71 results about "Bayesian inversion" patented technology

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) deformation monitoring system for landslide and a method thereof, and relates to the field of geological disaster monitoring. The method has a high-precision deformation monitoring effect, through multi-source SAR data fusion, interference pair screening optimization and multi-source error correction (such as troposphere and ionosphere delay correction), the fidelity of a deformation phase sequence is remarkably improved, and the precision limitation under a complex terrain is overcome; the method has an intelligent geological constraint inversion capability, introduces geological prior knowledge (such as fault and fracture characteristics), enables a deformation field to better conform to the actual geomechanical law through deep learning semantic segmentation and a Bayesian inversion model, and reduces misinformation and missing report. The method has self-adaptive landslide recognition, adopts deformation gradient field calculation and a dynamic threshold segmentation algorithm, automatically recognizes a potential sliding zone boundary, improves the landslide partitioning efficiency, and is suitable for large-range monitoring.

The invention discloses an offshore wind turbine foundation scouring full-period intelligent monitoring method and system based on multi-source data fusion and a dynamic coupling model, and relates to the technical field of intelligent monitoring, and the method comprises the steps: deploying a multi-source monitoring module, and constructing a finite element model; carrying out load calculation and parameter inversion; and training full-cycle dynamic updating of the washout failure function model. According to the method, a self-adaptive Kriging-Bayesian method is adopted, a Bayesian inversion framework and a self-adaptive agent model are fused to solve optimal soil body parameters, full-period model dynamic updating based on dynamic monitoring data is achieved, a multi-fidelity deep kernel learning model is adopted, three types of data are fused into a training set, full-period intelligent monitoring of offshore wind turbine foundation scouring is achieved, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. The dynamic identification of soil parameters is realized by combining a self-adaptive inversion framework with a displacement error closed-loop optimization mechanism, and the technical problem that a traditional static model cannot adapt to the spatial-temporal variability of seabed geology is solved.

The invention discloses a post-earthquake railway structure state evaluation method and system based on multi-source data fusion. The method comprises the steps that a finite element model is constructed according to the structure of a regional railway network, a simulation data set is generated, a physical information neural network model is trained, and a proxy model library is formed; acquiring multi-source observation data, and performing inversion according to the Bayesian theory, the structural damage parameters of the physical information neural network agent model and the multi-source observation data to obtain complete posterior probability distribution of the damage parameters; according to the method, statistical characteristics of damage parameters are extracted from posterior probability distribution, probability grading is carried out on the damage degree of the structure, driving constraint suggestions are generated according to the combination of damage probability grading and specifications, and Bayesian inversion time is reduced from several days to several hours through a physical information neural network agent model, so that the evaluation efficiency is improved; the output of the Bayesian method is probability distribution, the uncertainty range of the evaluation result is clearly displayed, and the uncertainty is quantified.

The invention provides a crustal stress prediction method and device, electronic equipment and a storage medium, and relates to the technical field of seismic survey. The method comprises the following steps: obtaining observation seismic data of seismic wavelets in a strong VTI medium, and constructing a to-be-inverted parameter matrix based on a PP wave reflection coefficient corresponding to the strong VTI medium; constructing a posterior probability function obeyed by an inversion parameter matrix corresponding to the to-be-inverted parameter matrix based on a Bayesian inversion theory and observation seismic data, and determining a target functional based on a prior probability function and a likelihood function corresponding to the posterior probability function; determining medium density and each stiffness matrix coefficient based on an inversion parameter matrix solving result of the target functional, and determining a flexibility matrix of the strong VTI medium based on each stiffness matrix coefficient; and predicting the ground stress distribution of the target profile in the strong VTI medium based on the medium density and the positive strain matrix and the flexibility matrix corresponding to the strong VTI. Therefore, the crustal stress prediction accuracy under the strong VTI medium is improved.

