VARIOGRAM

library(sp)
library(gstat)
library(dplyr)
library(ggplot2)
library(scales)
library(magrittr)

data = read.csv('D:/data (1).csv')

coordinates(data) = ~x.km. + y.km.
glimpse(data)

data %>% as.data.frame %>%
  ggplot(aes(x.km.,y.km.)) +
  geom_point(aes(size=K.Fracture),col="blue", alpha=0.6)+
  ggtitle("Permeabilitas (mD)") + coord_equal() + theme_bw()

(kfracture.vgm1 <- variogram(K.Fracture~1, data))
plot(kfracture.vgm1)

var.exp = kfracture.vgm1
plot(var.exp)
sill =2000
rnge = 0.3
nugget = 50
(kfracture.fit <- fit.variogram(var.exp, model=vgm(sill,model="Gau", rnge, nugget)))
plot(var.exp,kfracture.fit)

(kfracture.vgm1.ln.2 <- variogram(log(K.Fracture)~1, data,width = 0.05))
plot(kfracture.vgm1.ln.2)

var.exp = kfracture.vgm1.ln.2
plot(var.exp)
sill =2000
rnge = 0.3
nugget = 50
(kfracture.fit <- fit.variogram(var.exp, model=vgm(sill,model="Gau", rnge, nugget)))
plot(var.exp,kfracture.fit)

n = 0.005
kolom = seq(data@bbox[1,1]-n,data@bbox[1,2]+n,by=n)
baris = seq(data@bbox[2,1]-n,data@bbox[2,2]+n,by=n)
the.grid = expand.grid(x=kolom, y=baris)
coordinates(the.grid) <- ~x+y
gridded(the.grid) <- T

kfracture.kriged = krige(log(K.Fracture)~1, data,the.grid,model=kfracture.fit)
spplot(kfracture.kriged["var1.pred"],main="ordinary kriginf predictions 1")