Articles | Volume 24, issue 4
https://doi.org/10.5194/nhess-24-1223-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-24-1223-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Exploring inferred geomorphological sediment thickness as a new site proxy to predict ground-shaking amplification at regional scale: application to Europe and eastern Türkiye
GFZ German Research Centre for Geosciences, Helmholtz Centre Potsdam, 14467 Potsdam, Germany
Institute of Geosciences, University of Potsdam, 14469 Potsdam, Germany
Fabrice Cotton
GFZ German Research Centre for Geosciences, Helmholtz Centre Potsdam, 14467 Potsdam, Germany
Institute of Geosciences, University of Potsdam, 14469 Potsdam, Germany
Graeme Weatherill
GFZ German Research Centre for Geosciences, Helmholtz Centre Potsdam, 14467 Potsdam, Germany
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New generations of seismic hazard models are developed with sophisticated approaches to quantify uncertainties in our knowledge of earthquake processes. To understand why and how recent state-of-the-art seismic hazard models for France, Germany, and Europe differ despite similar underlying assumptions, we present a systematic approach to investigate model-to-model differences and to quantify and visualise them while accounting for their respective uncertainties.
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The 2020 European Seismic Hazard Model (ESHM20) is the latest seismic hazard assessment update for the Euro-Mediterranean region. This state-of-the-art model delivers a broad range of hazard results, including hazard curves, maps, and uniform hazard spectra. ESHM20 provides two hazard maps as informative references in the next update of the European Seismic Design Code (CEN EC8), and it also provides a key input to the first earthquake risk model for Europe.
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The ground motion models (GMMs) selected for the 2020 European Seismic Hazard Model (ESHM20) and their uncertainties require adaptation to different tectonic environments. Using insights from new data, local experts and developments in the scientific literature, we further calibrate the ESHM20 GMM logic tree to capture previously unmodelled regional variation. We also propose a new scaled-backbone logic tree for application to Europe's subduction zones and the Vrancea deep seismic source.
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The size of an earthquake is often described by a single number called the magnitude. Among the possible magnitude scales, the seismic moment (Mw) and the radiated energy (Me) scales are based on physical parameters describing the rupture process. Since these two magnitude scales provide complementary information that can be used for seismic hazard assessment and for seismic risk mitigation, we complement the Mw catalog disseminated by the GEOFON Data Centre with Me values.
Irina Dallo, Michèle Marti, Nadja Valenzuela, Helen Crowley, Jamal Dabbeek, Laurentiu Danciu, Simone Zaugg, Fabrice Cotton, Domenico Giardini, Rui Pinho, John F. Schneider, Céline Beauval, António A. Correia, Olga-Joan Ktenidou, Päivi Mäntyniemi, Marco Pagani, Vitor Silva, Graeme Weatherill, and Stefan Wiemer
Nat. Hazards Earth Syst. Sci., 24, 291–307, https://doi.org/10.5194/nhess-24-291-2024, https://doi.org/10.5194/nhess-24-291-2024, 2024
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For the release of cross-country harmonised hazard and risk models, a communication strategy co-defined by the model developers and communication experts is needed. The strategy should consist of a communication concept, user testing, expert feedback mechanisms, and the establishment of a network with outreach specialists. Here we present our approach for the release of the European Seismic Hazard Model and European Seismic Risk Model and provide practical recommendations for similar efforts.
Max Schneider, Fabrice Cotton, and Pia-Johanna Schweizer
Nat. Hazards Earth Syst. Sci., 23, 2505–2521, https://doi.org/10.5194/nhess-23-2505-2023, https://doi.org/10.5194/nhess-23-2505-2023, 2023
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Hazard maps are fundamental to earthquake risk reduction, but research is missing on how to design them. We review the visualization literature to identify evidence-based criteria for color and classification schemes for hazard maps. We implement these for the German seismic hazard map, focusing on communicating four properties of seismic hazard. Our evaluation finds that the redesigned map successfully communicates seismic hazard in Germany, improving on the baseline map for two key properties.
Juan Camilo Gómez Zapata, Massimiliano Pittore, Nils Brinckmann, Juan Lizarazo-Marriaga, Sergio Medina, Nicola Tarque, and Fabrice Cotton
Nat. Hazards Earth Syst. Sci., 23, 2203–2228, https://doi.org/10.5194/nhess-23-2203-2023, https://doi.org/10.5194/nhess-23-2203-2023, 2023
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To investigate cumulative damage on extended building portfolios, we propose an alternative and modular method to probabilistically integrate sets of single-hazard vulnerability models that are being constantly developed by experts from various research fields to be used within a multi-risk context. We demonstrate its application by assessing the economic losses expected for the residential building stock of Lima, Peru, a megacity commonly exposed to consecutive earthquake and tsunami scenarios.
