional system unit, and the method of coordinate matching is used to determine the initial depositional system unit and the passing position through which the fault passes. The passing position includes the three-dimensional coordinate points at which the fault enters and leaves the initial depositional system unit.

[0061] Step 143: For the initial depositional system unit through which the fault passes, analyze the spatial relationship between the extension direction of the fault in the unit and the paleocurrent direction, judge the influence range of the fault on the depositional process according to the characteristic variation amplitude of the fault, and adjust the spatial boundary of the initial depositional system unit according to the influence range to obtain the modified depositional system division result.

[0062] The computer device extracts extension direction data of the fracture in the initial depositional system unit, including the angle range of the extension direction, and extracts paleocurrent direction data in the unit, including the angle range of the paleocurrent direction; then analyzes the spatial relationship between the fracture extension direction and the paleocurrent direction, such as whether the two are parallel, perpendicular or at a certain angle; then judges the influence range of the fracture on the sedimentation process in combination with the characteristic variation range of the fracture, the characteristic variation range including the width variation and intensity variation of the fracture, and the influence range is determined according to the characteristic variation range of the fracture and the lithology composition of the depositional system unit; finally, the spatial boundary of the initial depositional system unit is adjusted according to the influence range, and the adjustment method includes expanding or reducing the spatial range of the unit, to obtain the modified depositional system division result.

[0063] Step 144: based on the modified depositional system division result, a depositional facies belt spatial distribution network is constructed, the fracture extension direction and the mutation position in the deep fracture characteristic parameter are integrated into the depositional facies belt spatial distribution network, and the intersection position and intersection mode of each fracture with different depositional facies belts are marked.

[0064] The computer device constructs a depositional facies belt spatial distribution network based on the modified depositional system division result, and the distribution network includes the spatial distribution, boundary coordinates and depositional characteristics of each depositional facies belt; then the fracture extension direction and the mutation position are extracted from the deep fracture characteristic parameter; then the above information is integrated into the depositional facies belt spatial distribution network, the extension direction of the fracture is associated with the distribution direction of the depositional facies belt, and the mutation position of the fracture is matched with the boundary of the depositional facies belt; finally, the intersection position of each fracture with different depositional facies belts is marked, including the three-dimensional coordinates of the intersection position, and the intersection mode such as cutting and tangency.

[0065] Step 1441: based on the spatial boundary coordinates and core depositional characteristics of each depositional system unit in the modified depositional system division result, the spatial range and vertical distribution level of each depositional facies belt are determined, and a three-dimensional depositional facies belt spatial distribution network is constructed, and the depositional facies belt spatial distribution network includes the unique identification and characteristic attribute index of each depositional facies belt.

[0066] The computer device determines the spatial range of each depositional facies belt based on the spatial boundary coordinates and core depositional characteristics of each depositional system unit in the modified depositional system division result, including the planar range and vertical depth range, and the vertical distribution level is determined according to the formation time of the depositional facies belt, such as the depositional facies belt formed earlier located in the lower layer and the depositional facies belt formed later located in the upper layer; then a three-dimensional depositional facies belt spatial distribution network is constructed, and the node coordinates of the distribution network correspond to the spatial coordinates of the depositional facies belt; finally, the unique identification and characteristic attribute index of each depositional facies belt are set in the depositional facies belt spatial distribution network, the unique identification is used to distinguish different depositional facies belts, and the characteristic attribute index is used to quickly query the characteristic attributes of the depositional facies belt.

[0067] Step 1442: Extract the fracture ID, spatial coordinate sequence, extension direction vector and characteristic mutation position coordinate of each fracture in the deep fracture feature parameter, and convert the spatial coordinate sequence of each fracture into coordinates in the coordinate system consistent with the sedimentary facies belt spatial distribution network.

[0068] Wherein, the computer device extracts the fracture ID, spatial coordinate sequence, extension direction vector and characteristic mutation position coordinate of each fracture from the deep fracture feature parameter, the fracture ID is the unique identifier of each fracture, the spatial coordinate sequence is the three-dimensional coordinate point set of the fracture, the extension direction vector is the spatial vector representing the extension direction of the fracture, and the characteristic mutation position coordinate is the three-dimensional coordinate of the fracture characteristic mutation; then the spatial coordinate sequence of each fracture is converted into coordinates in the coordinate system consistent with the sedimentary facies belt spatial distribution network, the conversion method includes coordinate translation, rotation, etc., to ensure the consistency of the coordinates.

[0069] Step 1443: Traverse the spatial coordinate sequence of each fracture, determine the sedimentary facies belt to which each coordinate point in the sequence belongs, and when the coordinate point enters the coordinate range of another sedimentary facies belt from the coordinate range of one sedimentary facies belt, record the coordinate point as the intersection position of the fracture and the sedimentary facies belt, and record the unique identification of the sedimentary facies belt before and after the intersection; for each intersection position, determine the intersection mode of the fracture and the sedimentary facies belt in combination with the extension direction vector of the fracture and the distribution characteristics of the adjacent coordinate points.

[0070] Wherein, the computer device traverses the spatial coordinate sequence of each fracture, for each coordinate point in the sequence, queries the sedimentary facies belt spatial distribution network to determine the sedimentary facies belt to which the coordinate point belongs; when the coordinate point enters the coordinate range of another sedimentary facies belt from the coordinate range of one sedimentary facies belt, record the coordinate point as the intersection position of the fracture and the sedimentary facies belt, and record the unique identification of the sedimentary facies belt before and after the intersection; then for each intersection position, determine the intersection mode of the fracture and the sedimentary facies belt in combination with the extension direction vector of the fracture and the distribution characteristics of the adjacent coordinate points, such as the extension direction of the fracture being perpendicular to the boundary of the sedimentary facies belt, then the intersection mode is cutting, and parallel is tangent.

[0071] Step 1444: Associate and store the fracture ID, extension direction vector, all intersection position coordinates, corresponding sedimentary facies belt identification and intersection mode of each fracture, and form a fracture-sedimentary facies belt association data table, and integrate the association data table as attribute information into the sedimentary facies belt spatial distribution network.

[0072] The computer device stores the fracture ID, the extension direction vector, all intersection position coordinates, the corresponding sedimentary facies belt identifier and the intersection mode of each fracture in association, in the form of a data table, and the fields of the data table include the fracture ID, the extension direction vector, the intersection position coordinates, the sedimentary facies belt identifier, and the intersection mode.

[0073] Step 145: Analyzing the association between the fracture characteristics and the sedimentary facies belt characteristics at the intersection position, and statistically analyzing the distribution density and extension rule of the fractures in different sedimentary facies belts to establish the spatial association rule between the fractures and the sedimentary facies belts.

[0074] The computer device analyzes the association between the fracture characteristics and the sedimentary facies belt characteristics at the intersection position, such as whether the extension direction of the fracture is consistent with the distribution direction of the sedimentary facies belt, whether the mutation position of the fracture coincides with the boundary of the sedimentary facies belt, etc.; then statistically analyzes the distribution density of the fractures in different sedimentary facies belts, such as the distribution density of the fractures in the shallow marine facies sedimentary facies belt is higher, and the distribution density of the fractures in the coastal facies is lower; further statistically analyzes the extension rule of the fractures, such as the fractures mainly extend linearly in the shallow marine facies sedimentary facies belt, and extend branchily in the delta facies; and finally establishes the spatial association rule between the fractures and the sedimentary facies belts, which includes the association rule between the extension direction of the fracture and the distribution direction of the sedimentary facies belt, and the association rule between the distribution density of the fracture and the type of the sedimentary facies belt.

