Kriging Method Research Articles

Regional ionospheric correction generation for GNSS PPP-RTK: theoretical analyses and a new interpolation method

The regional ionospheric corrections have become one of the critical parts of Precise Point Positioning (PPP)-based Real-Time-Kinematic (RTK) services to achieve fast positioning convergence. Several methods for regional ionospheric corrections supporting PPP-RTK have been developed and implemented over recent years, but little attention is given to the theoretical foundation of existing ionospheric correction methods and their performance comparison to find an optimal method in some sense. The optimality criterion of such methods should not only be based on the precision of the ionospheric correction itself, but also on its broadcasting strategies, and implementation aspects. This contribution studies ionospheric correction generation methods within the best linear unbiased predictor (BLUP) framework. Comparing the accuracy performances of the methods, we demonstrate that the Kriging method with trend, as a special case of BLUP, is the most appropriate method for large-scale networks (above 500 km). A strategy for the evaluation of the uncertainty of the grid-interpolated ionospheric corrections is also developed. In contrast to other empirical methods, this new method is rigorous in the sense that it avoids the underestimation of the uncertainty of predicted ionospheric corrections, especially when reference stations are close to a grid point.

Enhancing groundwater level prediction accuracy using interpolation techniques in deep learning models

Groundwater surface (GWS), which denotes the vertical extent of the water table or the volume of subterranean water within geologic formations, is pivotal for effective groundwater resource management. Accurately predicting GWS requires comprehensive and precise data to fully understand the influencing factors. The inherent temporal complexity and often incomplete datasets of GWS pose significant challenges to accurate assessments. This research aims to devise a comprehensive method that merges interpolation and prediction techniques to develop a functional model and dynamic system for GWS prediction. The study was conducted on the Azarshahr Plain aquifer in Iran, involving 34 observation wells with partially or entirely missing data. Initial analysis utilized three interpolation methods—Kriging, Support Vector Machine (SVM), and M5P—with the M5P method emerging as the most accurate, evidenced by the lowest Root Mean Square Error (RMSE) of 1.83. Two subsequent scenarios were examined: (1) using the M5P method to interpolate missing data for all 34 wells, and (2) using only data from 15 wells with complete records. GWS levels were predicted using Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models. Comparative analysis highlighted the superior performance of the CNN model in both scenarios, particularly noting its effectiveness in GWS prediction. The improvement of data quality through interpolation significantly enhanced predictive accuracy by approximately 90 percent, thereby increasing the reliability of the predictive models for future groundwater management decisions.

A novel piecewise linear transformation to moderate regional boundary effect on Real-Time regional ionospheric map (RT-RIM)

RT-RIM with high accuracy and dense spatiotemporal resolution of the ionospheric total electron content is crucial to diverse real-time GNSS applications. However, the uneven ionospheric pierce points distribution and boundary effect severely restrict the RT-RIM accuracy. We dedicate to the rapid and straightforward method to address the limited accuracy issue caused by the two crucial challenges. A novel piecewise linear transformation-adjusted spherical cap harmonic (PLT-ASCH) method is proposed to moderate the localized accuracy decrease in various latitudes areas for both active and stable ionospheric conditions. Especially, in the active ionospheric condition, the RMS improvements referring to IGS products are about 38.85%, 17.04%, 16.73% in Europe and 53.12%, 4.36%, 4.35% in South America, comparing to the inverse distance weighting, ASCH, and adjusted spherical harmonics adding Kriging methods. Moreover, the RMS improvements referring to differential slant TEC are about 48.11%, 16.53%, 16.99% in Europe and 61.01%, 4.87%, 4.33% in South America.

A novel adaptive sampling strategy for the Kriging surrogate-based control co-design method in the dynamic system

A novel adaptive sampling strategy for the Kriging surrogate-based control co-design method in the dynamic system

Computer model for detecting tsunami wave hazard on built-up land using machine learning and sentinel 2A satellite imagery

