Kriging Interpolation Algorithm Research Articles

A hybrid sparrow optimization Kriging model and its application in geological modeling

With the proposal of intelligent mines, the demand for drilling is increasing daily. Therefore, it is particularly crucial to gather more geological data by interpolation of limited drilling data for subsequent three-dimensional geological modeling. In this paper, a hybrid sparrow optimization Kriging model (HSSA), in which chaos theory and Levy flight are integrated into the initial population update algorithm of the sparrow algorithm and the location update algorithm of the entrants, is proposed. Next, the golden sine optimization algorithm is introduced into the reconnaissance and early warning mechanism of the sparrow algorithm to further improve the accuracy and local escape ability. By the correlation optimization of the original sparrow algorithm, the speed and accuracy of swarm intelligence optimization are further improved. In addition, the model solves the parameters of the variation function of the ordinary Kriging interpolation and reduces the generation error of the formation data interpolation. The results of relevant experiments show that the hybrid sparrow optimization Kriging model improves the accuracy and convergence speed compared with other swarm intelligence algorithms and that the accuracy of this model is improved by 8.4% compared with the original Kriging interpolation algorithm. Based on the hybrid sparrow optimization Kriging model, we propose a three-dimensional stratigraphic model for the Yangchangwan Coal Mine, which provides further support for mining operations and three-dimensional stratigraphic research in this area. The accuracy and applicability of the hybrid sparrow optimization Kriging model are further explained using a case study with the stratigraphic model data in the Yangchangwan Coal Mine. HSSA with significant potential for applications in industries such as coal mining and geological exploration. In these fields, the efficient acquisition, processing, and modeling of stratigraphic data are critical for enhancing geological interpretation and optimizing operational workflows.

A Spatial Interpolation Method for Meteorological Data Based on a Hybrid Kriging and Machine Learning Approach

ABSTRACTConventional spatial interpolation methods for meteorological data are usually based on linear interpolation. However, with the improvements in the temporal and spatial resolution of observational data, local neighbouring stations are susceptible to the influence of underlying surface changes and high terrain gradients. Moreover, for interpolation at a single time point, the inability to extract continuous change information effectively from adjacent times limits the interpolation performance. In this paper, an improved hybrid deep learning‐kriging method is proposed that combines a graph neural networks (GNNs) prediction model with the kriging interpolation algorithm. The GNNs considers dynamic changes over time and combines spatial and temporal information to estimate (interpolate) meteorological data at target weather stations using reanalysis data as input. The experimental results show that the hybrid method exhibits good performance in interpolating station data in complex terrain areas and under uneven surface conditions. The interpolation effectiveness of this method is markedly improved compared to that of traditional kriging methods. Moreover, when applied to station‐to‐grid interpolation, the hybrid method still provides better interpolation results than those of kriging methods. Therefore, this research provides a new method and perspective for meteorological data interpolation.

Suitability of rainwater harvesting in saline and arsenic affected areas of Bangladesh

A major portion of Bangladesh is currently experiencing a scarcity of safe drinking water because of arsenic contamination, high salinity and human-induced pollution. The objectives of this study were to identify locations with a high scarcity of drinking water and suitability of harvesting rainwater. Kriging interpolation algorithms of Geographical Information System (GIS) was employed to identify the probable water scarce zones as well as suitable zones of harvesting rain water from the available data of secondary sources. Statistical methods were employed to cluster, correlate, and regress variables such as rainfall, salinity, and As. The results showed that groundwater quality in the southwestern parts of Bangladesh is saline with high concentration (>10000 μS/cm). On the other hand, the northeastern and southwestern parts of Bangladesh are also vulnerable to arsenic contamination (60 %–97 % of tubewells), compared to other regions. The rainfall zonation map, covering the years 1951–2022, indicated that the Sylhet division had the highest potential for rainfall (ranging from 2600 to 3900 mm). From this study it was demonstrated that Sylhet, Noakhali, Bhola, Barishall, Patuakhali, Bagerhat, and Khulna were identified as suitable places for sustainable rainwater harvesting (RWH). The findings of this study may play significant role towards achieving sustainable potable water supply in vulnerable zones, if they receive attention from policymakers.

