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How geodesy can contribute to the understanding and prediction of earthquakes

  • Giuliano Francesco Panza
  • Antonella Peresan
  • Fernando Sansò
  • Mattia CrespiEmail author
  • Augusto Mazzoni
  • Andrea Nascetti
Satellite Positioning for Geosciences

Abstract

Earthquakes cannot be predicted with precision, but algorithms exist for intermediate-term middle-range prediction of main shocks above a pre-assigned threshold, based on seismicity patterns. Few years ago, a first attempt was made in the framework of project SISMA, funded by Italian Space Agency, to jointly use seismological tools, like CN algorithm and scenario earthquakes, and geodetic methods and techniques, like GPS and SAR monitoring, to effectively constrain priority areas where to concentrate prevention and seismic risk mitigation. We present a further development of integration of seismological and geodetic information, clearly showing the contribution of geodesy to the understanding and prediction of earthquakes. As a relevant application, the seismic crisis that started in Central Italy in August 2016 with the Amatrice earthquake and still going on is considered in a retrospective analysis of both GPS and SAR data. Differently from the much more common approach, here, GPS data are not used to estimate the standard 2D velocity and strain field in the area, but to reconstruct the velocity and strain pattern along transects, which are properly oriented according to the a priori information about the known tectonic setting. SAR data related to the Amatrice earthquake coseismic displacements are here used as independent check of the GPS results. Overall, the analysis of the available geodetic data indicates that it is possible to highlight the velocity variation and the related strain accumulation in the area of Amatrice event, within the area alarmed by CN since November 1st, 2012. The considered counter examples, across CN alarmed and not-alarmed areas, do not show any spatial acceleration localized trend, comparable to the one well defined along the Amatrice transect. Therefore, we show that the combined analysis of the results of intermediate-term middle-range earthquake prediction algorithms, like CN, with those from the processing of adequately dense and permanent GNSS network data, possibly complemented by a continuous InSAR tracking, may allow the routine highlight in advance of the strain accumulation. Thus, it is possible to significantly reduce the size of the CN alarmed areas.

Keywords

Earthquakes Prediction Neo deterministic method CN Geodesy 

Abbreviations

CN

California–Nevada algorithm for intermediate-term middle-range earthquake prediction

DGA

Design ground acceleration

GNSS

Global navigation satellite system

GPS

Global positioning system

InSAR

Interferometric synthetic aperture radar

M8

Global algorithm for intermediate-term middle-range prediction for the greatest (M ≥ 8) earthquakes worldwide

MCS

Macroseismic intensity scale Mercalli, Cancani, Sieberg

NDSHA

Neo deterministic seismic hazard assessment

PGV

Peak ground velocity

PGA

Peak ground acceleration

RAN-DPC

Rete Accelerometrica Nazionale–Dipartimento Protezione Civile

SAR

Synthetic aperture radar

Notes

Acknowledgements

The authors are indebted with Roberto Devoti, Federica Riguzzi, Riccardo Lanari, Manuela Bonano, Andrea Magrin, and Volodya Kossobokov for contributing GPS data, SAR data, and prior geological information, and for unselfish fruitful discussions that helped shaping up this paper.

