38 results about "Variogram" patented technology

The invention discloses a three-dimensional convolution method for decoding an imaginary language electroencephalogram topographic map. The method solves the problem that for a multi-rhythm electroencephalogram topographic map, an existing convolutional network method is insufficient in modeling capability, so that the decoding precision of the multi-rhythm electroencephalogram topographic map in an imaginary language is remarkably reduced. In the aspect of rectangular BEAM reconstruction, a frequency band specificity self-adaptive variation Kriging interpolation model is built, an optimal variation function is automatically selected according to spatial variation characteristics of electroencephalogram power of each frequency band, a high-resolution BEAM graph sequence under multiple frequency bands and multiple time steps is built, and spatial continuity and CNN structure adaptability are considered. A double-branch three-dimensional convolutional neural network is designed, high-dimensional feature extraction is performed on space-time and space-frequency band tensors, a multi-scale structure and dynamic expression ability of neural activity are mined, discrimination of two feature vector branches is dynamically weighted, and the recognition ability of a classifier to a language imagination electroencephalogram mode is improved.

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 belongs to the technical field of urban meteorological data processing and spatial information calculation, and particularly relates to an urban meteorological gridding data construction method and device based on spatial interpolation. Comprising the following steps: clustering meteorological data: carrying out partition optimization by utilizing an objective function during clustering; partition interpolation: according to a meteorological data clustering result, carrying out spatial division and processing on regions, carrying out statistics on the number of base stations for each region, and determining region types based on the spatial distribution density of the base stations; determining a global variation function and a region variation function for each region, and calculating an estimated value of a to-be-interpolated point by adopting a corresponding Kriging interpolation method for different region types based on the variation functions; region junction point interpolation: for points near different region junctions, interpolating by using the meteorological data of the related region base stations, and weighting to obtain a final result; finally, urban meteorological gridding data construction based on spatial interpolation is realized.

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.

The invention discloses a multi-period non-stationary hydrological variable valuation method, and relates to the technical field of hydrological prediction. Comprising the steps of obtaining hydrological variables of a plurality of observation stations in a drainage basin at different time points as hydrological time sequences of the observation stations; constructing an experimental variation function according to the hydrological time sequence of each observation station; selecting a drift model through a trend surface analysis method and model inspection; constructing a multi-period non-stationary variable equation set comprising a Lagrange multiplier and a hydrological variable weight based on the experimental variation function and the drift model; expressing the multi-period non-stationary variable equation set as a matrix form, and calculating a Lagrange multiplier and a hydrological variable weight through a matrix inversion method; and constructing an estimation formula according to the calculation result of the Lagrange multiplier and the hydrological variable weight, and performing multi-period spatial estimation on the hydrological variable of the to-be-measured drainage basin through the estimation formula. Dependence on the number of observation stations can be reduced, and accurate estimation can be achieved only through a small amount of observation station data.

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.

Embodiments of the present application disclose a well-seismic joint reservoir prediction method, device and electronic equipment. The well-seismic joint reservoir prediction method comprises: selecting seismic attributes and performing standardization processing on the selected seismic attributes; obtaining a seismic attribute variogram plan based on the standardization-processed seismic attributes; establishing a relationship between the standardization-processed seismic attributes and reservoir parameters based on the seismic attribute variogram plan; and predicting the reservoir based on the relationship. The selected seismic attributes are used to obtain a seismic attribute variogram plan, thereby establishing a relationship between the standardization-processed seismic attributes and reservoir parameters, and the prediction of the reservoir is realized based on the established relationship between the standardization-processed seismic attributes and reservoir parameters. The seismic attributes are integrated into the prediction of the reservoir parameters, thereby improving the accuracy of the prediction results.

