90 results about "Kriging method" patented technology

The invention relates to a near-surface modeling method using tomography inversion of a two-step method. The near-surface modeling method specifically comprises the following steps of: (1) picking and inputting short-refraction first-break; (2) carrying out tomography inversion on the short-refraction first-break and outputting explanation results capable of reflecting fine velocity change in an extremely shallow stratum as shown in picture 2; (3) picking a big shot and recording first-break time; (4) interpolating short-refraction inversion results in combination with explanation results of a microlog in a work area by using a kriging method and building near-surface velocity body of the extremely shadow stratum; (5) meshing and constraining inversion initial-velocity modeling, replacing a near-surface velocity body at the extremely shallow stratum obtained in the last step into the shallow stratum part of the initial model generated at the big-shot first-break, replenishing the velocity loss of the extremely shallow stratum resulting from the excessive minimum offset distance of the big shot; (6) constraining generation of a weight field; and (7) inversing the near-surface velocity model by using a constrained chromatography method, picking the top interface of a high-speed layer on the basis of a velocity-depth model obtained by tomography inversion, and calculating static correcting values of a shot point and a geophone position.

A method of modifying a geological model representative of an underground reservoir is disclosed which respects average proportions of the lithologic facies imposed by a production data calibration process which has application to petroleum reservoir development. A geographical zone Z is defined within the geological model and an average proportion in zone Z allowing the production data to be calibrated is determined for k facies, with an optimization process. The proportions of these facies are modified using a block indicator cokriging method constrained by the average proportions to be respected. A new geological model constrained by the modified facies proportions is simulated and the development of the underground medium is optimized by the simulated model.

The invention discloses a method for reflecting natural erosion degree of regional soil. The method comprises the following steps pf: inputting each soil erosion factor parameter in a universal geographic information system (GIS) and obtaining erosion modulus based on a universal soil loss (USLE) model; and interpolating the erosion modulus as a sample value by the krige method in the presence of winsurfer software interface to obtain an erosion modulus contour map of the regional soil. The method can directly reflect the spatial change of the erosion intensity of the regional soil and provide rapid and reliable erosion modulus for regional soil and water conservation planning and water and soil conservation, thus multiplying the acquisition and updating efficiency and reducing the fieldwork cost.

The invention discloses a mineralizing abnormal extraction method based on a multifractal Krige method. The mineralizing abnormal extraction method is characterized by comprising the following steps of: performing the inspection of normal distribution on an exploration geochemical data set to be analyzed, calculating an experimental variance function value for the data set which submits to the normal distribution, and fitting the experimental variance function value by a theoretical variance function model to acquire relevant parameters in the theoretical variance function model; and solving an abnormal lower limit value effectively according to a multifractal Krige interpolation result on the basis of establishing local high-value information and highlighting weak anomaly to acquire a reliable mineralizing abnormal area for prospecting and exploration.

The invention relates to a design method for a multi-target cooperative sampling scheme of randomly-distributed geographic elements, which includes the steps: 1 selecting multiple geographic elements with random distribution characteristics as sampling targets, and acquiring pre-sampling sample point data of the targets; 2 exploring spatial variation structures of the pre-sampling data by an ordinary Kriging method, fitting and generating a theory semi-variable function of each target, and obtaining a spatial distribution integral of all the targets by a Gaussian sequential random simulation method according to the theory semi-variable functions and the pre-sampling sample point data in the step 1; 3 computing the minimum cooperative sample size according to the precision requirements of the targets and prior variance, and setting a cooperative particle swarm optimization parameter; and 4 optimizing a current pre-sampling sample point scheme by means of cooperative particle swarm optimization, so that the multi-target cooperative sampling scheme is obtained. By the aid of the method, on-site sampling cost can be saved, self-organization and optimization during distribution of thesampling sample points are improved, and the design efficiency of the multi-target cooperative sampling scheme of the geographic elements is enhanced.