References

  1. Davis C, Keilis-Borok VI, Kossobokov V, Soloviev A (2012) Advance prediction of the March 11, 2011 Great East Japan Earthquake: a missed opportunity for disaster preparedness. Int J Disaster Risk Reduct 1:17–32. doi: 10.1016/j.ijdrr.2012.03.001 CrossRefGoogle Scholar
  2. Devoti R, Pietrantonio G, Riguzzi F (2014) GNSS networks for geodynamics in Italy. Física de la Tierra 26:11–24CrossRefGoogle Scholar
  3. DMG (2017) Department of Mathematics and Geosciences-University of Trieste. Intermediate-term middle-range earthquake prediction experiment (http://www.geologia.units.it/esperimento-di-previsione-dei-terremoti-mt/algorithm-cn). Accessed 10 Apr 2017
  4. Doglioni C, Panza GF (2015) Polarized plate tectonics. Adv Geophys 56:1–167. doi: 10.1016/bs.agph.2014.12.001 CrossRefGoogle Scholar
  5. Galvani A, Anzidei M, Devoti R, Esposito A, Pietrantonio G, Pisani A, Riguzzi F, Serpelloni E (2013) The interseismic velocity field of the central Apennines from a dense GPS network. Ann Geophys. doi: 10.4401/ag-5634 CrossRefGoogle Scholar
  6. Gruppo di Lavoro INGV sul terremoto di Amatrice (2016) Primo rapporto di sintesi sul Terremoto di Amatrice Ml 6.0 del 24 Agosto 2016 (Italia Centrale). doi: 10.5281/zenodo.61121
  7. IEPT (2017) Institute of earthquake prediction theory. Russian Academy of Sciences. (http://www.mitp.ru/en/predlist.html). Accessed 10 Apr 2017
  8. Indirli M, Razafindrakoto H, Romanelli F, Puglisi C, Lanzoni L, Milani E, Munari M, Apablaza S (2011) Hazard evaluation in Valparaiso: the MAR VASTO project. Pure Appl Geophys 168:543–582. doi: 10.1007/s00024-010-0164-3 (ISSN: 0033-4553) CrossRefGoogle Scholar
  9. INGV working group on the Amatrice earthquake (2016) Second summary report on the M6.0 Amatrice earthquake of August 24, 2016 (Central Italy). doi: 10.5281/zenodo.166241
  10. Kantorovich LV, Keilis-Borok VI (1991) Earthquake prediction and decision-making: social, economic and civil protection aspects. In: Proc. International Conference on Earthquake Prediction: State-of-the-Art, 586-593, Scientific-Technical Contributions, CSEM-EMSC, Strasbourg, France (based on “Economics of earthquake prediction’’ in Proc. UNESCO Conference on Seismic Risk, Paris, 1977)Google Scholar
  11. Kantorovich LV, Keilis-Borok VI, Molchan GM (1974) Seismic risk and principles of seismic zoning. In: Seismic design decision analysis. Department of Civil Engineering, MIT. Internal Study Report, p 43Google Scholar
  12. Keilis-Borok VI (1996) Intermediate term earthquake prediction. Proc Natl Acad Sci USA 93:3748–3755CrossRefGoogle Scholar
  13. Keilis-Borok V, Soloviev A (eds) (2003) Nonlinear dynamics of the lithosphere and earthquake prediction, chapters: 1, 4 and 5. Springer Verlag, Berlin. ISBN 978-3-662-05298-3Google Scholar
  14. Kossobokov VG (2014) Times of increased probabilities for occurrence of catastrophic earthquakes: 25 years of hypothesis testing in real time. Chapter 18. In: Wyss M, Shroder J (eds) Earthquake hazard, risk, and disasters. Elsevier, London, pp 477–504CrossRefGoogle Scholar
  15. Kossobokov VG, Soloviev AA (2015) Evaluating the results of testing algorithms for prediction of earthquakes. Dokl Earth Sci 460(2):192–194. doi: 10.1134/S1028334X15020208 CrossRefGoogle Scholar
  16. Kreemer C, Blewitt G, Klein EC (2014) A geodetic plate motion and global strain rate model. Geochem Geophys Geosyst 15:3849–3889. doi: 10.1002/2014GC005407 CrossRefGoogle Scholar
