MATLAB codes for comparing geostatistical simulation algorithms. Please do the following to use the codes:
- Copy-paste the codes in a specific directory of your computer.
- Go to ...\ ComparingGSAlgorithms\ComparingGSAlgorithms folder and open test.m file.
- uncomment and run the code for each case.
9 important functions are listed below, explaining how this algorithm works in detail. Functions:
- DistMtrx = calculateModelVar_MPH(realizations,TI,pyramid)
This function is used to calculate distance matrix between realizations, as well as between realizations and training image in 2D binary case. MPH approach is used in this function. First input: “realizations” is all the realizations needed: e.g.10110150 Second input: “TI” is the training image Third input: “pyramid” is the level of Pyramid of realizations and training image Output: ”DistMtrx”: distance matrix e.g. 105151 (10 is the level of Pyramid)
Notice: sometimes it might be out of contiguous memory, so rebooting or restarting MATLAB may work. When possible, running the code in 64-bit windows will solve such a problem.
- DistMtrx = calculate3DModelVar_MPH(realizations,TI,pyramid) This function is used to calculate distance matrix between realizations, as well as between realizations and training image in 3D binary case. MPH approach is used in this function.
First input: “realizations” is all the realizations needed: e.g.69693950 Second input: “TI” is the training image: e.g. 696939 Third input: “pyramid” is the level of Pyramid of realizations and training image Output: “DistMtrx”: distance matrix e.g. 1051*51 (10 is the level of Pyramid)
Notice: sometimes it might be out of contiguous memory, so rebooting or restarting MATLAB may work. When possible, running the code in 64-bit windows will solve such a problem.
- DistMtrx = calculateModelVar_CHP(realizations,TI,tempSize); This function is used to calculate distance matrix between realizations, as well as between realizations and training image in continuous case. CHP approach is used in this function.
First input: “realizations” is all the realizations needed: e.g.10110150 Second input: “TI” is the training image Third input: “tempSize” is the optimal size of template for training image Output: “DistMtrx”: e.g. distance matrix e.g. 105151 (10 is the level of Pyramid)
- show2DModelVar2(DisMtrx1,DisMtrx2,TI,realizations1,realizations2) This function is used to show the variability of the two sets of 2D realizations at different resolutions in the form of MDS plots.
First input: DisMtrx1 is the distance matrix for the first set of realizations Second input: DisMtrx2 is the distance matrix for the second sets of realizations Third input: “TI” is the training image e.g. 101101 Forth input: “realizations1” is the first set of realizations: e.g. 101101*50 Fifth input: “realizations2” is the second set of realizations
- show2DModelVar3(DisMtrx1,DisMtrx2,DisMtrx3,TI,realizations1,realizations2,realizations3) This function is used to show the variability of the three sets of 2D realizations at different resolutions in the form of MDS plots.
First input: DisMtrx1 is the distance matrix for the first set of realizations Second input: DisMtrx2 is the distance matrix for the second sets of realizations Third input: DisMtrx3 is the distance matrix for the third sets of realizations Forth input: “TI” is the training image e.g. 101101 Fifth input: “realizations1” is the first set of realizations: e.g. 101101*50 Sixth input: “realizations2” is the second set of realizations Seventh input: “realizations3” is the third set of realizations
- show3DModelVar2(DisMtrx1,DisMtrx2,TI,realizations1,realizations2) This function is used to show the variability of the two sets of 3D realizations at different resolutions in the form of MDS plots.
First input: DisMtrx1 is the distance matrix for the first set of realizations Second input: DisMtrx2 is the distance matrix for the second sets of realizations Third input: “TI” is the training image e.g. 696939 Forth input: “realizations1” is the first set of realizations: e.g. 696939*50 Fifth input: “realizations2” is the second set of realizations
- show3DModelVar3(DisMtrx1,DisMtrx2,DisMtrx3,TI,realizations1,realizations2,realizations3) This function is used to show the variability of the three sets of 3D realizations at different resolutions in the form of MDS plots.
First input: DisMtrx1 is the distance matrix for the first set of realizations Second input: DisMtrx2 is the distance matrix for the second sets of realizations Third input: DisMtrx3 is the distance matrix for the third sets of realizations Forth input: “TI” is the training image e.g. 696939 Fifth input: “realizations1” is the first set of realizations: e.g. 696939*50 Sixth input: “realizations2” is the second set of realizations Seventh input: “realizations3” is the third set of realizations
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RatioMtrx=QuantifyDist2(DisMtrx1,DisMtrx2) This function is used to calculate the ratio of between-realization distance and within-realization distance according to two different distance matrix. First input: DisMtrx1 is the distance matrix for the first set of realizations Second input: DisMtrx2 is the distance matrix for the second sets of realizations Output: RatioMtrx is a 3x2 matrix to represent ratios.
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RatioMtrx=QuantifyDist3(DisMtrx1,DisMtrx2,DisMtrx3) This function is used to calculate the ratio of between-realization distance and within-realization distance according to three different distance matrix. First input: DisMtrx1 is the distance matrix for the first set of realizations Second input: DisMtrx2 is the distance matrix for the second sets of realizations Third input: DisMtrx3 is the distance matrix for the second sets of realizations Output: RatioMtrx is a 3x3 matrix to represent ratios