U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2018–1131

Research to Improve ShakeAlert Earthquake Early Warning 

Products and Utility 

48 seconds

Light shaking

2% g 6%g

31 seconds

Moderate shaking

6% g

14 seconds

Strong shaking

14% g

PACIFIC 

OCEAN

PACIFIC 

OCEAN

PACIFIC 

OCEAN

San Francisco San Francisco San Francisco
Cover. Maps of potential warning times for different levels of expected ground motion in the city of San Francisco, California, from an 

evolving rupture on the northern San Andreas Fault. Background color is the peak ground acceleration with red indicating severe or higher 

ground motions. Panels from left to right show the progression of the earthquake as it grows in size and ruptures closer to the city. The 

expected ground motions in San Francisco become large only when the rupture approaches the city. The final size and rupture extent of an 

earthquake cannot be predicted in advance, so warning times for strong ground motion will be short. Longer warning times are possible if a 

lower threshold is used to issue an alert. Based on results from Minson and others (2018). %, percent; g, gravitational acceleration.
Research to Improve ShakeAlert Earthquake 

Early Warning Products and Utility

By Elizabeth S. Cochran, Brad T. Aagaard, Richard M. Allen, Jennifer Andrews, 

Annemarie S. Baltay, Andrew J. Barbour, Paul Bodin, Benjamin A. Brooks, 

Angela Chung, Brendan W. Crowell, Douglas D. Given, Thomas C. Hanks, J. 

Renate Hartog, Egill Hauksson, Thomas H. Heaton, Sara McBride, Men-Andrin 

Meier, Diego Melgar, Sarah E. Minson, Jessica R. Murray, Jennifer A. Strauss, 

and Douglas Toomey

Open-File Report 2018–1131

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

RYAN K. ZINKE, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2018

For more information on the USGS—the Federal source for science about the Earth,

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endorsement by the U.S. Government.

Although this information product, for the most part, is in the public domain, it also may

contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items

must be secured from the copyright owner.

Suggested citation:

Cochran, E.S., Aagaard, B.T., Allen, R.M., Andrews, J., Baltay, A.S., Barbour, A.J., Bodin, P., Brooks, B.A., Chung, A., 

Crowell, B.W., Given, D.D., Hanks, T.C., Hartog, J.R., Hauksson, E., Heaton, T.H., McBride, S., Meier, M-A., Melgar, 

D., Minson, S.E., Murray, J.R., Strauss, J.A., and Toomey, D., 2018, Research to improve ShakeAlert earthquake early 

warning products and their utility: U.S. Geological Survey Open-File Report 2018–1131, 17 p., https://doi.org/10.3133/

ofr20181131. 

ISSN 2331-1258 (online)
iii

Acknowledgments

The authors thank those who provided input during the development and revision of this 

document, including Ruth Harris, Stephen Hickman, Keith Knudsen, William Leith, and 

Andrew Michael. 

Contents

Executive Summary.......................................................................................................................................1

Introduction.....................................................................................................................................................2

Design of the ShakeAlert Operational System ................................................................................2

Current System Performance and Identified Needs.......................................................................3

Summary of Research and Development Opportunities................................................................3

Challenge 1—Understand EEW Capabilities and User Needs ..............................................................3

Theoretical Capabilities of EEW Systems.........................................................................................4

System Performance Metrics and Uncertainty Measures............................................................4

Public Alert Messaging........................................................................................................................5

Technical User Needs and Development of a Cost-Benefit Framework.....................................5

Alert Dissemination Technologies .....................................................................................................6

Evolution of Alert Information and Messaging ................................................................................6

Integrating ShakeAlert and Real-Time Earthquake Information Products .................................6

Challenge 2—Make Alerts as Fast and Accurate as Possible ..............................................................7

Improved Ground-Motion Predictions ..............................................................................................7

Improved Identification of Seismic Phases in Seismograms........................................................8

Development of Ground-Motion-Based Alert Methods.................................................................8

Increased Quality of Point-Source Algorithm Ground-Shaking Predictions ..............................9

Increased Quality of Finite-Fault Algorithm Predictions ................................................................9

Propagation of Uncertainties and Development of a Bayesian Framework ..............................9

Challenge 3—Ensure Reliability When It Counts ...................................................................................10

Appropriate Historical Datasets for System Performance Testing............................................10

Synthetic Waveforms for Performance Testing and Evaluation.................................................10

Expecting the Unexpected ................................................................................................................10

Challenge 4—Explore the Use of New Instrumentation .......................................................................11

Low-cost instrumentation .................................................................................................................11

Offshore Instrumentation ..................................................................................................................12

Strainmeters ........................................................................................................................................12

Conclusions...................................................................................................................................................13

References Cited..........................................................................................................................................13
iv

Abbreviations

ANSS Advanced National Seismic System

BART Bay Area Rapid Transit

BSM borehole strainmeter

EEW earthquake early warning

GMPE ground-motion prediction equation

GMICE ground-motion-to-intensity conversion equation

GNSS global navigation satellite system

GPS global positioning system

IPE intensity prediction equation

JCCEO Joint Committee on Communication, Education, and Outreach

MEMS microelectromechanical system

MMI Modified Mercalli Intensity

OBS ocean-bottom seismometers

PBO Plate Boundary Observatory

PDS peak dynamic strain

PGA peak ground acceleration

PGV peak ground velocity

PGD peak ground displacement

UC University of California

USGS U.S. Geological Survey

XML extensible markup language
Executive Summary

Earthquake early warning (EEW) is the rapid detection of 

an earthquake and issuance of an alert or notification to people 

and vulnerable systems likely to experience potentially damaging 

ground shaking. The level of ground shaking that is considered 

damaging is defined by the specific application; for example, 

manufacturing equipment may experience damage at a lower 

intensity ground shaking than would cause damage to a building. 

Along the West Coast of the United States, the warning times 

for ground shaking could range as high as tens of seconds for 

moderate levels of ground shaking, or potentially longer, if a 

lower ground-shaking threshold is used to issue alerts. However, 

it is not always possible to provide advance warning of ground 

shaking, particularly for locations close to an earthquake that are 

most likely to experience very strong ground shaking. EEW alerts 

may be useful to individuals who can use a few seconds to move 

to a safe zone and to electromechanical systems that can take 

automatic actions to reduce damage and injuries. An EEW system, 

ShakeAlert, has been under development in the United States 

since 2006. Federal and State governments, as well as the private 

sector, are now investing in the ShakeAlert prototype system that 

will, when completed, become an operational public system for 

the West Coast of the United States.

