Adapting Machine Learning Algorithms for Spatial Interpolation

Preface If you have been following my blog, you already know that a topic to which I devout quite a lot of time and thought both here and in my day job is spatial interpolation: the art of generalizing from a few localized samples to the regional structure of a feature.

Tips and Tricks for dealing with geo(logical) data in R

Preface Making maps is an essential (in fact, often the essential) part of working with spatial data. Your analysis is only as good as your ability to communicate the results to the decision makers.

Modeling the solubility of minerals in water with PHREEQC from R

Preface When working with contaminated groundwater, complex interactions between different solutes and sometimes the aquifer geology itself can lead to a plethora of reactions, potentially creating additional or even completely new problems, when it comes to remediation.

Kriging with R: Exploring gstat

Back in June I wrote a post about the basics of geospatial interpolation in R that, according to Twitter, resonated with a lot of people. It appears that there is a need for detailed tutorials on how to apply geospatial algorithms to real world data (at least in R).

Ionic Charge Balance - a functional approach

When working with water chemistry you are often presented with analytic results either send to you by a lab or probably even measured by yourself. From that analysis (usually from several) you are then supposed to draw insight about what is going on in your river/pond/aquifer.

A practical guide to geospatial interpolation with R

One of the most exciting things you can do with R is geospatial interpolation. This means that you have some kind of information (e.g. measurements of, say, soil temperature) for a limited number of locations and then you apply a mathematical model that will provide you with an educated guess of what your result might look like, if you would have measured at every possible location.

Constructing Mineral Phase Diagrams in R

Preface Preparing Input Data Example 1: Experimental Data set Example 2: Thermodynamic Model (MELTS) Data set Concluding Remarks Preface In petrology it is common to be confronted with or thinking about mineral phase stability.

Digital Point Counting for Mineralogical Thin Sections

Mineral point counting in geological thin sections is a simple yet very useful technique that aims at determining the relative proportion of different minerals within a rock. Quantifying this can give insight on the evolution of a magma within different stages of melt evolution or even underpin comparisons of similar melts from different magmatic complexes.

Working with climate data from the German Meteorological Service (DWD)

Many tasks in the realm of Hydrogeology require data from meteorological observations to be taken into account. If you are working on such a project that happens to be located in Germany, you can count yourself lucky, because the Deutscher Wetterdienst (DWD, German Meteorological Service) is providing a cornucopia of both brand new and historic data for free via its climate datacenter (CDC).