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Attenuation of random noise in GPR data by image processing
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Geosciences and Environmental Engineering. Univ Tehran, Inst Geophys, Tehran, Iran.
Univ Tehran, Inst Geophys, Tehran, Iran.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Geosciences and Environmental Engineering.ORCID iD: 0000-0002-5600-5375
Univ Tehran, Inst Geophys, Tehran, Iran.
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2018 (English)In: Arabian Journal of Geosciences, ISSN 1866-7511, E-ISSN 1866-7538, Vol. 11, no 21, article id 677Article in journal (Refereed) Published
Abstract [en]

Random noise in ground penetrating radar (GPR) data affects the signal-to-noise ratio, blurs the details, and complicates reconnaissance of the useful information. Many methods with different advantages and disadvantages have been proposed to eliminate or weaken the random noise. We have reviewed basic principles of various signal processing techniques including the curvelet transform (CT), non-local mean (NLM), median, and mean filters to remove the random noise and compared their performances using synthetic and actual GPR data. The performances of the four filters were analyzed on synthetic GPR data both in time and frequency domains. On noisy synthetic data, results indicate that the CT filter performs better than NLM, mean, and median filters at attenuating random noise and improving S/N of the GPR data. On the real data, the performance of only the NLM and CT filters was investigated. Comparing the results clearly shows the CT filter robustness for the random noise attenuation and simultaneously its signal preservation

Place, publisher, year, edition, pages
Heidelberg: Springer, 2018. Vol. 11, no 21, article id 677
Keywords [en]
Curvelet transform, GPR, Mean filter, Median filter, Non-local mean, Random noise
National Category
Geophysics
Research subject
Exploration Geophysics
Identifiers
URN: urn:nbn:se:ltu:diva-71857DOI: 10.1007/s12517-018-4035-zISI: 000449698200008Scopus ID: 2-s2.0-85056234003OAI: oai:DiVA.org:ltu-71857DiVA, id: diva2:1267444
Note

Validerad;2018;Nivå 2;2018-12-03 (inah)

Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2019-09-13Bibliographically approved

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Smirnov, Maxim

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