4439 results about "Geological exploration" patented technology

The invention relates to the technical field of geological exploration, and discloses an advanced geological forecasting method and system based on tunnel geological exploration. The method comprises the following steps: performing data denoising on a multi-source data set pre-collected in a tunnel construction area to obtain an original data set; performing preprocessing and multi-stage data fusion on the original data set to obtain a fused data set; performing three-dimensional space modeling and feature extraction on the fused data set to obtain an intelligent geological prediction result; performing multi-factor risk assessment on the intelligent geological prediction result to obtain a geological risk distribution map in front of the tunnel; performing construction scheme optimization on the geological risk distribution map to obtain a construction guidance scheme; and carrying out comprehensive analysis and dynamic updating on the real-time monitoring data, the construction guidance scheme, the intelligent geological prediction result and the geological risk distribution map to obtain an advanced geological prediction result. According to the method and the system, the defects of insufficient prediction precision, reliability and accuracy of a traditional geological prediction method are overcome, and the safety risk is reduced.

The invention provides a three-dimensional geological modeling parameter optimization method and system based on deep learning, and relates to the field of geological prospecting and modeling.The method comprises the steps that drilling data and remote sensing data of a geological prospecting area are obtained, and multi-source geological data of three-dimensional geological modeling are formed; generating geological profile map data of the geological features according to the drilling data, generating landform profile map data corresponding to the topographic features according to the remote sensing data, and extracting boundary information of the geological exploration area; carrying out gridding processing on a three-dimensional space, dividing a geologic body of the geological exploration area into a plurality of small grid units, and defining geological modeling parameters; the preliminary three-dimensional geological grid model is constructed according to the geological profile map data, the landform contour map data and the boundary information, the size and the resolution of the grid are dynamically adjusted according to the geological change area, the three-dimensional geological model is obtained through optimization and adjustment, the modeling precision is improved, manual intervention is reduced, and computing resources are saved.

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 relates to the technical field of petroleum and natural gas engineering, and discloses a wellbore life cycle management system and method, and the system comprises multiple modules of wellbore design, construction monitoring, integrity evaluation and the like. Accurate design parameters are generated by obtaining geological exploration data, multi-sensor data are collected in real time to guarantee construction safety, integrity is evaluated according to multi-source data, the corrosion rate is predicted, a time-space correlation algorithm is used for integrating data to recognize an abnormal coupling effect, then a risk level signal and an early warning instruction set are generated, and an optimization maintenance scheme is formulated. According to the method, the design accuracy and stability of the shaft can be remarkably improved, the condition of the shaft is comprehensively and accurately evaluated, corrosion is accurately predicted, and risk early warning and maintenance decision making are scientifically carried out. Meanwhile, the management efficiency is improved through visual display, many problems of traditional shaft management are effectively solved, the operation and maintenance cost is reduced, and the safety and economical efficiency of energy exploitation are improved.

The invention relates to a road collapse risk comprehensive assessment method based on multi-source data fusion, and the method comprises the following steps: collecting and preprocessing multi-source data related to road collapse, the multi-source data related to road collapse comprising remote sensing image data, underground water level monitoring data, geological exploration data and surface deformation monitoring data; spatio-temporal feature fusion and multi-level data fusion are carried out on the preprocessed multi-source data, the spatio-temporal feature fusion comprises spatial registration and time alignment, and the multi-level data fusion comprises low-level data fusion and high-level data fusion; and performing feature extraction on the fused multi-level data, inputting a feature extraction result into a road collapse risk assessment model constructed based on a machine learning model, outputting a road collapse risk occurrence probability, and generating a road collapse risk level according to the road collapse risk occurrence probability. Compared with the prior art, the method can comprehensively and accurately predict and evaluate the risk of road collapse.

The invention provides an innovative deep concealed ore deposit detection method, which comprehensively utilizes geology, geophysics, geochemistry and remote sensing technologies, and realizes effective integration of multi-source geological data by developing an advanced data fusion algorithm. According to the method, data collected by geological survey, wide-area electromagnetic method exploration, hydrocarbon-mercury superposition halo analysis and aviation hyperspectral remote sensing are subjected to standardized preprocessing, so that the consistency and accuracy of the data are ensured; and then, deep analysis is carried out on the fused data by utilizing machine learning and statistical analysis technologies so as to identify and predict mineralization modes and abnormal regions. Finally, a three-dimensional geologic model is constructed, the form and distribution of an ore deposit are visually displayed, and a scientific basis is provided for geological exploration. The method is characterized in that a multi-source data fusion technology and mineralization mode identification automation and precision are realized, so that the efficiency and the accuracy of deep hidden ore deposit detection are remarkably improved.