The invention discloses an extra-high arch dam deformation spatio-temporal series prediction method based on spatio-temporal integration. The method comprises following steps: as measured data alwayshas a random error item, when a dam sensor is used for monitoring data, a spatio-temporal process of the dam sensor is assumed to be decomposed into two parts: determinacy spatio-temporal change and small-scale random error change after the deterministic trend is removed, wherein the small-scale random error change after the deterministic trend is expected to be zero. A simple BP neural network isused for fitting a whole spatio-temporal trend; the residual item is acquired after the whole spatio-temporal trend is removed, the residual item is subjected to linear unbiased estimation, and a spatio-temporal Kriging method is selected for fitting a local spatio-temporal trend; a threshold circulation neural network is introduced to predict a time sequence of each measure point of the dam, a deformation value of the related measure point is predicted, the deformation value predicted by the threshold circulation neural network is compared with the deformation value predicted by the BP neural network, and if the predicted value of the threshold network is more accurate, the river direction displacement data after prediction is used as training data for optimizing the BP network.

The invention relates to an evaluation method of carbonate gas pool reserve utilization conditions. According to the evaluation method, logging information is fully utilized and subjected to multi-element combination to obtain percolation characteristic parameters which are used for evaluating gas pool reserve utilization conditions in a quantitative mode, and the parameters fully consider the lithology, shale content, gas content, permeability, pore structure types and pore structure coefficients of reservoir stratums and can accurately reflect the difference between inter-layer and in-layer percolation ability of carbonate gas pool small layers and the reserve utilization degree. The random modeling means is effectively utilized to set up a three-dimensional percolation characteristic parameter continuous geologic model, so that percolation characteristic parameter inter-well prediction is carried out, the smoothing effect of a traditional interpolation method (like the Kriging method) to parameters is overcome, the anisotropism of carbonate gas pool reservoirs is really reflected, technical support is provided for effective utilization of gas pool reserves, the continuous stable yield foundation of a gas pool is enhanced, and the purpose of improving the ultimate recovery factor of the gas pool is achieved.

The invention discloses a geostatistics-based wind power station wind speed spatio-temporal data modeling method. The method comprises the following steps: 1, constructing a space structure matrix of a wind power station according to geographical location information of fans, space convariance function of wind power station wind speed and variation function of wind power station wind speed to represent the spatial correlation of input wind speed of each fan; 2, performing layered modeling on the input wind speed of each fan by a universal Kriging method and a Bayesian algorithm and estimating model parameters by adopting Gibbs sampling; and 3, predicting the forward P steps of the wind speed of each fan to acquire simulation samples of P step forward prediction distribution of the wind speed of each fan, and sampling and averaging to acquire the optimal prediction result of the wind speed of each fan, wherein P is more than or equal to 1. By the method, the time and space correlation of wind speed data of different fan positions is comprehensively analyzed based on the physical characteristics of wind, so that a more accurate prediction model is built for the whole wind power station, and the prediction result is better than that of the traditional method.

The invention discloses a soil property interpolation estimation method. The method comprises the following steps of: S1, generating prior probability distribution of soil property to-be-measured variables by utilizing a Kriging method; S2, generating soft data by utilizing a correlational relationship between environment factors and prediction points; S3, updating the probability distribution of the soil property to-be-measured variables by utilizing a BME method; and S4, drawing a soil space property map by utilizing the probability distribution of soil the property to-be-measured variables. According to the method, the Kriging method which is simple to operate and mature and a Bayesian maximum entropy method which is accurate and reasonable are combined, so that the interpolation process is more simple and convenient and interpolation result is more reasonable.

The invention discloses a quality control method of a ground temperature observation material. The method includes the following steps: firstly acquiring temperature data of a meteorological station within an object region, then resolving an experimental variance function value, and using an arctan function that is proposed to carry out fitting on the experimental variance function value, then resolving the weight of each neighboring meteorological station to an object meteorological station by using a Kriging equation set, further using gradient Kriging method to estimate the temperature values of respective meteorological station and evaluate the precision of the temperature values, and finally conduct threshold examination on a temperature estimation value and an original observation value so as to determine whether the data is right or not and correct the data accordingly. According to the invention, the method addresses the problems of insufficient consideration of impact of space distribution, temperature space relativity and variance thereof and landform on temperature of current quality control methods.

