TFCGAN is a python package for strong ground motion simulation in time-frequency domain (TF) based on based on conditional generative adversarial network and phase retrieval approaches.
Used as command line application, TFCGAN can be used to create .npy files of synthetic seismic waveforms using a pre-trained ANN model.
Install python 3.10.0 (anaconda or venv), then:
Standard install:
pip install --upgrade pip setuptools && pip install .
Developers install (install editable package and tests libraries):
pip install --upgrade pip setuptools && pip install -e ".[dev]"
In the Python virtualenv and then:
Check versions: pip freeze should give tensorflow==2.11.1 and numpy==1.26.1
For developers, run tests via pytest ./tests
TFCGAN is a command line application to be launched from the terminal. For help,
type tfcgan --help in your terminal
TFCGAN can also be used in your Python code (disclaimer: the snippet below has not been tested, please provide feedback in case of import errors):
from tfcgan.tfcgan import TFCGAN
# setup your parameters. Example:
mag = 7
dist = 100
vs30 = 760
num_waveforms = 10
# Model
tfcgan = TFCGAN()
# Generate labels for a specific scenario
tfcgan.create_scenario(mag, dist, vs30, num_waveforms = num_waveforms)
# Generate the time-frequency representation
tf = model.get_tf_representation
# Generate waveform data
t, data = tfcgan.get_ground_shaking_synthesis
# Generate Fourier amplitude spectra
freq, fas = tfcgan.get_fas_response
# data is a Numpy Matrix of shape (num_waveforms, 4000).
# Each waveform delta time is 0.01 sec
# (i.e., each waveform is 40s long by default)- create model with tensorflow 12.16 or whatever,
- create models in hdf format (see tensorflow doc)
- check the running code by running the tests
Esfahani, Reza DD, Fabrice Cotton, Matthias Ohrnberger, and Frank Scherbaum.
"TFCGAN: Nonstationary Ground‐Motion Simulation in the Time–Frequency Domain
Using Conditional Generative Adversarial Network (CGAN) and Phase Retrieval Methods."
*Bulletin of the Seismological Society of America * 113, no. 1 (2023): 453-467.
https://doi.org/10.1785/0120220068