215 results about "Geologic map" patented technology

The invention provides a reservoir geologic modeling method based on a progressive conditional generative adversarial network, and relates to the field of reservoir geologic modeling, and the method comprises the steps: obtaining a geological image and logging data; constructing a training sample pair according to the geological image and the logging data; constructing a progressive conditional generative adversarial network based on logging data; training the progressive conditional generative adversarial network through the training sample pair in combination with a progressive training method of which the resolution is increased step by step; obtaining logging data to be predicted; and inputting to-be-predicted logging data into the progressive conditional generative adversarial network to generate a reservoir image conforming to both the geological mode and the logging data constraint. According to the technical scheme, the problems that in reservoir modeling, a geologic body space distribution mode is not enough to capture, and well logging data constraints are difficult to meet are solved.

The invention belongs to the technical field of mineral exploration, and particularly discloses a copper ore prospecting method based on three-dimensional geological modeling. The method comprises the following steps: acquiring geological data of a target area; after extreme values in the geological data are recognized and removed through a box plot, the geological data are mapped to an interval of [0, 1], and the geological data are divided into terrain data, stratum data and mineral resource data; adopting Kalman filtering to enable the data to form fusion data, and generating a thematic map comprising a geological map, a mineralization anomaly map and a topographic slope map; based on the data and the thematic map, constructing a three-dimensional geologic model by a finite element method, and performing mining path planning by adopting a specific path planning algorithm formula; exploring by the mining equipment according to the planned path, acquiring geological data, comparing the geological data with the predicted data, re-planning the path and continuing exploring if the path is blocked, the resource recovery is out of tolerance and new mineralization is abnormal, otherwise continuing exploring according to the planned path. The method has the advantages of being high in exploration efficiency and data precision, low in comprehensive cost and high in adaptability.

The invention provides a copper mine prediction method based on multi-source data, and the method comprises the steps: S11, taking a copper mine geological report as a data source, and enabling the copper mine geological report to comprise a geological map, geophysical data, a geochemical sample analysis result and a remote sensing image; s12, respectively carrying out vectorization processing on the data obtained in the step S11; s13, inputting the information processed in the step S12 into a machine learning model for feature extraction, and filling the extracted features into a copper ore distribution diagram; s14, constructing a multi-modal copper mine prediction model, inputting the copper mine distribution diagram into the multi-modal copper mine prediction model for training, and obtaining a trained multi-modal copper mine prediction model; and S15, inputting the newly obtained copper ore geological report into the multi-mode copper ore prediction model to obtain a copper ore distribution prediction map of the target area. According to the method, the relevance and similarity of the multi-source data in the copper mine geological report are extracted, the multi-mode copper mine prediction model is established to predict the potential position of the copper mine, and the success rate and efficiency of prospecting are improved.

The invention discloses a geological map-oriented breakpoint repairing and closed curve reconstruction method and system and a medium. The method comprises the following steps of: preprocessing an original geological map to obtain a grayscale image; carrying out binarization and morphological closed operation processing to obtain a grayscale image of the edge of the smooth contour curve; performing pixel skeletonization to obtain a pixel-level skeleton diagram; performing secondary edge extraction, connected domain marking and endpoint statistics on the pixel-level skeleton diagram, and screening out a non-closed curve and an endpoint set; carrying out nearest neighbor retrieval by adopting KD-Tree to obtain a candidate pairing set of each end point; performing priority pairing in combination with the spatial distance and the direction smoothness; and generating a transition line segment according to a pairing result to supplement the fracture part of the non-closed curve. According to the method, non-closed breakpoints generated by scanning or drawing errors in the geological map can be automatically restored, and a closed curve with topological integrity and a smooth boundary is generated.