The invention provides a tunnel activity measurement method with a bidirectional loading demand, and belongs to the technical field of tunnel activity measurement, and the method comprises the steps: collecting a multi-dimensional force signal and a displacement signal, carrying out the digital processing, building a mechanical coupling tensor matrix, and obtaining a preliminary decoupling force component through a condition number adaptive inversion strategy; tensor network high-order correlation modeling and density matrix reforming swarm optimization are utilized for optimization to obtain an accurate decoupling force component, a simulated annealing particle swarm optimization algorithm is combined for solving a non-convex optimization objective function to obtain real mechanical response parameters, defects are identified through ultrasonic detection, and an influence coefficient matrix is established; a Bayesian inversion algorithm is adopted to remove defect influence to obtain corrected tunnel activity parameters, tunnel safety monitoring is finally realized through time sequence analysis and stress redistribution evaluation, and the technical problem of insufficient measurement precision caused by multi-dimensional force signal coupling interference is solved.

The invention discloses a port area flexible load millisecond-level cooperative suppression method based on magnetic flux jump prediction, and the method comprises the steps: obtaining the external vibration and electrical data of a transformer, combining the Bayesian inversion of physical constraint, and reconstructing an internal three-dimensional magnetic flux field; based on time sequence evolution of the magnetic flux field, a dual-mode self-adaptive prediction algorithm is adopted, and a magnetic flux jump early warning vector containing jump time, amplitude and position is generated; and according to the early warning vector, through a multi-layer cooperative control strategy of time sequence decoupling, a control instruction for the flexible load of the harbor district is generated and executed. Advanced early warning and millisecond-level active suppression of the potential magnetic saturation risk are realized, and the operation reliability and equipment safety of a harbor district power distribution system are improved.

The invention discloses a small watershed nitrogen and phosphorus load accounting method based on key process parameter calibration, and the method comprises the steps: obtaining the basic geographic hydrological data of a target small watershed, the data comprising the areas of different land utilization types in the watershed, the flow length of a water collection region river channel, and the average flow velocity; based on the hydrology and water quality monitoring data of the long-time sequence of the catchment area, adopting a Bayesian inversion model to quantitatively identify the nitrogen and phosphorus output coefficient of each land utilization type; calculating a soil nitrogen and phosphorus output load in the catchment area, determining a natural reduction coefficient of a nitrogen and phosphorus first-order river, and calculating transport time of nitrogen and phosphorus from a starting point of a river channel of the catchment area to a water outlet and transport time of soil output nitrogen and phosphorus from a tributary estuary to the water outlet of the catchment area; and calculating the water collection area outlet nitrogen and phosphorus load. According to the method, the overestimation error caused by the fact that a production and pollution discharge coefficient method is adopted in a traditional method is remarkably reduced through fusion of'pollution source nitrogen and phosphorus entering the river-remaining soil output-along-the-way natural reduction 'ternary accounting, the accuracy of load accounting is improved, the feasibility and advantages of the technical scheme are verified, and the method is particularly suitable for small watersheds with deficient basic data.

The invention discloses a finite aperture elastic wave underground buried object reconstruction method based on Bayesian inversion, and belongs to the technical field of data processing. The method comprises the following steps of: firstly, performing structured reconstruction on non-phase scattering data collected under a finite aperture condition by utilizing the redundancy and multi-resolution characteristic of framework reconstruction; then, posterior distribution of anomalous body shape parameters is obtained by constructing prior distribution and a likelihood function, efficient sampling is carried out on the anomalous body shape parameters by adopting a precondition Crank-Nicolson parallel annealing (pCN-PT) algorithm, and robust reconstruction of the positions and the geometrical shapes of the underground buried objects is achieved. According to the method, the problem of data missing caused by the finite aperture is effectively solved through frame reconstruction, and the sampling efficiency and convergence performance of Bayesian inversion are improved in combination with the pCN-PT algorithm, so that the precision and reliability of underground buried object detection under the condition of finite aperture elastic waves are remarkably improved.