<p>The aim of this research is to compile a tsunami wave hazard scale based on built-up land density extracted and classified by machine learning from Sentinel 2A satellite and digital elevation model (DEM) imageries. This research was carried out in 5 stages, namely: (i) pre-processing of Sentinel 2A and DEM images, (ii) Classification of VI data using the machine learning algorithms, (iii) Spatial prediction using the ordinary kriging method, (iv) Field testing using the confusion matrix method, (v) Preparation of decision matrix for tsunami wave hazard. The results of the study show that the most accurate classification algorithm for classifying built-up indices data is the k-nearest neighbor (k-NN) algorithm. The results of the statistical accuracy test show that the most accurate is normalized difference built-up index (NDBI) with a mean of square error (MSE) value of 0.073 and a mean of absolute error (MAE) of 0.003. DEM analysis shows that the research area is at an altitude of 0–15 meters above sea level so it is in the high vulnerability to medium vulnerability category. Field testing showed user accuracy of 91.11%, manufacturer accuracy of 92.16%, and overall average accuracy of 91%.</p>

Meta model-based importance sampling combined with adaptive Kriging method for estimating failure probability function

The failure probability function (FPF) can provide the entire effect of the distribution parameter in the concerned region on failure probability (FP), which is important for decoupling the optimal parameter search and constraint analysis in the FP-constrained design optimization. To improve the efficiency of solving the FPF, this paper combines meta model-based importance sampling with adaptive Kriging (Meta-IS-AK). First, based on the theoretical optimal importance sampling density (ISD) for estimating the FP (ISD-FP) at the given distribution parameter, the theoretical optimal ISD for solving FPF (ISD-FPF) is constructed using an integral operator. Then, a Kriging model of the performance function is adaptively trained to establish the quasi-optimal ISD-FPF for approximating the optimal performance function, and a simple rejection sampling (SRS) method is designed to extract the samples of the quasi-optimal ISD-FPF. For recognizing the state of the ISD-FPF sample and completing the IS estimation of FPF, the Kriging model is secondly updated to establish ISD-FPF in the sample pool comprising these ISD-FPF samples. Using the proposed method, the whole FPF corresponding to arbitrary distribution parameters can be obtained using one IS reliability analysis based on the constructed ISD-FPF. Four examples and an application illustrate the advantages of the proposed method.

SPATIAL INSIGHTS INTO EARTHQUAKE STRENGTH: A SULAWESI CASE STUDY USING ORDINARY AND ROBUST KRIGING METHODS

The data from the Meteorology, Climatology and Geophysics Agency (BMKG) in the last 22 years shows that there have been 230 destructive earthquakes in Indonesia with the highest incidence in 2021. One of the islands frequently hit by earthquakes is Sulawesi Island. According to the 2020 Disaster Risk Index Book (IRBI), 63 of the 81 regencies/cities on Sulawesi Island have a high category earthquake risk index. Based on this, information is needed as a first step in disaster mitigation so that the government can take preventive and anticipatory actions to reduce risks associated with earthquakes and ensure the safety of people on the island of Sulawesi, one of which is obtained through spatial interpolation. In this study, the Kriging methods of interpolation, Ordinary Kriging (OK) and Robust Kriging (RK) were used. From the analysis with OK and RK, the best theoretical semivariogram model is the Exponential model with nugget, sill and range values of ​​respectively 0.40, 0.70, and 6.50 for OK and 0.35, 0.90 and 9.50 for RK. Both methods produced the results that most areas of Sulawesi Island have the potential for shallow earthquakes with a magnitude of around 3.2 to 4.0 on the Richter scale. The potential for earthquakes with high strength is more common around the seas to the east and north of Central Sulawesi Province. The highest estimation results are at the coordinates of 120,029° East Longitude, 1.159° North Latitude, namely in the sea north of South Dampal. According to the results of K-Fold Cross Validation and Leave One Out Cross Validation, the more accurate method for estimating earthquake strength on Sulawesi Island is the RK method because the RMSE and MAPE values ​​in the RK method are smaller than the OK method.

Statistical Analysis for Forest Fire Factors using Geography Information System (GIS) and Remote Sensing Imagery

About 33% of permanent forest reserve in Selangor is covered by peat swamp forest. The most important reason of peat swamp forests is to balance the ecosystem. Nevertheless, the evolution of technology and increasing population, with the needs of more space for land development has put peat swamp forest in Selangor under threat particularly because of forest fire. To overcome this risk problem, it is important to identifying and justifying these forest fires and a further study has been carried out to understand the problem. The study is carried out to find the factors that triggering forest fire at Kuala Langat South Forest Reserve (KLSFR) from 2013 until 2020. Temperature, rainfall, relative humidity, wind speed, NDVI, and LULC are choosing as to know the most triggered factors by measure their correlation value. The data are process using GIS and Remote Sensing (Landsat 8). The temperature, rainfall, relative humidity, wind speed data interpolate using Kriging method as a statistical analysis. While LULC is classify using Random Trees method. The value of correlation of temperature (0.4256), rainfall (-0.7613), relative humidity (-0.2484), wind speed (-0.8615), NDVI (0.1945) respectively. Furthermore, LULC is classify into five classes, which are high density forest, medium density forest, agriculture, bare soil, and waterbodies. Bare soil area shows highest correlation compare other classes, which is 0.6381. While rainfall and wind speed were identified as the most trigger factor to forest fire .

