Articles | Volume 16, issue 9
https://doi.org/10.5194/essd-16-3949-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-3949-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
03 Sep 2024
Data description paper |
| 03 Sep 2024
| 03 Sep 2024
Synthetic ground motions in heterogeneous geologies from various sources: the HEMEWS-3D database
Fanny Lehmann
CORRESPONDING AUTHOR
CEA, DAM, DIF, 91297 Arpajon, France
LMPS – Laboratoire de Mécanique Paris-Saclay, Université Paris-Saclay, CentraleSupélec, ENS Paris-Saclay, CNRS, Gif-sur-Yvette, France
Filippo Gatti
LMPS – Laboratoire de Mécanique Paris-Saclay, Université Paris-Saclay, CentraleSupélec, ENS Paris-Saclay, CNRS, Gif-sur-Yvette, France
Michaël Bertin
CEA, DAM, DIF, 91297 Arpajon, France
Didier Clouteau
LMPS – Laboratoire de Mécanique Paris-Saclay, Université Paris-Saclay, CentraleSupélec, ENS Paris-Saclay, CNRS, Gif-sur-Yvette, France
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Fanny Lehmann, Bramha Dutt Vishwakarma, and Jonathan Bamber
Hydrol. Earth Syst. Sci., 26, 35–54, https://doi.org/10.5194/hess-26-35-2022, https://doi.org/10.5194/hess-26-35-2022, 2022
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Many data sources are available to evaluate components of the water cycle (precipitation, evapotranspiration, runoff, and terrestrial water storage). Despite this variety, it remains unclear how different combinations of datasets satisfy the conservation of mass. We conducted the most comprehensive analysis of water budget closure on a global scale to date. Our results can serve as a basis to select appropriate datasets for regional hydrological studies.
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Many data sources are available to evaluate components of the water cycle (precipitation, evapotranspiration, runoff, and terrestrial water storage). Despite this variety, it remains unclear how different combinations of datasets satisfy the conservation of mass. We conducted the most comprehensive analysis of water budget closure on a global scale to date. Our results can serve as a basis to select appropriate datasets for regional hydrological studies.
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An earthquake focal mechanism catalog for source and tectonic studies in Mexico from February 1928 to July 2022
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A newly integrated ground temperature dataset of permafrost along the China–Russia crude oil pipeline route in Northeast China
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Earth Syst. Sci. Data, 16, 3261–3281, https://doi.org/10.5194/essd-16-3261-2024, https://doi.org/10.5194/essd-16-3261-2024, 2024
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The satellite gravimetry mission Gravity Recovery and Climate Experiment (GRACE) and its follower GRACE-FO play a vital role in monitoring mass transportation on Earth. Based on the latest observation data derived from GRACE and GRACE-FO and an updated data processing chain, a new monthly temporal gravity field series, HUST-Grace2024, was determined.
Francesca Pace, Andrea Vergnano, Alberto Godio, Gerardo Romano, Luigi Capozzoli, Ilaria Baneschi, Marco Doveri, and Alessandro Santilano
Earth Syst. Sci. Data, 16, 3171–3192, https://doi.org/10.5194/essd-16-3171-2024, https://doi.org/10.5194/essd-16-3171-2024, 2024
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We present the geophysical data set acquired close to Ny-Ålesund (Svalbard islands) for the characterization of glacial and hydrological processes and features. The data have been organized in a repository that includes both raw and processed (filtered) data and some representative results of 2D models of the subsurface. This data set can foster multidisciplinary scientific collaborations among many disciplines: hydrology, glaciology, climatology, geology, geomorphology, etc.