[0075] Step 146: Based on the sedimentary facies belt spatial distribution network, combining the spatial association rule and the complete deep fracture characteristic parameters, a three-dimensional model of the fracture system is constructed, which includes the spatial coordinates of the fractures, the extension mode, and the associated attributes of the sedimentary facies belt.

[0076] The computer device determines the spatial framework of the three-dimensional model of the fracture system based on the sedimentary facies belt spatial distribution network; then associates the extension direction and the distribution density of the fractures with the characteristics of the sedimentary facies belt based on the spatial association rule; further combines the complete deep fracture characteristic parameters, including the spatial coordinates, the extension mode, and the mutation position of the fractures; and finally constructs the three-dimensional model of the fracture system, which includes the spatial coordinate point set of the fractures, the three-dimensional geometric model of the extension mode, and the parameters of the associated attributes of the sedimentary facies belt.

[0077] Step 150: According to the three-dimensional stratigraphic model and the three-dimensional model of the fracture system, the association relationship between the lithological combination characteristics corresponding to the ore body deposition environment, the structural ore-controlling rule characteristics corresponding to the geological structure characteristic parameters, and the physical property anomaly characteristics corresponding to the geophysical data is identified, a ore body prediction model is established based on the association relationship as a constraint, spatial data of the target exploration area is input into the ore body prediction model, and a three-dimensional visual prediction result for the spatial distribution of the concealed ore body is obtained.

[0078] In this step, the computer equipment first extracts lithological combination information corresponding to the ore body's sedimentary environment from the 3D stratigraphic model, and obtains lithological combination characteristics through lithological type statistics and combination sequence analysis. Then, it filters ore-controlling structural attributes from geological structural characteristic parameters, and analyzes the control effect of structures on ore body formation by combining the fault distribution and extension characteristics in the 3D fault system model, thus obtaining structural ore-controlling characteristics. Next, it extracts ore body-related physical property information from geophysical data, and identifies the spatial distribution and intensity changes of physical property anomalies by combining anomalous characteristic areas in deep fault characteristic parameters, thus obtaining physical property anomaly characteristics. Then, it identifies the correlations between the above characteristics, such as the correlation between lithological combination characteristics and physical property anomaly characteristics, and the correlation between structural ore-controlling characteristics and ore body distribution. Then, it establishes an ore body prediction model based on these correlations, using machine learning algorithms such as random forests and support vector machines. Finally, it inputs spatial data of the target exploration area into the ore body prediction model, including stratigraphic data, structural data, and geophysical data. Finally, it obtains a 3D visualization prediction result for the spatial distribution of concealed ore bodies, including the spatial location, morphology, and distribution direction of the concealed ore bodies.

[0079] Step 151: Extract lithological combination information corresponding to the ore body depositional environment from the three-dimensional stratigraphic model, and obtain lithological combination characteristics through lithological type statistics and combination sequence analysis. The lithological combination characteristics include the proportion of lithological types, the combination arrangement order and the vertical variation law.

[0080] The computer equipment first delineates the spatial range of the ore body's sedimentary environment from a three-dimensional stratigraphic model. The determination of the spatial range is based on the known distribution location of the ore body and the characteristics of the sedimentary environment. Then, it extracts lithological assemblage information within this spatial range, including the name and thickness of each lithological type. Next, it statistically analyzes the lithological types, calculating the proportion of each lithological type's thickness to the total thickness, thus obtaining the lithological type percentage. Then, it performs sequence analysis on the lithological assemblage, analyzing the arrangement order of the lithological types, such as clastic rocks, carbonate rocks, and phosphate rocks from bottom to top. Finally, it analyzes the vertical variation patterns, such as the frequency of lithological type transitions and the trend of thickness changes, to obtain the lithological assemblage characteristics.

[0081] Step 1511: Based on the regional geological background and known geological clues, delineate the spatial range of the ore body depositional environment in the three-dimensional stratigraphic model, and extract the lithological information and spatial coordinate data of all stratigraphic units within the spatial range.

[0082] The computer device determines the spatial range of the ore body deposition environment in the three-dimensional stratum model based on the regional geological background, such as the geological structure evolution history of the target exploration area, the deposition environment of the known ore body, and the known geological clues, such as the lithological combination and distribution position of the discovered ore body, and determines the boundary coordinates of the spatial range according to the known geological clues; then extracts the lithological information of all stratum units in the spatial range, including the lithological type, color, structure, and spatial coordinate data, including the three-dimensional coordinate point set of the stratum unit.

[0083] Step 1512: Classify and count the extracted lithological information to determine the lithological types contained in the spatial range and the number of stratum units corresponding to each lithological type, calculate the proportion of the number of each lithological type in the total number of stratum units, and obtain the lithological type proportion data.

[0084] The computer device classifies and counts the extracted lithological information, classifies the lithological information by lithological type, determines the lithological types contained in the spatial range, such as carbonate rock, phosphate rock, and clastic rock, then counts the number of stratum units corresponding to each lithological type, and then calculates the proportion of the number of each lithological type in the total number of stratum units, which is obtained by dividing the number of stratum units of each lithological type by the total number of stratum units, to obtain the lithological type proportion data.

[0085] Step 1513: Sort the stratum units in the spatial range in the order of vertical depth in the spatial coordinate data to form a stratum sequence from shallow to deep, extract the lithological type of each stratum unit, record the appearance sequence of the lithological type in the order of sorting, and obtain the initial lithological combination sequence.

[0086] The computer device sorts the stratum units in the spatial range in the order of vertical depth in the spatial coordinate data, with the basis of the vertical depth of the stratum unit, with the stratum unit with shallow depth arranged in front and the stratum unit with deep depth arranged in back, to form a stratum sequence from shallow to deep, then extracts the lithological type of each stratum unit, and then records the appearance sequence of the lithological type in the order of sorting to obtain the initial lithological combination sequence, such as the initial lithological combination sequence being clastic rock, carbonate rock, phosphate rock, and carbonate rock.

[0087] Step 1514: Perform sequence analysis on the initial lithological combination sequence to identify the repeated lithological combination fragments and the appearance frequency of the fragments, determine the dominant lithological combination mode, and analyze the conversion mode and conversion frequency of the lithological types in the sequence to determine the combination arrangement order characteristics.

[0088] The computer device performs sequence analysis on the initial lithology combination sequence, adopts a sequence pattern mining algorithm, identifies repeatedly occurring lithology combination fragments in the sequence, such as a carbonate rock-phosphate rock as a repeatedly occurring fragment, and a frequency of occurrence of the fragment, determines a dominant lithology combination pattern, the dominant pattern is a lithology combination fragment with the highest frequency of occurrence, analyzes a conversion mode of the lithology types in the sequence, such as a gradual change or a sudden change from a clastic rock to a carbonate rock, and a conversion frequency is a number of times of conversion divided by a length of the sequence, and finally determines a combination arrangement order feature, the feature including an arrangement order of the lithology combination fragments, the conversion mode, and the conversion frequency.

[0089] Step 1515: constructing a lithology vertical distribution map with a vertical depth as a horizontal axis and a lithology type as a vertical axis, marking a depth interval and a thickness corresponding to each lithology type in the lithology vertical distribution map, identifying variation characteristics of the lithology types in different depth intervals, and calculating a conversion gradient of the lithology types with respect to the depth.

[0090] The computer device constructs a lithology vertical distribution map with a vertical depth as a horizontal axis and a lithology type as a vertical axis, marks a depth interval and a thickness corresponding to each lithology type in the lithology vertical distribution map, identifies variation characteristics of the lithology types in different depth intervals, such as a clastic rock as a main lithology type in a shallow depth interval, a carbonate rock as a main lithology type in a middle depth interval, and a phosphate rock as a main lithology type in a deep depth interval, calculates a conversion gradient of the lithology types with respect to the depth, and finally mines a variation rule of the lithology combination with respect to the vertical depth in combination with the lithology vertical distribution map and the conversion gradient data.