An efficient method for modeling and evaluating the bench terrain of open-pit mines

In order to quantitatively analyze the roughness of the bench floor during open-pit mine blasting, this study proposes a real-time measuring method for the three-dimensional terrain of the bench floor during the excavation process. Real-time monitoring is conducted at the boundary and discrete internal points of the workbench floor during electric shovel operation, utilizing real-time kinematic global navigation satellite system (RTK-GNSS) positioning technology. An improved convex hull algorithm is introduced to automatically extract the optimal boundary of discrete point clouds based on their spatial distribution characteristics. This study establishes a digital elevation model (DEM) using five interpolation algorithms for 3D terrain visualization simulation. Through cross-validation, a comparative analysis of the DEM accuracy, the simulation results of the ordinary kriging interpolation algorithm were found to be optimized. The optimized interpolation algorithm is applied to simulate the 3D terrain in the Dexing open-pit copper mine, and the relevant terrain parameters were calculated. This dataset can serve as a precise foundation for the real-time path planning of elevation blasting design and ground leveling operations.

Robot joint space grid error compensation based on three-dimensional discrete point space circular fitting

The poor absolute positioning accuracy of industrial robots has limited their application in fields such as aerospace manufacturing. To address this issue, the spatial grid compensation method has been proposed as an effective solution. In this paper, we propose a sampling method based on three-dimensional discrete point space circular fitting for grid points to significantly reduce the sampling workload and improve compensation accuracy compared to traditional joint space grid compensation methods. Additionally, we use the Kriging interpolation algorithm instead of the inverse distance weighting (IDW) algorithm for spatial interpolation prediction of pose error. Based on this, the proposed sampling and interpolation prediction method in this paper was verified on a Comau NJ500–2.7 manipulator equipped with a fiber-laying end effector. The experimental results demonstrate that using our proposed sampling method yields pose data of grid points that have only a small deviation from directly sampled results and are only slightly higher than the robot's repeat positioning accuracy. Moreover, our proposed method can significantly reduce the sampling workload by 60% under the experimental conditions of this study (sampling 10 groups of data within 90 degrees range), with increasing sampling range leading to more obvious efficiency improvements. Finally, we show that compared to the IDW algorithm, the Kriging interpolation algorithm yields better results and improves the mean absolute positioning accuracy of robots after compensation by 30%.

Translational medical bioengineering research of traumatic brain injury among Chinese and American pedestrians caused by vehicle collision based on human body finite element modeling

Based on the average human body size in China and the THUMS AM50 finite element model of the human body, the Kriging interpolation algorithm was used to model the Chinese 50th percentile human body, and the biological fidelity of the model was verified. We built three different types of passenger vehicle models, namely, sedan, sports utility vehicle (SUV), and multi-purpose vehicle (MPV), and used mechanical response analysis and finite element simulation to compare and analyze the dynamic differences and head injury differences between the Chinese 50th percentile human body and the THUMS AM50 model during passenger vehicle collisions. The results showed that there are obvious differences between the Chinese mannequin and THUMS in terms of collision time, collision position, invasion speed, and angle. When a sedan collided with the mannequins, the skull damage to the Chinese human body model was more severe, and when a sedan or SUV collided, the brain damage to the Chinese human body was more severe. The abovementioned results suggest that the existing C-NCAP pedestrian protection testing regulations may not provide the best protection for Chinese human bodies, and that the regulations need to be improved by combining collision damage mechanisms and the physical characteristics of Chinese pedestrians. This thorough investigation is positioned to shed light on the fundamental biomechanics and injury mechanisms at play. Furthermore, the amalgamation of clinically rooted translational and engineering research in the realm of traumatic brain injury has the potential to establish a solid foundation for discerning preventive methodologies. Ultimately, this endeavor holds the potential to introduce effective strategies aimed at preventing and safeguarding against traumatic brain injuries.

Study on the regional ASF prediction method based on the ordinary kriging interpolation