The invention discloses a method for establishing underwater landform digital elevation based on a Kriging method, which mainly aims to solve the problem that bigger water depth errors exist in some position points in a digital elevation establishing process when the water depth value change amplitude of the water depth data is large. The method comprises the following steps: loading initial water depth data; establishing underwater irregular triangular network landform according to the initial water depth data; performing space optimization on the irregular triangular network landform; analyzing the variable structure of the initial water depth data; performing the water depth Kriging interpolation process of the irregular triangulation network landform; performing error compensation on the water depth interpolation result. Compared with the method for directly generating regular grid digital elevation through initial water depth data interpolation, the method provided by the invention is used for selecting Kriging interpolation data to generate regular grid digital elevation through the topographic inequality of the irregular triangulation network landform, so that the problem of bigger regular grid digital elevation errors caused by large water depth value change amplitude of water depth data is solved.

The invention discloses an optimization model for hydrologic station networks based on the combination of a kriging method and an information entropy theory. According to the method, with maximization of information total quantity and minimization of estimation errors of the station networks as guidance, theoretical bases, namely NI-KSE standards, are put forward for the optimal arrangement of stations, and potential station networks are re-planned and rearranged. The kriging method and the information entropy theory are combined together, the potential hydrologic station networks are systematically evaluated, and therefore the defects are well overcome that the density in the station networks is too high, the local errors are too big, and the information redundancy is unreasonable; the rationality of information total quantity and spatial distribution between the stations is quantitatively described, the station networks are objectively evaluated and optimized, and the model has the advantages of rationality and effectiveness.

A plane seismic exploration signal decomposition method based on a Kriging method belongs to the field of exploration geophysics. The method comprises the following steps: acquiring a seismic exploration signal, setting the basic lag distance and the maximum lag distance, and calculating a two-dimensional experimental variation function of a seismic attribute slice to obtain a two-dimensional variation function graph; determining ellipse information corresponding to target information from the variation function graph, extracting experimental variation function curves of long-axis and short-axis directions (respectively corresponding to primary and secondary directions of the target information), and using a theoretical model to perform one-dimensional second-order nested fitting; determining the initial parameters, including sill, long-axis range, ellipse direction and the ratio of long axis to short axis, of the two-dimensional variation function according to the parameters of the separated variation function curves of the primary and secondary directions, tuning parameters, selecting different models (spherical model, exponential model, Gauss model), and making the fitted two-dimensional variation function more consistent with a target signal experimental variation function; and determining a two-dimensional theoretical variation function, substituting the function into a Kriging interpolation equation, and decomposing to obtain the target information.

The invention relates to a rapid economic evaluation method of ionic adsorption rare earth ore. The method comprises the following steps: carrying out geological exploration to collect geological survey borehole data and borehole sampling and testing data of a target mine area and establishing an ore deposit borehole database; performing 3D ore body modeling by using the borehole data and performing grade interpolation by using a kriging method to obtain an ore deposit ore body model; performing price sensitivity analysis by acquiring a large quantity of mineral products market data and establishing a mineral product price-boundary grade calculating model; using the model to determining the boundary grade of a specific ore deposit under different market prices and then using the boundary grade to constrain the ore deposit ore body model and to delineate an ore body range, and then performing reserves calculation. According to the change of the market price and the operating cost of the enterprise, the profit and loss balance point is determined according to the basic geologic characteristics of the weathering crust and the distribution of the rare earth elements, and the boundary grade and the industrial grade are determined by comprehensively considering the profit margin and the like. further, computer technology is used for delineating the ore bodies and to calculate the reserves of resources, and providing the basis for the reasonable determination of a mining plan.

The invention discloses an aircraft system optimization method based on dynamic multi-model fusion, and belongs to the technical field of multidisciplinary optimization in aircraft design. The methodincludes the steps: fusing high-and-low-accuracy analysis models in aircraft system design by a Co-Kriging method, and replacing an original model to perform optimization design; updating and managinga Co-Kriging surrogate model by a double sampling method in the optimization process; improving local convergence ability of optimizing search through trust region sampling, improving global exploration ability of an optimizing process through predictive variance sampling, and guiding the optimizing process to rapidly converge an optimal solution. The method can overcome the shortcomings that a traditional optimization method can only process single-accuracy analysis model and is high in optimization cost, multi-source response information cannot be sufficiently utilized and the like, simulation models with different accuracies in aircraft system design can be efficiently fused and optimized, and the aircraft system optimization method is of great significance for reduction of aircraft system design optimization cost, improvement of system design performance and the like.

