Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Tagungsbeiträge |
| DOI | 10.3997/2214-4609.201413765 |
| Titel (primär) | Predicting continuous distributions of sparse data under full consideration of tomographic reconstruction ambiguity |
| Titel (sekundär) | Near Surface Geoscience 2015 - 21st European Meeting on Environmental and Engineering Geophysics, Turin, Italy, 6-10 September 2015 |
| Autor | Asadi, A.; Dietrich, P.
; Paasche, H. |
| Erscheinungsjahr | 2015 |
| Department | MET |
| Seite von | WE21A06 |
| Sprache | englisch |
| UFZ Querschnittsthemen | 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. |
| dauerhafte UFZ-Verlinkung | https://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, WE21A06 10.3997/2214-4609.201413765 |
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