How “good” are real-time ground motion predictions from Earthquake Early Warning systems?

Real-time ground motion alerts, as can be provided by Earthquake Early Warning (EEW) systems, need to be both timely and sufficiently accurate to be useful. Yet how timely and how accurate the alerts of existing EEW algorithms are is often poorly understood. In part, this is because EEW algorithm performance is usually evaluated not in terms of ground motion prediction accuracy and timeliness but in terms of other metrics (e.g., magnitude and location estimation errors), which do not directly reflect the usefulness of the alerts from an end user perspective. Here we attempt to identify a suite of metrics for EEW algorithm performance evaluation that directly quantify an algorithm's ability to identify target sites that will experience ground motion above a critical (user-defined) ground motion threshold. We process 15,553 recordings from 238 earthquakes with M > 5 (mostly from Japan and southern California) in a pseudo-real-time environment and investigate two end-member EEW methods. We use the metrics to highlight both the potential and limitations of the two algorithms and to show under which circumstances useful alerts can be provided. Such metrics could be used by EEW algorithm developers to convincingly demonstrate the added value of new algorithms or algorithm components. They can complement existing performance metrics that quantify other relevant aspects of EEW algorithms (e.g., false event detection rates) for a comprehensive and meaningful EEW performance analysis

How “good” are real-time ground motion predictions from Earthquake Early Warning systems?

Real-time ground motion alerts, as can be provided by Earthquake Early Warning (EEW) systems, need to be both timely and sufficiently accurate to be useful. Yet how timely and how accurate the alerts of existing EEW algorithms are is often poorly understood. In part, this is because EEW algorithm performance is usually evaluated not in terms of ground motion prediction accuracy and timeliness but in terms of other metrics (e.g., magnitude and location estimation errors), which do not directly reflect the usefulness of the alerts from an end user perspective. Here we attempt to identify a suite of metrics for EEW algorithm performance evaluation that directly quantify an algorithm's ability to identify target sites that will experience ground motion above a critical (user-defined) ground motion threshold. We process 15,553 recordings from 238 earthquakes with M > 5 (mostly from Japan and southern California) in a pseudo-real-time environment and investigate two end-member EEW methods. We use the metrics to highlight both the potential and limitations of the two algorithms and to show under which circumstances useful alerts can be provided. Such metrics could be used by EEW algorithm developers to convincingly demonstrate the added value of new algorithms or algorithm components. They can complement existing performance metrics that quantify other relevant aspects of EEW algorithms (e.g., false event detection rates) for a comprehensive and meaningful EEW performance analysis

Evidence for Universal Earthquake Rupture Initiation Behavior

Earthquake onsets provide a unique opportunity to study physical rupture processes because they are more easily observable than later rupture stages. Despite this relative simplicity, the observational basis for rupture onsets is unclear. Numerous reports of evidence for magnitude-dependent rupture onsets (which imply deterministic rupture behavior, e.g. Colombelli et al., 2014) stand in contradiction to a large body of physics-based rupture modeling efforts, which are mostly based on inherently non-deterministic principles (e.g. Rice, 1993). Here we make use of the abundance of short-distance recordings available today; a magnitude-dependency of onsets should appear most prominently in such recordings. We use a simple method to demonstrate that all ruptures in the studied magnitude range (4 < M < 8) share a universal initial rupture behavior and discuss ensuing implications for physical rupture processes and earthquake early warning

Evolution of seismicity near the southernmost terminus of the San Andreas Fault: Implications of recent earthquake clusters for earthquake risk in southern California

Three earthquake clusters that occurred in the direct vicinity of the southern terminus of the San Andreas Fault (SAF) in 2001, 2009, and 2016 raised significant concern regarding possible triggering of a major earthquake on the southern SAF, which has not ruptured in more than 320 years. These clusters of small and moderate earthquakes with M ≤ 4.8 added to an increase in seismicity rate in the northern Brawley seismic zone that began after the 1979 M_w 6.5 Imperial Valley earthquake, in contrast to the quiet from 1932 to 1979. The clusters so far triggered neither small nor large events on the SAF. The mostly negative Coulomb stress changes they imparted on the SAF may have reduced the likelihood that the events would initiate rupture on the SAF, although large magnitude earthquake triggering is poorly understood. The relatively rapid spatial and temporal migration rates within the clusters imply aseismic creep as a possible driver rather than fluid migration

Generalized Seismic Phase Detection with Deep Learning

To optimally monitor earthquake-generating processes, seismologists have sought to lower detection sensitivities ever since instrumental seismic networks were started about a century ago. Recently, it has become possible to search continuous waveform archives for replicas of previously recorded events (template matching), which has led to at least an order of magnitude increase in the number of detected earthquakes and greatly sharpened our view of geological structures. Earthquake catalogs produced in this fashion, however, are heavily biased in that they are completely blind to events for which no templates are available, such as in previously quiet regions or for very large magnitude events. Here we show that with deep learning we can overcome such biases without sacrificing detection sensitivity. We trained a convolutional neural network (ConvNet) on the vast hand-labeled data archives of the Southern California Seismic Network to detect seismic body wave phases. We show that the ConvNet is extremely sensitive and robust in detecting phases, even when masked by high background noise, and when the ConvNet is applied to new data that is not represented in the training set (in particular, very large magnitude events). This generalized phase detection (GPD) framework will significantly improve earthquake monitoring and catalogs, which form the underlying basis for a wide range of basic and applied seismological research

Evidence for Universal Earthquake Rupture Initiation Behavior

Earthquake onsets provide a unique opportunity to study physical rupture processes because they are more easily observable than later rupture stages. Despite this relative simplicity, the observational basis for rupture onsets is unclear. Numerous reports of evidence for magnitude-dependent rupture onsets (which imply deterministic rupture behavior, e.g. Colombelli et al., 2014) stand in contradiction to a large body of physics-based rupture modeling efforts, which are mostly based on inherently non-deterministic principles (e.g. Rice, 1993). Here we make use of the abundance of short-distance recordings available today; a magnitude-dependency of onsets should appear most prominently in such recordings. We use a simple method to demonstrate that all ruptures in the studied magnitude range (4 < M < 8) share a universal initial rupture behavior and discuss ensuing implications for physical rupture processes and earthquake early warning