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Scenario-conditioned flow matching for probabilistic generation of three-component ground-motion waveforms
Yi Ding, Jinjun Hu, Su Chen, Xianwei Liu, Zhongxiang Zhang, Zongchao Li, Xiaojun Li, Lili Xie
https://arxiv.org/abs/2606.31340 https://arxiv.org/pdf/2606.31340 https://arxiv.org/html/2606.31340
arXiv:2606.31340v1 Announce Type: new
Abstract: Performance-based seismic risk assessment requires three-component acceleration histories compatible with specified source, path, and site conditions. Conventional ground-motion prediction equations provide scalar intensity measures, while many generative waveform models learn amplitude and waveform shape within a single high-dimensional target. We present WaveFlowGMM, a two-stage probabilistic ground-motion model that uses peak ground acceleration (PGA) as an amplitude interface between scenario conditioning and waveform generation. The amplitude stage uses physics-informed symbolic learning to estimate component-wise PGA medians and a full cross-component covariance. The waveform stage uses few-step AlphaFlow in an invertible wavelet-packet coefficient space to generate normalised three-component histories that are rescaled by sampled PGA. Tests on an event-level NGA-West2 holdout set show that the generated motions recover the main magnitude, distance, and site scaling, keep peak and spectral residuals close to zero, preserve three-component amplitude dependence, and yield velocity and displacement histories without systematic drift after integration of the generated three-component acceleration histories. The framework provides an interpretable and computationally efficient candidate component for waveform-level seismic hazard and risk analysis.
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Tech has flattened out so much in the last 20 years. Ok, there's Apple shipping 3-generations-previous Nvidia performance with lower power consumption, but there's no *alien technology*. No SGI CAVEs, no wild DEC Alpha CPU frequencies, just laptops and slightly nicer laptops and a few very nice laptops with a lot of RAM. And LLMs. Boring. Yawn.