Dino Bindi, Riccardo Zaccarelli, Angelo Strollo, Domenico Di Giacomo, Andres Heinloo, Peter Evans, Fabrice Cotton, and Frederik Tilmann
Earth Syst. Sci. Data, 16, 1733–1745, https://doi.org/10.5194/essd-16-1733-2024, https://doi.org/10.5194/essd-16-1733-2024, 2024
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Neda Darbeheshti, Martin Lasser, Ulrich Meyer, Daniel Arnold, and Adrian Jäggi
Earth Syst. Sci. Data, 16, 1589–1599, https://doi.org/10.5194/essd-16-1589-2024, https://doi.org/10.5194/essd-16-1589-2024, 2024
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Athina Peidou, Donald F. Argus, Felix W. Landerer, David N. Wiese, and Matthias Ellmer
Earth Syst. Sci. Data, 16, 1317–1332, https://doi.org/10.5194/essd-16-1317-2024, https://doi.org/10.5194/essd-16-1317-2024, 2024
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Lavinia Tunini, Andrea Magrin, Giuliana Rossi, and David Zuliani
Earth Syst. Sci. Data, 16, 1083–1106, https://doi.org/10.5194/essd-16-1083-2024, https://doi.org/10.5194/essd-16-1083-2024, 2024
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Wenbin Tang, Ji Zhou, Jin Ma, Ziwei Wang, Lirong Ding, Xiaodong Zhang, and Xu Zhang
Earth Syst. Sci. Data, 16, 387–419, https://doi.org/10.5194/essd-16-387-2024, https://doi.org/10.5194/essd-16-387-2024, 2024
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Arno Zang, Peter Niemz, Sebastian von Specht, Günter Zimmermann, Claus Milkereit, Katrin Plenkers, and Gerd Klee
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Quetzalcoatl Rodríguez-Pérez and F. Ramón Zúñiga
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Iman R. Kivi, Auregan Boyet, Haiqing Wu, Linus Walter, Sara Hanson-Hedgecock, Francesco Parisio, and Victor Vilarrasa
Earth Syst. Sci. Data, 15, 3163–3182, https://doi.org/10.5194/essd-15-3163-2023, https://doi.org/10.5194/essd-15-3163-2023, 2023
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Claudia Finger, Marco P. Roth, Marco Dietl, Aileen Gotowik, Nina Engels, Rebecca M. Harrington, Brigitte Knapmeyer-Endrun, Klaus Reicherter, Thomas Oswald, Thomas Reinsch, and Erik H. Saenger
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Jia-Hao Li, Zhao-Liang Li, Xiangyang Liu, and Si-Bo Duan
Earth Syst. Sci. Data, 15, 2189–2212, https://doi.org/10.5194/essd-15-2189-2023, https://doi.org/10.5194/essd-15-2189-2023, 2023
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Konstantinos Michailos, György Hetényi, Matteo Scarponi, Josip Stipčević, Irene Bianchi, Luciana Bonatto, Wojciech Czuba, Massimo Di Bona, Aladino Govoni, Katrin Hannemann, Tomasz Janik, Dániel Kalmár, Rainer Kind, Frederik Link, Francesco Pio Lucente, Stephen Monna, Caterina Montuori, Stefan Mroczek, Anne Paul, Claudia Piromallo, Jaroslava Plomerová, Julia Rewers, Simone Salimbeni, Frederik Tilmann, Piotr Środa, Jérôme Vergne, and the AlpArray-PACASE Working Group
Earth Syst. Sci. Data, 15, 2117–2138, https://doi.org/10.5194/essd-15-2117-2023, https://doi.org/10.5194/essd-15-2117-2023, 2023
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Muhammad Rizwan Asif, Nikolaj Foged, Thue Bording, Jakob Juul Larsen, and Anders Vest Christiansen
Earth Syst. Sci. Data, 15, 1389–1401, https://doi.org/10.5194/essd-15-1389-2023, https://doi.org/10.5194/essd-15-1389-2023, 2023
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To apply a deep learning (DL) algorithm to electromagnetic (EM) methods, subsurface resistivity models and/or the corresponding EM responses are often required. To date, there are no standardized EM datasets, which hinders the progress and evolution of DL methods due to data inconsistency. Therefore, we present a large-scale physics-driven model database of geologically plausible and EM-resolvable subsurface models to incorporate consistency and reliability into DL applications for EM methods.
Médéric Gravelle, Guy Wöppelmann, Kevin Gobron, Zuheir Altamimi, Mikaël Guichard, Thomas Herring, and Paul Rebischung
Earth Syst. Sci. Data, 15, 497–509, https://doi.org/10.5194/essd-15-497-2023, https://doi.org/10.5194/essd-15-497-2023, 2023
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Michal Kruszewski, Gerd Klee, Thomas Niederhuber, and Oliver Heidbach
Earth Syst. Sci. Data, 14, 5367–5385, https://doi.org/10.5194/essd-14-5367-2022, https://doi.org/10.5194/essd-14-5367-2022, 2022
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The authors assemble an in situ stress magnitude and orientation database based on 429 hydrofracturing tests that were carried out in six coal mines and two coal bed methane boreholes between 1986 and 1995 within the greater Ruhr region (Germany). Our study summarises the results of the extensive in situ stress test campaign and assigns quality to each data record using the established quality ranking schemes of the World Stress Map project.
Andrea Rovida, Andrea Antonucci, and Mario Locati
Earth Syst. Sci. Data, 14, 5213–5231, https://doi.org/10.5194/essd-14-5213-2022, https://doi.org/10.5194/essd-14-5213-2022, 2022
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EPICA is the 1000–1899 catalogue compiled for the European Seismic Hazard Model 2020 and contains 5703 earthquakes with Mw ≥ 4.0. It relies on the data of the European Archive of Historical Earthquake Data (AHEAD), both macroseismic intensities from historical seismological studies and parameters from regional catalogues. For each earthquake, the most representative datasets were selected and processed in order to derive harmonised parameters, both from intensity data and parametric catalogues.
