543 results about "Mineral Sources" patented technology

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 relates to the technical field of mineral resource management, in particular to a mineral resource dynamic prediction and mining management system which comprises a data perception and fusion layer, a unified digital twinborn model, a dynamic prediction and decision intelligent agent and a visualization and interaction control layer. The data perception and fusion layer collects structured data such as geological exploration and mining environment and market unstructured data, and generates a unified space-time tensor through processing; the unified digital twinborn model generates a dynamic comprehensive mining area situation map containing resource reserve risk economic indicators through a three-dimensional convolutional neural network embedded with an attention mechanism; the dynamic prediction and decision-making agent predicts future reserves and geological risks, and constructs a dual-objective optimization model to generate an optimal mining path equipment scheduling and resource allocation scheme; and the visualization and interaction control layer presents the mining area state and the decision scheme in a three-dimensional manner and provides an interaction interface. According to the invention, the data utilization rate and decision scientificity are improved, the safety risk is reduced, and mine management intellectualization is promoted.

The embodiment of the invention provides a BIM-GIS three-dimensional twin geological modeling method and system based on cognitive enhancement. The method is applied to the technical field of geological modeling, and comprises the following steps: acquiring different data sources to carry out geological data acquisition, and carrying out preprocessing to obtain preprocessed geological data; converting the geologic body into a voxel three-dimensional model according to the preprocessed geologic data; performing multi-level optimization processing on the voxel three-dimensional model, constructing an optimized model, and performing three-dimensional display; and performing mineral resource space distribution prediction according to the voxel three-dimensional model, predicting the position and the number of mineral resources in combination with historical exploration data and a convolutional neural network, and performing underground water flow simulation analysis in combination with an underground water flow model. In this way, by combining the BIM and GIS technologies, the three-dimensional geological data can be efficiently and accurately managed and displayed, and the method can be widely applied to the fields of geological exploration, mining development, urban construction and the like.

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 relates to the technical field of mineral resource estimation, and discloses a mineral reserve estimation method based on artificial intelligence, comprising the following steps: step 1, collecting and preprocessing data, acquiring multi-source data, and performing data standardization and feature extraction; 2, carrying out ore body modeling through a 3D convolutional neural network, constructing the 3D convolutional neural network, and carrying out ore body prediction through three-dimensional convolution calculation; step 3, optimizing ore body space prediction in combination with Kriging interpolation, preliminarily predicting ore body space distribution through Kriging interpolation, and further optimizing by using a deep learning model; and step 4, estimating reserves based on Bayesian optimization. The technical scheme of combining multi-source data fusion, a 3D convolutional neural network and Kriging interpolation is adopted, the effect of accurately capturing ore body space distribution under the complex geological condition is achieved, and compared with an existing scheme depending on a traditional geological statistical method, the problem that the fault zone and heterogeneous region modeling capacity is insufficient is solved.

The invention relates to the technical field of unmanned aerial vehicle geological survey, and discloses a path planning method and system for unmanned aerial vehicle geological survey, and the method comprises the steps: obtaining the image, spectrum and topographic data of a remote mountainous area through a multispectral sensor and a laser radar, and recognizing potential vein abnormal points through data fusion and geological feature analysis; and constructing a geological interestingness weight map and planning a flight path. Dense sampling flights are performed in a high interestingness region, high resolution data are processed in real-time to identify key reconnaissance targets, a weight map is updated, and a flight trajectory is dynamically adjusted. Through the closed-loop self-adaptive exploration process, accurate positioning and feature description of the hidden vein are realized, the mineral exploration efficiency is improved, the labor cost and the safety risk are reduced, and an innovative solution is provided for mineral resource exploration in remote areas.

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 embodiment of the invention discloses a mineral resource exploration data analysis method and device based on three-dimensional space modeling. The method comprises the steps that back-flushing pushing-covering structure feature information is extracted through structure interface occurrence measurement, lithology combination analysis and structure movement trace recognition and converted into geologic structure feature parameters; stratigraphic age data and lithology distribution information are integrated, and a three-dimensional stratigraphic model is constructed with a stratigraphic superposition relation as a constraint; geophysical data are imported to extract geophysical response characteristics of stratums with different depths, and lithologic boundary correction is combined to obtain deep fracture characteristic parameters; sedimentary system division is carried out on lithofacies paleogeographic information, and deep fracture characteristic parameters are fused to establish a fracture system three-dimensional model; the multi-feature association relationship is identified, the ore body prediction model is established, the spatial data is input to obtain the three-dimensional visual prediction result of the hidden ore body spatial distribution, and the exploration efficiency and accuracy in the deep phosphorite exploration process are improved.

