1458 results about "Lithology" patented technology

The invention discloses a reservoir bank slope geological disaster risk identification method based on an unmanned aerial vehicle laser radar, and relates to the technical field of reservoir bank slope prevention and control, an unmanned aerial vehicle is used for carrying laser radar equipment, flight scanning is carried out on a reservoir bank slope area, and three-dimensional point cloud data is obtained. According to the reservoir bank slope identification method based on the unmanned aerial vehicle laser radar detection data, high-precision terrain and geological information of the reservoir bank slope is rapidly acquired in a non-contact manner by using the three-dimensional imaging capability of the unmanned aerial vehicle laser radar technology, so that the data acquisition efficiency and safety are greatly improved; the method also remarkably improves the precision and integrity of data, accurately captures key topographic factors such as topographic relief, gradient, slope direction and the like through laser radar scanning, and provides a solid foundation for the accurate recognition of bank slope types in combination with geological factors such as geological structures, lithology and the like, so that the prediction and evaluation of bank slope disasters are more accurate. Preventive measures can be taken in advance, and disaster risks are reduced.

The invention discloses a prospecting target area prediction method and system based on geological three-dimensional modeling, and relates to the technical field of mineral resource exploration, and the method comprises the steps: obtaining geological, geophysical, geochemical and remote sensing data of a target area, carrying out the preprocessing, generating a modeling data set, and carrying out the geological modeling; constructing a lithology classification model by combining the three-dimensional geologic model with deep learning, and extracting shallow lithology features to predict the deep lithology of the target area; screening metallogenic factors according to the prediction result of the deep lithology of the target area, the deep geophysical data and the three-dimensional geologic model, and generating a metallogenic prediction factor set; and establishing a prospecting prediction model based on the graph convolutional network, importing the metallogenic prediction factor set to obtain a metallogenic distribution prediction result, and carrying out three-dimensional prospecting target area delineation. According to the method, deep lithologic stratum prediction is carried out through multi-source geological data and geological three-dimensional modeling, and target region delineation is carried out by using the graph convolutional neural network, so that the efficiency and precision of prospecting target region prediction are improved, and the exploration cost is reduced.

The invention relates to the technical field of risk analysis, in particular to a safety engineering comprehensive management system which comprises a geological parameter dynamic monitoring module, a risk threshold early warning module, a stability weight evaluation module and a risk comprehensive management and control module. According to the method, lithology data are extracted through a drilling profile to calculate the layer thickness sudden change rate and the strength attenuation rate, a lithology abnormal area is positioned through a variable coefficient method, lithology sudden change marks are analyzed through spatial clustering, a dynamic trend is captured through sliding window mean value comparison, and gradient amplitude and slope direction dispersion are calculated through a digital elevation model. Fusing the abnormal point density to generate a terrain instability index; establishing a multi-dimensional quantitative evaluation framework; carrying out fuzzy comprehensive evaluation and cross validation on the instability index and an engineering parameter trend; generating a dynamic priority sequence, carrying out stake number interval matching to construct a risk measure table, carrying out analytic hierarchy process dynamic resource allocation support drainage scheme, and carrying out real-time monitoring and prediction closed-loop iteration. And adaptively adjusting the control strategy.

A pore pressure prediction method for fine-grained hybrid sedimentary rock based on variable P-wave velocity includes: carrying out a P-wave velocity test of rock under different effective stresses, determining a function form of the relationship between acoustic velocity and effective stress, fitting the parameters in the function by optimization, and establishing an evaluation model of effective stress influence term by lithology. According to the measured formation pressure data and the effective stress influence term model, the acoustic velocity background value is deduced, the logging curve is preferred, and the acoustic velocity background value logging evaluation model is established. According to the logging curves of DT and DEN, combined with the lithology identification results, the acoustic velocity background value is calculated, and then the effective stress of the formation is obtained, and then the pore pressure is determined according to the overburden pressure.

The invention relates to the technical field of coal mine exploration, in particular to a coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, which comprises the steps of S1, integrating multi-source geological data; s2, data processing and feature enhancement; s3, machine learning modeling; s4, target spot optimization and dynamic verification; and S5, geological modeling and risk grading. According to the coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, a depth domain joint data set is constructed, multi-dimensional features such as seismic amplitude, lithology coding, fracture index and fault distance field are integrated, and multi-source data fusion and physical constraint machine learning are realized through a feature enhancement technology; locking a high-uncertainty region based on the prediction variance, and dynamically updating a target spot through a Gaussian process proxy model to realize targeted drilling and dynamic closed-loop optimization; a multi-attribute risk fusion model is constructed, a four-level risk map is output through the risk factors, grouting resources are guided to be preferentially put into a high-risk area, and risk grading early warning processing is achieved.