Audrey Bonnelye, Pierre Dick, Marco Bohnhoff, Fabrice Cotton, Rüdiger Giese, Jan Henninges, Damien Jougnot, Grzegorz Kwiatek, and Stefan Lüth
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The overall objective of the CHENILLE project is to performed an in-situ experiment in the Underground Reaserch Laboratory of Tournemire (Southern France) consisting of hydraulic and thermal stimulation of a fault zone. This experiment is monitored with extensive geophysical means (passive seismic, active seismic, distributed fiber optics for temperature measurements) in order to unravel the physical processes taking place during the stimulation for a better charactization of fault zones.
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Nat. Hazards Earth Syst. Sci., 21, 3599–3628, https://doi.org/10.5194/nhess-21-3599-2021, https://doi.org/10.5194/nhess-21-3599-2021, 2021
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We present variable-resolution boundaries based on central Voronoi tessellations (CVTs) to spatially aggregate building exposure models and physical vulnerability assessment. Their geo-cell sizes are inversely proportional to underlying distributions that account for the combination between hazard intensities and exposure proxies. We explore their efficiency and associated uncertainties in risk–loss estimations and mapping from decoupled scenario-based earthquakes and tsunamis in Lima, Peru.
Cited articles
Al Atik, L., Abrahamson, N., Bommer, J. J., Scherbaum, F., Cotton, F., and Kuehn, N.: The variability of ground-motion prediction models and its components, Seismol. Res. Lett., 81, 794–801, https://doi.org/10.1785/gssrl.81.5.794, 2010. a, b
Allen, T. I. and Wald D. J.: Topographic slope as a proxy for seismic site-conditions (VS30) and amplification around the globe, US Geol. Surv. Open-File Rept. 2007-1357, 69 pp., US Geological Survey [data set], https://doi.org/10.3133/ofr20071357, 2007. a
Amemiya, T.: Tobit models: A survey, J. Econometr., 24, 3–61, https://doi.org/10.1016/0304-4076(84)90074-5, 1984. a
Atkinson, G. M.: Single-station sigma, Bull. Seismol. Soc. Am., 96, 446–455, https://doi.org/10.1785/0120050137, 2006. a
Bates, D., Mächler, M., Bolker, B. M., and Walker, S. C.: Fitting linear mixed-effects models using lme4, J. Stat. Softw., 67, 1–48, https://doi.org/10.18637/jss.v067.i01, 2015. a
Bayless, J. and Abrahamson, N. A.: Summary of the BA18 ground-motion model for Fourier amplitude spectra for crustal earthquakes in California, Bull. Seismol. Soc. Am., 109, 2088–2105, https://doi.org/10.1785/0120190077, 2019. a
Bergamo, P., Hammer, C., and Fäh, D.: On the relation between empirical amplification and proxies measured at Swiss and Japanese stations: Systematic regression analysis and neural network prediction of amplification, Bull. Seismol. Soc. Am., 111, 101–120, https://doi.org/10.1785/0120200228, 2021. a
Bergamo, P., Hammer, C., and Fäh, D.: Correspondence between Site Amplification and Topographical, Geological Parameters: Collation of Data from Swiss and Japanese Stations, and Neural Networks-Based Prediction of Local Response, Bull. Seismol. Soc. Am., 112, 1008–1030, https://doi.org/10.1785/0120210225, 2022. a, b
Bindi, D., Spallarossa, D., and Pacor, F.: Between-event and between-station variability observed in the Fourier and response spectra domains: comparison with seismological models, Geophys. J. Int., 210, 1092–1104, https://doi.org/10.1093/gji/ggx217, 2017. a
Bishop, C. M. and Nasrabadi, N. M.: Pattern recognition and machine learning, in: vol. 4, Springer, https://doi.org/10.1007/978-0-387-45528-0, 2006. a
Boore, D. M., Thompson, E. M., and Cadet, H.: Regional correlations of VS 30 and velocities averaged over depths less than and greater than 30 meters, Bull. Seismol. Soc. Am., 101, 3046–3059, https://doi.org/10.1785/0120110071, 2011. a
