MPS

The 'MPS' type prior make use of the MPS library for mulitple-point based simulation. For compilation and installation help see Install SIPPI. MPS implements the SNESIM (using both a search tree and a list to stor conditional statistics), and the generalized ENESIM algoritm. The type of multiple-point algorithm is define in the method field.

To use the MPS type prior at least the type, dimension, as well as a training image must be provided:

ip=1; 
prior{ip}.type='mps';
prior{ip}.x=1:1:80;
prior{ip}.y=1:1:80;

A trainin imag emust be set in the 'ti' field, as 1D, 2D, or 3D matrix. If not set, the classical training image from Strebelle is used, equivalent to:

prior{ip}.ti=channels;

More examples of traning images are located in the 'mGstat/ti' folder.

MPS provides three different simulation aglrithms, which canbe chosen in the 'method' field as:

prior{ip}.method='mps_snesim_tree';
prior{ip}.method='mps_snesim_list';
prior{ip}.method='mps_genesim';

'mps_snesim_tree' is the simulation method selected by default if it is not specified.

options for MPS

All options for the MPS type simulation algorithm are be available in the prior{ip}.MPS data structure.

For example, to set the number of used multiple grids, set the MPS.n_multiple_grids as

ip=1; 
prior{ip}.type='mps';
prior{ip}.method='mps_snesim';
prior{ip}.x=0:.1:10;
prior{ip}.y=0:.1:20;
[m,prior]=sippi_prior(prior);
i=0;
for n_mul_grids=[0:1:4];
    prior{ip}.MPS.rseed=1;
    prior{ip}.MPS.n_multiple_grids=n_mul_grids;
    [m,prior]=sippi_prior(prior);
    i=i+1;subplot(1,5,i);
    imagesc(prior{1}.x,prior{1}.y,m{1});axis image
    title(sprintf('NMG = %d',n_mul_grids));
end

More details on the use of MPS can be found in the SoftwareX manuscript that describes MPS.