393 results about "Metallogeny" patented technology

The invention relates to the field of geological exploration, in particular to an intelligent prospecting prediction method and system for an ore deposit three-dimensional geological model, a terminal and a medium. The method comprises the steps of obtaining multi-source geological data of a target area to construct an ore deposit three-dimensional geological model, inputting the ore deposit three-dimensional geological model into a trained intelligent prospecting prediction model for ore-forming potential analysis, and optimizing the model or generating an intelligent prospecting prediction scheme according to a predicted resource quantity confidence degree condition; when the model is constructed, three-dimensional inversion calculation, element anomaly field construction and the like are carried out, the model can be optimized through transfer learning, exploration data can be accessed in real time to realize dynamic updating, and a multi-target optimization model is established to output an exploration scheme; the invention also relates to a corresponding system, a terminal and a storage medium. The method achieves the technical effects of improving the accuracy and efficiency of prospecting prediction, dynamically optimizing the model according to the actual situation, reasonably planning the exploration scheme, and reducing the exploration cost and risk.

The invention discloses an altered mineral analysis-based prospecting target prediction method and system, and relates to the technical field of prospecting target prediction. An altered mineral analysis-based prospecting target prediction system comprises a data acquisition module, a clustering analysis module, an alteration combination discrimination module, a spatial modeling module, a space-time coupling module and a metallogenic evaluation module. According to the method, altered minerals and symbiotic combinations of the altered minerals are subjected to layered clustering treatment by introducing mineral thermodynamic phase diagram constraints, multi-stage superposed alteration information in a complex structure area is effectively analyzed, and a mineral symbiotic combination structure model with cause difference expression ability is established; by constructing cause period labels and forming a time sequence decoupling model, systematic distinguishing of alteration bodies formed under the mineralization effect of different times is achieved, and a time sequence basis is provided for identification of the multi-stage mineralization process.

The invention discloses a mineral resource intelligent prediction method and system based on multi-source heterogeneous data fusion and deep learning, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data, and carrying out the standardization processing to form a structured data set; multi-source heterogeneous data fusion: realizing data layer space registration and feature layer weight dynamic allocation through an attention mechanism multi-modal fusion module, and outputting a high-dimensional metallogenic feature vector; constructing a CNN-LSTM mixed deep learning model and completing initialization training, and outputting an initial mineralization probability graph; and establishing a dynamic updating engine, performing model increment training based on transfer learning, correcting the mineralization probability through positive and negative sample reinforcement learning in combination with a newly added data type, and outputting a time sequence dynamic mineralization probability graph. According to the method, mineralization probability dynamic evaluation and risk quantitative updating are realized, the prediction precision and the model updating efficiency are improved, the method is adaptive to a multi-stage exploration scene, and accurate real-time support is provided for exploration decision making.

The invention discloses a multi-factor prospecting prediction method suitable for tin polymetallic ore, and relates to the technical field of geological science. The prospecting prediction method comprises the following steps: collecting data; fusing multi-source data; extracting and analyzing mineralization information; constructing a comprehensive prediction model; and predicting mineralization. According to the method, a potential tin polymetallic ore deposit area is preliminarily selected by comprehensively considering multi-source information such as geology, geophysics, geochemistry and remote sensing, drilling data and profile data are collected in the area, the drilling data and the profile data are utilized to construct a comprehensive prediction model, and the model is combined with advanced algorithms such as geostatistics and machine learning, so that the prediction accuracy of the tin polymetallic ore deposit area is improved. The multi-source data is efficiently integrated, and a three-dimensional visualization result is provided, so that the position and the scale of a potential ore body can be more accurately predicted, and the success rate of prospecting is improved.

The embodiment of the invention provides a mineral target area intelligent delineation method and system based on multi-source heterogeneous geoscience data, and relates to the technical field of mineral resource exploration, and the method comprises the steps: dividing a multi-source heterogeneous geoscience data set comprising geological structure, geochemistry, geophysics and remote sensing image data into a plurality of data modules according to a geological action influence range; each data module records spatial distribution and attribute information, then a dynamic mapping relation between the data modules and a mineral product mineralization mechanism is constructed, a mineralization association chain between the data modules is generated based on the dynamic mapping relation, a cooperative change rule of nodes and connection paths is defined, then a target region feature set conforming to the mineralization association chain is screened, and a target region feature set is obtained; and finally, according to a collaborative change rule, judging the integrating degree of a target region feature set space unit and a mineralization condition, dynamically adjusting a space boundary range, and generating a mineral target region delineation result, thereby comprehensively utilizing multi-source data, deeply mining a relationship with a mineralization mechanism, and improving the accuracy and reliability of mineral target region delineation.

