scholarly journals Earthquake Precursory Detection Using Diurnal GPS-TEC and Kriging Interpolation Maps: 12 May 2008, Mw7.9 Wenchuan Case Study

MethodsX ◽  
2022 ◽  
pp. 101617
Author(s):  
Prapas Thammaboribal ◽  
N.K. Tripathi ◽  
Sarawut Ninsawat ◽  
Indrajit Pal
Keyword(s):  
Kriging Interpolation ◽  
Gps Tec

scholarly journals A Spatial-Temporal Analysis of the Effects of Households’ Land-use Behaviors on Soil Available Potassium in Cropland: A Case Study from Urban Peripheral Region in Northeast China

Land ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 160
Author(s):  
Hongbin Liu ◽  
Zhanli Sun ◽  
Xiaojuan Luo ◽  
Xiuru Dong ◽  
Mengyao Wu
Keyword(s):  
Land Use ◽  
Urban Area ◽  
Northeast China ◽  
Temporal Analysis ◽  
Peripheral Region ◽  
Kriging Interpolation ◽  
Gis Tools ◽  
Data Points ◽  
Spatial Temporal Analysis

Available potassium (AVK) in the soil of cropland is one of the most important factors determining soil quality and agricultural productivity. Thus, it is crucial to understand the variation of AVK and its influencing factors for sustaining soil fertility and mitigating land degradation. Farm households are the ultimate land users, and their land-use behaviors inevitably play an important role in the variation of AVK. This paper, therefore, aims to explore the effects of households’ land-use behaviors on soil AVK from spatial and temporal perspectives. Taking an urban peripheral region in Northeast China as the study area, we firstly use geostatistics (Kriging interpolation) and GIS tools to map out the spatial AVK distributions in 1980, 2000, and 2010, based on soil sampling data points, and then assess the impacts of land-use behaviors on AVK using econometric models. The results show that, although the AVK content in the study area has a largely downward trend over the 30 years, there are distinct trends in different stages. The disparity of trends can be attributed to the changes in households’ land-use behaviors over time. The spatial variation of AVK is also substantial and intriguing: the closer to the urban area, the greater the decline of soil AVK content, while the farther away from the urban area, the greater the rise of soil AVK content. This spatial disparity can too be largely explained by the obvious differences in households’ land-use behaviors in various regions.

Solar terminator-related ionosphere derived from GPS TEC measurements: A case study

2011 ◽  
Vol 60 (4) ◽  
pp. 1224-1235 ◽  
Author(s):  
Marcin Grzesiak ◽  
Anna Świątek
Keyword(s):  
Gps Tec ◽  
Solar Terminator

scholarly journals Comparison of Ordinary and Universal Kriging interpolation techniques on a depth variable (a case of linear spatial trend), case study of the Šandrovac Field

2016 ◽  
Vol 31 (2) ◽  
pp. 41-58
Author(s):  
Ivana Mesić Kiš
Keyword(s):  
Input Data ◽  
Universal Kriging ◽  
Kriging Interpolation ◽  
Geological Data ◽  
Geological Structures ◽  
Spatial Trend ◽  
Common Trend ◽  
Cardinal Direction ◽  
Surface Spread

Universal Kriging has not until now been used for mapping of geological data in Croatia. However, it is one of the most frequently used methods of Kriging, probably the most adequate in cases when the input data is marked by a common trend. That exact feature is often an attribute of deep geological data, and thereby that of structural maps. Mapped surfaces in a row of examples have a structural trend towards one cardinal direction, or a sequence of geological structures, like anticlinorium, is a part of a structural unit of a higher order such as regional monocline. An example is given of geographical trend recognition in e-log Z’ surface spread in Šandrovac Field as well as successful mapping of that marker depth variable by using Universal Kriging.

scholarly journals Forecasting and Mapping Coffee Borer Beetle Attacks Using GSTAR-SUR Kriging and GSTARX-SUR Kriging Models

2020 ◽  
Vol 11 (2) ◽  
pp. 65-73
Author(s):  
Henny Pramoedyo ◽  
Arif Ashari ◽  
Alfi Fadliana
Keyword(s):  
Spatial Clustering ◽  
Kriging Model ◽  
Seemingly Unrelated Regression ◽  
Kriging Interpolation ◽  
Percentage Error ◽  
Eastern Region ◽  
Unrelated Regression ◽  
Interpolation Technique ◽  
The Mean

