• Aucun résultat trouvé

1D geological imaging of the subsurface from geophysical data with Bayesian Evidential Learning

N/A
N/A
Protected

Academic year: 2021

Partager "1D geological imaging of the subsurface from geophysical data with Bayesian Evidential Learning"

Copied!
38
0
0

Texte intégral

Figure

Figure 1: Illustration of the BEL1D process. See text for abbreviation.
Figure 2 : Example of the effect of noise on the computation of the kernel density estimator for the probability
Figure 3 : Simulated data for the SNMR experiment. The black curves represent the noisy dataset and the red
Table 2: Prior model space for the SNMR experiment
+7

Références

Documents relatifs

fois avec désespoir " ﻞﻣﺃ ﻙﺎﻨﻫ ﻥﻮﻜﻳ ﻻ ﻯﺮﺧﺃ ﺕﺍﺮﻣ ﻦﻜﻟ ﺓﺎﻴﳊﺍ ﰲ ﻞﻣﺃ ﻙﺎﻨﻫ ﻥﻮﻜﻳ ﺕﺍﺮﻣ ﻱﺃ ، ﺓﺎﻴﳊﺍ ﰲ. ﺔﻋﱰﻟﺍ ﻆﺣﻼﻧ ﺎﻨﻫﻭ ﺔﻟﺎﳊﺍ ﻯﺪﻟ ﺔﻴﻨﻳﺪﻟﺍ. ﱐﺎﻌﻳ ﺪﻴﺴﻟﺍ ﻥﺃ ،ﺔﻟﺎﺤﻠﻟ

We have considered model stochastic ODEs with additive and multiplicative Brownian noise (1)/(2), and have derived the deterministic equations in variational form satisfied by the

In this work, we describe a new class of global gridded crop model emulators for five crops (maize, soybean, rice, and spring and winter wheat) and nine process-based crop models,

In the present study, we identified novel viral miRNAs encoded by TFV and analyzed the diversity of miRNA expression in fish (ZF4) and mammalian (HepG2) cells to define the

Indique en-dessous si elles sont parallèles ou perpendiculaires entre elles, en utilisant les symboles géométriques // et ┴...  Place un point B en dehors de

This prior information is formulated as pairwise constraints on instances: a Must-Link constraint (respectively, a Cannot-Link constraint) indicates that two objects belong

In the proposed method, the tree parameters are estimated through the maximization of an evidential likelihood function computed from belief functions, using the recently proposed E 2