The application provides a fracturing fracture real-time monitoring method and system based on time-varying electric field dynamic data, and specifically, time-varying electric field data sequences collected by electric field sensors arranged in a fracturing working area are acquired; for each monitoring time step k in the electric field data sequences, the following sequential Bayesian inversion is performed: a. based on samples of a fracture state parameter posterior distribution of a time step k-1, a state parameter prior distribution of a time step k is constructed by using kernel density estimation; b. a target posterior probability density function composed of a prior distribution, a likelihood function and a physical regularization term is established; c. in Markov chain Monte Carlo iteration, the target posterior probability density function is sampled; d. after iteration, a sample set output by the Markov chain is taken as a fracture state parameter posterior distribution of the time step k, and fracture three-dimensional geometric morphology and distribution information is extracted from the sample set.

This invention discloses a seismic inversion method for inclined fracture parameters and brittleness indicator factors, comprising the following steps: (1) calculating Young's modulus and brittleness indicator factors; calculating two inclined fracture parameters using fracture density and fracture dip angle; performing interpolation and extrapolation based on seismic stratigraphic data and well logging data to obtain an initial model of Young's modulus, brittleness indicator factors, density, fracture density, and two inclined fracture parameters; (2) extracting azimuth angle seismic wavelets; (3) combining azimuth partial angle superimposed seismic data, azimuth angle seismic wavelets, and the initial model, obtaining Young's modulus, brittleness indicator factors, density, fracture density, and two inclined fracture parameters through Bayesian inversion; calculating the fracture dip angle based on the inverted two inclined fracture parameters. This invention can provide stable and reliable prediction results for brittleness indicator factors, fracture density, and fracture dip angle, which is helpful for conducting seismic characterization of brittleness and inclined fractures in shale gas reservoirs.

The invention relates to the technical field of vehicle-mounted mobile meteorological detection, in particular to a vehicle-mounted mobile meteorological detection system and method. The method comprises the following steps: activating a laser radar system of a vehicle-mounted end to collect back scattering signals of a target air mass corresponding to each collection point on a voyage route, performing inversion based on the back scattering signals to obtain an extinction coefficient of the target air mass, and determining the boundary of the target air mass based on the variation of the extinction coefficient of the target air mass on the voyage route; determining a wind field vertical profile of the target air mass based on the heterodyne spectrum; and obtaining a posterior failure probability of the moving trend of the target air mass based on Bayesian inversion analysis, and predicting the moving trend of the target air mass based on the posterior failure probability. According to the method, the air mass boundary can be accurately determined, the height of the vertical profile of the wind field is calculated, the air mass moving trend is analyzed and predicted through Bayesian inversion, and the accuracy of air mass moving trend prediction is improved.

This invention relates to the field of numerical simulation and computational technology for seismic wave propagation, and discloses a method and apparatus for generating seismic ground motions based on target spectra and Green's functions. It aims to address the difficulty in achieving a balance between physical consistency, computational efficiency, and spectral compatibility in existing methods. The scheme mainly includes: preprocessing environmental noise records and extracting empirical Green's functions through unsupervised clustering; constructing a spatially continuous propagation tensor by performing depth correction and gradient interpolation based on surface wave eigenfunctions; discretizing the kinematically finite fault model into sub-source units and generating long-period seismic ground motions including path effects using propagation tensor convolution; optimizing source parameters through Bayesian inversion using the GMPE target spectrum as a constraint; and finally generating high-frequency components and fusing them with the long-period waveform to obtain a broadband seismic ground motion time history. This invention achieves a unification of physical propagation mechanisms and statistical spectral constraints, significantly improving computational efficiency while ensuring spectral compatibility, and is particularly suitable for basin areas.

