Integration of diverse physical-property models: Subsurface zonation and petrophysical parameter estimation based on fuzzy c -means cluster analyses

Détails

ID Serval
serval:BIB_DF874747F2FB
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Integration of diverse physical-property models: Subsurface zonation and petrophysical parameter estimation based on fuzzy c -means cluster analyses
Périodique
Geophysics
Auteur⸱e⸱s
Paasche H., Tronicke J., Holliger K., Green A., Maurer H.
ISSN-L
0016-8033
Statut éditorial
Publié
Date de publication
2006
Peer-reviewed
Oui
Volume
71
Pages
H33-H44
Langue
anglais
Résumé
Inversions of an individual geophysical data set can be highly nonunique,
and it is generally difficult to determine petrophysical parameters
from geophysical data. We show that both issues can be addressed
by adopting a statistical multiparameter approach that requires the
acquisition, processing, and separate inversion of two or more types
of geophysical data. To combine information contained in the physical-property
models that result from inverting the individual data sets and to
estimate the spatial distribution of petrophysical parameters in
regions where they are known at only a few locations, we demonstrate
the potential of the fuzzy c -means (FCM) clustering technique. After
testing this new approach on synthetic data, we apply it to limited
crosshole georadar, crosshole seismic, gamma-log, and slug-test data
acquired within a shallow alluvial aquifer. The derived multiparameter
model effectively outlines the major sedimentary units observed in
numerous boreholes and provides plausible estimates for the spatial
distributions of gamma-ray emitters and hydraulic conductivity.
Mots-clé
rocks, statistical analysis, data acquisition, geophysical techniques, pattern, clustering, geophysics computing, fuzzy systems
Création de la notice
25/11/2013 19:28
Dernière modification de la notice
20/08/2019 17:03
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