Skip to main content
SpringerLink
Log in

Assessing the impact of an updated spatial correlation model of ground motion parameters on the italian shakemap

  • Original Article
  • Published:
Bulletin of Earthquake Engineering Aims and scope Submit manuscript

Abstract

This study develops a new spatial correlation model for Italy using the most up-to-date and densest dataset of accelerometer and velocimeter records available. The objective is to estimate the average correlation length and assess its impact on the prediction accuracy of the Italian Shakemap compared to the global model (Loth and Baker, 2013–LB13) adopted in the default configuration of the program. We compute the spatial covariance structure using a geostatistical approach based on traditional variography applied to standardized residuals within the events of a reference ground motion model (ITA10). We observe spatial clusters of the correlation lengths and a wide variability over the Italian territory linked to the profound heterogeneity of the geological and geomorphological context. The obtained estimates are then implemented within the LB13 co-regionalization model in place of the default values while assuming the same cross-correlation coefficients among spectral parameters. Although our results are quite consistent with previous models calibrated for Italy, we find that the inclusion of the new correlation lengths in the Shakemap predictions, assessed through a leave-one-out cross-validation technique, results in a non-appreciable improvement over the global model, thus indicating that the adopted approach is not able to resolve the regional features and the corresponding spatial correlation with reference to individual scenarios. These findings may suggest the need to move towards nonergodic models in the Shakemap computing to better capture the spatial variability or to determine different co-regionalisation matrices more suitable for the regional applications.

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
Fig. 12
Fig. 13

Similar content being viewed by others

Data Availability

The original dataset (Brunelli et al., 2022a) used to calibrate the spatial correlation model proposed in this study is accessible at the following URL: https://itaca.mi.ingv.it/ItacaNet_32/#/products/itacaext_flatfile. Velocimetric records are automatically downloaded from the European Integrated Data Archive (EIDA http://www.orfeus-eu.org/eida/eida.html) within the ESM database. Input data for the ShakeMap validation test are available at ESM (https://esm-db.eu/#/home), ITACA (https://itaca.mi.ingv.it/ItacaNet_32/#/home) and INGV (http://terremoti.ingv.it/webservices_and_software).

References

  • Al-Atik L, Abrahamson N, Bommer JJ, Scherbaum F, Cotton F, Kuehn N (2010) The variability of ground-motion prediction models and its components. Seismol Res Lett 81(5):794–801

    Article  Google Scholar 

  • Baker JW, Chen Y (2020) Ground motion spatial correlation fitting methods and estimation uncertainty. Earthq Eng Struct Dyn. https://doi.org/10.1002/eqe.3322

    Article  Google Scholar 

  • Banerjee S, Gelfand A, Carlin B (2004) Multivariate spatial modelling. Hierarchical modeling and analysis for spatial data CandH/CRC monographs on statistics and applied probability. Chapman and Hall/CRC, UK

    Google Scholar 

  • Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Software 67(1):1–48

    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(6):1899–1920. https://doi.org/10.1007/s10518-011-9313-z

    Article  Google Scholar 

  • Bindi D, Spallarossa D, Pacor F (2017) Between-event and between-station variability observed in the Fourier and response spectra domains: comparison with seismological models. Geophys J Int 210(2):1092–1104. https://doi.org/10.1093/gji/ggx217

    Article  Google Scholar 

  • Brunelli G, Lanzano G, D’Amico MC, Felicetta C, Luzi L, Mascandola C, Pacor F, Russo E, Sgobba S (2022a) ITACAext flatfile. Istituto Nazionale Di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/itaca32/itacaext_flatfile.1.0

    Article  Google Scholar 

  • Brunelli G, Lanzano G, Luzi L, Sgobba S (2022b). Data-driven zonations for modelling the regional source and propagation effects into ground motion models in Italy. Soil Dyn Earth Eng

  • CEN, 2004. EN 1998–1:2004 Eurocode 8: design of structures for earthquake resistance—Part 1: general rules, seismic actions and rules for buildings. European Committee for Standardization, Brussels

  • Chen Y, Baker J (2019) Spatial correlations in cybershake physics-based ground motion simulations. Bull Seismol Soc Am. https://doi.org/10.1785/0120190065

    Article  Google Scholar 

  • Chen FW, Liu CW (2012) Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 10:209–222