The invention discloses a metal ore body identification method based on remote sensing interpretation, relates to the technical field of mineral resource exploration, and aims to solve the problems of low identification precision, neglect of geological laws and poor generalization caused by dependence on single remote sensing data in the prior art. After standardization processing, spectrum and texture features are fused to generate a composite vector; extracting a multi-scale mineralization signal by using a convolutional neural network, and interpreting and constructing an ore control area in combination with a terrain gradient and a curvature; alteration zoning is identified through spectrum angle matching, and a favorable mineralization area is determined by combining lithology combination analysis; and dynamically weighting geological elements by adopting a deep learning fusion network, outputting an ore body position according to a comprehensive score, and finally optimizing a boundary through spatial clustering and morphological filtering. The method significantly improves the automation degree and reliability of ore body identification in a complex environment, and is suitable for metal mineral exploration target area delineation.

The invention relates to the technical field of mineral resource exploration. The prospecting method, device and equipment based on the multi-source geological relation data and the medium are provided, the method comprises the steps that space-time alignment and standardization processing are conducted on obtained geological historical evolution data, geophysical field data, geochemical migration data and remote sensing alteration information, and a dynamic knowledge graph is generated; performing mineralization stage division processing on the geological historical evolution data to obtain a mineralization stage division result; integrating the fluid migration space-time path network, performing three-dimensional dynamic modeling processing, and generating a staged three-dimensional metallogenic evolution model; extracting a stage-specific geological mark combination from the staged three-dimensional metallogenic evolution model, performing knowledge reasoning processing based on a preset historical ore deposit rule base, and outputting an ore prospecting target region and a metallogenic process constraint basis which meet a preset confidence coefficient condition, so as to improve the precision of a structural model, generate a staged fluid dynamic field, and improve the precision of the metallogenic process. And the target region prediction confidence is enhanced.

The invention relates to the technical field of mineral resource prediction and analysis, and discloses a regional mineral resource prediction and analysis method and system, and the method comprises the steps: carrying out the collection and standardization, and constructing a standardized physical data set; organizing the physical parameters in a spatial dimension, and constructing a multi-dimensional tensor structure; tensor evolution modeling is carried out to generate tensor time sequence data; obtaining an optimal weight combination according to geological unit division; calculating the mineralization probability of each space unit; evaluating the credibility of the mineralization probability result, and outputting a mineralization probability graph and a credibility scoring graph; the system comprises a data acquisition and standardization module, a tensor modeling module, an evolution modeling module, a feature optimization module, a probability prediction module, a credibility evaluation module and a visual output module. According to the method, a space-parameter joint expression mode based on a four-order tensor structure is introduced, various kinds of physical field data are subjected to integrated processing in a unified space framework, and a data model reflecting multi-dimensional geological response characteristics is effectively constructed.

The invention belongs to the technical field of data processing, and particularly relates to a rutile type titanium ore prospecting model construction method. Comprising the following steps: collecting remote sensing time sequence, real-time geophysical prospecting and geochemical data, carrying out geological causal feature analysis after standardization so as to construct a metallogenic causal graph, and meanwhile, collecting anti-fact features to generate virtual mineralization feature vectors; a prospecting model comprising an input layer, a weight optimization layer (comprising a causal attention module and a particle swarm anti-fact comparison module) and a mineralization probability output layer is constructed, and a high-frequency data acquisition mechanism is triggered with assistance of a metallogenic event threshold. According to the method, the data reliability and the model generalization ability are improved, the mineralization probability calculation precision and the potential mineralization target region judgment accuracy are remarkably improved, and a scientific support is provided for efficient prospecting of rutile type titanium ore.

