74 results about "Variogram" patented technology

A method for generating one or more geological models for oil field exploration. The method includes receiving one or more well facies logs, a vertical facies proportion curve, a lateral proportion map, a variogram model and a global target histogram. The method then includes generating a facies probability cube using a modified Sequential Gaussian Simulation (SGSIM) algorithm, the well facies logs, the vertical facies proportion curve, the lateral proportion map and the variogram model. After generating the facies probability cube, the method includes matching the facies probability cube to the global histogram and generating the geological models based on the matched facies probability cube.

A novel database and method of classifying and searchably retrieving measurement data derived from a plurality of rock core and plug sample images that are analyzed to define their principal geostatistical attributes and characteristics, with the resulting analytical data being retrievably stored in a database, the method including calculating spatial variability of images, such as CT scan images, porosity images and other types of available images, quantifying the main image characteristics utilizing multi-azimuth variograms and simplified pattern recognition based on the histogram and variography analysis to thereby provide a means to correlate data from various geographical regions or fields by analyzing data which has the same variographic parameters.

A computer-implemented method and system for simulating reservoir property trends, including petrophysical trends within facies geobodies, is provided. One embodiment includes discretizing a range of values of the reservoir property to be modeled into at least two categories and utilizing the categories to discretize the associated well data and training image into categorical well data and a categorical training image. An embodiment includes applying MPS simulation using the categorical training image on a stratigraphic grid of nodes discretizing the reservoir volume to create a categorical property model. The embodiment additionally includes applying variogram-based simulation in each sub-region of the categorical property model to create a reservoir property model that reproduces the category histogram of each associated sub-region and models the reservoir property trends.

The invention discloses a spatial variability layout plan generation method of heavy metal content in reclamation land soil, belonging to the technical field of environmental monitoring. The spatial variability layout plan generation method comprises: obtaining the soil sample of a sampling point in a reclamation land to be measured; carrying out data pretreatment to the numerical value of heavy metal content in the soil sample of a sampling point to respectively obtain the quasi-interpolation subsample of each soil heavy metal element; carrying out exploring spatial data analysis treatment to the quasi-interpolation subsample to obtain each anisotropy parameter, step length and step number of the soil sample of the sampling point; carrying out function fitting to the numerical value of heavy metal content in the soil sample of the sampling point to obtain a theoretical semi-variogram model suitable for obtaining heavy metal content interpolation; and drawing the spatial variability predicting result layout plan. The layout plan obtained by the invention can accurately evaluate the spatial condition of the heavy metal content in the reclamation land soil.

The invention discloses a SAR (Synthetic Aperture Radar) image speckle suppression method based on dictionary learning in wavelet domain, which mainly solves the problems that the edge is not clear enough and the homogenous region is not smooth enough in the existing speckle reduction technology. The implementation process of the method comprises the following steps of: firstly, segmenting an original SAR image Y by a variogram method to obtain a smooth mark matrix SY and an edge mark matrix EY; performing N-level stationary wavelet transformation on the original SAR image Y to obtain sub-band images WY(s); modeling for a non-logarithmic additive noise in the WY(s) by zero-mean-value Guassian distribution; using an approximation KSVD (Singular Value Decomposition) algorithm to obtain a learner's dictionary D's and a sparse representative matrix Lambda's of each sub-band image WY(s), obtaining a reconstructed sub-band image according to the D's and the Lambda's, and obtaining an edge region of the sub-band images WY(s) by the edge mark matrix EY, and substituting the edge region in the reconstructed sub-band image to obtain modified sub-band images W'Y(s); performing inverse stationary wavelet transformation on the W'Y(s) to obtain the speckle- reduced image. The method has the advantages that the edge information after speckle reduction is complete and the homogenous region issmooth, and can be used for the pretreatment process of SAR image understanding.

Systems and methods for computing a variogram model, which utilize a variogram map and a rose diagram to compute the variogram model. The variogram model may be validated in real-time to provide immediate feedback without the need to interpolate or simulate the real data.

The invention discloses a multispectral remote sensing image variation detection method based on spectral reflectivity variation analysis. The method mainly solves the problem of whole luminance difference sensitivity between different time phase images in the prior art. The method comprises the steps that (1) two time phase multispectral image sets registered in the same region are input, and subjected to wiener filtering, noise removal and normalization processing; (2) the processed image sets are converted into relative ground object spectral reflectivity image sets; (3) the variance and the modulus of a spectral reflectivity variation are calculated, a variogram and a modulus value figure of the spectral reflectivity variation are obtained and enhanced respectively, and an enhanced variogram and an enhanced modulus value figure are obtained; and (4) the enhanced variogram and the enhanced modulus value figure are divided, and a variation detection result figure is obtained by fusing divided binary images. The method requires no manual participation, is high in detection accuracy, and can be used for land utilization and cover monitoring, vegetation cover monitoring, and water resource and mineral resource monitoring.