The invention belongs to the technical field of geological information processing, and discloses a geological mineral exploration data extraction system which comprises the following steps: constructing an air-ground-hole three-in-one acquisition network, and respectively acquiring earth surface mineral spectrum data, seismic longitudinal wave and transverse wave reflection data and rock core main quantity and trace element composition data; processing the collected data to form a five-dimensional data cube containing space coordinates, spectrum parameters, physical property parameters, element parameters and time indexes, performing physical layer attribute extraction on the five-dimensional data cube, performing analysis to obtain a wave impedance value and a lithology category probability array, performing semantic layer attribute extraction on the five-dimensional data cube, and performing semantic layer attribute extraction on the five-dimensional data cube; an alteration type probability array is obtained through analysis, an alteration zoning graph layer is generated, the mineralization potential index and a five-dimensional data cube are fused, a four-dimensional dynamic database is constructed, a mineralization potential prediction graph layer and a drilling priority graph are generated based on the four-dimensional dynamic database, and therefore the decision-making level of ore body drilling deployment is improved.

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 provides a geological BIM modeling and engineering quantity calculation method based on point cloud sparse optimization, and relates to the technical field of geological engineering, and the method comprises the steps: obtaining high-resolution point cloud data through an unmanned aerial vehicle oblique photography technology, combining the high-resolution point cloud data with drilling exploration data, and carrying out the denoising, classification, segmentation, lithology discrimination and registration, thereby obtaining a point cloud point cloud model; a geological BIM model before excavation is constructed; area point cloud data are obtained again after excavation, the point cloud precision of the slope toe area is optimized through an inverse distance weighted interpolation algorithm, and a geological BIM model after excavation is generated; and in combination with a classified and optimized digital elevation model, detecting the elevation difference of the model before and after excavation pixel by pixel by adopting a robust elevation difference calculation method based on a neighborhood window, and accurately calculating the volume change of each lithologic region to obtain the engineering quantity. Through the point cloud sparse optimization and elevation change detection technology, the precision of the geological BIM model and the reliability of volume calculation are improved, and the method is widely suitable for geological engineering and earth volume calculation scenes.

The invention relates to the technical field of mine visualization, in particular to a three-dimensional visualization method and system for a mine. The method comprises the following steps: establishing a three-dimensional terrain model of a mine; designing exploration planning space distribution parameters based on the three-dimensional terrain model of the mine, and executing exploration drilling operation through the exploration planning space distribution parameters; mine point rock stratum lithology data are collected according to exploration drilling operation; mapping the mine point location rock stratum lithology data to the mine three-dimensional terrain model to obtain optimized mine point location rock stratum lithology mapping data; analyzing mine rock stratum boundary structure data and rock stratum lithology fusion characteristic data according to the optimized mine point location rock stratum lithology mapping data, and performing mine rock stratum lithology structure analysis to generate mine rock stratum lithology structure data; and based on the lithologic structure data of the mine rock stratum, lithologic fusion differentiation rendering three-dimensional modeling of the mine rock stratum is carried out, and a three-dimensional visual rendering model of the mine rock stratum is generated. According to the invention, accurate visual analysis of the internal structure of the mine is realized.

The invention discloses a coal mine rock burst risk assessment system which comprises a data acquisition module, an energy accumulation rate calculation module, a roadway network modeling module, a risk assessment module and an early warning regulation and control module. The method comprises the following steps of: firstly, calculating an energy accumulation rate of a roadway, then constructing a roadway network diagram, identifying a high stress conduction path, calculating a dynamic risk index and delimiting a risk area by integrating factors such as a historical impact event and an energy index, and automatically triggering corresponding early warning and regulation measures by a system according to a risk level so as to realize accurate early warning and dynamic regulation. And mine operation decision making and accident prevention are assisted.