While the current prototype is delivering alerts to test users, 

improvements to the accuracy, timeliness, and utility of the alerts 

are needed. For this reason, it is essential that the ShakeAlert 

system be continuously improved through targeted research, 

involving not only the current ShakeAlert partner organizations, 

but also the broader scientific, engineering, and emergencyresponse communities. To this end, this report describes the 

opportunities for improvement that can be addressed through 

research and development over the next 5 years.

Our recommendations are organized into four areas: (1) 

understand EEW capabilities and user needs, (2) make alerts 

as fast and accurate as possible, (3) ensure reliability when it 

counts, and (4) explore the use of new instrumentation. 

The first challenge is to understand EEW capabilities 

and user needs. EEW must deliver actionable information to 

Research to Improve ShakeAlert Earthquake Early 

Warning Products and Utility 

By Elizabeth S. Cochran, Brad T. Aagaard, Richard M. Allen, Jennifer Andrews, Annemarie S. Baltay, Andrew 

J. Barbour, Paul Bodin, Benjamin A. Brooks, Angela Chung, Brendan W. Crowell, Douglas D. Given, Thomas 

C. Hanks, J. Renate Hartog, Egill Hauksson, Thomas H. Heaton, Sara McBride, Men-Andrin Meier, Diego 

Melgar, Sarah E. Minson, Jessica R. Murray, Jennifer A. Strauss, and Douglas Toomey

people and to automated systems to mitigate short- and longterm impacts of damaging ground shaking, so development 

of EEW must be motivated by the needs of users. Within 

this challenge, we must study the technical capabilities and 

limitations of EEW in general, and the ShakeAlert system 

specifically. This includes development of performance 

metrics that assess the timeliness and accuracy of alerts to 

understand the value and utility of the ShakeAlert EEW 

product(s) for various user groups, including different 

industry sectors, emergency-management agencies, and the 

public. Research is needed to define the alerting choices 

that maximize the utility of the system for users and to 

determine what the available communication pathways are for 

providing timely alert information. Additionally, we engage 

users to assess how alerts will be used by different sectors to 

mitigate losses and to inform EEW product design. Further, 

social-science research is needed to develop alert messaging, 

including what relevant prior and follow-up information are 

required, to ensure effective use of alerts. 

The second challenge is to make alerts as fast and as 

accurate as possible. The timeliness and accuracy of an EEW 

alert is important because it will set in motion a series of 

actions and downstream products. An EEW alert will trigger 

notification across emergency-alert systems and across multiple 

communication channels to populations in impacted regions. 

The EEW alert region may grow as the earthquake fault-rupture 

length increases, and the EEW system’s characterization of 

it, evolves. We must continue research into new or improved 

seismic and geodetic waveform-processing methods necessary to 

rapidly characterize the expected ground shaking and associated 

uncertainties. It is important to thoroughly evaluate whether new 

methods improve alerts through more accurate ground-motion 

estimates and (or) reduced latencies (that is, longer warning times). 

New methods could include tracking the extent of a large rupture 

in real time (known as finite-fault algorithms) and ground-motionbased EEW algorithms. Additionally, ground motion predictions 

could be optimized for each earthquake as the earthquake fault 

rupture progresses by using, for example, event terms to shift 

ground-motion curves for more (or less) energetic ruptures. 
2 Research to Improve ShakeAlert Earthquake Early Warning Products and Utility

The third challenge is to ensure reliability when it counts. 

This challenge requires us to explore approaches that assess 

the expected performance of ShakeAlert across the range of 

earthquake magnitudes, locations, and depths that may occur 

within the alerting region. Large, damaging earthquakes and 

their associated aftershock sequences matter most for hazard 

and for EEW, but these large-earthquake sequences occur 

infrequently. We expect ShakeAlert to respond robustly to 

these large-earthquake sequences despite potentially long 

periods of relative seismic quiescence in the intervening years, 

and in spite of inevitable communication challenges that 

arise during and after a large earthquake. We must develop 

methods to utilize the broadest available datasets to test EEW 

performance, including ground-motion data recorded in 

other parts of the world. The observational period for large, 

damaging earthquakes in any particular region has been short 

in comparison to estimated large-earthquake recurrence times. 

Ground-motion records for very large, damaging western 

United States events and major aftershock sequences do not 

yet exist, nor do data exist for all potential sources of noise 

and spurious signals that ShakeAlert must be “tuned” to reject. 

In addition, robust synthetic data could provide the flexibility 

to test a wider range of earthquake magnitude, tectonic-setting, 

and noise scenarios than are covered by existing observational 

data. Synthetic ground-motion data must be thoroughly vetted 

against records of smaller magnitude earthquakes to ensure 

that they accurately capture both the onset and the amplitude 

of the ground shaking. 

The final challenge is to explore the use of new 

instrumentation. The development of EEW around the world 

to date has focused on the use of high-quality, scientific-grade 

seismic and geodetic instrumentation. The use of additional 

types of instrumentation or information may also improve 

EEW products by filling gaps in sensor coverage in countries 

that already have dense seismic networks or enable EEW 

in countries without such networks. We must keep up with 

these developments and continuously assess their value in 

supplementing existing EEW systems, such as ShakeAlert, 

or enabling EEW where such systems do not exist. Such 

developments include low-cost instrumentation with 

microelectromechanical system (MEMS) sensors and global 

positioning system (GPS)/global navigation satellite system 

(GNSS) antennas embedded in low-cost consumer electronics, 

sea-floor seismometers, geodetic instrumentation deployed 

along the Cascadia and Alaska megathrust margins of western 

North America, and borehole strainmeters that are already 

deployed across the region.

Introduction

Earthquake early warning (EEW) systems propose to 

trigger actions that may mitigate losses and reduce injuries 

from earthquakes (for example, Nakamura, 1988; Wu and 

others, 1998; Kanamori, 2005; Strauss and Allen, 2016). 

EEW can provide up to tens of seconds of warning for 

moderate ground shaking (Modified Mercalli Intensity [MMI] 

IV–VI) (Meier and others, 2017; Minson and others, 2018); 

however, it is not always possible to provide advance warning 

particularly for locations close to an earthquake that may 

experience very strong ground shaking. EEW systems are 

operational in several countries around the world, including 

Mexico, Japan, Turkey, China, Taiwan, Italy, and Romania 

(Espinosa-Aranda and others, 1995; Hoshiba and others, 

2008; Allen and others, 2009). ShakeAlert is an EEW system 

being developed for high-seismic-risk areas in the United 

States (Given and others, 2014). ShakeAlert development 

began in 2006 and is currently operating as a prototype 

system in California, Washington, and Oregon (Kohler and 

others, 2018). In 2018, ShakeAlert will begin a gradual public 

rollout, and expansion of public alerting will occur after the 

system attains the necessary sensor coverage, system alert 

performance metrics are achieved, and training and education 

of users is completed. 

