High Frequency Earthquake Modelling

November 1, 2023
In Progress
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Earthquakes are among the most devastating natural hazards humanity is confronted with. Earthquake shaking spans a very wide range of frequencies, with classic sensors designed to record signals over four orders of magnitude in frequency. The low frequency part of seismic signals is readily used to study earthquake processes and the interior structure of the Earth. Using the high frequency parts of earthquake wavefields, on the other hand, is more challenging, because i) high frequency signals are hard to observe: seismometers are usually too far away and too sparse to record high frequency wavefields with the necessary resolution; ii) high frequency wavefields are hard to model: the computational cost of wavefield simulations grows with the 4th power of frequency, and we lack the detailed knowledge of underground geological structures that affect and modify the signals at this small scales. Next level underground laboratories, such as ETHZ’s Bedretto Lab or Utah Forge, however, strongly reduce the observational limitations, in that they facilitate earthquake monitoring from unusually close distances, and with high sensor densities. Here we propose to use this opportunity, and to jointly leverage high performance computing, generative deep modeling and the massive, near-field earthquake data sets that the novel laboratories provide, to model and study earthquake waveforms up to very high frequencies. We will use these modelling capabilities to achieve a range of science goals, including improved seismicity monitoring algorithms, and to study small scale earthquake source processes and ground motion site response.



SDSC Team:
Simon Dirmeier

PI | Partners:

ETH Zurich, Department of Earth Sciences:

  • Dr. Men-Andrin Meier
  • Dr. Laura Ermert
  • Dr. Maria Koroni

More info



The prime observation to study earthquake phenomena are seismic waveforms. Inferences
on earthquake sources and earth interior structure are made by comparing them to increasingly sophisticated numerical models. A current gap in our capabilities is the generation of realistic high
frequency (HF) synthetic seismic waveforms. The computational cost of deterministic physics-based
waveform simulations puts limits to the resolvable frequency range. Moreover, detailed modelling
of high-frequency waveforms requires considerable prior geologic knowledge, such as gradients and interfaces between rock types, cracks and shear zones, and fine-scale topography.

Proposed Approach / Solution

For the project HighFEM, we are developing generative models for synthesis of seismic waveforms.  Since waveforms are long-range time series data sets, approaches based on forecasting methods, such as recurrent neural networks and autoregressive models (e.g., LSTMs or pixelCNNs), combined with adversarial training (e.g., GANs) pose a promising research direction.


The project will allow to better characterize and understand natural and induced earthquakes; to study earthquake rupture, including at small scales; and to improve site-specific seismic hazard estimates.



Additional resources


  1. Florez, Manuel A., et al. "Data‐driven synthesis of broadband earthquake ground motions using artificial intelligence." Bulletin of the Seismological Society of America 112.4 (2022): 1979-1996.


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