Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog
Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog
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DOI：
10.26443/seismica.v2i2.978
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Tim Dittmann;Jade Morton;B. Crowell;Diego Melgar;Jensen DeGrande;David Mencin
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作者：
Tim Dittmann;Jade Morton;B. Crowell;Diego Melgar;Jensen DeGrande;David Mencin
识别地球物理信号的数据驱动方法已被证明在模型驱动方法不足的高维环境中是有益的。全球导航卫星系统提供了一个非饱和地面运动观测源，这种观测是地面运动预测和快速地震危险评估及警报的通用数据。然而，这些来自全球导航卫星系统的信号叠加在受地球大气层、低成本或星载振荡器以及复杂射频环境影响的依赖硬件、位置和时间的噪声特征上。在这种情况下，避开启发式或基于物理的模型，采用数据驱动的方法是自主信号识别的一个进步。然而，数据驱动方法的性能取决于具有准确分类的大量代表性样本，而更复杂的算法架构则需要更深入的科学见解。现有的高速率（≥1Hz）GNSS地面运动目录相对有限。在这项工作中，我们在半球网络的GNSS速度测量的概率噪声模型和评估。我们生成随机噪声时间序列，以增强现有惯性目录中70公里内强事件（≥ MW 5.0）的低噪声强运动信号。我们利用已知的信号和噪声信息来评估特征提取策略并量化增强效益。我们发现，与仅在真实GNSS速度目录上训练的模型相比，在此扩展的伪合成目录上训练的分类器模型提高了泛化能力，并为未来增强的数据驱动方法提供了框架。
Data-driven approaches to identify geophysical signals have proven beneficial in high dimensional environments where model-driven methods fall short. GNSS offers a source of unsaturated ground motion observations that are the data currency of ground motion forecasting and rapid seismic hazard assessment and alerting. However, these GNSS-sourced signals are superposed onto hardware-, location- and time-dependent noise signatures influenced by the Earth’s atmosphere, low-cost or spaceborne oscillators, and complex radio frequency environments. Eschewing heuristic or physics based models for a data-driven approach in this context is a step forward in autonomous signal discrimination. However, the performance of a data-driven approach depends upon substantial representative samples with accurate classifications, and more complex algorithm architectures for deeper scientific insights compound this need. The existing catalogs of high-rate (≥1Hz) GNSS ground motions are relatively limited. In this work, we model and evaluate the probabilistic noise of GNSS velocity measurements over a hemispheric network. We generate stochastic noise time series to augment transferred low-noise strong motion signals from within 70 kilometers of strong events (≥ MW 5.0) from an existing inertial catalog. We leverage known signal and noise information to assess feature extraction strategies and quantify augmentation benefits. We find a classifier model trained on this expanded pseudo-synthetic catalog improves generalization compared to a model trained solely on a real-GNSS velocity catalog, and offers a framework for future enhanced data driven approaches.