22 results about "Variogram" patented technology

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 tunnel geological information modeling and dynamic updating method and system. The method comprises the following steps: firstly, collecting drilling data before construction and stratum information exposed by a tunnel face in a construction process, defining a lithology category set after standardization processing, and converting the lithology category set into an indication variable; empirical semi-variance is calculated based on the standardized data set, and variation parameters are obtained through variation function fitting; determining a neighborhood radius according to the variation parameter, and constructing an initial geologic model by adopting an indicating Kriging method; in the tunneling process, newly revealed stratum information is fused in an incremental mode, variation parameters are adjusted in a self-adaptive mode, a geologic model is updated, and meanwhile uncertainty quantification is conducted; and finally, determining the most probable lithology category of each spatial position based on the updated geologic model, and generating a lithology distribution diagram. According to the method, lithology category probability prediction and geologic model dynamic updating are realized, tunnel geologic spatial features can be accurately described, prediction uncertainty is quantified, and prediction precision and reliability are improved.

This invention discloses a method, apparatus, equipment, and storage medium for estimating geostress parameters in the longitudinal profile of a deep-buried, long tunnel, relating to the field of geological surveying technology. The method treats the tunnel geostress field as a random field and the measured geostress as several realizations of the random field. After obtaining the measured values ​​of geostress parameters from multiple points on the tunnel's longitudinal profile, the method first uses these measured data to fit multiple experimental variogram models to obtain model coefficients. Then, through error analysis, the optimal model is selected. Finally, using this optimal model, spatial interpolation calculations of geostress are performed to obtain the geostress results along the tunnel axis. This method eliminates the need for detailed and accurate geological data and soil parameters, and can quickly and efficiently obtain the geostress distribution results along the tunnel axis that meet the requirements and accuracy of tunnel engineering demonstration and design stages, using only a small amount of measured data from a few points. This shortens the required time and has significant theoretical and practical value.

The invention belongs to the field of oil and gas field exploration and development, and discloses a numerical reservoir model optimization and history fitting method and related device.The method comprises the steps that firstly, geological exploration data are utilized, a spatial clustering algorithm is adopted to automatically determine the local updating range of a structural model, and structural layers and stratums are optimized through spatial interpolation; outputting an optimized construction model and uncertainty measurement; based on the model and logging lithofacies attribute data, adaptively identifying a lithofacies updating key area and variation function parameters by applying a machine learning algorithm, and performing lithofacies simulation to output an optimized lithofacies model; combining the porosity, permeability and saturation attribute data, adaptively determining an attribute optimization range and variation function parameters by adopting a probabilistic neural network, and outputting an optimized attribute model through phase control attribute modeling; a three-dimensional oil-water two-phase black oil seepage model is constructed, a global optimization algorithm is adopted to automatically adjust parameters based on a target function, simulation data is calculated until convergence, optimal parameters are output to complete historical fitting, and efficient and reliable intelligent support is provided for complex oil reservoir development.

The invention discloses a millimeter wave radar point cloud generation method with freezing mechanism static filtering and local interpolation angle measurement. According to the method, exponential moving average (EMA) static clutter suppression with a freezing mechanism is carried out on a distance-Doppler unit, and a smoothing coefficient is temporarily improved to freeze background update when a suspected human body scattering target is detected, so that a slow target is prevented from being filtered out; and calculating a coarse grid spatial spectrum for the candidate units by adopting adaptive diagonal loading two-dimensional minimum variance undistorted response (MVDR), reconstructing a fine grid spatial spectrum in a peak neighborhood based on local Kriging interpolation of a Gaussian variation function so as to obtain a high-resolution azimuth angle and a pitch angle, and finally generating a three-dimensional point cloud in combination with the distance. Compared with the prior art, the method has the advantages that clutter suppression robustness is improved and false alarm and leak detection are reduced under the complex indoor slowly-changing background, and meanwhile, the angle measurement resolution is improved on the premise that the calculated amount is controlled through local interpolation.

The application provides a differential pressure transmitter comprehensive compensation method based on Kriging interpolation and cubic spline interpolation, which comprises the following steps: measuring the calibration output voltage of a differential pressure transmitter at multiple temperature calibration points, static pressure calibration points and differential pressure calibration points to obtain data calibration points; fitting a variogram function theoretical model by using Kriging interpolation and obtaining a compensation matrix; establishing an output voltage model by using cubic spline interpolation; when the measured value of the ambient temperature is a temperature calibration point, the measured value of the static pressure is directly brought into the output voltage model to obtain a theoretical voltage, three calibration output voltages adjacent to the theoretical voltage are selected, a differential pressure function relationship is established by using cubic spline interpolation, and the measured output voltage is brought into the differential pressure function relationship to obtain a to-be-measured differential pressure; when the measured value of the ambient temperature is not a temperature calibration point, a compensation coefficient is determined by using the variogram function theoretical model, and the to-be-measured differential pressure is calculated by using the compensation coefficient. The application adopts cubic spline interpolation at the modeling temperature and adopts Kriging interpolation at the non-modeling temperature, so that the algorithm with large calculation amount can be used to improve the compensation precision, the repeated interpolation at each temperature and static pressure interval is avoided, the workload is reduced, and the real-time performance of the compensation is improved.

