3196 results about "Geological exploration" patented technology

The invention discloses a coal mine hole fracture seepage analysis and prediction system based on a deep learning model, and relates to the technical field of coal mine safety. Comprising a geological data acquisition and three-dimensional model construction module, a data preprocessing and cleaning module, a deep learning feature extraction and training module, a graph neural network optimization and topology modeling module and a seepage prediction and risk assessment module, geological exploration data and hydrogeological information of a coal mine area are obtained, and a three-dimensional geological model containing pores and fractures is constructed. Through the deep learning method combining the convolutional neural network and the graph neural network, the accuracy of coal mine fracture seepage analysis is remarkably improved. The CNN extracts local features, the GNN optimizes a global topological structure, and the seepage prediction precision is improved. The model not only can accurately predict a seepage path, but also can evaluate safety risks, help mine managers to identify hidden dangers in advance, reduce water damage and gas accumulation accidents, and optimize mine safety management.

The invention discloses a rockburst risk early warning method based on TBM multi-source data fusion and a hybrid algorithm, and the method comprises the steps: collecting TBM tunneling parameters, geological exploration data and micro-seismic monitoring data of different rockburst levels, achieving feature complementation through multi-source data fusion, improving the completeness and reliability of early warning, constructing a hybrid neural network model based on CNN, LSTM and an attention mechanism, and carrying out the early warning of the rockburst risk. Feature weight distribution is optimized by introducing an attention mechanism, so that the model can adaptively focus key risk signals, and the applicability of model early warning is improved; and in combination with a fuzzy comprehensive evaluation algorithm and a Bayesian probability model, outputting four rockburst grades of no rockburst, slight rockburst, medium rockburst and strong rockburst and occurrence probabilities thereof, and realizing real-time dynamic early warning and probabilistic early warning of the rockburst grades. Compared with an existing method, the method has the advantages that the accuracy, timeliness and engineering applicability of rockburst early warning are remarkably improved, and real-time and accurate early warning of potential rockburst and the grade of the potential rockburst can be achieved.

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 provides an underground cavern construction method generation method and system based on multi-source data fusion, and the method comprises the steps: collecting geological exploration, construction machinery operation and environment monitoring data, carrying out the time-space alignment processing, generating a standardized monitoring data set, carrying out the multi-source feature extraction of the standardized monitoring data set, and carrying out the multi-source feature extraction of the standardized monitoring data set; obtaining a construction state feature set and a geological risk feature set, inputting a reinforcement learning model for multi-target parameter optimization to obtain a preliminary construction method parameter set, inputting the preliminary construction method parameter set into a digital twin simulation system for simulating mechanical response, and iteratively optimizing parameters when a preset safety threshold is exceeded to obtain a preliminary construction method parameter set; the optimized construction method parameter set is matched and verified with a preset construction specification knowledge base, manual correction is carried out when illegal items exist, finally, full-period risk prediction is carried out on the corrected construction method parameter set, dynamic regulation and control are carried out according to a construction risk distribution map, a final construction method instruction set is generated, and scientific and intelligent construction method generation is achieved.

The invention discloses a multi-source collaborative advanced geological exploration method based on TBM construction, and belongs to the technical field of tunnel engineering geological exploration. A three-dimensional geological model is constructed through multi-scale collaborative operation of directional long exploration drilling, branch short exploration drilling and in-hole transient electromagnetic detection; the directional long exploration adopts a secondary drilling structure and a screw motor directional drilling technology to realize accurate control of a deep stratum, the branch short exploration realizes dynamic and rapid verification through a TBM integrated drilling machine, meanwhile, in-hole transient electromagnetic exploration adopts three-component measurement and an intelligent inversion algorithm to fill the blank of geological information between drill holes, and by means of mutual data correction of the three methods, the detection accuracy is improved. A three-in-one detection system of drilling, geophysical prospecting and intelligent analysis is formed, and then accurate advanced prediction is achieved, so that the problems that a traditional single detection technology is low in precision and insufficient in coverage range are effectively solved, the water inrush risk is remarkably reduced, construction safety is guaranteed, and the method is effectively suitable for deep tunnel TBM safety construction.

