Skip to main content
SpringerLink
Log in

Prediction of ground motion parameters for the volcanic area of Mount Etna

  • Original Article
  • Published:
Journal of Seismology Aims and scope Submit manuscript

Abstract

Ground motion prediction equations (GMPEs) have been derived for peak ground acceleration (PGA), velocity (PGV), and 5 % damped spectral acceleration (PSA) at frequencies between 0.1 and 10 Hz for the volcanic area of Mt. Etna. The dataset consists of 91 earthquakes with epicentral distances between 0.5 and 100 km. Given the specific characteristics of the area, we divided our data set into two groups: shallow events (SE, focal depth <5 km), and deep events (DE, focal depth >5 km). The range of magnitude covered by the SE and the DE is 3.0 ≤ M L ≤ 4.3 and 3.0 ≤ M L ≤ 4.8, respectively. Signals of DE typically have more high frequencies than those of SE. These differences are clearly reflected in the empirical GMPEs of the two event groups. Empirical GMPEs were estimated considering several functional forms: Sabetta and Pugliese (Bull Seism Soc Am 77:1491–1513, 1987) (SP87), Ambraseys et al. (Earth Eng Struct Dyn 25:371–400, 1996) (AMB96), and Boore and Atkinson (Earth Spectra 24:99–138, 2008) (BA2008). From ANOVA, we learn that most of the errors in our GMPEs can be attributed to unmodeled site effects, whereas errors related to event parameters are limited. For DE, BA2008 outperforms the simpler models SP87 or AMB96. For SE, the simple SP87 is preferable considering the Bayesian Information Criterion since it proves more stable with respect to confidence and gives very similar or even lower prediction errors during cross-validation than the BA2008 model. We compared our results to relationships derived for Italy (ITA10, Bindi et al. Bull Earth Eng 99:2471–2488, 2011). For SE, the main differences are observed for distances greater than about 5 km for both horizontal and vertical PGAs. Conversely, for DE the ITA10 heavily overestimates the peak ground parameters for short distances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Abrahamson NA, Silva W (1997) Empirical response spectral attenuation relations for shallow crustal earthquakes. Seismol Res Lett 68:94–127

    Article  Google Scholar 

  • Akkar S, Bommer JJ (2010) Empirical equations for the prediction of PGA, PGV and spectral accelerations in Europe, the Mediterranean region and the Middle East. Seismol Res Lett 81:195–206

    Article  Google Scholar 

  • Akkar S, Çagnan Z, Yenier E, Erdoğan Ö, Sandikkaya A, Gülkan P (2010) The recently compiled Turkish strong motion database: preliminary investigation for seismological parameters. J Seismol 14:457–479

    Article  Google Scholar 

  • Albarello D, Bosi V, Bramerini F, Lucantoni A, Naso G, Peruzza L, Rebez A, Sabetta F, Slejko D (2000) Carte di pericolosità sismica del territorio nazionale. Quad Geofis 12, ING, Roma, 7 pp., CD-Rom, 4 annexes (in Italian)

  • Amato A, Azzaro R, Basili A, Chiarabba C, Cocco M, Di Bona M, Selvaggi G (1995) Main shock and aftershocks of the December 13, 1990 Eastern Sicily earthquake. Ann Geophys 38:255–266

    Google Scholar 

  • Ambraseys NN, Simpson KA, Bommer JJ (1996) Prediction of horizontal response spectra in Europe. Earthq Eng Struct Dyn 25:371–400

    Article  Google Scholar 

  • Aster RC, Borchers B, Thurber CH (2005) Parameter estimation and inverse problems. Elsevier Academic Press, Amsterdam, p 301

    Google Scholar 

  • Azzaro R (2004) Seismicity and active tectonics in the Etna region: constraints for a seismotectonic model. In: Bonaccorso A, Calvari S, Coltelli M, Del Negro C, Falsaperla S (eds) Mt. Etna: Volcano Laboratory, AGU Monograph 143. 205–220

  • Azzaro R, D’Amico S, Mostaccio A, Scarfì L, Tuvè T (2006) Terremoti con effetti macrosismici in Sicilia orientale nel periodo gennaio 2002-dicembre 2005. Quad Geofis 41, INGV, Roma (in Italian)