The application discloses a dam monitoring analysis method and system based on a superconducting magnetic quantum sensor, relates to the technical field of dam safety monitoring and intelligent analysis, and comprises the following steps: constructing a FOC-DFT signal processing combined with a DPD cost function to realize high-resolution positioning of an abnormal source, enhance weak signal extraction and noise resistance, realizing independent extraction of stress, seepage and crack characteristics by combining spatial clustering with PICA blind source decomposition, improving the resolution capability for multi-physical coupling effects, realizing inversion calculation of stress, seepage and magnetic permeability parameters by combining a multi-physical field coupling model with sparse Bayesian inversion and compressed sensing, improving the accuracy of abnormal mechanism analysis, and realizing trend prediction and risk assessment of dam hidden dangers by combining LSTM time series modeling with a comprehensive abnormal scoring mechanism, and improving the foresight and intelligent level of early warning.

The invention provides a geopolymer coating and steel bonding strength reliability modeling method, and relates to the technical field of steel bonding strength reliability analys.The geopolymer coating and steel bonding strength reliability modeling method includes the steps that through full-process data driving and probability modeling, a system collects multi-source influence factor data and constructs joint probability distribution, and the mapping relation between constitutive parameters and influence factors is introduced into a constitutive model; multi-factor self-adaption and space coupling of structural performance are achieved; a Markov degradation model is combined, the time sequence degradation process of the steel bonding strength in the service period is dynamically described, a Bayesian inversion method is further introduced to achieve dynamic updating of parameter distribution, and the probability that the structure meets the safety standard is quantified through reliability integration. According to the overall scheme, the durability and safety of the structure under the complex actual working condition can be truly reflected, the scientificity and coverage of reliability evaluation under the long-term service environment are effectively improved, and a quantifiable traceable decision basis can be provided for engineering design, acceptance and operation and maintenance management.

The invention discloses an electric shock combined gas-bearing prediction method, electronic equipment, a storage medium and a device. The method comprises the following steps: constructing a rock physical model based on a rock core data test result, and establishing a relationship among an elastic parameter, an electrical parameter, mineral content, porosity and gas saturation; carrying out pre-stack inversion based on the logging data and the seismic data to obtain elastic parameter data; acquiring electrical parameter data based on the electrical prospecting data; bayesian inversion is carried out based on the rock physical model, the elastic parameter data and the electrical parameter data, and gas-bearing prediction is completed. According to the method, the electrical method and the seismic multi-domain data are combined as input, compared with gas-bearing prediction by independently using the seismic data, the multiplicity of prediction results is obviously reduced, and the gas-bearing prediction precision is improved.

The application relates to the technical field of biological signal processing, and discloses a multi-modal physiological signal coupling analysis method, a system, a terminal and a storage medium. The method comprises the following steps: synchronously collecting multi-modal physiological signals of a subject when the subject performs a specific experimental paradigm; constructing a multi-modal dynamic causal model comprising a neuron dynamics model and an observation model corresponding to each signal; adopting a staged Bayesian inversion strategy to perform parameter estimation, fixing a first type of signal parameter to invert a second type of signal related parameter, fixing the inverted parameter to invert the first type of signal parameter, and obtaining a joint posterior distribution; and finally generating a biomarker of the subject based on the model parameters obtained through inversion. Through the staged inversion strategy, the technical problems of high computational complexity and unstable parameter estimation caused by the large time scale difference of multi-modal data and the large number of model parameters are effectively solved, and the biomarker can be generated and used for clinical motor function evaluation.

The application discloses a kind of oil-water interface seismic prediction method, equipment and storage medium, belong to oil and gas exploration technical field.The method includes the following steps: S1.Based on well logging data, the intersection interpretation template of longitudinal wave impedance and shear wave impedance curve of reservoir actual state and water-saturated state is established;S2.Based on the prestack bayesian inversion of bayesian sparse inversion theory and model soft constraint, the inversion result of longitudinal wave impedance and shear wave impedance in study area is obtained;S3.According to the intersection interpretation template, the inversion result of longitudinal wave impedance and shear wave impedance is predicted for the distribution of water-bearing layer in study area;S4.The time-depth relationship that meets the depth requirement is established, and the time-depth conversion is carried out for the distribution of water-bearing layer in study area, and the shallowest depth position of water-bearing layer is the position of oil-water interface.The application can qualitatively-semi-quantitatively predict oil-water interface, and can estimate oil-water interface according to a small amount of well data in early exploration, to provide technical support for next step exploration of oil field.