Bora, S. S., Cotton, F., and Scherbaum, F.: NGA-West2 empirical Fourier and duration models to generate adjustable response spectra, Earthq. Spectra, 35, 61–93, https://doi.org/10.1193/110317EQS228M, 2019. a
CEN: Eurocode 8: Design of structures for earthquake resistance – part 1: general rules, seismic actions and rules for buildings, European Committee for Standardization, Brussels, https://www.saiglobal.com/PDFTemp/Previews/OSH/IS/EN/2005/I.S.EN1998-1-2005.pdf (last access: 21 February 2024), 2004. a
Crowley, H., Dabbeek, J., Despotaki, V., Rodrigues, D., Martins, L., Silva, V., Romão, X., Pereira, N., Weatherill, G., and Danciu, L.: European seismic risk model (ESRM20), EFEHR Technical Report, 002 V1.0.1, ETH Zürich, 1–85 , https://doi.org/10.3929/ethz-b-000590388, 2021. a, b, c
Cultrera, G., Cornou, C., Di Giulio, G., and Bard, P.-Y.: Indicators for site characterization at seismic station: recommendation from a dedicated survey, Bull. Earthq. Eng., 19, 4171–4195, https://doi.org/10.1007/s10518-021-01136-7, 2021. a
Derras, B., Bard, P. Y., and Cotton, F.: VS30, slope, H800 and f0: Performance of various site-condition proxies in reducing ground-motion aleatory variability and predicting nonlinear site response 4. Seismology, Earth Planets Space, 69, 133, https://doi.org/10.1186/s40623-017-0718-z, 2017. a, b
Edwards, B., Michel, C., Poggi, V., and Fäh, D.: Determination of site amplification from regional seismicity: application to the Swiss National Seismic Networks, Seismol. Res. Lett., 84, 611–621, https://doi.org/10.1785/0220120176, 2013. a
Foster, K. M., Bradley, B. A., McGann, C. R., and Wotherspoon, L. M.: A VS30 map for New Zealand based on geologic and terrain proxy variables and field measurements, Earthq. Spectra, 35, 1865–1897, https://doi.org/10.1193/121118EQS281M, 2019. a
Holbrook, W. S., Riebe, C. S., Elwaseif, M., L. Hayes, J., Basler-Reeder, K., L. Harry, D., Malazian, A., Dosseto, A., C. Hartsough, P., and W. Hopmans, J.: Geophysical constraints on deep weathering and water storage potential in the Southern Sierra Critical Zone Observatory, Earth Surf. Proc. Land., 39, 366–380, https://doi.org/10.1002/esp.3502, 2014. a
Hollender, F., Roumelioti, Z., Maufroy, E., Traversa, P., and Mariscal, A.: Can we trust high-frequency content in strong-motion database signals? Impact of housing, coupling, and installation depth of seismic sensors, Seismol. Res. Lett., 91, 2192–2205, https://doi.org/10.1785/0220190163, 2020. a
Koller, M.: robustlmm: an R package for robust estimation of linear mixed-effects models, J. Stat. Softw., 75, 1–24, https://doi.org/10.18637/jss.v075.i06, 2016. a
Kotha, S. R., Bindi, D., and Cotton, F.: Site-corrected magnitude-and region-dependent correlations of horizontal peak spectral amplitudes, Earthq. Spectra, 33, 1415–1432, https://doi.org/10.1193/091416eqs150m, 2017. a
Kotha, S. R., Cotton, F., and Bindi, D.: A new approach to site classification: mixed-effects ground motion prediction equation with spectral clustering of site amplification functions, Soil Dynam. Earthq. Eng., 110, 318–329, https://doi.org/10.1016/j.soildyn.2018.01.051, 2018. a, b, c, d
Kotha, S. R., Weatherill, G., Bindi, D., and Cotton, F.: A regionally-adaptable ground-motion model for shallow crustal earthquakes in Europe, Bull. Earthq. Eng., 18, 4091–4125, https://doi.org/10.1007/s10518-020-00869-1, 2020. a
Kotha, S. R., Bindi, D., and Cotton, F.: A regionally adaptable ground-motion model for fourier amplitude spectra of shallow crustal earthquakes in Europe, Bull. Earthq. Eng., 20, 711–740, https://doi.org/10.1007/s10518-021-01255-1, 2022. a, b, c, d
Ktenidou, O.-J., Roumelioti, Z., Abrahamson, N., Cotton, F., Pitilakis, K., and Hollender, F.: Understanding single-station ground motion variability and uncertainty (sigma): lessons learnt from EUROSEISTEST, Bull. Earthq. Eng., 16, 2311–2336, https://doi.org/10.1007/s10518-017-0098-6, 2018. a
