413 results about "Geological structure" patented technology

The invention relates to the technical field of tunnel engineering, in particular to a tunnel surrounding rock grading method and system, comprising intelligent sensing and data acquisition, multi-source data fusion and modeling, hybrid model dynamic grading, real-time decision and support optimization, online learning and dynamic feedback, and risk early warning and emergency response. Compared with the prior art that a geological data acquisition mode combining manual drilling coring and low-resolution geophysical prospecting is adopted, efficiency is low, subjective errors are large, and a complex geological structure is difficult to cover, unmanned aerial vehicle LiDAR scanning, intelligent rock core image analysis and a high-density IoT sensor network work cooperatively, and the working efficiency is greatly improved. Real-time dynamic acquisition of full-section geological information is achieved, manual intervention errors are eliminated in combination with a multi-source data fusion algorithm, the automation level and three-dimensional space representation precision of data acquisition are remarkably improved, and a high-resolution holographic data base is provided for surrounding rock classification.

The invention provides a field electrical prospecting data anomaly identification method based on big data analysis, and the method comprises the steps: collecting multi-source heterogeneous data to construct a digital twin model, and generating a virtual electrical prospecting data set in combination with geological physical law constraints; performing multi-device cooperative training through a federated learning framework to obtain a self-evolution anomaly detection model, deploying the model to an edge computing node to obtain an anomaly recognition result, and visually displaying the anomaly recognition result; and iteratively optimizing the geological digital twinning and anomaly detection model to obtain a field electrical prospecting data anomaly recognition result. Space-time synchronization is realized based on related technologies, a three-dimensional geological structure model is constructed in combination with a geological knowledge graph, and geological logic constraints are embedded; real geological scene features are injected through closed-loop feedback of a generative geological simulator and a federated learning framework, and an electrical method response mapping relation is corrected, so that the model adapts to geological structure changes; and mapping an anomaly recognition result to the three-dimensional geologic model for visual display, thereby solving the problem of low matching degree of a traditional method.

The invention discloses a geological disaster monitoring system based on multi-modal data, and relates to the technical field of geological disaster monitoring, and the system comprises a crack analysis module which carries out the time-space correlation mining of the crack propagation rate of a monitoring region, and analyzes the nonlinear evolution characteristics and spatial differentiation rules of rock mass fracture; and the critical identification module is used for performing wavelet packet energy spectrum analysis on the inclination angle change rate of the geologic body, identifying a critical turning point of rigidity attenuation of the geologic structure in combination with a preset algorithm, judging whether the overall stability enters an instability acceleration stage or not, and performing multi-parameter collaborative detection, crack evolution cross validation and dynamic trend stability verification to obtain the stability of the geologic structure. The accuracy and reliability of geological structure rigidity attenuation critical turning point recognition are remarkably improved, a more accurate rigidity attenuation stage judgment basis is provided for an early warning module, and the capturing capacity of a multi-modal data geological disaster monitoring system for structure instability precursor is enhanced.

The invention discloses a slope settlement data processing method under an infiltration surface effect based on a water-soil coupling model, and relates to the technical field of civil engineering geomechanical analysis. And establishing a water-soil coupling numerical model containing a mutual coupling relationship between water flow permeation and soil deformation. According to the method, the multi-source monitoring data and the sudden change recognition algorithm are fused, so that the recognition precision and real-time performance of the sudden change point of the slope infiltration surface are improved. And a parameter adaptive correction mechanism is introduced, so that the model can dynamically adjust key parameters, and the adaptability to a complex geological environment and the prediction stability are enhanced. Meanwhile, a dynamic processing flow integrating data driving, model calculation and risk identification is constructed, intelligent linkage early warning of the side slope settlement risk is achieved, the landslide accident risk is effectively reduced, and the scientificity and safety of geological disaster monitoring are improved.