The invention provides a pre-stack seismic inversion method, device, equipment and product based on multi-source data, and the method comprises the steps: obtaining target multi-source data of an inversion research region, the target multi-source data is obtained according to the preprocessed initial multi-source data, and the target multi-source data comprises a logging elastic parameter trend, seismic attributes, geological prior information and horizon constraints; according to a Bayesian hierarchical modeling algorithm, hierarchical fusion is carried out on the target multi-source data, a non-stationary low-frequency model of elastic parameters is constructed, and statistical characteristics of the non-stationary low-frequency model change spatially according to horizon constraints; according to the non-stationary low-frequency model, generating an elastic parameter change trend matched with the inversion research area; and inputting the elastic parameter change trend into a Bayesian inversion framework to complete pre-stack seismic inversion, thereby improving reservoir prediction precision and reliability under complex geological conditions.

The application discloses a post-earthquake railway structure state evaluation method and system based on multi-source data fusion, which comprises the following steps: constructing a finite element model according to the structure of a regional railway network, generating a simulation data set, training a physical information neural network model, and forming a proxy model library; collecting multi-source observation data, inverting the complete posterior probability distribution of the damage parameters according to the Bayesian theory, the structure damage parameters of the physical information neural network proxy model and the multi-source observation data; extracting the statistical characteristics of the damage parameters from the posterior probability distribution, probabilistically grading the damage degree of the structure, generating driving constraint suggestions according to the damage probability grading and combining the specifications, and reducing the Bayesian inversion time from several days to several hours through the physical information neural network proxy model, thereby improving the evaluation efficiency; the output of the Bayesian method is a probability distribution, which clearly shows the uncertainty range of the evaluation result and quantifies the uncertainty.

The invention discloses a complex stratum exploration method and device based on geological radar and drilling data fusion, and relates to the technical field of geological exploration, and the method comprises the steps: collecting geological radar waveform data and acoustic logging data, obtaining the prior geological information of a work area, carrying out the uncertainty quantification processing, and generating posterior probability distribution; performing Bayesian inversion on the posterior probability distribution and the prior geological information to generate posterior statistics, and generating an interpretation confidence cloud picture based on the posterior statistics; and constructing a spatial uncertainty weighting matrix by using the interpretation confidence cloud atlas, fusing the anisotropy parameters at the drill hole and the anisotropy characteristics of the geological radar data, establishing anisotropy constraint terms of the geological radar data, and generating an anisotropy full-waveform inversion target function. According to the method, fusion of different types of data and quantification of space-time uncertainty are realized, and the underground structure prediction precision and the model reliability are improved.

The invention provides a geothermal resource comprehensive geophysical exploration method, which comprises the following steps of: acquiring surface thermal anomaly data, and generating a thermal anomaly distribution diagram; collecting regional construction data, and obtaining a fault zone spatial distribution result; micro-crack dynamic signals are collected, and a real-time positioning result is obtained; high-temperature logging data in a well are collected, and a thermal reservoir porosity distribution three-dimensional image is generated. And constructing a thermal reservoir temperature field, porosity and permeability three-dimensional joint distribution model, and outputting an exploration result report. According to the method, multiple types of data are integrated, a multi-parameter continuum data field under a unified coordinate system is constructed, cross gradient constraint inversion and generative adversarial network simulation are combined, an underground medium physical parameter space coupling relation is described, and the problem that a traditional method is insufficient in resolution is solved; geological prior knowledge is embedded into a Bayesian inversion framework, inversion multiplicity is reduced, the physical rationality of an imaging result is improved, and finally, full-chain high-precision exploration capability is formed through visualization, parameter statistics and reserve calculation.

