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Integrating collocated auxiliary parameters in geostatistical simulations using joint probability distributions and probability aggregation
Author(s) -
Mariethoz Grégoire,
Renard Philippe,
Froidevaux Roland
Publication year - 2009
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2008wr007408
Subject(s) - joint probability distribution , probability distribution , probability density function , conditional probability , variable (mathematics) , computer science , field (mathematics) , conditional probability distribution , random variable , relation (database) , domain (mathematical analysis) , marginal distribution , algorithm , function (biology) , data mining , mathematics , statistics , mathematical analysis , evolutionary biology , biology , pure mathematics
We propose a new cosimulation algorithm for simulating a primary attribute using one or several secondary attributes known exhaustively on the domain. This problem is frequently encountered in surface and groundwater hydrology when a variable of interest is measured only at a discrete number of locations and when the secondary variable is mapped by indirect techniques such as geophysics or remote sensing. In the proposed approach, the correlation between the two variables is modeled by a joint probability distribution function. A technique to construct such relation using underlying variables and physical laws is proposed when field data are insufficient. The simulation algorithm proceeds sequentially. At each location of the domain, two conditional probability distribution functions (cpdf) are inferred. The cpdf of the main attribute is inferred in a classical way from the neighboring data and a model of spatial variability. The second cpdf is inferred directly from the joint probability distribution function of the two attributes and the value of the secondary attribute at the location to be simulated. The two distribution functions are combined by probability aggregation to obtain the local cpdf from which a value for the primary attribute is randomly drawn. Various examples using synthetic and remote sensing data demonstrate that the method is more accurate than the classical collocated cosimulation technique when a complex relation relates the two attributes.