Suqin Zhang, Changhua Fu, Jianjun Wang, Guohao Zhu, Chuanhua Chen, Shaopeng He, Pengkun Guo, and Guoping Chang
Earth Syst. Sci. Data, 14, 5195–5212, https://doi.org/10.5194/essd-14-5195-2022, https://doi.org/10.5194/essd-14-5195-2022, 2022
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The Sheshan observatory has nearly 150 years of observation history, and its observation data have important scientific value. However, with time, these precious historical data face the risk of damage and loss. We have carried out a series of rescues on the historical data of the Sheshan observatory. New historical datasets were released, including the quality-controlled absolute hourly mean values of three components (D, H, and Z) from 1933 to 2019.
Guoyu Li, Wei Ma, Fei Wang, Huijun Jin, Alexander Fedorov, Dun Chen, Gang Wu, Yapeng Cao, Yu Zhou, Yanhu Mu, Yuncheng Mao, Jun Zhang, Kai Gao, Xiaoying Jin, Ruixia He, Xinyu Li, and Yan Li
Earth Syst. Sci. Data, 14, 5093–5110, https://doi.org/10.5194/essd-14-5093-2022, https://doi.org/10.5194/essd-14-5093-2022, 2022
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A permafrost monitoring network was established along the China–Russia crude oil pipeline (CRCOP) route at the eastern flank of the northern Da Xing'anling Mountains in Northeast China. The resulting datasets fill the gaps in the spatial coverage of mid-latitude mountain permafrost databases. Results show that permafrost warming has been extensively observed along the CRCOP route, and local disturbances triggered by the CRCOPs have resulted in significant permafrost thawing.
Alessandro Cicoira, Samuel Weber, Andreas Biri, Ben Buchli, Reynald Delaloye, Reto Da Forno, Isabelle Gärtner-Roer, Stephan Gruber, Tonio Gsell, Andreas Hasler, Roman Lim, Philippe Limpach, Raphael Mayoraz, Matthias Meyer, Jeannette Noetzli, Marcia Phillips, Eric Pointner, Hugo Raetzo, Cristian Scapozza, Tazio Strozzi, Lothar Thiele, Andreas Vieli, Daniel Vonder Mühll, Vanessa Wirz, and Jan Beutel
Earth Syst. Sci. Data, 14, 5061–5091, https://doi.org/10.5194/essd-14-5061-2022, https://doi.org/10.5194/essd-14-5061-2022, 2022
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This paper documents a monitoring network of 54 positions, located on different periglacial landforms in the Swiss Alps: rock glaciers, landslides, and steep rock walls. The data serve basic research but also decision-making and mitigation of natural hazards. It is the largest dataset of its kind, comprising over 209 000 daily positions and additional weather data.
Xiaoli Chang, Huijun Jin, Ruixia He, Yanlin Zhang, Xiaoying Li, Xiaoying Jin, and Guoyu Li
Earth Syst. Sci. Data, 14, 3947–3959, https://doi.org/10.5194/essd-14-3947-2022, https://doi.org/10.5194/essd-14-3947-2022, 2022
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Based on 10-year observations of ground temperatures in seven deep boreholes in Gen’he, Mangui, and Yituli’he, a wide range of mean annual ground temperatures at the depth of 20 m (−2.83 to −0.49 ℃) and that of annual maximum thawing depth (about 1.1 to 7.0 m) have been revealed. This study demonstrates that most trajectories of permafrost changes in Northeast China are ground warming and permafrost degradation, except that the shallow permafrost is cooling in Yituli’he.
Alice C. Frémand, Julien A. Bodart, Tom A. Jordan, Fausto Ferraccioli, Carl Robinson, Hugh F. J. Corr, Helen J. Peat, Robert G. Bingham, and David G. Vaughan
Earth Syst. Sci. Data, 14, 3379–3410, https://doi.org/10.5194/essd-14-3379-2022, https://doi.org/10.5194/essd-14-3379-2022, 2022
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This paper presents the release of large swaths of airborne geophysical data (including gravity, magnetics, and radar) acquired between 1994 and 2020 over Antarctica by the British Antarctic Survey. These include a total of 64 datasets from 24 different surveys, amounting to >30 % of coverage over the Antarctic Ice Sheet. This paper discusses how these data were acquired and processed and presents the methods used to standardize and publish the data in an interactive and reproducible manner.