    Article  Google Scholar 

  • Colavitti L, Lanzano G, Sgobba S, Pacor F, Gallovič F (2022) Empirical evidence of frequency-dependent directivity effects from small-to-moderate normal fault earthquakes in Central Italy. J Geophys Res: Solid Earth. https://doi.org/10.1029/2021JB023498

    Article  Google Scholar 

  • Crowley H, Stafford P, Bommer JJ (2008) Can earthquake loss models be validated using field observations? J Earthq Eng 12(7):1078–1104

    Article  Google Scholar 

  • Dreger DS (2003) 85.11 TDMT_INV: Time domain seismic moment tensor INVersion. International Handbook of Earthquake and Engineering Seismology. Elsevier, p 1627. https://doi.org/10.1016/S0074-6142(03)80290-5

    Chapter  Google Scholar 

  • Esposito S, Iervolino I (2011) PGA and PGV spatial correlation models based on European multi-event datasets. Bull Seismol Soc Am 101(5):2532–2541

    Article  Google Scholar 

  • Esposito S, Iervolino I (2012) Spatial correlation of spectral acceleration in European data. Bull Seismol Soc Am 102(6):2781–2788. https://doi.org/10.1785/0120120068

    Article  Google Scholar 

  • Goda K, Hong HP (2008) Spatial correlation of peak ground motions and response spectra. Bull Seismol Soc Am 98(1):354–365

    Article  Google Scholar 

  • Heresi P, Miranda E (2019) Uncertainty in intraevent spatial correlation of elastic pseudo-acceleration spectral ordinates. Bull Earthq Eng 17(3):1099–1115. https://doi.org/10.1785/0120070078

    Article  Google Scholar 

  • Hofierka J, Cebecauer T, Šúri M (2007) Optimisation of inter- polation parameters using cross-validation. In: Peckam RJ, Jordan G (eds) Digital Terrain Modelling. Springer, Berlin Heidelberg, pp 67–82

    Chapter  Google Scholar 

  • Huang C, Galasso C (2019) Ground-motion intensity measure correlations observed in italian strong-motion records. Earthq Eng Struct Dynam 48(15):1634–1660. https://doi.org/10.1002/eqe.3216

    Article  Google Scholar 

  • Infantino M, Smerzini C, Lin J (2021) Spatial correlation of broadband ground motions from physics-based numerical simulations. Earthq Eng Struct Dyn. https://doi.org/10.1002/eqe.3461

    Article  Google Scholar 

  • Jayaram N, Baker JW (2009) Correlation model for spatially distributed ground-motion intensities. Earthq Eng Struct Dynam. https://doi.org/10.1002/eqe

    Article  Google Scholar 

  • Lanzano G, Sgobba S, Luzi L, Puglia R, Pacor F, Felicetta C, D’Amico M, Cotton F, Bindi D (2018) The pan-European engineering strong motion (ESM) flatfile: compilation criteria and data statistics. Bull Earthq Eng 17(2):561–582. https://doi.org/10.1007/s10518-018-0480-z

    Article  Google Scholar 

  • Lanzano G, Luzi L, Pacor F, Felicetta C, Puglia R, Sgobba S, D’Amico M (2019) A revised ground-motion prediction model for shallow crustal earthquakes in Italy. Bull Seismol Soc Am 109(2):525–540

    Article  Google Scholar 

  • Loth C, Baker JW (2013) A spatial cross-correlation model of ground motion spectral accelerations at multiple periods. Earthq Eng Struct Dynam 42:397–417

    Article  Google Scholar 

  • Loth C, Baker JW (2020) Erratum: a spatial cross-correlation model for ground motion spectral accelerations at multiple periods. Earthq Engng Struct Dyn 49:315–316. https://doi.org/10.1002/eqe.3233

    Article  Google Scholar 

  • Matheron G, (1962) Traité de géostatistique appliquée. 1 (1962). Editions Technip

  • Menafoglio A, Sgobba S, Lanzano G, Pacor F (2020) Simulation of seismic ground motion fields via object-oriented spatial statistics with an application in Northern Italy. Stoch Environ Res Risk Assess 32(12):3421–3437

    Article  Google Scholar 

  • Michelini A, Faenza L, Lauciani V, Malagnini L (2008) ShakeMaps implementation in Italy. Seismol Res Lett 79:688–697

