Details zur Publikation

Kategorie Textpublikation
Referenztyp Tagungsbeiträge
DOI 10.3997/2214-4609.201600795
Titel (primär) 2D probabilistic prediction of sparsely measured geotechnical parameters constrained by tomographic ambiguity and measurements errors
Titel (sekundär) 78th EAGE Conference and Exhibition, Vienna, Austria, 30 May - 2 June 2016
Autor Asadi, A.; Dietrich, P. ORCID logo ; Paasche, H.
Erscheinungsjahr 2016
Department MET
Seite von 1
Seite bis 5
Sprache englisch
Abstract We present a new approach for 2 D probabilistic prediction of sparsely measured target parameters, e.g.,
measured by direct push technology or borehole logging. Geophysical tomography is used to constrain the
prediction. The presented approach fully accounts for tomographic ambiguity and transduces it into
prediction uncertainty. Furthermore, errors of the logging data can be considered to avoid overfitting when
learning the optimal link between tomograms and logging data by means of Artificial Neural Networks.
Consideration of errors results in improved predictions, which we exemplary illustrate here by 2D sleeve
friction prediction.
dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=18165
Asadi, A., Dietrich, P., Paasche, H. (2016):
2D probabilistic prediction of sparsely measured geotechnical parameters constrained by tomographic ambiguity and measurements errors
78th EAGE Conference and Exhibition, Vienna, Austria, 30 May - 2 June 2016
1 - 5 10.3997/2214-4609.201600795