The invention discloses a three-dimensional geologic model modeling method and device. The three-dimensional geological model modeling method comprises the following steps: acquiring to-be-established geological region data and actual geological boundary data; generating an initial stratum interface grid according to the to-be-established geological region data; correcting the initial stratigraphic interface grid to obtain a corrected stratigraphic interface grid; carrying out geological rule constraint on the corrected stratigraphic interface grid so as to form simple three-dimensional stratigraphic interface model data; correcting the simple three-dimensional stratigraphic interface model data according to the seismic profile information so as to obtain a composite geologic structure model; generating a three-dimensional fault plane model according to the composite geologic structure model; and generating a composite geological structure model including a fault network according to the three-dimensional fault plane model and the composite geological structure model. Through the three-dimensional geological model modeling method, the deviation between the model and an actual geological boundary can be remarkably reduced.

The invention relates to the technical field of geological disaster monitoring, and discloses a landslide dynamic simulation monitoring and early warning method based on multi-source sensing fusion, and the method comprises the steps: collecting multi-source data of a landslide body through a plurality of heterogeneous sensors, and enabling the multi-source data to be in space-time alignment after preprocessing; building a multi-parameter fusion model fusing a displacement field, a mechanical field and an environment field based on the preprocessed data, enabling the multi-parameter fusion model to output a deformation rate and a stability coefficient, and dynamically adjusting the weights of the displacement field, the mechanical field and the environment field according to a landslide evolution stage; predicting a future deformation trend of the landslide mass in combination with a geological structure and historical data, comparing a stability coefficient with a dynamic safety threshold to judge a risk level, and generating early warning information; and dynamically correcting a reference weight coefficient in the model based on the deviation between the monitoring data and the model output, so that the model is adaptively optimized. The problems of single monitoring dimension and static model solidification in the prior art are solved, and accurate and adaptive monitoring and early warning of the risk state of the landslide mass are realized.

The invention discloses a post-stack seismic data denoising method based on multi-dip-angle structure guided filtering, and belongs to the field of seismic data denoising, and the method comprises the following steps: carrying out the processing of input seismic data through guided filtering, and obtaining a fault likelihood attribute; dividing the seismic data into a fault region and a continuous stratum region based on fault likelihood attributes; in the fault region, performing filtering processing on the seismic data by using an edge-preserving smoothing algorithm to obtain a filtering result of the fault region; in the continuous stratum area, calculating a local inclination angle of the seismic data, and performing filtering processing on the seismic data by adopting a structure guiding filtering algorithm which performs smoothing along the direction of the local inclination angle to obtain a filtering result of the continuous stratum area; and fusing the filtering result of the fault region and the filtering result of the continuous stratum region to obtain final de-noised seismic data. According to the method, efficient noise suppression can be performed on post-stack seismic data, and meanwhile, the protection capability on a complex geological structure is remarkably improved.

The invention relates to the technical field of mining, in particular to a mine disaster prediction method and system. The method comprises the following steps: acquiring geologic structure data corresponding to a mine, wherein the geologic structure data comprises rock stratum distribution, fault occurrence and underground water occurrence state; constructing a coupling model corresponding to the mine based on the rock stratum distribution, the fault occurrence and the underground water occurrence state, and determining a rock mass constitutive relation and coupling parameters in the coupling model; determining a prediction boundary condition of the coupling model based on the rock constitutive relation and the coupling parameters; and acquiring field monitoring data of the mine, correcting the coupling model based on the field monitoring data and the prediction boundary condition so as to determine a potential disaster point based on the corrected coupling model, and generating a risk early warning strategy and a risk recovery strategy corresponding to the potential disaster point. According to the embodiment of the invention, a full-chain prevention and control system from disaster prediction to emergency disposal is constructed, and the safety management level and disaster prevention and control capability of mine engineering are remarkably improved.