The invention belongs to the field of intelligent monitoring, and discloses an active and passive fusion clean room environment intelligent monitoring method, which constructs an active and passive fusion monitoring system based on covariable statistics. A two-parameter variation function is established by introducing real-time wind field difference as a key covariable, and the defect that a traditional model ignores flow field dynamics is overcome; a comprehensive blind area guiding index is constructed by using a deviation between a basic information field generated by a fixed sensor and a correction information field formed by robot fusion data and combining an inherent coverage risk and a joint space disparity degree, and an absolute monitoring blind area is accurately identified; therefore, the control mode of fixed route inspection to blind area driving type active detection is converted. According to the method, on the premise that the hardware cost is not increased, the full-domain monitoring coverage rate, precision and sudden pollution response speed of the clean room are remarkably improved.

Embodiments of the present application disclose a method and device for establishing an ultra-shallow velocity model, electronic equipment and a storage medium. The method comprises: obtaining micro-logging data of a target area, wherein the micro-logging data comprises at least one measuring point and a corresponding velocity value of each measuring point, and the velocity value is obtained by measuring the velocity at the corresponding measuring point; for each analysis direction in at least one preset analysis direction, analyzing the micro-logging data in the analysis direction to obtain a function parameter, and determining a variogram corresponding to the analysis direction according to the function parameter; performing interpolation processing on the micro-logging data according to the variogram corresponding to each analysis direction, to obtain an interpolation result, and establishing an ultra-shallow velocity model of the target area according to the interpolation result. The technical scheme of the embodiments of the present application can be used to establish an accurate ultra-shallow velocity model.

The application provides a tunnel face information-based dynamic geological model updating and predicting method, which combines the tunnel face grid point data obtained in the tunnel excavation process, adopts Kriging interpolation and variogram fitting technology, and constructs and dynamically updates the geological model. The method comprises the following steps: collecting the face data, cleaning and standardizing the data, and extracting the tunnel starting end face and the ending end face; generating an initial geological model by using a three-dimensional variogram model and Kriging interpolation; integrating the newly added face data, dynamically adjusting the model parameters, and updating the geological distribution; and finally displaying the dynamically updated geological model through three-dimensional visualization technology. The application solves the problem that the traditional static model is difficult to adapt to the dynamic changes of geological conditions in the excavation process, can optimize the geological model in real time, provides accurate and reliable geological information support for tunnel construction, and significantly improves the construction efficiency and safety.

The invention relates to the technical field of geological exploration, and discloses a geological exploration drill hole number optimization method and device, equipment and a storage medium, and the method comprises the steps: obtaining the drill hole data of an existing drill hole in a target exploration region, including space coordinate data and geological attribute data, and constructing a variation function model representing the geological attribute space continuity; according to the method, the number of the drill holes is optimized, interpolation is carried out based on the model, the standard deviation of each interpolation point is calculated, the number of the drill holes is optimized according to the distribution condition of the standard deviation, the number of unnecessary drill holes can be reasonably reduced, or the drill holes are added for an area with high data uncertainty, and optimal configuration of the number of the drill holes is achieved. By means of the optimal configuration, exploration work is more efficient, and time and cost consumption caused by blind drilling is avoided.

The application discloses a tunnel geological information modeling and dynamic updating method and system. The method first collects drilling data before construction and stratum information exposed in a working face during construction, defines a lithology category set and converts the lithology category set into an indicator variable after standardization processing; calculates an empirical semi-variance based on the standardized data set, obtains a variation parameter through variogram fitting; determines a neighborhood radius according to the variation parameter, and constructs an initial geological model by using an indicator Kriging method; in the tunneling process, newly exposed stratum information is incrementally integrated, the variation parameter is adaptively adjusted, the geological model is updated, and uncertainty quantification is simultaneously performed; finally, the most possible lithology category of each spatial position is determined based on the updated geological model, and a lithology distribution map is generated. The application realizes lithology category probability prediction and geological model dynamic updating, can accurately depict tunnel geological spatial characteristics, quantitatively predict uncertainty, and improves prediction accuracy and reliability.