[0091] Step 152: screening a structure attribute related to ore control from the geological structure feature parameters, analyzing a control action of the structure on the formation of the ore body in combination with a fracture distribution and an extension feature in the fracture system three-dimensional model, obtaining a structure ore-controlling rule feature, and the structure ore-controlling rule feature including a fracture ore-controlling range, a correlation of structure intersection relationships and the ore body.

[0092] Among them, the computer device screens the structural properties related to ore control from the geological structure feature parameters, such as the properties of faults, the shapes of folds, the occurrences of structural interfaces, etc.; then combines the fault distribution and extension characteristics in the three-dimensional model of the fault system to analyze the control of the faults on the ore body, such as whether the extension direction of the fault is consistent with the distribution direction of the ore body, whether the intersection position of the fault is the enrichment area of the ore body, etc.; then analyzes the relevance of the structural intersection relationship and the ore body, such as whether the intersection of faults of different directions is easy to form an ore body; and finally obtains the structural ore-controlling regularity characteristics, which include the three-dimensional coordinate range of the fault ore-controlling range, the correlation rules of the structural intersection relationship and the ore body distribution, etc.

[0093] Step 153: Extracting the physical property information related to the ore body from the geophysical data, combining the abnormal feature area in the deep fault feature parameters, identifying the spatial distribution and intensity variation of the physical property anomaly, and obtaining the physical property anomaly feature, which includes the abnormal spatial coordinates, the intensity gradient, and the spatial correlation with the fault.

[0094] Among them, the computer device extracts the physical property information related to the ore body from the geophysical data, such as density, magnetism, and electrical properties; then combines the abnormal feature area in the deep fault feature parameters, such as gravity anomaly area, magnetic anomaly area, etc.; then identifies the spatial distribution of the physical property anomaly, including the three-dimensional coordinate range of the abnormal area, and the intensity variation, including the intensity value and the variation trend of the anomaly; finally analyzes the spatial correlation of the physical property anomaly and the fault, such as whether the physical property anomaly area coincides with the distribution range of the fault, whether the intensity variation of the anomaly is consistent with the characteristic variation of the fault, etc., to obtain the physical property anomaly feature.

[0095] Step 154: Standardizing the lithological combination feature, the structural ore-controlling regularity feature, and the physical property anomaly feature respectively to eliminate dimensional differences and convert them into feature vectors of a unified dimension, identifying the interdependence relationship and influence weight through feature correlation analysis, and forming a correlation relationship set.

[0096] Among them, the computer device standardizes the lithological combination feature, the structural ore-controlling regularity feature, and the physical property anomaly feature respectively, the standardization methods include mean standardization, standard deviation standardization, etc., to eliminate dimensional differences; then converts the standardized features into feature vectors of a unified dimension, the dimension of the feature vector is determined according to the number of features; then identifies the interdependence relationship and influence weight between the features through feature correlation analysis, such as Pearson correlation coefficient analysis, Spearman rank correlation analysis, etc.; finally arranges the interdependence relationship and influence weight into a correlation relationship set.

[0097] Step 155: constructing a model structure of the ore body prediction model with the set of association relationships as constraint conditions, the model structure comprising a feature input layer, an association analysis layer and a result output layer; wherein the association analysis layer embeds the interdependence rules and influence weights in the set of association relationships.

[0098] The computer device determines a model structure of the ore body prediction model with the set of association relationships as constraint conditions, the model structure comprising a feature input layer, an association analysis layer and a result output layer; the feature input layer is used to receive input feature data, the association analysis layer is used to analyze the association relationships between features, and the result output layer is used to output prediction results; wherein the association analysis layer embeds the interdependence rules and influence weights in the set of association relationships, the interdependence rules are used to determine the dependency relationship between features, and the influence weights are used to determine the influence degree of features on prediction results.

[0099] Step 1551: obtaining input indicators and output indicators of the ore body prediction model, the input indicators being lithology combination feature data, structure ore-controlling regularity feature data and physical property anomaly feature data of the target exploration area, and the output indicators being spatial distribution probability and morphological parameters of the concealed ore body.

[0100] The computer device obtains input indicators and output indicators of the ore body prediction model, the input indicators being lithology combination feature data, structure ore-controlling regularity feature data and physical property anomaly feature data of the target exploration area, the lithology combination feature data including lithology type proportion, combination arrangement order and vertical variation law, the structure ore-controlling regularity feature data including fault ore-controlling range, structure intersection relationship and association with the ore body, and the physical property anomaly feature data including abnormal spatial coordinates, intensity gradient and spatial association with the fault; the output indicators being spatial distribution probability and morphological parameters of the concealed ore body, the spatial distribution probability indicating the probability of existence of the concealed ore body at each position in the target exploration area, and the morphological parameters indicating the shape and size of the concealed ore body.

[0101] Step 1552: constructing a feature input layer; wherein the feature input layer comprises a data preprocessing branch and a feature standardization branch, the data preprocessing branch being used to unify the format and remove noise of the input feature data, and the feature standardization branch being used to convert the preprocessed feature data into a standardized feature vector with uniform dimension.

[0102] The computer device constructs a feature input layer, and the feature input layer includes a data preprocessing branch and a feature standardization branch. The data preprocessing branch uniformly formats the input feature data, converts feature data in different formats into the same format, such as converting text format into numerical format, and simultaneously removes noise, removes abnormal values and missing values in the feature data, and the like. The feature standardization branch converts the preprocessed feature data into a standardized feature vector with a uniform dimension, and the standardization method includes mean standardization, standard deviation standardization, and the like.

[0103] Step 1553: constructing a correlation analysis layer; the correlation analysis layer includes a rule storage sub-branch, a weight distribution sub-branch, and a correlation calculation sub-branch. The rule storage sub-branch is used to store interdependent rules in the correlation relationship set, convert the rules into logical judgment statements executable by the model, the weight distribution sub-branch is used to distribute the influence weight in the correlation relationship set to the corresponding feature index, determine the contribution degree of each feature index to the prediction result, and the feature index with a high contribution degree is assigned a higher weight coefficient. The correlation calculation sub-branch embeds the logical judgment statement and the weight coefficient, is used to receive the standardized feature vector from the feature input layer, analyzes the matching of each feature index in the feature vector through the logical judgment statement, and calculates the correlation score of each feature index in combination with the weight coefficient.

[0104] The computer device constructs a correlation analysis layer, and the correlation analysis layer includes a rule storage sub-branch, a weight distribution sub-branch, and a correlation calculation sub-branch. The rule storage sub-branch stores interdependent rules in the correlation relationship set, converts the rules into logical judgment statements executable by the model, such as “if the phosphate rock proportion in the lithology type proportion is greater than 50%, and the fracture ore-controlling range in the structure ore-controlling law feature contains the position, then the position has a higher probability of containing a concealed ore body”. The weight distribution sub-branch distributes the influence weight in the correlation relationship set to the corresponding feature index, such as the influence weight of the phosphate rock proportion is 0.6, and the influence weight of the fracture ore-controlling range is 0.4. The correlation calculation sub-branch embeds the logical judgment statement and the weight coefficient, receives the standardized feature vector from the feature input layer, analyzes the matching of each feature index in the feature vector through the logical judgment statement, and calculates the correlation score of each feature index in combination with the weight coefficient. The calculation method of the correlation score is that the standardized value of the feature index is multiplied by the weight coefficient.