The invention provides an urban underground space high-precision detection method, system and device and a storage medium, and relates to the technical field of geological exploration. The method comprises the following steps: acquiring reflection seismic data, high-resolution electromagnetic response data, environmental noise data, well-side seismic data and logging data of a target area, and performing refined processing on the reflection seismic data, the high-resolution electromagnetic response data, the environmental noise data, the well-side seismic data and the logging data to obtain multi-source multi-scale detection data; performing constraint condition extraction based on the multi-source multi-scale detection data to obtain a multi-source constraint condition, and constructing an urban underground space three-dimensional information model based on the multi-source constraint condition and the multi-source multi-scale detection data; and performing data mining based on the urban underground space three-dimensional information model to obtain three-dimensional geological data. According to the scheme, the accuracy of urban underground space detection can be improved.

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 provides a BIM (Building Information Modeling)-based rail transit construction geological exploration profile generation method, which belongs to the technical field of exploration measurement and comprises the step of comprehensively improving the accuracy of rail transit construction geological exploration through an advanced data acquisition and intelligent analysis method. In a construction site, multi-dimensional data of geological drilling, geological radar, geophysical prospecting testing and the like are acquired through various precise instruments, and a three-dimensional coordinate system is firstly established; and deep feature extraction is performed on complex data by using a deep learning algorithm, and key information such as geological stratification, rock and soil parameters and underground water level is accurately obtained. Intelligent fusion of multi-source data is realized by constructing a geological stratification model, a rock and soil parameter model and an underground water level model, and a comprehensive geological information model is generated. And importing the extracted geological profile data into a BIM platform, and carrying out strict precision verification and optimization by adopting a professional geological information collaborative equation set to finally form a highly accurate and reliable rail transit construction geological exploration profile.

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 invention relates to the technical field of geological exploration, in particular to a geological exploration safety monitoring and early warning method and system, and the method comprises the following steps: carrying out the exploration region division based on a geological exploration data set, carrying out the risk assessment and density assignment of geological units in combination with the risk degree, and generating a geological risk density map. According to the method, through geological risk assessment and density assignment, exploration area division and risk assessment, and in combination with risk level judgment of geological units, safety monitoring and early warning in the geological exploration process are more refined and scientific, and by means of stress analysis simulation and terrain sensitivity analysis, the safety of the geological exploration process is improved. According to the geological disaster early warning method, the stress response of the geological unit under natural and artificial loads is revealed, the capability of identifying risk points and sensitive areas in an exploration area is improved, early warning is carried out on geological disasters in the early stage, the real-time performance and pertinence of geological disaster early warning are optimized, and losses caused by the geological disasters are reduced.

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 an imaging method and system based on cosmic ray muon transmission, which are applied to a control mechanism, and the control mechanism is electrically connected with at least one detection module. The imaging method based on cosmic ray muon transmission comprises the steps that the response state of at least one detection module is obtained, the detection module comprises a plurality of sensor units, and the sensor units are arranged in a string shape. And preprocessing the response state to obtain muon flux in unit area, unit time and unit solid angle. Based on the topographic data, the landform data and the muon flux, the density distribution in the muon ray irradiation range is reversely deduced through a preset algorithm. And performing visual imaging based on a density distribution result. According to the method, the working efficiency of muon geological imaging is improved, the system complexity is reduced, the adaptability in a narrow working environment is remarkably enhanced, and an efficient and convenient solution is provided for the fields of geological exploration, engineering detection and the like.