The invention relates to the technical field of reservoir bedrock anti-seepage grouting, and discloses a reservoir bedrock anti-seepage grouting method which comprises the following steps: acquiring fracture data through geological exploration, and constructing a reservoir bedrock fracture distribution model; simulating a flow path and permeability characteristics of the slurry in the fracture based on the fracture model; the grouting pressure and flow are determined through an optimization algorithm, and a grouting scheme is formulated; screening grouting materials according to fracture characteristics, and analyzing stress-strain and permeability of the grouting materials; a construction process is formulated, the grouting state is monitored in real time, and parameters are dynamically adjusted; and after grouting, carrying out permeation and pressure test to evaluate whether the curing effect reaches the standard or not. Based on the three-dimensional fracture structure modeling and flow field simulation technology, high-precision recognition and dynamic evolution tracking of the fracture permeation path in the complex rock mass of the reservoir bank are achieved, so that grouting hole layout is more accurate, the slurry flow direction is more controllable, and the method is different from a point-selecting grouting mode of a traditional empirical method.

The invention relates to the technical field of geological survey and measurement, in particular to a rock mass anomaly detection method based on vibration and visual data multi-modal fusion, which comprises the following steps: firstly, respectively acquiring vibration data and image data, and then performing data feature extraction and fusion through a deep learning model; specifically, a combined model of a long short-term memory network LSTM and a convolutional neural network CNN is used to extract time sequence features and frequency information of vibration data, a Swinin-T model is used to extract spatial features of image data, and then a shared Transform encoder and a cross attention mechanism are used to realize multi-modal data alignment and fusion. And inputting the fused features into an anomaly detection module for training and tuning, and finally deploying the trained model to edge equipment for anomaly detection of the rock mass. According to the invention, through multi-modal data fusion, abnormity can be accurately identified in various complex environments, so that the probability of false alarm and missing alarm is reduced, and meanwhile, by combining with an edge computing architecture, field deployment and real-time monitoring are facilitated, and the method is suitable for various application scenes.

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 invention relates to the technical field of design analysis, in particular to a soft soil deep foundation pit earth excavation construction scheme planning method and system.The soft soil deep foundation pit earth excavation construction scheme planning method comprises the following steps that a three-dimensional geological model is built based on geological exploration data, and rheological parameters, shear strength parameters and permeability coefficients of all soil layers are extracted to serve as layering parameters; according to the layering parameters and preset supporting structure rigidity parameters, a soil body rheological rate compensation coefficient is calculated, and an earthwork block excavation scheme with the grading thickness difference is generated; and in the excavation process, the displacement rate and pore water pressure data of the supporting structure are collected in real time, and when a monitoring value exceeds a preset threshold value, a dynamic adjustment instruction of the block excavation scheme is triggered. According to the method, the layering thickness and the blocking form are intelligently controlled according to different sensitivity levels, the matching coping capacity for soft soil instability inducements is improved, and the limitation that a single physical parameter dominates in traditional blocking design is broken through.

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 invention discloses a road slope modeling method based on an unmanned aerial vehicle inspection route, and relates to the technical field of unmanned aerial vehicle inspection. The method comprises the following steps: measuring the road slope length, the landform and the geological complexity, and selecting an adaptive unmanned aerial vehicle type and sensor combination according to the road slope length, the landform and the geological complexity; planning unmanned aerial vehicle surface inspection and shallow geological exploration routes, ensuring space matching, and synchronously obtaining high-resolution images and ground penetrating radar data; and processing image data through a photogrammetry technology, generating a digital elevation model and an orthophoto map, and combining to construct a three-dimensional model of the earth surface. Geophysical inversion is carried out on the ground penetrating radar data, reflection features are extracted and projected to the ground surface three-dimensional model, and space correlation between the ground surface and a shallow layer structure is established; the reflection features are digitalized and geometrically reconstructed into three-dimensional surface elements, the three-dimensional surface elements are embedded into an earth surface model to form a three-dimensional geologic body model, time dimensions are introduced, and a dynamic evolution model is constructed. According to the dynamic evolution model, the dynamic analysis of the slope stability is realized.

The invention relates to the technical field of computer simulation, and discloses a computer simulation-based overlying strata multilevel grouting design optimization method, which comprises the following steps of: constructing a three-dimensional geomechanical model of a goaf according to geological exploration data; calculating development characteristics of each layer in the overlying strata of the goaf, and constructing a grouting bearing layer position of the goaf according to the development characteristics; fracture development characteristics and horizon stress distribution are analyzed; according to fracture development characteristics and horizon stress distribution, analyzing grouting simulation parameters of the horizon of the grouting bearing layer; generating a multi-level layered grouting simulation scheme of the position of the grouting bearing layer; generating a digital track of the position of a grouting bearing layer according to the multi-level layered grouting simulation scheme, and generating a grouting control simulation instruction according to the digital track; and according to the grouting control simulation instruction, multi-level grouting simulation operation is carried out on the position of the grouting bearing layer, and a multi-level grouting simulation result is output. According to the method, the accuracy of the grouting level of overlying strata multi-level design optimization can be improved.

