363 results about "Mineral exploration" 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 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 provides a multi-source geophysical prospecting data fused Zenzenite rock mass contact zone anomaly method and system, and relates to the technical field of geophysical prospecting, the method comprises the following steps: executing joint denoising processing and time-frequency feature enhancement processing on a TFEM data set in a three-dimensional fused data volume, and outputting the optimized three-dimensional fused data volume; and based on the optimized three-dimensional fusion data body, by establishing a mapping relationship between magnetic, electric and polarization anomalies and a geologic body, quantifying the correlation between a multi-parameter combination and a mineralization index and screening an up-to-standard abnormal region, and finally delineating a magnetic anomaly gradient zone, a low-resistance region and a high-polarization anomaly co-occurrence ore prospecting target region three-dimensional distribution diagram. Through multi-source geophysical prospecting method collaboration, data deep fusion, quality optimization and accurate mapping, full-chain technology upgrading from data acquisition to target area delineation is realized, and the high efficiency of mineral exploration is 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 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 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 relates to a lithium ore prospecting prediction method and system, and relates to the technical field of mineral exploration. According to the lithium ore prospecting prediction method and system, the prediction accuracy of ore body identification is high, the exploration period can be shortened, and the exploration cost can be reduced. According to the method, the multi-dimensional data fusion is combined with the intelligent algorithm, the three-dimensional mineralization probability is output through the prediction neural network model, the prediction accuracy of ore body recognition is high, the exploration period can be shortened, and the exploration cost can be reduced.

The invention discloses a hydrothermal vein type gold deposit alteration zoning remote sensing identification method, and the method specifically comprises the following steps: S1, carrying out multi-platform remote sensing cooperative interpretation, and obtaining the initial alteration zoning information of a regional scale. According to the hydrothermal vein type gold deposit alteration zoning remote sensing identification method, through organic combination of multi-platform remote sensing cooperative interpretation and unmanned aerial vehicle fine mineral mapping, cross-scale detection from regional scale preliminary alteration zoning identification to target region scale alteration lithofacies accurate delineation is realized; the problems that a traditional remote sensing method is single in scale and insufficient in precision in alteration zoning recognition are effectively solved. According to the method, the complementation advantages of multi-temporal optical remote sensing, synthetic aperture radar and thermal infrared remote sensing data are utilized, accurate recognition of key altered minerals is achieved in combination with hyperspectral data of the unmanned aerial vehicle, and the certainty of alteration zoning boundaries and the accuracy of lithofacies classification are remarkably improved.

The invention discloses a deep niobium phosphate ore positioning physical and chemical exploration method based on multi-method cooperation, and belongs to the technical field of geological mineral exploration. According to the method, a three-stage exploration system of region optimization, target region delineation and fine verification is constructed, multi-parameter cooperation of a magnetic method, an electromagnetic method, an induced polarization method, a three-component magnetic logging method and a geochemical method is combined, and each step is constrained by a basic rock-niobium phosphorite metallogenic model. An ore prospecting mark is determined through regional geological background analysis and metallogenic model construction, a metallogenic prospective region is delineated through regional optimization, a specific target region is delineated and locked, engineering verification is carried out through fine verification, and finally, a result is integrated to estimate the resource quantity. According to the method, the multiplicity of solutions of a single method is effectively reduced, the positioning precision of the deep niobium phosphorite is improved, the exploration efficiency and the target area ore finding rate are improved, and the method is suitable for accurate exploration of the deep niobium phosphorite.

The invention relates to the technical field of deep mineral exploration, and discloses a deep metal mine multi-source geophysical data real-time fusion system. The system comprises a data access module which divides detection dimensions according to physical field types so as to receive original data; the space-time registration module is used for establishing mapping between original data and a unified space-time reference system through timestamps and space coordinates; the feature extraction module is used for extracting key features from the registration data, determining weights according to physical meanings and constructing a feature association network; the fusion decision module identifies an abnormal value and adjusts a network topology structure based on the credibility of the network evaluation data; and the real-time updating module is used for optimizing the fusion priority and algorithm parameters and generating a real-time fusion result library. According to the system, real-time fusion of multi-source geophysical data is realized, and systematicness and timeliness of data processing are improved.