Cited articles
Aki, K. and Richards, P. G.: Quantitative Seismology, Freeman, San Francisco, 1980. a
Altindal, A. and Askan, A.: SIGMOID-TR: A Simulated Ground Motion Dataset for Turkey, Zenodo [data set], https://doi.org/10.5281/ZENODO.7007917, 2022. a
Annon: Simulate CO2 Flow with Open Porous Media, Github [code], https://github.com/microsoft/AzureClusterlessHPC.jl/tree/main/examples/opm, 2022. a
Arias, A.: A Measure of Earthquake Intensity, in: Seismic design for nuclear plants, edited by: Hansen, R. J., The MIT Press, 438–483, ISBN 978-0-262-08041-5, 1970. a
Arroucau, P.: A Preliminary Three-Dimensional Seismological Model of the Crust and Uppermost Mantle for Metropolitan Franc, Tech. Rep. SIGMA2-2018-D2014, https://www.sigma-2.net/medias/files/sigma2-2018-d2-014-3d-velocity-model-france-approved-public-.pdf (last access: 12 April 2022), 2020. a
Atkinson, G. M.: Ground-Motion Prediction Equation for Small-to-Moderate Events at Short Hypocentral Distances, with Application to Induced-Seismicity Hazards, B. Seismol. Soc. Am., 105, 981–992, https://doi.org/10.1785/0120140142, 2015. a, b, c, d
Atkinson, G. M. and Boore, D. M.: Earthquake Ground-Motion Prediction Equations for Eastern North America, B. Seismol. Soc. Am., 96, 2181–2205, https://doi.org/10.1785/0120050245, 2006. a, b
Bahrampouri, M., Rodriguez-Marek, A., Shahi, S., and Dawood, H.: An Updated Database for Ground Motion Parameters for KiK-net Records, Earthquake Spectra, 37, 505–522, https://doi.org/10.1177/8755293020952447, 2021. a
Baker, J. W.: GMM, Github [code], https://github.com/bakerjw/GMMs/tree/master (last access: 9 July 2024), 2022. a
Bonev, B., Kurth, T., Hundt, C., Pathak, J., Baust, M., and Kashinath, K.: Modelling Atmospheric Dynamics with Spherical Fourier Neural Operators, in: ICLR 2023 Workshop on Tackling Climate Change with Machine Learning, https://www.climatechange.ai/papers/iclr2023/47 (last access: 24 August 2023), 2023. a
Castro-Cruz, D., Gatti, F., and Lopez-Caballero, F.: High-Fidelity Broadband Prediction of Regional Seismic Response: A Hybrid Coupling of Physics-Based Synthetic Simulation and Empirical Green Functions, Natural Hazards, 108, 1997–2031, https://doi.org/10.1007/s11069-021-04766-x, 2021. a
Chaljub, E., Celorio, M., Cornou, C., Martin, F. D., Haber, E. E., Margerin, L., Marti, J., and Zentner, I.: Numerical Simulation of Wave Propagation in Heterogeneous and Random Media for Site Effects Assessment in the Grenoble Valley, in: He 6th IASPEI/IAEE International Symposium: Effects of Surface Geology on Seismic Motion, 2021. a
Chernov, L. A.: Wave Propagation in a Random Medium, Translated by Richard A. Silverman, Mineola, New York, dover publications Edn., 1960. a
Chiou, B. S.-J. and Youngs, R. R.: Update of the Chiou and Youngs NGA Model for the Average Horizontal Component of Peak Ground Motion and Response Spectra, Earthquake Spectra, 30, 1117–1153, https://doi.org/10.1193/072813EQS219M, 2014. a, b
Colvez, M.: Influence of the Earth's Crust Heterogeneities and Complex Fault Structures on the Frequency Content of Seismic Waves, Ph.D. thesis, Université Paris Saclay, Paris-Saclay, https://theses.hal.science/tel-03551874 (last access: 11 March 2023), 2021. a
Convertito, V., De Matteis, R., Amoroso, O., and Capuano, P.: Ground Motion Prediction Equations as a Proxy for Medium Properties Variation Due to Geothermal Resources Exploitation, Sci. Rep., 12, 12632, https://doi.org/10.1038/s41598-022-16815-x, 2022. a
de Carvalho Paludo, L., Bouvier, V., and Cottereau, R.: Scalable Parallel Scheme for Sampling of Gaussian Random Fields over Very Large Domains: Parallel Scheme for Sampling of Random Fields over Very Large Domains, Int. J. Numer. Meth. Eng., 117, 845–859, https://doi.org/10.1002/nme.5981, 2019. a
