Tomographic reconstruction of soil gas distribution from multiple gas sources based on sparse sampling
- A monitoring method is introduced that creates twodimensional (2D) maps of the soil gas distribution. The method combines linear gas sensing technology for in-situ monitoring of gases in soil with the mapping capabilities of Computed Tomography (CT) to reconstruct spatial and temporal resolved gas distribution maps. A weighted iterative algebraic reconstruction method based on Maximum Likelihood with Expectation Maximization (MLEM) in combination with a source-by-source reconstruction approach is introduced that works with a sparse setup of orthogonally-aligned linear gas sensors. The reconstruction method successfully reduces artifact production, especially when multiple gas sources are present, allowing the discrimination between true and non-existing so-called ghost source locations. Experimental validation by controlled field experiments indicates the high potential of the proposed method for rapid gas leak localization and quantification with respect to Pipeline or underground gasA monitoring method is introduced that creates twodimensional (2D) maps of the soil gas distribution. The method combines linear gas sensing technology for in-situ monitoring of gases in soil with the mapping capabilities of Computed Tomography (CT) to reconstruct spatial and temporal resolved gas distribution maps. A weighted iterative algebraic reconstruction method based on Maximum Likelihood with Expectation Maximization (MLEM) in combination with a source-by-source reconstruction approach is introduced that works with a sparse setup of orthogonally-aligned linear gas sensors. The reconstruction method successfully reduces artifact production, especially when multiple gas sources are present, allowing the discrimination between true and non-existing so-called ghost source locations. Experimental validation by controlled field experiments indicates the high potential of the proposed method for rapid gas leak localization and quantification with respect to Pipeline or underground gas storage issues.…
Autor*innen: | Patrick P. NeumannORCiD, D. Lazik, Matthias BartholmaiORCiD |
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Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Verlagsliteratur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | IEEE Sensors Journal |
Jahr der Erstveröffentlichung: | 2016 |
Herausgeber (Institution): | IEEE |
Verlag: | IEEE - Inst. Electrical Electronics Engineers Inc |
Verlagsort: | Hoes Lane, NJ, USA |
Jahrgang/Band: | 16 |
Ausgabe/Heft: | 11 |
Erste Seite: | 4501 |
Letzte Seite: | 4508 |
DDC-Klassifikation: | Naturwissenschaften und Mathematik / Chemie / Analytische Chemie |
Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Angewandte Physik | |
Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Sanitär- und Kommunaltechnik; Umwelttechnik | |
Freie Schlagwörter: | Computed tomography; Discrimination of multiple gas sources; Distributed linear sensor; Gas distribution mapping and gas source localization; Gas storage areas; Membrane-based gas sensing; Subsurface monitoring |
DOI: | 10.1109/JSEN.2016.2545103 |
ISSN: | 1530-437X |
Verfügbarkeit des Dokuments: | Datei im Netzwerk der BAM verfügbar ("Closed Access") |
Datum der Freischaltung: | 26.05.2016 |
Referierte Publikation: | Ja |
Datum der Eintragung als referierte Publikation: | 02.06.2016 |