Design of the ShakeAlert Operational System 

The ShakeAlert system uses the earthquake-monitoring 

infrastructure of the Advanced National Seismic System 

(ANSS), a nationwide group of cooperating seismic networks 

coordinated by the U.S. Geological Survey (USGS) (Given 

and others, 2014). Additional investment and development are 

being undertaken to bolster the ability of the seismic networks 

on the West Coast of the United States to provide the rapid 

and dense ground-motion observations necessary for issuing 

earthquake alerts. Sufficient density (seismic-station spacing 

of ~20 kilometers [km] or less) of recording sensors is needed 

near earthquake-generating faults that can generate strong 

ground shaking (Kuyuk and Allen, 2013). Communication of 

ground-motion data from field stations to central processing 

sites, with minimal latencies, has become possible through 

upgrades to data handling (for example, sending seismograms 

in second or sub-second data packets) and improved telemetry 

(that is, conversion of dial-up modems to radio and [or] 

cellular modems). The seismic stations that contribute to 

ShakeAlert send continuous real-time data back to processing 

centers by some combination of radio, government-owned 

microwave, commercial cellular, commercial Internet, 

commercial frame-relay, satellite, and partner-owned telemetry 

systems. Further improvements are planned to ensure that 

seismic-network design and telemetry is optimized for speed, 

reliability, and redundancy.

EEW seismic-data processing and the generation of 

alerts takes place at ANSS regional network centers. Groundmotion data are transmitted to the nearest regional ANSS 

center where EEW-relevant parameters are computed. 

These EEW parameters are then shared between regional 

network centers for redundant EEW alert generation. The 

ShakeAlert operational system for the 2018 gradual public 

rollout includes an earthquake “point-source” algorithm that 
Challenge 1—Understand EEW Capabilities and User Needs 3

provides an estimate of the location, magnitude, and origin 

time of detected earthquakes and a “finite-fault” algorithm 

that estimates the rupture extent and magnitude of larger 

earthquakes. These algorithms characterize an evolving 

earthquake fault rupture by processing seismic-waveform 

data to detect significant ground shaking and then measure 

EEW-relevant parameters such as peak ground shaking and 

the frequency content of the ground shaking. Alert messages 

are formatted with extensible markup language (XML) and 

distributed via the Internet to ShakeAlert users. 

Current System Performance and Identified Needs

The Testing and Certification Platform evaluates the 

performance of the ShakeAlert system using analysis of 

alerts generated by real-time data and by replay of a test suite 

of waveforms from historical earthquakes and problematic 

events (noise spikes, teleseisms, and so forth) (see Cochran 

and others, 2018, for more information). The ShakeAlert 

real-time prototype system achieved several milestones over 

the past few years. The ShakeAlert system detected the 2014 

magnitude (M) 6.0 South Napa, California, earthquake, and 

provided an alert 5 seconds before the S-wave arrival to the 

University of California police on the UC Berkeley campus, 

which experienced MMI IV at a distance of 38 km from the 

epicenter of the South Napa earthquake. An alert was also 

issued 8 seconds before the S-wave arrival to the emergency 

operations center in San Francisco, which experienced MMI 

IV at a distance of 50 km from the epicenter. In response to 

the shaking alert, Bay Area Rapid Transit (BART) activated 

their automated train-slowing system; however, no trains were 

in operation at the time of the earthquake alert (at 3:24 a.m.) 

because the earthquake occurred in the early morning. 

In southern California, the ShakeAlert system detected 

the 2015 M3.8 earthquake in View Park (Los Angeles) that 

occurred at a depth of 11.1 km, resulting in an alert issued 

3.4 seconds after the origin time, shortly before the S-wave 

reached the Earth’s surface. Other successful examples include 

the 2018 M4.4 Hayward Fault earthquake for which the 

prototype ShakeAlert system issued alerts up to 12 seconds 

prior to human-felt ground motion (MMI II), and the 2018 

M5.3 Santa Cruz Island, California, earthquake for which the 

system provided as long as 29 seconds advance notification for 

human-felt ground motion (MMI II). 

Evaluations have also identified deficiencies in the 

ShakeAlert system. The system has issued false alerts caused 

by misinterpreting ground motion from one earthquake 

as several simultaneous earthquakes (“split events”) and 

incorrect triggering on noise, timing pulses, or teleseismic 

arrivals (Cochran and others, 2018). Some alerts are also of 

lower quality owing to large errors in estimated earthquakesource parameters (magnitude or location), typically in 

regions of sparse seismic-station coverage or near the edges 

of the seismic network. Alerts are occasionally not issued for 

M4.5+ earthquakes, referred to as missed events, though these 

usually occur in areas where the seismic-station density is low. 

Since the system went online in February 2016, there have 

been no missed events with magnitude M4.5 or greater near 

any major city on the West Coast. The six M4.5+ events that 

ShakeAlert failed to alert on between February 2016 and May 

2018 occurred in remote and (or) offshore regions where there 

were too few nearby seismic stations to identify the event. 

Additionally, some alerts have provided correct information, 

but were issued too late for users to benefit. ShakeAlert 

must not only accurately detect whether an earthquake is 

happening, but it must also accurately calculate the expected 

ground shaking in order to identify which regions or users to 

alert. Work is currently underway to assess the timeliness and 

accuracy of ShakeAlert ground-motion estimates.

Summary of Research and Development 

Opportunities

A number of research and development opportunities 

exist that will advance our understanding of EEW and improve 

the performance of the ShakeAlert system. We detail some of 

these opportunities here, formulating a set of challenges for 

the ShakeAlert system. These challenges are to (1) understand 

EEW capabilities and user needs, (2) make alerts as fast and 

accurate as possible, (3) ensure reliability when it counts, and 

(4) explore the use of new instrumentation. We believe these 

challenges can be met over the next 5 years. 

