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Ground penetrating radar use in flood prevention

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main goal of the work is to create an automatic method of locating weak zones within flood embankments structure based on ground penetrating radar (GPR) measurements. The presented research shows the possibilities of using advanced methods of GPR signal processing and its analysis with the help of signal attributes for detecting zones threatening the stability of the structure of flood embankments. Obtained results may help in quick detection of potential weak zones of the embankments and consequently give means to ameliorate them, which may prevent damage to the embankments during rise in the level of river water. The presented analyses were carried out on GPR data obtained for the flood banks of the Rudawa River (Kraków, Poland) in the area of their visible degradation. The use of signal attributes, such as Energy, instantaneous frequency, similarity, curvature gradient, dominant frequency, allowed initial indication of anomalous zones threatening the stability of embankment. Advanced processing supported by the use of advanced filters such as GLCM, Grubbs filter threshold and Convolve Prewitt helped in the analysis of the structure of the embankments. Artificial neural networks (ANNs) in the supervised and unsupervised variants were used to perform the automatic classification of weakened zones within the embankments. The results demonstrated the usefulness of GPR geophysical method through integration of ANN in the analysis of the data.
Czasopismo
Rocznik
Strony
1955--1965
Opis fizyczny
Bibliogr. 39 poz.
Twórcy
  • Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Mickiewicza 30 Ave., 30‑059 Kraków, Poland
Bibliografia
  • 1. Anchuela Ó, Pueyo EL (2018) Internal characterization of embankment dams using ground penetrating radar (GPR) and thermographic analysis: a case study of the Medau Zirimilis Dam (Sardinia, Italy). Eng Geol 237:129–139
  • 2. Annan AP (1999) Practical processing of GPR data. Sensor and Software Inc., Canada
  • 3. Barnes AE (1993) Instantaneous spectral bandwidth and dominant frequency with applications to seismic reflection data. Geophysics 58(3):419–428
  • 4. Boniger U, Tronicke J (2012) Subsurface utility extraction and characterization: combining GPR symmetry and polarization attributes. IEEE Trans Geosci Remote Sens 50(3):736–746
  • 5. Bradford JH, Wu Y (2007) Instantaneous spectral analysis: Time-frequency mapping via wavelet matching with application to contaminated-site characterization by 3D GPR. Lead Edge 26(8):1018–1023
  • 6. Catakli A, Mahdi H, Al Shukri H (2011) Texture analysis of GPR data as a tool for depicting soil mineralogy. In: 2011 IEEE applied imagery pattern recognition workshop (AIPR). IEEE, pp 1–8
  • 7. Chopra S (2007) Seismic attributes for prospect identification and reservoir characterization. Society of Exploration Geophysicists, Tulsa
  • 8. Chopra S, Alexeev V (2005) Application of texture attribute analysis to 3D seismic data. CSEG Recorder, pp 29–32
  • 9. DGB Beheer BV (2019) OpendTect user documentation version 6.4
  • 10. Di Prinzio M et al (2010) Application of GPR to the monitoring of river embankments. J Appl Geophys 71(2–3):53–61
  • 11. Gao D (2013) Integrating 3D seismic curvature and curvature gradient attributes for fracture characterization: methodologies and interpretational implications. Geophysics 78(2):021–031
  • 12. Gołębiowski T, Małysa T (2018) Application of GPR method for detection of loose zones in flood levee. In: E3S web of conferences, 30. EDP Sciences
  • 13. Gołębiowski T, Tomecka-Suchoń S, Farbisz J (2012) Zastosowanie kompleksowych metod geofizycznych do nieinwazyjnego badania stanu technicznego wałów przeciwpowodziowych, Współczesne problemy ochrony przeciwpowodziowej: sympozjum europejskie, Paryż-Orlean, pp 28–30
  • 14. Gołębiowski T, Pasierb B, Porzucek S, Łój M (2018) Complex prospection of medieval underground salt chambers in the village of Wiślica Poland. Archaeol Prospect 25(3):243–254
  • 15. Haduch B, Tadeusiewicz R (2018) Neural networks as a tool to test the origin of motor gasoline. Przemysł Chemiczny 97(11):1843–1847 (in polish)
