Probabilistic prediction of rupture length, slip and seismic ground motions for an ongoing rupture: implications for early warning for large earthquakes

Earthquake EarlyWarning (EEW) predicts future ground shaking based on presently available data. Long ruptures present the best opportunities for EEW since many heavily shaken areas are distant from the earthquake epicentre and may receive long warning times. Predicting the shaking from large earthquakes, however, requires some estimate of the likelihood of the future evolution of an ongoing rupture. An EEW system that anticipates future rupture using the present magnitude (or rupture length) together with the Gutenberg-Richter frequencysize statistics will likely never predict a large earthquake, because of the rare occurrence of ‘extreme events’. However, it seems reasonable to assume that large slip amplitudes increase the probability for evolving into a large earthquake. To investigate the relationship between the slip and the eventual size of an ongoing rupture, we simulate suites of 1-D rupture series from stochastic models of spatially heterogeneous slip. We find that while large slip amplitudes increase the probability for the continuation of a rupture and the possible evolution into a ‘Big One’, the recognition that rupture is occurring on a spatially smooth fault has an even stronger effect.We conclude that anEEWsystem for large earthquakes needs some mechanism for the rapid recognition of the causative fault (e.g., from real-time GPS measurements) and consideration of its ‘smoothness’. An EEW system for large earthquakes on smooth faults, such as the San Andreas Fault, could be implemented in two ways: the system could issue a warning, whenever slip on the fault exceeds a few metres, because the probability for a large earthquake is high and strong shaking is expected to occur in large areas around the fault. A more sophisticated EEW system could use the present slip on the fault to estimate the future slip evolution and final rupture dimensions, and (using this information) could provide probabilistic predictions of seismic ground motions along the evolving rupture. The decision on whether an EEW system should be realized in the first or in the second way (or in a combination of both) is user-specific

PreSEIS: A Neural Network-Based Approach to Earthquake Early Warning for Finite Faults

The major challenge in the development of earthquake early warning (EEW) systems is the achievement of a robust performance at largest possible warning time. We have developed a new method for EEW—called PreSEIS (Pre-SEISmic)—that is as quick as methods that are based on single station observations and, at the same time, shows a higher robustness than most other approaches. At regular timesteps after the triggering of the first EEW sensor, PreSEIS estimates the most likely source parameters of an earthquake using the available information on ground motions at different sensors in a seismic network. The approach is based on two-layer feed-forward neural networks to estimate the earthquake hypocenter location, its moment magnitude, and the expansion of the evolving seismic rupture. When applied to the Istanbul Earthquake Rapid Response and Early Warning System (IERREWS), PreSEIS estimates the moment magnitudes of 280 simulated finite faults scenarios (4.5≤M≤7.5) with errors of less than ±0.8 units after 0.5 sec, ±0.5 units after 7.5 sec, and ±0.3 units after 15.0 sec. In the same time intervals, the mean location errors can be reduced from 10 km over 6 km to less than 5 km, respectively. Our analyses show that the uncertainties of the estimated parameters (and thus of the warnings) decrease with time. This reveals a trade-off between the reliability of the warning on the one hand, and the remaining warning time on the other hand. Moreover, the ongoing update of predictions with time allows PreSEIS to handle complex ruptures, in which the largest fault slips do not occur close to the point of rupture initiation. The estimated expansions of the seismic ruptures lead to a clear enhancement of alert maps, which visualize the level and distribution of likely ground shaking in the affected region seconds before seismic waves will arrive

Real-time Finite Fault Rupture Detector (FinDer) for large earthquakes

To provide rapid estimates of fault rupture extent during large earthquakes, we have developed the Finite Fault Rupture Detector algorithm, ‘FinDer’. FinDer uses image recognition techniques to detect automatically surface-projected fault ruptures in real-time (assuming a line source) by estimating their current centroid position, length L, and strike θ. The approach is based on a rapid high-frequency near/far-source classification of ground motion amplitudes in a dense seismic network (station spacing <50 km), and comparison with a set of pre-calculated templates using ‘Matching by Correlation’. To increase computational efficiency, we perform the correlation in the wavenumber domain. FinDer keeps track of the current dimensions of a rupture in progress. Errors in L are typically on the same order as station spacing in the network. The continuously updated estimates of source geometries as provided by FinDer make predicted shaking intensities more accurate and thus more useful for earthquake early warning, ShakeMaps, and related products. The applicability of the algorithm is demonstrated for several recorded and simulated earthquakes with different focal mechanisms, including the 2009 M_w 6.3 L’Aquila (Italy), the 1999 M_w 7.6 ChiChi (Taiwan) and the M_w 7.8 ShakeOut scenario earthquake on the southern San Andreas Fault (California)