The invention discloses a power transmission tower static pressure prefabricated pipe pile soil squeezing effect assessment method and system, and relates to the technical field of soil squeezing effect assessment, and the method comprises the following steps: obtaining geological exploration data of a construction area, building a three-dimensional geological model, dividing soil body units, and endowing a nonlinear constitutive relation; based on the three-dimensional geologic model, a stress propagation path and a soil squeezing range of soil around the pile are obtained, and a stress concentration area and a local liquefaction risk area are identified; according to the stress concentration area and the local liquefaction risk area, soil compaction effect sudden change risk characteristics at the junction of the hard interlayer and the soft lenticular body are analyzed; and correcting the soil squeezing range by combining the soil squeezing effect sudden change risk characteristics with the real-time pile sinking resistance data, and generating a construction risk early warning map. According to the method, the risk prediction of the soil squeezing effect is provided, the risk is dynamically corrected in combination with the real-time construction data, a visual construction risk early warning map is generated, constructors can find and deal with potential risks in time, and smooth construction is ensured.

The invention discloses an earth geology physical three-dimensional modeling method based on multi-data fusion, which comprises the following steps: A, data acquisition: acquiring geological data through geological exploration and measurement technologies, comprising geological profile, underground water level, lithology, structure, surface digital elevation model (DEM) data, remote sensing image data, surface geographic information data, drilling data, geological plane data, profile data, stratum isoline data, fault data, geophysical prospecting data, dynamic data and related document data. According to the invention, data of geological profile, underground water level, lithology, structure, earth surface digital elevation model (DEM) data, remote sensing image data, earth surface geographic information data, drilling data, geological plane data, profile data, stratum isoline data, fault data, geophysical prospecting data, dynamic data and related document data are collected. And the data can be effectively integrated.

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 discloses a thin mutual coal seam fracturing, gasification and carbon sequestration integrated method. The method comprises the steps of geological exploration and coal seam refined evaluation; a middle horizontal well is drilled downwards from the ground; top and bottom horizontal wells are planned and drilled in the uppermost and lower sandstone stratums of the thin mutual coal seam; a plurality of top and bottom fishbone wells are drilled obliquely downwards and upwards in the top and bottom horizontal wells; oxygen-enriched water vapor is injected into the middle horizontal well through a continuous oil pipe, the oxygen-enriched water vapor is squeezed into the reservoir under the action of high pressure, and fracturing operation is conducted; initiating an underground gasification process of the coal seam, and synchronously performing extraction operation of the coal seam gas; supercritical CO2 fracturing operation is conducted on the top horizontal well and the bottom horizontal well; injecting water-based fracturing fluid into the top horizontal well and the bottom horizontal well; gasified materials generated by coal seam combustion gasification and desorbed methane gas are extracted through the middle horizontal well, and supercritical carbon dioxide injected before is sealed for a long time. According to the method, the limitation of a traditional hydraulic fracturing technology can be solved, and the transformation effect of the reservoir can be improved.

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 field of coal mine underground roadway supporting processes, and discloses a roadway supporting process for regional high-stress discrete solidification. The method comprises the steps of geological exploration and stress testing, roadway support processing, support sorting, parameter determination and real-time monitoring and feedback, in the roadway support processing, a stress data processing module is constructed, stress data around a roadway are discretized into a plurality of area units, support sorting is conducted on the area units, and the roadway support processing module is constructed; and the high-stress area and the secondary high-stress area are sequentially subjected to supporting construction, parameters of anchor rods and anchor cables are determined before supporting construction, and the supporting effect is ensured. According to the roadway supporting method suitable for regional high-stress discrete solidification, the stability of the roadway is enhanced, the service life is prolonged, and the safety is improved by perfecting the supporting process.

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 relates to the technical field of geological disaster prediction, in particular to a field geological exploration monitoring system based on remote sensing data, which comprises a data acquisition module, a data preprocessing module, a multi-source data fusion module, a deep learning model module and an early warning module. The data acquisition module, the data preprocessing module, the multi-source data fusion module, the deep learning model module and the early warning module are connected in sequence. Through sequential connection of the modules, the system realizes closed loop of data from acquisition, analysis and early warning, and ensures integrity, real-time performance and accuracy of the data. The data preprocessing module eliminates the interference of source data while ensuring the data quality, and the fusion module improves the synergistic effect of multi-source data through an intelligent algorithm. The high-precision prediction of the deep learning model module ensures the timeliness and effectiveness of disaster early warning. The early warning module responds in time to help related departments to do disaster prevention and reduction work, and risks caused by geological disasters are effectively reduced.