[0105] Step 1554: constructing a result output layer; the result output layer includes a result fusion sub-branch and a format conversion sub-branch. The result fusion sub-branch is used to comprehensively fuse the correlation scores output by the correlation calculation sub-branch to obtain the spatial distribution probability value of the concealed ore body. The format conversion sub-branch is used to convert the spatial distribution probability value and the corresponding morphological parameters into a standardized output format.

[0106] The computer device constructs a result output layer, and the result output layer includes a result fusion sub-branch and a format conversion sub-branch; the result fusion sub-branch comprehensively fuses the correlation scores output by the correlation analysis sub-branch, and the fusion method includes weighted summation, logistic regression, etc., to obtain the spatial distribution probability value of the concealed ore body; and the format conversion sub-branch converts the spatial distribution probability value and the corresponding morphological parameters into a standardized output format, such as a JSON format, an XML format, etc.

[0107] Step 1555: connecting the feature input layer, the correlation analysis layer and the result output layer in a data transmission order to form the model structure.

[0108] The computer device connects the feature input layer, the correlation analysis layer and the result output layer in a data transmission order, the output of the feature input layer is connected to the input of the correlation analysis layer, the output of the correlation analysis layer is connected to the input of the result output layer, and the model structure is formed.

[0109] Step 156: collecting geological data of known ore bodies as training samples, inputting the feature data in the training samples into the ore body prediction model, and adjusting the internal parameters of the ore body prediction model to complete the construction of the ore body prediction model.

[0110] The computer device collects geological data of known ore bodies as training samples, and the training samples include lithological combination feature data, structure ore-controlling regularity feature data, physical property anomaly feature data and corresponding spatial distribution probability and morphological parameters of the concealed ore body; then the feature data in the training samples is input into the ore body prediction model; then the internal parameters of the ore body prediction model are adjusted, such as the weight coefficients in the correlation analysis layer and the threshold values of the logical judgment statements, and the adjustment method includes the gradient descent method and the genetic algorithm; finally, the construction of the ore body prediction model is completed.

[0111] Step 157: inputting the spatial data of the target exploration area into the constructed ore body prediction model to obtain the spatial coordinates, morphological parameters and distribution trend of the concealed ore body through the correlation analysis layer.

[0112] The computer device collects spatial data of the target exploration area, and the spatial data includes stratum data, structure data and geophysical data; then the spatial data is input into the constructed ore body prediction model; then the correlation between the features is analyzed through the correlation analysis layer to obtain the spatial coordinates, morphological parameters and distribution trend of the concealed ore body; finally, the processing result is output.

[0113] Step 1571: Collect spatial data of the target exploration area covering stratum lithology, geological structure, geophysical exploration and spatial coordinates, and after preprocessing the spatial data, extract lithology combination features, structure ore-controlling regularity features and physical property anomaly feature related data according to the input indicators of the ore body prediction model to generate an original input data set.

[0114] Among them, the computer device collects spatial data of the target exploration area, and the data covers stratum lithology, geological structure, geophysical exploration and spatial coordinates, and the data sources include field geological survey, geophysical exploration, well logging, etc.; then the spatial data is preprocessed, the preprocessing includes format unification, noise removal, etc.; then the lithology combination features, structure ore-controlling regularity features and physical property anomaly feature related data are extracted according to the input indicators of the ore body prediction model; finally, the original input data set is generated.

[0115] Step 1572: calling the feature input layer of the ore body prediction model to complete data standardization to obtain a standardized feature vector, inputting the standardized feature vector into the correlation calculation sub-branch of the correlation analysis layer, and calling the interdependent rules stored in the rule storage sub-branch by the correlation calculation sub-branch to perform one-by-one logical judgment on the matching relationship between each feature index in the standardized feature vector and the ore body.

[0116] Among them, the computer device calls the feature input layer of the ore body prediction model to standardize the original input data set and obtain a standardized feature vector; then the standardized feature vector is input into the correlation calculation sub-branch of the correlation analysis layer; then the correlation calculation sub-branch calls the interdependent rules stored in the rule storage sub-branch to perform one-by-one logical judgment on the matching relationship between each feature index in the standardized feature vector and the ore body, such as whether the phosphate rock proportion in the lithology type proportion is greater than the threshold value, whether the fracture ore-controlling range in the structure ore-controlling regularity feature contains the position, etc.

[0117] Step 1573: synchronously acquiring the weight coefficients assigned to each feature index by the weight distribution sub-branch through the correlation calculation sub-branch, calculating the correlation score corresponding to each standardized feature vector based on the logical matching result and combining the weight coefficients through the weighted summation algorithm; wherein the weighted summation algorithm is based on the standardized feature vector.

[0118] The computer equipment synchronously obtains the weight coefficients assigned to each feature index by the weight allocation sub-branch through the association calculation sub-branch; then, based on the logical matching results, such as whether the matching relationship between the feature index and the ore body is yes or no, it calculates the association score corresponding to each standardized feature vector by combining the weight coefficients through a weighted summation algorithm. The calculation method of the weighted summation algorithm is to multiply the standardized value of each feature index by the corresponding weight coefficient, and then add the products; the weighted summation algorithm is based on the standardized feature vectors to ensure the accuracy of the calculation results.

[0119] Step 1574: Input the association score into the result fusion sub-branch of the result output layer, perform complementary verification on the single feature association score through a multi-feature fusion algorithm, obtain the distribution probability of concealed ore bodies at each spatial location of the target exploration area, and screen out potential areas where the probability of the presence of concealed ore bodies is higher than the distribution probability threshold based on the preset distribution probability threshold.

[0120] The computer equipment inputs the association score into the result fusion sub-branch of the result output layer; then, the single feature association score is complementarily verified by a multi-feature fusion algorithm, including Bayesian fusion, DS evidence theory, etc., to obtain the distribution probability of concealed ore bodies at each spatial location in the target exploration area; then, based on a preset distribution probability threshold, such as 0.7, potential areas with a concealed ore body existence probability higher than 0.7 are selected.

[0121] Step 1575: Combine the spatial coordinate data carried in the standardized feature vector to locate the three-dimensional spatial coordinate range of the potential area. Analyze the boundary contour, extension length and thickness changes of the potential area through the geological body morphology analysis algorithm to obtain morphological parameters. Analyze the spatial positional relationship and morphological correlation of adjacent potential areas to obtain the distribution trend of the concealed ore body.

[0122] The computer equipment, combined with the spatial coordinate data carried in the standardized feature vector, locates the three-dimensional spatial coordinate range of the potential area. The coordinate range is determined based on the maximum and minimum coordinate values ​​in the spatial coordinate data. Then, the boundary contour of the potential area is analyzed through geological body morphology analysis algorithms, such as circular, elliptical, and irregular shapes, as well as the extension length, such as the length of the longest extension direction of the potential area, and the thickness variation, such as the vertical thickness variation of the potential area, to obtain morphological parameters. Next, the spatial positional relationship and morphological correlation of adjacent potential areas are analyzed, such as whether adjacent potential areas are connected or whether their shapes are similar, to obtain the distribution trend of the concealed ore body. The distribution trend includes the extension direction and distribution range of the concealed ore body.

[0123] Step 158: Based on the spatial coordinates, morphological parameters and distribution trend of the concealed ore body, combined with the spatial structure network of the three-dimensional stratum model, a three-dimensional visualization prediction result for the spatial distribution of the concealed ore body is generated, which is used to present the spatial positional relationship between the concealed ore body and the stratum and the fault.