The invention discloses a geophysical prospecting signal denoising method based on VMD (variational mode decomposition) combined with an improved wavelet threshold function, and belongs to the technical field of mineral exploration geophysical prospecting signal process.The method includes the steps that firstly, a decomposition mode number K of a signal is determined through VMD in a self-adaptive mode, a plurality of IMFs (intrinsic mode components) are obtained, and then according to the frequency characteristic and noise distribution of each IMF component, a wavelet threshold function is obtained; and carrying out targeted noise suppression by adopting an improved wavelet threshold function containing an adjustment parameter alpha, and finally, carrying out linear superposition reconstruction on all the processed IMF components to obtain a de-noised geophysical prospecting signal. Experimental verification shows that compared with a traditional method, the method has the advantages that the signal-to-noise ratio and the correlation coefficient of the noisy geophysical prospecting signals can be effectively increased, the root-mean-square error can be effectively reduced, the inherent defects of the traditional method are overcome, the geologic features of the geophysical prospecting signals can be effectively reserved, the method is suitable for various mineral exploration scenes, and reliable data support is provided for anomaly recognition in mineral exploration.

The invention discloses a mineral exploration sampling method for mineral geology, and belongs to the technical field of geological exploration. Comprising the following steps: generating a standardized alteration anomaly spatial distribution map; accurate prediction of the mineralization probability is realized, and a mineralization probability distribution diagram is generated; the sampling depth is intelligently determined by combining the alteration-mineralization space relation of the mineralization type, and an initial sampling grid scheme is formed; establishing a digital file including a coordinate position, a sampling depth and geological features for each sampling point; the on-site detection module is controlled to implement on-site sampling according to the optimized sampling grid, and an on-site detection result is obtained in real time; whenever significant deviation exists between the field detection result and remote sensing alteration prediction, the sampling point generating deviation is marked as an abnormal deviation point; if the sampling integrity does not reach the preset exploration standard, dynamically adjusting a subsequent sampling scheme and continuing sampling; and if the sampling integrity reaches the preset exploration standard, generating a final mineralization probability distribution diagram and an exploration sampling report.

The invention discloses an uncertainty perception deep learning mineral product prediction method, and relates to the technical field of mineral product exploration, and the uncertainty perception deep learning mineral product prediction method mainly comprises the steps: carrying out the preprocessing and feature processing of a multi-source geological data set, and obtaining a training set and a test set; constructing an uncertainty perception deep learning model and a loss function, training the uncertainty perception deep learning model by using the loss function and the training set to obtain a trained uncertainty perception deep learning model, predicting Dirichlet parameters of the training set and the test set, and calculating a prediction probability and uncertainty of each sample; and a prediction probability graph and an uncertainty graph are obtained. By implementing the uncertainty perception deep learning mineral product prediction method provided by the invention, the uncertainty of the mineral product prediction result can be measured and simulated, and the robustness of mineral product prediction is improved.

The invention belongs to the technical field of rock core cutting, and particularly relates to a solid mineral exploration rock core automatic cutting device which comprises a box body, and a fixing frame is fixed to the upper portion of the box body; the fixing assembly is used for fixing a rock core, the fixing assembly comprises a mounting plate arranged in the box body, a pair of U-shaped frames are arranged on the mounting plate, a supporting rod is arranged in the middle of each U-shaped frame, the two ends of each supporting rod are fixedly connected with the side wall of the corresponding U-shaped frame through connecting rods, and pressing rods are slidably arranged on the connecting rods on the two sides respectively; the hydraulic cylinder is arranged below the mounting plate and used for controlling the mounting plate to ascend or descend; the hydraulic cylinder is arranged to sink a rock core into water before cutting and after cutting, on one hand, floating dust on the surface of the rock core can be removed before cutting, and fly ash generated in the cutting process is reduced; on the other hand, mud attached to the fixing assembly and the rock core in the cutting process can be effectively removed, and the cleaning effect is improved.