  • Bianca M, Monaco C, Tortorici L, Cernobori L (1999) Quaternary normal faulting in southeastern Sicily (Italy): a seismic source for the 1693 large earthquake. Geophys J Int 139:370–394

    Article  Google Scholar 

  • Bindi D, Luzi L, Pacor F (2009) Interevent and interstation variability computed for the Italian accelerometric archive (ITACA). Bull Earthq Eng 99:2471–2488

    Google Scholar 

  • Bindi D, Luzi L, Massa M, Pacor F (2010) Horizontal and vertical ground motion prediction equations derived from the Italian Accelerometric Archive (ITACA). Bull Earthq Eng 8:1209–1230. doi:10.1007/s10518-009-9130-9

    Article  Google Scholar 

  • Bindi D, Pacor F, Luzi L, Puglia R, Massa M, Ameri G, Paolucci R (2011) Ground motion prediction equations derived from the Italian strong motion database. Bull Earthq Eng 9:1899–1920. doi:10.1007/s10518-011-9313-z

    Article  Google Scholar 

  • Bonforte A, Guglielmino F, Puglisi G (2013) Interaction between magma intrusion and flank dynamics at Mt. Etna in 2008, imaged by integrated dense GPS and DInSAR data. Geochem Geophys Geosyst 14:2818–2835. doi:10.1002/ggge.20190

    Article  Google Scholar 

  • Boore DM, Atkinson GM (2008) Ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5 %-damped PSA at spectral periods between 0.01 s and 100 s. Earthquake Spectra 24:99–138

    Article  Google Scholar 

  • Boschi E, Guidoboni E, Dante M (1995) Seismic effects of the strongest historical earthquakes in the Syracuse area. Ann Geophys 38:223–253

    Google Scholar 

  • Bousquet JC, Lanzafame G (2004) The tectonics and geodynamics of Mt. Etna: synthesis and interpretation of geological and geophysical data. In: Bonaccorso A, Calvari S, Coltelli M, Del Negro C, Falsaperla S (eds) Mt. Etna: Volcano Laboratory. AGU Monograph 143:29–45

  • Burnham KP, Anderson DR (2004) Multimodal inference. Understanding AIC and BIC in model selection. Social Methods Res 33:261–304. doi:10.1177/0049124104268644

    Article  Google Scholar 

  • Castello B, Selvaggi G, Chiarabba C, Amato A (2006) CSI Catalogo della sismicità italiana 1981–2002, versione 1.1. INGV-CNT, Roma. http://www.ingv.it/CSI/

  • CEN (Comitè Europèen de Normalisation) (2003) prEN 1998-1- Eurocode 8: design of structures for earthquake resistance. Part 1: General rules, seismic actions and rules for buildings. Draft No 6, Doc CEN/TC250/SC8/N335, January 2003, Brussels

  • Convertito V, Caccale M, De Matteis R, Emolo A, Wald D, Zollo A (2013) Fault extent estimation for near-real-time ground shaking map computation purposes. Bull Seismol Soc Am 102:661–679

    Article  Google Scholar 

  • D’Agostino N, Selvaggi G (2004) Crustal motion along the Eurasia-Nubia plate boundary in the Calabrian Arc and Sicily and active extension in the Messina Straits from GPS measurements. J Geophys Res 109. doi:10.1029/2004JB002998

  • Douglas J, Gehl P (2008) Investigating strong ground-motion variability using analysis of variance and two-way-fit plots. Bull Earthq Eng 6:389–405

    Article  Google Scholar 

  • Efron B, Tibshirani RJ (1994) An introduction to the bootstrap. Chapman & Hall/CRC, Boca Raton, Florida, ISBN 978–0412042317, 456 pp

  • Emolo A, Convertito V, Cantore L (2011) Ground-motion predictive equations for low-magnitude earthquakes in the Campania-Lucania area, Southern Italy. J Geophys Eng 8:46–60

    Article  Google Scholar 

  • Fisher RA (1990) Statistical methods, experimental design, and scientific inference. Oxford University Press, Oxford

    Google Scholar 

  • Frisenda M, Massa M, Spallarossa D, Ferretti G, Eva C (2005) Attenuation relationship for low magnitude earthquakes using standard seismometric records. J Earthq Eng 9:23–40