This application provides a dynamic simulation system and method for coupled soil organic carbon and inorganic carbon. The system utilizes an internal observation data input layer, a mechanistic model layer with a coupled carbon model deployed in the system, a Bayesian inversion layer, and a posterior analysis and application layer. The coupled carbon model is used to describe the co-evolution process of organic carbon and inorganic carbon. Thus, the system provides an automated and reproducible modeling framework from observation → mechanistic constraints → posterior learning → future prediction → uncertainty quantification. It realizes a closed-loop process of parameter estimation, mechanism analysis, and scenario prediction of the coupled carbon model, thereby obtaining a carbon change prediction system for experimental sites with mechanistic interpretability and risk quantification capabilities, which can improve the prediction accuracy and decision interpretability of carbon change.

The invention discloses a dam monitoring analysis method and system based on a superconducting magnetic quantum sensor, and relates to the technical field of dam safety monitoring and intelligent analysis, and the method comprises the steps: combining FOC-DFT signal processing with DPD cost function construction, achieving the high-resolution positioning of an abnormal source, enhancing the weak signal extraction and anti-noise performance, and achieving the high-resolution positioning of the abnormal source. Spatial clustering is combined with PICA blind source decomposition, independent extraction of stress, seepage and fracture characteristics is achieved, and the resolution capacity for the multi-physical coupling effect is improved; the multi-physical field coupling model is combined with sparse Bayesian inversion and compressed sensing, so that inversion calculation of parameters such as stress, seepage and magnetic conductivity is realized, and the accuracy of abnormal mechanism analysis is improved; through combination of LSTM time sequence modeling and a comprehensive abnormal scoring mechanism, trend prediction and risk assessment of dam hidden dangers are realized, and the perspectiveness and the intelligent level of early warning are improved.

The invention relates to the technical field of coal mine safety engineering and geological disaster prevention and control, and discloses a complex coal seam group gas occurrence dynamic reconstruction method based on a spatio-temporal evolution field theory, which comprises the following steps: S1, constructing a multi-scale geological structure evolution basic model; s2, establishing a dual-medium gas occurrence parameter spatio-temporal evolution equation; s3, carrying out multi-source data dynamic correction and multi-scale Bayesian inversion reconstruction; wherein the step S1 provides a permeability distribution model and geological constraints for the step S2, the step S2 provides a forward modeling core framework for the step S3, and the step S3 reversely corrects evolution equation parameters of the step S2 through real-time data to form closed-loop linkage. According to the method, high-precision and real-time dynamic reconstruction of the gas occurrence state of the coal seam containing the complex structure is realized, and the precise decision-making capability of mine gas disaster prevention and control is effectively improved.

The invention discloses an ancient stratum earth-moon system parameter reconstruction method and system based on Bayesian inversion. The method comprises the following steps: acquiring a cycle indication data sequence of a pre-selected layer section of a target ancient stratum, and preprocessing the cycle indication data sequence into an equal-interval sampling sequence; determining a candidate deposition rate range and candidate frequency bands of eccentricity-related periodic components and age-related periodic components based on the sequence; prior distribution at least including an earth axis age difference constant k is constructed, and a TimeOpt objective function is established based on an eccentricity rate to age difference amplitude modulation relation; performing Bayesian inversion by adopting Markov chain Monte Carlo analysis to obtain posterior distribution of k; and inputting the posterior distribution into a preset earth-moon dynamics conversion model to calculate and output LOD and / or earth-moon distance EMD. According to the method, process calculation is formed through modulation constraint and probability inversion, depth-time parameters are quantitatively reconstructed, uncertainty is represented, dependence on scarce geological records and subjective matching is reduced, and result rechecking performance is enhanced.