The invention discloses a while-drilling intelligent rock drill and method for self-calibrating a tunnel blast hole drilling track, and the while-drilling intelligent rock drill comprises a rock drill main body, a drill rod, a drill bit, an air leg, a monitoring module, an execution module and a control module, the monitoring module is used for receiving and collecting real-time data such as the drilling depth, the drill bit posture, the linear speed, the angular speed and the contact pressure of a drill bit and a rock stratum in real time and transmitting the data to the control module. The control module is used for receiving data of the monitoring module in real time, drawing an actual drilling track of a drill hole, calculating a drill hole deviation with a preset track, predicting lithology change in combination with an LSTM deep learning model, and outputting a correction instruction to the execution module; and the execution module dynamically adjusts the drilling deviation according to the received correction instruction. The method is realized based on the intelligent rock drill while drilling. The system has the advantages that the system has good real-time deviation monitoring and automatic calibration capabilities, the drilling precision can be effectively improved, and the problem of drilling deviation caused by lithology changes and personal errors is avoided.

The invention provides a rock image classification method based on edge enhancement and multi-scale feature fusion. By combining the edge enhancement and multi-scale feature fusion technology, the accuracy of mineral classification in the rock slice image is remarkably improved. According to the method, rock slice image interference is eliminated through filtering and denoising, and mineral particle edges are enhanced by fusing morphological top-hat transformation and a Laplace operator; and performing multi-scale pyramid decomposition on the enhanced image to extract high-frequency information, performing multi-direction response enhancement to generate a direction feature map, and performing channel-level fusion on the original image, the edge enhanced image and the direction feature map. And finally, inputting the fused image into a convolutional neural network to complete rock type classification. According to the method, mineral boundary expression is effectively enhanced, the classification accuracy is improved, and a reliable technical scheme is provided for geological analysis and lithology identification.

The invention discloses a high-precision logging lithology intelligent identification method based on DenseNet-Transformer depth fusion. The method comprises the following steps: step 1, carrying out data preprocessing; and step 2, constructing a deep learning model in which a DenseNet mechanism and a Transform mechanism are fused. According to the method disclosed by the invention, efficient feature multiplexing is realized by using the dense connection characteristic of the DenseNet through a deep learning technology, and the global feature modeling capability is enhanced in combination with the Transform network, so that the lithology identification accuracy and generalization capability are remarkably improved. The method is suitable for intelligent analysis of complex logging data, especially shows excellent performance in the aspects of high-dimensional data processing and fine feature extraction, greatly improves the recognition difficulty of a traditional method in a complex stratum structure, and provides important technical support and efficient decision basis for the fields of oil-gas exploration, geological survey and the like.

The invention discloses a three-dimensional geologic model establishment method, a storage medium and equipment, and relates to the field of three-dimensional geologic body modeling, and the method comprises the steps: carrying out the discretization sampling of continuous geologic feature data, and dividing the sampling data into a training set and a test set; constructing a lithology category prediction model which comprises an encoder and a decoder, and training and testing the lithology category prediction model by using the training set and the test set; generating a regular grid point array by using a DEM model of a research area, and predicting lithology category labels of regular grid points by using the trained lithology category prediction model; a regular grid point is used as an angular point of a voxel grid, the step length of a regular grid point array is used as the size of the voxel grid, a regular grid voxel model is constructed, and a gradient function is set according to lithology category labels of the angular point of the voxel grid to simulate lithology composition of each voxel unit. The model is more in line with the basic rule of stratigraphic distribution, and the precision of the model is improved.

The invention discloses a rock debris lithology analysis method based on a multilevel fusion image algorithm and X-ray diffraction. The method comprises the following steps: S1, acquiring rock debris image data under different resolutions; s2, generating a multi-level rock debris image feature matrix; s3, preliminarily generating a lithology classification result; s4, generating supplementary data of X-ray diffraction analysis; s5, fusing the supplementary data of the lithology analysis and the preliminarily generated lithology classification result, and finally generating a comprehensive lithology classification result; s6, the comprehensive lithology classification result is input into a dynamic adaptive analysis system, the dynamic adaptive analysis system adaptively adjusts an analysis strategy according to the rock debris sample complexity, and parameters of a dual-path convolutional neural network model and a multi-modal analysis algorithm are optimized through a real-time feedback and learning mechanism; and S7, outputting an analysis report containing mineral distribution, crystal structure and comprehensive lithology classification results. The classification accuracy of different types of rock debris samples is improved by about 15%.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster hidden danger point detection method and system. Comprising the following steps: S1, synchronously acquiring vegetation canopy penetration data, multispectral soil humidity spatial distribution data and lithology distribution data by adopting an unmanned aerial vehicle carried image data acquisition device and a multi-band laser radar, and generating an orthoimage and a three-dimensional point cloud data set; s2, fusing the orthoimage and the three-dimensional point cloud data through an aerial triangulation encryption algorithm to establish a three-dimensional model, loading the multispectral soil humidity spatial distribution data into the three-dimensional model to generate a dynamic digital elevation model, and generating a contour line topological structure through a TIN triangulation network algorithm; s3, extracting a slope factor, topographic relief, vegetation coverage, fracture distance and a lithologic factor based on the three-dimensional model to construct a geological disaster hidden danger system; according to the system, the geological disaster occurrence time can be estimated, and the rescue and protection treatment period can be effectively planned based on the geological disaster occurrence time.

