Preprints
https://doi.org/10.5194/essd-2023-470
https://doi.org/10.5194/essd-2023-470
09 Jan 2024
 | 09 Jan 2024
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

Synthetic ground motions in heterogeneous geologies: the HEMEW-3D dataset for scientific machine learning

Fanny Lehmann, Filippo Gatti, Michaël Bertin, and Didier Clouteau

Abstract. The ever-improving performances of physics-based simulations and the rapid developments of deep learning are offering new perspectives to study earthquake-induced ground motion. Due to the large amount of data required to train deep neural networks, applications have so far been limited to recorded data or two-dimensional simulations. To bridge the gap between deep learning and high-fidelity numerical simulations, this work introduces a new database of physics-based earthquake simulations.

The HEMEW-3D database comprises 30,000 simulations of elastic wave propagation in three-dimensional (3D) geological domains. Each domain is parametrized by a different geological model built from a random arrangement of layers augmented by random fields that represent heterogeneities. For each simulation, ground motion is synthetized at the surface by a grid of virtual sensors. The high frequency of waveforms (fmax = 5 Hz) allows extensive analyses of surface ground motion.

Existing and foreseen applications range from statistic analyses of the ground motion variability and machine learning methods on geological models, to deep learning-based predictions of ground motion depending on 3D heterogeneous geologies.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Fanny Lehmann, Filippo Gatti, Michaël Bertin, and Didier Clouteau

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-470', Anonymous Referee #1, 12 Mar 2024
  • RC2: 'Comment on essd-2023-470', Anonymous Referee #2, 19 Mar 2024
  • AC1: 'Comment on essd-2023-470', Fanny Lehmann, 07 May 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-470', Anonymous Referee #1, 12 Mar 2024
  • RC2: 'Comment on essd-2023-470', Anonymous Referee #2, 19 Mar 2024
  • AC1: 'Comment on essd-2023-470', Fanny Lehmann, 07 May 2024
Fanny Lehmann, Filippo Gatti, Michaël Bertin, and Didier Clouteau

Data sets

Physics-based Simulations of 3D Wave Propagation: a Dataset for Scientific Machine Learning Fanny Lehmann https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/LAI6YU

Model code and software

HEMEW3D Fanny Lehmann https://github.com/lehmannfa/HEMEW3D

Fanny Lehmann, Filippo Gatti, Michaël Bertin, and Didier Clouteau

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Short summary
Numerical simulations are a promising approach to characterize the intensity of ground motion in the presence of geological uncertainties. However, the computational cost of three-dimensional 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 HEMEW-3D dataset can be helpful for geophysicists, seismologists, and machine learning scientists, among others.
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