The invention relates to a geological analysis technology, and discloses a multi-head attention model training method fused with geological rules, which is used for improving the scientificity and accuracy of geological body model prediction and improving the interpretability of a geological analysis process. According to the scheme, firstly, feature vectors of geological attribute data and geological coordinate data are spliced to obtain a fusion feature vector, then spatial locality and directivity are considered at the same time, geomorphic multi-head attention calculation is conducted on the fusion feature vector, and an attention feature sequence is obtained; then extracting multi-scale features from the input geological map, constructing a cross-fault weight mask matrix based on fault constraints, and obtaining comprehensive features through fusion; calculating a loss value by adopting a joint loss function containing prediction loss and fault non-penetrability constraint terms, and updating model parameters through a back propagation algorithm to complete model training; and finally, drawing an attention thermodynamic diagram for visual display, and superposing the attention thermodynamic diagram with the three-dimensional geologic model.

The invention discloses a magmatic rock mineral analysis method and system based on geological images, and relates to the technical field of mineral analysis. A magmatic rock mineral analysis system based on geological images comprises a magmatic rock feature analysis module and a magmatic rock comprehensive analysis module. According to the method, the magmatic rock mineral image and the magmatic rock geological auxiliary data are fused, so that the mineralization evolution history and the future mineralization potential of the magmatic rock can be disclosed; high-precision automatic recognition and classification of minerals in magmatic rocks are achieved through the mineral classification model, manual intervention is reduced, and the recognition efficiency and accuracy are improved; the extracted mineral crystallization characteristics are integrated with the oxygen fugacity characteristics, so that the conjoint analysis of the physical structure and the chemical property of the mineral is realized, and more comprehensive data support is provided for subsequent geological evolution research and mineral product prediction.

The invention discloses a method for constructing a large mineralization potential evaluation model based on LLM, and relates to the field of models.The method for constructing the large mineralization potential evaluation model based on LLM comprises the following steps that 1, a mineral geological map file is converted into a structured JSON format data set; step 2, constructing cue words, wherein the cue words at least comprise geological information extraction cue words, function task cue words and entity, relation and attribute extraction cue words; compared with the prior art, the method has the beneficial effects that the large mineralization potential evaluation model constructed by the method realizes text and map multi-mode mineral product geological map information identification and analysis, automatic mineralization geological feature analysis, mineralization feature information extraction and mineralization potential evaluation; the problems that in the prior art, man-made subjective influence exists, professional knowledge dependence is high, the geological information recognition accuracy rate in the recognition process is low, and information is lost during knowledge retrieval are solved.

The invention relates to the technical field of geological image processing, and particularly discloses a three-dimensional modeling method for a geological section, which is used for solving the problem of how to accurately identify and hide irrelevant geological areas and improve the modeling efficiency in geological three-dimensional modeling. Comprising the steps of data acquisition and preprocessing, geologic model constraint, implicit modeling and machine learning optimization, three-dimensional modeling and graphic rendering optimization, model correction and optimization and dynamic interaction and analysis, and four analysis formulas based on hidden requirements are provided; according to the method, by setting the hiding demand judgment formula for the coal seam, the rock stratum, the mined area and the inactive fault, the geological area irrelevant to target research is screened, the geological three-dimensional modeling process is optimized, the accuracy and efficiency of the model are improved, interference of irrelevant data is reduced, and the method has good application prospects. It is ensured that the mineral resource evaluation result better conforms to the actual geological condition, and the precision and credibility of mineral resource prediction are effectively improved.

The invention belongs to the technical field of remote sensing image processing, and particularly relates to a hyperspectral lithology intelligent identification method based on fuzzy clustering, which comprises the following steps: acquiring a geological map of a research area, vectorizing the geological map of the research area, and acquiring initial lithology distribution vector data; preprocessing the hyperspectral data; performing data dimension reduction on the preprocessed hyperspectral data by using principal component analysis transformation to obtain dimension-reduced data; clustering the dimension-reduced data by adopting a spatial fuzzy C-means clustering algorithm, and segmenting a clustering result based on a neighborhood similarity criterion to obtain the clustering result; and according to the initial lithology distribution vector data, determining the lithology category with the maximum proportion in different clustered patches from the clustering result, and taking the category as the lithology category corresponding to the clustered patches to obtain an intelligent lithology identification result. According to the method, the influence of human interference factors can be effectively reduced, and the lithology identification precision and the mineralization-related geological element identification capability are improved.