The embodiment of the invention discloses a landslide disaster evolution analysis method and system based on a coupling model, and the method comprises the steps: carrying out the multi-source data integration of geological structure monitoring data, landform distribution data and hydrological environment monitoring data of a target seismic region, and generating a comprehensive monitoring data set; based on the data set, establishing an interaction association relationship of the target equation set, and constructing a geology-landform-hydrology coupling model; performing dynamic simulation and quantitative evaluation on the instability probability and the landslide scale of the potential landslide mass by using the coupling model according to a preset scene parameter combination, and generating an image-text annotation quantitative evaluation report; and finally, carrying out vulnerability analysis and risk assessment on the disaster-bearing body based on the report, generating a comprehensive guidance report containing a disaster-bearing body vulnerability spatial distribution diagram, landslide risk prevention and control measure suggestions and a regional space planning optimization scheme, and providing a scientific basis for landslide disaster prevention and control and regional planning.

The invention provides a coal mine high-precision three-dimensional geological modeling method and system based on multi-source heterogeneous data fusion, and relates to the technical field of coal mine geological modeling. The method comprises the following steps: establishing a unified semantic reference system and a time-space coordinate reference, and generating an alignment anchor point set; mapping the multi-source observation data to form an alignment feature set and calculating a source weight; according to a multi-evidence consistency rule and geomechanical prior inversion, a discontinuous geological structure is obtained; constructing an implicit stratigraphic function and applying a boundary jump condition to solve a stratigraphic boundary set; and taking the stratum boundary as a framework, and executing anisotropic joint estimation to obtain a property distribution model and an uncertainty result. According to the method, integrated modeling of the geological structure and the property of the complex structure area is achieved, and the precision, the stability and the credibility of the three-dimensional geological model are remarkably improved.

The invention discloses a water injection induced earthquake monitoring and fault activation detection system, and belongs to the field of earthquake monitoring and fault activation detection. The earthquake observation module is used for obtaining earthquake monitoring data; the seismic phase data extraction module is used for carrying out micro-seismic identification according to the seismic monitoring data to obtain seismic phase data; the earthquake initial positioning module is used for obtaining an earthquake directory of initial positioning; the earthquake fine positioning module is used for carrying out secondary positioning to obtain an earthquake directory of fine positioning; the fault and structure modeling module is used for integrating the finely positioned seismic catalog into a three-dimensional geologic structure model and carrying out spatial positioning and visualization of seismic activities; and the fault activation and earthquake risk analysis module is used for analyzing the earthquake activity time change trend, b value change, fault activation and earthquake risk according to the three-dimensional geologic structure model. The method solves the problem that an existing method lacks monitoring of fault activation and weakening of the shale gas development area and water injection induced earthquakes.

The invention provides a geological disaster intelligent early warning method based on fusion of a physical information neural network and space-air-ground monitoring, and the method comprises the following steps: S10, obtaining geological structure features of different depths under the ground of a city, and recognizing the spatial distribution and thickness of an underground abnormal body; acquiring a surface deformation time sequence, crack and landform information, a three-dimensional point cloud and a continuous vibration signal of a surface-shallow stratum; s20, constructing a three-dimensional twin substrate, performing space-time alignment and resampling on multi-source heterogeneous data, mapping the data to a three-dimensional grid of a unified coordinate system, and extracting and fusing geological disaster precursor features; s30, taking the generated fusion feature field as an input training neural network model, carrying out geological disaster forward prediction and parameter inversion, and outputting future stability probability distribution, a potential slip plane and key parameter evolution; and S40, adaptively adjusting a risk threshold and an early warning rule, constructing an incremental data set, and carrying out periodic incremental training and parameter optimization on the physical information neural network in the step S30.

