Alert Optimization of the PLUM Earthquake Early Warning Algorithm for the Western United States

ABSTRACT We determine an optimal alerting configuration for the propagation of local undamped motion (PLUM) earthquake early warning (EEW) algorithm for use by the U.S. ShakeAlert system covering California, Oregon, and Washington. All EEW systems should balance the primary goal of providing timely alerts for impactful or potentially damaging shaking while limiting alerts for shaking that is too low to be of concern (precautionary alerts). The PLUM EEW algorithm forward predicts observed ground motions to nearby sites within a defined radius without accounting for attenuation, avoiding the earthquake source parameter estimation step of most EEW algorithms. PLUM was originally developed in Japan where the alert regions and ground motions for which alerts are issued differ from those implemented by ShakeAlert. We compare predicted ground motions from PLUM to ShakeMap-reported ground motions for a set of 22 U.S. West Coast earthquakes of magnitude 4.4–7.2 and evaluate available warning times. We examine a range of prediction radii (20–100 km), thresholds used to issue an alert (alert threshold), and levels of impactful or potentially damaging shaking (target threshold). We find optimal performance when the alert threshold is close to the target threshold, although higher target ground motions benefit from somewhat lower alert thresholds to ensure timely alerts. We also find that performance, measured as the cost reduction that a user can achieve, depends on the user’s tolerance for precautionary alerts. Users with a low target threshold and high tolerance for precautionary alerts achieve optimal performance when larger prediction radii (60–100 km) are used. In contrast, users with high target thresholds and low tolerance for precautionary alerts achieve better performance for smaller prediction radii (30–60 km). Therefore, setting the PLUM prediction radius to 60 km balances the needs of many users and provides warning times of up to ∼20 s.

Using LSTM Neural Networks for Onsite Earthquake Early Warning

Onsite earthquake early warning (EEW) systems determine possible destructive S waves solely from initial P waves and issue alarms before heavy shaking begins. Onsite EEW plays a crucial role in filling in the blank of the blind zone near the epicenter, which often suffers the most from disastrous ground shaking. Previous studies suggest that the peak P-wave displacement amplitude (Pd) may serve as a possible indicator of destructive earthquakes. However, the attempt to use a single indicator with fixed thresholds suffers from inevitable errors because the diversity in travel paths and site effects for different stations introduces complex nonlinearities. In addition, the short warning time poses a threat to the validity of EEW. To conquer the aforementioned problems, this study presents a deep learning approach employing long short-term memory (LSTM) neural networks, which can produce a highly nonlinear neural network and derive an alert probability at every time step. The proposed LSTM neural network is then tested with two major earthquake events and one moderate earthquake event that occurred recently in Taiwan, yielding the results of a missed alarm rate of 0% and a false alarm rate of 2.01%. This study demonstrates promising outcomes in both missed alarms and false alarms reduction. Moreover, the proposed model provides an adequate warning time for emergency response.

Efficacy and Usefulness of an Independent Public Earthquake Early Warning System: A Case Study—The Earthquake Network Initiative in Peru

Public earthquake early warning (PEEW) systems are intended to reduce individual risk by warning people ahead of shaking and allowing them to take protective action. Yet very few studies have assessed their actual efficacy from a risk-reduction perspective. Moreover, according to these studies, a majority of people do not undertake safety actions when receiving the warning. The spectrum of PEEW systems has expanded, with a greater diversity of actors (from citizens to private companies), increased independence from national authorities, and greater internationality. Beyond differences in warning and messaging strategies, systems’ characteristics may impact the way the public perceive, trust, understand, and respond to these warnings, which in turn will influence PEEW systems’ efficacy and perceived usefulness, enhancing the need for additional research. We take the example of earthquake network, an independent, voluntary, community-based and free system that offers a PEEW service. Through a quantitative survey (n = 2625), we studied users’ perception and reaction to a warning sent related to an M 8.0 earthquake in Peru (where no national system existed). We observed that even though only a minority of users actually took protective action, the system was appreciated and perceived as useful by the majority because it enabled mental preparation before the shaking. We found evidence for a tolerance for perceived late, missed, and false alerts. However, because it is a voluntary and independent system, the social dimension of the warning was incomplete because only a fringe of the population benefited from the warning. Therefore, many users’ first reaction was to warn their relatives. We discuss the need for partnerships between PEEW operators and national authorities to guarantee universal access to the service and maximize PEEW system efficacy.

The Impact of 3D Finite-Fault Information on Ground-Motion Forecasting for Earthquake Early Warning

ABSTRACT We identify aspects of finite-source parameterization that strongly affect the accuracy of estimated ground motion for earthquake early warning (EEW). EEW systems aim to alert users to impending shaking before it reaches them. The U.S. West Coast EEW system, ShakeAlert, currently uses two algorithms based on seismic data to characterize the earthquake’s location, magnitude, and origin time, treating it as a point or line source. From this information, ShakeAlert calculates shaking intensity and alerts locations where shaking estimates exceed a threshold. Several geodetic EEW algorithms under development would provide 3D finite-fault information. We investigate conditions under which this information produces sufficiently better intensity estimates to potentially improve alerting. Using scenario crustal and subduction interface sources, we (1) identify the most influential source geometry parameters for an EEW algorithm’s shaking forecast, and (2) assess the intensity alert thresholds and magnitude ranges for which more detailed source characterization affects alert accuracy. We find that alert regions determined using 3D-source representations of correct magnitude and faulting mechanism are generally more accurate than those obtained using line sources. If a line-source representation is used and magnitude is calculated from the estimated length, then incorrect length estimates significantly degrade alert region accuracy. In detail, the value of 3D-source characterization depends on the user’s chosen alert threshold, tectonic regime, and faulting style. For the suite of source models we tested, the error in shaking intensity introduced by incorrect geometry could reach levels comparable to the intrinsic uncertainty in ground-motion calculations (e.g., 0.5–1.3 modified Mercalli intensity [MMI] units for MMI 4.5) but, especially for crustal sources, was often less. For subduction interface sources, 3D representations substantially improved alert area accuracy compared to line sources, and incorrect geometry parameters were more likely to cause error in calculated shaking intensity that exceeded uncertainties.