The invention discloses a prospecting exploration method for ion adsorption type heavy rare earth ore in middle and low mountainous areas, and belongs to the technical field of geological mineral exploration. According to Y, Dy and Nb abnormity and Y-Nb combination abnormity areas such as water chemical exploration and soil chemical exploration of a working area, high-silicon, potassium-rich, alkali-rich and sphene-rich heavy rare earth secondary mineral mineralization mother rocks are preferably selected, and a favorable mineralization area of the ion adsorption type heavy rare earth ore is delineated by combining characteristic parameters that Y / Ce is greater than 0.9 and Dy / Ce is greater than 0.2; screening south and east-south-east abrupt slope (gradient of about 30 degrees) areas by utilizing a GIS spatial analysis technology, overlapping favorable mineralization areas of the ion adsorption type heavy rare earth ore, and comparing and determining an ion adsorption type heavy rare earth ore prospecting target area in a working area; whether mineralization abnormity exists in the heavy rare earth ore prospecting target area or not is verified by using a manual impact sampling drill and a rare earth ion leaching-titration qualitative analysis method; chemical samples are preliminarily collected, 15 components of ion phases are analyzed, whether heavy rare earth ore bodies exist or not is judged and evaluated, the prospecting exploration range of the ion adsorption type heavy rare earth ore is determined, and the prospecting efficiency of the ion adsorption type heavy rare earth ore is improved. According to the method, the mineralization prospective area of the ion adsorption type heavy rare earth ore can be rapidly delineated in a large range, the prospecting target area is narrowed, and the prospecting efficiency of the ion adsorption type heavy rare earth ore is improved.

The invention discloses a mineral sample acquisition optimization method based on stratum change identification, and particularly relates to the technical field of mineral exploration sampling, comprising the following steps: acquiring lithologic characteristics, fault distribution, underground stress information and historical sampling data, fusing to form spatial reference data and generating a stratum change level distribution map, then constructing a sample collection candidate area map; generating a sampling path group and adjusting path layout according to the candidate area map, matching a sampling mode for each path, recording multiple types of data in real time in the sampling process and comparing the data with spatial reference data, and dynamically adjusting the sampling path or mode according to a comparison result; spatial reference data are generated through multi-source geological information fusion, and high-precision stratum change recognition is achieved; the sampling path layout is optimized in combination with disturbance distribution and operation constraint, so that the operation safety and the coverage integrity are improved; in the sampling process, response data are compared in real time, a sampling strategy is dynamically adjusted, and the geological consistency and reliability of a sampling result are ensured.

The invention relates to a refined three-dimensional geological modeling method based on a geophysical prospecting three-dimensional inversion algorithm, and belongs to the field of geological exploration, and the method comprises the steps: obtaining multi-source data, constructing an initial model, and obtaining a three-dimensional physical property model through the combined constraint of the three-dimensional inversion algorithm; creating a lithology classifier, generating a three-dimensional lithology probability body according to the three-dimensional physical property model, performing geological structure boundary enhancement, and performing geological classification based on the probability body; and extracting a contour surface from the three-dimensional lithologic probability body, generating a preliminary geological interface, performing construction processing, performing attribute model coupling, and outputting a model. According to the method, the spatial continuous model is directly constructed through three-dimensional joint inversion, and interpolation errors between measuring lines are avoided; drilling data forced matching and geophysical prospecting data trend control consider both local precision and global rationality; the whole process algorithm is driven from inversion to lithology classification, and manual intervention is reduced; the method is suitable for high-precision scenes such as urban underground space, mineral exploration and geological disaster assessment.

The invention provides a gold mineral exploration method, device and system and a storage medium. Relates to the technical field of mineral exploration. The gold mineral exploration method comprises the following steps: acquiring a backscattered electron image of a sample to be detected, and screening bright mineral phase particles based on a preset gray threshold; performing energy spectrum analysis to obtain an actually measured energy spectrum corresponding to each bright mineral phase particle; and determining the gold-bearing minerals in the to-be-detected sample according to the actually measured energy spectrum. According to the method provided by the invention, the gold-bearing mineral is determined by analyzing the actually measured energy spectrum, and the identification accuracy and the automation efficiency of the micro-fine gold are improved; the method can effectively solve the problem of missed judgment caused by energy spectrum signal mixing and low matching degree due to fine or wrapped particles in a traditional method, so that more accurate process mineralogy data is obtained, and a reliable basis is provided for formulating an efficient dressing and smelting recovery scheme.

The invention relates to the technical field of mineral exploration, in particular to a high-precision electromagnetic method mineral exploration device suitable for complex terrains, which comprises a self-adaptive mobile frame, and an electromagnetic detection module, a data processing and transmission module, a composite energy module and a central control module are arranged on the self-adaptive mobile frame. According to the high-precision electromagnetic method mineral exploration device suitable for the complex terrain and the detection method, through the combined design of the track assembly, the electric hydraulic rod and the shock absorber, laser radar terrain scanning and path planning are utilized, and autonomous movement and horizontal keeping of the complex terrain are adapted; non-contact electromagnetic detection is adopted, deep and shallow detection is covered by a multi-frequency signal generator, the anti-interference capability is improved according to electromagnetic shielding and adaptive filtering processing, mineral product space distribution and reserve estimation is realized through convolutional neural network imaging, and false positive signals are eliminated through iterative optimization.

