simpgs {RGeostats}  R Documentation 
Plurigaussian conditional simulations
simpgs(dbin = NA, dbout, dbprop = NA, rule = rule.input(), model1 = NA, model2 = NA, neigh=neigh.input(), props = NA, nostat=NA, domain=0, flag.gaus = FALSE, flag.prop=FALSE, flag.check=FALSE, flag.show = FALSE, seed = 232131, nbsimu = 1, nbtuba = 100, ngburn = 10, ngiter = 100, ngint=5, percent=0.1, toleps=1, delta = NA, flag.spde=FALSE, triswitch="nqQ", gext=NA, numparts=NA, npiter=10, verbose=FALSE, accept.fun = NA, accept.nloop.max = 10, accept.verbose=TRUE, radix = "SimPGS", modify.target = db.locmod())
dbin 
The 
dbout 
The 
dbprop 
The 
rule 
The 
model1 
The 
model2 
The 
neigh 
The 
props 
Array giving the (constant) proportions of the different facies
involved in the Lithotype rule. See details in 
nostat 
List of nonstationary parameters.
For details see 
domain 
When (strictly) positive, it corresponds to the reference domain value. If a Db attribute is defined as a Domain variable in a Db file, a sample will be selected only if the corresponding Domain variable is equal to the reference domain value. This Domain selection can serve as a secondary selection for the sample in the Input Db or as a mask in the Output Db. 
flag.gaus 
When FALSE, the simulation outcome is coded into facies. When TRUE, the (conditional) simulation is kept in the gaussian scale 
flag.prop 
When TRUE, the procedure returns the proportion of facies, rather than the simulations 
flag.check 
When TRUE (and if dbin is defined), the facies of conditioning data is compared to the facies of the closest grid node. 
flag.show 
When TRUE (and if dbin is defined), the grid node which coincides with a data is represented with the data facies (with a negative sign). 
seed 
Seed used for the generation of random numbers. When 0, the seed is not initialized. 
nbsimu 
Number of simulations 
nbtuba 
Number of turning bands 
ngburn 
Number of iterations performed for bootstrap 
ngiter 
Maximum number of iterations for calculating the conditional expectation 
ngint 
Number of iterations inside the Gibbs sampler iterative algorithm (only for SPDE technology) 
percent 
Amount of nugget effect added to the model if this initial model only contains Gaussian structures. If set to zero, the initial model is kept unchanged. This amount is defined as a percentage of the global variance. 
toleps 
Relative difference between the previous and the new value at a sample location below which the sample is considered as immobile 
delta 
Spatial distance for generating the replicates in case of conditional simulations for shadow option. 
flag.spde 
When TRUE, the simulation is carried out using SPDE technique. Otherwise the Turning Bands method is used. 
triswitch 
Command line for the internal triangulation step. For more information see

gext 
When 'dbout' is organized as a grid, it may be dilated by gext. This argument designates an array, with its dimension equal to the dimension of the space and which contains the extension defined in number of grid nodes. If not defined, the grid is not dilated and the simulated results may suffer some edge effect problems. 
numparts 
Array for subdividing the field into parts. Its dimension must be equal to the space dimension. If not defined (or equal to 1 in each space direction), the space is not subdivided. Subdividing in parts reduces the dimension of the matrices and can be used in the case of large files (input or output). In order to reduce the artifacts that may be induced by the subdivision, two steps of subdivisions are actually processed. For illustration purpose, let us assume that the field (SX by SY) with origin (X0,Y0) is subdivided into NX ny NY parts.

npiter 
When the field is subdivided into several parts, several iterations are necessary to glue the parts. This parameter defined the number of iterations to be processed. 
verbose 
Verbose flag 
accept.fun 
An acceptation function. See details for more information. 
accept.nloop.max 
Maximum number of iterations before 'nbsimu' acceptable simulation outcomes is reached. 
accept.verbose 
When TRUE, the activity of the acceptation function is echoed. 
radix 
Radix of the name given to the simulation outcomes stored in the output Db. 
modify.target 
Decides whether or not the newly created variables will have their
locator defined or not. For more information, see 
The data Db where the simulations outcomes have been added.
# Acceptation function # accept < function(db,iatt) { # Locations of wells in the grid # ranks = c(5021, 5081) # Perform the Connected components # a = db a = db.locate(a,iatt,"z") a = morpho(a,1,2,oper="cc") # Do all wells belong to same CC # test = TRUE cc = a[ranks[1],a$natt] for (i in 1:length(ranks)) if (cc != a[ranks[i],a$natt]) test = FALSE test } # Perform a conditional simulation of facies # nbsimu = 25 grid = db.create(nx=c(100,100)) model = model.create(vartype="Cubic",range=30) rule = rule.create(c("S","F1","F2")) x1 = c(20,80,50) x2 = c(50,50,50) z1 = c(1,1,2) data=db.create(x1=x1,x2=x2,z1=z1) neigh = neigh.create(type=0) grid = simpgs(data,grid,rule=rule,model1=model,neigh=neigh, accept.fun=accept,nbsimu=nbsimu,nbtuba=1000) grid = db.compare(grid,names="SimPGS*",fun="mean") plot(grid,title="Connectivity probability",pos.legend=1) plot(data,add=TRUE,name.post=1,pch=19,col=c("yellow","red")[z1]) rm(grid,model,rule,data,neigh,nbsimu,accept,x1,x2,z1)