- Published on
Using GANs to Generate Realistic Broadband Earthquake Accelorograms
- Authors
- Name
- Pai Buabthong
- @paippb
Florez, M. A., Caporale, M., Buabthong, P., Ross, Z. E., Asimaki, D., and Meier, M.-A., "Data-Driven Synthesis of Broadband Earthquake Ground Motions Using Artificial Intelligence." Bulletin of the Seismological Society of America, (2022). DOI: 10.1785/0120210264
tl;dr
- Engineers have to design buildings to withstand earthquakes and seismic activities.
- Larger-magnitude earthquakes typically have more disastrous effects on the buildings. However, the number of seismograms follows a power law, meaning we have many small-magnitude data, but exponentially fewer larger-magnitude seismograms.
- In this paper, our goal is to generate realistic seismic waves conditioned on the magnitude, epicenter distance, and shear-wave velocity (V30).
- The model employs two separate networks trained concurrently: a generator with the goal of generating 3D accelerograms from a Gaussian seed, magnitude, distance, and V30, and a discriminator with the goal of distinguishing whether the waves are from the real dataset or from the generator.
- Our model is able to generate realistic waveforms which have all the important characteristics of the real seismograms. It can even generate accelerograms from conditions that were not observed before!
Comments
- GAN is such a powerful technique. I hope to see people in other fields start to use this in their research.
- Unfortunately, evaluating GAN, or any generative model is quite challenging. Currently, it's highly dependent on the field. Developing a benchmark standard for generative models would be extremely impactful.