The invention relates to the technical field of geological exploration and three-dimensional modeling, in particular to a mining unmanned aerial vehicle high-precision three-dimensional geological modeling system which comprises a multispectral imaging acquisition module, a point cloud-image high-precision registration module, a geological feature fusion extraction module, a three-dimensional geological model construction module and a geological attribute visualization output module. Wherein the multispectral imaging acquisition module is used for acquiring registration image data and original point cloud data; the point cloud-image high-precision registration module is used for carrying out consistency matching and outputting a registration point cloud-image data set; the geological feature fusion extraction module is used for identifying geological boundaries and outputting geological feature vector data; and the three-dimensional geologic model construction module is used for generating a three-dimensional grid model with attribute topology. According to the method, through accurate three-dimensional modeling and lithology visualization, geological features and lithology information are efficiently combined, and reliable data support is provided for rock mass stability analysis.

The invention discloses a high cut slope stability evaluation method based on multi-source data fusion, and particularly relates to the technical field of data analysis. By integrating topographic data, geological exploration data, remote sensing images, monitoring sensor data and geophysical detection data, an inversion algorithm is adopted to automatically extract abnormal structure features such as shear wave velocity sudden change and resistivity valley, the abnormal structure features and geological profiles are subjected to cross validation to generate an underground structure risk map layer, and a fusion model is combined with machine learning and a mechanical algorithm to obtain an underground structure risk map layer. According to the method, slope stability indexes including the safety coefficient and the sliding probability are comprehensively output, intelligent identification and grading of potential sliding dangerous areas are achieved, finally, an evaluation result is visually displayed in a three-dimensional GIS platform in a layer superposition mode, the identification precision and evaluation scientificity of slope hidden risks are remarkably improved, and the method is suitable for popularization and application. And a more reliable decision basis is provided for early warning and disposal of a high-risk slope.

The invention provides an intelligent optimization method and system for tunnel smooth blasting blast holes in layered rock mass, and belongs to the technical field of tunnel engineering and blasting. The method comprises the steps of collecting stratified rock geological data, evaluating and extracting features, constructing a multi-objective optimization function, solving by an intelligent optimization algorithm, performing construction feedback and dynamic optimization and the like. The method comprises the following steps of: acquiring data by using various geological exploration equipment, performing evaluation processing on the data by using a support vector machine model, constructing an optimization function considering multiple indexes, solving an optimal blast hole parameter combination by combining a genetic algorithm and a particle swarm optimization algorithm, and finally feeding back a blasting effect to dynamically adjust an optimization scheme. According to the intelligent optimization method and system for the smooth blasting blast holes of the tunnel in the layered rock mass, the blasting effect can be improved, over-break and under-break and concrete consumption can be reduced, the construction cost can be reduced, the construction safety and efficiency can be enhanced, and remarkable economic and social benefits can be achieved for layered rock mass tunnel engineering construction.

The invention relates to the technical field of geological exploration, and discloses a water-rich sandy stratum tunnel base settlement prediction method and system, and the method comprises the steps: constructing a multi-source cooperative exploration system; deploying a sanding-seepage-stress coupling model at an edge computing node, and mining the relevance of data of different dimensions through a space-time attention fusion network; based on the prediction matrix and mineral exploitation working condition parameters; when the predicted settlement exceeds a safety threshold value or the sanding expansion rate is abnormal, a high-precision remeasurement mechanism is triggered, and a cross-hole radar and acoustic logging combined verification module is activated; and iteratively optimizing coupling model parameters by adopting a transfer learning algorithm through deviation analysis of measured data and a prediction result. According to the method, a scientific basis is provided for stratum stability evaluation and prevention and control measure formulation in the mineral exploitation process, and the probability of engineering risks caused by base settlement of the water-rich sandy stratum is effectively reduced.