This invention discloses an intelligent monitoring method for vegetation in spoil heaps based on optimal layout, belonging to the field of open-pit mine ecological restoration monitoring technology. This invention employs spatial variogram and simulated annealing algorithms to achieve optimal monitoring point layout; it automatically acquires multi-angle images and videos using adjustable supports, and performs keyframe extraction, multi-angle registration, and adaptive denoising preprocessing; the images are input into a lightweight dual-stream feature fusion network, and vegetation areas are accurately segmented through RGB and NDVI dual-stream attention fusion and combined loss function optimization; based on the segmentation results and environmental factors, vegetation coverage, plant height, and tiller number are automatically calculated; the time series is decomposed using STL, and the logistic growth model is integrated into the LSTM network as a physical constraint to output a growth trend prediction that conforms to biological laws. This invention achieves fully automated monitoring from layout to prediction, significantly improving the accuracy and efficiency of monitoring reclaimed grassland in spoil heaps.

The application discloses a method for predicting seismic spatial structure parameters, comprising the following steps: reading observed seismic data; defining a prior probability density function of spatial structure parameters based on a variogram type of model parameters and the number of spatial structure parameters; defining a seismic forward operator and calculating a likelihood function between the spatial structure parameters and the observed seismic data based on the seismic forward operator; and calculating a posterior solution of a posterior probability density function of the spatial structure parameters based on the prior probability density function and the likelihood function through a Monte Carlo sampling method. The application also discloses a device for predicting seismic spatial structure parameters, a computer device and a readable storage medium. The application combines the Bayesian theory with the Monte Carlo inversion method, fully utilizes the volume constraint characteristics of seismic data, and simultaneously infers the longitudinal and lateral spatial structure of the underground medium in a data-driven manner.

This invention discloses a method for predicting earthwork volume in shield tunneling construction based on Kriging interpolation and classification regression trees, relating to the field of earthwork volume prediction in shield tunneling construction. This invention solves the prediction accuracy problem of traditional Kriging interpolation in areas of local geological variation by real-time acquisition of construction parameters, geological parameters, and environmental parameters, combined with a dynamic adjustment of the variogram function and a multi-scale interpolation fusion strategy. Simultaneously, it improves the accuracy of spatial prediction by dynamically correcting the stratum interpolation results using a Bayesian update method. Furthermore, by employing a dynamic weight adjustment strategy combined with a Bayesian fusion model, the prediction model weights at different stages can be adjusted according to error feedback during construction, thereby achieving more accurate earthwork volume prediction.

The invention discloses an earth surface deformation monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of space calculation, and the method comprises the steps: obtaining discrete point data collected by a ground-based synthetic aperture radar, carrying out the preprocessing of the discrete point data, and obtaining rectangular coordinate points and corresponding observation values; and constructing a variation function model based on the rectangular coordinate points and the corresponding observation values. Adjusting neighborhood parameters according to the current data density, and constructing a mobile neighborhood for each to-be-interpolated point based on sector search. Based on the mobile neighborhood and the variation function model, spatial interpolation is carried out on the to-be-interpolated point, spatial deformation data are obtained, and the spatial deformation data are used for monitoring the earth surface deformation condition. By calculating data density and dynamically adjusting neighborhood parameters, spatial adaptive local optimization can be realized. Local high-frequency information is reserved based on distance-first neighborhood selection, local details can be enhanced, the problem that local features are weakened due to the fact that too many long-distance points are used is avoided, and high-precision deformation monitoring is achieved.

The invention provides an artificial intelligence virtual well sample expansion method based on geostatistics. The sample expansion method comprises the following steps: performing statistical analysis according to logging data to obtain prior information; a sequential indication simulation algorithm simulates lithology conforming to prior information; checking statistical parameters of the simulated lithofacies and dividing the lithofacies of the virtual well; performing sequential Gaussian simulation on the fractal phase, and simulating physical properties and elastic parameters by a sequential Gaussian co-simulation algorithm; combining each well to obtain lithology, physical property and elastic parameters of the complete well; and physical properties and elastic parameters of the simulated lithofacies are checked by using the statistical magnitude and the variation function. The accuracy of the simulation data is verified through statistical analysis, the consistency of the simulation data and actual well data is ensured, and the credibility of the virtual well sample is enhanced.

The invention relates to the technical field of environmental engineering and geoscience statistics, in particular to a soil remediation-oriented soil data visual management method and system.The method comprises the steps that S1, plot survey data are obtained and discretized to generate a three-dimensional voxel grid, constructing a three-dimensional geologic model based on the drilling lithology data, mapping a geologic lithology label to a voxel grid, and establishing a three-dimensional space data structure with stratum constraint; s2, analyzing underground water flow field data to determine a main flow direction azimuth angle of pollutant migration, and accordingly constructing an anisotropic search ellipsoid and setting variation function parameters; according to the method, the hybrid model combining machine learning trend prediction and anisotropic residual error correction is constructed, the global trend is fitted by utilizing the stratum lithology data, and the local deviation is corrected by combining the hydrodynamic factor, so that the smoothing effect of the traditional single interpolation method under the heterogeneous geological condition is improved; and the goodness of fit between a three-dimensional pollutant distribution simulation result and an actual geological environment is improved.