The invention discloses a deep learning prospecting prediction method and system for multi-modal geological data, and relates to the technical field of mineral resource exploration and prediction. The method comprises the following steps: acquiring multi-modal geological data, and preprocessing the multi-modal geological data to obtain preprocessed geological sensing data; constructing a prospecting prediction model based on a hybrid model architecture; the hybrid model architecture comprises a CNN branch, an RNN branch and a GNN branch, and each branch is connected with the full connection layer through splicing operation; inputting the multi-modal geological data into a trained prospecting prediction model for processing to obtain a mineralization potential prediction result; the mineralization potential prediction result comprises a mineralization type, an ore body scale and an ore body grade; and based on the mineralization potential prediction result, constructing a three-dimensional mineralization potential model of the target area by using a depth generation model for visual display. According to the invention, the precision and generalization ability of prospecting prediction can be improved.

The invention relates to a gold mine air-ground-in-well rapid collaborative exploration method, belongs to the field of geological exploration, and aims to solve the technical problems of data splitting, low target area positioning precision, insufficient deep prediction capability and the like in traditional gold mine exploration. By integrating air remote sensing, surface geological survey and underground detection data, a multi-source data standardization processing flow is established, and a progressive exploration system from wide-area screening to accurate positioning is realized by combining geological, geophysical and geochemical comprehensive analysis methods. The system adopts a dynamic feedback mechanism to optimize an exploration process, forms closed-loop analysis and prediction, improves exploration efficiency and accuracy, and reduces resource waste and exploration cost. The method is suitable for gold ore resource exploration under complex geological conditions, has remarkable economic benefits and application value, and can be widely applied to the field of mineral resource development.

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.

A dynamic adaptive learning method for mineral prediction includes: collecting a dataset including geological data and labels of the geological data; extracting features from the geological data, initializing parameters of a training model and optimizing the parameters to obtain training parameters; performing an associative training on the training model based on the training parameters and the labels in a dynamic adaptive learning framework to obtain a mineral prediction model, algorithms of the associative training including a variational expectation algorithm and a variational maximization algorithm, and the variational expectation algorithm including an unsupervised learning mode, a semi-supervised learning mode, and a fully supervised learning mode; and predicting, by using the mineral prediction model, a mineral to obtain a mineral prediction result. The method can break through limitations of the traditional machine learning technology, offering a more efficient, universal, and stable strategy for geophysical data analysis and mineral resource assessment.

The invention provides a layered multi-agent-based full-process intelligent decision-making method and system for mineral development. The method comprises the following steps: constructing a three-level intelligent decision-making main body-based full-process layered collaborative decision-making architecture for mineral development; designing a communication protocol; and establishing a quantitative coupling relationship between the cross-link coupling constraint and the process parameters. And constructing a state space and an action space of the three-level intelligent decision-making main body. Designing a global objective function, a link objective function and a key parameter collaborative optimization model; and establishing an adaptive weight optimization mechanism. And designing a distributed collaborative optimization mechanism based on an alternating direction multiplier method. And implementing an agent collaborative decision-making process of the evolutionary game. The selected decision scheme is converted into a specific production instruction to be issued and executed, the execution effect and feedback data are monitored in real time, and the strategy is dynamically adjusted and optimized. According to the invention, the multi-objective collaborative optimization of the full life cycle of mineral development is realized, and the intelligent, green and sustainable development of mineral resource development is promoted.

The invention relates to a dynamic detection method, system, equipment and medium for a blind gold mine in a coverage area, and belongs to the technical field of mineral resource exploration, the method comprises the following steps: collecting historical geochemical exploration data and geographic coordinate data of a target coverage area, and screening a target test profile based on drilling mine finding position and coverage layer thickness information; collecting soil shallow gas data on the test section and performing anomaly analysis to generate a gas anomaly space coordinate set; taking an area marked by the gas anomaly space coordinate set as a preset sampling point, acquiring soil sample attribute data of the preset sampling point, inputting the soil sample attribute data into a pre-configured granularity-element correlation model, and outputting a coverage type judgment label; and calling a corresponding geochemical method combination from a preset method library, generating an execution instruction list, carrying out element abnormal data acquisition and spatial overlay analysis on the target coverage area, and generating an ore body prediction probability distribution diagram and a drill hole layout coordinate list. The accuracy of ore body positioning can be improved.