The invention discloses a multi-period non-stationary multi-hydrological variable spatial valuation method, and relates to the technical field of hydrogeology. Comprising the steps of obtaining a univariate time sequence; combining the univariate time sequences of all observation stations in the drainage basin to obtain a multivariate random time sequence; calculating a long-term average level of each observation station through a space-time random function to obtain a long-term trend; subtracting the multivariable random time sequence of each observation station from the corresponding long-term trend to obtain a random residual error; according to the random residual error, determining a covariance function and a variation function between any two observation stations in the drainage basin; and on the basis, a multi-period non-stationary multivariable space valuation model is constructed by combining a multivariable Kriging model theory, and multi-hydrological variable valuation is performed on the drainage basin to be measured. According to the invention, internal correlation between variables can be revealed, and the prediction precision and reliability can be effectively improved.

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.

The invention discloses a soil heavy metal pollution interpolation prediction method of a variation coupling graph attention mechanism, and relates to the field of soil heavy metal detection, and the method comprises the following steps: collecting a target area soil sample, and calculating the Euclidean distance between every two sampling points; dividing a plurality of distance intervals; carrying out empirical mode decomposition on the heavy metal concentration and verifying an intrinsic part; calculating semi-variation values of the heavy metal concentration intrinsic part under different distances; fitting a theoretical variation function model; constructing a graph structure; calculating an attention weight coefficient between the nodes; obtaining node-level mapping features; and taking the node-level mapping characteristics output by the last layer of GAT structure as the input of a pre-trained multi-layer perceptron, and outputting a soil heavy metal pollution interpolation prediction result. According to the method, the variation function and the distance are integrated into the GAT model, the understanding of the soil heavy metal pollution mode is enhanced, the method can adapt to different geographical conditions and pollution scenes, and a more accurate and reliable prediction result is provided.

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 present invention discloses a GRNN smoothing factor setting method and system based on a variogram model, comprising: preprocessing sampled data; obtaining an omnidirectional experimental variogram of the preprocessed sampled data, and fitting the omnidirectional experimental variogram to obtain a range value; using the range value as a smoothing factor to perform GRNN prediction; constructing a GRNN model based on the smoothing factor that satisfies the prediction result and the preprocessed sampled data, and predicting the grade value at an unknown location using the GRNN model. The present invention makes it easier to obtain the optimal smoothing factor, improves the efficiency of parameter search, and has greater adaptability.

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 present invention discloses a quantitative evaluation method for the stationarity of training images based on a variogram, which calculates the strength of the spatial stationarity of the training image through the variogram, thereby providing an objective quantitative index for judging whether the training image is suitable for modeling. First, the variogram is used to quantify the variability differences between different spatial anchor points and other areas of the training image to obtain an anchor point distance model. Then, the stationarity coefficient of the anchor point is calculated through the anchor point distance model, and finally, the stationarity coefficients of all distance models are statistically analyzed to form a quantitative index for the stationarity of the training image. The present invention aims to solve the limitations of the quantification of the stationarity of training images, greatly improve the accuracy and efficiency of image analysis, systematically evaluate the stationarity of training images, and provide a more accurate and reliable spatial variability analysis method for geological modeling.

A three-dimensional convolution method for imagined language electroencephalography (EEG) topography decoding. It solves the problem of the deficiency of the existing convolutional neural network method in modeling ability for multi-rhythm EEG topography, which leads to a significant decline in decoding accuracy in imagined language. In the aspect of rectangular BEAM reconstruction, a frequency-specific adaptive variogram kriging interpolation model is built, which automatically selects the optimal variogram function according to the spatial variation characteristics of the EEG power of each frequency band, and constructs a high-resolution BEAM map sequence under multi-frequency and multi-time steps, taking into account the spatial continuity and the adaptability of the CNN structure. A double-branch three-dimensional convolutional neural network is designed to extract high-dimensional features from spatial-time and spatial-frequency tensors, respectively, to explore the multi-scale structure and dynamic expression ability of neural activity. The discriminative ability of the two feature vector branches is dynamically weighted to improve the recognition ability of the classifier for the EEG pattern of language imagination.

