Application of InSAR in measuring Earth’s surface deformation caused by groundwater extraction and modeling its behavior using time series analysis by artificial neural networks

• Autorzy: Rahmani Y. , Ahmadi F. F.

Identyfikatory

DOI

10.1007/s11600-018-0182-6

• Języki publikacji: EN

Abstrakty

EN

Measuring ground displacement is very important, considering its destructive physical and financial effects, and one of the most efficient methods for this purpose is the differential interferometry that uses data with high spatial resolution which, in this research, are 11 TerraSAR-X images over 11-month period. The study area is the plain in the southwest of Tehran, and the subsidence was displayed by extracted interferograms. The maximum displacement of the pairs of consecutive images in this area was 18 cm, and the displacement rate was 13 cm/year (cm/year). By analyzing the time series using neural network, displacement for 12th month was predicted at 32 cm. The obtained results of this research were evaluated and validated by using radar and GPS data associated with similar research. The result of the evaluation indicates compliance of the obtained results in this research with other researches in the field.

Słowa kluczowe

EN

artificial neural network; groundwater extraction; InSAR; subsidence; TerraSAR-X

PL

sztuczna sieć neuronowa; wydobywanie wód podziemnych; InSAR; osiadanie; TerraSAR-X

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2018
• Tom: Vol. 66, no. 5
• Strony: 1171--1184
• Opis fizyczny: Bibliogr. 23 poz.

Twórcy

autor

Rahmani Y.

• Department of Geomatics Engineering, Faculty of Civil Engineering University of Tabriz Tabriz Iran

autor

Ahmadi F. F.

• Department of Geomatics Engineering, Faculty of Civil Engineering University of Tabriz Tabriz Iran

Bibliografia

• 1. Agirre-Basurko E, Ibarra-Berastegi G, Madariaga I (2006) Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area. Environ Model Softw 21:430–446
• 2. Agram PS (2010) Persistent scatterer interferometry in natural terrain. Stanford University, Stanford
• 3. Azoff EM (1994) Neural network time series forecasting of financial markets. Wiley, London
• 4. Gray ALAFM, Farris-Manning PJ (1993) Repeat-pass interferometry with airborne synthetic aperture radar. IEEE Trans Geosci Remote Sens 31:180–191
• 5. G.Z.Sh Engineering (2004) Subsidence and it’s undesirable effects throughout Iran and the world. Geologic and Environmental Management, Ministry of Industry and Mining
• 6. Hooper A (2008) A statistical cost approach to unwrapping the phase of SAR interferogram time series manuscript in preparation. Delft Institute of Earth Observation and Space Systems, Delft University of Technology, Delft, Netherlands, pp 1–2
• 7. Hooper A, Zebker HA (2007) Phase unwrapping in three dimensions with application to InSAR time series. JOSA A 24:2737–2747
• 8. Kampes BM (2006) Radar interferometry: persistent scatterer technique. Springer, Berlin
• 9. Lanari R, Lundgren P, Manzo M, Casu F (2004) Satellite radar interferometry time series analysis of surface deformation for Los Angeles, California. Geophys Res Lett 31:L23613
• 10. Li FAG, Goldstein R (1987) Studies of multi-baseline spaceborne interferometric synthetic aperture radar. Int Geosci Remote Sens Symp 2:1545–1550
• 11. Massonnet D (1997) Satellite radar interferometry. Sci Am 276:43–56
• 12. Massonnet D, Holzer T, Vadon H (1997) Land subsidence water by the east mesa geothermal field, California, observed using SAR interferometry. Geophys Res Lett 24:901–904
• 13. Mirshahi FS, Valadan Zoj MJ (2012) Measuring earth’s surface displacement with TerraSAR-X images using InSAR technique. Faculty of Geodesy and Geomatics Engineering, K.N.T University, p 70,1391
• 14. Pepe A, Sansosti E, Berardino P, Lanari R (2005) On the generation of ERS/ENVISAT DInSAR time-series via the SBAS technique. IEEE Geosci Remote Sens Lett 2:265–269
• 15. Sarmap (2006) ASAR Product Handbook. ESA
• 16. Sarmap (2009) SAR-Guidebook. The earth observation information gateway, pp 36–40
• 17. Sneed M, Brandt JT (2013) Detection and measurement of land subsidence using global positioning system surveying and interferometric synthetic aperture radar, Coachella Valley, California, from 1996 to 2005. U.S. GEOLOGICAL SURVEY Scientific Investigations Report 2007–5251, vol 2.0
• 18. Sousa JJ, Ruiz AM, Hanssen RF, Bastos L, Gil AJ, Galindo-Zaldívar J et al (2010) PS-InSAR processing methodologies in the detection of field surface deformation, Study of the Granada basin (Central Betic Cordilleras, Southern Spain). J Geodyn 49:181–189
• 19. Stancliffe RPWAVDK, Van der Kooij MWA (2001) The use of satellite-based inteferometry to monitor production activity at the Cold Lake heavy oil field, Alberta, Canada. AAPG Bull 85:781–793G
• 20. Touzi R, Lopes A, Bruniquel J, Vachon PW (1999) Coherence estimation for SAR imagery. IEEE Trans Geosci Remote Sens 37:135–149
• 21. Tehran Clean Water Resources. Tehran Megacity Atlas. http://atlas.tehran.ir/Default.aspx?tabid=227
• 22. Wright TJ (2000) Crustal deformation in Turkey from synthetic aperture radar interferometry. University of Oxford, Oxford
• 23. Zebker HA, Rosen PA, Hensley S (1997) Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic maps. J Geophys Res Solid Earth (1987–2012) 102:7547–7563