    Article  Google Scholar 

  • Michelini A, Faenza L, Lanzano G, Lauciani V, Jozinović D, Puglia R, Luzi L (2020) The new ShakeMap in Italy: progress and advances in the last 10 Yr. Seismol Res Lett. https://doi.org/10.1785/0220190130

    Article  Google Scholar 

  • Oliver MA, Webster R (2014) A tutorial guide to geostatistics: Computing and modelling variograms and kriging. Catena 113:56–69. https://doi.org/10.1016/j.catena.2013.09.006

    Article  Google Scholar 

  • Pagani M, Monelli D, Weatherill G, Danciu L, Crowley H, Silva V, Henshaw P, Butler L, Nastasi M, Panzeri L, Simionato M, Vigano D (2014) OpenQuake engine: an open hazard (and Risk) software for the global earthquake model. Seismol Res Lett 85(3):692–702. https://doi.org/10.1785/0220130087

    Article  Google Scholar 

  • Paolucci R, Pacor F, Puglia R, Ameri G, Cauzzi C, Massa M (2011) Record processing in ITACA, the new Italian strong-motion database. In: Akkar S, Gulkan P, Van Eck T (eds) Earthquake Data in Engineering Seismology, vol 14. Geotechnical, Geological and Earthquake Engineering Series. Springer, Dordrecht, The Netherlands, pp 99–113

    Chapter  Google Scholar 

  • Park J, Bazzurro P, Baker JW, (2007) Modeling spatial correlation of ground motion Intensity measures for regional seismic hazard and portfolio loss estimation. Appl Stat Prob Civil Eng pp. 1–8

  • Pondrelli S, (2002) European-mediterranean regional centroid-moment tensors catalog (RCMT). Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/rcmt/euromed

  • Schiappapietra E, Douglas J (2020) Modelling the spatial correlation of earthquake ground motion: insights from the literature, data from the 2016–2017 Central Italy earthquake sequence and ground-motion simulations. Earth-science Rev. https://doi.org/10.1016/j.earscirev.2020.103139

    Article  Google Scholar 

  • Schiappapietra E, Douglas J (2021) Assessment of the uncertainty in spatial-correlation models for earthquake ground motion due to station layout and derivation method. Bull Earthq Eng 19:5415–5438. https://doi.org/10.1007/s10518-021-01179-w

    Article  Google Scholar 

  • Schiappapietra E, Smerzini C (2021) Spatial correlation of broadband earthquake ground motion in Norcia (Central Italy) from physics-based simulations. Bull Earthq Eng. https://doi.org/10.1007/s10518-021-01160-7

    Article  Google Scholar 

  • Schiappapietra E, Stripajová S, Pažák P, Douglas J, Trendafilosk G (2022) Exploring the impact of spatial correlations of earthquake ground motions in the catastrophe modelling process: a case study for Italy. Bull Earthq Eng 20:5747–5773. https://doi.org/10.1007/s10518-022-01413-z

    Article  Google Scholar 

  • Sedaghati F, Pezeshk S (2017) Partially nonergodic empirical ground-motion models for predicting horizontal and vertical PGV, PGA, and 5% damped linear acceleration response spectra using data from the Iranian Plateau. Bull Seismol Soc Am 107:934–948

    Article  Google Scholar 

  • Sgobba S, Lanzano G, Pacor F, Puglia R, D’Amico M, Felicetta C, Luzi L (2019) Spatial correlation model of systematic site and path effects for ground-motion fields in Northern Italy. Bull Seismol Soc Am. https://doi.org/10.1785/0120180209

    Article  Google Scholar 

  • Sgobba S, Lanzano G, Pacor F (2021) Empirical non-ergodic shaking scenarios based on spatial correlation models: an application to Central Italy. Earthq Eng Struct Dyn. https://doi.org/10.1002/eqe.3362

    Article  Google Scholar 

  • Sokolov V, Wenzel F (2011) Influence of spatial correlation of strong ground motion on uncertainty in earthquake loss estimation. Earthq Eng Struct Dyn 40:993–1009. https://doi.org/10.1002/eqe.1074