The invention discloses an intelligent mineral resource reserve management method and system based on big data, and the method comprises the steps: obtaining a mineral resource data set, mining the potential correlation between different data modes in the mineral resource data set through a self-attention mechanism, and visualizing key features through an attention weight; based on the cross-modal mineral feature data, introducing a GNN graph neural network to be combined with an InSAR technology, constructing a dynamic three-dimensional geological map, and modeling an ore body, a fault and a rock stratum into graph nodes and edges to represent a spatial topological relation; and generating a reserve report and a mining plan according to the ore body space structure map, encrypting the reserve report and the mining plan by using a Hash algorithm, and uploading the encrypted reserve report and mining plan to a block chain network for storage. The actual situation of mineral resources can be reflected more accurately and comprehensively, and the resource mining efficiency is improved.

The invention relates to a near-surface longitudinal wave velocity modeling method and device based on micro-motion exploration, and the method comprises the steps: obtaining a surface survey-based ground geological map of a target work area, and correcting a geological boundary of the surface survey-based ground geological map; according to stratum, fault and stratum occurrence information provided by the corrected ground geological map, a geological cross section is drawn along a seismic survey line, and a shallow ground structure model of the target work area is obtained; obtaining the shallow surface shear wave velocity of each preset measuring point in the target work area, and converting the shallow surface shear wave velocity of each preset measuring point into a longitudinal wave velocity; with the shallow ground structure model as a constraint, spatial interpolation is carried out on the longitudinal wave velocity of each preset measuring point to obtain a near-surface longitudinal wave velocity model based on the shallow ground structure model and a shallow velocity field based on the shallow ground structure model, the inversion velocity is close to the stratum velocity, and the precision is high; and a good near-surface longitudinal wave velocity model is provided for subsequent pre-stack depth migration.

The invention discloses a rock core identification method and system based on regional geological data, and the method comprises the steps: S1, collecting a rock core sample image, and constructing a rock core image data set; s2, dividing the core image data set into a training set and a test set, and training and testing the constructed classification model based on artificial intelligence; and S3, extracting formation lithology information in the regional geological map, and adjusting and optimizing the trained classification model by using the extracted formation lithology information. When the rock core recognition model is constructed, the regional geological data is introduced to optimize the model, the initial weight assignment of the model is optimized by introducing the regional geological data, the training efficiency, the accuracy and the generalization ability of the model are improved, and the model can be applied to different geological conditions and different regions.

The invention relates to the technical field of geological disaster early warning, and discloses a geological deformation early warning method and system based on AI vision, and the method comprises the steps: obtaining surface micro-deformation features through an AI vision system, and constructing a geological map structure network; fusing a nonlinear geomechanical equation in a graph neural network to realize physical constraint driven feature extraction; creating a multi-dimensional graph structure model representing different depth layers, and realizing cross-layer information transmission through a physical information guide type graph attention mechanism; deploying a graph neural network of a Bayesian probability framework to carry out geological stress field distribution modeling, and quantizing prediction uncertainty; and finally, generating intelligent early warning decision information with risk grade division. According to the method, the AI vision and the physical constraint graph neural network are combined, accurate inference of the underground stress field is realized, the uncertainty can be quantitatively predicted, and the reliability and timeliness of geological disaster early warning are improved.

