Real‐Time Moment Magnitude and Stress Drop with Implications for Real‐Time Shaking Prediction

Abstract

Despite the potentially dramatic effect of the stress drop on groundmotion intensity, currently available earthquake early warning systems that deliver peak ground-motion predictions do not account for the effect of this parameter. To address this issue, a new evolutionary algorithm for determining stress drop and moment magnitude in real time is described. It consists of two distinct modules: one processes data recorded by individual stations and another computes event-average stress drops and moment magnitudes. To speed up the analysis, the real-time algorithm deviates from standard procedures of stress-drop determination in several ways. Because these time-saving measures come at the price of accuracy, a quality-control parameter is introduced, which quantifies the discrepancy between the observed and modeled ground motion.The results of implementing the algorithm offline using KiK-borehole data from Japan are presented. It is shown that it is possible to recover the moment magnitudes and the stress drops in real time. Two example timelines of seismic moment and stress drop are presented. These show that the source parameters of small-to-moderate earthquakes may be estimated quite accurately within 5 to 10 s since the first trigger, whereas those of larger magnitudes (i.e., M w > 6) take 20-30 s. Finally, ground-motion prediction equations for the velocity's root mean square and peak ground velocity are presented. Once the epicenter, seismic moment, and stress drop are determined using a few stations nearest to the epicenter, their values can be input into those equations to get the ground-motion intensity at sites further away from it.

Source Parameters of Moderate-To-Large Chinese Earthquakes From the Time Evolution of P-Wave Peak Displacement on Strong Motion Recordings

“…More recently, new strategies have been proposed to improve the accuracy of source parameter estimation for EEW applications and provide an estimate of the rupture area extent and the slip distribution on the fault. Among these strategies, some of them are based on the rapid inversion of geodetic and/or accelerometer data or fitting the spectrum in realtime (Allen and Ziv, 2011;Ohta et al, 2011;Colombelli et al, 2013;Caprio et al, 2011;Ziv and Lior, 2016). However, the rapid inversion of geodetic and/or accelerometer data need a catalog of the active faults for the construction of a rupture model plane in real-time (Colombelli et al, 2013).…”

“…However, the rapid inversion of geodetic and/or accelerometer data need a catalog of the active faults for the construction of a rupture model plane in real-time (Colombelli et al, 2013). The azimuth dependency and the simplifying assumptions (e.g., directivity and segmentation) may introduce large discrepancies between modeled and observed spectra, leading to large variability in corner frequency estimates during the real-time spectrum inversion method (Ziv and Lior, 2016).…”

Harnessing Distributed Acoustic Sensing for Earthquake Early Warning: Magnitude Estimation and Ground Motion Prediction

“…Stress drop, Δτ, is a fundamental earthquake source parameter that strongly affects ground motion intensities 36,[39][40][41] (See "The relation between earthquake source parameters and ground motions" in Methods). For optimal ground motion prediction, both magnitude and stress drop should be determined, as demonstrated by recent studies 2,30,32,36 . Since in this framework we only use one ground motion metric, i.e., ground accelerations rms, Arms, we may only estimate the magnitude (see "Magnitude estimation from bandlimited ground accelerations" in Methods)…”

“…The robustness of these relations largely relies on the quality, quantity, and magnitude range of available earthquake observations 30 . Since DAS is a relatively new seismic measurement technology 8 , current earthquake DAS datasets are insufficient to devise robust empirical methods, and a physics-based approach that does not rely on data availability should be developed [30][31][32] . Recently, a holistic physics-based approach for earthquake source parameter (magnitude and stress drop) estimation and ground motion prediction has been proposed 30 .…”

The Relation Between Ground Motion, Earthquake Source Parameters, and Attenuation: Implications for Source Parameter Inversion and Ground Motion Prediction Equations

“…This is advantageous, since the modeling of ground motion spectra is nontrivial and introduces elements of subjectivity into the process. For example, some researchers resample the power spectrum coefficients at constant log of frequency units (e.g., Allmann & Shearer, 2009;Lior & Ziv, 2017;Ziv & Lior, 2016), some weight them proportionally to 1/ log (f) (e.g., Kaneko & Shearer, 2015;Trugman & Shearer, 2017), and some model them as is (e.g., Chen & Shearer, 2011;Prieto et al, 2004;Shearer et al, 2006). Because different schemes yield notably different results ( Figure 2), seeking ways to bypass the biases inherited in the modeling of ground motion spectra is instructive.…”