The invention relates to the technical field of multi-source information analysis, in particular to a niobium-tantalum metal ore metallogenic evaluation system based on multi-source information analysis, which comprises a geologic structure association module, a mineral distribution interpretation module, a metallogenic environment adaptation module, a spatial feature analysis module and a potential region judgment module. According to the method, by introducing a multi-source data analysis and fusion method, the evaluation precision and comprehensive capability of resource exploration are effectively improved, analysis after information fusion is combined with relevance and spatial modes of different data types, it is ensured that mineralization potential evaluation is more accurate, the limitation of a single data source and manual analysis is overcome, and the method is suitable for popularization and application. According to the method, the identification precision and reliability of the mineralization law are improved, the potential area can be identified more accurately, resource potential analysis is optimized in combination with the adaptability of the mineralization environment, the efficiency and accuracy of determining the mineralization area are remarkably improved, the problems of information isolation and analysis deviation in a traditional method are avoided, and the scientificity and the practical application value of the exploration process are enhanced.

The invention belongs to the technical field of mineral resource exploration, and provides a multi-source remote sensing data and AI combined copper ore exploration method and system, and the method comprises the following steps: obtaining multi-source remote sensing data of a target region through a plurality of remote sensing devices; preprocessing the multi-source remote sensing data to obtain fusion data; the fusion data are analyzed through an AI algorithm, and mineralization alteration areas and geological structure information are obtained through recognition; performing overlay analysis on the mineralization alteration area and the known geochemical anomaly area, analyzing the spatial relationship between the mineralization area and the structure in combination with the geological structure information, and judging the favorable mineralization part of the copper ore to obtain an analysis result; performing field investigation on the potential copper ore area determined by the analysis result, collecting rock samples for laboratory analysis, and verifying the accuracy of the analysis result; according to the method, the multi-source remote sensing data and the AI technology are comprehensively utilized, the target area is rapidly analyzed, the potential copper ore area is positioned, the workload of blind exploration is reduced, and the exploration efficiency is improved.

The invention discloses a satellite-air-ground-well multi-method collaborative shallow coverage area copper-gold ore exploration method, and relates to the technical field of mineral resource exploration, regional alteration mineral information is extracted in a shallow coverage area, and a porphyry type copper-gold ore prospecting prospective area is determined according to alteration mineral space distribution and zoning characteristics thereof; in the prospecting prospective area, a key exploration area is selected according to metallogenic geological conditions, aviation geophysical prospecting and aviation hyperspectral remote sensing investigation are carried out, and a prospecting favorable area is delineated; performing prospecting prediction on related ore types in the favorable prospecting area, and delineating a prospecting target area; determining a new drilling position in the target area; drilling verification and well logging are carried out, ground-well geophysical prospecting is carried out to describe the spatial form of the metallogenic geologic body, and the prospecting effect is expanded. According to the method, the favorable prospecting area and the prospecting target area can be more accurately delineated, the spatial form of the ore body is finely delineated, a reliable basis is provided for prospecting of the porphyry type copper-gold ore, and the prospecting success rate and efficiency of the porphyry type copper-gold ore in the shallow coverage area are greatly improved.

The invention discloses an intelligent prospecting prediction method based on a three-in-one metallogenic prediction theory, and relates to the technical field of prospecting prediction, and the method comprises the steps: determining various geological elements formed by a regional control ore deposit, obtaining data related to the elements formed by the regional control ore deposit, and storing the data in a database; comprising geological survey, engineering exploration, geophysics, geochemistry and remote sensing data; performing data preprocessing on the geological element data and dividing the geological element data into a plurality of categories; unsupervised learning is carried out on the geological data according to a deep learning algorithm, and the geological structure of the mining area is described in detail; in the areas with relatively high reconstruction errors, selecting the areas with relatively high geochemical anomaly and geophysical anomaly indication variables or reconstruction error values, comparing the selected areas with known typical ore deposits to determine the correlation of space structures, and extracting ore-forming favorable anomaly feature information from the selected areas; and the extracted abnormal information is matched with various geological elements to delineate the target region, so that the accuracy and efficiency of prospecting prediction are improved.

The invention discloses a deep concealed ore deposit detection method based on multi-source geological data fusion, and the method comprises the following steps: 1, carrying out a dynamic multi-source data fusion algorithm: introducing a fuzzy logic algorithm, dynamically adjusting the data fusion weight according to the feature significance of different geological units, and developing a cross-scale feature pyramid network, hierarchical feature extraction and fusion are carried out on different detection depth data; 2, optimizing an intelligent model based on prior knowledge; step 3, constructing a real-time interactive three-dimensional modeling system; 4, establishing an efficient multi-means verification system; and step 5, intelligent integration of the exploration process. According to the method, multi-source data are fused through fuzzy logic dynamic weight adjustment, the mineralization mode and the reinforcement learning optimization model are combined, three-dimensional modeling and multi-means combined verification are updated in real time, and the effects of accurate fusion of the multi-source data, dynamic model adjustment and optimization, efficient and accurate modeling, short verification period and low cost are achieved; and the detection efficiency and scale of the deep concealed ore deposit are improved.