The invention discloses a three-dimensional geologic body model construction method based on sedimentary simulation, and the method comprises the following steps: S1, data collection: collecting various geological data of a target region, including but not limited to drilling data, seismic exploration data, a geological map, paleocurrent direction data and the like; the collected data are preprocessed, the data quality is checked, and wrong or abnormal data are removed; s2, feature parameter extraction: extracting feature parameters related to sedimentary facies from the preprocessed geological exploration data; the parameters can comprise a particle size median Md, a sorting coefficient SO, skewness SK, kurtosis Kg, a logging curve characteristic value, natural gamma GR and resistivity Rt; and S3, parameter mapping of the sedimentary facies. According to the preparation method, through parameter mapping and fine identification of sedimentary facies, the sedimentary characteristics of the geologic body are reflected more accurately, and the calculation amount is reduced and the modeling efficiency is improved while the precision of the model is ensured by the application of a self-adaptive grid division technology and a finite volume method.

The invention discloses a geological profile map processing method based on cross-hole CT and drilling information multi-dimensional cooperation, and relates to the technical field of intelligent processing of engineering geological image achievements. Cross-hole CT data can provide continuous distribution information of rock-soil bodies among drill holes, and lithology distribution can be depicted more finely in combination with drilling data; the advantage that cross-hole CT can detect and recognize inter-hole rock-soil body structures is combined with the advantage that drilling can obtain different-depth rock core distribution, the characteristics of the inter-hole rock-soil body structures obtained through the cross-hole CT are combined with artificial intelligence, intelligent correction of engineering geology profile maps with multi-dimensional cooperation of the cross-hole CT and drilling information is achieved, and the engineering geology profile maps with the multi-dimensional cooperation of the cross-hole CT and the drilling information are obtained. The corrected engineering geological profile map is finer and closer to the real situation, and has better reference value for engineering design and engineering decision; the risk of emergencies in the construction process can be reduced through accurate geological information, the requirement for the number of drilled holes can be reduced, and therefore the exploration cost is reduced, and the exploration time is shortened.

The invention relates to the technical field of slope monitoring, in particular to a mine field slope instability trend-based radar monitoring and early warning method and system, and the method comprises the following steps: recognizing a slope region with an instability risk on a geological image through a pre-trained Fast R CNN model, and taking the slope region as an instability risk region; scanning the instability risk area in real time through a slope radar to obtain a radar image sequence of the instability risk area; and acquiring a geological image sequence of the instability risk area with the same time sequence as the radar image sequence, determining an instability trend of the instability risk area based on the radar image sequence and the geological image sequence, and then allocating an early warning level according to the instability trend. According to the method, locking of instability factors in advance is realized on the basis of the Fast R CNN model, falling rock and soil loosening sliding track trends are quickly analyzed in real time on the actual falling rock and soil loosening track obtained through multi-modal data solution on the basis of the LSTM network, and thus integrated display, focusing monitoring and graded early warning of mining area risk points are realized.

The invention belongs to the technical field of geological prospecting evaluation, and discloses an ore body positioning method and a rapid sampling device for niobium-tantalum ore target region prediction and evaluation. According to the method, based on existing geological data, various geologic bodies in a 1: 250,000-1: 10,000 geological map are analyzed, distribution, form, invasion contact relation, formation times and other characteristics of alkaline rock masses are found out, and a favorable metallogenic area is delineated; a linear structure, an annular structure and alteration information are recognized through satellites and aerial images, and target area positioning is assisted; the method comprises the following steps: exploring and detecting ore-bearing rock mass and fracture structures through a magnetic method, delineating potential positions of ore bodies, and inferring geological attributes of abnormal sources in combination with geological data; measuring the total amount of gamma at different positions in the niobium-tantalum prediction target area by using radioactive detection equipment, and preliminarily judging the position of the niobium-tantalum ore according to a gamma abnormal result; and carrying out systematic sampling and assay analysis on the target area by using the rapid sampling device. By means of the method and device, the metallogenic geological condition and the prospecting prospect can be evaluated, and the exploration network degree is optimized.

