 |
| Titel |
Using Bayesian methods for the parameter estimation of deformation monitoring networks |
| VerfasserIn |
E. Tanir, K. Felsenstein, M. Yalcinkaya |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1561-8633
|
| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 8, no. 2 ; Nr. 8, no. 2 (2008-04-11), S.335-347 |
| Datensatznummer |
250005413
|
| Publikation (Nr.) |
 copernicus.org/nhess-8-335-2008.pdf |
|
|
|
|
|
| Zusammenfassung |
In order to investigate the deformations of an area or an object, geodetic
observations are repeated at different time epochs and then these
observations of each period are adjusted independently. From the coordinate
differences between the epochs the input parameters of a deformation model
are estimated. The decision about the deformation is given by appropriate
models using the parameter estimation results from each observation period.
So, we have to be sure that we use accurately taken observations (assessing
the quality of observations) and that we also use an appropriate
mathematical model for both adjustment of period measurements and for the
deformation modelling (Caspary, 2000). All inaccuracies of the model,
especially systematic and gross errors in the observations, as well as
incorrectly evaluated a priori variances will contaminate the results and
lead to apparent deformations. Therefore, it is of prime importance to
employ all known methods which can contribute to the development of a
realistic model. In Albertella et al. (2005), a new testing procedure from
Bayesian point of view in deformation analysis was developed by taking into
consideration prior information about the displacements in case estimated
displacements are small w.r.t. (with respect to) measurement precision.
Within our study, we want to introduce additional parameter estimation from
the Bayesian point of view for a deformation monitoring network which is
constructed for landslide monitoring in Macka in the province of Trabzon in
north eastern Turkey. We used LSQ parameter estimation results to set up
prior information for this additional parameter estimation procedure. The
Bayesian inference allows evaluating the probability of an event by
available prior evidences and collected observations. Bayes theorem
underlines that the observations modify through the likelihood function the
prior knowledge of the parameters, thus leading to the posterior density
function of the parameters themselves. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|