Challenge 1—Understand EEW 

Capabilities and User Needs 

The goal of the ShakeAlert system is to provide users 

with advance warning of potentially damaging ground motions 

so that the users, in turn, can take protective action. The level 

of ground shaking that is considered as damaging is defined 

by the specific application; for example, manufacturing 

equipment may experience damage at a much lower intensity 

(perhaps MMI IV) than would cause damage to a building 

(typically MMI VII+). As such, conducting research on the 

expected capabilities of EEW systems as well as the needs 

of a diverse user community are central to the design and 

distribution of alerts. 

During an earthquake, EEW algorithms generate a stream 

of information from the first detection of an earthquake to an 

expanding characterization of the seismic source that might 

include information about the extent of the fault rupture and 

the distribution of slip on the fault. This information is then 

converted into products (alerts) that are issued to users within 

geographic regions expected to experience ground shaking 

exceeding some threshold. The first challenges for EEW 

are identifying the scientific and technical capabilities of 

current EEW systems, the types of information most critical 

for users, how alerts can most efficiently be distributed, and 
4 Research to Improve ShakeAlert Earthquake Early Warning Products and Utility

how to integrate these alerts with other real-time earthquake 

information products. These challenges will require a 

broad collaboration among not only geoscientists, but also 

researchers in the fields of technology and social sciences.

Continued and expanded engagement between 

ShakeAlert project members and users is essential. Research 

and development are needed in the following broad areas:

• Theoretical capabilities of EEW systems;

• System performance metrics and uncertainty measures;

• Public alert messaging, including post-alert 

information;

• Technical user needs and development of a cost-benefit 

framework; 

• Alert dissemination technologies;

• Evolution of alert information and messaging; and

• Integrating ShakeAlert and other real-time earthquake 

information products.

Theoretical Capabilities of EEW Systems

Current work on theoretical capabilities of earthquakesource-based EEW methods indicates that theory-based 

limitations should be considered when designing an effective 

system, including considerations of the timeliness of alerts for a 

physically realistic evolution of earthquake fault rupture (Meier, 

2017; Minson and others, 2018) and the accuracy of groundmotion predictions. Additional work is required to assess the 

expected performance for various measures of ground motion, 

including peak ground acceleration (PGA), velocity (PGV), and 

displacement (PGD), and for different earthquake frequency/

magnitude distributions, regional attenuation, rupture behavior, 

site/basin characteristics, rupture directivity, and so forth. 

These predictions should be tested against observations, where 

available. Additionally, we must understand the advantages 

and disadvantages of algorithms that describe earthquakes 

using point-source parameters (for example, Allen and 

Kanamori, 2003; Allen, 2007; Wu and others, 2007; Allen 

and others, 2009; Böse and others, 2009; Kuyuk and Allen, 

2013), algorithms that describe earthquakes using finite-fault 

parameters (Böse and others, 2012; Allen and Ziv, 2011; 

Grapenthin and others, 2014; Minson and others, 2014; Crowell 

and others, 2016), and algorithms that predict expected ground 

motions from observed ground motions without determining 

earthquake magnitude, location, or rupture extent (for example, 

Hoshiba, 2013; Hoshiba and Aoki, 2015; Kodera and others, 

2016; Kodera and others, 2018; Yang and others, 2018). The 

results will allow for a greater understanding of the expected 

performance of EEW systems that predict ground shaking from 

source magnitude and location information as well as systems 

that directly predict expected ground-shaking intensities from 

the earliest shaking observations.

System Performance Metrics and Uncertainty 

Measures

An ideal EEW performance metric would be applicable 

to any kind of EEW algorithm and any type of earthquake 

scenario; it would also consider the evolving ground-motionprediction accuracies during an earthquake, from the first 

alert through to the final alert issued by the EEW system. 

Such a metric may be the basis for evaluating performance 

of algorithms in absolute terms (how good are the warnings 

that EEW systems can provide?) as well as relative to other 

algorithms or algorithm versions. This approach would 

enable the community to methodically evaluate the benefit of 

EEW system modifications on all scales (for example, new 

algorithms, new features of existing algorithms, and so forth), 

and to determine which alerting strategies lead to optimal 

outcomes. Furthermore, it may provide guidance on how 

ground-motion predictions from multiple algorithms should 

be combined to produce a more accurate ensemble groundshaking prediction. The metrics should also allow evaluation 

of the tradeoffs between gain and complexity when different 

variants of EEW algorithms are compared—implementation 

of a more complex algorithm is only warranted if it leads to a 

significant increase in warning time and accuracy of predicted 

ground shaking.

EEW-performance evaluation is evolving from proxy 

metrics, such as magnitude and location errors, to the actual 

target metric of EEW—predicted ground motion and its 

uncertainties. These ground-motion-based metrics help us 

answer these questions:

• How accurately can an EEW algorithm predict ground 

motions, and with what timeliness relative to the onset 

of strong ground motion? 

• How does this accuracy translate into correct, false, and 

missed alerts in threshold-based end-user applications?

Additionally, a performance assessment might consider weighting 

the performance by the local population density within the alert 

region in addition to the accuracy of ground-motion estimates 

and warning times. Continuing research will assess how the 

ShakeAlert system performs using the developed metrics, 

identify deficiencies in current EEW approaches, and use these to 

prioritize further improvements to the system. Additionally, socialscience research assessing user needs and perception will drive 

development of improved performance metrics. 

Realistic uncertainty measures should be determined that 

are consistent across all EEW algorithms. A shaking-intensity 

uncertainty is needed, as well as the likelihood that an alert 

constitutes a true event. These two uncertainty measures would 

capture both the historical performance of the EEW system as 

well as the information (for example, available data or quality 

of solution) from which the current alert is derived; these 

uncertainties must be combined and provided to technical 

users along with the alert for use in decision-making to initiate 

mitigating actions. 
Challenge 1—Understand EEW Capabilities and User Needs 5

Public Alert Messaging

Before an EEW system is used to issue public alerts, it 

is necessary to understand expectations from the public and 

to produce appropriate training material that maximize users’ 

understanding and effective use of the alerts (Kamigaichi and 

others, 2009). The relatively low seismicity rates along the West 

Coast compared to other nation’s regions with EEW systems mean 

that first-time public users in the United States will likely have 

limited, if any, experience of what “strong” ground shaking feels 

like and how EEW works. 