[0124] In the method, the computer device determines the position and morphology of the concealed ore body in the three-dimensional space based on the spatial coordinates, morphological parameters and distribution trend of the concealed ore body, combined with the spatial structure network of the three-dimensional stratum model; then generates a three-dimensional visualization prediction result for the spatial distribution of the concealed ore body, which is presented in the form of a three-dimensional graph and contains the spatial position, morphology, distribution trend of the concealed ore body, and the spatial positional relationship with the stratum and the fault; and finally outputs the three-dimensional visualization prediction result for guiding mineral resource exploration.

[0125] In another embodiment, the method further comprises:

[0126] Step 161: Extract the spatial correlation features of the concealed ore body area and the surrounding stratum from the three-dimensional visualization prediction result, and call the original stratum attribute data of the concealed ore body area from the three-dimensional stratum model, to obtain attribute difference information of the ore body associated stratum through feature comparison, and generate a feature difference data set.

[0127] In the method, the computer device extracts the spatial correlation features of the concealed ore body area and the surrounding stratum from the three-dimensional visualization prediction result, including the positional relationship and contact mode of the concealed ore body area and the surrounding stratum; then calls the original stratum attribute data of the concealed ore body area from the three-dimensional stratum model, including the lithology, thickness and age of the stratum; then compares the attribute difference between the concealed ore body area and the surrounding stratum through feature comparison, to obtain the attribute difference information of the ore body associated stratum; and finally organizes the attribute difference information into a feature difference data set.

[0128] Step 162: Fuse the feature difference data set with the original geological structure feature parameters of the target exploration area, perform feature vector conversion on the fused data, establish a mapping relationship between the difference features and the structure parameters through semantic correlation analysis, and obtain a fused feature set.

[0129] In the method, the computer device fuses the feature difference data set with the original geological structure feature parameters of the target exploration area, and the fusion method includes feature splicing and feature weighting; then performs feature vector conversion on the fused data, converts the fused data into feature vectors; then establishes a mapping relationship between the difference features and the structure parameters through semantic correlation analysis, such as the mapping relationship between the lithology difference in the attribute difference information and the fault property in the geological structure feature parameters; and finally obtains a fused feature set.

[0130] Step 163: dynamically adjust the lithology combination attribute and spatial distribution parameter of the corresponding stratigraphic unit in the three-dimensional stratigraphic model based on the fusion feature set, and generate an initial updated model; wherein the adjustment process maintains the overall structural consistency of the strata based on the stratigraphic superimposition relationship constraint condition.

[0131] wherein the computer device determines the stratigraphic unit that needs to be adjusted based on the fusion feature set; then dynamically adjusts the lithology combination attribute and spatial distribution parameter of the corresponding stratigraphic unit in the three-dimensional stratigraphic model, the adjustment of the lithology combination attribute is based on the lithology difference information in the feature difference data set, and the adjustment of the spatial distribution parameter is based on the spatial position difference information in the feature difference data set; then generate an initial updated model; wherein the adjustment process maintains the overall structural consistency of the strata based on the stratigraphic superimposition relationship constraint condition, such as the stratigraphic superimposition order being unchanged.

[0132] Step 164: spatially match and verify the initial updated model with the three-dimensional fracture system model, identify the spatial connection contradiction area of the strata and the fractures, and correct the boundary parameters of the initial updated model based on the feature difference data of the contradiction area to obtain the feature update result of the three-dimensional stratigraphic model.

[0133] wherein the computer device spatially matches and verifies the initial updated model with the three-dimensional fracture system model, the verification method includes coordinate matching, shape matching, etc.; then identifies the spatial connection contradiction area of the strata and the fractures, such as the boundary of the strata not matching the position of the fractures; then corrects the boundary parameters of the initial updated model based on the feature difference data of the contradiction area, the correction of the boundary parameters is based on the spatial position difference information in the feature difference data set; finally obtains the feature update result of the three-dimensional stratigraphic model.

[0134] In another embodiment, the method further comprises:

[0135] Step 171: extract the spatial coordinate boundary of the concealed ore body from the three-dimensional visualization prediction result, combine the spatial distribution data of the fractures in the three-dimensional fracture system model, identify the intersection features of the ore body boundary and the fractures, and generate a fracture-ore body correlation feature vector.

[0136] wherein the computer device extracts the spatial coordinate boundary of the concealed ore body from the three-dimensional visualization prediction result, the boundary coordinates include the maximum and minimum three-dimensional coordinates of the concealed ore body; then combines the spatial distribution data of the fractures in the three-dimensional fracture system model, including the three-dimensional coordinate point set of the fractures; then identifies the intersection features of the ore body boundary and the fractures, including the three-dimensional coordinates of the intersection position, the intersection mode, etc.; finally generates a fracture-ore body correlation feature vector, the dimension of the feature vector is determined according to the number of intersection features.

[0137] Step 172: Collecting regional tectonic evolution time series data of the target exploration area, dividing the time series data into stages and converting it into a space-time vector form, aligning the space-time vector with the fault-ore body correlation feature vector in dimension, and obtaining a space-time fusion vector through feature fusion.

[0138] Wherein, the computer device collects regional tectonic evolution time series data of the target exploration area, and the data includes time stages of tectonic evolution, tectonic features of each stage, etc.; then the time series data is divided into stages, and the tectonic evolution is divided into multiple stages; then the tectonic features of each stage are converted into a space-time vector form, the direction of the space-time vector represents the time sequence of tectonic evolution, and the length represents the intensity of tectonic features; then the space-time vector is aligned with the fault-ore body correlation feature vector in dimension to ensure the consistency of the vector dimensions; finally, a space-time fusion vector is obtained through feature fusion, and the fusion method includes vector splicing, vector weighting, etc.

[0139] Step 173: Taking the space-time fusion vector as the iteration basis, the extension direction and spatial form parameters of the corresponding fault in the fault system three-dimensional model are time-sequenced and corrected to generate an initial iteration model, and the correction process keeps the association attributes of the fault and the sedimentary facies unchanged.

[0140] Wherein, the computer device takes the space-time fusion vector as the iteration basis to determine the fault that needs to be corrected; then the extension direction and spatial form parameters of the corresponding fault in the fault system three-dimensional model are time-sequenced and corrected, the correction of the extension direction is based on the time stage information in the space-time fusion vector, and the correction of the spatial form parameter is based on the tectonic feature intensity information in the space-time fusion vector; then an initial iteration model is generated; wherein, the correction process keeps the association attributes of the fault and the sedimentary facies unchanged, such as the intersection position and intersection mode of the fault and the sedimentary facies.

[0141] Step 174: Spatial correlation verification of the initial iteration model and the three-dimensional stratum model with feature update, identification of the spatial connection contradiction area of stratum and fault, boundary parameter correction of the initial iteration model based on the feature difference data of the contradiction area, obtaining the space-time vector iteration result of the fault system three-dimensional model, and the iteration result is used to improve the time sequence adaptability of ore body prediction.

[0142] The computer device performs spatial correlation verification on the initial iteration model and the three-dimensional stratum model after feature updating, and the verification method includes coordinate matching, shape matching, and the like; then, a spatial connection contradiction area of the stratum and the fracture is identified, such as an extension direction of the fracture being inconsistent with a distribution direction of the stratum; then, a boundary parameter of the initial iteration model is corrected based on feature difference data of the contradiction area, and the correction of the boundary parameter is based on spatial position difference information in the feature difference data set; and finally, a time-space vector iteration result of the fracture system three-dimensional model is obtained, and the iteration result is used to improve time sequence adaptability of ore body prediction, so that the ore body prediction result is more in line with the law of regional tectonic evolution.