The invention discloses a dual-model-driven intelligent reservoir logging evaluation method, which comprises the following steps of: establishing a lithology identification logging data set based on a rock slice identification result and conventional logging data, and constructing a data model-driven lithology identification pre-training model; establishing a porosity prediction data set of different lithology based on a core porosity analysis result, and constructing a dual-model driven porosity prediction model of a data model and a knowledge model under the guidance of a lithology classification result; establishing a permeability prediction data set of different lithology based on a permeability analysis result, and constructing a dual-model driven permeability prediction model of a data model and a knowledge model under the guidance of a lithology classification result; on the basis of different lithologic saturation parameters measured in a laboratory, the water saturation of different lithologies is calculated in combination with physical property prediction results driven by double models of different lithologies and conventional logging data. The method can effectively solve the well logging evaluation problem faced by a complex reservoir.

The present invention belongs to the technical field of geological exploration deep Earth. The system of the present invention is configured to: collect seismic data, and preprocess original seismic data to obtain standardized gamma ray logging parameters; extract features from the standardized gamma ray logging parameters to obtain a variance attribute curve while drilling; obtaining a formation thickness prediction model of each set based on the variance attribute curve while drilling; acquire frequency-division seismic data based on the standardized seismic data by means of a frequency-division processing method based on wavelet transform; acquire optimal seismic frequency band information based on a predicted formation thickness value of each set; establish a geological prediction model of the current formation based on the frequency-division seismic data, the optimal seismic frequency band information, historical gamma ray logging parameters and the standardized gamma ray logging parameters.

The invention discloses a method for determining the plugging thickness of a complex lithologic layer based on simplified lithologic combination, and the method comprises the steps: determining the vertical superposition relation of a silty rock layer and a shale layer and the total thickness of the silty rock layer and the shale layer according to the lithologic longitudinal distribution characteristics of an overlying stratum; integrally forming and preparing a roof rock sample by combining the properties of siltstone and shale; arranging a plurality of groups of temperature sensors, pressure sensors and strain sensors on the side surface of the rock sample at set intervals; heating the rock sample until the bottom temperature is the set temperature, pressurizing until the bottom pressure is the set pressure, applying stress to the bottom of the rock sample step by step from small to large until the set stress, and stabilizing at the temperature, the pressure and the stress until the measured value of the sensor is stable; determining the lower limit of the roof plugging thickness according to the measured value of each sensor and the up-and-down superposition relationship; and similarly, determining the bottom plate plugging thickness lower limit. According to the method, through simplified lithology and combined simulation of a temperature field, a pressure field and a stress field, the lower limit of the plugging thickness of the shale reservoir complex lithology roof and floor is reasonably quantified.

The invention discloses a method for determining the plugging thickness of a complex lithologic layer based on temperature-pressure field simulation, and the method comprises the steps: determining the vertical superposition relation of a silty rock layer and a shale layer and the total thickness of the silty rock layer and the shale layer according to the lithologic longitudinal distribution characteristics of an overlying stratum in a set range; respectively obtaining formulas for preparing siltstone and shale through experimental fitting; integrally forming and preparing a roof rock sample; arranging multiple groups of temperature sensors and pressure sensors on the side surface of the rock sample at set intervals; heating the rock sample until the bottom temperature is the set temperature, pressurizing the rock sample until the bottom pressure is the set pressure, and stabilizing at the temperature and the pressure until the measured value of the sensor is stable; determining the lower limit of the roof plugging thickness according to the measured value of each sensor and the up-and-down superposition relationship; and similarly, determining the bottom plate plugging thickness lower limit. According to the method, the lower limit of the plugging thickness of the shale reservoir complex lithology top and bottom plates can be reasonably quantified through simplified lithology and joint simulation of a temperature field and a pressure field.