The invention relates to an electromagnetic spectrum map construction method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring monitoring data and a to-be-estimated point position, and acquiring path attenuation component estimation by using a trend surface fitting algorithm; subtracting the monitoring data from the path attenuation component estimation value of the corresponding position, performing position clustering on the obtained residual monitoring data, and estimating a clustering result in an optimal neighborhood by adopting a Kriging method to obtain shadow fading component estimation; and obtaining an estimation result according to the path attenuation component and the shadow fading component. Electromagnetic spectrum map construction is decomposed into estimation of path attenuation and shadow fading components, so that the electromagnetic spectrum map construction precision is effectively improved; through monitoring data clustering and optimal neighborhood selection, the calculated amount is reduced under the condition of ensuring the construction precision, and the electromagnetic spectrum map construction speed is effectively improved; and the method does not need prior information such as a radiation source and a propagation model, and has good adaptability.

The invention provides a low resistance object explaining and imaging method and system based on reflection coefficient analysis. The method comprises the steps of simulating a formation condition tobe analyzed, setting a measuring line length and an electrode distance matched with each other by using a plurality of electrode arrangement modes, building a formation model, and collecting electrical prospecting data of an area to be detected; inverting the prospecting data, finely gridding original data acquired by inversion and using a Kriging method interpolation to image the grid data, and extracting the coordinate value and resistivity value of a grid node in a longitudinal direction of the center position of a low resistance object; computing the reflection coefficient of the extracteddata and normalizing the same, building a function of an amplification index and a depth value, computing the amplification index power of the reflection coefficient to serve as an influence value, drawing a relation curve of the influence value and the depth value, according to the waveform of the relation curve and the depth coordinate of a peak value, comparing original model data, and correcting the function; and adjusting a model parameter, and repeating the above-mentioned steps until the relation curve expresses the depth value of the low resistance body interface in the formation.

The invention relates to a design method of an expansion coupling sleeve for solving stress concentration and light weight. The method mainly solves the technical problems that an existing expansion sleeve size design method is insufficient in reliability, materials are wasted and the like. The specific method comprises the following steps of 1) according to an expansion coupling sleeve transmission torque requirement, a material yield strength requirement and a light-weight minimum mass requirement, determining the design indexes as follows the minimum contact pressure p1min of the main shaftand the shaft sleeve, the maximum equivalent stress Emax of the whole machine and the minimum mass mmin; 2) determining the design parameters of the expansion sleeve according to the design indexes,and obtaining the sample data by utilizing orthogonal test design based on finite element simulation; 3) concluding the sample data, taking the design index as a function dependent variable, taking the design parameter as an independent variable, obtaining an objective function by utilizing a Kriging method, and constructing a prediction model which meets the requirements of torque, equivalent force and minimum mass; 4) obtaining a multi-objective optimization function through a simultaneous prediction model, and obtaining a final design parameter by solving a second-generation genetic algorithm (NSGA-II).

The invention relates to a preparation method and system of a supercritical pressure fluid heat exchanger. The preparation method comprises the steps of: obtaining the boundary conditions of a supercritical pressure fluid heat exchanger; adjusting a heat exchanger preliminary frame based on the boundary conditions; dividing each fluid channel along the fluid flowing direction based on the heat exchanger preliminary frame, and establishing a heat balance control model for each divided fluid channel; based on the heat balance control model, combining a Gaussian regression process and a collaborative Kriging method to construct a machine heat transfer model; determining on-way thermal parameters of flowing heat transfer of a working medium in each fluid channel according to the machine heat transfer model; determining the area of the heat exchanger according to the on-way thermal parameters; judging whether a heat exchange area reaches a heat exchange area standard or not, and if yes, preparing a supercritical pressure fluid heat exchanger according to the heat exchanger primary frame; and if not, readjusting the heat exchanger primary frame until the heat exchange area reaches the heat exchange area standard. The heat exchanger performance of the supercritical pressure fluid heat exchanger can be improved.

