378 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 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 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 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 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.

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 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 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 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.

The invention belongs to the technical field of geological sample analysis, and particularly relates to a heavy mineral fidelity pretreatment and morphology automatic characterization method based on artificial intelligence. The method is based on a medium-low hardness heavy mineral fidelity separation and purification technology, an integrated multifunctional target manufacturing technology, a microscopic image intelligent acquisition technology and intelligent identification and feature extraction based on deep learning. The method comprises the following steps: acquiring fidelity heavy mineral particles which maintain the original three-dimensional morphology, are not reset in internal thermosensitive geological records and are not subjected to any systematic deviation screening on population composition, calculating a series of morphological parameters such as an extension coefficient, roundness, color and the like of each heavy mineral particle, and finally outputting a structured quantitative data report. According to the method, multidirectional and intelligent fidelity is realized from the source, and the analysis flux and objectivity are greatly improved by an automatic process, so that systematic and quantitative mineral resource general survey on massive fine particles in a giant deposition system becomes possible.

The invention discloses a stratigraphic water content layering inversion method based on a shallow seismic full waveform, and relates to the technical field of geophysical exploration data processing, and the method comprises the following steps: 1, under the same survey line condition, synchronously obtaining a seismic wave full waveform, complex impedance and an induced polarization frequency spectrum; 2, constructing a layered initial model containing a layer boundary line and an in-layer water content initial value; based on the layered initial model, executing dual-domain quantile alignment interface keeping iteration until convergence, and obtaining a final layer boundary line and an in-layer water content field; the double-domain quantile alignment interface maintaining iteration comprises the following steps: updating an in-layer moisture content field and a layer boundary line according to a seismic wave full waveform; and performing total variation regularization processing on the updated in-layer water content field to maintain the transition characteristic of the layer boundary line. According to the method, an efficient and reliable solution with actual guidance value is provided for mineral resource exploration, geological structure detection, groundwater survey and engineering geology evaluation.

The utility model discloses a magnetic suction lining and double-medium energy-saving ore grinding system, which comprises an ore feeding port, an overflow type mill cylinder, a magnetic suction lining, double grinding media, a hollow shaft neck and a reverse spiral blade, the whole magnetic suction lining has magnetism and is tightly attached to the overflow type mill cylinder by means of magnetic force; double grinding media are arranged in the overflow type mill cylinder and are used for grinding minerals; a hollow shaft neck is arranged at one end part of the overflow type mill cylinder body and is used for discharging mineral products; an ore feeding opening is formed in the other end part of the overflow type mill cylinder body and is used for guiding a sand setting product of the hydraulic classification cyclone to enter the overflow type mill cylinder body; reverse spiral blades are arranged on the hollow shaft neck, and the rotating direction of the blades in the ore grinding operation is opposite to the rotating direction of the overflow type mill barrel. Through the multi-layer and multi-link fine design, the whole ore grinding process is more efficient and accurate, and the utilization rate and the production efficiency of mineral resources are greatly improved.

The invention discloses a mineralization potential evaluation and target region optimization method and system based on machine learning, and relates to the technical field of mineral resource exploration. The method comprises the following steps: acquiring multi-dimensional data of a target area; preprocessing the multi-dimensional data to obtain preprocessed key data; a metallogenic system conceptual model is constructed, and a hydrothermal migration path and a metallogenic fluid pressure field of the key data are calculated through finite element simulation for visual display; generating an evidence layer based on the key data to obtain multi-dimensional evidence layer data; and constructing a mineralization potential evaluation model, and inputting the multi-dimensional evidence layer data into the mineralization potential evaluation model for prediction to obtain a mineralization probability prediction result. Through multi-source data fusion and machine learning algorithm innovation, intelligentization and precision of mineralization potential evaluation and target region optimization are realized, and an efficient technical means is provided for mineral resource exploration.

The invention relates to the technical field of mineral resource exploration, in particular to a deep concealed ore body prediction method and system. The method comprises the following steps: acquiring geological exploration data, geophysical data and lithologic physical property probability distribution data of a target area; constructing a three-dimensional geological model according to the geological exploration data; performing inversion calculation on the geophysical data by taking the three-dimensional geologic model and the lithologic physical property probability distribution data as constraints to obtain a three-dimensional geophysical model; fusing the three-dimensional geologic model and the three-dimensional geophysical model to obtain a three-dimensional geologic-geophysical model; and determining the mineralization probability of the deep concealed ore body according to the three-dimensional geology-geophysical model. The problem that in the prior art, deep hidden ore body prediction is not accurate enough is solved, the three-dimensional geology-geophysical model is gradually constructed by obtaining the geological exploration data, the geophysical data and the lithologic physical property probability distribution data of the target area to be explored, then ore body prediction is conducted, and the accuracy of ore body prediction is improved.