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 discloses a gravity-magnetoelectricity three-dimensional joint inversion method based on reweighting, and belongs to the technical field of geophysics. According to the method, the resolution and physical property difference characteristics of data of geophysical gravity, a magnetic method and an electromagnetic method are fully utilized, and a more reliable underground structure model is obtained in a joint inversion mode; according to the method, gravity, magnetic and electric multi-field data are processed at the same time, and the method is suitable for mineral resource exploration, oil and gas reservoir exploration, deep structure research and other scenes under complex geological conditions, and especially has remarkable advantages in hidden ore body boundary recognition, lithologic contact zone positioning and the like; by integrating reweighting, cross gradient and efficient iteration technologies, the manual intervention and parameter debugging cost is greatly reduced, the automatic inversion process from data input to model output is realized, and the engineering application efficiency of geophysical exploration is 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 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 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 discloses a mineral resource image classification method based on VGG, and the method comprises the steps: an image preprocessing stage: segmenting a remote sensing image into small blocks according to the coordinates of a mineral point and a classification result, making labels, and carrying out the upsampling, sharpening, filtering and other operations of the image; and a model construction stage: designing a VGG-based classification model, then dividing the sorted images into a training set and a test set, training the model by using the training set, and evaluating the model effect by using the test set. Then model parameters are adjusted, and an optimal parameter combination is found; and a mineral resource prediction stage: carrying out containerization deployment on the trained model, inputting a predicted picture, and obtaining an output result. According to the method, the VGG algorithm is used, a good effect is achieved in mineral resource image classification under real geological data, and the method has wide application value and use prospects in the geological mineral field.

The invention provides a method for quickly converting coordinate formats of mineral resource reserve databases in batches and related equipment. The method comprises the following steps: firstly, reading a mining area number and a registration classification number in a row coordinate record, and a character string in a mining area or mine range coordinate field and a character string in a calculation coordinate field from a coordinate management table; secondly, independently storing block coordinates in the two character strings by utilizing two one-dimensional arrays, and independently storing initial positions and inflection points of the block coordinates by utilizing two two-dimensional arrays; and finally, according to the information in the four arrays, in combination with an encoding rule of the character string, reading block coordinates from the corresponding arrays in sequence, forming a coordinate pair format, and writing the coordinate pair format into a corresponding text file, so that forward conversion from the coordinate string to the coordinate pair is realized. In addition, reverse conversion of coordinate pairs to coordinate strings is also provided. According to the invention, the coordinate pair data meeting the coordinate system transformation format requirement can be quickly realized and provided.

The invention provides a mineral resource overburden risk assessment method and system, and the method comprises the steps: obtaining multi-source heterogeneous geological data, carrying out the deep fusion of the multi-source heterogeneous geological data through employing a game theory gradient control robust neural network training method, and obtaining a fusion data set; performing deep mining and reasoning on high-dimensional geological data by utilizing the pattern recognition and correlation analysis capability of a large model and combining a manifold perception regularization technology, and recognizing geological structure features; establishing a dynamic safety mining depth evaluation model by using a fractional peak differential equation neural network with efficient accompanying parameter training, and calculating safety mining depth intervals and risk probabilities under different confidence degrees by integrating geological structure risks, mining disturbance effects and engineering safety thresholds; and constructing an assessment report generation module including core risk point analysis, key technology demonstration and prospective risk early warning, and generating an intelligent overburden risk assessment report. According to the invention, the accuracy and reliability of the pressing and covering risk assessment can be improved.