The invention discloses a spatial registration method for tunnel multi-source heterogeneous data, and the method comprises the steps: constructing a lightweight digital twinborn model based on a design parameter library, a construction log library and a real-time monitoring library; mapping the multi-source sensor data in the real-time monitoring library to a digital twin model space node through a space-time synchronization algorithm; probability distribution model-based abnormal value elimination is performed on the geological exploration data, the while-drilling parameters and the geological sketch, and a discrete smooth interpolation algorithm is adopted to generate a surrounding rock mechanical parameter three-dimensional distribution field; the surrounding rock mechanical parameter three-dimensional distribution field serves as input, and front surrounding rock classification probability distribution is dynamically predicted through a machine learning model optimized through transfer learning; based on the three-dimensional point cloud data of the tunnel face in the digital twin model, extracting geometric parameters of the structural plane and constructing a fracture network model constrained by a normal vector; according to the method, the surrounding rock risk location is accurately positioned based on double-threshold triggering, and multi-scale model seamless fusion is realized through gradient constraint topology fusion.

The invention discloses a geological structure three-dimensional reconstruction and analysis system based on multi-modal data fusion, and relates to the technical field of geological exploration information. The method comprises the following steps: acquiring and normalizing multi-source heterogeneous geological observation data through a preprocessing module, and generating basic data and a data coverage density map; the adaptive fusion module performs analysis to generate a dynamic adaptive weight field, and performs weighted fusion on the data to obtain a primary three-dimensional geological data volume; the construction and optimization module constructs a three-dimensional space residual field in a vector form based on the difference between the fusion body and the original data, and deduces the geometric morphology of the geological interface; the reconstruction module ensures the consistency between model layers through hierarchical bidirectional belief propagation processing; the quantification module calculates an uncertainty index; and the visualization module finally generates a three-dimensional geological scene. According to the method, the problems of conflict resolution and reliability evaluation in multi-source geological data fusion are effectively solved, and the precision of the three-dimensional geological model and the engineering decision support capability are improved.

The invention discloses a weak intercalated layer control-based closed conduit-slope system optimization layout method, which comprises the following steps of S1, geological survey and parameter acquisition: accurately positioning a weak intercalated layer through a geological radar and drilling coring, and determining a rock-soil body strength parameter by combining a laboratory rock-soil mechanical test, geological radar data and rock-soil body strength parameters are analyzed through a random forest algorithm, and the interlayer space distribution rule is predicted. By combining geological survey, numerical simulation, intelligent optimization and dynamic monitoring technologies, scientific layout and adjustment of a closed conduit-side slope system are realized, a weak intercalated layer seepage path is accurately cut off, the safety coefficient of the side slope is effectively improved, meanwhile, expansion of a plastic zone is effectively inhibited, settlement of an overlying soil body or large-range deformation of the side slope is reduced, and the stability of the side slope is improved. The seepage stability and the stress slow release capacity of the system are enhanced, the safety of the water diversion project in a complex geological area is improved, and an operable technical path and basis can be provided for layout of similar projects in the future.

The invention relates to the technical field of geological exploration, provides a gold ore metallogenic three-dimensional prediction method and system based on structure information fusion, and breaks through the limitation that a traditional three-dimensional modeling method stops in the aspect of geometric description through fusion processing of multi-source data such as geological structures, ore body evolution processes and geochemical anomalies. Ore control structure information is fused with a geochemical trend and a space structure for the first time, a structure-geochemistry-ore body form collaborative prediction mechanism is realized, and blind ore recognition and peripheral prospecting capabilities are effectively enhanced. Meanwhile, a target region division mechanism based on multi-source layer overlapping is constructed, the credibility and precision of a prediction result are remarkably improved, and the prospecting requirement of a high-work-degree gold mining area is met. By adopting a mode of combining engineering modeling and mid-segment graph modeling, space errors generated by traditional interpolation modeling in a data sparse or form complex area are effectively avoided, and the authenticity and continuity of an ore body structure are improved.

The invention discloses a three-dimensional geological modeling method based on multi-source data, and particularly relates to the technical field of geological modeling. The method comprises the following steps: firstly, acquiring and preprocessing multi-source heterogeneous data including geological exploration, geophysics, remote sensing and drilling data, and carrying out cleaning, format unification and coordinate registration; performing semantic association, topological integration and uncertainty quantization processing on the data through a fusion rule to generate unified multi-source fusion data; then, an implicit or explicit modeling algorithm is adopted to automatically construct an initial three-dimensional geologic model, the model is optimized based on geologic law constraints such as geologic stack overlay and structural continuity, and a final model with reasonable geologic significance is generated; and finally, dynamically updating and maintaining the model through a version management and increment fusion technology. According to the method, efficient fusion of multi-source data and automatic model construction are realized, and the precision, reliability and updating efficiency of the three-dimensional geologic model are improved.

