Connotea
CiteULike
del.ico.us
BibSonomyAbstract
Nearest-neighbor classification for facies delineation
Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, California, USA
Group T-7, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Group T-7, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Dipartimento di Ingegneria Idraulica, Ambientale, Infrastrutture Viarie e Rilevamento, Politecnico di Milano, Milan, Italy
Geostatistics has become the dominant tool for probabilistic estimation of properties of heterogeneous formations at points where data are not available. Ordinary kriging, the starting point in the development of other geostatistical techniques, has a number of serious limitations, chief among which is the intrinsic hypothesis of the (second-order) stationarity of the underlying random field. Attempts to overcome this limitation have led to the development of ever more complex flavors of kriging. We pursue an opposite strategy that consists of finding the simplest possible technique that is adequate for the task of facies delineation. Guided by the principle of parsimony, we identify nearest-neighbor classification (NNC) as a viable alternative to geostatistics among deterministic techniques. We demonstrate that when used for the purpose of facies delineation, NNC, which has no fitting parameters and operational assumptions, outperforms indicator kriging, which has several parameters.
Received 13 February 2007; accepted 4 June 2007; published 20 July 2007.
Citation: (2007), Nearest-neighbor classification for facies delineation, Water Resour. Res., 43, W07201, doi:10.1029/2007WR005968.
Cited By