The invention discloses a skarn type deposit three-dimensional metallogenic prediction method, system and equipment and a medium, and relates to the technical field of mineral exploration. The method comprises the following steps: collecting and sorting geological information of a to-be-studied area and a surrounding area; according to the collected information, performing gravity-magnetic three-dimensional physical property inversion and lithology mapping; carrying out gravity-magnetic three-dimensional comprehensive geological modeling by adopting a discrete body inversion method; on the basis of the constructed model, performing three-dimensional metallogenic favorable contrast prediction; constructing a three-dimensional mineralization comprehensive prediction model based on the mineralization favorable degree value; and carrying out target region delineation and verification by using the three-dimensional metallogenic comprehensive prediction model to complete three-dimensional metallogenic prediction under the support of regional gravity and magnetic data. The method has the advantages of wider prediction range, high prediction reliability and the like.

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 big data analysis, and discloses a prospecting target area positioning method and system based on big data, and the method comprises the steps: collecting multi-source exploration data in real time through distributed nodes, completing coordinate normalization, semantic alignment and time synchronization through a spatial heterogeneous data flow engine, and generating a standardized incremental data block; performing local feature sensitivity analysis based on the historical model library, identifying a newly added feature dimension, and performing parameter increment updating by adopting a sliding window gradient descent method; inputting the updated model into a target evolution model driven by a Bayesian space-time probability field, and dynamically calculating the metallogenic probability of each space grid in combination with a stress field, an element migration path and historical verification data; and generating high, medium and low three-level target area maps according to probability sorting, and pushing the high, medium and low three-level target area maps to a three-dimensional visual decision terminal. According to the method, minute-level dynamic response of the target region under triggering of newly-added data is realized, computing resource consumption is reduced to be less than 5% of that of an original system, and prospecting efficiency and abnormal region identification timeliness are improved.

The invention relates to the technical field of intelligent geological exploration, in particular to a reserve exploration area evaluation method and system based on a classification algorithm, and the method comprises the following steps: extracting an included angle between a tectonic line and a lithologic zone trend, and generating a tectonic trend label according to space combination, calling a label to obtain mineralized element concentration, calculating a distance between a distribution center and a mass center to generate an enrichment type, dividing density grades through an area ratio, and generating an alteration gradient classification set in combination with a grid thickness trend and a mineral concentration ratio. According to the method, the spatial combination is identified by extracting the relationship between the structure and the lithology direction, the coupling expression of the geologic structure and the metallogenic mode is improved, the feature consistent region is screened by combining the spatial distance between the mineralized element enrichment center and the regional mass center, the target region focusing precision is improved, and the abnormal cluster is extracted based on the convergence of the abnormal body point location and the quantity feature; abnormal intensity quantification is enhanced, and the multi-factor collaborative recognition path significantly enhances the multi-source data fusion judgment capability and spatial precision.

The invention discloses a geological mineralization causal knowledge extraction method, a storage medium, equipment and a product, and relates to the field of geological mineralization, and the method comprises the steps: constructing a causal hypothesis of a causal role from an unstructured geological text; constructing a causal data set based on causal hypothesis, and training the natural language inference model by using the causal data set; wherein the input vector of the causal data set and the vector of the causal role are fused to form the input vector of the model; a bias matrix of a causal role is introduced to calculate a self-attention weight; obtaining gating weights of the causal role and the global context based on [CLS] embedding, and performing weighted summation on embedding of the causal role and [CLS] embedding by using the gating weights to obtain a final fusion representation; and identifying a high-confidence causal chain from the geological text by using the trained model to obtain a typical causal mode related to the specific mineralization. According to the invention, the chain type identification of the complex mineralization mode can be realized.