The invention provides a synthetic aperture radar image non-local mean filter method, belonging to the radar image processing field. The invention introduces the heterogeneous property measure into the non-local average algorithm, and improves the weight measurement mode of the non-local average algorithm. Firstly, the traditional non-local mean method is weighted by equal weights in calculating the distance measure. In this paper, variogram coefficients are introduced to calculate the weighted weights of the distance measure of pixels in the window of an image block. Because the variogram coefficient can represent the texture direction of the image, the similarity of the image blocks weighted by the variogram coefficient can capture the image blocks with similarity in texture and direction more effectively, thus avoiding the blurring of edge and direction. Then, an adaptive degeneration parameter function based on variogram coefficients is designed. Adaptive adjustment of degenerationparameters can effectively ensure that the heterogeneous regions are not over-smoothed. Therefore, the invention can more effectively keep the texture edge information while filtering out the coherent speckles.

Systems and methods for computing a variogram model, which utilize a variogram map and a rose diagram to compute the model.

The invention discloses a reservoir parameter prediction method and system based on Bayesian classification. The method comprises the following steps of: establishing a first inversion equation of reservoir physical property parameters based on known reservoir elastic parameters; based on the Bayesian formula, transforming the first inversion equation into the second inversion equation about the likelihood function of the reservoir elastic parameter; based on a Bayesian-sequential Gaussian method, solving the likelihood function of reservoir elastic parameters; obtaining reservoir physical parameters by solving the second inversion equation based on the likelihood function of the obtained reservoir elastic parameters. The invention does not need to carry out complex model initialization, fully considers the advantage of stochastic characteristics of geological and geophysical characteristics, and makes the inversion results have more practical geological significance, and it can also solve the problem that the variogram function is not reliable in the case of less well data, or even in the case of uneven distribution of wells in the work area in the geostatistical stochastic simulation method.

The invention provides a magnetic resonance image reconstruction method and device based on compressed sensing. The method includes the steps that under-sampled original K space data are acquired through magnetic resonance scanning; image reconstruction is conducted on the original K space data through a compressed sensing method; after an image reconstruction result tends to converge, disturbance is added to the original K space data, variances of the reconstruction result obtained after disturbance is added are counted, and then a first variogram is acquired; a region of interest in the first variogram is extracted; image mask processing is conducted on the region of interest, and then a second variogram is acquired; the second variogram is added to the image reconstruction result through a non-linear function, and then a reconstructed image is acquired. By the adoption of the method and device, the accuracy of the magnetic resonance image reconstruction result is improved.

The invention discloses an inertial device value simulation method. An Adis16480 inertial device is taken as a simulated object, and results show that a simulation value and a simulated value have high fidelity. The inertial device value simulation method disclosed by the invention is mainly characterized in that 1, simulated random noise sequences are subjected to band-pass filtering according tothe random noise frequency bandwidth characteristics, so that the value simulation is more realistic; 2, random noise power spectrum parameters of the simulated inertial device are identified throughan Allan variogram, and the simulation is more targeted, so that the simulation value and simulated object value characteristics are more realistic. The inertial device value simulation method disclosed by the invention can realistically simulate an inertial device of any model and meets the demands on study on different performance inertial navigation and integrated navigation algorithms.

The invention relates to an omnidirectional search mechanism variogram based optimal remote sensing scale selection method, which comprises the following steps of: firstly, selecting a research area by using an obtained remote sensing image; then, calculating corresponding variogram values of the remote sensing image at different space intervals in each direction by using an omnidirectional search algorithm; on this basis, establishing a curvilinear relation between the space intervals and the variogram values of the space intervals, and then solving a corresponding local optimal space scale of the remote sensing image in each direction based on the spherical model regression fitting in a variogram model; and finally, taking the local optimal space scale corresponding to the direction with the maximum degree of fitting as the optimal space scale of the remote sensing image. By using the method disclosed by the invention, the problem of 'false' local optimal scales caused by that when a traditional variogram algorithm is used, in the process of selecting the optimal space scale of a remote sensing image, only an isotropic factor is taken into consideration is solved, thereby improving the accuracy of optimal scale selection.

The invention discloses an ultra-short-term photovoltaic generation power prediction method based on a composite data source autoregression model. The ultra-short-term photovoltaic generation power prediction method based on the composite data source autoregression model comprises the steps that data are input to enable parameters of the autoregression model to be obtained; input data required by photovoltaic generation power prediction are input into the autoregression model which is determined according to the parameters of the autoregression model, so that a prediction result is obtained; model training basic data are input, order determination is conducted on the autoregression model AR(p) according to a residual variogram method, and the parameters of the model AR(p) with the determined order are estimated according to a moment estimation method. Key information is provided for new energy power generation real-time scheduling, a new energy power generation day-ahead plan, a new energy power generation monthly plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the photovoltaic generation power generated during photovoltaic generation. The ultra-short-term photovoltaic generation power prediction accuracy is effectively improved due to the fact a composite data source is introduced, and thus the on-grid energy of new energy resources is effectively increased on the premise that safe, stable and economical operation of a power grid is guaranteed.