The eLoran system functions as a robust backup to the Global Navigation Satellite System (GNSS), providing substantial signal power, robust anti-jamming capabilities, and an easily maintainable ground system. Nonetheless, the propagation time delay of the system, primarily driven by the Additional Secondary Phase Factor (ASF), significantly influences the positioning accuracy. Compensating for ASF can effectively enhance the positioning performance. Traditional propagation delay theories frequently yield substantial discrepancies between the predicted and measured values in regions characterized by extended propagation distances and varying topographic features. To address this issue, we conducted ASF measurements within a selected test area using a limited number of measurement points. We employed the ordinary Kriging interpolation method to predict ASF values across the entire test area, and used cross-validation to validate our predictions. The results confirmed the accuracy and effectiveness of the Kriging interpolation algorithm in predicting ASF values within specific regions. The cross-validation demonstrated that the errors remained within acceptable ranges. Furthermore, we applied Ordinary Kriging, Inverse Distance Weighting, and Radial Basis Function Interpolation methods to evaluate the positioning accuracy of the test area before and after ASF correction. Compared to other methods, using the Ordinary Kriging interpolation algorithm for predicting ASF values resulted in a corrected positioning accuracy of up to 68.8 m at various test locations. This approach effectively resolves the challenge of low accuracy in theoretical calculations in complex environments. By utilizing ordinary Kriging interpolation, we required measurements of only a few ASF values within a specific region to create an ASF correction map, addressing the challenges related to inaccurate theoretical calculations in complex pathways and avoiding time-consuming and labor-intensive large-scale measurements. The results of this study offer valuable theoretical support for improving the accuracy of land-based navigation systems.

Characterising a rock fracture rough surface using spatial continuity and kriging: a new approach to meshing coupled thermo–hydraulic–mechanical–chemical (THMC) models

Abstract. Fluid flow through low permeability rocks is mainly accomplished through fractures. In order to model fluid flow, coupled thermo–hydraulic–mechanical–chemical (THMC) numerical models are used, which rely on fracture surface representations to construct a distribution model of the empty space (aperture) between the two fracture faces. The traditionally used statistical representations of fracture surfaces often overlook spatial continuity (SC), i.e. how well correlated points are in direction and distance. Examples are the fractures' aperture distribution random sampling to the joint roughness coefficient. This may result in a poor representation of the aperture distribution and thus a poor model. The first aim of this study is to investigate the possibility of characterising a fracture surface roughness using its SC parameters, an upscaled fracture surface and ordinary kriging (OK) interpolation algorithm. This method provides better control over the aperture model creation, which will have implications for its complexity and computation times. The second aim is to utilise the SC information and the distribution of a fracture in order to extrapolate (i.e. blind predict) the distribution of the fracture where no observations exist. A statistical analysis was performed in a greywacke in order to acquire the parameters necessary to describe the SC of the fracture surface topography. The surface was then interpolated using the OK algorithm. These parameters and the surface distribution will be used to inform the OK algorithm to extrapolate the fracture to where no data has yet been acquired. A reasonable match between the kriged and original surfaces has been achieved and the fit quantified by analysing the error between the two and by R2, which offer positive measurements of methodological quality. The aperture can easily be calculated from the difference between both complementary surfaces. The aperture between the two original fracture surfaces versus the two kriged fracture surfaces was also quantified and compared, yielding good results. This method may provide a new alternative to current storing and computing solutions for fracture representation, especially in aperture distribution calculation for coupled THMC numerical models and simulations. To verify advances in accuracy and computing times for this method, results between models derived from the original versus kriging aperture data will have to be compared. Another potential applicability of the SC information is to know a priori from any modelling which directions are those of Darcy's flow and which are the directions of the highest and lowest dilation rates with shearing, which will have to be confirmed with future planned modelling work.

XCO2 Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker

In view of the urgent need for high coverage and high-resolution atmospheric CO2 data in the study of carbon neutralization and global CO2 change research, this study combines the Kriging interpolation and the Triple Collision (TC) algorithm to fuse three XCO2 datasets, OCO-2, GOSAT, and CarbonTracker, to obtain a 1° × 1° half-monthly average XCO2 dataset. Through a sub division of the Kriging interpolation, the average coverages of the OCO-2 and GOSAT XCO2 interpolating datasets are increased by 53.65% and 48.5%, respectively. In order to evaluate the accuracy of the TC fusion dataset, this study used a reliable reference dataset, TCCON data, as the verification data. Through comparative analysis, the MAE of the fusion dataset is 0.6273 ppm, RMSE is 0.7683 ppm, and R2 is 0.8279. It can be seen that the combination of Kriging interpolation and TC algorithm can effectively improve the coverage and accuracy of the XCO2 dataset.