De Martin, F., Chaljub, E., Thierry, P., Sochala, P., Dupros, F., Maufroy, E., Hadri, B., Benaichouche, A., and Hollender, F.: Influential Parameters on 3-D Synthetic Ground Motions in a Sedimentary Basin Derived from Global Sensitivity Analysis, Geophys. J. Int., 227, 1795–1817, https://doi.org/10.1093/gji/ggab304, 2021. a
Deng, C., Feng, S., Wang, H., Zhang, X., Jin, P., Feng, Y., Zeng, Q., Chen, Y., and Lin, Y.: OpenFWI: Large-scale Multi-Structural Benchmark Datasets for Full Waveform Inversion, in: Advances in Neural Information Processing Systems, edited by: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., and Oh, A., Curran Associates, Inc., 35, 6007–6020, https://proceedings.neurips.cc/paper_files/paper/2022/file/27d3ef263c7cb8d542c4f9815a49b69b-Paper-Datasets_and_Benchmarks.pdf (last access: 24 August 2023), 2022. a, b, c, d, e, f
Ding, Y., Chen, S., Li, X., Wang, S., Luan, S., and Sun, H.: Self-Adaptive Physics-Driven Deep Learning for Seismic Wave Modeling in Complex Topography, Eng. Appl. Artif. Intel., 123, 106425, https://doi.org/10.1016/j.engappai.2023.106425, 2023. a
Duverger, C., Mazet-Roux, G., Bollinger, L., Trilla, A. G., Vallage, A., Hernandez, B., and Cansi, Y.: A Decade of Seismicity in Metropolitan France (2010–2019): The CEA/LDG Methodologies and Observations, Bulletin de la Société Géologique de France, 192, p. 25, https://doi.org/10.1051/bsgf/2021014, 2021. a
El Haber, E., Cornou, C., Jongmans, D., Lopez-Caballero, F., Youssef Abdelmassih, D., and Al-Bittar, T.: Impact of Spatial Variability of Shear Wave Velocity on the Lagged Coherency of Synthetic Surface Ground Motions, Soil Dyn. Earthq. Eng., 145, 106689, https://doi.org/10.1016/j.soildyn.2021.106689, 2021. a
Equinor: Sleipner 2019 Benchmark Model, CO2DataShare [data set], https://doi.org/10.11582/2020.00004, 2020. a
Faccioli, E., Maggio, F., Paolucci, R., and Quarteroni, A.: 2d and 3D Elastic Wave Propagation by a Pseudo-Spectral Domain Decomposition Method, J. Seismol., 1, 237–251, https://doi.org/10.1023/A:1009758820546, 1997. a
Feng, S., Wang, H., Deng, C., Feng, Y., Liu, Y., Zhu, M., Jin, P., Chen, Y., and Lin, Y.: EFWI: Multiparameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties, in: Thirty-Seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, https://openreview.net/forum?id=3BQaMV9jxK (last access: 10 June 2024), 2023. a, b
Fu, H., He, C., Chen, B., Yin, Z., Zhang, Z., Zhang, W., Zhang, T., Xue, W., Liu, W., Yin, W., Yang, G., and Chen, X.: 18.9-Pflops Nonlinear Earthquake Simulation on Sunway TaihuLight: Enabling Depiction of 18-Hz and 8-Meter Scenarios, in: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '17, Association for Computing Machinery, New York, NY, USA, ISBN 978-1-4503-5114-0, https://doi.org/10.1145/3126908.3126910, 2017. a, b
Gadylshin, K., Lisitsa, V., Gadylshina, K., Vishnevsky, D., and Novikov, M.: Machine Learning-Based Numerical Dispersion Mitigation in Seismic Modelling, in: Computational Science and Its Applications – ICCSA 2021, edited by: Gervasi, O., Murgante, B., Misra, S., Garau, C., Blečić, I., Taniar, D., Apduhan, B. O., Rocha, A. M. A. C., Tarantino, E., and Torre, C. M., Springer International Publishing, Cham, 34–47, ISBN 978-3-030-86653-2, 2021. a
Gatti, F. and Clouteau, D.: Towards Blending Physics-Based Numerical Simulations and Seismic Databases Using Generative Adversarial Network, Comput. Method. Appl. M., 372, 113421, https://doi.org/10.1016/j.cma.2020.113421, 2020. a
Grady, T. J., Khan, R., Louboutin, M., Yin, Z., Witte, P. A., Chandra, R., Hewett, R. J., and Herrmann, F. J.: Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEs, Comput. Geosci., 178, 105402, https://doi.org/10.1016/j.cageo.2023.105402, 2023. a
Grassberger, P. and Procaccia, I.: Measuring the Strangeness of Strange Attractors, Physica D, 9, 189–208, 1983. a
Hartzell, S., Harmsen, S., and Frankel, A.: Effects of 3D Random Correlated Velocity Perturbations on Predicted Ground Motions, B. Seismol. Soc. Am., 100, 1415–1426, https://doi.org/10.1785/0120090060, 2010. a
