TimesNet-Gen：基于深度学习的场地特异性强震动生成模型

TimesNet-Gen: Deep Learning-based Site Specific Strong Motion Generation

December 4, 2025

作者: Baris Yilmaz, Bevan Deniz Cilgin, Erdem Akagündüz, Salih Tileylioglu

cs.AI

摘要

有效的地震风险防控依赖于精准的场地特异性评估，这需要能够体现局部场地条件对地震动特征影响的模型。在此背景下，从记录的地震动中学习场地控制特征的数据驱动方法提供了可行方向。本文基于时域加速度计记录研究强地震动生成问题，提出TimesNet-Gen——一种时域条件生成模型。该方法采用站点特定的潜在瓶颈结构，通过对比各台站真实与生成记录的HVSR曲线及场地基频f_0分布进行评估，并基于f_0分布混淆矩阵构建评分体系以量化台站特异性。实验表明TimesNet-Gen在台站级数据对齐方面表现优异，相较于基于频谱图的条件VAE基线模型，在场地特异性强震动合成任务中更具优势。相关代码已开源：https://github.com/brsylmz23/TimesNet-Gen。

English

Effective earthquake risk reduction relies on accurate site-specific evaluations. This requires models that can represent the influence of local site conditions on ground motion characteristics. In this context, data driven approaches that learn site controlled signatures from recorded ground motions offer a promising direction. We address strong ground motion generation from time-domain accelerometer records and introduce the TimesNet-Gen, a time-domain conditional generator. The approach uses a station specific latent bottleneck. We evaluate generation by comparing HVSR curves and fundamental site-frequency f_0 distributions between real and generated records per station, and summarize station specificity with a score based on the f_0 distribution confusion matrices. TimesNet-Gen achieves strong station-wise alignment and compares favorably with a spectrogram-based conditional VAE baseline for site-specific strong motion synthesis. Our codes are available via https://github.com/brsylmz23/TimesNet-Gen.