The invention discloses an LIBS rare earth multi-element quantitative detection method based on a deep learning model, and the method comprises the steps: constructing a multi-scale feature fusion deep learning architecture MSFF-Net, carrying out the weighted fusion of local features extracted by a 1D-CNN and long-range dependence features of BiLSTM modeling through a channel attention mechanism, and enhancing weak signal features in combination with a first-order derivative spectrum. Meanwhile, a synthetic data enhancement strategy based on a physical model is adopted to simulate a matrix effect and a spectral line overlapping scene, and spectral feature migration under different matrixes is achieved in cooperation with a matrix self-adaptive migration learning strategy. And after data acquisition and preprocessing, a trace element concentration prediction value is synchronously output through a multi-task learning framework. According to the method, the detection precision and the anti-interference capability of the LIBS technology on trace elements are effectively improved, and the method has remarkable application value in the industrial fields of mineral resource exploration, strategic metal recovery and the like.

The invention provides a well-ground frequency domain electromagnetic detection method and device and electronic equipment, and the method comprises the steps: setting a perfect matching layer based on stretching coordinate transformation at the outer side of a model based on a three-dimensional geologic model, constructing an overall computational domain, facing the overall computational domain, and implementing adaptive mesh generation according to posterior error estimation. And generating an unstructured tetrahedral mesh which is locally refined in a target area, and on the tetrahedral mesh, performing spatial discretization and solution on a frequency domain Maxwell equation by adopting a first-order or second-order vector finite element in an H (curl) space to realize well-ground frequency domain electromagnetic forward modeling calculation. According to the method, through cooperation of the adaptive grids and the vector finite elements under the microframework, both precision and efficiency are considered, forward modeling stability and imaging resolution are improved, and a scientific basis is provided for efficiency improvement and cost saving of mineral resource exploration and development.

The invention provides a carbonate clay type lithium ore element spectral data acquisition device which comprises a display module used for displaying a first interface comprising a preset map. The preset map comprises at least one sub-region. And the data acquisition module is used for acquiring hyperspectral data of the target elements corresponding to the sampling points included in any sub-region. The target element is associated with carbonate clay-type lithium ore. The display module is further used for displaying a second interface comprising a label selection area and a data display area. The label selection area is used for displaying a label selection control. And the data analysis module is used for determining a sampling point to be processed in response to an input selection operation on any label selection control. And the data analysis module is also used for determining a spectral reflection coefficient peak comparison curve corresponding to the to-be-processed sampling point based on the hyperspectral data of the target element corresponding to the to-be-processed sampling point. The display module is further used for displaying the spectral reflection coefficient peak comparison curve so as to visually display the exploration result, and the exploration efficiency of mineral resources is improved.

The invention relates to the technical field of mineral exploration, and discloses a mineral resource intelligent identification method based on multi-source remote sensing data, and the method constructs an intelligent identification model based on a deviation analysis and correction mechanism through integrating the multi-source remote sensing data, geological data and mineral reaction characteristic signals, can dynamically adapt to different environmental conditions, and improves the identification efficiency. According to the method, the mineral resource exploration precision and reliability are remarkably improved, through dynamic correction and multi-coefficient integration, the misjudgment rate is effectively reduced, the recognition accuracy and precision ratio of the mineral target area are improved, potential mining areas can be rapidly screened out in a large-range area, manual intervention is reduced, the exploration efficiency is remarkably improved, and the method is suitable for popularization and application. By generating a plurality of mineral marker guidance coefficients KWX, the mineral resource potential is comprehensively evaluated from multiple angles, and the accuracy and stability of mineral recognition are remarkably improved.

The invention discloses a salt lake brine mining method and system for hydraulically gradient-driven water replenishing and ore dissolving of a sedimentation funnel, and relates to the technical field of salt lake mineral resource development, the method comprises the following steps: S1, collecting water level data of a sedimentation funnel area, calculating gradient distribution, dividing functional areas, and generating zoning parameters; s2, based on the partition parameters, a water injection well network is arranged in the gradient direction, water injection parameters are determined according to the water level difference, and a well network scheme is generated; s3, based on a well pattern scheme, injecting the proportioning solvent into an ore bed in a layered manner to form a directional seepage field, and generating solute transport data; s4, on the basis of solute transport data, enriched brine is extracted in the collection area, the concentration is monitored, and brine parameters are generated; and S5, based on the deviation between the brine parameter and the target value, calculating the regulation amount, adjusting the injection-production parameter to maintain dynamic balance, and outputting the salt lake brine with the target concentration. By regulating and controlling the hydraulic gradient, the form of the settlement funnel is stabilized, the phenomena of stratum disturbance and ground settlement are reduced, and the ecological risk is reduced.