The invention discloses a soil organic carbon density spatial heterogeneity-considered Kriging interpolation model. According to the Kriging interpolation model, influences, on common Kriging methods, of low-spatial heterogeneity and spatial abnormal values are reduced by combining land utilization information; and according to different trend term functions, the Kriging interpolation model comprises a virtual variable regression Kriging method, a mean centralized Kriging method and a median centralized Kriging method. By utilizing the model, high-precision SOCD prediction and mapping are realized, and finally, an optimum prediction method and a prediction result thereof are selected according to model evaluation indexes. The reliability, prediction rationality, prediction precision and decision coefficient R2 of the model are remarkably improved; and meanwhile, the model realizes high-precision SOCD spatial mapping, so that significance is provided for realizing precision agriculture and improving the agricultural production efficiency.

The invention relates to a soil organic carbon content prediction method based on a random forest-ordinary Kriging method. The method comprises the following steps: firstly, determining organic carbons in a soil sample; secondly, extracting environmental factors and screening the environmental factors related to the soil organic carbon content; finally, predicting the spatial distribution of the soil organic carbon content based on a random forest-ordinary Kriging model and the related environmental factors. The method provided by the invention realizes the spatial prediction of the soil organic carbon content by means of a relationship between the soil organic carbons and the environmental factors (topography, climate and vegetation).

The present invention relates to a numerical control machine tool generalized space cutting stability prediction and optimization method, belonging to the numerical control machine tool field. Known sample information is constructed, an approximate function relation of least limit cutting depth, which is a cutting stability characteristic index, and machine tool space position coordinates is established based on the Kriging method to predict a cutting stability space evolution rule, the improved particle swarm optimization is employed to determine the processing position having the least limit cutting depth maximum value, a cutting experiment is employed to determine a machine tool easy flutter mode so as to determine a machine tool weak joint portion based on the energy distribution theory, a joint portion dynamic stiffness optimization configuration scheme is provided to improve the least limit cutting depth value, predict the cutting stability space differentiation features and enlarge the stability region selection range. The deficiency is made up that a traditional space cutting stability analysis neglects correlation characteristics of a variable in a processing space to form an optimal processing position scheme on this basis so as to obtain the optimal cutting parameter scheme of the processing space.

The invention relates to the technical field of underground water level restoration, in particular to an underground water level missing data restoration method based on geostatistics and a neural network. According to the invention, a restoration model is constructed from the aspects of space and time, and the accuracy and reliability of data missing interpolation are improved. The method comprises the following steps: dividing a spatio-temporal data set of an underground water level into a spatio-temporal sequence data set and a time sequence data set; according to the time-space sequence data set, adopting a Kriging method to interpolate missing values to carry out spatial interpolation based on geostatistics, and obtaining a restored spatial data set; establishing a time sequence missing value restoration model based on a BP neural network, and restoring missing data of the time sequence data set; carrying out training and learning according to the space restoration result, the time restoration result and the actual detection data, continuously adjusting the space-time element weight through machine learning, and constructing a mixed restoration model of the underground water level space-time sequence missing data; and repairing missing data in the underground water level space-time monitoring data set by utilizing the hybrid repair model.

The invention discloses a spatial interpolation method for long-span bridge monitoring data. The spatial interpolation method comprises the following steps: (1) spatial auto-correlation analysis on sensors of the same kind is performed based on distribution of measurement points of certain monitoring data on the long-span bridge; the spatial auto-correlation analysis is embodied by spatial variation function; (2) search of a reference evaluation point is performed by combination of real monitoring distribution points of the bridge; a four-direction search scheme is carried out by steps of dividing a plane into four quadrants based on an x-coordinate and a y-coordinate of an interpolating point, taking the length of a range a as a searching radius, searching a known sample point, which is nearest to the interpolating point, from each quadrant; (3) the spatial interpolation analysis on the stress-strain monitoring data is performed by combination of the variation function of the real monitoring distribution points of the bridge by adopting a Kriging method so as to obtain the estimated value of an unknown point. The spatial interpolation method for the long-span bridge monitoring data provided by the invention is complete in the monitoring information and relatively high in accuracy.