The invention provides a reservoir prediction large sample data set construction method and system, and relates to the technical field of geophysical exploration and reservoir evaluation, and the method comprises the following steps: obtaining multi-dimensional parameters of reservoir rock samples, and carrying out the preprocessing to obtain a sample parameter data set; screening the rock physical model by adopting a machine learning algorithm to obtain a first rock physical model; performing preliminary optimization on the first rock physical model to obtain a second rock physical model; performing Bayesian inversion based on the sample parameter data set and the second rock physical model to obtain an inversion result; calculating a model prediction deviation based on an inversion result, and iteratively optimizing the second rock physical model to obtain a third rock physical model; and geological constraint conditions are defined, and a reservoir prediction large sample data set is constructed in combination with the sample parameter data set and the third rock physical model. The generated samples are close to actual geological conditions, the availability is high, the problem of insufficient samples is effectively solved, and key support is provided for intelligent evaluation of the reservoir.

The invention relates to the technical field of water environment prediction, in particular to a water quality prediction method, device and equipment under incomplete information and a medium, and the method comprises the steps: obtaining data and monitoring data of a water quality monitoring station in a target area; the method comprises the following steps: preprocessing data and monitoring data, generating a data set according to preprocessed multi-source data, training a pre-constructed water quality prediction model by using the data set, randomly generating multiple groups of network structures of a water quality preset model in the training process, determining a target network structure from the multiple groups of network structures according to the training structure, and predicting the target network structure according to the target network structure. Under the condition that part of monitoring data is missing, correcting the model state and model parameters of the water quality prediction model based on data assimilation and Bayesian inversion; and performing water quality prediction on the target area by using the trained water quality prediction model. Therefore, the problems of low precision, poor stability and the like of a related technology water quality prediction method under incomplete information are solved.

The invention belongs to the technical field of sensors and measurement, and provides a deep and thick covering layer multi-parameter mutual feedback type in-hole testing device and method based on spatial variability, and the main scheme is as follows: obtaining shear wave velocity, conical tip resistance and lateral pressure modulus which are synchronously measured from a point-line multi-channel data acquisition mother board through a DSP chip; and operating a Bayesian inversion model based on shear wave velocity-conical tip resistance-lateral pressure modulus, correcting the lateral pressure modulus in real time by taking the shear wave velocity as prior information and the conical tip resistance as observation data to obtain a corrected value of the lateral pressure modulus, recording space coordinates during measurement each time, and performing edge calculation in continuous different depth tests to obtain the lateral pressure modulus. The spatial variability is quantized into a covariance matrix that varies with the test depth. According to the invention, the comprehensive energy consumption during the in-hole test can be reduced, and the accuracy and the test efficiency of the in-hole test can be improved.

The invention discloses a VTI medium fluid parameter Bayesian inversion method based on independent prior information. According to the method, independent prior information between to-be-inverted fluid parameters (fluid factors, shear modulus and density) and anisotropy parameters () is extracted and applied to a Bayesian inversion technology, and finally, a final inversion result is obtained through inverse independent prior information transformation. According to the technology, the problem of coupling of conventional fluid parameter inversion results is solved, and a fluid parameter inversion result more fitting a real value can be obtained.

The invention discloses a microseismic-seepage coupled real-time inversion method for fracture evolution of a mine water sealing layer. Microseismic and seepage data are collected in real time through the three-dimensional microseismic monitoring sensor array and the seepage monitoring device; establishing a three-dimensional seepage numerical model, and constructing a microseismic-seepage bidirectional coupling mechanism; a Bayesian inversion algorithm is adopted to invert fracture parameters in real time, and the model is corrected; early warning is triggered according to the permeability increment, the hydraulic opening sudden increase rate and the like, and closed-loop feedback is formed; and generating a real-time distribution map, an evolution curve and an early warning report guidance project. The three-dimensional micro-seismic monitoring sensor array comprises a DAS system and a short-period geophone, and a sliding time window spectrum analysis method is used for waveform inversion. The Bayesian inversion algorithm is combined with a Markov chain Monte Carlo method. According to the method, the independent simulation defect is overcome, the model prediction error is reduced by 40%, and real-time accurate inversion of fracture evolution and early warning of the instability risk are achieved.