The research aimed to use Generalized Space Time Autoregressive (GSTAR) and GSTARX modeling with the Seemingly Unrelated Regression (SUR) approach and combine them with the Kriging interpolation technique in an unobserved location. The case study was coffee borer beetle forecasting in Probolinggo Regency, East Java, Indonesia, with Watupanjang Village as the unobserved location. The results show that GSTAR-SUR Kriging and GSTARX-SUR Kriging models can predict coffee borer beetle attacks in unobserved areas with high accuracy. It is indicated by the Mean Absolute Percentage Error (MAPE) values of less than 10%. The addition of exogenous variables (rainfall) into the model is proven to improve the accuracy of the model. The Root-Mean-Square Error (RMSE) value of the GSTARX-SUR Kriging model is smaller than the GSTAR-SUR Kriging model. The structure of the model produced from the research, GSTARX-SUR (1,[1,12])(10,0,0), can be used as a reference in modeling coffee borer beetle attacks in other regencies. Map of forecasting coffee borer beetle attack shows that the spread of coffee borer beetle attack is spatial clustering with the attack center located in the eastern region of Probolinggo Regency.

scholarly journals Heterogeneity Evaluation of Soil Engineering Properties Based on Kriging Interpolation Method. Case Study: North East of Iran, West of Mashhad

2018 ◽  
Vol 41 (2) ◽  
pp. 193-202 ◽  
Author(s):  
Mahin Etemadifar ◽  
Nasibeh Sadat Vaziri ◽  
Iman Aghamolaie ◽  
Naser Hafezi Moghaddas ◽  
Gholamreza Lashkaripour
Keyword(s):  
Interpolation Method ◽  
Engineering Properties ◽  
Kriging Interpolation ◽  
North East ◽  
Kriging Interpolation Method ◽  
East Of Iran

scholarly journals Geomechanical Modelling Using Artificial Neural Networks Combined With Geostatistics

2020 ◽  
Author(s):  
Son Tung Pham
Keyword(s):  
Oil And Gas ◽  
Horizontal Stress ◽  
Kriging Interpolation ◽  
Empirical Formulas ◽  
Stress Estimation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Minimum Horizontal Stress ◽  
Oil And Gas Reservoir

The principal minimum horizontal stress plays an important role in the study of reservoir characteristics, modeling of oil and gas reservoir, drilling, production and stimulating wells. However, it is currently not possible to measure the minimum horizontal stress along the wellbore as a geophysical parameter logging. Minimum horizontal stress is measured by leak-off test (LOT) at only several points in a well. In order to have the values all along the wellbore, experimental formulas were established to determine the minimum horizontal stresses for different fields. Then these formulas must be calibrated with LOT data whose number is usually limited, even sometimes unavailable. On the other hand, the empirical formulas of one field might not be accurate for another. This study presents a new approach to solve the problem of minimum horizontal stress estimation by a combination of artificial intelligence and geostatistics. The method consists of using artificial neural network (ANN) to build a model of minimum horizontal stress estimation from relevant parameters such as true vertical depth, pore pressure and vertical stress, then combined with Kriging interpolation to obtain the distribution in space of the minimum horizontal stress. Hence, this method can estimate the minimum horizontal stress with a limited amount of available data and therefore we do not need to drill new wells or to find empirical formulas for each survey area. The method was then applied in a case study involved real geomechanical dataset of Hai Thach - Moc Tinh field in Nam Con Son basin, Vietnam.

scholarly journals Author Correction: Revelation of early detection of co-seismic ionospheric perturbations in GPS-TEC from realistic modelling approach: Case study

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Dhanya Thomas ◽  
Mala S. Bagiya ◽  
Poikayil Sukumaran Sunil ◽  
Lucie Rolland ◽  
Anakuzhikkal Sudarsanan Sunil ◽  
...  
Keyword(s):  
Early Detection ◽  
Ionospheric Perturbations ◽  
Gps Tec ◽  
Modelling Approach

scholarly journals Spatial distribution of interpolation uncertainity: Case study of isotherm map of Serbia (1991-2009)

2012 ◽  
Vol 92 (4) ◽  
pp. 31-50
Author(s):  
Milutin Pejovic ◽  
Branislav Bajat ◽  
Jelena Lukovic
Keyword(s):  
Monte Carlo Simulation ◽  
Spatial Distribution ◽  
Simulation Modeling ◽  
Kriging Interpolation ◽  
Geostatistical Methods ◽  
Method Of Evaluation ◽  
Product Maps ◽  
Mean Annual Air Temperature

More widely used geostatistical methods for modeling distributed phenomena requires the evaluation of the quality of the product (maps) obtained by their application. The method of evaluation of uncertainty i.e. quality of the map was described on the example of a map of mean annual air temperature in Serbia for the period from the years 1991 to 2009 that was obtained from a relatively small number of samples for the whole country area (110 meteorological stations). The uncertainty of the map, obtained by kriging interpolation was evaluated by applying a Monte Carlo simulation modeling method.

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