09 Jan 2024
 | 09 Jan 2024
Status: this preprint is currently under review for the journal ESSD.

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.

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

Status: open (until 06 Mar 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
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

Model code and software

HEMEW3D Fanny Lehmann

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.