    Google Scholar 

  • Gresta S, Langer H (2002) Assessment of seismic potential in southeastern Sicily. In: Brebbia CA (ed) Risk Analysis III. WITpress Southampton, Boston, pp 617–626

    Google Scholar 

  • Gresta S, Patanè D (1987) Review of seismological studies at Mount Etna. Pure Appl Geophys 125:951–970

    Article  Google Scholar 

  • Gruppo Analisi Dati Sismici (2015) Catalogo dei terremoti della Sicilia Orientale - Calabria Meridionale (1999–2015). Istituto Nazionale di Geofisica e Vulcanologia, Catania http://www.ct.ingv.it/ufs/analisti/catalogolist.php

  • Hastie T, Tibshirani R, Friedman JH (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New York, 533 pp

    Book  Google Scholar 

  • Hesterberg T, Monaghan S, Moore DS, Clipson A, Epstein R (2003) Bootstrap and permutation tests—companion chapter 18 to the “Practice of business statistics”, in the practice of business statistics. Freeman, WH & Co, New York

    Google Scholar 

  • Joyner WB, Boore DM (1981) Peak horizontal acceleration and velocity from strong motion records including records from the 1979 Imperial Valley, California, earthquake. Bull Seismol Soc Am 71:2011–2038

    Google Scholar 

  • Kadane JB, Lazar NA (2004) Methods and criteria for model selection. J Am Stat Assoc 99:279–290. doi:10.1198/016214504000000269

    Article  Google Scholar 

  • Langer H, Raffaele R, Scaltrito A, Scarfi L (2007) Estimation of an optimum velocity model in the Peloritani Mountains—assessment of the variance of model parameters and variability of earthquake locations. Geophys J Int 170:1151–1164. doi:10.1111/j.1365-246X.2007.03459.x

    Article  Google Scholar 

  • Lilliefors HW (1967) On the Kolmogorov-Smirnov test for normality with mean and variance unknown. J Am Stat Assoc 62:399–402

    Article  Google Scholar 

  • Lilliefors HW (1969) On the Kolmogorov-Smirnov test for the exponential distribution wioth mean unknown. J Am Stat Assoc 64:387–389

    Article  Google Scholar 

  • Luzi L, Sabetta F, Hailemikael S, Bindi D, Pacor F, Mele F (2008) ITACA (ITalian ACcelerometric Archive): a web portal for the dissemination of Italian strong motion data. Seismol Res Lett 79:717–723

    Article  Google Scholar 

  • Margottini C, Molin D, Narcisi B, Serva L (1987). Intensity vs. acceleration: Italian data. C Margotinni and L Sena (Eds.), Proceeding, workshop on historical seismicity of Central-Eastern Mediterranean region, ENEA, Rome, 213–226

  • Massa M, Marzorati S, D’Alema E, Di Giacomo D, Augliera P (2007) Site classification assessment for estimating empirical attenuation relationships for Central-Northern Italy earthquakes. J Earthq Eng 11:943–967

    Article  Google Scholar 

  • Montaldo V, Faccioli E, Zonno G, Akinci A, Malagnini L (2005) Treatment of ground-motion predictive relationships for the reference seismic hazard map of Italy. J Seismol 9:295–316

    Article  Google Scholar 

  • Pacor F, Paolucci R, Ameri G, Massa M, Puglia R (2011) Italian strong motion records in ITACA: overview and record processing. Bull Earthq Eng 9:1741–1759

    Article  Google Scholar 

  • Press W, Teukolsky S, Vetterling W, Flannery B (1992) Numerical recipes in C: the art of scientific computing. Cambridge University, Cambridge

    Google Scholar 

  • Sabetta F, Pugliese A (1987) Attenuation of peak horizontal acceleration and velocity from Italian strong-motion records. Bull Seismol Soc Am 77:1491–1513

    Google Scholar 

  • Sabetta F, Pugliese A (1996) Estimation of response spectra and simulation of nonstationary earthquake ground motions. Bull Seismol Soc Am 86:337–352

