{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.31340",
  "author_total": 8,
  "authors": [
    {
      "name": "Yi Ding"
    },
    {
      "name": "Jinjun Hu"
    },
    {
      "name": "Su Chen"
    },
    {
      "name": "Xianwei Liu"
    },
    {
      "name": "Zhongxiang Zhang"
    },
    {
      "name": "Zongchao Li"
    },
    {
      "name": "Xiaojun Li"
    },
    {
      "name": "Lili Xie"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8ehh2ykqyfxs9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.31340",
  "fulltext_url": null,
  "id": "8ehh2ykqyfxs9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.31340",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.geo-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2606.31340",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8ehh2ykqyfxs9.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:14Z",
      "native_id": "2606.31340",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.31340"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.geo-ph",
      "subfield": "geophysics"
    }
  ],
  "title": "Scenario-conditioned flow matching for probabilistic generation of three-component ground-motion waveforms",
  "title_text": "Scenario-conditioned flow matching for probabilistic generation of three-component ground-motion waveforms",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:50Z",
  "url": "https://research.draconitic.com/paper/8ehh2ykqyfxs9",
  "venue": "arXiv",
  "versions": []
}