The invention discloses a digital rock mass establishment and application method for well drilling. The establishment method comprises the steps of establishing a stratigraphic framework model of a set region; obtaining well drilling engineering geological attribute values of drilled tracks; calculating a variogram function of each attribute distributed in each layer; traversing each grid for each attribute in each layer, and endowing the attribute to each vertex of the grid if the attribute exists in the grid; calculating to obtain the attribute value by utilization of the variogram function if each vertex of the grid is fully or partially empty, and endowing the attribute value to the vertex of the empty value; and storing all vertex data obtained in each layer to obtain a digital rock mass. In an well drilling design or actual drilling process of a new well, if the geological parameters need to be employed, each vertex parameter of the grid where a coordinate is can be obtained through the coordinate of each point on each well track, further each engineering parameter of the coordinate is calculated, and finally all engineering parameters of all well tracks can be obtained.

The present invention relates to the field of geological exploration, and specifically relates to a geological information interpolation calculation method based on spatial variation analysis. A geological information interpolation calculation method based on spatial variation analysis, comprising the following steps: (1) geological exploration; (2) data conversion; (3) calculating the variation function of mineralized points, and calculating the variation for each mineralized point (4) Generation of fitting structure of variation function, calculating mineralization grade information for all non-exploration points; (5) Using Kriging variance method to evaluate the quality of fitting structure, and draw conclusions on whether re-exploration is needed. The remarkable effect of the present invention is: use the variation function fitting structure to calculate the mineralization information of unexplored points, fully consider the degree of influence of known points on unexplored points, and the method can not only perform interpolation calculation for shallow mineralization information, but also Interpolation calculations can be performed on deep mineralization information.

Systems and methods for updating a property map during conditional simulation or unconditional simulation using interactive azimuth guidelines, well data and/or variogram parameters.

The invention provides a building area extraction method and system based on an SAR image. The building area extraction method based on the SAR image includes: acquiring a first image on the basis ofthe SAR image; generating a local space index image and a variogram image on the basis of the first image; respectively screening sampling points on the basis of the local space index image, the variogram image and the first image to obtain corresponding sets; and processing sampling points in each set according to preset growth criteria to respectively construct to-be-selected building area images, superimposing all the to-be-selected building area images to obtain a final building area image. Through the method or the system provided by the invention, a building area can be quickly and accurately extracted from the Sentinel-1 SAR image, automatic extraction of construction land of a large region is realized, and a foundation is laid for global building area mapping.

A system and a method, implemented on a computer, for calculating property distribution uncertainty of spatially correlated petrophysical data. The method includes inputting, into the computer, a sample petrophysical data comprising correlated data; applying, using the computer, a variogram to the sample petrophysical data to select a plurality of subsets of data, the subsets of data being substantially less correlated than the sample petrophysical data; and applying, using the computer, a bootstrap process on each of the plurality of subsets of data to obtain a plurality of bootstrap data sets from each of the plurality of subsets of data. The method further includes calculating data distributions for each of the obtained plurality of bootstrap data sets; ranking the data distributions by using a selected statistical parameter to obtain ranked data distributions; and characterizing the uncertainty based on the ranked data distributions.

The invention relates to a full-reference mixed-distortion image quality evaluation method based on sparse decomposition residuals. The method comprises steps: a natural image is selected, and image blocks are selected from the natural image as training image blocks to train a dictionary D; a sparse decomposition residual energy graph is acquired; and the image distortion level is evaluated basedon the sparse decomposition residual energy graph. The third step comprises the following sub steps: the local residual energy feature similarity is calculated; a variogram solved by a reference imageis used as a weight to acquire a local residual mass fraction Qrl; a global residual Gres<r> is solved by the local residual energy graph; the mass fraction of the global residual feature is calculated; and the local residual mass fraction Qrl and the global residual mass fraction Qrg are integrated to obtain the evaluation mass fraction Qr of the final residual feature.

The present invention relates to a multi-window fusion-based digital image repairing algorithm. The method comprises the steps of for a to-be-repaired image I, setting a repair window omega n at different sizes; in the repair window omega n, completing the repairing process of the to-be-repaired image I by adopting the Criminisi repair algorithm, and marking the repaired image to be In; calculating an updated to-be-repaired image UI, and calculating a repair variogram VI; calculating the priority of a target pixel point Pu, and changing the priority calculation mode of the target pixel point in the Criminisi repair algorithm to be the calculation mode of Pu(pu); and repairing the UI according to the Criminisi repair principle so as to obtain a final repair result IP.