SeisMamba：面向空间分布式地震预警的低延迟单台站地震震级估计
SeisMamba: Low-Latency Single-Station Seismic Magnitude Estimation for Spatially Distributed Earthquake Early Warning
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- The University of Sydney（悉尼大学）
- Accenture（埃森哲）
- The University of New South Wales（新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本文提出轻量级架构SeisMamba，结合分层卷积编码等技术实现低延迟单台站地震震级估计，在STEAD基准及跨区域实验中均表现优异，为分布式低成本地震预警提供新方案。
中文摘要 AI 辅助
快速地震震级估计是地震预警的核心，但许多运行系统依赖密集的区域地震台网和特定区域校准，这给传感基础设施稀疏的高风险区域带来了空间覆盖障碍。单台站学习提供了一种更低成本的替代方案，但现有模型常面临精度-延迟权衡问题，且可能在区域分布偏移下性能下降。本文提出SeisMamba，一种轻量级、基于Mamba的架构，用于从单台站记录的经最少处理的三分量地震波形中进行低延迟震级估计。SeisMamba结合了分层卷积编码、稀疏选择性状态空间建模、多尺度特征融合以及辅助时间预测头，以支持高效的长序列波形分析。在STEAD基准测试中，SeisMamba在测试的基线中取得了最佳的均方误差（MSE）、均方根误差（RMSE）和决定系数（R²），同时在NVIDIA T4 GPU上处理32个波形的批次仅需0.55毫秒，比基于Transformer的基线快约3倍。本文还开展了智利-台湾区域留一实验作为跨区域部署的诊断测试，结果显示SeisMamba在地理上未见过的地震区域仍保持有用的性能。这些结果表明，选择性状态空间波形建模为空间分布式、低成本的地震预警提供了一种有前景的精度-延迟主干。
英文摘要
Rapid earthquake magnitude estimation is central to earthquake early warning, yet many operational systems depend on dense regional seismic networks and region-specific calibration. This creates a spatial coverage barrier for high-risk areas with sparse sensing infrastructure. Single-station learning offers a lower-cost alternative, but existing models often face an accuracy--latency trade-off and may degrade under regional distribution shift. We present SeisMamba, a lightweight Mamba-based architecture for low-latency magnitude estimation from minimally processed three-component seismic waveforms recorded at a single station. SeisMamba combines hierarchical convolutional encoding, sparse selective state-space modelling, multi-scale feature fusion, and an auxiliary temporal prediction head to support efficient long-sequence waveform analysis. On the STEAD benchmark, SeisMamba achieves the best MSE, RMSE, and $R^2$ among tested baselines while requiring only 0.55 ms for a batch of 32 waveforms on an NVIDIA T4 GPU, making it about three times faster than transformer-based baselines. We further conduct a Chile--Taiwan regional hold-out experiment as a diagnostic test of cross-region deployment, where SeisMamba retains useful performance on geographically unseen seismic regions. These results suggest that selective state-space waveform modelling provides a promising accuracy--latency backbone for spatially distributed, low-cost earthquake early warning.