The invention discloses a gold mine resource potential prediction method fusing a geological knowledge graph and a large model, and belongs to the technical field of mineral resource prediction, and the method comprises the steps: aggregating geological exploration data streams, constructing a space-time event knowledge graph, driving a pre-trained large model to carry out continuous tensor projection, and carrying out prediction. Encoding the metallogenic mechanism into a gauge tensor to construct a geological semantic Riemannian manifold; constructing a neural differential equation set representing metallogenic dynamics on the manifold; by taking observation data as a boundary, calculating an energy dissipation minimum value path through variational solution and geodesic line optimization; locking and representing a negentropy flow accumulation area of the ore body through phase-space reconstruction and persistence coherence test; and holographic mapping is carried out on the accumulation area to form a materialized prospecting target area, and a dynamic causal transcription report is generated. According to the method, a generative dynamic inversion normal form based on a physical mechanism is adopted, metallogenic prediction is reconstructed into solving of an evolution problem of a differential equation in a manifold space, the space-time inevitability of a metallogenic system can be replayed from static observation data, and a prospecting target region with causal interpretation is generated.

The invention discloses an experimental device and method for controlling migration and mineralization of ore-forming fluid in a wrinkle deformation process, and belongs to the technical field of ore deposit geology. The device comprises an ore liquid synthesis unit and a mechanical deformation simulation and mineralization analysis unit. The mechanical deformation simulation unit comprises a box body, an extrusion deformation system, a liquid injection system and a heating temperature control system, and can synchronously realize visual analysis of structure extrusion, fluid injection and mineralization precipitation at high temperature. The experimental method comprises the following steps: firstly, preparing a simulated metallogenic fluid containing FeS2 (pyrite); laying simulated surrounding rock and wrinkles in the box body, arranging a separation layer, and heating; extruding the sand layer and injecting fluid to simulate a mineralization process; and slicing, sampling and analyzing after model consolidation, and establishing a mineralization space distribution model. The device is simple in structure and convenient to operate, the whole process of fold extrusion, crack extension, fluid migration and mineral precipitation can be repeatedly represented, and an experimental basis is provided for hydrothermal deposit mineralization mechanism research and prospecting prediction.

The invention relates to the technical field of big data processing, and discloses a data extraction method and system for geological mineral exploration, and the method comprises the steps: carrying out the standardization preprocessing of original data containing space coordinates, lithology texts, mineral components, logging curves and mineralization labels; spatial, semantic, concentration and time sequence deep representations are extracted in parallel through a multi-modal geologic feature encoder and fused into high-dimensional vectors; applying structured sparse constraint to the features by using a graph attention mechanism guided by an expert knowledge graph, and strengthening a mineralization association dimension; a dynamic incremental learning engine is combined with an elastic weight solidification mechanism to realize local fine tuning of model parameters and historical knowledge retention; and finally outputting a mineralization potential score, a mineralization factor sequence and an abnormal element combination. According to the method, through triple mechanisms of multi-modal fusion, knowledge embedding and incremental evolution, the accuracy, interpretability and timeliness of data extraction are improved, and the real-time analysis requirement of large-scale mineral exploration is met.

The invention discloses a mineral resource metallogenic law simulation and target prediction system based on big data, and relates to the technical field of geological exploration. The multi-source data acquisition and standardization module supports acquisition of five types of data, and the data are preprocessed and then transmitted to the distributed database; mining key elements by using an improved Apriori algorithm, and constructing a correlation graph; building a model based on Unity 3D, and integrating three types of dynamic simulation; performing three-level prediction by using a CNN-LSTM model; three-dimensional rendering and multifunctional display are supported; and automatically updating data, models and rules to form an optimized closed loop. According to the method, multi-source data is integrated to improve ore-forming element recognition comprehensiveness, dynamic simulation fits geological reality, AI prediction is accurate, uncertainty analysis is included, visual interaction is practical, iterative optimization continuously improves efficiency, invalid exploration cost is reduced, and mineral exploration is efficiently guided.

The invention relates to the technical field of mineral exploration, and discloses a method for determining a heat source center of a gold and copper ore area containing alunite minerals, which comprises the following steps of: determining a to-be-detected area of a target mining area, sampling surface rocks according to a preset grid or section, and acquiring a plurality of rock samples and corresponding original acquisition points; carrying out mineral component analysis on the plurality of rock samples, screening the alunite-containing samples to obtain a plurality of alunite-containing samples, and screening effective samples; performing elemental component analysis on the effective sample, and extracting elements and contents related to gold mineralization or copper mineralization in the effective sample; setting a potential heat source center, calculating the distance between each effective sample and the potential heat source center, introducing a distance attenuation index, establishing an objective function of the element content and the distance, and adopting a gradient descent method to perform inversion solution by minimizing the objective function to obtain coordinates of the potential heat source center, namely the determined heat source center of the gold ore or the copper ore; the method improves the accuracy of gold and copper ore area heat source center exploration.