The invention discloses a three-dimensional dynamic prediction method and system for deep brine resource quantity, particularly relates to the technical field of mineral resource exploration, and is used for solving the problem of resource evaluation misalignment caused by a well group interference effect in intensive mining. The method comprises the following steps: constructing a spatial topology network model which takes a well location as a node and a well spacing as an edge weight by obtaining a mining area well group space coordinate and mining dynamic time sequence data; excavating pressure conduction characteristics, resource concentration migration characteristics and shaft temperature time sequence data; calculating a thermal buoyancy gradient based on temperature data, determining a stress deflection angle in combination with a network model azimuth angle, and fusing to construct an interference strength coefficient matrix; tracking an inter-well resource siphon path by using a network edge weight, and generating a time-lag compensation factor by coupling a time-lag rule of pressure and concentration characteristics; finally, the interference matrix, the siphon path and the time delay factor are fused to dynamically correct the single well evaluation value, the total area brine resource amount is output, and the intensive mining area resource evaluation precision is remarkably improved.

The invention provides a safety monitoring method and system for deep well heat damage, relates to the technical field of deep well safety, and solves the technical problems of high energy consumption and insufficient monitoring precision of deep well high-temperature heat damage treatment in the prior art. The method comprises the following steps: constructing a heat damage risk prediction model based on a machine learning algorithm, inputting real-time deep well environment data into the heat damage risk prediction model, and outputting a current heat damage risk level; based on the current heat damage risk grade, refrigerating equipment and a ventilation system are dynamically controlled, and the environment is cooled; and waste heat generated during operation of the refrigeration equipment and the mine equipment is recycled, and stepped recycling and power generation treatment are conducted on the waste heat. The method is used for the full-period operation process of deep well mineral resource exploitation, and is particularly suitable for core production links such as the deep well tunneling stage and the stoping operation stage.

The invention relates to a mineral resource prediction method and system for a shallow-hidden fluorite mine, and the method comprises the steps: carrying out the metallogenic characteristic analysis of a typical fluorite deposit, and constructing a prospecting prediction conceptual model according to an analysis result; performing evaluation analysis on the resource potential, the mineralization favorable degree and the prospecting prediction matching degree of the prospecting area to obtain an optimal mineralization prediction geological unit; on the basis of the prospecting prediction conceptual model, a prospecting prediction quantitative model is obtained in combination with geological information of the typical fluorite deposit, and fluorite mine quantitative prediction is conducted on the optimal metallogenic prediction geological unit through the prospecting prediction quantitative model, so that fluorite mine prediction quantity is obtained; and under the condition that the fluorite mine prediction amount reaches a set threshold value, fluorite mine resource evaluation analysis is carried out according to the fluorite mine prediction amount in combination with the obtained geological exploration data of the ore searching area, and fluorite mine resource distribution data is obtained. From the perspective of green prospecting, the method can quickly and accurately carry out prospecting prediction on shallow hidden or deep fluorite mines.

The invention discloses a mineral resource reserve estimation method and system based on satellite data, and the method comprises the steps: S1, obtaining multispectral satellite remote sensing data, extracting surface mineral alteration information and geological structure features through a spectral feature recognition algorithm, and generating a surface feature data set; s2, based on the earth surface feature data set, establishing a spatial correlation model of earth surface anomaly and deep mineralization by adopting a geostatistics method, and obtaining a three-dimensional probability distribution matrix of deep mineralization intensity based on the spatial correlation model; s3, dividing a to-be-estimated region into three-dimensional grid units based on the three-dimensional probability distribution matrix; and S4, calculating an in-mineral resource reserve estimation value of each grid unit in the three-dimensional grid units, and completing mineral resource reserve estimation based on satellite data based on the in-mineral resource reserve estimation value.

Belonging to the field of marine resource exploration equipment, the invention relates to a portable pressure maintaining gravity sampler, which comprises an extraction hole, an empennage, a counterweight, a connecting piece, a control piece, a large spring, a control mechanism shell, a positioning block, a release pipe, a small spring, a spacer pin, a sliding pipe, a pressure maintaining pipe, a closed ring, a mandrel, a flap valve, a check valve ball, a sampling pipe, a one-way flap, a tool bit, a drain hole, a limiter and a pipe body. According to the design scheme, the sampler is provided, through the design of the control mechanism shell, the pressure maintaining pipe and the sampling pipe, a light-weight pressure maintaining structure is provided, and complex pressure compensation or a hydraulic system is not needed; in addition, a sealing and pressure maintaining device is optimized, the in-situ pressure of the seabed sediment can be stably maintained so as to guarantee the integrity of the sample, meanwhile, the operation process of the sampling process is simplified, the manufacturing cost and the fault risk are reduced, and the dual requirements of marine geological research, mineral resource exploration and other scenes for high-quality samples and efficient operation are met.

The invention discloses a 2.5 D statistical optimal distributed modeling method for a geologic structure of a thin coal seam group, and belongs to the technical field of geologic modeling and mineral resource development. The method comprises the steps that drilling space information and sequence constraints of all stratums are obtained based on drilling data, elevation estimation values and corresponding estimation value variances of all points in estimation value grids of all the stratums are obtained through independent Kriging interpolation operation of all the stratums, and constraint conditions are constructed according to the sequence of all the stratums; and constructing a target function of'minimizing the sum of variance with the original value 'by utilizing the elevation estimation value and the corresponding variance on each stratum estimation value grid point, and solving the elevation value of each stratum on the grid point in the estimation value range. The method fuses adjacent horizon information and spatial statistical constraints, can more accurately describe and express complex three-dimensional spatial relationships such as stratum pinching, crossing and merging in the thin coal seam group, remarkably improves the spatial fidelity of the model, realizes automatic and quantitative correction of the stratum sequence in strip mine full-stratum modeling, eliminates manual intervention errors, and improves the modeling efficiency. And the modeling efficiency and the geometric accuracy are improved.