A software toolbox installing and using method comprises the following steps of S1, installing a main program, S2, opening a main program interface to select a module needing to be used, S3, achieving intelligent layer management and control through a layer management module, S4, achieving intelligent processing of geological elements through a geological map management module, and S5, achieving intelligent processing of the geological elements through a geological map management module. The method comprises the following steps: S1, carrying out intelligent construction of a two-dimensional profile map through a profile map generation module, S6, carrying out batch processing of reserve elements through a reserve map, S7, carrying out lithological layering, sampling position and geological structure discrete data of a drill hole through a drill hole dynamic block, and S8, providing a high-frequency tool rapid channel through a common tool box module. Compared with the prior art, the tool box has the advantages that the tool box is seamlessly integrated with the CAD environment, so that a user can quickly access and utilize efficient working tools on the basis of keeping the existing CAD skills; meanwhile, the section map generation module and the drilling dynamic block display module further simplify the visualization process of two-dimensional and three-dimensional data.

The invention belongs to the technical field of three-dimensional geologic modeling, and particularly relates to a three-dimensional modeling method for a structural plane based on strike control and occurrence constraint, which comprises the following steps of: extracting strata, fault strike lines and occurrence data based on a plane geologic map of a modeling target area, and extracting coordinates and azimuth angles of points on the strike lines through a curvature-constrained equal-interval sampling method; generating layer control points in batches by changing the sampling depth in combination with occurrence data; expanding a layer control point set by utilizing a stratum or fault layering point disclosed by a drill hole or a geological section, and further creating an irregular triangulation network of a structural plane; and extracting a structural surface intersection line by adopting a collision detection algorithm, reconstructing a stratum contact relation and a fault network space topological relation, finally obtaining a layer data file of the structural model, and completing data storage and three-dimensional visualization of the model. According to the method, the model of the structural plane such as the stratum and the fault can be quickly established, the spatial topological relation is reconstructed, and data file generation and three-dimensional visualization of the structural model are realized.

The invention discloses a rock identification method and equipment based on geological semantic guidance, and a medium, belongs to the technical field of geological rock detection and identification, and is used for solving the problems that blindness and redundancy exist in a feature fusion process, and the efficiency is high due to lack of effective modeling of geoscience features in existing rock target detection. And the identification requirements of complex geological images are difficult to adapt. The method comprises the following steps: performing semantic analysis on effective data in multi-source geological original data under related rock characteristics to generate a geological semantic map; performing feature fusion processing under related feature mapping between the geological semantic map and the feature pyramid to obtain a target fusion feature under the current rock image; performing collaborative recognition based on visual features on rock physical attributes in the current multi-source geological data, and outputting target detection features under the current rock image; outputting a geological rock classification result of the current rock image; and carrying out visualization processing on the geological rock classification result under the integrated platform.

The invention relates to the technical field of geophysical exploration, in particular to an optimal selection method for an oil-gas structure favorable area of a low-exploration-degree area of a mountain-front thrust zone. The method comprises the following steps: selecting an oil and gas prospective area, carrying out surface geological survey based on a regional geological map, compiling a surface fracture structure outline map and determining a main structure trend; performing wavelet decomposition on the gravity data to determine a substrate fracture adaptation result; deploying a magnetotelluric sounding profile in a direction perpendicular to the structure to obtain an electrical model, and compiling a substrate fracture diagram; deploying a sounding profile and a wide-line two-dimensional seismic profile fixed low-resistance strip in a parallel structure trend; a base depth map is calibrated, and a hidden strike-slip fracture layout is tracked; performing mesh generation to calculate indexes, performing standardized assignment, and performing mean value gridding; and preferably selecting an oil-gas structure favorable area by combining a drilling gas content fixed boundary. By adopting the ground measurement and multi-geophysical technology, the method is low in cost, simple to operate, high in efficiency and adaptive to a low exploration area, the optimization accuracy is improved, and powerful technical support is provided for optimization of an oil-gas exploration target area.