The invention relates to a mineral green prediction method and device based on cross-domain self-adaption and multi-modal fusion, and belongs to the technical field of mineral resource exploration and remote sensing. The method comprises the following steps: acquiring multi-source data, inputting the multi-source data into a transfer learning module to learn transferable model parameters, fusing the acquired data through a modal gating network, then constructing a line map through a map construction model, and enhancing the line map by using a map convolutional network; the enhanced multi-modal data is input into a Transformer encoder of an embedded graph convolutional network, a mining area space topological structure and spectral features are extracted, and an ore forming body potential probability graph is obtained through prediction of a prediction head; and an environmental risk assessment model is constructed, ore body prediction accuracy maximization and environmental risk minimization are realized through multi-objective optimization, and the accuracy of ore body prediction is verified through drilling activities. The method not only improves the regional generalization ability of the model, but also realizes the interpretability of green mineral exploration and results, and improves the precision, efficiency and sustainability of mineral exploration.

The invention discloses a hyperspectral anomaly detection method and system for enhancing low rank and significance. The method comprises the following steps: acquiring hyperspectral image tensor data of a to-be-detected region; carrying out band-by-band normalization processing; decomposing into a background tensor and an abnormal tensor; designing an improved weighted tensor nuclear norm based on tensor singular value decomposition; constructing a visual saliency sparse weight tensor; establishing an anomaly detection model; obtaining an optimal abnormal tensor; and detecting the obtained optimal abnormal tensor to generate an abnormal detection result graph. According to the method, comprehensive mining of background low-rank features and full utilization of abnormal target saliency can be realized, and the detection rate of hyperspectral anomaly detection is effectively improved; the abnormal target can be clearly and accurately detected, the shape of the abnormal target can be completely reserved, good detection performance is still kept under the complex background and noise interference, and reliable technical support is provided for related applications such as mineral exploration and military reconnaissance.

The invention provides a gold mine prospecting target area intelligent delineation method and system based on multi-source geological data, and relates to the technical field of mineral exploration. Remote sensing images, geophysical exploration and earth surface rock chemical data associated with gold mine mineralization are collected, and according to the gold mine mineralization characteristics, the gold mine prospecting target area intelligent delineation method and system based on the multi-source geological data are obtained; correcting gold element space distribution difference, decomposing mineral spectrum mixing information, and strengthening mineralization related characteristic information. Then mining a data feature association relationship based on the feature information, the physical exploration data and the geochemical data, constructing a metallogenic association network, and inputting the feature information to obtain metallogenic association parameters; calculating mineralization probability values point by point in combination with spatial distribution characteristics of the multi-source geological data, generating a continuous mineralization probability curved surface, finally screening areas with probability values higher than a preset threshold value, locking a conforming closed area in combination with a typical gold ore deposit model, and recording coordinate boundary information to generate a gold ore prospecting target area map. The intelligent and accurate delineation of the gold ore prospecting target area can be realized.

A system and method for distribution anomaly prediction for new mineral exploration using mineralogical understanding and artificial intelligence analysis of geochemical data are provided, the system comprising: a database development unit configured to collect regional distribution data including pre-collected geochemical data and geologic spatial data of a region and to integrate map data and data set to construct integrated data into a database; a model learning unit configured to generate and learn an exploration mineral distribution anomaly model based on machine learning, wherein the exploration mineral distribution anomaly model enables prediction of distribution anomaly based on geologic and ore deposit geological understanding of exploration mineral using the integrated data; and a distribution anomaly prediction unit configured to predict the distribution anomaly of the exploration minerals in an exploration region using the learned exploration mineral distribution anomaly model.

The invention relates to the technical field of geological information, and discloses a mineral resource potential rapid evaluation method and system based on big data, and the method comprises the steps: automatically collecting geological, remote sensing, geophysical and geochemical exploration and mining data through a multi-source interface, executing intelligent format recognition and structural analysis, and storing the data into a distributed data lake; scheduling parallel computing nodes according to task parameters, synchronously operating five sub-models including space superposition, geochemical anomaly identification, construction of mine control factor extraction, remote sensing alteration enhancement and mining intensity regression; and generating a comprehensive potential evaluation map through dynamic weighted fusion, and outputting a visual result and an uncertainty quantitative index. According to the technical scheme, the evaluation period is shortened from several weeks to several hours, the data processing accuracy is improved to 97% or above, the hit rate of the evaluation result in the known ore deposit area reaches 89%, and the mineral exploration efficiency and decision scientificity are improved.