The invention provides a method for constructing a three-dimensional mixed geological model of a mine. The method comprises the following steps: step 1, constructing a geological database; 2, respectively constructing a stratigraphic interface model, a closed surface model and a solid model; step 301, according to the stratigraphic interface model and the closed surface model constructed in the step 2, identifying contact and interpenetration relations between geologic bodies; and step 302, on the basis of the contact and interpenetration relationship between the geologic bodies identified in the step 301, fusing the stratigraphic interface model and the closed surface model constructed in the step 2 through union set and difference set operation, and processing a conflict region of the stratigraphic interface model and the closed surface model constructed in the step 2 by adopting a Lagrange multiplier method, obtaining a three-dimensional preliminary mixed geologic model of the mine; and step 303, on the basis of the mine three-dimensional preliminary mixed geologic model obtained in the step 302, constructing a variation function model by combining the sequence information of the stratigraphic interface model constructed in the step 2 and the fracture density of the solid model, and obtaining a mine three-dimensional mixed geologic model. The method covers a macrostructure to a microcosmic fracture, and the integrity and reliability of a geologic model are remarkably improved.

The invention discloses a photovoltaic power generation power ultra-short-term prediction method and device based on Kriging spatial interpolation, and the method comprises the steps: firstly, calculating the relative distance and semi-variance value between stations according to the power data and latitude and longitude coordinates of real photovoltaic stations in a region, and obtaining a variation function curve through the fitting of a nonlinear least square method; then, reasonably setting the number and spatial distribution of the virtual sites, and determining longitude and latitude coordinates of the virtual sites; based on the variation function obtained through fitting, the relative distance and the semi-variance value between each virtual site and the real site are further calculated; a Kriging equation set is constructed and solved to obtain a weight coefficient of the power data of the virtual site relative to each real site, and finally the power data of the virtual site is generated in a weighted summation mode. After power data of a real site and power data of a virtual site are combined, a sliding window method is adopted to divide a sequence into input samples and output samples, and the input samples and the output samples are used for training a combined prediction model fusing a space-time diagram convolutional neural network and a self-attention mechanism.

This invention belongs to the field of oil and gas field exploration and development, and discloses a three-dimensional geological modeling method and related apparatus based on neural networks and variograms. It constructs a structured three-dimensional geological modeling workflow, defining a complete sequence of steps from data input to model output. An initial model based on neural networks and variograms is trained using preprocessed multi-source geological data to generate a parameter generation model that automatically outputs key parameters for each stage of the workflow. When executing the workflow, the parameters are invoked, and the intermediate geological model is quantitatively evaluated. If the model does not meet the standards, the parameters are fine-tuned and the process is repeated until a satisfactory three-dimensional geological model is output. This method significantly improves modeling efficiency, avoids inconsistencies caused by expert subjective differences, enhances model repeatability and objectivity, reduces labor costs, and meets the needs of high efficiency and large-scale oil and gas field exploration and development.

The application discloses a method and device for reservoir geological modeling based on a full-space variation function. The method comprises the following steps: performing grid division on single-well reservoir attribute interpretation hard data and inter-well reservoir attribute trend soft data through well site coordinates to obtain well data and divide the well data into a training data set and a test data set; training an initial full-space variation function model through cross-validation of the training data set to obtain a target full-space variation function model; judging the accuracy of the target full-space variation function model according to the test data set; in the case that the accuracy is less than or equal to a preset accuracy, continuously training the target full-space variation function model until the accuracy is greater than the preset accuracy; and in the case that the accuracy is greater than the preset accuracy, using the target full-space variation function model for target reservoir geological model construction. The application effectively improves the accuracy of reservoir geological modeling of continuous and discrete reservoir attribute spatial distribution by establishing a full-space variation function model.

The application discloses a method and system for realizing integration of geological modeling and stress field simulation, and belongs to the technical field of geological exploration and development. The method comprises the following steps: project setting and data loading, seismic interpretation and attribute analysis, structural modeling, discrete fracture network modeling and stress field simulation. The application takes a three-dimensional structural model as a unified carrier, takes stratum boundaries, fracture distribution and stratum layering as basic constraints of mechanical modeling, realizes complete matching of a geological framework and a mechanical model grid and a spatial range, adopts a fracture distance constraint and a variogram hierarchical control coupling method, distinguishes a main fracture, a secondary fracture and a surrounding rock area, sets different interpolation rules, takes lithology and a tectonic deformation zone as partition constraints, and completes spatial simulation of mechanical parameters under geological recognition constraints. The structural model provides a spatial boundary for mechanical attribute modeling, and mechanical parameter inversion results feed back and correct the geological model, so that two-way data transmission and collaborative constraints are formed.