Possibility and quality assessment in seafloor modeling relative to the sea surface using hybrid data

Possibility and quality assessment in seafloor modeling relative to the sea surface using hybrid data

Expert's experience-informed hierarchical kriging method for aerodynamic data modeling

Data-driven models, such as kriging, have gained popularity in aerospace engineering due to their capability of predicting multidimensional, nonlinear aerodynamic characteristics of an aircraft. However, they are still suffering from the problem associated with poor extrapolation capability and physical interpretability, which in turn has great impacts on aerodynamic performance and flight safety. To address this problem, this article proposes to incorporate an empirical aerodynamic model obtained from expert's understanding of aerodynamics in the fitting process of a kriging model. First, empirical aerodynamic models based on expert's understanding and experience are derived. Second, the regression term of a kriging model is replaced by the expert's experience-informed model so that the global trend can be consistent with physical laws and provides extra knowledge in the subregion(s) without training data, especially for the extrapolation region(s). Finally, the expert's experience-informed hierarchical kriging (EEI-HK) model is built in a sequential way. The proposed method is validated with analytical test examples and demonstrated by aerodynamic data modeling of an AGARD-B missile and an FDL-5A hypersonic flight vehicle. Results show that, compared with ordinary and universal kriging models, the proposed EEI-HK model can dramatically improve the prediction accuracy in the extrapolation domain and slightly enhance the interpolation accuracy, with assistance from physical information provided by an empirical model. In consequence, it can be a promising approach for aerodynamic data extrapolation and saving the cost of establishing an aerodynamic database.

Improving spatiotemporal image fusion incorporating unmixing step by considering the point spread function effect

ABSTRACT In this paper, an improving spatiotemporal image fusion (STIF) incorporating unmixing step by considering the point spread function (PSF) effect model (CPSF) is proposed to address the problem that unmixing accuracy in STIF models incorporating unmixing step is vulnerable to the PSF effect. First, an unsupervised clustering algorithm is used to cluster the fine image at previous date into a land cover map, and the coarse class fraction matrix is generated from this land cover map. Second, the fine class fraction matrix uncontaminated by the PSF effect is calculated by upsampling the coarse class fraction matrix using the Area-to-Point Kriging (ATPK) method under the PSF constraint. Third, this fine class fraction matrix is convolved with the ideal square wave filter to downsample, and then normalized to obtain the coarse class fraction matrix without the influence of the PSF effect. Finally, the CPSF-processed class fraction matrix is used to replace the original one in the following unmixing and prediction process to generate the fine resolution image at the prediction date. Experiments on two remote sensing data sets show that the proposed method can effectively improve the accuracy of spatiotemporal image fusion methods incorporating unmixing step.

Sequentially Quadratic Surrogate Algorithm for Time-dependent Reliability and Reliability Sensitivity Analysis

Time-dependent reliability and reliability sensitivity analysis in presence of random uncertainty is widespread in equipment structures. To this end, this paper establishes a sequentially quadratic surrogate method. Firstly, the global reliability sensitivity analysis (GRS) is transformed into the classification problem of the time-dependent performance function outputs by means of conditional probability formula. Secondly, referring to the strategy of the Meta-IS method, the Kriging model of time-dependent performance function is employed to construct the importance sampling function to generate the importance sampling (IS) samples of failure domain efficiently. Furthermore, the Kriging model is updated in the IS samples set through the single-loop adaptive Kriging method to realize the accurate identification of the failure indicator function of IS samples, as well as simulation of time-dependent failure probability. Finally, utilize the information of the failure samples obtained by the estimation of time-dependent reliability to evaluate GRS. The proposed algorithm has excellent computational efficiency and applicability due to the conversion of the conditional probability formula, which enables the computational consumption of the time-dependent reliability and GRS analysis independent of the dimensions of the inputs, as well as the Meta-IS method, which improves the sampling efficiency and is applicable to the case of complex implicit performance function. The given examples fully verify the conclusions.