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

The invention discloses a carbon storage space construction method for an artificial reconstruction plugging key layer, and the method comprises the following steps: S1, carrying out geological exploration, and determining the thickness and lithology of each overlying rock stratum; s2, determining the height of a gas guide fissure zone generated after the coal seam is mined by filling based on theoretical analysis and actual measurement data; s3, on the basis of geological data and the height of the gas-conducting fissure zone, the position of an artificial reconstruction plugging key layer and the position of a grouting layer are determined; s4, filling mining is carried out, grouting is carried out on overlying strata to reconstruct and block a key layer, and a carbon storage space is formed; and S5, carbon dioxide gas is injected into the goaf through a pipeline, carbon dioxide concentration monitoring is conducted on the working face while negative carbon filling is conducted, earth surface rock movement monitoring is conducted on the earth surface, and the negative carbon filling effect is judged. According to the method, the carbon storage space is formed by manually rebuilding and blocking the key layer while the gangue is filled, the space after coal mining can be effectively utilized for storing carbon dioxide, and emission of greenhouse gas is effectively reduced.

The invention provides a sedimentary body lithology identification system and method for oil-gas exploration, and the system comprises a data collection module which is used for collecting seismic data and logging data; the data processing module is used for preprocessing the seismic data and the logging data; the data fusion module is used for fusing the preprocessed seismic data, logging data and a geologic model to generate a comprehensive data set; the lithology identification module is used for analyzing the comprehensive data set by using a machine learning algorithm and identifying the lithology type of the sedimentary body; the result verification module is used for performing comparison verification on the identification result and actual core analysis data; and the display module is used for visualizing the recognition result. The system provided by the invention not only can identify the lithology of the sedimentary body, but also can generate a visual three-dimensional lithology distribution diagram through three-dimensional geological modeling and a visualization technology. The visualization results enable geologists and engineers to clearly understand the underground structure and lithologic distribution so as to make more accurate exploration decisions and development plans.

The invention relates to a reservoir porosity and permeability prediction method based on a dynamic committee integration model, and the method comprises the following steps: obtaining an original data set which comprises shale content, porosity, permeability, GR, AC, CNL, DEN, RT, RXO, SP, CALC, CALI, depth and lithologic labels; performing data enhancement on the original data set; determining main control factors influencing the porosity and the permeability; constructing a dynamic committee integration model; inputting the main control factors into a dynamic committee integration model; and utilizing the trained dynamic committee integration model to respectively predict the porosity and the permeability. According to the method, the logging data is processed by adopting a machine learning method, reservoir parameters can be efficiently and accurately predicted, and favorable support can be provided for oil-gas exploration and development; the dynamic committee integrated model constructed by the invention can dynamically adjust the weight of each model according to different geological conditions and data features, can more flexibly adapt to the geological condition of a research area compared with a single model, and improves the prediction precision and generalization of the model.

The invention relates to an intelligent stratum lithology identification method based on logging data. The method comprises the following steps: collecting the logging data; preprocessing the data; extracting features; an LSTM-CRF model is adopted, training set data serve as input of the LSTM model, the CRF model is applied to output of the LSTM model, and stratum lithology is output; and evaluating the model. The method combines the long-term dependence of the recurrent neural network and a deep learning model of the sequence labeling capability of the CRF, and deduces the formation lithology of clay, sandstone, glutenite, limestone, mudstone and dolomite through logging data of natural gamma, sound wave, resistivity and the like. According to the method, the features are automatically extracted from the logging data through a machine learning algorithm, and the lithology identification is carried out by using the features, so that the identification accuracy can be effectively improved; the method does not depend on geological conditions of a specific region, is suitable for logging data of different regions, and has wide applicability.