The invention provides a rapid karst cave modeling method and system based on multi-source data, and the method comprises the steps: collecting multi-source data including geological exploration data, remote sensing data and unmanned aerial vehicle aerial photography data, and carrying out the preprocessing of the multi-source data; fusing the preprocessed data by adopting a weighted fusion or principal component analysis method; extracting shape features, size features and position features from the fused data; the extracted karst cave features are converted into point cloud data or grid data, and a karst cave model is constructed through three-dimensional modeling software; performing vertex editing and patch adjustment on the initial model, and correcting the model form in combination with expert experience; and comparing the constructed karst cave model with actual geological data, and if an error exceeds a preset threshold value, iteratively adjusting model parameters until a precision requirement is met. According to the method, efficient extraction, accurate modeling and dynamic optimization of the karst cave geological information can be realized.

The invention relates to an engineering geological exploration and evaluation method based on a remote sensing technology, and relates to the technical field of remote sensing, a laser radar and an unmanned aerial vehicle are used for aerial photography to obtain ground three-dimensional point cloud data, soil samples are collected through geological exploration to obtain geological data, and the obtained point cloud data and geological data are cleaned and denoised to obtain an evaluation result. According to the processed point cloud data, generating a digital elevation model to analyze topographic features, respectively performing feature extraction on the point cloud data and the geological data, establishing a landslide risk assessment model by using a machine learning method, and performing landslide risk assessment and grade division on a target area according to a result of the model. The point cloud data and the geological data are combined, fusion of multi-source data can be achieved, richer input is provided for a landslide risk assessment model, and the precision of landslide risk assessment is improved.

The invention discloses a geological condition engineering risk auxiliary analysis method and system based on a large language model, and the method comprises the steps: S1, carrying out the multi-modal information analysis and fusion of geological exploration data, and obtaining structured geological data; s2, combining the engineering information and the structured geological data to generate an engineering-geological situation label; s3, constructing a geological risk knowledge graph, and performing knowledge graph matching according to the engineering-geological situation tag to obtain a knowledge graph matching result; and S4, constructing cue words in combination with a knowledge graph matching result, performing geological risk automatic identification and reasoning by utilizing a large language model according to the constructed cue words, and outputting a geological risk identification and reasoning result. According to the method, the geological information in the investigation report is intelligently analyzed, and the engineering type, the standard knowledge and the expert experience are combined to assist in identifying key geological risks, so that the accuracy and the normalization of risk analysis are improved, and the problem of insufficient experience of engineers is solved.

The invention belongs to the technical field of geological exploration, particularly relates to a high-adaptability multi-type geological exploration sampling device and an accurate sampling method.The high-adaptability multi-type geological exploration sampling device and the accurate sampling method aim to solve the problems that a drill bit is stuck and stratified sampling is difficult, and the high-adaptability multi-type geological exploration sampling device comprises a base, a vertical plate, a lifting plate and a driving cylinder with the drill bit; a first annular groove is formed in the outer wall of the driving cylinder and matched with the first circular ring, downward-pressing drilling and upward-lifting resetting of the drill bit are achieved, a second annular groove is formed in the drill bit, and the flow direction of hydraulic oil is controlled through a second circular ring connected with a fixing rod; during lifting, hydraulic oil is injected into the second hydraulic channel, the trapezoidal collecting box extends out to scrape hole wall samples, the electromagnet and the iron block are matched to adjust the position of the second circular ring, the working states of the teeth and the collecting box are switched, drill bit jamming is reduced, hole wall sampling at any depth is supported, and exploration efficiency is improved.

The invention provides a karst area pile foundation intelligent positioning and precise construction method which comprises the steps that a karst development characteristic data set of a target engineering area is obtained, and the karst development characteristic data set comprises a three-dimensional geological exploration data set and a karst attribute data set; and preprocessing the data set to obtain a karst preprocessed data set. And based on a feature extraction model, performing feature extraction on the preprocessed three-dimensional geological exploration data set to generate a three-dimensional geological feature map. And identifying the map through a karst cavity identification model to generate a karst cavity distribution thermodynamic diagram. And karst cave feature conversion processing is conducted on the karst preprocessing data set, and a pile foundation construction feature data set is obtained. And according to the karst cavity distribution thermodynamic diagram, constraint processing is conducted on the pile foundation construction characteristic data set, and a pile foundation constraint boundary diagram is generated. And outputting a pile foundation end hole positioning coordinate set according to a pile foundation positioning optimization algorithm and the pile foundation constraint boundary diagram. The construction safety and accuracy of the pile foundation in the karst area can be improved, and the construction risk and cost are reduced.

