Maas, Christian, Schmalzl, Jörg (2013) Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar. Computers & Geosciences, 58. 116-125 doi:10.1016/j.cageo.2013.04.012

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Reference Type	Journal (article/letter/editorial)
Title	Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar
Journal	Computers & Geosciences
Authors	Maas, Christian		Author
Authors	Schmalzl, Jörg		Author
Year	2013 (August)	Volume	58
Publisher	Elsevier BV
DOI	doi:10.1016/j.cageo.2013.04.012Search in ResearchGate
	Generate Citation Formats
Mindat Ref. ID	453572	Long-form Identifier	mindat:1:5:453572:4
GUID	0
Full Reference	Maas, Christian, Schmalzl, Jörg (2013) Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar. Computers & Geosciences, 58. 116-125 doi:10.1016/j.cageo.2013.04.012
Plain Text	Maas, Christian, Schmalzl, Jörg (2013) Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar. Computers & Geosciences, 58. 116-125 doi:10.1016/j.cageo.2013.04.012
In	(2013) Computers & Geosciences Vol. 58. Elsevier BV

	*Koyan, Philipp, Tronicke, Jens (2020) 3D modeling of ground-penetrating radar data across a realistic sedimentary model. Computers & Geosciences, 137. 104422 doi:10.1016/j.cageo.2020.104422*
	*Irving, James, Knight, Rosemary (2006) Numerical modeling of ground-penetrating radar in 2-D using MATLAB. Computers & Geosciences, 32 (9) 1247-1258 doi:10.1016/j.cageo.2005.11.006*
	*Hunziker, J., Slob, E.C., Irving, J. (2023) Fast 3D ground penetrating radar simulations for glaciers. Computers & Geosciences, 173. 105320 doi:10.1016/j.cageo.2023.105320*
	*Schmalzl, Jörg (2003) Using standard image compression algorithms to store data from computational fluid dynamics. Computers & Geosciences, 29 (8) 1021-1031 doi:10.1016/s0098-3004(03)00098-0*
	Fang, Hongyuan, Lin, Gao (2012) Symplectic partitioned Runge–Kutta methods for two-dimensional numerical model of ground penetrating radar. Computers & Geosciences, 49. 323-329 doi:10.1016/j.cageo.2012.01.016
	Wang, Xiangyu, Liu, Hai, Meng, Xu, Hu, Hesong (2025) GPR-FWI-Py: Open-source Python software for multi-scale regularized full waveform inversion in Ground Penetrating Radar using random excitation sources. Computers & Geosciences, 197. doi:10.1016/j.cageo.2025.105870
	*Bradford, John H. (2006) Applying reflection tomography in the postmigration domain to multifold ground-penetrating radar data. Geophysics, 71 (1) doi:10.1190/1.2159051*
	*Moysey, Stephen M. (2010) Hydrologic trajectories in transient ground-penetrating-radar reflection data. Geophysics, 75 (4) doi:10.1190/1.3463416*
	Lucky Moffat, Kebabonye Laletsang (2014) Mapping Spontaneous Ground Fissures using Seismic Refraction, Reflection, and Ground Penetrating Radar in the Letlhakeng Area, Kweneng District, Botswana. Journal of Geology & Geosciences, 3 (6). doi:10.4172/2329-6755.1000175
	Młynarczuk, Mariusz, Górszczyk, Andrzej, Ślipek, Bartłomiej (2013) The application of pattern recognition in the automatic classification of microscopic rock images. Computers & Geosciences, 60. 126-133 doi:10.1016/j.cageo.2013.07.015
	*Lunt, I.A., Hubbard, S.S., Rubin, Y. (2005) Soil moisture content estimation using ground-penetrating radar reflection data. Journal of Hydrology, 307 (1). 254-269 doi:10.1016/j.jhydrol.2004.10.014*

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Maas, Christian, Schmalzl, Jörg (2013) Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar. Computers & Geosciences, 58. 116-125 doi:10.1016/j.cageo.2013.04.012

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