  17. Lavecchia G et al (2016) Ground deformation and source geometry of the 24 August 2016 Amatrice earthquake (Central Italy) investigated through analytical and numerical modeling of DInSAR measurements and structural-geological data. Geophys Res Lett. doi: 10.1002/2016GL071723 CrossRefGoogle Scholar
  18. Livi Bacci M, Panza GF (eds) (2016) Resilienza delle città d’arte ai terremoti – XXXIII giornata dell’ambiente – Atti dei Convegni Lincei 306, p 632 (ISBN:978-88-218-1141-8)Google Scholar
  19. Molchan GM, Romashkova L, Peresan A (2017) On some methods for assessing earthquake predictions. Geophysical J Int. doi: 10.1093/gji/ggx239
  20. Panza GF, Peresan A, Magrin A, Vaccari F, Sabadini R, Crippa B, Marotta AM, Splendore R, Barzaghi R, Borghi A, Cannizzaro L, Amodio A, Zoffoli S (2011) The SISMA prototype system: integrating geophysical modeling and earth observation for time-dependent seismic hazard assessment. Nat Hazards (2013) 69:1179–1198. doi: 10.1007/s11069-011-9981-7 CrossRefGoogle Scholar
  21. Peresan A (2017) Recent developments in the detection of seismicity patterns for the Italian region. In: Ouzounov D, Pulinets S, Hattori K, Taylor P (eds) Pre-earthquake processes: a multi-disciplinary approach to earthquake prediction studies. AGU/Wiley, p 35xGoogle Scholar
  22. Peresan A, Kossobokov V, Romashkova L, Panza GF (2005) Intermediate-term middle-range earthquake predictions in Italy: a review. Earth Sci Rev 69(2005):97–132CrossRefGoogle Scholar
  23. Peresan A, Kossobokov V, Panza GF (2012) Operational earthquake forecast/prediction. Rend Fis Acc Lincei 23:131–138. doi: 10.1007/s12210-012-0171-7 CrossRefGoogle Scholar
  24. Peresan A, Gorshkov A, Soloviev A, Panza GF (2015) The contribution of pattern recognition of seismic and morphostructural data to seismic hazard assessment. Boll Geofis Teorica ed Applicata 56:295–328. doi: 10.4430/bgta0141 CrossRefGoogle Scholar
  25. Peresan A, Kossobokov VI, Romashkova L, Magrin A, Soloviev A, Panza GF (2016) Time-dependent neo-deterministic seismic hazard scenarios: Preliminary report on the M6.2 Central Italy earthquake, 24th August 2016. New Concepts Global Tecton 4(3):487–493Google Scholar
  26. Piccardi L et al (2016) The August 24, 2016, Amatrice earthquake (Mw 6.0): field evidence of on- fault effects. Preliminary report. http://www.isprambiente.gov.it/files/notizie-ispra/notizie-2016/sisma-italia-centrale/REPORT_Amatrice_en_2016_09_16.compressed.pdf
  27. Riguzzi F, Crespi M, Devoti R, Doglioni C, Pietrantonio G, Pisani AR (2013) Strain rate relaxation of normal and thrust faults in Italy. Geophys J Int 195(2):815–820. doi: 10.1093/gji/ggt304 CrossRefGoogle Scholar
  28. Rotwain IM, Keilis-Borok VI, Botwina L (1997) Premonitory transformation of steel fracturing and seismicity. Phys Earth Planet Interiors 101:61–71CrossRefGoogle Scholar
  29. Wright TJ (2016) The earthquake deformation cycle. A&G 57(4):4.20–4.26. doi: 10.1093/astrogeo/atw148

Copyright information

© Accademia Nazionale dei Lincei 2017

Authors and Affiliations

  • Giuliano Francesco Panza
    • 1
    • 2
    • 3
  • Antonella Peresan
    • 3
    • 4
  • Fernando Sansò
    • 2
    • 5
  • Mattia Crespi
    • 6
    Email author
  • Augusto Mazzoni
    • 6
  • Andrea Nascetti
    • 6
  1. 1.Institute of GeophysicsChina Earthquake AdministrationBeijingChina
  2. 2.Accademia Nazionale dei LinceiRomeItaly
  3. 3.International Seismic Safety Organization (ISSO)RomeItaly
  4. 4.CRS-Istituto Nazionale di Oceanografia e Geofisica SperimentaleTriesteItaly
  5. 5.DICA-Politecnico di MilanoMilanItaly
  6. 6.Geodesy and Geomatics Division-DICEAUniversity of Rome “La Sapienza”RomeItaly

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