[0143] The embodiment of the application extracts and converts feature information of the thrust nappe structure of the target exploration area into geological structure feature parameters, integrates stratum age data and lithology distribution information to construct a three-dimensional stratum model, imports geophysical data to extract geophysical response features of strata at different depths and corrects the features in combination with lithology boundaries to obtain deep fracture feature parameters, divides a sedimentary system based on facies paleogeographic information and integrates the deep fracture feature parameters to establish a fracture system three-dimensional model, and finally identifies multi-feature correlation and establishes an ore body prediction model to obtain a three-dimensional visual prediction result of spatial distribution of the concealed ore body.

[0144] In this way, multi-dimensional data fusion and analysis of geological structure, stratum spatial distribution, geophysical response, facies paleogeography, and ore body prediction are realized as a whole, scientific problems such as unclear sedimentary microfacies of the phosphorus-bearing strata and unclear relationship between the sedimentary microfacies and phosphorus enrichment mineralization and unclear spatial distribution law caused by large spatial distribution variation of the deep phosphorus ore are effectively solved, deep phosphorus ore exploration technology is established, fine reconstruction technology of facies paleogeography based on big data, three-dimensional prediction technology of deep phosphorus ore in a mineralization area based on multi-source data, green and efficient ore prediction technology based on fusion of geological, geophysical, geochemical, and remote sensing big data and artificial intelligence, and deep phosphorus ore spatial accurate positioning technology are broken through, comprehensive and accurate technical support is provided for exploration and prediction of the deep phosphorus ore, and efficiency and accuracy of deep phosphorus ore exploration are improved.

[0145] Referring to Figure 2 As shown in the figure, the figure is a schematic diagram of a basic structure of a computer device 20 provided by an embodiment of the application, and the computer device 20 includes:

[0146] a processor 201;

[0147] a storage device 202, on which a computer program 2020 is stored;

[0148] When the computer program 2020 is executed by the processor 201, the processor 201 implements any of the mineral resource exploration data analysis methods based on three-dimensional space modeling.

[0149] Based on the above, a readable storage medium is provided, and the readable storage medium stores programs or instructions, and the programs or instructions are executed by a processor to implement the steps of the above method.

[0150] Referring to Figure 3 As shown in the figure, the figure is a function module block diagram of a mineral resource exploration data analysis device based on three-dimensional space modeling provided by an embodiment of the application, and the mineral resource exploration data analysis device based on three-dimensional space modeling comprises:

[0151] A feature information extraction module is configured to extract feature information of thrust nappe structure of a target exploration area by interface occurrence measurement, lithological combination analysis and tectonic movement trace identification, and convert the feature information into geological structure feature parameters.

[0152] A stratum model construction module is configured to integrate stratum age data and lithological distribution information of the target exploration area based on the geological structure feature parameters, and construct a three-dimensional stratum model reflecting stratum spatial distribution of the target exploration area by taking the determined stratum superimposition relationship by tectonic sequence division as a constraint condition.

[0153] A fault feature determination module is configured to import geophysical data of the target exploration area based on the three-dimensional stratum model, extract geophysical response features of strata at different depths from the geophysical data, and correct the extraction results in combination with lithological boundary correction in the three-dimensional stratum model to obtain deep fault feature parameters.

[0154] A spatial correlation analysis module is configured to divide a sedimentary system of the target exploration area to obtain a sedimentary system division result, integrate the deep fault feature parameters into the sedimentary system division result, and establish a fault system three-dimensional model reflecting spatial distribution of faults and a relationship with deposition by spatial correlation analysis of faults and sedimentary facies belts.

[0155] A mineral body prediction processing module is configured to identify an association relationship between lithological combination features corresponding to a mineral body deposition environment, tectonic ore-controlling regularity features corresponding to geological structure feature parameters, and physical property anomaly features corresponding to geophysical data based on the three-dimensional stratum model and the fault system three-dimensional model, establish a mineral body prediction model by taking the association relationship as a constraint, input spatial data of the target exploration area into the mineral body prediction model, and obtain a three-dimensional visual prediction result for spatial distribution of a concealed mineral body.

[0156] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0157] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments.

[0158] In addition, each functional unit in the embodiments of the present application can be integrated in a processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of a software functional unit.

[0159] If the integrated unit 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, the technical solutions of the present application essentially or the part that makes a contribution to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing 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 the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0160] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for analyzing mineral resource exploration data based on three-dimensional spatial modeling, characterized in that, include: By measuring the occurrence of structural interfaces, analyzing lithological combinations, and identifying traces of tectonic movement, characteristic information of thrust-nappe structures in the target exploration area is extracted, and the characteristic information is converted into geological structural characteristic parameters. Based on the geological structural characteristic parameters, the stratigraphic age data and lithological distribution information of the target exploration area are integrated, and the stratigraphic superposition relationship determined by the structural sequence division is used as a constraint to construct a three-dimensional stratigraphic model that reflects the spatial distribution of the stratigraphy in the target exploration area. Based on the three-dimensional stratigraphic model, geophysical data of the target exploration area are imported, and geophysical response features of strata at different depths are extracted from the geophysical data. Combined with the lithological boundary correction extraction results in the three-dimensional stratigraphic model, deep fault characteristic parameters are obtained. The lithofacies paleogeographic information of the target exploration area is used to classify the sedimentary system, and the sedimentary system classification results are obtained. The deep fault characteristic parameters are integrated into the sedimentary system classification results. A three-dimensional model of the fault system reflecting the spatial distribution of faults and their relationship with sedimentation is established through spatial correlation analysis between faults and sedimentary facies zones. Based on the three-dimensional stratigraphic model and the three-dimensional fault system model, the correlation between the lithological combination characteristics corresponding to the sedimentary environment of the ore body, the tectonic ore-controlling law characteristics corresponding to the geological structural characteristic parameters, and the physical property anomaly characteristics corresponding to the geophysical data are identified. The ore body prediction model is established with the correlation as a constraint. The spatial data of the target exploration area is input into the ore body prediction model to obtain the three-dimensional visualization prediction results for the spatial distribution of the concealed ore body.

2. The method as described in claim 1, characterized in that, Based on the three-dimensional stratigraphic model, geophysical data of the target exploration area is imported. Geophysical response features of strata at different depths are extracted from the geophysical data, and combined with the lithological boundary correction extraction results in the three-dimensional stratigraphic model, deep fault characteristic parameters are obtained, including: Using the spatial distribution range of strata and the coordinates of lithological interfaces in the three-dimensional stratigraphic model as a reference, the processing range of geophysical data is defined, and the geophysical data is divided into multiple data subsets according to the depth gradient. Each data subset corresponds to a stratigraphic unit in the three-dimensional stratigraphic model. Geophysical response feature extraction is performed on each data subset. An initial response feature set containing amplitude features, phase features, and continuity features is obtained through signal feature decomposition. Simultaneously, the lithological boundary coordinates and lithological variation attributes of the corresponding stratigraphic units in the three-dimensional stratigraphic model are extracted. The initial response feature set is spatially matched with the lithological boundary coordinates. Based on the lithological change attributes, it is determined whether there are abnormal feature points in the initial response feature set. If there are, the spatial location corresponding to the abnormal feature point is marked and its spatial relationship with the lithological boundary is analyzed. For the marked anomalous feature points, backtrack to the corresponding geophysical data subset, re-perform signal feature decomposition and adjust the decomposition parameters to obtain the corrected response feature set. The corrected response feature set is then correlated and compared with the response feature sets of adjacent stratigraphic units at different depths. Regions with abrupt feature changes during the correlation comparison process are extracted. The spatial range of these regions is determined by combining the lithological boundary coordinates. The abrupt feature change patterns and spatial morphology of these regions are analyzed to generate deep fracture feature parameters that include the location of the abrupt change, the direction of extension, and the magnitude of the feature changes.