MEASUREMENT AND ENHANCEMENT OF GRAVITY DATA FOR GEOPHYSICAL INVESTIGATIONS IN ABUJA, NIGERIA

The availability of gravity data is useful to individuals, governments and organizations that wish to carry out a geophysical investigation in Abuja. The data provides an accurate model for environmental monitoring and disaster studies. This research was aimed at measurement and prediction of gravity data in Abuja, Nigeria. The aim was achieved by observation of terrestrial gravity data at forty (40) gravity stations around the federal capital territory Abuja, prediction of the gravity data at other points and the evaluation of the predicted data. Gravity reduction was carried out on the measured data to account for drift correction, free air correction, bouguer correction latitude correction and terrain correction. The data was further enhanced by interpolation using the Kriging method. A total of 59 points were predicted from the 40 measured stations. The predicted gravity anomalies were validated by comparing with their corresponding gravity anomalies obtained from the Earth Gravity Model 2008 (EGM08) because of their well-established reliability. For the number of observations, n = 59, the RMSE and SE were computed as 4.797mGal (4.787x10-5 ms-2) and 4.880mGal (4.880 x10-5 ms-2) respectively. Also, the correlation value obtained was 0.991 which is significant at 0.01 level. This shows a strong relationship signifying a perfect possible agreement in the values obtained. It was therefore concluded that Kriging is a good interpolator that provides an estimate with high reliability and dependency. In this regard, considering the high cost of geophysical data collection, gravity prediction can make geophysical data collection more efficient and cost-effective.

Spatial variability of heavy metals concentrations in soil of auto-mechanic workshop clusters in Nsukka, Nigeria

The indiscriminate disposal of spent engine oils and other hazardous waste at auto mechanic workshops clusters in Nsukka, Enugu State, Nigeria is an environmental concern. This study examines the concentration of heavy metals in the soil inside the workshop cluster and in the unpolluted soil outside the workshop cluster at approximately 100 m. Ten sampling points were randomly selected from within the cluster and another ten from outside the cluster. Using a hand-held Global Positioning System, the coordinates of the selected points were established and used to create a digital map. Soil samples at depths of 0–30 cm and 30–60 cm, were analyzed for Cu, Fe, Zn, Pb, As and Cd using Spectrophotometer. Moisture content determination and particle size analysis were also done on the samples. Spatial variability of heavy metals concentrations of the studied site was also mapped with ArcGIS 10.2.2 using interpolation methods. Results showed that the soil ranged from sandy loam to sandy clay loam. Cadmium and Zinc had the lowest and highest concentration, respectively, in the studied area. Comparing the concentrations of heavy metals in soils within and outside the auto mechanic cluster revealed notable differences across various depths (0–30 cm and 30–60 cm). The analysis results for soil samples within the cluster exhibited concentration levels (mg/kg) ranging from 0.716–0.751 (Cu), 2.981–3.327 (Fe), 23.464–30.113 (Zn), 1.115–1.21 (Pb), 2.6–2.912 (As), and 0.133–0.365 (Cd) demonstrating a variation pattern in the order of Zn > Fe > As > Pb > Cu > Cd. Conversely, for soil samples outside the cluster, concentration levels (mg/kg) ranged from 0.611–0.618 (Cu), 2.233–2.516 (Fe), 12.841–15.736 (Zn), 0.887–0.903 (Pb), 1.669–1.911 (As), and 0.091–0.091 (Cd). To assess the disparity in heavy metal concentration levels between samples collected within and outside the clusters, ANOVA test was performed. The test showed significant difference in heavy metal concentrations between samples within and outside the auto mechanic cluster (p < 0.05), implying auto mechanic activities significantly impact heavy metal levels within the cluster compared to outside areas. The assessment of soil pollution utilized indices including the Geo-accumulation Index (Igeo), Contamination factor (Cf), and anthropogenic metal concentration (QoC). Zinc, Cadmium, and Arsenic showed the highest contamination factors, indicating significant soil contamination likely due to anthropogenic activities. The concentrations of the metals analyzed were within WHO permissible limits while the metals concentrations were also observed to decrease as depth was increased. Using ArcGIS 10.2.2, spatial maps showing heavy metal distribution were developed, with the Kriging method proving superior. This study suggests that heavy metal levels in the soil at the area be monitored on a regular basis.

