Earthquake Magnitude Scaling Using Peak Ground Velocity Derived from High-Rate GNSS Observations

Abstract

Rapid response to destructive tsunami and seismic events requires rapid determination of the earthquake magnitude. We propose a new method that employs peak ground velocities (PGVs) derived from Global Navigation Satellite System (GNSS) data to estimate earthquake magnitudes. With a total of 1434 records from 22 events as the constraints, we perform the regression and obtain a PGV scaling law for magnitude determination. The advantage of the new method is that the PGVs are extracted from the GNSS velocity waveforms, which can be easily computed using broadcast GNSS ephemeris. In contrast, the peak ground displacement (PGD) depends on a sophisticated high-precision GNSS-processing subject to external correction data, realization of which cannot be kept robust constantly, especially in real time. The results show that the PGV magnitudes agree with reported moment magnitudes with mean absolute deviation of 0.26 magnitude units for the 22 events and also agree well with the PGD magnitude. We further demonstrate that GNSS-derived PGV and the modified Mercalli intensity values can be consistent with their counterparts from the U.S. Geological Survey ShakeMap products and therefore the GNSS-derived PGVs have the potential to be included in the ShakeMap as a complementary constraint, especially in areas with sparse seismic station coverage for large earthquake.

Earthquake Magnitude Estimation from High-Rate GNSS Data: A Case Study of the 2021 Mw 7.3 Maduo Earthquake

“…EEW is currently being carried out on a global scale [9,21,33]. PGD and PGV from high-rate GNSS data provide a way to quickly estimate the earthquake magnitude [16,32], as shown during the Maduo earthquake in this study. Although this method does not require complex fault rupture models, there are some issues that need attention and resolution.…”

“…where N v (t), E v (t) and U v (t) show the velocity components of the north, east and up in m/s, respectively. The regression model between PGV and moment magnitude is expressed as [16]:…”

“…where Mw is the moment magnitude; R is the hypocentral distance in km; and A, B and C are the regression coefficients. We used the regression coefficients (A = −5.025, B = 0.741 and C = −0.111) from Fang et al [16], which were extracted from modeling of GNSS observational data taken during 22 moderate-to-large (Mw 6.0-9.1) earthquakes. Similar to the PGD magnitude, the PGV magnitude was updated at every epoch until it reached convergence.…”

“…PPP relies on precise orbit and clock products, and requires a relatively long convergence or re-convergence time (20-30 min) due to complicated error corrections and parameter estimation [13,14]. Relative positioning has an accuracy of sub-centimeters, but it needs at least one stable reference station to acquire displacements and thereby might be affected by the reference station [15,16]. Nonetheless, it has been widely used to extract real-time coseismic displacements.…”

“…The regression model was tested and updated using more earthquake cases in recent years [24][25][26]. The past few years benefited from the accumulation of high-rate GNSS data in earthquake case studies, and a regression model between earthquake magnitude and peak ground velocity (PGV) of high-rate GNSS was established [16]. Using the data from 22 earthquakes with magnitudes ranging from Mw 6.0 to Mw 9.1, Fang et al [16] found that the PGV magnitudes were comparable with PGD magnitudes.…”