Using SeaFreeze and GSW, the ocean water EOS calculations return garbage values, e.g. negative densities, when pressures and temperatures relevant to pore spaces in the silicate layers are supplied. This causes problems with calculating the properties in porous silicate layers, because the bulk properties of each layer depend on both the matrix material and the material filling the pore space.
To fix, we will probably need to define the boundaries of valid results in constructing the EOS functions, and set a fill value for the extrapolation. Unfortunately, RectBivariateSpline in python does not have a fill_value kwarg, unlike interp2d. We are using RectBivariateSpline because it is much, much faster, so we will have to make some kind of compromise here.
Using SeaFreeze and GSW, the ocean water EOS calculations return garbage values, e.g. negative densities, when pressures and temperatures relevant to pore spaces in the silicate layers are supplied. This causes problems with calculating the properties in porous silicate layers, because the bulk properties of each layer depend on both the matrix material and the material filling the pore space.
To fix, we will probably need to define the boundaries of valid results in constructing the EOS functions, and set a fill value for the extrapolation. Unfortunately, RectBivariateSpline in python does not have a fill_value kwarg, unlike interp2d. We are using RectBivariateSpline because it is much, much faster, so we will have to make some kind of compromise here.