The invention relates to the technical field of mineral exploration, and discloses a method for estimating the mineralization probability of residual slope product type anatase based on multi-source parameters, and the method comprises the steps: determining a geographic region, collecting geological data, sampling a geological sample, and collecting the characteristics of the sample; extracting the first characteristic index set to judge the region classification of the geographic region, wherein the region classification comprises a conventional exploration region, a main mineralization mode region and a secondary mineralization mode region; extracting a second characteristic index set from the geological data to calculate a standard mineralization probability; calculating a weathering correction coefficient and a maturity correction coefficient; and correcting the standard mineralization probability to generate a mineralization probability, and judging whether the geographic region is a high-potential target region or not according to the mineralization probability. According to the technical scheme, the metallogenic probability judgment standard can be determined and corrected under different conditions, and judgment of a high-potential target region is achieved.

The invention discloses a tungsten deposit deep edge metallogenic prediction method based on machine learning, and particularly relates to the technical field of geological prediction and data processing. The method comprises the following steps: acquiring multi-source geological data, constructing a multi-dimensional space grid, and dividing candidate prediction units; quartz vein density, alteration indexes, geochemical combination anomaly weights and high-frequency magnetic anomaly waveform features are extracted, and an ore control feature tensor matrix is constructed; extracting a spatial dependency relationship among the candidate units based on a graph neural network, constructing a metallogenic potential prediction model, and outputting a metallogenic probability graph and a metallogenic hot spot graph; model parameters and feature weights are dynamically optimized through spatial superposition analysis and an abnormal response recognition mechanism; according to the method, the precision and efficiency of deep edge tungsten ore prospecting can be remarkably improved, and good self-adaptability and popularization and application value are achieved.

The invention relates to an ore control element extraction and weight determination method based on a knowledge graph, and the method comprises the following steps: constructing a geological mineral knowledge graph: collecting geological text data, extracting entities and semantic relationships in the geological text data, and constructing the geological mineral knowledge graph; network analysis and simplification: utilizing a community clustering algorithm to divide an ore deposit, and combing and simplifying a complex geological knowledge map in combination with a modularity algorithm process; subgraph generation: constructing a series of subgraphs according to community categories of nodes, and forming an information set which completely and structurally represents specific geologic features and metallogenic conditions; weight calculation and integration: quantifying the indicating significance of different geological entities on ore deposit prospecting through an entity-relation weight result, and defining the weight of ore control elements; and element screening: sorting according to the weight values, and rapidly screening out geological entities which have important influence on ore deposit mineralization as ore control elements. According to the invention, an intelligent and automatic decision support tool is provided for geological prospecting work.

The invention belongs to the technical field of vanadium enrichment mechanisms of phosphorus vanadium ores, and discloses a research method of a vanadium enrichment mechanism in a diagenetic stage of phosphorus vanadium ores, which comprises the following steps: S1, sample collection and treatment; s2, rock ore identification and mineral composition analysis; s3, analyzing the occurrence state and microcell morphology of vanadium; s4, performing geochemical analysis on the whole rock; s5, in-situ microcell geochemical analysis is carried out; s6, phosphorus nodule analysis; s7, comprehensive analysis and mineralization model establishment; according to the method, mineralogy, total rock geochemistry, micro-area surface scanning and other multi-scale and multi-means research methods are comprehensively utilized, and the vanadium occurrence state, the ore-forming fluid evolution and the phosphorus-vanadium coupling relation are finely described; in-situ microcell geochemical analysis is introduced into phosphorus nodule analysis, the internal structure, the growth sequence and element distribution of the phosphorus nodule are directly revealed, obvious frontier and systematicness are achieved, and reliable theoretical support is provided for accurate exploration of vanadium ore resources and green and efficient dressing and smelting process research and development.

The invention provides an intelligent prediction method of an ion adsorption type rare earth ore metallogenic target region based on a deep neural network, and provides a novel convolutional neural network model RCACNet fusing cross-level cavity features and attention, and the model adopts a deep residual network as a backbone network. The densely connected cavity space pyramid pooling module, the cross-level feature fusion module and the convolution block attention module embedded in the backbone network are fused, and through cooperative work of the four modules, complex challenges such as similar spectrum, variable scale and fine features of the ion adsorption type rare earth ore target can be effectively handled; therefore, high-precision segmentation is 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.