Aftershocks (ASs) following strong mainshocks (MSs) can exacerbate structural damage or lead to collapse. However, the scarcity of recorded data necessitates reliance on artificial sequences, which have difficulty in characterizing the time‐frequency correlation between MSs and ASs. This study innovatively converts the AS time history prediction into an image translation task, exploiting the invertible transformation between accelerograms and time‐frequency representations. An encoder–decoder neural network is developed to encode the MS information into the latent space of a pre‐trained generative adversarial network, enabling accurate AS predictions through the decoder. The integration of seismic parameters further improves the AS prediction performance. Comparative analyses demonstrate that the proposed method outperforms the traditional ones on accuracy and robustness and reproduces the non‐stationarity of ASs.

中文翻译：

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通过生成对抗网络的潜在空间编码进行主震-余震序列模拟


强主震 (MS) 后发生的余震 (AS) 可能会加剧结构损坏或导致倒塌。然而，记录数据的稀缺需要依赖人工序列，这很难表征 MS 和 AS 之间的时频相关性。这项研究创新性地将 AS 时程预测转化为图像翻译任务，利用了加速度图和时频表示之间的可逆变换。开发了编码器-解码器神经网络，将 MS 信息编码到预先训练的生成对抗网络的潜在空间中，从而通过解码器实现准确的 AS 预测。地震参数的整合进一步提高了AS预测性能。比较分析表明，该方法在准确性和鲁棒性方面优于传统方法，并再现了 AS 的非平稳性。