The invention provides a pore-scale underground natural hydrogen migration and accumulation behavior prediction method, and belongs to the field of underground natural hydrogen exploration and exploitation. Comprising the following steps: reconstructing an underground porous medium according to a real geological structure image; establishing an empirical formula of fluid attributes such as wettability, interfacial tension and viscosity of the hydrogen-water-rock system and temperature, pressure, salinity and rock type; establishing a flow equation of the hydrogen-water two-phase system in the porous medium; after initial conditions and boundary conditions are determined, the initial distribution, migration and aggregation process of natural hydrogen in the porous medium is simulated; and after the flow is stable, a migration and accumulation image of the hydrogen-water-rock system and a hydrogen recovery rate change curve are obtained. The hydrogen-water two-phase migration-accumulation model constructed by the method is suitable for the characteristics of high density ratio and high viscosity ratio of hydrogen and water, and is good at treating a porous medium complex structure and identifying a hydrogen-water interface; and the method is oriented to a real underground reservoir, and is suitable for predicting the hydrogen-water-rock migration and accumulation process under no environmental factors.

The invention discloses a geological modeling method for fusing remote sensing and seismic data based on a multi-mode auto-encoder, and the method comprises the steps: collecting a remote sensing image covering a target area and seismic exploration data, and converting the attribute of the seismic exploration data into a resolution consistent with the remote sensing image; respectively extracting remote sensing image features and seismic attribute features by using a parallel double-branch coding structure of a multi-mode encoder; a fusion module is arranged in the middle layer of the remote sensing image encoder and the seismic attribute encoder, fusion is carried out in the fusion module, remote sensing images and seismic data are reconstructed by using a decoder based on fusion features, and difference data between reconstructed data and the remote sensing images and the seismic data are calculated based on a combined loss function introducing regular terms. Training a multi-modal encoder by using the difference data to obtain an encoding feature and a difference heat map; and on the basis of the coding features and the difference heat map, key geological structure region identification and visual display are carried out, and key geological structures comprise faults and lithology.

The invention discloses a digital twin-driven foundation settlement multi-physics field coupling simulation platform and a use method thereof, relates to the technical field of foundation settlement analysis, and solves the technical problem of settlement prediction deviation accumulation caused by incapability of synchronously updating seepage field and stress field coupling boundary conditions. According to the invention, the twinborn construction unit and the multi-physics coupling solving unit are installed, so that the rapid construction of the digital twinborn of the stratified geologic structure and the multi-physics coupling effect calculation of a stress field, a seepage field and a temperature field of a soil body are realized, and the problem of insufficient precision of traditional single-field simulation is solved; by installing a noise filtering unit and a data standardization unit, elimination of high-frequency interference components of sensor signals and time-space alignment of multi-source data are achieved, the problem that noise interference of geological exploration data is incompatible with heterogeneous formats is solved, and the accuracy and accuracy of foundation settlement analysis are improved. And the reliability of input data and the stability of a simulation result are improved.

The invention relates to the technical field of intelligent geological exploration, in particular to a reserve exploration area evaluation method and system based on a classification algorithm, and the method comprises the following steps: extracting an included angle between a tectonic line and a lithologic zone trend, and generating a tectonic trend label according to space combination, calling a label to obtain mineralized element concentration, calculating a distance between a distribution center and a mass center to generate an enrichment type, dividing density grades through an area ratio, and generating an alteration gradient classification set in combination with a grid thickness trend and a mineral concentration ratio. According to the method, the spatial combination is identified by extracting the relationship between the structure and the lithology direction, the coupling expression of the geologic structure and the metallogenic mode is improved, the feature consistent region is screened by combining the spatial distance between the mineralized element enrichment center and the regional mass center, the target region focusing precision is improved, and the abnormal cluster is extracted based on the convergence of the abnormal body point location and the quantity feature; abnormal intensity quantification is enhanced, and the multi-factor collaborative recognition path significantly enhances the multi-source data fusion judgment capability and spatial precision.