The invention discloses a geological exploration service management method and system based on cloud intelligence, and relates to the technical field of geological exploration management, and the system comprises a standard specification system, a base layer, a data layer, a platform layer, an application layer and a safety guarantee system. Data collaborative acquisition, processing and sharing are realized through a multi-level architecture design, project approval analysis, work layout, task distribution auditing and data management are realized, multi-specialty collaborative acquisition and safety management of a field data acquisition APP are supported, rapid mapping and standardized filing of an interior work data processing tool are supported, a standardized process is formed, and data processing efficiency is improved. The working efficiency and the data quality are improved through indoor and outdoor real-time collaboration, multi-source data verification, edge calculation and the like, the data safety is guaranteed, the team collaboration and achievement customization ability is enhanced, meanwhile, integrated data sharing is achieved through the cloud computing pool, and the efficiency of geological exploration work is improved.

The invention relates to the technical field of geological exploration, and particularly discloses a geological structure automatic identification and interpretation method based on deep learning, and the method comprises the steps: obtaining seismic data, logging data, gravity data and / or magnetic data, and generating a fusion tensor through space-time alignment and dynamic weight distribution; performing space-frequency domain joint coding on the fusion tensor by using an improved Transform encoder, and generating multi-scale geologic feature representation; a generative adversarial network is constructed, a generator of the generative adversarial network outputs a geological structure pseudo label, and a discriminator and a geological expert rule base collaboratively optimize a prediction result; and quantizing a confidence interval of a prediction result based on Bayesian deep learning, and combining geological priori knowledge to generate a dynamic interpretation scheme. Through multi-source data fusion, an innovative algorithm and model collaboration, accurate recognition and scientific interpretation of the geological structure are achieved, the accuracy and efficiency are improved, the cost is reduced, and the method has remarkable advantages in complex geological exploration.

The invention discloses a continuous deflecting coring method for realizing wire-line coring orientation, which relates to the technical field of drilling, and specifically comprises the following steps of: selecting a bent screw drill and a matched wire-line coring tool according to geological conditions and a designed drilling track, assembling and connecting the bent screw drill and the matched wire-line coring tool, and putting the bent screw drill and the matched wire-line coring tool into a drilling hole; in the drilling process, the hole bottom power drilling tool provides drilling power, a bent angle of the bent screw drilling tool enables a drilled hole to be inclined according to the design direction, and a rotor cavity of the hollow screw drilling tool allows an internal assembly and a core barrel to pass through. According to the continuous deflecting coring method for wire-line coring orientation, the high-precision directional drilling technology and the wire-line coring technology are organically combined, the problem of exploration of irregular deposited ore deposits and concealed ore deposits is solved, the precision and efficiency of geological exploration are improved, and by adopting the bent screw drill and the matched wire-line coring tool, the geological exploration efficiency is improved. Continuous directional drilling and continuous wire-line coring are carried out at the same time, the number of times of drill lifting is reduced, and the auxiliary time is shortened.

The invention discloses a metal resource assessment method and system based on geological exploration, relates to the technical field of mineral data processing, and effectively solves key problems in geological resource assessment through multi-module collaboration. A data fusion module integrates drilling data Zk, geophysical data Dw and remote sensing data Yg; the confidence Rx is calculated by using an adaptive weighting algorithm and probability filling is carried out, so that the data sparsity error is remarkably reduced; the boundary optimization module is used for calculating a mineralization continuity index Lx and generating a boundary optimization coefficient Ly in combination with the temperature dynamic factor Tx so as to realize intelligent correction of the boundary of the ore body; the depth correction module constructs a depth compensation model through the signal intensity M, the stability V and the noise level N, the deep data credibility Sx is improved, Monte Carlo simulation and dynamic parameter adjustment are matched, the system overcomes the defects of a traditional method in the aspects of data uncertainty, boundary ambiguity and deep precision, and the method has the advantages of being high in accuracy and high in reliability. And full-process automation and high-precision resource evaluation and early warning are realized.