Below are the concepts implemented in this package. For understanding how these concepts are used, please consult the Examples section.

Hard data

Voxels can be assigned values that will be honored by the simulation. HardData() is a dictionary of locations and associated values specified by the user:

well = HardData((i,j,k)=>value(i,j,k) for i=10, j=10, k=1:100)
iqsim(..., hard=well)

Soft data

Given 3D data of size (gridsizex, gridsizey, gridsizez) and dataTI of size size(training_image), local relaxation can be performed with:

# 3D seismic as auxiliary data
iqsim(..., soft=[(seismic,seismicTI)])

Multiple pairs of data can be passed as well:

iqsim(..., soft=[(data1,dataTI1), (data2,dataTI2), ...])

Masked grids

Masked grids are a special case of hard data conditioning where inactive voxels are marked with the value NaN. The algorithm handles this hard data differently as it shouldn't be considered in the pattern similarity calculations.

training_image can also have inactive voxels marked with NaN. Convolution results are only looked up in active regions.