Re: [ai-geostats] Regression vs. Kriging vs. Simulation vs. IDW
I was probably a bit misleading to say regression
is not an estimation technique. The word regression
meaning to revert back to the original, or find the
underlying real equation for a set of data. "Kriging"
is a form of what is called "generalised linear regression"
which is one of the most advanced forms of regression.
The simpler forms of regression can be used to fit
parametrics equations to data, such as linear regression
to fit an equation of a line to a set of data points,
or non-linear regression to fit a polynomial surface
to a scattered set of say topography data points.
Not really estimation, but equation fitting. I use non-linear
regression to fit equations to drillhole survey points
to plot their curves. In it's more advanced form when
you wish to fit equations to say a set of two dimensional
data points, or three dimensional orebody samples,
this is called trend surface fitting. Unfortunately normally
the equations developed from trend surface fitting
become massively too complex to handle to be practical,
and hence estimation is opted for.
- For ore resource modelling I've used IDW on a highly skewed lognormally
distributed deposit, where no variograms could be produced. With lognormally
distributed data often found in ore resources, having a good variogram is
important, to avoid large errors in kriging hence it may be preferential to
IDW and a topcut. However if your data is not so highly skewed even
a variogram can provide superior results. I used to model topography
and Kriging with a 'guessed' variogram produced good results compared to
IDW which produced highly spiked and erroneous results.