Investigating user needs and expectations of an EEW 

system is critical to the success of ShakeAlert. We do have 

some information about the needs of users from several studies 

conducted around the world. For example, several alerts were 

issued by Mexico’s EEW system (SASMEX; Espinosa-Aranda 

and others, 1995) in September 2017, including for the 2017 M7.1 

Puebla earthquake that caused significant damage in Mexico 

City. In October 2017, a reconnaissance team of seismologists 

and social scientists examined public perception of the Mexico 

EEW system (Allen and others, 2017). Their findings suggest 

that alerts for incoming shaking should be simple and only 

provide information that is immediately actionable, for example, a 

message like “earthquake shaking expected”; however, initial alert 

information should be followed rapidly by information about the 

earthquake that occurred and post-event actions that users might 

take. Additionally, follow-up messages should declare an alert 

as false (if it was) to minimize confusion. We must also consider 

what follow-up messaging is necessary for cases where an alert 

is not issued for an earthquake that the system was designed to 

detect, or for regions that may have not have received a timely 

alert prior to strong shaking. In a study of early ShakeAlert users 

in California (Johnson and others, 2016), most technical users plan 

to provide warnings to individuals (employees and [or] customers) 

rather than activate automated responses to reduce damage to 

equipment or other infrastructure. More information about users’ 

needs is necessary, but these studies point to the importance of 

investigating the appropriate alert thresholds, alert messaging, and 

technology pathways for rapid alert dissemination.

We must provide public users with enough information 

to know how to react before, during, and after an EEW alert. 

Advance education should include information about size of 

earthquakes and range of ground-shaking levels for which the 

system is designed to issue alerts. Additionally, it is critical to 

convey that a user may not always receive a timely warning 

or that they may receive a false alert (for example, an alert 

with no associated earthquake). The alert message itself must 

provide sufficient information to ensure understanding of the 

hazard and to encourage appropriate action. Rapid follow-up 

information may include how long to shelter after the alert 

has been received; this is especially true if a user does not feel 

ground shaking at their location. And, if shaking is strong, 

follow-up messages could provide information about expected 

aftershock information and (or) steps to take in the period 

immediately following the earthquake. The need to optimize 

the messaging is a reason why ShakeAlert needs to investigate 

end-user/public messaging. User messaging should be paired 

with new earthquake information and response products 

that could potentially help save lives and guide emergency 

response immediately after a large earthquake. Some of these 

new products are described in the section titled “Integrating 

ShakeAlert and Real-Time Earthquake Information Products” 

below. Some efforts may require coordination with other 

emergency management agencies (for example, Federal 

Emergency Management Agency and [or] State and local 

emergency-management agencies) to deliver public safety 

information, such as a response checklist with information 

about keeping phone lines free and stopping gas leaks, 

customized according to the user’s location and earthquake 

shaking intensity and impact.

 The ShakeAlert education and training is being 

developed by the Joint Committee on Communication, 

Education, and Outreach (JCCEO), a committee comprised 

of communications experts at USGS and partner universities 

as well as emergency managers in California, Oregon, and 

Washington. The JCCEO is currently developing a roadmap 

for implementation of a comprehensive and actionable training 

program for all audiences that integrates best practices from 

social-science research on hazard communications. This plan 

may need to consider potential differences between areas 

where alerts are distributed, such as a community’s perception 

of their seismic risk and (or) how often alerts are expected 

to be issued. It will also need to develop best practices for 

training various groups and communicating early warning 

to various communities, sectors, and a broad audience with 

diverse needs. 

Technical User Needs and Development of a 

Cost-Benefit Framework

To date, technical users are engaged through personto-person communication along the West Coast through the 

ShakeAlert JCCEO. A portal is available for technical users to 

request new alert capabilities and submit reports of technical 

problems. ShakeAlert should undertake regular (annual or 

bi-annual) feedback surveys of technical users and analyze the 

results to determine whether or not alert information aligns 

with user needs and to update performance metrics so that 

the system is evaluated (and improvements prioritized) based 

on maximizing user benefits. Surveys from other countries 

with EEW systems have provided information to guide future 

developments (Suárez and others, 2009; Nakamura and others, 

2013). The user-feedback surveys should be augmented 

by workshops for technical users within different industry 

sectors (for example, transportation, emergency management, 

communications, and so forth), where key stakeholders can 

discuss new advances in ShakeAlert science as well as their 

specific needs. A framework for collecting feedback from 

these workshops, extracting common areas of concern, and 

integrating the highest priority items into the ShakeAlert 

project-management plan will need to be formalized. 
6 Research to Improve ShakeAlert Earthquake Early Warning Products and Utility

Technical users must also be trained to understand how 

to maximize their benefits from the EEW system. The goal 

of this work is to give users a basic framework to undertake 

specific cost-benefit actions for their organization in order to 

determine whether and how to utilize EEW alerts. The utility 

of an EEW system requires consideration of the expected 

performance of the ShakeAlert system, the expected distribution 

of earthquakes, a user’s alerting and damage thresholds, and the 

required time for completing the mitigating action. This may 

require developing, in a generalized sense, user-specific cost 

functions for both the mitigating actions and amount of damage 

prevented. For example, ground-motion cost-based performance 

metrics could be applied to analyze the utility of EEW for 

various damage and alerting thresholds and for a range of 

expected earthquake ruptures, such as provided by the Uniform 

California Earthquake Rupture Forecast probabilities (Field and 

others, 2014) or similar studies. 

Alert Dissemination Technologies

Currently, the ShakeAlert system distributes an XMLformatted alert message via public Internet to a limited set of beta 

and pilot users. These users utilize ShakeAlert products via direct 

access to XML messages, or through use of computer or mobile 

applications that parse and display information contained in the 

XML messages. For automated response, these users typically 

want a direct connection to the XML-formatted alert messages 

that are disseminated via public Internet by the ShakeAlert servers. 

For alerting to individuals, ShakeAlert currently provides a 

demonstration computer application (UserDisplay) and a mobile 

phone application (MyEEW) that parse the XML messages and 

display alert information. So far, these Internet-push distribution 

methods are limited in the number and types of people they can 

reach and are therefore not sufficient for broad dissemination of 

alerts. However, UserDisplay may have a role to play in providing 

a comprehensive view of system information to technical users 

and key stakeholders. Research is needed to understand the 

needs of technical user groups to drive future development of 

the UserDisplay program and extrapolate what other users want 

beyond what is currently provided by UserDisplay to inform 

future design choices. 

