Earthquake Early Warning(EEW)is the rapid acquisition of earthquake epicenter, magnitude, and occurrence time after a destructive earthquake has started to issue alerts to the public before the arrival of transverse waves and long-period surface waves. Magnitude estimation plays a significant role in EEW algorithm research, serving as a fundamental component for early warning, post-earthquake disaster assessment, and emergency response. Seismic monitoring methods primarily focus on technologies like High-rate Global Navigation Satellite System (HR-GNSS) and strong-motion instruments. HR-GNSS is capable of capturing high-precision ground deformation signals and offers the advantages of a non-saturation recording range, making it crucial for rapid estimation of earthquake magnitudes during major seismic events. However, due to the low GNSS sampling rate and high instrument noise, observational noise often overshadows the deformation signals obtained during low-magnitude earthquakes. Additionally, the sparse distribution of GNSS stations currently impacts the accuracy and timeliness of magnitude estimation. Strong-motion observation methods, characterized by high sampling rates, low noise, and dense station distribution, are widely applied in magnitude estimation. Prevalent methods for strong-motion magnitude estimation often rely on P-wave arrival time information for timely determination of magnitude, commonly used in earthquake early warning systems. Yet, these methods are susceptible to saturation effects, leading to underestimation of magnitudes for large earthquakes. Moment magnitude estimation methods are closely associated with rupture characteristics of the seismic source and hold clear physical significance. However, determining this magnitude necessitates knowledge of the rupture extent and slip distribution along the fault plane, which are challenging to precisely obtain at the moment of earthquake occurrence. Hence, such methods are generally employed for post-event magnitude calculations.
Addressing these challenges, this paper proposes a novel method for rapidly estimating earthquake magnitudes using Peak Ground Velocity(PGV)derived from strong motion. First, a comprehensive dataset of strong-motion acceleration records is compiled, covering nearly 20 years and including 5 596 records from 23 global seismic events with magnitudes ranging from 6.0 to 9.0. These records encompass epicentral distances from 1km to 1 000km, with source depths within 60km. A uniform processing approach is applied to standardize the records in terms of time domain orientation, measurement units(converted to cm/s^{2}), and file formats. Data from each station is categorized into three directions: East-West(EW), North-South(NS), and Vertical(UD). Subsequently, the data is converted into the Seismic Analysis Code(SAC)file format, which is specialized for digital seismic waveform data exchange. Ensuring accurate PGV measurements from strong-motion data involves meticulous data preprocessing. This includes removing the mean acceleration from the first 5 seconds before the seismic event for simple bias correction, followed by baseline correction using a high-pass filter with a cutoff frequency of 0.02Hz. The preprocessed strong-motion acceleration records are then integrated to obtain velocity, enabling the measurement of PGV. A robust PGV-based magnitude estimation model, suitable for rapid earthquake magnitude estimation, is constructed using the least-squares regression method.
Furthermore, the constructed PGV-based magnitude estimation model undergoes comprehensive experimental analysis. Initially, the residuals between observed PGV values from 5596 strong-motion records and PGV values predicted by the regression model are computed to evaluate the precision of the constructed PGV-based magnitude estimation model. The model is validated using four earthquake events not included in its construction: the 2021 Damasi M_{W}6.3 earthquake, the 2012 Nicoya M_{W}7.6 earthquake, the 2008 Wenchuan M_{W}7.9 earthquake, and the 2014 Iquique M_{W}8.2 earthquake. This validation process assesses the reliability of the constructed magnitude estimation model. Finally, the paper conducts a study on rapid magnitude estimation to evaluate the timeliness and accuracy of the PGV-based magnitude estimation model within this context.
The experimental results indicate that the predicted values of strong-motion PGV are largely consistent with the observed values for 23 seismic events, with a root mean square error of residuals measuring 0.296. For the four seismic events that were not included in the modeling process, the estimated magnitudes based on strong-motion PGV correspond closely to the moment magnitudes reported by the United States Geological Survey(USGS). The absolute deviations for these events are 0.15, 0.14, 0.05, and 0.13 magnitude units, with an average absolute deviation of 0.12 magnitude units. In the investigation of rapid magnitude estimation, the following outcomes were observed: For the Damasi M_{W}6.3 earthquake, an initial magnitude of 5.03 was calculated at 13 seconds, approaching the theoretical magnitude at 63 seconds, and reaching a convergent magnitude of 6.09 at 76 seconds. Regarding the Nicoya M_{W}7.6 earthquake, a preliminary magnitude of 4.57 was computed within 6 seconds, approximating the theoretical magnitude at 30 seconds, and converging to 7.46 at 50 seconds. In the case of the Wenchuan M_{W}7.9 earthquake, a preliminary magnitude of 4.06 was determined within 19 seconds. At 50 seconds, the calculated magnitude approached the theoretical value, and it converged to 7.81 at 84 seconds. For the Iquique M_{W}8.2 earthquake, an initial magnitude of 6.45 was estimated within 2 seconds, nearing the theoretical magnitude at 55 seconds, and achieving a convergent magnitude of 8.04 at 70 seconds. The convergence time for rapid magnitude estimation for all four events was consistently under 90 seconds.
This experimental findings underscore the applicability of the constructed PGV-based magnitude estimation model for rapid earthquake magnitude estimation. The model's ability to counter saturation effects and prevent magnitude underestimation reinforces its robustness and offers substantial technical support for earthquake early warning systems and post-earthquake emergency response strategies.