The invention discloses a multi-source three-dimensional geological data fusion and management method and related equipment, and relates to the technical field of geological information, and the method comprises the steps: obtaining standardized geological data; fusing the standardized geological data based on a Bayesian fusion framework; constructing a multi-scale three-dimensional geologic model by adopting a potential field interpolation method based on probability constraints provided by posterior probability distribution; performing uncertainty quantification on the multi-scale three-dimensional geologic model by adopting a Monte Carlo simulation method; and storing the multi-scale three-dimensional geologic model and the uncertainty quantification result to a data management platform conforming to an OGC standard, and providing visualization, query and access of a geologic map layer through a Web service interface. According to the method, the sharing efficiency, the reuse value and the application convenience of the geological result data are greatly improved, and the intelligent level and the decision support capability of geological data analysis are enhanced on the whole.

The invention provides a geological map three-dimensional visualization method and system based on a database technology, and belongs to the field of geological map visualization. The method focuses on geological map resources, deeply fuses spatial information and attribute information of the geological map, designs a geological map three-dimensional coordinate system based on geological age strata, designs a geological map three-dimensional space data and attribute data storage structure, and provides a relatively effective solution for multi-modal geological resource association. The geological map resources and associated data thereof are systematically stored and managed through a database, the geological map resources are standardized and efficiently integrated, and the innovative purpose of effectively supporting true three-dimensional visualization expression and analysis of geological elements is achieved by relying on a three-dimensional visualization platform. The geological map three-dimensional visualization method based on the database technology comprises the following steps: step (1), data and association thereof; step (2), data processing; step (3), database design; step (4), application; multi-modal geological data is stored based on a database, and a geological map three-dimensional visualization platform is built in combination with a three-dimensional visualization engine.

The present invention relates to the field of slope monitoring technology, and more specifically to a radar monitoring and early warning method and system based on the instability trend of mine slopes. The method comprises the following steps: using a pre-trained Fast R-CNN model to identify slope areas at risk of instability on a geological image as instability risk areas; using a slope radar to scan the instability risk areas in real time to obtain a radar image sequence of the instability risk areas; obtaining a geological image sequence of the instability risk areas that is co-sequential with the radar image sequence, and determining the instability trend of the instability risk areas based on the radar image sequence and the geological image sequence, and then assigning an early warning level based on the instability trend. The present invention uses the Fast R-CNN model to achieve early locking of instability factors, and based on the LSTM network, quickly and in real time analyzes the sliding trajectory trends of falling rocks and loose soil based on the actual trajectory of falling rocks and loose soil obtained by solving multimodal data, thereby achieving integrated display of risk points in the mining area, focused monitoring, and graded early warning.

The invention relates to the technical field of gold mine geological modeling exploration, in particular to a gold mine modeling and reserve estimation method under multi-source information constraint. Geological images and exploration data of the target mining area are collected; performing interpolation operation according to the geological image in combination with the elevation data and the drilling data to establish a geological model; establishing a fracture model by adopting a deterministic modeling method according to the geological image; constructing a lithologic model by adopting an envelope surface modeling method according to the drilling data and the geophysical exploration data; constructing a gold ore body model by adopting an envelope surface modeling method according to the elevation data and the geophysical exploration data; obtaining mineralization information to construct a variation function model, and establishing a grade model by adopting a Kriging difference method according to the variation function parameter value of each fault; and estimating the gold ore reserves of the target mining area based on the established model. According to the method, the model can be established in combination with multi-source information, and the ore body structure can be accurately represented, so that the estimation precision of the gold ore is effectively improved.

