GeoStats.jl v0.73
New features
Multivariate interpolation with CoKriging that exploits the cross-variable correlation at any lag distance besides the auto-correlation. We support any number of variables, as well as any geostatistical function, including variograms, covariances and transiograms.
CoKriging interpolation is quite useful when a subset of one or more variables are under-sampled, and another subset of densely-sampled variables are linearly correlated to the first subset. The cross-variable correlation can greatly improve the interpolation of the under-sampled variables. Below is an example where we interpolate 5 variables jointly along a well trajectory:
Please reach out in our #geostats.jl channel if you need help setting up multivariate interpolation.
Anisotropic variogram models can now be constructed with ranges and rotation. There is no need to create a MetricBall manually:
GaussianVariogram(ranges = (1, 2, 3), rotation=I)
Breaking changes
We took this opportunity to review and optimize the Interpolate and InterpolateNeighbors transforms. Their syntax was simplified to
Interpolate(domain; model=model, ...)
and we no longer support the complex syntax with variable names. This is important now that we have multivariate interpolation routines. If you pass a geotable with N>1 columns to a univariate interpolation model, you will get an assertion error. Please Select(variable) before Interpolate or InterpolateNeighbors in this case.
The variants InterpolateMissing and InterpolateNaN were removed in favor of more explicit pipelines with DropMissing and DropNaN respectively.