In recent years, coseismic velocity from high-rate global navigation satellite systems (GNSS) carrier phase data has been widely utilized to estimate instrumental seismic intensity, thereby guiding earthquake early warning and emergency response. However, using carrier phase data only yields displacement, displacement increment, and average velocity but not instantaneous velocity at the epoch level. In large earthquakes, using average velocity over a brief time span (e.g., 1 s) to quantify instantaneous coseismic velocity is less reliable for recovering accurate deformation dynamics, especially for the near-field region. In this study, we first introduce GNSS raw Doppler-based instantaneous velocity into seismology, expanding carrier phase-based traditional GNSS seismology. We also propose a new integrated GNSS velocity estimation method that employs a Kalman filter to integrate raw Doppler-based instantaneous velocity and carrier phase-based average velocity. The GNSS data from shake table experiments and two real-world earthquake events (i.e., the 2016 Mw 6.6 Norcia earthquake and the 2011 Mw 9.1 Tohoku-oki earthquake) are used to investigate the impact of high-rate GNSS raw Doppler on capturing coseismic velocity waveforms and predicting instrumental seismic intensity. The simulated sine wave experiment results indicate that the accuracy of instantaneous and average velocity for the 1 Hz sampling rate case is 1.20 cm/s and 12.67 cm/s, respectively. A similar case holds for the simulated quake wave experiment. The retrospective analysis of the ultra-high-rate (20 Hz) GNSS data for the Norcia earthquake shows the average velocities exhibit more aliasing and have a smaller peak ground velocity value than instantaneous velocities in all cases (i.e., 1, 2, 4, 5, 10, and 20 Hz). For the 2011 Mw 9.1 Tohoku-oki earthquake, results show that incorporating raw Doppler data enhances the consistency between the GNSS intensity map and the United States Geological Survey intensity map for near-field regions. Therefore, high-rate GNSS RD data as it becomes more widely available should be incorporated into data processing of high-rate GNSS seismology to capture more accurate instantaneous coseismic velocity waveforms and predict more realistic instrumental seismic intensity in future analyses.

近年来，来自高速全球导航卫星系统 （GNSS） 载波相位数据的同震速度已被广泛用于估计仪器地震强度，从而指导地震预警和应急响应。然而，使用载流子相位数据只能产生位移、位移增量和平均速度，而不会产生 epoch 级别的瞬时速度。在大地震中，使用短时间跨度（例如 1 s）的平均速度来量化瞬时同震速度对于恢复准确的变形动力学不太可靠，尤其是对于近场区域。在本研究中，我们首先将基于 GNSS 原始多普勒的瞬时速度引入地震学，扩展了基于载波相位的传统 GNSS 地震学。我们还提出了一种新的集成 GNSS 速度估计方法，该方法采用卡尔曼滤波器来集成基于原始多普勒的瞬时速度和基于载波相位的平均速度。来自振动台实验和两次真实世界地震事件（即 2016 年 Mw 6.6 Norcia 地震和 2011 年 Mw 9.1 东北冲地震）的 GNSS 数据用于研究高速 GNSS 原始多普勒对捕获同震速度波形和预测仪器地震强度的影响。模拟正弦波实验结果表明，1 Hz 采样率情况下的瞬时速度和平均速度精度分别为 1.20 cm/s 和 12.67 cm/s。模拟地震波实验也有类似的情况。对 Norcia 地震的超高速率 （20 Hz） GNSS 数据的回顾性分析表明，在所有情况下，平均速度都表现出更多的混叠，并且具有比瞬时速度更小的峰值地速度值（即、1、2、4、5、10 和 20 Hz）。对于 2011 年 Mw 9.1 东北冲地震，结果表明，结合原始多普勒数据可以增强 GNSS 强度图与美国地质调查局近场区域强度图之间的一致性。因此，随着高速率 GNSS RD 数据越来越广泛地获得，应将其纳入高速 GNSS 地震学的数据处理中，以捕获更准确的瞬时同震速度波形，并在未来的分析中预测更真实的仪器地震强度。