The present invention provides a lithology classification model construction method and a lithology classification method, and relates to the technical field of lithology classification. The lithology classification model construction method comprises: extracting the lithology characteristics of each lithology in a lithology data set, and constructing a lithology adjacency matrix and a lithology adjacency submatrix according to the co-occurrence relationship of all lithologies; obtaining output lithology characteristics and output lithology sub-features according to the lithology adjacency matrix, the lithology adjacency submatrix and the lithology characteristics respectively; obtaining a graph representation and a node representation according to all output lithology characteristics, and obtaining a subgraph representation and a subnode representation according to all output lithology subfeatures; obtaining a model graph representation and a model subnode representation according to the graph representation and the subnode representation, and obtaining a model subgraph representation and a model node representation according to the subgraph representation and the node representation; and constructing a lithology classification model according to the model graph representation, the model node representation, the model subgraph representation and the model subnode representation. The present invention can effectively improve the efficiency of lithology classification of geological maps.

The invention belongs to the field of oil and gas field geological modeling, and discloses an oil and gas field three-dimensional geological modeling and visualization method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining a geological map of a to-be-modeled region, and collecting geological data of an oil and gas field through a ground penetrating radar, the geological data comprising rock stratum data and soil data; preprocessing the geological map and the geological data to obtain preprocessed data, and fusing the preprocessed data to obtain a three-dimensional geological data set; based on the three-dimensional geological data set, constructing a three-dimensional geological model of the oil and gas field by using a finite element method and a deep learning technology; and displaying the three-dimensional geologic model through an AR technology to complete visualization of the model. Key problems in the prior art can be effectively solved, powerful technical support is provided for efficient exploration and development of oil and gas fields, and important practical application value and wide market prospects are achieved.

The present invention discloses a mine disaster tracing method based on a knowledge graph, applied in coal mine safety. First, a mine disaster-related knowledge graph is built, involving three main steps: constructing a conceptual model, extracting entities from relational databases and geological maps, and establishing entity relationships based on location and process logic. Next, characteristic indexes and transmission rules for entity objects are set, categorizing entities into four types: discrete reporting, continuous monitoring, geological structure, and geological continuity. When an early warning occurs, disaster tracing is performed using the entity transmission rules and a graph traversal algorithm, identifying direct and root causes. This invention offers systematic and timely disaster tracing by utilizing a specialized knowledge graph and graph algorithms.

The invention relates to the technical field of three-dimensional geologic modeling, in particular to a fine three-dimensional modeling method for a thin-vein ore body. According to the method, an ore body contour line sequence and an ore body outer boundary line are generated through vectorization of geological maps such as a given exploration line profile map, line segment nodes of the ore body outer boundary line are encrypted through natural cubic spline function interpolation, and then corresponding biharmonic spline functions are fitted respectively; and adaptive dynamic interpolation is carried out on scanning lines in the sequence based on typical features of the thin-vein ore body. The curved surface meeting the specific smoothness condition is generated, it is ensured that the morphological change of the model is more reasonable, meanwhile, the interpenetration phenomenon is avoided, detailed features of areas such as faults and boundaries are effectively recognized, the accuracy of the model and the authenticity of geological expression are improved, and the method is suitable for popularization and application. Therefore, a more reliable thin-vein ore body refined three-dimensional modeling method is provided for geological exploration and resource evaluation.

The invention discloses a structured corpus generation method and device for geological map multi-modal large model training, and the method comprises the steps: preprocessing geological vector data, and constructing a semantic aligned geological data set; randomly selecting a data viewport, and setting the geographic space range and size of a map viewport; configuring a plurality of visual parameter items, performing controllable random adjustment on text labels, element symbols and map maps, and customizing and producing geological map samples as required; extracting multi-dimensional attribute information of the geological map sample to construct structured meta-information annotation data, and constructing a geological element mask annotation file to obtain a structured corpus for geological map multi-modal large model training. According to the method, large-scale production of the training samples with diversity, high quality and image-text alignment features is realized, the geological map corpus meets the requirements of multi-modal large model training on data scale, diversity, labeling precision and modal alignment, and the training efficiency and generalization ability of a geological map intelligent interpretation understanding model are remarkably improved.