The improvement of strain estimation using universal kriging

Abstract

In this paper, universal kriging with linear trend is used to interpolate the strain tensor elements over a region along San Andreas Fault in California. The main goal of this paper is to improve the ordinary kriging interpolation results. A 7-year time series (2006)(2007)(2008)(2009)(2010)(2011)(2012) of 12 permanent stations is utilized to obtain the coordinate changes in UTM coordinates system and calculate the strain tensor elements by means of finite difference method. Comparing the results we can find an improvement about 40 % for universal kriging at critical points in which ordinary kriging can't be appropriate method of interpolation.

Spatial-Temporal Analysis and Short-Term Forecasting of Hydrotechnical Facility Displacements

“…By applying geostatistical methods one can accurately model spatial data, estimations and estimation accuracy and so determine the reliability of the estimation results [8,[11][12][13][14][15][16][17][18][19][20]. For years geostatistical methods have been used in geological sciences, mining, environmental protection, agriculture, geochemistry, epidemiology, meteorology, oceanography, forestry and materials science, to a lesser or greater extent [15][16][17][18][21][22][23][24][25][26][27][28] and also in geodesy [29][30][31][32][33][34]. They are also used in thematic cartography, e.g.…”

Multilevel landscape utilization of the Siberian flying squirrel: Scale effects on species habitat use

“…That accounts spatial dependence of a variable as two components: large‐scale trend surface interpolation (regional effects), and local structures of spatial autocorrelation (landscape patchiness) (Lam, ; Legendre & Fortin, ). The two‐scale procedure is about 40% more effective for describing spatial patterns than ordinary kriging, which does not consider the large‐scale trend (Ghiasi & Nafisi, ).…”

“…Population patches and gaps are defined as areas of higher (+) and lower (−) local abundance in relation to the global trend of regional density. In the analysis, gross values of site occupancy (i + ii + iii) and landscape-level abundance (ii + iii) were explained by landscape metrics, while the partial components of landscape-level abundance (ii) and regional density (iii) were used as covariates accounting for the effects of lower level patterns kriging, which does not consider the large-scale trend (Ghiasi & Nafisi, 2015).…”

Modeling the Horizontal Velocity Field of the Earth’s Crust in a Regular Grid from GNSS Measurements

“…They are universal for generating gridded data for movement and deformation fields regardless of the studied geological process or phenomenon. These methods include geostatistical methods [Bogusz et al, 2013;Ghiasi and Nafisi, 2015], distance-weighting methods [Bogusz et al, 2013;Shen et al, 1996Shen et al, , 2015, spline and polynomial methods [Bogusz et al, 2013;Sandwell, 1987], machine learning methods [Aleshin et al, 2022;Grishchenkova, 2017;Manevich et al, 2021;Manevich and Tatarinov, 2017;Tatarinov et al, 2018], and others.…”

“…In modeling recent crustal movement fields, classical spatial interpolation methods are regularly employed, such as the inverse distance method, kriging, and the natural neighbour method [Bogusz et al, 2013;Ghiasi and Nafisi, 2015;Matheron, 1970;Shen et al, 2015;Srivastava and Isaaks, 1989;Wackernagel, 1994]. Their application is justified by their ease of implementation in GIS environments and the ability to finely tune parameters.…”