The invention provides a nickel ore target region prediction method and system based on hyperspectral remote sensing data analysis, and relates to the technical field of mineral resource exploration, and the method comprises the steps: firstly obtaining a hyperspectral remote sensing data body of a to-be-predicted region, then carrying out alteration feature space clustering construction processing on the hyperspectral remote sensing data body, forming an alteration feature space cluster body, and carrying out the clustering construction processing on the alteration feature space cluster body; the method comprises the following steps: identifying spatial distribution of various geological alteration types and binding spectral features, then carrying out mineralization alteration combination sequential spatial evolution processing to obtain a mineralization alteration sequential evolution body, then carrying out target area indication feature anchoring shaping construction processing on the mineralization alteration sequential evolution body to form a target area indication space shaping body, and carrying out target area indication feature anchoring shaping construction processing on the target area indication space shaping body. And positioning key indication characteristics of the nickel ore target region, and finally carrying out nickel ore target region space consolidation delineation treatment to generate a nickel ore target region prediction result. According to the method, spectrum and space information of hyperspectral remote sensing data is fully utilized, and the accuracy and reliability of nickel ore target region prediction are effectively improved.

The invention discloses a borehole wall coupling effect-oriented downhole transient electromagnetic response modeling and signal correction integrated method. The method comprises the following steps of 1) constructing a wellbore-stratum-anomalous body three-dimensional geometric model; 2) deriving an electromagnetic field control equation based on a time domain Maxwell equation set, and continuously processing an interface in the wellbore-stratum-anomalous body three-dimensional geometric model by combining a tangential electric field and normal magnetic flux density; 3) calculating a response signal Vwell (t) and a response signal Video (t) by adopting an FDTD (Frequency Division Time Division) method; 4) analyzing the time-space and frequency spectrum characteristics of the signals, and calculating a well wall effect difference value delta V (t) by using the response signal Vwell (t) and the response signal Video (t); and 5) optimizing the time-varying correction coefficient alpha (t), and correcting the transient electromagnetic response signal in the well based on the time-varying correction coefficient alpha (t) and the well wall effect difference value delta V (t) to obtain a correction signal. According to the method, reliable data support can be provided for high-resolution inversion of stratum electrical parameters, rapid and accurate recognition of complex underground anomalous bodies is achieved, and the application effect and engineering practicability of the BTEM technology in the fields of oil-gas exploration, geothermal resource evaluation, mineral resource exploration, deep geological survey and the like are remarkably improved.

The invention discloses a gold mine prediction method based on deep ground drilling data, and relates to the field of mineral resource exploration. The problems that a traditional method depends on artificial experience, ignores a multi-source geological element space coupling relation and lacks three-dimensional probability expression, so that prediction precision is insufficient are solved. Carrying out weight distribution calculation on each key mineralization factor by adopting the coupling strength coefficient, and carrying out comprehensive processing on numerical values of the plurality of mineralization factors through a weighted fusion algorithm to obtain a comprehensive mineralization index distribution diagram layer; based on the numerical value distribution characteristics of the three-dimensional mineralization index field, establishing a mineralization probability prediction model by adopting a Bayesian network algorithm, and if the mineralization index of a certain spatial position is higher than a critical value, calculating a gold ore occurrence probability numerical value of the position; and converting the gold ore occurrence probability value into a probability distribution body under a space coordinate system through a three-dimensional modeling algorithm, and performing rendering processing on the probability distribution body by adopting a volume rendering technology to obtain a three-dimensional visual model of the gold ore occurrence probability.

The invention discloses a new-type barite deposit mineralization mode construction method based on a same sedimentary fault. The method belongs to the field of geological exploration and mineral resource prediction, and comprises the following steps: analyzing the earth crust cracking activity history of an earthquake-Hangjian broken basin, and recovering a basin'checkerboard 'structure prototype; by means of field geological survey and research, drilling engineering, geophysics, remote sensing geological big data and the like, north-east-east-direction and north-west-direction co-sedimentary fault networks are identified and modeled, and a deep metallogenic fluid rising and jet flow channel is determined; the metallogenic process is concluded into a hydrothermal jet flow stage and an underwater gravity flow deposition stage, deep fluid is revealed to rise, jet flow and precipitate along a fault, and an ore-rich zone of a composite seabed fan body group is formed; and a geochemical prospecting prediction model combining trace element and sulfur isotope characteristics is established. The method systematically clarifies a coupling mechanism of ore control of the same sedimentary fault, ore-forming fluid migration and gravity flow deposition, and provides a theoretical basis and a technical means for prospecting prediction of the barite deposit in the Hendan-Hangjian fault basin.