Lanzano, G., Sgobba, S., Luzi, L., Puglia, R., Pacor, F., Felicetta, C., D'Amico, M., Cotton, F., and Bindi, D.: The pan-European Engineering Strong Motion (ESM) flatfile: compilation criteria and data statistics, Bul. Earthq. Eng., 17, 561–582, https://doi.org/10.1007/s10518-018-0480-z, 2019. a, b
Lemoine, A., Douglas, J., and Cotton, F.: Testing the applicability of correlations between topographic slope and VS30 for Europe, Bull. Seismol. Soc. Am., 102, 2585–2599, https://doi.org/10.1785/0120110240, 2012. a, b
Li, M., Rathje, E. M., Cox, B. R., and Yust, M.: A Texas-specific VS30 map incorporating geology and VS30 observations, Earthq. Spectra, 38, 521–542, https://doi.org/10.1177/87552930211033622, 2022. a
Loviknes, K., Kotha, S. R., Cotton, F., and Schorlemmer, D.: Testing nonlinear amplification factors of ground-motion models, Bull. Seismol. Soc. Am., 111, 2121–2137, https://doi.org/10.1785/0120200386, 2021. a
Loviknes, K., Cotton, F., and Weatherill, G.:. Mapping site proxies and proxy-based site amplification predictions (Version 02), Zenodo [code], https://doi.org/10.5281/zenodo.10686867, 2023. a
Luzi, L., Lanzano, G., Felicetta, C., D'Amico, M., Russo, E., Sgobba, S., and Pacor, F.: ORFEUS Working Group 5: Engineering Strong Motion Database (ESM) (Version 2.0), INGV – Istituto Nazionale di Geofisica e Vulcanologia [data set], https://doi.org/10.13127/ESM.2, 2020. a, b, c
Melgar, D., Taymaz, T., Ganas, A., Crowell, B., Öcalan, T., Kahraman, M., Tsironi, V., Yolsal-Çevikbilen, S., Valkaniotis, S., Irmak, T. S., Eken, T., Erman, C., Özkan, B., Dogan, A. H., and Altuntaş, C.: Sub- and super-shear ruptures during the 2023 Mw 7.8 and Mw 7.6 earthquake doublet in SE Türkiye, Seismica, 2, 2, https://doi.org/10.26443/seismica.v2i3.387, 2023. a, b
Mori, F., Mendicelli, A., Moscatelli, M., Romagnoli, G., Peronace, E., and Naso, G.: A new VS30 map for Italy based on the seismic microzonation dataset, Eng. Geol., 275, 105745, https://doi.org/10.17632/8458tgzc73.1, 2020. a
Nakano, K., Matsushima, S., and Kawase, H.: Statistical properties of strong ground motions from the generalized spectral inversion of data observed by K-NET, KiK-net, and the JMA Shindokei network in Japan, Bull. Seismol. Soc. Am., 105, 2662–2680, https://doi.org/10.1785/0120140349, 2015. a
Paolucci, R., Aimar, M., Ciancimino, A., Dotti, M., Foti, S., Lanzano, G., Mattevi, P., Pacor, F., and Vanini, M.: Checking the site categorization criteria and amplification factors of the 2021 draft of Eurocode 8 Part 1–1, Bull. Earthq. Eng., 19, 4199–4234, https://doi.org/10.1007/s10518-021-01118-9, 2021. a
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D.: A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, J. Adv. Model. Earth Syst., 8, 41–65, https://doi.org/10.1002/2015MS000526, 2016a. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D.: Global 1-km Gridded Thickness of Soil, Regolith, and Sedimentary Deposit Layers, ORNL DACC [data set], https://doi.org/10.3334/ORNLDAAC/1304, 2016b. a, b
Petersen, G. M., Büyükakpinar, P., Vera Sanhueza, F. O., Metz, M., Cesca, S., Akbayram, K., Saul, J., and Dahm, T.: The 2023 Southeast Türkiye Seismic Sequence: Rupture of a Complex Fault Network, Seismic Rec., 3, 134–143, https://doi.org/10.1785/0320230008, 2023. a, b
Rodriguez-Marek, A., Cotton, F., Abrahamson, N. A., Akkar, S., Al Atik, L., Edwards, B., Montalva, G. A., and Dawood, H. M.: A model for single-station standard deviation using data from various tectonic regions, Bull. Seismol. Soc. Am., 103, 3149–3163, https://doi.org/10.1785/0120130030, 2013. a
Seyhan, E. and Stewart, J. P.: Semi-empirical nonlinear site amplification from NGA-West2 data and simulations, Earthq. Spectra, 30, 1241–1256, https://doi.org/10.1193/063013EQS181M, 2014. a