3. The method as described in claim 1, characterized in that, The process involves classifying the sedimentary system based on the lithofacies paleogeographic information of the target exploration area, obtaining sedimentary system classification results, incorporating the deep fault characteristic parameters into the sedimentary system classification results, and establishing a three-dimensional model of the fault system reflecting the spatial distribution of faults and their relationship with sedimentation through spatial correlation analysis between faults and sedimentary facies zones. This includes: Collect lithofacies paleogeographic information covering sedimentary facies types, paleocurrent directions, and lithological facies transition characteristics of the target exploration area. Based on the spatial distribution of sedimentary facies types and the gradient changes of lithological facies transition characteristics, divide the target exploration area into sedimentary systems to obtain multiple initial sedimentary system units. Extract the spatial boundary coordinates, core sedimentary features, and lithological composition information of each initial sedimentary system unit. Spatially superimpose the fracture location coordinates in the deep fracture feature parameters with the spatial boundary coordinates of the initial sedimentary system unit to determine the initial sedimentary system unit and the location through which the fracture passes. For the initial sedimentary system unit through which the fault passes, the spatial relationship between the extension direction of the fault and the direction of the paleocurrent within the unit is analyzed. The influence range of the fault on the sedimentary process is determined by combining the characteristic variation amplitude of the fault. The spatial boundary of the initial sedimentary system unit is adjusted according to the influence range to obtain the corrected sedimentary system division result. Based on the corrected sedimentary system division results, a spatial distribution network of sedimentary facies zones is constructed. The fracture extension direction and abrupt change location in the deep fracture characteristic parameters are integrated into the spatial distribution network of sedimentary facies zones, and the intersection location and intersection mode of each fracture with different sedimentary facies zones are marked. The correlation between fracture characteristics and sedimentary facies zone characteristics at the intersection was analyzed, the distribution density and extension patterns of fractures in different sedimentary facies zones were statistically analyzed, and spatial correlation rules between fractures and sedimentary facies zones were established. Based on the spatial distribution network of sedimentary facies zones, and combined with the spatial correlation rules and complete deep fracture characteristic parameters, a three-dimensional model of the fracture system is constructed, which includes fracture spatial coordinates, extension morphology, and sedimentary facies zone correlation attributes.

4. The method as described in claim 3, characterized in that, The collection of lithofacies paleogeographic information covering sedimentary facies types, paleocurrent directions, and lithological facies transition characteristics of the target exploration area, based on the spatial distribution patterns of sedimentary facies types and the gradient changes in lithological facies transition characteristics, divides the target exploration area into sedimentary systems, obtaining multiple initial sedimentary system units, including: Collect lithofacies paleogeographic information of the target exploration area, and classify and organize the lithofacies paleogeographic information to determine sedimentary facies types, paleocurrent direction data and lithological facies transition characteristics; The sedimentary facies types are classified, the main sedimentary facies types and typical lithological markers of each type are determined, the paleocurrent direction data are converted into spatial vector form, the lithological facies transition characteristics are quantified, and lithological facies transition gradient data are obtained. Based on the planar coordinates of the target exploration area, a two-dimensional analysis grid is constructed, and the classified sedimentary facies types, paleocurrent directions in spatial vector form, and quantified lithological facies transition gradient data are mapped to the corresponding nodes of the two-dimensional analysis grid. The similarity of sedimentary facies types, the angle between paleocurrent direction vectors, and the difference in lithological facies gradients among adjacent nodes in a two-dimensional analysis grid are calculated. Similarity thresholds, angle thresholds, and difference thresholds are set. Adjacent nodes that meet the following criteria are grouped into the same unit: sedimentary facies type similarity is higher than the similarity threshold, paleocurrent direction vector angle is lower than the angle threshold, and lithological facies gradient difference is lower than the difference threshold. Among these criteria, sedimentary facies type similarity is calculated based on type matching degree, paleocurrent direction vector angle is based on spatial vector angle measurement, and lithological facies gradient difference is based on lithological change intensity quantification. Traversing the entire two-dimensional analysis grid and repeating the node merging process, multiple initial sedimentary units on the plane are formed. Combining the stratigraphic thickness information and vertical lithological variation characteristics in the three-dimensional stratigraphic model, the initial sedimentary units on the plane are extended vertically to obtain multiple initial sedimentary system units containing vertical dimension information. The vertical boundary coordinates, planar distribution range, and core feature parameters of each initial sedimentary system unit are extracted. The core feature parameters include the dominant sedimentary facies type, the dominant paleocurrent direction, and the average lithological facies gradient. The dominant paleocurrent direction is the paleocurrent flow direction that dominated the target exploration area during the target sedimentary period. Based on the corrected sedimentary system division results, a spatial distribution network of sedimentary facies zones is constructed. The fracture extension direction and abrupt change locations from the deep fracture characteristic parameters are integrated into the spatial distribution network. The intersection locations and intersection methods of each fracture with different sedimentary facies zones are marked, including: Based on the spatial boundary coordinates and core sedimentary features of each sedimentary system unit in the revised sedimentary system division results, the spatial range and vertical distribution level of each sedimentary facies zone are determined, and a three-dimensional spatial distribution network of sedimentary facies zones is constructed. The spatial distribution network of sedimentary facies zones includes the unique identifier and characteristic attribute index of each sedimentary facies zone. Extract the fracture ID, spatial coordinate sequence, extension direction vector, and characteristic abrupt change location coordinates of each fracture from the deep fracture feature parameters, and convert the spatial coordinate sequence of each fracture into coordinates in a coordinate system consistent with the spatial distribution network of sedimentary facies zones; Traverse the spatial coordinate sequence of each fault, determine the sedimentary facies zone to which each coordinate point in the sequence belongs, and when a coordinate point enters the coordinate range of another sedimentary facies zone from one sedimentary facies zone, record the coordinate point as the intersection of the fault and the sedimentary facies zone, and record the unique identifier of the sedimentary facies zone before and after the intersection; for each intersection position, combine the extension direction vector of the fault and the distribution characteristics of adjacent coordinate points to determine the intersection mode of the fault and the sedimentary facies zone. The fracture ID, extension direction vector, coordinates of all intersection locations, corresponding sedimentary facies zone identifiers, and intersection methods of each fracture are associated and stored to form a fracture-sedimentary facies zone association data table. This association data table is then integrated as attribute information into the spatial distribution network of sedimentary facies zones.