Optimization and innovative design of dental implants under dynamic finite element analysis

ABSTRACT Dental implants’ usage life and strength are critical factors for implant patients. This paper examines the optimization of dental implant threads by modifying the C-Tech implant system model to ascertain thread design’s impact on micromotion through finite element analysis (FEA). The fundamental measurements of the redesigned C-Tech implant system are established by dynamic (FEA). Six implant parameters are chosen as the control factors to be advanced. Experimental simulations are built using a uniform design (UD) method. The dynamic FEA tool ANSYS/LS-DYNA is utilized for each experimental simulation to identify the maximal micromotion in the modified C-Tech implant system. The optimum design model is acquired by minimizing the micromotion by applying the Kriging interpolation (KGI) and genetic algorithm (GA). The improved design has a micromotion of 12.19 µm, as opposed to the original design’s micromotion of 38.11 µm. The improvement rate is 68.02%. Finally, the following innovative design is to add a secondary thread to the implant body. After conducting simulations, the micromotion is reduced to 4.72 µm. Further, it shows a 61.28% improvement compared with the optimization design version and an 87.62% improvement compared with the primary implants.

Global‐Krigger: A Global Kriging Interpolation Toolbox With Paleoclimatology Examples

AbstractMany applications in Earth sciences require spatial prediction, that is, obtaining a continuous scalar field from a set of discrete scalar data points on the Earth's surface. Such applications include model‐data comparisons and derivation of continuous scalar fields as input for Earth system models. The advantage of kriging as an interpolation method is that it provides predictions with confidence intervals for data sets of irregularly distributed points in space. However, the theory of kriging for non‐Euclidean domains such as oblate spheroids (e.g., the Earth's surface) is poorly developed, and existing kriging algorithms for global interpolation oftentimes cannot guarantee the validity of their predictions. Here, we present Global‐Krigger, a new kriging interpolation algorithm adapted for local to global applications that (a) incorporates a numerical check to guarantee that the necessary conditions for the kriging system of linear equations are met, and (b) derives a combined uncertainty field due both to spatial variations in data density and measurement error. The robustness of the method is demonstrated by cross‐validating predictions against reanalysis fields of traditional climatological scalar variables. We also show an example application in paleoclimatology for Holocene mineral dust deposition fluxes. The toolbox includes a user‐friendly graphical user interface that guides users through a range of choices during data pre‐interpolation analysis, kriging, and post‐processing.

Exploring modern bathymetry: A comprehensive review of data acquisition devices, model accuracy, and interpolation techniques for enhanced underwater mapping

Building a high-precision bathymetry digital elevation model is essential for navigation planning, marine and lake resource planning, port construction, and underwater archaeological projects. However, existing bathymetry methods have yet to be effectively and comparatively analyzed. This paper comprehensively reviews state-of-the-art bathymetry methods, including data acquisition techniques, model accuracy, and interpolation algorithms for underwater terrain mapping. First, We assess the merits and drawbacks of novel data acquisition devices, such as single-beam/multi-beam echo sounders and light detection and ranging systems. After that, we analyze the accuracy of the ETOPO1, GEBCO_2022 and SRTM15 to provide valuable insights into their performance. Furthermore, we evaluate ANUDEM, Inverse Distance Weighting, Kriging and Nearest Neighbor interpolation algorithms in different underwater terrains by comparing their applicability, reliability, and accuracy in various underwater environments. Finally, we discuss the development trends and challenges in underwater bathymetry technology and offer a forward-looking perspective on the future of this essential field.

Air pollution is associated with the risk of neurodegenerative disorders: a prominent role of PM10

Abstract Background Studies revealed an implication of air pollution in neurodegenerative disorders, although this link remains unclear. Here, we investigated this testing multiple pollutants simultaneously. Methods In the Moli-sani cohort (N = 24,325; ≥35 years; 51.9% women, baseline 2005-2010), we estimated yearly levels of exposure to nitrogen oxides (NOX, NO, NO2), ozone (O3), particulate matter (PM10) and BTX hydrocarbons (benzene, toluene and xylene) in 2006-2018, applying residence geo-localization of participants and Kriging interpolation algorithm to land measurements of air pollutants. We performed a principal component analysis and tested association of the resulting principal components (PCs) with the incident risk of Parkinson (PD) and Alzheimer disease (AD), through multivariable Cox PH regressions adjusted for age, sex and education level completed. Results Over 24,308 subjects with pollution data available (51.9% women, 55.8(12.0) years), we extracted three PCs explaining ≥5% of pollution exposure variance: PC1 (38.2%, tagging PM10 exposure), PC2 (19.5%, O3/CO/SO2), PC3 (8.5%, NOx/BTX hydrocarbons). Over a mean follow-up of 10.9(2.1) years, we observed statistically significant associations of PC1 with an increased risk of PD (HR[CI] = 1.04[1.02-1.05]; 405 incident cases) and AD (1.06[1.04-1.08]; 218 cases). These associations were confirmed when we analyzed PM10 levels averaged over follow-up time, in models further adjusted for professional exposures like working class, compartment and toxic compounds and lifestyles like smoking and drinking habits, physical activity and adherence to Mediterranean diet (PD: 1.27 [1.19-1.37]; AD: 1.22[1.16-1.28] per 1 μg/m3 increase of PM10). Conclusions This evidence supports an influence of air pollution - especially PM10 - on increased neurodegenerative risk in the Italian population, independent on concurring risk factors. This suggests reducing PM10 pollution as a potential strategy to reduce neurodegenerative risk. Key messages • PM10 levels are associated with increased Parkinson and Alzheimer disease risk. • This suggests to act on air pollution to reduce neurodegenerative risk in the general population.