The present invention discloses a method, device, and apparatus for automatic variogram modeling based on deep learning, which relates to the field of geostatistics and can solve the current technical problems of inaccurate and low efficiency in variogram modeling. The method comprises: constructing a variogram surface dataset to be trained and constructing an initial theoretical variogram parameter prediction model, wherein the variogram surface dataset to be trained includes the variogram surface to be trained and the corresponding variogram parameters to be trained; training the initial theoretical variogram parameter prediction model based on the variogram surface to be trained and the corresponding variogram parameters to be trained to obtain a trained theoretical variogram parameter prediction model; obtaining experimental variogram values, calculating the variogram surface corresponding to the experimental variogram values, and using the theoretical variogram parameter prediction model to predict the theoretical variogram parameters corresponding to the variogram surface, thereby obtaining a theoretical variogram based on the theoretical variogram parameters.

The application discloses a kind of self-adapting multi-beam survey line layout method based on kriging interpolation prediction, belong to underwater surveying and mapping technical field, for multi-beam survey line layout, including using extreme value weighted depth model to predict and layout based on strip coverage ratio survey line, according to the measured water depth abrupt change situation adaptive adjustment extreme value weighting weight, using kriging interpolation model based on measured water depth water area to unmeasured water depth water area carries out water depth space interpolation prediction, according to the terrain difference of water area, switch variogram model to optimize interpolation prediction result, based on strip coverage ratio and the water depth space of interpolation prediction, the spatial position of next survey line is planned, according to the boundary of to-be-measured deep water area, the two end points of new survey line are determined, and complete survey line is planned.The application can effectively capture local water depth variation characteristics, and then provide reliable basis for subsequent survey line strip width estimation and interval control, and automatically generate multi-beam survey line with high coverage and continuity.

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 discloses a Kriging interpolation prediction-based adaptive multi-beam survey line layout method, which belongs to the technical field of underwater surveying and mapping, is used for multi-beam survey line layout, and comprises the following steps of: predicting and laying survey lines based on a strip coverage rate by using an extreme value weighted water depth model; the weighted weight of an extreme value is adaptively adjusted according to the sudden change condition of the water depth of a measured water area, a Kriging interpolation model is used for carrying out water depth space interpolation prediction on a water area with an unmeasured water depth based on the water area with the measured water depth, and a variation function model is switched according to the terrain difference of the water area to optimize an interpolation prediction result. And based on the strip coverage rate and the water depth space predicted by interpolation, planning the spatial position of the next survey line, determining two end points of the new survey line according to the boundary of the water area to be deepened, and planning a complete survey line. According to the method, local water depth change characteristics can be effectively captured, a reliable basis is provided for follow-up survey line strip width estimation and spacing control, and continuous multi-beam survey lines with high coverage rate are automatically generated.

The present invention discloses a method for automatically constructing a theoretical variogram model, which relates to the field of geostatistics and specifically includes the following steps: preprocessing spatial data; obtaining variogram point cloud data from the preprocessed spatial data; and fitting a theoretical variogram model based on the variogram point cloud data. Compared with conventional experimental variogram fitting methods, the present invention uses point cloud data to fit the theoretical variogram model, which reduces the process of selecting various parameters when constructing the experimental variogram, and can more simply obtain a relatively reasonable theoretical variogram model. The present invention uses a particle swarm optimization algorithm to automatically optimize the key parameters of the theoretical variogram model, effectively solving the subjective problem of parameter setting in the theoretical variogram model and improving the accuracy and stability of the theoretical variogram model construction.

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 present disclosure relates to systems, methods, and non-transitory computer-readable media for dynamically utilizing offset drill-well data generated within a threshold geographic area to determine formation-top trends and identify formation-top depths at a subject drill-well site. To do so, in some embodiments, the disclosed systems estimate a variogram for observed formation-top depths of a subset of offset drill-wells, and, in turn, map a predicted response from the estimated variogram. For example, using weighted combinations (e.g., with Kriging weights) of the formation-top depths of the subset of offset drill-wells, the disclosed systems can map a continuous surface of a formation and identify a top-depth thereof. Moreover, the disclosed system can do so for multiple formations at the subject drill-well site, and (in real-time in response to a user input) provide for display at a client device, the associated formation-top depths, various predicted drilling events and / or predicted drilling metrics.