Heinecke, A., Breuer, A., Rettenberger, S., Bader, M., Gabriel, A. A., Pelties, C., Bode, A., Barth, W., Liao, X. K., Vaidyanathan, K., Smelyanskiy, M., and Dubey, P.: Petascale High Order Dynamic Rupture Earthquake Simulations on Heterogeneous Supercomputers, in: SC '14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, New Orleans, LA, USA, ISBN 2167-4337, 3–14, https://doi.org/10.1109/SC.2014.6, 2014. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Imperatori, W. and Mai, P. M.: Broad-Band near-Field Ground Motion Simulations in 3-Dimensional Scattering Media, Geophys. J. Int., 192, 725–744, https://doi.org/10.1093/gji/ggs041, 2013. a
Jessell, M., Guo, J., Li, Y., Lindsay, M., Scalzo, R., Giraud, J., Pirot, G., Cripps, E., and Ogarko, V.: Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications, Earth Syst. Sci. Data, 14, 381–392, https://doi.org/10.5194/essd-14-381-2022, 2022. a, b
Karimpouli, S. and Tahmasebi, P.: Physics Informed Machine Learning: Seismic Wave Equation, Geosci. Front., 11, 1993–2001, https://doi.org/10.1016/j.gsf.2020.07.007, 2020. a
Khazaie, S., Cottereau, R., and Clouteau, D.: Influence of the Spatial Correlation Structure of an Elastic Random Medium on Its Scattering Properties, J. Sound Vib., 370, 132–148, https://doi.org/10.1016/j.jsv.2016.01.012, 2016. a
Komatitsch, D. and Tromp, J.: Introduction to the Spectral Element Method for Three-Dimensional Seismic Wave Propagation, Geophys. J. Int., 139, 806–822, https://doi.org/10.1046/j.1365-246x.1999.00967.x, 1999. a
Lehmann, F.: Physics-based Simulations of 3D Wave Propagation with Source Variability: HEMEW∧S-3D, Recherche Data Gouv [data set], https://doi.org/10.57745/LAI6YU, 2023. a, b, c
Lehmann, F.: Lehmannfa/HEMEW3D: Initial Version, Zenodo [code], https://doi.org/10.5281/ZENODO.13625608, 2024. a
Lehmann, F., Gatti, F., Bertin, M., and Clouteau, D.: Machine Learning Opportunities to Conduct High-Fidelity Earthquake Simulations in Multi-Scale Heterogeneous Geology, Front. Earth Sci., 10, https://doi.org/10.3389/feart.2022.1029160, 2022. a, b
Lehmann, F., Gatti, F., and Clouteau, D.: Multiple-Input Fourier Neural Operator (MIFNO) for Source-Dependent 3D Elastodynamics, arXiv [preprint], https://doi.org/10.48550/arXiv.2404.10115, 2024. a, b
Levina, E. and Bickel, P.: Maximum Likelihood Estimation of Intrinsic Dimension, in: Advances in Neural Information Processing Systems, edited by Saul, L., Weiss, Y., and Bottou, L., MIT Press, vol. 17, https://proceedings.neurips.cc/paper_files/paper/2004/file/74934548253bcab8490ebd74afed7031-Paper.pdf (last access: 17 April 2023), 2004. a
Liu, B., Yang, S., Ren, Y., Xu, X., Jiang, P., and Chen, Y.: Deep-Learning Seismic Full-Waveform Inversion for Realistic Structural Models, Geophysics, 86, R31–R44, https://doi.org/10.1190/geo2019-0435.1, 2021. a
Maechling, P. J., Silva, F., Callaghan, S., and Jordan, T. H.: SCEC Broadband Platform: System Architecture and Software Implementation, Seismol. Res. Lett., 86, 27–38, https://doi.org/10.1785/0220140125, 2015. a, b
Mansoor, K., Buscheck, T., Yang, X., Carroll, S., and Chen, X.: LLNL Kimberlina 1.2 NUFT Simulations June 2018 (V2), https://doi.org/10.18141/1603336, 2020. a
Michelini, A., Cianetti, S., Gaviano, S., Giunchi, C., Jozinović, D., and Lauciani, V.: INSTANCE – the Italian seismic dataset for machine learning, Earth Syst. Sci. Data, 13, 5509–5544, https://doi.org/10.5194/essd-13-5509-2021, 2021. a, b
Moczo, P., Kristek, J., Bard, P.-Y., Stripajová, S., Hollender, F., Chovanová, Z., Kristeková, M., and Sicilia, D.: Key Structural Parameters Affecting Earthquake Ground Motion in 2D and 3D Sedimentary Structures, B. Earthq. Eng., 16, 2421–2450, https://doi.org/10.1007/s10518-018-0345-5, 2018. a
Molinari, I. and Morelli, A.: EPcrust: A Reference Crustal Model for the European Plate: EPcrust, Geophys. J. Int., 185, 352–364, https://doi.org/10.1111/j.1365-246X.2011.04940.x, 2011. a, b, c, d
