Especificamente sobre petróleo, veja
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.4850
e mais coisas em
http://www.math.ntnu.no/ure/refs.html
Certa vez vi uma apresentação num congresso de alguem que
trabalhava numa petrolífera. O grande problema era a necessidade
de predição em alta resolução. Nesta direção há três abordagens
desenvolvidas recentemente
1 - process convolution models (Higdon, 1998; Xia and Gelfand,
2005)
2 - predictive process, or fixed-rank Kriging, models (Banerjee
et al., 2008; Cressie and Johannesson, 2008),
3 - stochastic partial differential equation models of Lindgren
et al. (2011).
D. Higdon. A
process-convolution approach to modelling temperatures in the
North Atlantic Ocean. Environmental and Ecological Statistics,
5(2):173–190, 1998
G. Xia and A. Gelfand.
Stationary process approximation for the analysis of large
spatial datasets. ISDS Discussion Paper 2005-24, Duke
University, Durham, NC, 2005
S. Banerjee, A. E.
Gelfand, A. O. Finley, and H. Sang. Gaussian predictive
process models for large spatial datasets. Journal of the
Royal Statistical Society, Series B, 70(4):825–848, 2008.
N. A. C. Cressie and
G. Johannesson. Fixed rank kriging for very large spatial data
sets. Journal ofthe Royal Statistical Society, Series B,
70(1):209–226, 2008
Lindgren, H. Rue, and J. Lindstr ̈om. An explicit link between
Gaussian fields and Gaussian Markov random fields: The stochastic
partial differential equation approach (with discussion).Journal
of the Royal Statistical Society. Series B. Statistical
Methodology, 73(4):423–498, September 2011.
Att.
Elias
On 27/08/13 21:01, Marcelo Albuquerque wrote: