metadata
license: mit
pipeline_tag: other
tags:
- seismology
- geophysics
- time-series
TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation
TimesNet-Gen is a time-domain conditional generator designed for site-specific strong ground motion synthesis from accelerometer records. It employs a latent bottleneck with station identity conditioning to capture local site influences on ground motion characteristics.
- Paper: TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation
- GitHub Repository: brsylmz23/TimesNet-Gen
- Demo: Hugging Face Space
Installation
To use the model, first clone the repository and install the required dependencies:
git clone https://github.com/brsylmz23/TimesNet-Gen.git
cd TimesNet-Gen
pip install -r requirements.txt
Sample Usage
After downloading the pre-trained checkpoints (as described in the GitHub README), you can generate samples using the provided script:
# Generate 50 samples per station for the default stations
python generate_samples.py --num_samples 50
# Generate for specific stations (e.g., station IDs 0205 and 1716)
python generate_samples.py --stations 0205 1716 --num_samples 100
The output includes generated waveforms in NPZ format along with HVSR curves and f₀ distribution comparisons.
Citation
If you find this work useful, please cite:
@misc{yilmaz2025timesnetgendeeplearningbasedsite,
title={TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation},
author={Baris Yilmaz and Bevan Deniz Cilgin and Erdem Akagündüz and Salih Tileylioglu},
year={2025},
eprint={2512.04694},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2512.04694},
}