5. The method according to any one of claims 1-4, characterized in that, Based on the three-dimensional stratigraphic model and the three-dimensional fault system model, the correlation between lithological combination characteristics corresponding to the ore body sedimentary environment, tectonic ore-controlling characteristics corresponding to geological structural parameters, and physical property anomalies corresponding to geophysical data is identified. An ore body prediction model is established using these correlations as constraints. Spatial data of the target exploration area is input into the ore body prediction model to obtain a three-dimensional visualization prediction result for the spatial distribution of concealed ore bodies, including: The lithological assemblage information corresponding to the ore body depositional environment is extracted from the three-dimensional stratigraphic model. The lithological assemblage characteristics are obtained through lithological type statistics and assemblage sequence analysis. The lithological assemblage characteristics include the proportion of lithological types, the order of assemblage arrangement, and the vertical variation law. From the geological structural feature parameters, structural attributes related to ore control are screened. Combined with the fault distribution and extension characteristics in the three-dimensional model of the fault system, the control effect of structure on ore body formation is analyzed to obtain the structural ore control law characteristics. The structural ore control law characteristics include the fault ore control range, structural intersection relationship and the correlation with ore body. Physical property information related to the ore body is extracted from the geophysical data. Combined with the abnormal feature areas in the deep fault characteristic parameters, the spatial distribution and intensity changes of physical property anomalies are identified to obtain physical property anomaly characteristics. The physical property anomaly characteristics include anomaly spatial coordinates, intensity gradient and spatial correlation with the fault. The lithological combination characteristics, the tectonic ore-controlling regularity characteristics, and the physical property anomaly characteristics are standardized to eliminate dimensional differences and converted into feature vectors of a unified dimension. Through feature correlation analysis, the interdependence and influence weights are identified to form a set of correlation relationships. Using the set of relationships as constraints, a model structure for predicting ore bodies is constructed. The model structure includes a feature input layer, a relationship analysis layer, and a result output layer. The relationship analysis layer embeds the interdependence rules and influence weights in the set of relationships. Geological data of known ore bodies are collected as training samples. Feature data from the training samples are input into the ore body prediction model, and the internal parameters of the ore body prediction model are adjusted to complete the construction of the ore body prediction model. The spatial data of the target exploration area is input into the constructed ore body prediction model, and the spatial coordinates, morphological parameters and distribution trend of the concealed ore body are obtained through the correlation analysis layer. Based on the spatial coordinates, morphological parameters, and distribution trend of the concealed ore body, and combined with the spatial structure network of the three-dimensional stratigraphic model, a three-dimensional visualization prediction result for the spatial distribution of the concealed ore body is generated. The three-dimensional visualization prediction result is used to present the spatial positional relationship between the concealed ore body and the strata and faults.

6. The method as described in claim 5, characterized in that, The lithological assemblage information corresponding to the ore body's sedimentary environment is extracted from the three-dimensional stratigraphic model. Lithological assemblage characteristics are obtained through lithological type statistics and assemblage sequence analysis. These characteristics include the proportion of lithological types, the order of assemblage arrangement, and the vertical variation patterns, including: Based on the regional geological background and known geological clues, the spatial range of the ore body depositional environment is delineated in the three-dimensional stratigraphic model, and the lithological information and spatial coordinate data of all stratigraphic units within this spatial range are extracted. The extracted lithological information is classified and statistically analyzed to determine the lithological types included in the spatial range and the number of stratigraphic units corresponding to each lithological type. The proportion of each lithological type to the total number of stratigraphic units is calculated to obtain the lithological type proportion data. According to the vertical depth order in the spatial coordinate data, the stratigraphic units within the spatial range are sorted to form a stratigraphic sequence from shallow to deep. The lithology type of each stratigraphic unit is extracted, and the occurrence sequence of lithology types is recorded according to the sorting order to obtain the initial lithology combination sequence. Sequence analysis was performed on the initial lithological assemblage sequence to identify recurring lithological assemblage segments and their frequency of occurrence, determine the dominant lithological assemblage pattern, and analyze the transformation mode and frequency of lithological types in the sequence to determine the characteristics of the assemblage arrangement order. A lithology vertical distribution map is constructed with vertical depth as the horizontal axis and lithology type as the vertical axis. The depth range and thickness corresponding to each lithology type are marked in the lithology vertical distribution map. The variation characteristics of lithology types in different depth ranges are identified, and the transformation gradient of lithology type with depth is calculated. By combining lithological vertical distribution maps and transformation gradient data, we can explore the variation law of lithological assemblage with vertical depth. This variation law includes the vertical zonation characteristics of lithological types and the dominant lithological types in different depth segments. By combining the proportion of lithological types, the characteristics of their combination and arrangement, and the vertical variation patterns, lithological combination characteristics are generated.

7. The method as described in claim 5, characterized in that, The model structure for constructing the ore body prediction model using the set of relationships as constraints includes: The input and output indicators of the ore body prediction model are obtained. The input indicators are the lithological combination characteristics, structural ore-controlling regularity characteristics, and physical property anomaly characteristics of the target exploration area. The output indicators are the spatial distribution probability and morphological parameters of the concealed ore body. Construct a feature input layer; wherein the feature input layer includes a data preprocessing branch and a feature standardization branch. The data preprocessing branch is used to unify the format and remove noise from the input feature data, and the feature standardization branch is used to convert the preprocessed feature data into a standardized feature vector with a unified dimension. A correlation analysis layer is constructed, comprising a rule storage sub-branch, a weight allocation sub-branch, and a correlation calculation sub-branch. The rule storage sub-branch stores the interdependence rules in the correlation set and converts the rules into logical judgment statements that the model can execute. The weight allocation sub-branch assigns the influence weights in the correlation set to the corresponding feature indicators, determines the contribution of each feature indicator to the prediction result, and assigns higher weight coefficients to feature indicators with higher contribution. The correlation calculation sub-branch embeds logical judgment statements and weight coefficients, receives standardized feature vectors from the feature input layer, analyzes the matching of each feature indicator in the feature vector through logical judgment statements, and calculates the correlation score of each feature indicator in combination with the weight coefficients. Construct a result output layer; wherein, the result output layer includes a result fusion sub-branch and a format conversion sub-branch. The result fusion sub-branch is used to comprehensively fuse the correlation scores output by the correlation calculation sub-branch to obtain the spatial distribution probability value of the concealed ore body. The format conversion sub-branch is used to convert the spatial distribution probability value and the corresponding morphological parameters into a standardized output format. The feature input layer, the correlation analysis layer, and the result output layer are connected in the order of data transmission to form the model structure.

8. The method as described in claim 5, characterized in that, The process of inputting spatial data of the target exploration area into the constructed ore body prediction model, and obtaining the spatial coordinates, morphological parameters, and distribution trend of the concealed ore body through correlation analysis layer processing includes: Spatial data covering stratigraphy, geological structure, geophysical exploration, and spatial coordinates of the target exploration area are collected. After preprocessing the spatial data, relevant data on lithological combination characteristics, structural ore-controlling regularity characteristics, and physical property anomaly characteristics are extracted based on the input indicators of the ore body prediction model to generate the original input dataset. The feature input layer of the ore body prediction model is called to complete data standardization and obtain a standardized feature vector. The standardized feature vector is then input into the correlation calculation sub-branch of the correlation analysis layer. The correlation calculation sub-branch calls the interdependence rules stored in the rule storage sub-branch to make logical judgments on the matching relationship between each feature index in the standardized feature vector and the ore body. The association calculation sub-branch synchronously obtains the weight coefficients assigned to each feature index by the weight allocation sub-branch. Based on the logical matching results, the association score corresponding to each standardized feature vector is calculated by weighted summation algorithm in combination with the weight coefficients. The weighted summation algorithm is based on the standardized feature vectors. The association score is passed to the result fusion sub-branch of the result output layer. The single feature association score is complemented and verified by the multi-feature fusion algorithm to obtain the distribution probability of the concealed ore body at each spatial location of the target exploration area. Based on the preset distribution probability threshold, potential areas with a probability of existence of concealed ore bodies higher than the distribution probability threshold are screened out. By combining the spatial coordinate data carried in the standardized feature vector, the three-dimensional spatial coordinate range of the potential area is located. The boundary contour, extension length and thickness variation of the potential area are analyzed by the geological body morphology analysis algorithm to obtain morphological parameters. The spatial position relationship and morphological correlation of adjacent potential areas are analyzed to obtain the distribution trend of the concealed ore body.

9. A computer device, characterized in that, include: processor; A storage device having a computer program stored thereon, which, when executed by the processor, causes the processor to implement the mineral resource exploration data analysis method based on three-dimensional spatial modeling as described in any one of claims 1-8.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions, which, when executed by a processor, implement the mineral resource exploration data analysis method based on three-dimensional spatial modeling as described in any one of claims 1-8.

Images