    Article  Google Scholar 

  • Sokolov V, Wenzel F, Kuo-Liang W (2010) Uncertainty and spatial correlation of earthquake ground motion in Taiwan. TAO Terr Atmos Ocean Sci 21(6):9. https://doi.org/10.3319/TAO.2010.05.03.01(T)

    Article  Google Scholar 

  • Stafford PJ (2014) Crossed and nested mixed-effects approaches for enhanced model development and removal of the ergodic assumption in empirical ground-motion models. Bull Seismol Soc Am 104(2):702–719

    Article  Google Scholar 

  • Tomczak M (1998) Spatial interpolation and its uncertainty using automated anisotropic inverse distance weighting (IDW)-cross- validation/jackknife approach. J Geogr Inf Decision Anal 2(2):18–30

    Google Scholar 

  • Verros SA, Wald DJ, Worden CB, Hearne M, Ganesh M (2017) Computing spatial correlation of ground motion intensities for ShakeMap. Comput Geosci 99:145–154. https://doi.org/10.1016/j.cageo.2016.11.004

    Article  Google Scholar 

  • Weatherill GA et al (2015) Exploring the impact of spatial correlations and uncertainties for portfolio analysis in probabilistic seismic loss estimation. Bull Earthq Eng 13(4):957–981. https://doi.org/10.1007/s10518-015-9730-5

    Article  Google Scholar 

  • Worden CB, Wald DJ, Allen TI, Lin K, Garcia D, Cua G (2010) A revised ground-motion and intensity interpolation scheme for ShakeMap. Bull Seismol Soc Am 100:3083–3096

    Article  Google Scholar 

  • Worden CB, Thompson EM, Baker JW, Bradley BA, Luco N, Wald DJ (2018) Spatial and spectral interpolation of ground motion intensity measure observations. Bull Seism Soc Am 108(2):866–875. https://doi.org/10.1785/0120170201

    Article  Google Scholar 

  • Worden CB, Thompson EM, Hearne M, Wald DJ (2020) ShakeMap Manual Online: technical manual, user’s guide, and software guide. US Geol Survey. https://doi.org/10.5066/F7D21VPQ

    Article  Google Scholar 

  • Zerva A, Zervas V (2002) Spatial variation of seismic ground motions: an overview. Appl Mech Rev 55(3):271. https://doi.org/10.1115/1.1458013

    Article  Google Scholar 

  • Zimmaro P, Scasserra G, Stewart JP, Kishida T, Tropeano G, Castiglia M, Pelekis P (2018) Strong ground motion characteristicsfrom 2016 Central Italy earthquake sequence. Earthq Spectra 34(4):1611–1637. https://doi.org/10.1193/091817EQS184M

    Article  Google Scholar 

Download references

Acknowledgements

This study was partially funded in the framework of INGV and Dipartimento della Protezione Civile (INGV-DPC) AGREEMENT B2 2019–2021, within the Task related to ShakeMap implementation in Italy. The authors thank Lucia Luzi and Alberto Michelini for their fruitful suggestions during the preparation of the work. The authors are also grateful to the two anonymous Reviewers and to the Associate Editor John Douglas for their valuable comments.

Funding

This study was partially funded in the framework of INGV and Dipartimento della Protezione Civile (INGV-DPC) AGREEMENT B2 2019–2021, within the Task related to ShakeMap implementation in Italy.

Author information

Authors and Affiliations

  1. Istituto Nazionale di Geofisica e Vulcanologia, Via Alfonso Corti, 12, 20133, Milan, Italy

    Sara A. Sgobba, Giulio Brunelli & Giovanni Lanzano

  2. Istituto Nazionale di Geofisica e Vulcanologia, Via Donato Creti, 12, 40128, Bologna, Italy

    Licia Faenza

Authors
  1. Sara A. Sgobba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Licia Faenza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giulio Brunelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giovanni Lanzano
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the material preparation, data collection and analysis. Study conception and design was performed by Sara Sgobba. The first draft of the manuscript was written by Sara Sgobba and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sara A. Sgobba.

Ethics declarations

Conflict of Interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sgobba, S.A., Faenza, L., Brunelli, G. et al. Assessing the impact of an updated spatial correlation model of ground motion parameters on the italian shakemap. Bull Earthquake Eng 21, 1847–1873 (2023). https://doi.org/10.1007/s10518-022-01581-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10518-022-01581-y

Keywords

Search

Navigation