Publication Details

Reference Category Conference contributions
DOI / URL link
Title (Primary) Predicting continuous distributions of sparse data under full consideration of tomographic reconstruction ambiguity
Title (Secondary) Near Surface Geoscience 2015 - 21st European Meeting on Environmental and Engineering Geophysics, Turin, Italy, 6-10 September 2015
Author Asadi, A.; Dietrich, P.; Paasche, H.;
Year 2015
Department MET;
Language englisch;
POF III (all) T31;
UFZ wide themes RU5;
Abstract We present a novel methodology to probabilistically predict spatial distributions of sparsely measured
borehole logging data constrained by multiple geophysical crosshole tomograms. In doing so, we fully
account for the ambiguity of the tomographic model reconstruction procedure by taking advantage of a
recently developed fully non-linear inversion approach. We use Artificial Neural Networks to link the
results of the non-linear inversion with sparse information of tip resistance logging data. Additionally, we
achieve information during the training phase of the ANN about the compliancy of tomographic models
found by the inversion with the available logging data, which may help to identify those tomographic
models that may reconstruct the subsurface more realistically.
ID 16678
Persistent UFZ Identifier http://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=16678
Asadi, A., Dietrich, P., Paasche, H. (2015):
Predicting continuous distributions of sparse data under full consideration of tomographic reconstruction ambiguity
Near Surface Geoscience 2015 - 21st European Meeting on Environmental and Engineering Geophysics, Turin, Italy, 6-10 September 2015
European Association of Geoscientists and Engineers (EAGE), Houten, p. WE21A06