Characterization of saturation and pressure distribution based on deep learning for a typical carbonate reservoir in the Middle East

The typical carbonate reservoir in the Middle East has huge potential, but due to low permeability and high gas-oil ratio, the characteristics of reservoir saturation and pressure distribution are not clear, which makes reservoir development very difficult. As an intelligent prediction and analysis technique, deep learning provides a novel solution to solve this problem. In this study, firstly, the actual static geological data and dynamic production data from the target reservoir were gathered to provide a data foundation for model training and analysis. And then, Random Forest algorithms, Reservoir Engineering methods, and Kriging interpolation algorithms were selected to predict and supplement the missing data of pressure and saturation field. After that, the Convolutional Long Short-term Memory (ConvLSTM) neural network algorithm was employed to build an optimal model to forecast the oil/water/gas saturation distribution and pressure distribution fields. The results showed that the optimal ConvLSTM model achieved good prediction performance. Not only the calculation efficiency, but also the prediction accuracy was higher, in which the average R2 was 0.8825 and the average accuracy was 0.865. Furthermore, we proposed five development optimizations for the remaining oil potential tapping strategies according to the prediction results. The workflow proposed in this study provides a new direction for the characterization of saturation and pressure distribution in a typical carbonate reservoir.

A hybrid spatial model based on identified conditions for 3D pore pressure estimation

Accurate spatial modeling of pore pressure helps to provide extensive and continuous information regarding the insightful downhole geological environment for safe drilling plans, performance analysis, and efficient reservoir modeling. Considering the varying drilling depth and pore pressure of study wells, a hybrid spatial modeling method is developed to estimate the pore pressure of an area. The method comprises three parts. In the first part, an appropriate number of pressure conditions is determined adaptively based on quantization error modeling (QEM), and similar pressure conditions are identified based on fuzzy c-means (FCM) method. In the second part, a kriging interpolation (Kriging) algorithm is introduced to produce profiles of different pressure subspaces. Afterward, random forest (RF) submodels are built separately according to the pressure profiles. In the last part, all of these submodels coalesced into one whole model. Verification of the proposed method is carried out on real case studies using field data from Songliao basin. The results show that the proposed method achieves the best prediction accuracy than the other five well-known methods for six wells, especially for well XS3, the mean absolute error and mean absolute percentage error of the proposed method achieve 0.717 and 6.063, which are at least 31% less than those of other five well-known methods.

Quantifying the Effect of LiDAR Data Density on DEM Quality


 
 
 
 LiDAR sensors capture three-dimensional point clouds with high accuracy and density; since they are regularly obtained, interpolation methods are required to generate a regular grid. Given the large size of its files, processing becomes a challenge for researchers with not very powerful computer stations. This work aims to balance the sampling density and the volume of data, preserving the sensitivity of representation of complex topographic shapes as a function of three surface descriptors: slope, curvature, and roughness. This study explores the effect of the density of LiDAR data on the accuracy of the Digital Elevation Model (DEM), using a ground point cloud of 32 million measurements obtained from a LiDAR flight over a complex topographic area of 156 ha. Digital elevation models with different relative densities to the total point dataset were produced (100, 75, 50, 25, 10, and 1 % and at different grid sizes 23, 27, 33, 46, 73, and 230cm). Accuracy was evaluated using the Inverse Distance Weighted and Kriging interpolation algorithms, obtaining 72 surfaces from which their error statistics were calculated: root mean square error, mean absolute error, mean square error, and prediction effectiveness index; these were used to evaluate the quality of the results in contrast with validation data corresponding to 10 % of the original sample. The results indicated that Kriging was the most efficient algorithm, reducing data to 1 % without statistically significant differences with the original dataset, and curvature was the morphometric parameter with the most significant negative impact on interpolation accuracy.
 
