I teach basic hydrogeology at undergrad level at Dept of Geology. My lecture applies quantitative-statistical approaches, while most of the classes in the basic geology field uses more qualitative sense. I invite opinions from the RG community on what are the frequently used statistical analyses in geology?
The question received many responses from the following RG members:
- Prahar Iqbal from Indonesian Institute of Sciences
Jon Salmanton-Garcia from Instituto de Salud Carlos III
Aline Concha Dimas, independent researcher
- Ketil Haarstad from Bioforsk Soil and Water Environment
Theodoros M. Tsapanos from Aristotle University of Thessaloniki, Dept of Geology, Greece
Alessandro Comunian from University of Milan, Dept of Earth Sciences
Evangelos Tziritis from Hellenic Agricultural Organization
- Ahmed S. Elshall, independent researcher
Mathematical Statistics and Data Analysis must be one of the substantial courses in the geology departments, as mentioned by Theodoros. He noted the necessity to include: probability theory, random variables, limit theory, distributions, survey sampling, comparison of two samples, analysis of variance, Bayesian decision theory, cluster analysis, and factor analysis. He argued all of these statistics to be very important for students’ knowledge and their future career.
- fractal-based statistics as proposed in Bailey R and Smith D (2010, doi:10.3997/1365-2397.2010001) and
- connectivity metrics in hydrogeology in a paper by Renard P and Allard D (2013, doi:10.1016/j.advwatres.2011.12.001).
- Jef Caers (2011), Modeling Uncertainty in the Earth Sciences, Wiley
- Martin Trauth et al. (2010), MATLAB® Recipes for Earth Sciences, Springer
As also mentioned by Ahmed, the 1st book contains modeling uncertainty in the Earth sciences as it covers key issues such as: Spatial and time aspect; large complexity and dimensionality; computation power; costs of ‘engineering’ the Earth; uncertainty in the modeling and decision process.
The 2nd book contains a wide range of applications of Matlab in geosciences, such as image processing in remote sensing, the generation and processing of digital elevation models, and the analysis of time series. This book introduces methods of data analysis in geosciences using MATLAB, such as basic statistics for univariate, bivariate and multivariate datasets, jackknife and bootstrap resampling schemes, processing of digital elevation models, gridding and contouring, geostatistics and kriging, processing and georeferencing of satellite images, digitizing from the screen, linear and nonlinear time-series analysis, and the application of linear time-invariant and adaptive filters.