GEOSTATISTICS SHORT COURSE
Geostatistics is a branch of applied mathematics concerned with spatial correlation and interpolation. Originally developed to improve ore-grade estimation in the mining industry, geostatistical methods have proven useful across a wide range of geo-engineering fields, from reservoir modeling and simulation to environmental site characterization. The two-day short-course, Introduction to Geostatistics: Theory and Applications in R, covers the theoretical basis for spatial autocorrelation, spatial interpolation, and stochastic simulation, as well as their application using the statistical software package, R. The course is taught by Dr. Ryan M. Pollyea, assistant professor in the Department of Geosciences at Virginia Tech. No previous experience in R is necessary, but Dr. Pollyea requests that students install R prior to class.
Jayne & Pollyea (2018)
Please contact Dr. Ryan M. Pollyea for inquires and/or scheduling information.
2019 | November 7 - 8: Department of Civil & Environmental Engineering, University of Strathclyde, Glasgow, Scotland. Class Notes.
Data, Downloads & Information
To make the most of our time together, please install the statistical software package, R, which is freely available open-source software available for download from the Comprehensive R Archive Network (CRAN). Other R distributions should work just as well, e.g., R Studio. Please also install the R library called, gstat, which can be installed internally through the R console, or built from source (also available through CRAN).
The data set that we'll use is from the text Geostatistics for Natural Resource Evaluation, by Pierre Goovaerts, Oxford University Press, 1997. The data set comprises 259 sample locations, each coded by Land Use (L) and Rock Type (R) with concentration data for cadmium, cobalt, chromium, nickel, lead, and zinc. Additionally, the Jura Border file comprises polygon vertices to delineate the Jura study area. We'll use this latter file for visualizing results.
The following list includes reference information that students may find useful.
1. GSTAT User's Manual by Edzer J. Pebesma, Utrecht University
2. Journel, A.G. 1986. Geostatistics: Models and Tools for the Earth Sciences, Mathematical Geology, Vol. 18, No. 1, p. 119 - 140.
3. Matheron, G. 1963. Principles of Geostatistics, Economic Geology, Vol. 58, p. 1246 - 1266.
One of the original works on the Theory of Regionalized Variables.
4. Jayne, R.S. and Pollyea, R.M., 2018. Permeability correlation structure of the Columbia River Plateau and implications for fluid system architecture in continental large igneous provinces. Geology, Vol. 46, No. 8, pp.715-718.
An example of univariate spatial correlation as evidence of a geologic process.
5. Pollyea, R.M., Mohammadi, N., Taylor, J.E. and Chapman, M.C., 2018. Geospatial analysis of Oklahoma (USA) earthquakes (2011–2016): Quantifying the limits of regional-scale earthquake mitigation measures, Geology, Vol. 46, No. 3, pp.215-218.
An example of temporal changes to bi-variate spatial correlation to show changing wastewater injection volumes affect earthquake occurrence in Oklahoma.
6. Pollyea, R.M. and Fairley, J.P., 2012. Implications of spatial reservoir uncertainty for CO2 sequestration in the east Snake River Plain, Idaho (USA). Hydrogeology Journal, Vol. 20, No. 4, p.689-699.
An example of geostatistical reservoir characterization combined with Monte Carlo numerical simulation.