Once public alerting is initiated, alerts must be efficiently 

disseminated to the public with minimal delays to ensure 

alerts get to people who need them; thus, the mechanisms by 

which ShakeAlert distributes products must be adapted and 

expanded. The ability to deliver alerts in a timely manner, 

release users after the warning has expired, and provide 

follow-up information explaining why the user did or did 

not receive an alert are all important to both the actual 

and perceived success of ShakeAlert. Research evaluating 

the alert latencies for current and developing distribution 

technologies is necessary; the results of this research will 

need to be regularly communicated to the JCCEO to guide 

ShakeAlert outreach. The single most important component 

of this task is to determine how ShakeAlert can distribute the 

information required for successful EEW alerting in a manner 

that is timely, scalable to millions of people, and robust during 

and following large earthquakes. Based on these results, we 

will need rapid internal technical development to implement 

the recommendations, and work closely with the JCCEO to 

educate the public on the types of information they should 

expect from ShakeAlert. While immediate large-scale activity 

is required to design and implement alerting technology for 

ShakeAlert before public rollout, it is important to recognize 

that this is not a one-time effort. As technology evolves and 

the ways people receive information changes, ShakeAlert will 

need to continuously reevaluate how it distributes alerts.

Evolution of Alert Information and Messaging

The planned EEW alert product is simple and conveys 

that earthquake shaking above a minimum threshold is 

expected in a region. Through feedback collected from users, 

we may learn that additional alert information would be 

beneficial. For example, users may want to know the peak 

amplitude of the shaking and (or) the expected arrival time 

for a given threshold of shaking at their location. Further, the 

duration over which ground motion exceeds a certain threshold 

is important for damage generation (for example, Nakamura 

and others, 2011) and for how shaking is perceived. Research 

is needed to explore whether existing or new methods could 

provide such information and what the expected uncertainties 

are on additional information. In addition, optimal EEWmessaging strategies for delivering more complex information 

will need to be developed. Examples include messages such 

as “expect shaking to continue for more than 10 seconds”, 

or sending an “event over” message based on the expected 

duration. Sending an “event over” message might also provide 

an opportunity to share subsequent information about the 

likelihood of aftershocks to better prepare people for what is 

likely to follow a large-magnitude event.

Integrating ShakeAlert and Real-Time 

Earthquake Information Products

Development of new science products that use 

information from ShakeAlert may be possible. For example, 

rapid magnitude estimates, finite-fault locations, and (or) 

surface-slip estimates could potentially be used to estimate 

surface displacement at critical lifeline infrastructure, create 

maps of real-time peak-ground shaking, improve tsunami 

warning, and forecast aftershock probabilities. These products 

should be driven by priorities identified from user and 

stakeholder feedback and will require development of new 

methodologies, testing, and eventual implementation of each 

new operational product. Each potential product requires a 

proof-of-concept demonstration and assessments by relevant 

users before evolving into an operational product through 

comprehensive testing and software-engineering support.
Challenge 2—Make Alerts as Fast and Accurate as Possible 7

Challenge 2—Make Alerts as Fast and 

Accurate as Possible

The quality of real-time ground motion warnings may 

vary significantly over time; quality may increase as more data 

become available upon which warnings can be based, but it 

may also decrease if an earthquake grows into a more complex 

event for which ground motion is difficult to predict until the 

earthquake fault rupture has grown to sufficient size. Depending 

on the EEW alert distribution pathway, the frequency at which 

updates can be delivered to users is expected to vary and is as 

yet unknown. It may take several seconds to distribute EEW 

alerts across a large region, possibly hindering the speed with 

which updates can be sent. And it is likely that different user 

groups may require diverse types of alert information, such as 

expected shaking intensities versus peak ground acceleration or 

spectral amplitudes, as seen with the Japanese warning system 

(Fujinawa and Noda, 2013).

The goal of EEW system design is to maximize the warning 

accuracy and timeliness at any given point in time, such that 

the most accurate ground-motion estimates are provided with 

maximum time before the arrival of shaking that exceeds a given 

ground-motion threshold. How EEW quality can be maximized 

may depend on the size of the target earthquake: for medium-size 

events (M5–7), algorithms can be relatively simple but need to be 

fast, because affected regions are close to the epicenter, whereas 

very large events (M7+) require more complex algorithms that 

can characterize effects on ground motions from long earthquake 

fault ruptures in real time. It is these largest earthquakes for which 

real-time geodetic data, for example, from high-rate scientific 

GNSS stations, have the most potential for earthquake sourcebased algorithms to improve EEW because of the tendency 

of earthquake-magnitude estimates from the initial P wave to 

saturate for M≥6.5 (Kanamori, 2005; Rydelek and Horiuchi, 2006; 

Hoshiba and others, 2011). EEW systems must be able to provide 

accurate warnings across the expected range of magnitudes, 

earthquake fault-rupture lengths, locations, and earthquake-source 

types (focal mechanisms, directivities, and fault slip) that produce 

damaging ground motions within the alert region.

In the context of this challenge, we suggest how the 

quality of EEW ground-motion predictions can be evaluated 

and outline strategies that have the potential to increase EEW 

quality for different kinds of scenarios. The key areas that 

need to be addressed are:

• Improved ground-motion predictions;

• Improved identification of seismic phases in seismograms;

• Development of ground-motion-based EEW alert 

methods;

• Increased quality of earthquake point-source algorithm 

predictions;

• Increased quality of earthquake finite-fault algorithm 

predictions; and

• Propagation of uncertainties and development of a 

Bayesian framework.

Improved Ground-Motion Predictions

The EEW system is inherently a ground-motion warning 

system—users care about the level of shaking they experience 

at their location. For algorithms that only provide an estimate 

of an earthquake magnitude and location, the expected 

ground motions must be estimated by using a ground-motion 

prediction equation. However, the method of simply applying 

an existing ground-motion prediction equation (GMPE) to an 

earthquake magnitude and location results in large inherent 

uncertainties in EEW ground-motion predictions at the user’s 

location (Allen, 2007). The choice of ground-motion models 

(GMPEs, ground motion to intensity conversion equations 

[GMICEs], and intensity prediction equations [IPEs]) is 

a crucial element in EEW systems, and there is room for 

improvement. The quality metrics suggested earlier will 

allow evaluation of which ground-motion models leads to the 

strongest EEW performance. 

Most existing ground-motion models have not been 

specifically designed for EEW applications, and they are 

often dominated by recordings made at distances of tens to a 

few hundred kilometers from earthquake-generating faults. 

Many GMPEs also include parameters not readily available 

in real time or are not modeled in EEW algorithms. For 

example, most GMPEs use the earthquake depth as a model 

parameter (for example, Abrahamson and others, 2014), as 

it is a major predictor of the energetics of the earthquake 

and, hence, observed ground motion, yet current ShakeAlert 

point-source algorithms use a fixed depth for all earthquakes. 