Moseley, B., Markham, A., and Nissen-Meyer, T.: Solving the Wave Equation with Physics-Informed Deep Learning, ArXiv [preprint], https://doi.org/10.48550/arxiv.2006.11894, 2020. a
Mousavi, S. M. and Beroza, G. C.: Machine Learning in Earthquake Seismology, Annu. Rev. Earth Pl. Sc., 51, 105–129, https://doi.org/10.1146/annurev-earth-071822-100323, 2023. a, b, c
Mousavi, S. M., Sheng, Y., Zhu, W., and Beroza, G. C.: STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI, IEEE Access, 7, 179464–179476, https://doi.org/10.1109/ACCESS.2019.2947848, 2019. a, b
Ovadia, O., Kahana, A., Stinis, P., Turkel, E., and Karniadakis, G. E.: ViTO: Vision Transformer-Operator, ArXiv [preprint], https://doi.org/10.48550/arXiv.2303.08891, 2023. a
Paolucci, R., Smerzini, C., and Vanini, M.: BB-SPEEDset: A Validated Dataset of Broadband Near-Source Earthquake Ground Motions from 3D Physics-Based Numerical Simulations, B. Seismol. Soc. Am., 111, 2527–2545, https://doi.org/10.1785/0120210089, 2021. a, b
Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., Azizzadenesheli, K., Hassanzadeh, P., Kashinath, K., and Anandkumar, A.: FourCastNet: A Global Data-driven High-resolution Weather Model Using Adaptive Fourier Neural Operators, ArXiv [preprint], https://doi.org/10.48550/arXiv.2202.11214, 2022. a
Petrone, F., Abrahamson, N., McCallen, D., Pitarka, A., and Rodgers, A.: Engineering Evaluation of the EQSIM Simulated Ground-motion Database: The San Francisco Bay Area Region, Earthquake Eng. Struc., 50, 3939–3961, https://doi.org/10.1002/eqe.3540, 2021. a
Poursartip, B., Fathi, A., and Tassoulas, J. L.: Large-Scale Simulation of Seismic Wave Motion: A Review, Soil Dyn. Earthq. Eng., 129, 105909, https://doi.org/10.1016/j.soildyn.2019.105909, 2020. a
Qiu, H., Yang, Y., and Pan, H.: Underestimation Modification for Intrinsic Dimension Estimation, Pattern Recogn., 140, 109580, https://doi.org/10.1016/j.patcog.2023.109580, 2023. a, b
Raissi, M., Perdikaris, P., and Karniadakis, G.: Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations, J. Comput. Phys., 378, 686–707, https://doi.org/10.1016/j.jcp.2018.10.045, 2019. a
Rasht-Behesht, M., Huber, C., Shukla, K., and Karniadakis, G. E.: Physics-Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions, J. Geophys. Res.-Sol. Ea., 127, e2021JB023120, https://doi.org/10.1029/2021JB023120, 2022. a
Rekoske, J. M., Gabriel, A.-A., and May, D. A.: Instantaneous Physics-Based Ground Motion Maps Using Reduced-Order Modeling, J. Geophys. Res.-Sol. Ea., 128, e2023JB026975, https://doi.org/10.1029/2023JB026975, 2023. a
Ren, P., Rao, C., Chen, S., Wang, J.-X., Sun, H., and Liu, Y.: SeismicNet: Physics-informed Neural Networks for Seismic Wave Modeling in Semi-Infinite Domain, Comput. Phys. Commun., 295, https://doi.org/10.1016/j.cpc.2023.109010, 2024. a
Ross, D. A., Lim, J., Lin, R.-S., and Yang, M.-H.: Incremental Learning for Robust Visual Tracking, Int. J. Comput. Vis., 77, 125–141, https://doi.org/10.1007/s11263-007-0075-7, 2008. a
Rosti, A., Smerzini, C., Paolucci, R., Penna, A., and Rota, M.: Validation of Physics-Based Ground Shaking Scenarios for Empirical Fragility Studies: The Case of the 2009 L'Aquila Earthquake, B. Earthquake Eng., 21, 95–123, https://doi.org/10.1007/s10518-022-01554-1, 2023. a
Scalise, M., Pitarka, A., Louie, J. N., and Smith, K. D.: Effect of Random 3D Correlated Velocity Perturbations on Numerical Modeling of Ground Motion from the Source Physics Experiment, B. Seismol. Soc. Am., 111, 139–156, https://doi.org/10.1785/0120200160, 2021. a
Shahjouei, A. and Pezeshk, S.: Alternative Hybrid Empirical Ground-Motion Model for Central and Eastern North America Using Hybrid Simulations and NGA-West2 Models, B. Seismol. Soc. Am., 106, 734–754, https://doi.org/10.1785/0120140367, 2016. a, b
Shinozuka, M. and Deodatis, G.: Simulation of Stochastic Processes by Spectral Representation, Appl. Mech. Rev., 44, 191–204, https://doi.org/10.1115/1.3119501, 1991. a