The invention provides a large-scale three-dimensional tunnel surrounding rock parameter random field model construction method and system, and relates to the technical field of tunnel surrounding rock modeling, and the method comprises the steps: carrying out the fitting construction of a probability distribution model reflecting surrounding rock parameters; selecting a plurality of surrounding rock parameters as clustering features, quantitatively classifying global geologic structures in tunnel surrounding rocks, and performing global decomposition on a tunnel model into continuous sub-domains containing overlapped buffer areas; for a first sub-domain from any direction of the whole domain of the tunnel model, performing covariance matrix spectral decomposition by adopting a KL decomposition method to generate a first sub-domain random field, and extracting random field values of overlapped regions among the sub-domains as boundary condition data of a recursive generation process; on the basis of the boundary condition data, conditional random fields of subsequent sub-domains are generated layer by layer; and seamlessly splicing the conditional random fields generated by recursion of the sub-fields into a global parameter random field, converting the global parameter random field into probability distribution by adopting a probability mapping method, and performing instruction encapsulation by developing a cross-platform data interface engine to realize a construction process of a parameter random field model.

The invention provides a shale reservoir fracture prediction method and system based on three-dimensional full-waveform inversion and a medium, and relates to the technical field of data analysis, and the method comprises the steps: arranging a multi-channel seismic exploration instrument in a shale reservoir region, and collecting full-waveform seismic data, including longitudinal waves, transverse waves and reflected waves; performing three-dimensional full-waveform inversion based on the logging data, the geological structure map and the full-waveform seismic data to generate a shale reservoir attribute model; a multi-scale inversion strategy is constructed, fracture sensitivity analysis and fracture form dynamic prediction are carried out, and reservoir fracture distribution characteristics and reservoir fracture form characteristics are obtained; discrete fracture point connection and fracture network reconstruction are carried out, a reservoir three-dimensional fracture network model is constructed, and fracture prediction visualization display is carried out. The technical problems that in the prior art, only inference based on limited data can be provided, comprehensive capture of a complex fracture network is lacked, distribution and form of fractures cannot be comprehensively and accurately described, and reservoir analysis and mining decisions are affected are solved.

The invention relates to the technical field of geophysical exploration data processing, and discloses a multi-element prospecting data cleaning and feature extraction system based on machine learning, and the system comprises a structure field generation unit which is used for constructing a multi-scale structure tensor field according to three-dimensional seismic data and extracting a structure vector representing a geological structure; the self-adaptive cleaning unit is used for carrying out abnormal value judgment and interpolation on non-seismic data by using the structural vector as prior guide information; the coupling feature generation unit is used for generating single-scale and cross-scale coupling features capable of deeply quantizing the relationship between the attribute change and the structure by calculating the projection of the non-seismic data gradient on different-scale structure vectors; and the feature output unit is used for combining the cleaned data, coupling the features and the original seismic attributes and constructing a high-dimensional final feature vector. According to the method, input with more geological significance is provided for machine learning, so that the precision and reliability of prospecting prediction are remarkably improved.

The invention discloses a seismic wave velocity-resistivity joint inversion method based on a time dimension, belongs to the technical field of geophysical exploration, and can solve the problem that the dynamic change of a geological structure cannot be determined in the prior art. The method comprises the following steps: constructing a seismic wave velocity distribution model according to seismic exploration data of a target area, and constructing a resistivity distribution model according to electromagnetic exploration data of the target area; determining an objective function of seismic wave velocity-resistivity joint inversion according to the seismic wave velocity distribution model and the resistivity distribution model; updating and iterating the seismic wave velocity distribution model and the resistivity distribution model according to the seismic exploration data and the electromagnetic exploration data of the plurality of periods until the objective function is minimized; and according to the corresponding seismic wave velocity distribution model and the resistivity distribution model when the target function is minimized, respectively determining seismic wave velocity inversion results and resistivity inversion results of the target area in multiple periods. The method is used for inverting the seismic wave velocity and the resistivity.