Consideration of the depth or making an adjustment when it 

is not known is critical for accurately predicting the ground 

motions. Additionally, most existing GMPEs and IPEs use 

rupture distance, not epicentral/hypocentral distance (for 

example, Abrahamson and others, 2014; Boore and others, 

2014) as the most meaningful source-to-receiver distance 

metric; yet, rupture-distance estimates for large earthquakes 

often only become available late in an EEW sequence when a 

finite-fault EEW algorithm comes into play. The ShakeAlert 

community should assess the potential performance 

improvements of using EEW-specific ground-motion models 

or the adaptation/optimization of existing relations. All 

ground-motion prediction models express strong variability 

of observed ground motion owing to variations in earthquake 

source parameters, seismic-wave propagation, and local site 

effects near the Earth’s surface; these uncertainties should 

be included both in determining optimal alert criteria and in 

communicating expected system performance to users.

Implementing specific source, site, or seismic-wave 

propagation path components into early warning GMPEs 

will reduce the uncertainty in ground-motion estimation, yet 

necessarily lead to increased complexity and computational 

time associated with these improvements. For example, 
8 Research to Improve ShakeAlert Earthquake Early Warning Products and Utility

including a source-specific term, akin to an earthquakeenergy stress drop or earthquake-magnitude adjustment, 

can reduce the uncertainty in the ground-motion prediction. 

Also, including finite-fault information, particularly for 

large-earthquake fault ruptures, will lead to more accurate 

near-field ground-motion prediction, which is of particular 

importance for EEW. The addition of wave-propagation 

directivity information as the rupture unfolds could improve 

our ground-motion accuracy, but may prove to be challenging. 

Additionally, we need to consider how to incorporate site 

parameters (for example, VS30, the shear-wave velocity in the 

upper 30 meters of Earth’s crust) or basin characterization 

in EEW to reduce uncertainties in ground-motion estimates. 

It is also important to consider how to best adapt current 

engineering-oriented GMPEs, which often include 

sophisticated terms that are typically not immediately known 

in an early warning situation, such as the differences in ground 

shaking on the upper versus lower sides of a dipping fault, 

known as the hanging-wall effect.

Improved Identification of Seismic Phases in 

Seismograms

In the current ShakeAlert system, EEW alerts using 

point-source algorithms are based on P-wave detections at a 

small number of seismic stations (Kohler and others, 2018). 

Therefore, EEW system performance can be enhanced by 

refining our ability to distinguish P-wave energy on seismic 

waveforms from all other sources of motion. Algorithms 

currently use a variety of waveform characteristics, but better 

approaches are needed.

The ShakeAlert algorithm uses a combination of methods 

to distinguish local events from regional or teleseismic 

earthquakes including filtering based on characteristic or 

peak period parameter and peak-displacement amplitude, 

setting minimum thresholds for different parameters, and 

requiring multiple stations to confirm events (Allen and 

Kanamori, 2003; Allen, 2007; Wu and others, 2007; Allen and 

others, 2009; Böse and others, 2009; Kuyuk and Allen, 2013; 

Hartog and others, 2016). A number of measures are used to 

distinguish earthquake signals from noise, but these methods 

occasionally admit environmental noise or teleseismic signals, 

or reject true earthquake arrivals (Hartog and others, 2016; 

Cochran and others, 2018). Further research aimed at noise 

discrimination at the single-station level is needed. Methods 

may include using either parametric information (for example, 

consistency of multiple waveform attributes such as peak 

ground motions, frequency content, and so forth) or machinelearning techniques to automatically categorize waveforms as 

local seismic phases or noise (for example, Ross and others, 

2018), to decrease alerting time by placing greater confidence 

in the earliest alerts, as well as to reduce false and missed 

earthquakes. The machine-learning techniques may require 

use of graphics processing units within a cloud that provides 

computing resources on demand. 

Future EEW software codes may require serverless 

architecture for improved performance in a cloud 

computational facility, which would enable the use of opensource software tools for a variety of routine tasks. Testing 

and developing EEW algorithms as a platform using modern 

software frameworks will enable EEW capacity to evolve 

with other emerging technologies. This is necessary to 

facilitate interoperability between EEW and emerging data 

communications and automatic decision tools deployed by our 

most sophisticated EEW users. 

The current point-source ShakeAlert algorithm requires 

phase discrimination so that only P-wave information is 

used and S waves are excluded. If S-wave detections are not 

correctly discarded, magnitude and location estimates may 

be erroneous and split-event alerts can occur. Split events 

are multiple alerts issued for a single event that are based on 

poor location and magnitude estimates (Hartog and others, 

2016; Cochran and others, 2018). Alternatively, when P-wave 

detections are incorrectly discarded, earthquake alerts have 

been delayed, degraded, or prevented (Hartog and others, 

2016). Current methods do not ideally distinguish between Pand S-wave phases. While there is ongoing research focused 

on using polarization analysis for P- and S-wave phase 

discrimination, additional techniques should be explored.

Development of Ground-Motion-Based Alert 

Methods

Ground-motion-based EEW alerting methods (for example, 

Hoshiba, 2013; Hoshiba and Aoki, 2015; Kodera and others, 2016; 

Yang and others, 2018) may provide rapid warnings for moderate 

to strong ground shaking and may have improved performance 

during aftershock sequences or other periods of high seismicity 

rates. The method deployed in Japan (Kodera and others, 2018) 

is straightforward: if strong ground motions are observed at a 

seismic station, an alert is immediately issued to all locations 

within ~30 km of that station, warning those regions to expect 

similar shaking levels. The approach may improve the accuracy of 

the ground-motion prediction relative to source-parameter-based 

approaches by automatically adjusting the median prediction to 

account for between-earthquake variability of ground motion (that 

is, the “event term” in GMPEs). Furthermore, ground-motionbased EEW methods are less likely to be confused at times when 

multiple earthquakes occur nearly simultaneously, such as during 

aftershock sequences (Kodera and others, 2018); they also may 

not suffer from the same failure modes of earthquake source-based 

methods, including split events and teleseisms mischaracterized 

as local earthquakes. Finally, the method is attractive for use with 

low-cost seismic sensors that can provide dense observations 

of PGA and PGV (Kodera and others, 2016). This method may 

be extended to include basic attenuation functions to estimate 
Challenge 2—Make Alerts as Fast and Accurate as Possible 9

expected ground motions at greater distances from the source. 