Smerzini, C., Paolucci, R., and Stupazzini, M.: Comparison of 3D, 2D and 1D Numerical Approaches to Predict Long Period Earthquake Ground Motion in the Gubbio Plain, Central Italy, B. Earthquake Eng., 9, 2007–2029, https://doi.org/10.1007/s10518-011-9289-8, 2011. a
Song, C., Liu, Y., Zhao, P., Zhao, T., Zou, J., and Liu, C.: Simulating Multi-Component Elastic Seismic Wavefield Using Deep Learning, IEEE Geosci. Remote Sens. Lett., 20, 1–5, https://doi.org/10.1109/LGRS.2023.3250522, 2023. a
Ta, Q.-A., Clouteau, D., and Cottereau, R.: Modeling of Random Anisotropic Elastic Media and Impact on Wave Propagation, Eur. J. Comput. Mech., 19, 241–253, https://doi.org/10.3166/ejcm.19.241-253, 2010. a
Tarbali, K. and Bradley, B.: Ground Motion Selection for Scenario Ruptures Using the Generalised Conditional Intensity Measures (GCIM) Method, Earthq. Eng. Struct. D., 44, 1601–1621, https://doi.org/10.1002/eqe.2546, 2015. a
Thompson, E. M., Baise, L. G., and Kayen, R. E.: Spatial Correlation of Shear-Wave Velocity in the San Francisco Bay Area Sediments, Soil Dyn. Earthq. Eng., 27, 144–152, https://doi.org/10.1016/j.soildyn.2006.05.004, 2007. a
Touhami, S., Gatti, F., Lopez-Caballero, F., Cottereau, R., de Abreu Corrêa, L., Aubry, L., and Clouteau, D.: SEM3D: A 3D High-Fidelity Numerical Earthquake Simulator for Broadband (0–10 Hz) Seismic Response Prediction at a Regional Scale, Geosciences, 12, 112, https://doi.org/10.3390/geosciences12030112, 2022. a
Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E.: Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE T. Image Process., 13, 600–612, https://doi.org/10.1109/TIP.2003.819861, 2004. a
Wen, G., Li, Z., Long, Q., Azizzadenesheli, K., Anandkumar, A., and Benson, S. M.: Real-Time High-Resolution CO2 Geological Storage Prediction Using Nested Fourier Neural Operators, Energ. Environ. Sci., 16, 1732–1741, https://doi.org/10.1039/d2ee04204e, 2023. a
Witte, P. A., Konuk, T., Skjetne, E., and Chandra, R.: Fast CO2 Saturation Simulations on Large-Scale Geomodels with Artificial Intelligence-Based Wavelet Neural Operators, Int. J. Greenhouse Gas Control, 126, 103880, https://doi.org/10.1016/j.ijggc.2023.103880, 2023. a, b
Wu, Y., Aghamiry, H. S., Operto, S., and Ma, J.: Helmholtz Equation Solution in Non-Smooth Media by Physics-Informed Neural Network with Incorporating Quadratic Terms and a Perfectly Matching Layer Condition, Geophysics, 88, 1–66, https://doi.org/10.1190/geo2022-0479.1, 2023. a
Zhang, T., Trad, D., and Innanen, K.: Learning to Solve the Elastic Wave Equation with Fourier Neural Operators, Geophysics, 88, 1–63, https://doi.org/10.1190/geo2022-0268.1, 2023. a
Zhu, C., Riga, E., Pitilakis, K., Zhang, J., and Thambiratnam, D.: Seismic Aggravation in Shallow Basins in Addition to One-dimensional Site Amplification, J. Earthquake Eng., 24, 1477–1499, https://doi.org/10.1080/13632469.2018.1472679, 2020. a
Zhu, W., Hou, A. B., Yang, R., Datta, A., Mousavi, S. M., Ellsworth, W. L., and Beroza, G. C.: QuakeFlow: A Scalable Machine-Learning-Based Earthquake Monitoring Workflow with Cloud Computing, Geophys. J. Int., 232, 684–693, https://doi.org/10.1093/gji/ggac355, 2022. a
Short summary
Numerical simulations are a promising approach to characterizing the intensity of ground motion in the presence of geological uncertainties. However, the computational cost of 3D simulations can limit their usability. We present the first database of seismic-induced ground motion generated by an earthquake simulator for a collection of 30 000 heterogeneous geologies. The HEMEWS-3D dataset can be helpful for geophysicists, seismologists, and machine learning scientists, among others.
Numerical simulations are a promising approach to characterizing the intensity of ground motion...
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