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Recent advances in scalable non-Gaussian geostatistics: The generalized sub-Gaussian model
- Publication Year :
- 2018
-
Abstract
- Geostatistical analysis has been introduced over half a century ago to allow quantifying seemingly random spatial variations in earth quantities such as rock mineral content or permeability. The traditional approach has been to view such quantities as multivariate Gaussian random functions characterized by one or a few well-defined spatial correlation scales. There is, however, mounting evidence that many spatially varying quantities exhibit non-Gaussian behavior over a multiplicity of scales. The purpose of this minireview is not to paint a broad picture of the subject and its treatment in the literature. Instead, we focus on very recent advances in the recognition and analysis of this ubiquitous phenomenon, which transcends hydrology and the Earth sciences, brought about largely by our own work. In particular, we use porosity data from a deep borehole to illustrate typical aspects of such scalable non-Gaussian behavior, describe a very recent theoretical model that (for the first time) captures all these behavioral aspects in a comprehensive manner, show how this allows generating random realizations of the quantity conditional on sampled values, point toward ways of incorporating scalable non-Gaussian behavior in hydrologic analysis, highlight the significance of doing so, and list open questions requiring further research.
- Subjects :
- Spatial correlation
Computer science
Non-Gaussian distributions
Gaussian
0208 environmental biotechnology
Multivariate normal distribution
Generalized sub-Gaussian model
02 engineering and technology
Geostatistics
Scaling
020801 environmental engineering
Heavy-tailed distributions
symbols.namesake
Water Science and Technology
Scalability
symbols
Statistical physics
Gaussian network model
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....e88514b0a178b165c19acc8cfdd44b2c