Silva, V., Amo-Oduro, D., Calderon, A., Costa, C., Dabbeek, J., Despotaki, V., Martins, L., Pagani, M., Rao, A., Simionato, M., Viganò, D., Yepes-Estrada, C., Acevedo, A., Crowley, H., Horspool, N., Jaiswal, K., Journeay, M., and Pittore, M.: Development of a global seismic risk model, Earthq. Spectra, 36, 372–394, https://doi.org/10.1177/8755293019899953, 2020. a
Stewart, J. P., Afshari, K., and Goulet, C. A.: Non-ergodic site response in seismic hazard analysis, Earthq. Spectra, 33, 1385–1414, https://doi.org/10.1193/081716eqs135m, 2017. a, b
Thompson, E., Wald, D. J., and Worden, C.: A VS30 Map for California with Geologic and Topographic Constraints, Bull. Seismol. Soc. Am., 104, 2313–2321, https://doi.org/10.1785/0120130312, 2014. a
Thompson, E. M. and Wald, D. J.: Uncertainty in VS30-based site response, Bull. Seismol. Soc. Am., 106, 453–463, https://doi.org/10.1785/0120150214, 2016. a
Thompson, E. M., Baise, L. G., Kayen, R. E., Tanaka, Y., and Tanaka, H.: A geostatistical approach to mapping site response spectral amplifications, Eng. Geol., 114, 330–342, https://doi.org/10.1016/j.enggeo.2010.05.010, 2010. a
Tobin, J.: Estimation of relationships for limited dependent variables, Econometrica, 26, 24–36, https://doi.org/10.2307/1907382, 1958. a
Trifunac, M. D.: Site conditions and earthquake ground motion – A review, Soil Dynam. Earthq. Eng., 90, 88–100, https://doi.org/10.1016/J.SOILDYN.2016.08.003, 2016. a
Vilanova, S. P., Narciso, J., Carvalho, J. P., Lopes, I., Quinta-Ferreira, M., Pinto, C. C., Moura, R., Borges, J., and Nemser, E. S.: Developing a Geologically Based VS30 Site-Condition Model for Portugal: Methodology and Assessment of the Performance of ProxiesDeveloping a Geologically Based VS30 Site-Condition Model for Portugal, Bull. Seismol. Soc. Am., 108, 322–337, https://doi.org/10.1785/0120170213, 2018. a, b, c
Wang, Z., Nakano, K., Ito, E., Kawase, H., and Matsushima, S.: A hybrid approach for deriving horizontal site amplification factors considering both the similarity of HVSRe and the vertical amplification correction function, Earthq. Eng. Struct. Dynam., 52, 128–146, 2023. a
Weatherill, G., Crowley, H., Roullé, A., Tourlière, B., Lemoine, A., Gracianne, C., Kotha, S. R., and Cotton, F.: Modelling site response at regional scale for the 2020 European Seismic Risk Model (ESRM20), Bull. Earthq. Eng., 21, 665–714, https://doi.org/10.1007/s10518-022-01526-5, 2023. a, b, c, d, e, f, g, h, i, j, k
Weatherill, G. A., Crowley, H., Roullé, A., Tourlière, B., Lemoine, A., Gracianne Hidalgo, C., Kotha, S. R., Cotton, F., and Dabbeek, J.: European Site Response Model Datasets Viewer (v1.0), European Site Response Model Datasets Viewer [data set], https://doi.org/10.7414/EUC-EUROPEAN-SITE-MODEL-DATA-VIEWER, 2021. a
Zhu, C., Weatherill, G., Cotton, F., Pilz, M., Kwak, D. Y., and Kawase, H.: An open-source site database of strong-motion stations in Japan: K-NET and KiK-net (v1. 0.0), Earthq. Spectra, 37, 2126–2149, https://doi.org/10.1177/8755293020988028, 2021. a, b
Zhu, C., Cotton, F., Kawase, H., Haendel, A., Pilz, M., and Nakano, K.: How well can we predict earthquake site response so far? Site-specific approaches, Earthq. Spectra, 38, 1047–1075, https://doi.org/10.1177/87552930211060859, 2022. a, b
Short summary
Earthquake ground shaking can be strongly affected by local geology and is often amplified by soft sediments. In this study, we introduce a global geomorphological model for sediment thickness as a protentional parameter for predicting this site amplification. The results show that including geology and geomorphology in site-amplification predictions adds important value and that global or regional models for sediment thickness from fields beyond engineering seismology are worth considering.
Earthquake ground shaking can be strongly affected by local geology and is often amplified by...
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