The invention discloses a method for predicting information on a same-position co-kriging by using a Markov model, which is used for implementing the approach to a global co-kriging method. The approach is based on a screening hypothesis of Markov. According to a screening effect hypothesis of the Markov model, the co-kriging method can be approached, and the same-position co-kriging also can implement the approach according to the Markov model. Thus, in the screening effect of the Markov model, hypothesis hard data can screen the influence on soft data at the position of the hypothesis hard data caused by other hard data beyond the position of the hypothesis hard data. So, under the condition of a plurality of variables, the same-position soft data which is closest to and most approximated to the correlation coefficients of the hard data often comprises the most predicted information. The method makes full use of the abundant soft data and can improve the accuracy of a prediction model. The method can be widely used in certain engineering and industrial fields such as geology, meteorology, mining and the like.

The invention aims at providing an electromagnetic relay quick calculation method based on a pseudo model, and belongs to the technical field of relay product performance analysis. The method comprises the steps of firstly based on an interpolation thought, constructing a user-defining function reflecting an electromagnetic relay output characteristic and a process variable as a primary function of the Kriging model; secondly, through a Latin hypercube sampling method, establishing an error function of the Kriging model, and thus constructing an electromagnetic system pseudo model based on a Kriging method; then, based on a deformation energy method, establishing a counterforce characteristic calculation pseudo model; finally, based on a numerical value method, solving a relay dynamic characteristic equation set, and completing quick calculation of electromagnetic relay dynamic characteristics. According to the electromagnetic relay quick calculation method based on the Kriging model, the calculation precision and the calculation speed are both taken into account, and the quick calculation method can be used in the field of analysis of electromagnetic relay structural optimization, stability design and the like.

The invention belongs to the technical field of structural health examination, and provides a structure damage area identification method based on frequency band weighted least squares. An input response and an output response of the structure under the action of an earthquake are divided into a plurality of frequency intervals within a certain frequency range; a Chebyshev band-pass filter is adopted to obtain time-history signals of different frequency bands, least square parameter identification is carried out on the signals of all the frequency bands, then energy of all the frequency bandsis adopted as weights, a common Kriging method is adopted to solve the weights, and weighted calculation is carried out on modal parameters. The method has the beneficial effects that (1) frequency division weighting is carried out, so that the precision is improved; and (2) the damaged area is identified through vibration mode changes, and the identification is more visual and accurate.

The invention aims to provide a gas compressor characteristic prediction method based on Kriging model optimization and a neural network. The gas compressor characteristic prediction method mainly comprises the steps that two Kriging methods are selected, three semi-variation functions are selected for each Kriging method, a Kriging equation set is solved to obtain six sets of weighting coefficients, and six prediction curved surfaces are obtained by fitting the weighting coefficients; a prediction error average value of each Kriging method is obtained by adopting an improved reserved cross validation method, and an optimal Kriging method is selected; and the characteristics of the gas compressor are identified by using an optimal Kriging method, and finally he characteristics of the gas compressor under all working conditions are predicted by using a neural network method. The method can predict, encrypt and extrapolate the characteristics of the gas compressor under the condition that the characteristic lines of the gas compressor are incomplete or sparse, and has the advantages of high precision and high calculation speed. The method is universal, and has reference significancefor characteristic prediction of impeller machinery such as an axial-flow turbine and the like.

The invention discloses a method for reserves computation, which is realized through the following steps: based on the grid method, and through the adoption of the Kriging method and the surfer gridded data, contour line data is generated through the data conversion function, computation parameters are assigned to all grid cells through the mapgis software, and the spatial overlay technology is adopted for the overlapping with the computed grid range. The method overcomes the problem that the selection result of reserves computation parameter control data, such as coal-seam thickness and rock stratum dip angle, is affected by human factors.