ConSeisGen: Controllable Synthetic Seismic Waveform Generation

Yuanming Li, Dongsik Yoon, Bonhwa Ku, Hanseok Ko

Research output: Contribution to journalArticlepeer-review


While generative adversarial network (GAN) models have shown success in generating synthetic data of acoustic, image, and speech, research on generating seismic waves using GAN is receiving great attention. Although some methods have been successful in generating seismic data, they lack the ability to control the generated seismic waves according to earthquake parameters. This letter proposes a novel approach for controllable seismic wave synthesis using auxiliary classifier GAN (ACGAN). Our method focuses on the generation of synthetic seismic waveforms associated with earthquakes of different epicenteral distances. To incorporate distance information into our model, we introduce a distance regression loss function. In addition, we incorporate a feature-level diversity improvement regularization into our model to enhance the diversity of the generated seismic data. The proposed model was trained on KiK-net datasets, and the quality of the generated data was rigorously validated using various validation methods. Experimental results demonstrate the effectiveness of our proposed model in generating seismic waves by adjusting the earthquake epicenter distance.

Original languageEnglish
Article number3000105
Pages (from-to)1-5
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.


  • Conditional generative adversarial networks (cGANs)
  • deep learning
  • seismic synthesis

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering


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