 
 

Deep Convolutional Neural Network for Rice Density Prescription Map at Ripening Stage Using Unmanned Aerial Vehicle-Based Remotely Sensed Images

In this paper, UAV (unmanned aerial vehicle, DJI Phantom4RTK) and YOLOv4 (You Only Look Once) target detection deep neural network methods were employed to collected mature rice images and detect rice ears to produce a rice density prescription map. The YOLOv4 model was used for rice ear quick detection of rice images captured by a UAV. The Kriging interpolation algorithm was used in ArcGIS to make rice density prescription maps. Mature rice images collected by a UAV were marked manually and used to build the training and testing datasets. The resolution of the images was 300 × 300 pixels. The batch size was 2, and the initial learning rate was 0.01, and the mean average precision (mAP) of the best trained model was 98.84%. Exceptionally, the network ability to detect rice in different health states was also studied with a mAP of 95.42% in the no infection rice images set, 98.84% in the mild infection rice images set, 94.35% in the moderate infection rice images set, and 93.36% in the severe infection rice images set. According to the severity of rice sheath blight, which can cause rice leaves to wither and turn yellow, the blighted grain percentage increased and the thousand-grain weight decreased, the rice images were divided into these four infection levels. The ability of the network model (R2 = 0.844) was compared with traditional image processing segmentation methods (R2 = 0.396) based on color and morphology features and machine learning image segmentation method (Support Vector Machine, SVM R2 = 0.0817, and K-means R2 = 0.1949) for rice ear counting. The results highlight that the CNN has excellent robustness, and can generate a wide range of rice density prescription maps.

Establishing a Marine Gravity Database around Egypt from Satellite Altimetry-Derived and Shipborne Gravity Data

For the purpose of marine geoid modeling and many other geodetic and geophysical applications, a marine gravity map around Egypt is established by the integration of gravity data provided by satellite altimetry and shipborne gravimetric observations. Firstly, the collected shipborne data were compared with GO_CONS_GCF_2_TIM_R6 and XGM2019e GGMs and with SSv29.1 and DTU17 altimetry models. Then, a pre-refinement of ship marine surveys was done with a rigorous condition, in which a number of 6525 points have been removed from the dataset. After that, 87709 points were deducted from the pre-filtered shipborne dataset to fit the study area and the cross-validation approach with the kriging interpolation algorithm were applied. A rigorous level of confidence was decided in this step where the points which have differences between the estimated and the observed values more than twice the STD of the residuals were removed until the STD reached a value less than 1 mGal. Finally, the filtered shipborne gravity data were combined with DTU17 (the best evaluation model) using the least-squares collocation technique (LSC). The final gravity map was tested using 8000 randomly chosen shipborne stations, which were not included when applying LSC, revealing the significant enhancement gained after the integration process.

A three-dimensional modeling method of overburden separated layer of TIN-DEM integrated model

In order to realize the three-dimensional visualization of overlying strata separation after coal seam mining, this paper proposes a three-dimensional modeling method of overburden separated layer of TIN-DEM integrated model. First of all, the original drilling data are combined with the Kriging interpolation algorithm to generate virtual drilling. And a TIN model is established before mining. Then, UDEC is used to simulate a working surface in two dimensions. And a DEM model of overburden separated layer is established. By correcting the pre-mining model, the stratum model (TIN-DEM) containing the overburden separated layer is established. Finally, instance verification is carried out through real data of a certain mine.

A variable scene modeling method of seabed terrain based on discrete point

In order to fit seabed terrain based on discrete points, many methods are proposed. However, fitting accuracy and fitting efficiency are contradictory in these methods. Therefore, a novel modeling method of seabed terrain based on variable scene is put forward. The threshold Θ C is defined as the basis to judge the distance of variable scene. When the distance is far, weighted ordinary Kriging interpolation algorithm is adopted; otherwise, Gridding Delaunay triangulation growth algorithm is adopted. The experiment analysis shows that the variable scene modeling method is superior to Grid in fitting precision and superior to TIN in the fitting efficiency, which proves that the method proposed in this paper has an overall optimal effect. This method is suitable for large-scale modeling of seabed terrain with large number of discrete data. • A novel modeling method of seabed terrain based on variable scene is put forward. • The weighted ordinary Kriging interpolation algorithm is proposed based on Grid. • The Gridding Delaunay triangulation growth algorithm is proposed based on TIN.