  • 16. Hall-Beyer M (2012) GLCM texture tutorial. https://prism.ucalgary.ca/bitstream/handle/1880/51900/texture%20tutorial%20v%203_0%20180206.pdf?sequence=11&isAllowed=y. Accessed 01 Apr 2019
  • 17. Łanczont M, Madeyska T, Mrocze P, Hołub B, Żogała B, Bogucki A (2015) Relief and palaeorelief analyses of the Kraków Spadzista Palaeolithic site as the tools used for explanation of the site location. Quat Int 359:89–95
  • 18. Łój M, Porzucek S, Gołębiowski T, Everett ME (2018) Microgravimetric and GPR surveys for detection of unconsolidated zones in a levee. In E3S web of conferences, vol 66, 01022. EDP Sciences
  • 19. Marcak H, Gołębiowski T, Tomecka-Suchoń S (2005) Analysis of possibility of using GPR refraction for location changes in River Embankments. Geologia/Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie 31(3–4):259–274 (in polish)
  • 20. Mazurkiewicz E, Tadeusiewicz R, Tomecka-Suchoń T (2016) Application of neural network enhanced ground-penetrating radar to localization of burial sites. Appl Artif Intell 30(9):844–860
  • 21. McClymont AF et al (2008) Visualization of active faults using geometric attributes of 3D GPR data: an example from the Alpine Fault Zone New Zealand. Geophysics 73(2):B11
  • 22. Mori G (2009) The use of ground penetrating radar and alternative geophysical techniques for assessing embankments and dykes safety. Diss, Alma
  • 23. Nemytova OV, Rinkevich AB, Perov DV (2012) Instantaneous frequency estimation used for the classification of echo signals from different reflectors. Russ J Nondestruct Test 48(11):649–661
  • 24. Peng TY, Zhao Y (2013) CUDA-based parallel Prewitt algorithm implementation and its application on GPR. Appl Mech Mater 380:4002–4006
  • 25. Perri MT et al (2014) River embankment characterization: the joint use of geophysical and geotechnical techniques. J Appl Geophys 110:5–22
  • 26. Rich JP (2008) Expanding the applicability of curvature attributes through clarification of ambiguities in derivation and terminology. In: 78th annual international meeting, SEG, expanded abstracts, pp 884–888
  • 27. Rich JP, Marfurt K (2013) Curvature gradient attributes for improved fault characterization. SEG Houston annual meeting, pp 1319–1323
  • 28. Sandmeier KJ (2012) Reflexw 6.0 Manual Sandmeier Software. Karlsruhe, Germany
  • 29. Sasiada M, Fraczek-Szczypta A, Tadeusiewicz R (2017) Efficiency testing of artificial neural networks in predicting the properties of carbon nanomaterials as potential systems for nervous tissue stimulation and regeneration. Bio-Algorithms Med-Syst 13(1):25–35
  • 30. Słowik M (2011) Changes of river bed pattern and traces of anthropogenic intervention: the example of using GPR method (the Obra River, western Poland). Appl Geogr 31(2):784–799
  • 31. Szymczyk P, Marcak H, Tomecka-Suchoń S, Szymczyk M, Gajer M, Gołębiowski T (2014) Zaawansowane metody przetwarzania danych georadarowych oraz automatyczne rozpoznawanie anomalii w strukturach geologicznych. Elektronika: konstrukcje, technologie, zastosowania 55(12):56–61
  • 32. Szymczyk P, Marcak H, Tomecka-Suchoń S, Szymczyk M, Gajer M, Gołębiowski T (2015a) Komputerowe przetwarzanie i analiza danych georadarowych. Wydawnictwo Naukowe IAE
  • 33. Szymczyk P, Tomecka-Suchoń S, Szymczyk M (2015b) Neural networks as a tool for georadar data processing. Int J Appl Math Comput Sci 25(4):955–960
  • 34. Tadeusiewicz R (2015) Neural networks in mining sciences—general overview and some representative examples. Arch Min Sci 60(4):971–984
  • 35. Taner MT (2001) Seismic attributes. CSEG Recorder, pp 48–56
  • 36. Taner MT, Koehler F, Sheriff RE (1979) Complex seismic trace analysis. Geophysics 44(6):1041–1063
  • 37. Xu X et al (2010) GPR detection of several common subsurface voids inside dikes and dams. Eng Geol 111(1–4):31–42
  • 38. Zhao W et al (2013) Ground penetrating radar (GPR) attribute analysis for archaeological prospection. J Appl Geophys 97:107–117
  • 39. Zhao W, Forte E, Pipan M (2016) Texture attribute analysis of GPR data for archaeological prospection. Pure Appl Geophys 173(8):2737–2751
Uwagi
Korekta artykułu w Vol. 67, iss. 6/2019, na stronach 1679--1691 Nr DOI korekty: 10.1007/s11600-019-00371-6
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-996c71a7-e32b-4322-bd41-e0f6cd281542
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