Rapid Source Parameter Estimations of Southern California Earthquakes Using PreSEIS

Earthquake early warning (EEW) systems provide real-time estimates of earthquake source and ground motion parameters to users before strong ground shaking occurs at sites of interest (Kanamori et al. 1997; Kanamori 2005). They make use of the fact that the most destructive ground shaking during an earthquake is caused by S- and surface waves, which travel much slower than P waves and also slower than electromagnetic signals carrying warnings to potential users. Real-time information systems can minimize loss of life and property damage and are therefore an important tool in short-term seismic hazard mitigation and disaster management (Wenzel et al. 2001). If an alarm can be issued seconds before the onset of the strong ground motions, automatic emergency actions can be initiated such as slowing down high speed trains or shutting down computers or gas distribution, for instance (Goltz 2002). EEW systems are of two main types, regional and on-site. The former uses a dense network of seismic stations to locate the earthquake, determine its magnitude, and estimate the ground motion at given sites of interest. The latter uses the observations at a single sensor to estimate the ensuing ground motion at the same site (Kanamori 2005). While regional systems work more accurately, they need more time to estimate earthquake source parameters

Evaluation and optimization of seismic networks and algorithms for earthquake early warning – the case of Istanbul (Turkey)

Earthquake early warning (EEW) systems should provide reliable warnings as quickly as possible with a minimum number of false and missed alarms. Using the example of the megacity Istanbul and based on a set of simulated scenario earthquakes, we present a novel approach for evaluating and optimizing seismic networks for EEW, in particular in regions with a scarce number of instrumentally recorded earthquakes. We show that, while the current station locations of the existing Istanbul EEW system are well chosen, its performance can be enhanced by modifying the parameters governing the declaration of warnings. Furthermore, unless using ocean bottom seismometers or modifying the current EEW algorithm, additional stations might not lead to any significant performance increase

CyberShake-derived ground-motion prediction models for the Los Angeles region with application to earthquake early warning

Real-time applications such as earthquake early warning (EEW) typically use empirical ground-motion prediction equations (GMPEs) along with event magnitude and source-to-site distances to estimate expected shaking levels. In this simplified approach, effects due to finite-fault geometry, directivity and site and basin response are often generalized, which may lead to a significant under- or overestimation of shaking from large earthquakes (M > 6.5) in some locations. For enhanced site-specific ground-motion predictions considering 3-D wave-propagation effects, we develop support vector regression (SVR) models from the SCEC CyberShake low-frequency (415 000 finite-fault rupture scenarios (6.5 ≤ M ≤ 8.5) for southern California defined in UCERF 2.0. We use CyberShake to demonstrate the application of synthetic waveform data to EEW as a ‘proof of concept’, being aware that these simulations are not yet fully validated and might not appropriately sample the range of rupture uncertainty. Our regression models predict the maximum and the temporal evolution of instrumental intensity (MMI) at 71 selected test sites using only the hypocentre, magnitude and rupture ratio, which characterizes uni- and bilateral rupture propagation. Our regression approach is completely data-driven (where here the CyberShake simulations are considered data) and does not enforce pre-defined functional forms or dependencies among input parameters. The models were established from a subset (∼20 per cent) of CyberShake simulations, but can explain MMI values of all >400 k rupture scenarios with a standard deviation of about 0.4 intensity units. We apply our models to determine threshold magnitudes (and warning times) for various active faults in southern California that earthquakes need to exceed to cause at least ‘moderate’, ‘strong’ or ‘very strong’ shaking in the Los Angeles (LA) basin. These thresholds are used to construct a simple and robust EEW algorithm: to declare a warning, the algorithm only needs to locate the earthquake and to verify that the corresponding magnitude threshold is exceeded. The models predict that a relatively moderate M6.5–7 earthquake along the Palos Verdes, Newport-Inglewood/Rose Canyon, Elsinore or San Jacinto faults with a rupture propagating towards LA could cause ‘very strong’ to ‘severe’ shaking in the LA basin; however, warning times for these events could exceed 30 s