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High-resolution ground motion generation with time–frequency representation

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Bulletin of Earthquake Engineering Aims and scope Submit manuscript

Abstract

Data-driven deep learning application in earthquake engineering highlights the insufficient quantity and the imbalanced feature distribution of measured ground motions, which can be mitigated with artificial ones. Traditional ground motion generation techniques tend to extend the catalogs conditioning on existing records, while current deep learning-based methods such as generative adversarial networks (GANs) only provide limited duration or sampling rate, thus obstructing further applications. In this paper, an invertible time–frequency transformation process is employed, based on which the transformed earthquake representation is implicitly modeled by advanced GANs for high-resolution and unconditional generation. Moreover, leveraging the disentangling property of the GAN’s latent space, the newly developed latent space walking method is adopted to assure the generations with controllable time–frequency features. A feature-balanced generated ground motion dataset has been constructed in combination with the proposed methods, and the application potential was demonstrated through comparative experiments of different datasets.

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Data Availability

The pre-trained models and codes will be available on GitHub at “https://github.com/xzk8559/StyleGAN-EQ” after the paper is published.

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Funding

The authors acknowledge the financial support provided by the MOST, National Key Technology R&D Program (2023YFC3805800) and National Natural Science Foundation of China (52178151).

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Authors and Affiliations

  1. Department of Structural Engineering, Tongji University, Shanghai, People’s Republic of China

    Zekun Xu & Jun Chen

  2. State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, People’s Republic of China

    Jun Chen

  3. Shanghai Research Institute of Building Sciences Co. Ltd, Shanghai, People’s Republic of China

    Zekun Xu

  4. Shanghai Key Laboratory of Engineering Structure Safety, SRIBS, Shanghai, People’s Republic of China

    Zekun Xu

Authors
  1. Zekun Xu
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  2. Jun Chen
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Contributions

Both authors contributed to the study conception, material preparation, and data collection. Analysis and the first draft of the manuscript were contributed by Zekun Xu. Both authors commented on previous versions of the manuscript. Both authors read and approved the final manuscript.

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Correspondence to Jun Chen.

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Xu, Z., Chen, J. High-resolution ground motion generation with time–frequency representation. Bull Earthquake Eng (2024). https://doi.org/10.1007/s10518-024-01912-1

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