Improving hydrodynamic performance of surface piercing propeller through trailing-edge optimization

Automated optimization is increasingly used in engineering applications. In this study, RANS-based CFD, the NSGA II algorithm, and Kriging were used to optimize a section of a marine surface piercing Propeller (SPP) set. The hydrodynamic performance of the SPP is also determined using the CFD tool. The optimization process involves the NSGA II algorithm in combination with the Kriging method. The optimized geometry is simulated using the CFD tool. Then, the obtained results are added to the initial population and the optimization is repeated in the next iteration. Thus, fewer simulations were required because the addition of the data with the surrogate method was accompanied by a good distribution, using surface methods for the replacement of the main part in the required calculations. As shown, the trailing edge optimization can improve Kt in J = 1 by almost 10.5%, and Kq changes by almost 12%, so this method can be used as an optimization package for similar problems.

Immersive Situational Analysis Method Based on Generalized Augmented Grid Statistic

The governance of Virtual-Reality Integration, which seamlessly merges the virtual world (metaverse) with the physical reality, represents an emerging approach to addressing perception and comprehension challenges in complex computational environments. Such Virtual-Reality Integration systems have the capability to streamline data analysis complexity, offer real-time visualization, and provide user-centric interaction, thereby delivering crucial support for data analysis and profound decision-making in complex computational settings. In this paper, we introduce a real-time perception and interaction methodology that combines computer vision with Virtual-Reality Integration technology. We employ the Grid-ORB algorithm-based approach for high-precision feature extraction and three-dimensional registration tracking on resource-constrained devices, enabling the perception of physical entities. Furthermore, we utilize the Kriging method, augmented with a drift term, to fill gaps in numerical physical space data, aiding users in observing real-world physical values and trend fluctuations. To facilitate a unified cognitive experience for data and knowledge, we devise a user-centric interaction interface using augmented reality technology. Within this interface, users can interact with charts and controls through methods such as eye movement and gestures. Finally, we validate our system within a real thermodynamics experimental environment, with results demonstrating a significant enhancement in user efficiency for comprehending data and knowledge within complex environments.

Risk assessment of radon exposure inside residential dwellings in Safi city, Morocco

ABSTRACT A radon study was conducted in 50 residences in Safi city, Morocco, using LR-115 detectors. The annual effective dose and Excess Lifetime Cancer Risk were also estimated. The measured radon concentrations ranged between the Lower Limit of Detection (LLD) and 132 Bq.m−3 with an arithmetic mean of 53 Bq.m−3 and a geometric mean of 41 Bq.m−3 (2.32). The results indicated that 58% of the obtained values exceeded the worldwide average value (39 Bq.m−3). In addition, the results revealed significant impacts of floor level and region on indoor radon levels. Inhabitants of the surveyed houses were found to receive a mean annual effective dose of 1.5 ± 0.1 mSv.y−1, corresponding to an Excess Lifetime Cancer Risk of 0.6%. Importantly, all annual effective dose values were below the recommended action level by the ICRP (3–10 mSv.y−1). Using the Ordinary Kriging method implemented in the QGIS software, an indoor radon map was created based on the collected data. The findings in this study serve as baseline values for future studies concerning radon concentrations and their fluctuations.

Bathymetric Modelling of High Mountain Tropical Lakes of Southern Ecuador

Very little is known on high mountain tropical lakes of South America. Thus, the main motivation of this research was obtaining base bathymetric data of 119 tropical lakes of the Cajas National Park (CNP), Ecuador, that could be used in future geomorphological studies. Eleven interpolation methods were applied with the intention of selecting the best one for processing the scattered observations that were collected with a low-cost fishing echo-sounder. A split-sample (SS) test was used and repeated several times considering different proportions of available observations, selected randomly, for training of the interpolation methods and accuracy evaluation of the respective products. This accuracy was assessed through the use of empirical exceedance probability distributions of the mean absolute error (MAE). A single best interpolation method could not be identified. Instead, the study suggested six better-performing methods, including the complex methods Kriging (ordinary), minimum curvature (spline), multiquadric, and TIN with linear interpolation but also the much simpler methods natural neighbour and nearest neighbour. A sensitivity analysis (SA), considering several data error magnitudes, confirmed this. This advocated that sophisticated interpolation methods do not always produce the best products as geomorphological characteristics of the study site(s) together with observation data characteristics are likely to play important roles in their performance. As such, this type of assessment should be carried out in any terrestrial mapping of bathymetry that is based on the interpolation of scattered observations. Upon the analysis of the relative hypsometric curves of the 119 study lakes, they were classified into three average form categories: convex, concave, and mixed. The separated accuracy analysis of these three groups of lakes did not help in identifying a single best method. Finally, the interpolated bathymetries of 114 of the study lakes were incorporated into the best DEM of the study site by equalising their elevation reference systems. It is believed that the resulting enhanced DEM could be a very useful tool for a more appropriate management of these very beautiful but fragile high mountain tropical lakes.