The invention discloses an engineering geology grouting reinforcement device, and relates to the field of geology grouting reinforcement, the engineering geology grouting reinforcement device comprises a mounting rack, the mounting rack is fixedly connected with a first telescopic driving part, the output end of the first telescopic driving part is fixedly connected with a mounting plate, and the mounting plate is fixedly connected with a grouting pipe. The grouting pipe is used for pressurizing and grouting, so that the grout enters a reinforcing gap under the action of pressure; after filling is conducted for a period of time, the injection-compression pipe moves downwards, the sealing plate blocks the grouting opening under the action of the spring to prevent grout from flowing back, meanwhile, the injection-compression pipe moves downwards to extrude the grout, the extrusion force is further increased, and geological gaps are filled with the grout more sufficiently. Through the reciprocating operation, the geological reinforcement position can be fully grouted, the stability and strength of the geological structure are effectively enhanced, the quality and reliability of grouting reinforcement are greatly improved, and the reinforcement requirement under the complex geological condition is met.

The invention relates to the technical field of geological data analysis, discloses a bank slope deep unloading developmental zone identification and evaluation method and system based on a sliding window, and aims at solving the problems that an existing method is low in efficiency and high in subjectivity, and evaluation results are discrete and cannot be quantized. Based on set window parameters, space convolution calculation is carried out through a sliding window, and a continuous feature signal sequence is generated; segmenting the sequence by using a preset background noise threshold value, and identifying candidate sections; matching and calibrating the boundary of the candidate section and the position of a known geological structural plane, and delineating a depth-keeping unloading development zone; the comprehensive development strength index of each deep unloading development zone is calculated, and quantitative evaluation is achieved. According to the method, automatic and continuous space recognition and quantitative representation of the deep unloading development zone are achieved, efficiency and accuracy are improved, and the method is particularly suitable for safe site selection and construction of major projects.

The invention provides a geological structure intelligent detection system and method for a deep special space, and relates to the technical field of geological exploration. The geological structure intelligent detection system and method for the deep special space comprises the steps that S1, a distributed sensor network is deployed in the deep special space, and seismic wave, electromagnetic field, sound wave, temperature and strain multi-physical field original detection data are synchronously collected; constructing a noise channel model based on a quantum computing principle, and modeling the original detection data; a quantum inspired adaptive filtering algorithm is applied, and noise characteristics are estimated in real time through variational optimization. Through synchronous acquisition of a distributed multi-physics field sensor network and an adaptive filtering technology of quantum inspiration, effective enhancement of weak characteristic signals is realized in a strong noise environment, and the signal-to-noise ratio and the target geological structure identification precision are significantly improved; and a neural network inversion model is combined with physical information constraint.

The invention discloses a surface mine geological parameter optimization modeling method based on deep reinforcement learning, and the method comprises the steps: achieving the spatial unified expression of multi-source data through constructing a three-dimensional non-uniform grid frame, and obtaining the spatial attribute description with a hierarchical discrimination degree on the basis of embedding a multi-scale feature extractor; and the consistency of initial parameter distribution and historical observation data is improved in combination with a dynamic weight distribution mechanism. According to the method, a strategy gradient updating mechanism is further introduced to form a learnable parameter field, collaborative optimization of a grid structure and parameter evolution is achieved through space-time alignment and topology reconstruction, and therefore the adaptability and dynamic updating performance of the model to a complex geological structure are remarkably enhanced. The finally output geologic model not only can accurately reflect the spatial relevance among the multi-source data of the mining area, but also has good continuity and interpretability, and effectively solves the problem that the multi-source heterogeneous geospatial data fusion modeling is difficult.

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

A software toolbox installing and using method comprises the following steps of S1, installing a main program, S2, opening a main program interface to select a module needing to be used, S3, achieving intelligent layer management and control through a layer management module, S4, achieving intelligent processing of geological elements through a geological map management module, and S5, achieving intelligent processing of the geological elements through a geological map management module. The method comprises the following steps: S1, carrying out intelligent construction of a two-dimensional profile map through a profile map generation module, S6, carrying out batch processing of reserve elements through a reserve map, S7, carrying out lithological layering, sampling position and geological structure discrete data of a drill hole through a drill hole dynamic block, and S8, providing a high-frequency tool rapid channel through a common tool box module. Compared with the prior art, the tool box has the advantages that the tool box is seamlessly integrated with the CAD environment, so that a user can quickly access and utilize efficient working tools on the basis of keeping the existing CAD skills; meanwhile, the section map generation module and the drilling dynamic block display module further simplify the visualization process of two-dimensional and three-dimensional data.