    Google Scholar 

Download references

Acknowledgments

This work has been carried out within the project “Estensione e Potenziamento dei sistemi di monitoraggio vulcanico e sismico della Sicilia” funded by the Istituto Nazionale di Geofisica e Vulcanologia (INGV) and the Regione Siciliana (Convenzione INGV- Regione Siciliana 2006–2011). This study has also benefited from funding provided by the Italian Presidenza del Consiglio dei Ministri - Dipartimento della Protezione Civile (DPC), Project V3. This paper does not necessarily represent DPC official opinion and policies. G.T. benefited from funding provided by the MED-SUV project and SIGMA PON01_00683 project. The MED-SUV project has received funding from the European Union Seventh Framework Programme (FP7) under Grant agreement no. 308665. The SIGMA PON01_00683 project is co-funded by FESR – Fondo Europeo di Sviluppo Regionale. We sincerely thank the editor Mariano Gracía Frenández, Antonio Emolo, and the anonymous reviewer who helped improve the quality of the manuscript. Our work benefited from the support by Stephen Convay for the english language correction.

Compliance with ethical standards

ᅟ

Conflict of interest

The authors declare that they have no competing interests.

Author information

Authors and Affiliations

  1. Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Catania - Osservatorio Etneo, Piazza Roma 2, 25125, Catania, Italy

    Giuseppina Tusa & Horst Langer

Authors
  1. Giuseppina Tusa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Horst Langer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giuseppina Tusa.

Appendices

Appendix 1

Table 8 The same as Table 3 but without considering the data from stations with C-soil class. Mean bootstrap and St.Dev. bootstrap are the mean value and the standard deviation of each coefficient, respectively, estimated by applying the bootstrap technique (see text for details). R 2 is the determination coefficient
Full size table
Table 9 Coefficients of Eq. 9 (Boore and Atkinson’s model, BA2008) for the prediction of PGA and PGV, both horizontal (PGAh and PGVh) and vertical (PGAv and PGVv)
Full size table
Table 10 Regression coefficients for PSA (cm/s2) at the different periods for the SE and obtained for the SP87/AMB96 model. Mean bootstrap and St.Dev. bootstrap are the mean value and the standard deviation of each coefficient, respectively, estimated by applying the bootstrap technique. R 2 is the determination coefficient
Full size table
Table 11 Regression coefficients for PSA (cm/s2) at the different periods for the DE obtained for the SP87/AMB96 model. Mean bootstrap and St.Dev. bootstrap are the mean value and the standard deviation of each coefficient, respectively, estimated by applying the bootstrap technique. R 2 is the determination coefficient
Full size table
Table 12 Regression coefficients for PSA (cm/s2) at the different periods for the SE by applying the BA2008 model. Mean bootstrap and St.Dev. bootstrap are the mean value and the standard deviation of each coefficient, respectively, estimated by applying the bootstrap technique. R 2 is the determination coefficient
Full size table
Table 13 Regression coefficients for PSA (cm/s2) at the different periods for the DE by applying the BA2008 model. Mean bootstrap and St.Dev. bootstrap are the mean value and the standard deviation of each coefficient, respectively, estimated by applying the bootstrap technique. R 2 is the determination coefficient
Full size table

Appendix 2

Fig. 12
figure 12

Residuals of logarithm of vertical PGA for a shallow and b deep events. In the upper panels, histograms of the number of data grouped according to the Log(PGAv) residuals for the BA2008 and SP87/AMB96 models. In the middle and lower panels, distributions of the Log(PGAv) residuals as a function of epicentral distance and magnitude, respectively

Full size image
Fig. 13
figure 13

Residuals of logarithm of horizontal PGV for a shallow and b deep events. In the upper panels, histograms of the number of data grouped according to the Log(PGVh) residuals for the BA2008 and SP87/AMB96 models. In the middle and lower panels, distributions of the Log(PGVh) residuals as a function of epicentral distance and magnitude, respectively

Full size image
Fig. 14
figure 14

Residuals of logarithm of vertical PGV for a shallow and b deep events. In the upper panels, histograms of the number of data grouped according to the Log(PGVv) residuals for the BA2008 and SP87/AMB96 models. In the middle and lower panels, distributions of the Log(PGVv) residuals as a function of epicentral distance and magnitude, respectively

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tusa, G., Langer, H. Prediction of ground motion parameters for the volcanic area of Mount Etna. J Seismol 20, 1–42 (2016). https://doi.org/10.1007/s10950-015-9508-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10950-015-9508-x

Keywords

Search

Navigation