Quantifying uncertainty in simulations of the West African monsoon with the use of surrogate models

Abstract. Simulating the West African monsoon (WAM) system using numerical weather and climate models suffers from large uncertainties, which are difficult to assess due to nonlinear interactions between different components of the WAM. Here we present a fundamentally new approach to the problem by approximating the behavior of a numerical model – here the Icosahedral Nonhydrostatic (ICON) model – through a statistical surrogate model based on universal kriging, a general form of Gaussian process regression, which allows for a comprehensive global sensitivity analysis. The main steps of our analysis are as follows: (i) identify the most important uncertain model parameters and their probability density functions, for which we employ a new strategy dealing with non-uniformity in the kriging process. (ii) Define quantities of interest (QoIs) that represent general meteorological fields, such as temperature, pressure, cloud cover and precipitation, as well as the prominent WAM features, namely the tropical easterly jet, African easterly jet, Saharan heat low (SHL) and intertropical discontinuity. (iii) Apply a sampling strategy with regard to the kriging method to identify model parameter combinations which are used for numerical modeling experiments. (iv) Conduct ICON model runs for identified model parameter combinations over a nested limited-area domain from 28° W to 34° E and from 10° S to 34° N. The simulations are run for August in 4 different years (2016 to 2019) to capture the peak northward penetration of rainfall into West Africa, and QoIs are computed based on the mean response over the whole month in all years. (v) Quantify sensitivity of QoIs to uncertain model parameters in an integrated and a local analysis. The results show that simple isolated relationships between single model parameters and WAM QoIs rarely exist. Changing individual parameters affects multiple QoIs simultaneously, reflecting the physical links between them and the complexity of the WAM system. The entrainment rate in the convection scheme and the terminal fall velocity of ice particles show the greatest effects on the QoIs. Larger values of these two parameters reduce cloud cover and precipitation and intensify the SHL. The entrainment rate primarily affects 2 m temperature and 2 m dew point temperature and causes latitudinal shifts, whereas the terminal fall velocity of ice mostly affects cloud cover. Furthermore, the parameter that controls the evaporative soil surface has a major effect on 2 m temperature, 2 m dew point temperature and cloud cover. The results highlight the usefulness of surrogate models for the analysis of model uncertainty and open up new opportunities to better constrain model parameters through a comparison of the model output with selected observations.

Surrogate Model of Solved Poisson Kriging Method for Radiation Field Reconstruction

Reverse reconstruction methods for the radiation field do not require information on the radioactive source and are capable of constructing the radiation field using a small amount of monitoring data, showing huge significance for radiation protection. However, in previous studies, inverse reconstruction methods have given less consideration to variations in the time dimension. Herein, the principle of the Poisson Kriging method solved by the surrogate model has been analyzed, and the Poisson Kriging method has been applied to the inverse reconstruction of two-dimensional radiation fields at different moments. On this basis, this work also investigated the effects of the principal function and correlation coefficient model on the objective function, the results of which demonstrate that the quadratic polynomial principal function and the Gaussian model correlation coefficient have good stability and convergence. Compared with the inverse distance weighting methods and the radial basis function methods, the Poisson Kriging method has smaller errors, showing that it is more suitable for reconstructing complex radiation fields. Finally, the Poisson Kriging method was applied to the Fukushima nuclear accident radiation field calculation. The Pearson correlation coefficient of its results was r = 0.49, reflecting the validity of this method. Our work provides a calculation method for the spatial distribution and trend of the radiation field in the early stages of a nuclear accident, which is helpful for furthering radiation protection and emergency responses to nuclear accidents.