The invention provides a mineralization domain spatial distribution prediction method and system based on geological priori knowledge, and relates to the technical field of mineral exploration, and the method comprises the steps: obtaining drilling core test analysis data and magnetic measurement data of a target mining area; processing the geochemical data to obtain an element concentration data set, and performing potential field conversion on the magnetic measurement data to generate a magnetization direction data set; identifying geochemical anomaly information from the concentration data set by using a fractal algorithm, determining an anomaly range, fusing the anomaly range and the magnetization direction data set, performing three-dimensional visual modeling, and constructing a three-dimensional geologic model; and finally, extracting feature data in the three-dimensional geologic model, and inputting the feature data into a machine learning prediction model for processing to obtain a distribution diagram representing the existence probability of the mineralization domain in the mining area space. According to the invention, the accuracy of blind mineralization domain spatial position prediction is improved.

The invention provides a mineral variable spatial autocorrelation enhancement method and system based on second-order clustering, and the method comprises the steps: firstly obtaining a mineral variable original collection set corresponding to a plurality of sampling positions in a mineral exploration region, and carrying out the spatial correlation preliminary analysis of the mineral variable original collection set, thereby obtaining a spatial correlation relation set; and executing first-order clustering processing based on the spatial association relationship set to generate a first-order clustering unit. Performing variable association deepening analysis on mineral variable information in each first-order clustering unit to obtain an internal association feature set, and performing second-order clustering processing based on all the internal association feature sets to generate a second-order clustering unit; and finally, enhancing the spatial autocorrelation characteristics of the original collection set of the mineral variables in combination with a second-order clustering unit to obtain an enhanced mineral variable set, so that the spatial autocorrelation characteristics of the mineral variables can be comprehensively and deeply enhanced.

The utility model relates to platform support technical field provides a platform support device for mineral exploration, including the drill tower body (1) and the operation platform (2) of fixed connection in the drill tower body (1), the vertical board (3) is fixedly connected on the operation platform (2) upper end face, the extension platform (4) is pasted and slides in the inside of operation platform (2), the free end upper surface of extension platform (4) is fixedly connected with the fixed frame (5), the one side fixedly connected with the sliding plate (6) of fixed frame (5) near operation platform (2), the sliding plate (6) is adjacent and slides in the vertical board (3) and is penetrated, the one end side wall fixedly connected with the limit strip (7) of sliding plate (6) away from fixed frame (5), the utility model can promote the security after extension platform extension.

The application discloses a method for realizing mineral resource detection by using spatial distribution rules of metallogenic metal elements, which utilizes the data accumulated in existing mineral exploration and mining, firstly finds an index metal relative quantization parameter index, and then establishes a geological sample model of the index metal relative quantization parameter three-dimensional spatial distribution rule by a computer; drilling construction is conducted on a prospecting target area obtained through early geological survey, a geological model of the index metal relative quantization parameter three-dimensional spatial distribution rule of the prospecting target area is established by using data obtained through the drilling construction, and the model is used as a key prospecting target area, and then a pit exploration project is implemented to expose a vein; in the mineral resource detection method, the drilling project and the pit exploration project are implemented in steps, and the pit exploration project with higher cost is implemented on the basis of the data accumulated in the previous mineral exploration and mining, so that the prospecting cost is greatly reduced.

The application discloses a core RQD segmentation method based on frequency-space feature enhancement and long-distance dependence, comprising: spatial feature coding is performed on a core tank image to obtain output features, and a core feature map is determined; a morphological center axis of a core instance mask is extracted, and an accurate pixel length is calculated; a plurality of cores satisfying a core length threshold are screened, and a rock quality index in a region corresponding to the core tank is calculated. The application reduces technical bottlenecks such as loss of micro-fissure features, limited long-distance spatial dependence perception, discontinuous long-strip core segmentation and complex background interference in core segmentation; the average precision mean on the NEU-Rock dataset reaches 95.5%, which significantly reduces the parameter quantity and calculation amount compared with the prior art; and the core image can be converted into an accurate rock RQD index in real time and with high fidelity, thereby providing objective and robust digital support for mineral exploration and rock mass stability evaluation.