The invention discloses a power transmission and transformation project geological zoning automatic adjustment method and system, and relates to the technical field, and the method comprises the following steps: obtaining multi-source geological data, carrying out the data standardization preprocessing, and obtaining a high-dimensional feature data table from the preprocessed data based on a multi-source Bayesian fusion model; performing feature construction and standardization processing on the high-dimensional feature data table to obtain a high-dimensional geological feature set; performing feature compression on the high-dimensional geological feature set based on VAE, and performing unsupervised clustering based on GMM to construct a deep clustering model; controlling the power transmission project system to perform adaptive geological region division through a deep clustering model, and generating a path control strategy set based on reinforcement learning; according to the method, adaptive intelligent adjustment of the geological division and closed-loop optimization of a path control strategy are realized by calling a deep clustering model and reinforcement learning path optimization method, and the problems that the geological division is high in static property and lacks a real-time feedback mechanism and path control is not deeply coupled with a geological structure are solved.

The invention relates to the field of environmental analysis, and provides a gas hydrate three-phase coexistence region modeling method and system. The method comprises the following steps: acquiring three-dimensional seismic data and original logging data, and preprocessing the original logging data to obtain corrected logging data; respectively performing logging interpretation and structural interpretation according to the corrected logging data and the three-dimensional seismic data to obtain interpretation results; converting a structure interpretation result in the interpretation results into a three-dimensional geological structure model, and performing grid division on the research area by adopting a corner grid system to obtain a structure grid model; and performing attribute modeling on the constructed grid model based on variation function analysis, and predicting porosity and saturation in a three-dimensional space through a Kriging interpolation algorithm to obtain a three-phase coexistence region model. According to the method, the influence of complex geological conditions on the accuracy of the model is reduced, and the precision of the three-phase coexistence region model is remarkably improved.

The invention discloses a magnetotelluric sounding measurement method for multidirectional aggregation observation, and the method comprises the steps: building a novel device layout: designing a reasonable measurement point layout scheme according to geological features and electromagnetic response distribution; processing data and establishing an unstructured grid: acquiring an electromagnetic field component, completing data acquisition, and inputting the processed electromagnetic field data into an unstructured grid model; impedance tensor calculation: on the basis of the unstructured grid, performing impedance calculation by utilizing multi-component electromagnetic field data and combining an interpolation idea, and constructing an extended impedance tensor of an electromagnetic field component to obtain an impedance value component; and three-dimensional inversion and visualization: performing three-dimensional inversion through a vector finite element method to obtain an inversion result. According to the method, richer observation data are provided for deep geologic structure inversion.

The invention relates to the field of mine disaster prediction, and discloses an AI-based mine disaster prediction and risk assessment method, which comprises the steps of deploying a multi-source sensor network which comprises a microseismic monitoring sensor, a gas concentration sensor, a stress sensor and a hydrological sensor, the data acquisition module is used for acquiring micro-seismic waveform data, gas concentration time sequence data, stress data and hydrological parameter data of a mining working face, a roadway and a geological structure area; and performing space-time alignment processing on the microseismic waveform data, the gas concentration time sequence data, the stress data and the hydrological parameter data to generate a space-time aligned heterogeneous data set, and performing encryption feature interaction with an external system through a federated learning framework. According to the method, a multi-modal deep learning model is constructed, microseismic waveforms, gas concentration time sequences and geologic structure space correlation characteristics are extracted, rock mechanics equation constraint conditions are injected in model training, and the recognition capacity of rock stratum fracture precursor signals and the disaster chain coupling process is remarkably enhanced.