The invention provides a new method for calculating the borehole wall collapse pressure of a fractured shale stratum, which comprises the following steps of: 1, preparing an experimental rock sample, and carrying out sound wave characteristic test on bed rock and a fractured rock sample and mechanical experiment test on the rock sample; step 2, calculating an integrity coefficient K upsilon, a geological strength index GSI, a rock mass disturbance coefficient D and a lithology index mi of the rock; 3, a Hoek-Brown strength criterion for evaluating the fractured formation based on sound waves is established; 4, a principal stress expression of the well walls of the inclined well and the horizontal well is obtained through calculation; and 5, establishing a fractured oil shale formation borehole wall collapse pressure calculation model. According to the new method for calculating the well wall collapse pressure of the fractured shale formation, the problem that a conventional homogeneous formation collapse pressure calculation model is not suitable for well wall stability prediction of the fractured shale formation is solved, and a new theoretical basis and technical support are provided for the well wall stability problem in the oil shale exploitation process.

The invention relates to the technical field of geological data processing, in particular to a prospecting method and system based on geological text data, and the method comprises the following steps: obtaining exploration drilling data, extracting lithologic description, mineral composition and fracture filling conditions of a rock core, calculating the correlation index of drilling depth and mineralization thickness, screening the change trend of ore grade, and obtaining the geological text data. And obtaining mining area borehole lithology distribution data. According to the method, by deeply analyzing drilling data and lithologic characteristics, accurately recognizing a mineralized fluid accumulation area and improving the prediction precision of an ore body occurrence position, boundary control of a mineralized zone is optimized through measurement and calculation of spatial homogeneity, the extension precision of the mineralized zone is enhanced, scientific adjustment of a prospecting range is ensured through detailed analysis of altered mineral characteristics, and the method is suitable for large-scale popularization and application. The reasonability of demarcation of the prospecting target area is improved, multi-dimensional data and parameter analysis are integrated, a complete prospecting logic chain is constructed, and mineral product prediction precision and resource exploration efficiency are remarkably improved.

The invention provides a mountain fissure geological disaster information monitoring and early warning method, which comprises the steps that fissure form data of a mountain fissure area is acquired, the fissure form data comprises the length, width, depth and trend of a fissure, and real-time acquisition is performed through a preset sensor network; geologic characteristic parameters of the crack area are obtained, the geologic characteristic parameters comprise lithology, structure and stress states, and data acquisition is carried out through a pre-arranged geologic sensor network; transmitting fracture form data and geological characteristic parameters acquired in the field to a monitoring platform in real time; according to the dynamically updated crack division result, designing a visual display module, and generating a crack spatial distribution map and an attribute feature map; and carrying out correlation analysis on the visually displayed crack division result and the rainfall, the surface displacement and other monitoring information in the monitoring platform, and generating a disaster early warning report.

The invention relates to an accurate and intelligent identification method for lithology of surrounding rock of underground engineering, and belongs to the technical field of lithology identification. The method comprises the following steps: S1, preparing a rock test piece: synthesizing a rock sample through field sampling or artificial simulation, and preparing the test piece meeting experimental requirements; s2, an indoor drilling experiment is conducted, and drilling parameters are collected; s3, performing a borehole peeping experiment and storing a peeping image as a data set; s4, carrying out data preprocessing and fusion; s5, carrying out machine learning model training: selecting a graph neural network to carry out training and lithology classification identification; s6, evaluating and verifying the machine learning model; and S7, performing field optimization and application. According to the method, the advantages of the drilling peeping technology and the machine learning technology are combined, the drilling parameters are obtained based on the indoor drilling experiment, the drilling image is obtained through drilling peeping, and accurate and intelligent lithology identification is achieved by coupling data of the drilling image and the drilling parameters.

The invention provides a method for characterizing the heterogeneity of a millimeter-level shale oil and gas reservoir. The method mainly solves the problem that the reliability and accuracy of a reservoir evaluation result of an existing volume sampling method are low. The method is characterized by comprising the following steps that S1, shale millimeter-level mineral components are recognized and drilled; s2, based on the obtained powdery sample, oil-containing and non-oil-containing tests are carried out; determining occurrence pore size distribution to carry out reservoir characterization through comparison before and after pumping; s3, on the basis of the obtained powdery sample, carrying out oiliness characterization on different lamina / layered components; and S4, carrying out millimeter-level shale oil reservoir heterogeneity characterization on the basis of the reservoir property and oiliness results of the different strata / layered components. According to the method provided by the invention, the identification precision of the shale formation / stratified structure can be enhanced, millimeter-level fine representation of lithology of different formation / strata is realized, and favorable support is provided for shale oil enrichment mechanism, sweet spot prediction and the like.