Philipp Koyan, M.Sc.
AG Angewandte Geophysik
Wissenschaftlicher Mitarbeiter
Campus Golm
Haus 27, Raum 0.35
Karl-Liebknecht-Str. 24-25
14476 Potsdam-Golm
CV
| 2012-2015 | B.Sc., Geowissenschaften (Universität Potsdam) |
| 2015-2018 | M.Sc., Geowissenschaften (Universität Potsdam) |
| 2018-2023 | Doktorand, Angewandte Geophysik (Universität Potsdam) |
| 2023- | Wissenschaftlicher Mitarbeiter, Angewandte Geophysik (Universität Potsdam) |
Forschung
In meiner Forschung konzentriere ich mich auf die Analyse von 2D/3D-Georadar-Daten in sedimentären Systemen, welche sowohl durch Modellierung als auch durch Akquisition im Feld generiert wurden. Klassischerweise werden solche Daten manuell und damit zeitaufwendig, subjektiv und selten reproduzierbar interpretiert. Ziel des Projektes ist es daher, zur Interpretation Datenattribute zu analysieren und zu klassifizieren, um 2D/3D Georadar-Faziesmodelle für den zu charakterisierenden sedimentären Untergrund zu generieren.
Publikationen
https://orcid.org/0000-0002-3647-7260
Zeitschriftenartikel
Allroggen, N., Heincke, B.H., Koyan, P., Wheeler, W., Rønning, J.S., 2022: 3D ground-penetrating radar attribute classification: A case study from a paleokarst breccia pipe in the Billefjorden area on Spitsbergen, Svalbard. Geophysics. doi: https://doi.org/10.1190/geo2021-0651.1
Koyan, P., Tronicke, J., Allroggen, N, 2021: 3D ground-penetrating radar attributes to generate classified facies models: A case study from a dune island. Geophysics. doi: https://doi.org/10.1190/geo2021-0204.1. Vorgestellt in: Behura, J., 2022: Geophysics Bright Spots. The Leading Edge. doi: https://doi.org/10.1190/tle41010062.1.
Koyan, P., Tronicke, J., 2020: 3D modeling of ground-penetrating radar data across a realistic sedimentary model. Computers and Geosciences. doi: https://doi.org/10.1016/j.cageo.2020.104422.
Konferenzbeiträge
Koyan, P., Tronicke, J., Klose, T., Guillemoteau, J., 2023: 3D GPR to explore peat deposits: Strategies for data acquisition, processing, and interpretation. In: 12th International Workshop on Advanced Ground Penetrating Radar, Lisbon.
Klose, T., Guillemoteau, J., Vignoli, G., Koyan, P., Walter, J., Herrmann, A., Tronicke, J., 2023: Structurally-constrained FD-EMI data inversion using a Minimum Gradient Support (MGS) regularization. In: EGU General Assembly 2023, Vienna, Austria. doi: https://doi.org/10.5194/egusphere-egu23-7067.
Koyan, P., Tronicke, J., 2023: The gradient structure tensor (GST): An efficient tool to analyze 3D GPR data for archaeological prospection. In: 15th International Conference of Archaeological Prospection, Kiel. doi: 10.38072/978-3-928794-83-1/p86.
Koyan, P., Tronicke, J., 2022: 3D Classified GPR Facies Models from Multi-frequency Data Volumes: A Synthetic Study. In: 19th International Conference On Ground Penetrating Radar (GPR), Golden, Colorado. doi: https://doi.org/10.1190/gpr2022-042.1.
Koyan, P., Tronicke, J., 2020: Analyzing 3D multi-frequency ground-penetrating radar (GPR) data simulated across a realistic sedimentary model. In: 18th International Conference On Ground Penetrating Radar (GPR), Golden, Colorado. doi: https://doi.org/10.1190/gpr2020-073.1.
J. Tronicke, Koyan, P., Allroggen, N., 2020. The redundant wavelet transform to process and interpret GPR data. In: 18th International Conference On Ground Penetrating Radar (GPR), Golden, Colorado. doi: https://doi.org/10.1190/gpr2020-104.1.
Koyan, P., Tronicke, J., Allroggen, N., Kathage, A., Willmes, M., 2018. Estimating moisture changes in concrete using GPR velocity analysis: potential and limitations. In: 17th International Conference On Ground Penetrating Radar (GPR), Rapperswil (CH). doi: 10.1109/ICGPR.2018.8441572.
Guillemoteau, J., Koyan, P., Tronicke, J., 2017: Processing of Densely Sampled Electromagnetic Induction Data Collected across Peat Deposits. In: 23rd European Meeting of Environmental and Engineering Geophysics, Malmö (Sweden). doi: https://doi.org/10.3997/2214-4609.201701983.
Datensätze
Koyan, P., Tronicke, J., 2019. A synthetic 3D ground-penetrating radar (GPR) data set across a realistic sedimentary model. Mendeley Data. doi: http://dx.doi.org/10.17632/by3yh79hx4.1.
Eingeladene Vorträge
Koyan, P., 2020. 3D GPR data simulated across a realistic sedimentary model: Modelling, analyses and applications. (Online Workshop on Ground-Penetrating Radar modelling using gprMax, Newcastle upon Tyne 2020). link: https://www.youtube.com/watch?v=sw5zncmyKU0