How vertical range could be larger than horizontal range in variogram?

I am caught in a strange situation. I have been doing some kriging using gstat package, but the data is exhibiting a lot of bad signs. It has a second order strong trend surface, with almost all the coefficients declared significant with three asterisks *** in lm(). Though R^2 is small, but the empirical variograms are clearly different in two cases i.e. variogram(attribute~1) and variogram(attribute~SO(x, y)) have different sills. Furthermore, the directional variograms also show both zonal and geometric anisotropies. When I try to fit a variogram model, I see a big change between the fitted ranges and sills of the simple (no anisotropy assumed) and anisotropy corrected variogram models. How do I deal with this analysis?    

I am investigating methods to reconstruct the strain field within a wall subjected to a concentrated top load. Scattered strain data is available from DFOS (distributed fiber optic sensing) measurements.

The objective is to interpolate the data to create a continuous strain field across the entire wall structure. Universal Kriging was initially used with spherical, exponential, and Gaussian variograms but resulted in significant validation errors. While default parameters for nugget effect and sill were used, their potential impact on improving results is unclear for me.

What is the recommended approach for constructing an accurate normal strain field based on scattered DFOS measurements (1 dimensional tangential strain) and a known loading scenario?

As I am a beginner in using R, I am still confused about fitting variogram in gstat.

i uploaded my data already and converted it into a spatial point data frame through a coordinates command.

but still have problems in selecting the exact numbers for sill, range, and nugget.

vt.fit<-fit.variogram(vt.c,model=vgm(1, "Sph", 7860, 0.5))

it gives me a warning message that I have to put other numbers

How i can fit the exact curve for variogram without spending time and to be sure everything will be correct. thanks in advance

I want to simulate a random field Z(u) that has a nested variogram, say gamma(h)=gamma1(h) + gamma2(h) + gamma3(h), assuming the variogram is isotropic. Whether can I simuate independently three random fields: Z1(u) with correlation structure gamma1(h), Z2(u) with correlation structure gamma2(h), Z3(u) with correlation structure gamma3(h), and then sum them up Z(u) = Z1(u) + Z2(u) + Z3(u)?

I am planning to distribute temperature data using kriging with external drift in R. I have time series data of temperature and i guess i need to make variogram for each day. Is there any way through which we can automatically generate variogram model and run kriging with external drift. Thanks in advance.

As we know erratic and unstable shape of variograms could be caused by different reasons such as incorrectly chosen lag and/or directional tolerance, the complex geometry of orebody, strongly skewed distribution, containing outliers, preferential sampling from the high grade ore, proportional effect, etc. Between all those reasons outlier values are the main causes of instability of the variogram. Noisy variogram shows less structure and fitting a theoretical model on it is very difficult. Therefore, different procedures developed to mitigate the noisy behavior of variograms including using correlogram, pairwise relative variogram, and transforming the data to a normal standard distribution (µ=0 and σ2=1). Between these techniques the normal score transformation is the best alternative to measure the spatial continuity. However, the variogram of normal score could be used in Gaussian techniques such as Sequential Gaussian Simulation but it cannot be used directly in the ordinary kriging or other types of kriging. Therefore, it is necessary to back-transform the variogram model to the original unit. There are two techniques to back-transform the normal score variogram model to original unit, known as Monte Carlo Simulation and Hermite polynomials. These back-transformation techniques are available in some software packages like Leapfrog and Geovariances but in software that is available for me, Datamine Studio RM, I did not find any of these two techniques.

Instead of back-transforming the normal score variogram model I used the normal score data as well as normal score variogram model to estimate the variable in unsampled locations. Then I back-transformed the estimated distribution to original unit by using the original data distribution and its corresponding normal score data. I am not sure if this procedure is correct. I appreciate if I can get any comment.

Workflow for estimation using normal score data and back-transform it to original unit is presented in the attached picture.

i need to plot a simple variogram with same distances, like this

ppm cu

20-15-12-8-6

15-20-8-6-7

8-15-20-5-3

- = 50 feets or meters

help please!

I am looking at the spatial distribution of two invasive plants using point pattern analysis to explore how the plants disperse within a community. My data is inhomogenous and I need to account for that in my analysis. It is pretty straight forward with pair correlation function (PCF), Ripley's K, G or L functions as they all have inhomogeneous functions. However, I have not found the same to be true of mark correlation and mark variogram functions. Do they have inhomogeneous functions or not? If so, how can I apply them in spatstats? In this case my marks are the basal diameter of the plant. If not, please explain why. I am a spatstats novice.

Assume that the given semi-variogram can be described as the sum of two Gaussian variograms with no nugget effects.