Further research is needed on ground-motion-based methods, 

including quantitative evaluation of potential gains in accuracy 

and timing of ground-motion warnings in comparison to other 

methods under consideration for use in ShakeAlert. 

Increased Quality of Point-Source Algorithm 

Ground-Shaking Predictions

A variety of strategies to improve point-source type EEW 

algorithm predictions have been proposed, but not all of them have 

been extensively tested or optimized based on ground-motionperformance metrics. Some of the strategies to consider are: 

• Better location estimates using, for example, real-time 

seismic-phase detection and azimuth estimation;

• Configurable parameter measurement windows, 

including use of variable-length windows to estimate 

parameters such as peak displacement (Pd) beyond 

their current limit (for example, 4 seconds); 

• The use of station corrections that correct for local site 

effects to improve magnitude estimates;

• Frequency-dependent ground-motion predictions (for 

example, low-frequency ground-motion prediction for 

large buildings, such as spectral acceleration); and

• Denser stations/redundant data—more seismic stations 

(than the “target” 10- to 20-km spacing) may make 

the detection faster and more accurate. Designing an 

optimal station density requires knowledge of how 

station density changes the timeliness of alerts and the 

quality of the expected ground-motion estimates.

Increased Quality of Finite-Fault Algorithm 

Predictions

Finite-fault earthquake source algorithms will be most 

important for M>7.0 earthquakes and subduction zone 

earthquakes. How good and how timely are ground motion 

predictions at sites that are far away from the hypocenter, yet 

close to the earthquake fault-rupture plane? 

Finite-fault methods that are currently under development 

involve both seismic and geodetic data. Seismic methods can 

identify regions in the near field of ruptures and track the evolving 

earthquake fault-rupture source (Böse and others, 2012; Böse and 

others, 2015; Böse and others, 2018). Through identification of the 

extent of the finite rupture, the predicted ground motions along an 

extended rupture are improved. Research is needed to understand 

how quickly the seismic-based algorithms identify significant 

finite-fault effects in the distribution of ground motion and 

optimize these methods to reduce latency and improve groundmotion predictions. 

Large static offsets associated with fault slip during 

earthquake ruptures can be measured with geodetic 

instrumentation in real time. Several algorithms that use GNSS 

data, alone or in combination with seismic records, have been 

developed for potential EEW applications (Allen and Ziv, 2011; 

Grapenthin and others, 2014; Minson and others, 2014; Crowell 

and others, 2016). Initial testing of these approaches demonstrates 

that geodetic information can enable more accurate and rapid 

magnitude and finite-fault estimates that, in turn, might improve 

alert accuracy for large (M>7.0) earthquakes. The scope of 

ShakeAlert products could also be expanded to include products 

that predict PGD and (or) peak dynamic strain (PDS). Quantitative 

evaluation of the tradeoff between accuracy and timeliness of 

shaking estimates derived from geodetically constrained source 

models, as well as the improvement to alerting offered by a joint 

seismic-geodetic system compared to a seismic-only system, 

is required in order to assess the value that geodetic data and 

methods would bring to the ShakeAlert system.

Propagation of Uncertainties and Development 

of a Bayesian Framework

Obtaining accurate and comparable estimates of uncertainties 

in source and (or) ground-motion information provided by each 

algorithm should be a priority. The relationship between the 

uncertainties must be compared with empirical uncertainties that 

can be evaluated with retrospective performance analysis of a large 

dataset to determine whether they agree. Also, analyses of how 

uncertainties change as an event evolves and more information 

becomes available are needed. 

With accurate time-dependent uncertainties available for 

all algorithm components, the entire system could be placed into 

a Bayesian framework. One approach, built on a probabilistic 

decision module, considers the probabilities of predicted 

ground motions through comparisons to available observed 

ground motions (for example, Minson and others, 2017). 

The underlying logic of such a probabilistic decision module 

should be developed and tested to study how real-time groundmotion predictions from different algorithms are optimally 

combined to generate better ensemble estimates. It may also 

be possible to consider prior seismic information, such as the 

locations of recent seismicity (that are potentially foreshocks) 

and known active faults. As a further step, the EEW system can 

use Bayesian decision theory to calculate the cost/benefit of 

proposed actions in real time (Wu and others, 2013, 2014). Thus, 

it is important to not only test whether ShakeAlert is providing 

timely and accurate information about expected ground-shaking 

intensity, but whether this information is being presented in 

the appropriate cost-benefit framework to enable decisions 

about whether or not a user should take an action (for example, 

stopping trains, extinguishing open flames, and so forth).
10 Research to Improve ShakeAlert Earthquake Early Warning Products and Utility

Challenge 3—Ensure Reliability When 

It Counts

ShakeAlert must perform well for large, potentially 

damaging earthquakes. The background rate of these events 

is once or twice per decade in the western continental United 

States, although such events are likely to cluster in mainshockaftershock sequences. A challenge for any EEW system is 

therefore to ensure the system’s performance is optimal in the 

instant that such an event (or cluster of events) occurs, and 

does not degrade in what may be many years between such 

events. Development of robust historical datasets and scenario 

waveforms is critical for ShakeAlert. We also have not 

observed the full spectrum of earthquakes (or sources of noise) 

for which ShakeAlert must be ready along the West Coast, so 

we must work to develop a robust system capable of handling 

the unexpected. Assuring reliability when it counts is divided 

into the following broad topics:

• Appropriate historical datasets for system performance 

testing;

• Synthetic waveforms for performance testing and 

evaluation; and

• Expecting the unexpected.

Appropriate Historical Datasets for System 

Performance Testing

Verification of algorithm performance is needed to determine 

if ShakeAlert products meet performance standards. The Testing 

and Certification Platform was developed in order to test new 

versions of ShakeAlert algorithms and (or) new algorithms prior 

to deployment in the production system. Evaluation of algorithms 

is undertaken using a suite of previously recorded waveforms of 

historical earthquakes in California, Oregon, and Washington, 

as well as teleseismic, noisy, or anomalous waveforms that have 

caused (or may be expected to cause) the system to issue false 

alerts (Cochran and others, 2018). Current test suites that are used 

in performance testing are limited in the magnitude ranges and 

focus on individual events rather than longer time series; therefore, 

it is necessary to examine the composition of the test data to 

ensure accurate assessment of system performance under a variety 

of conditions. For example, to address the limited number of large 

earthquakes, it may be important to consider inclusion of locally 

recorded data from earthquakes from around the world, such 

as those recorded in Japan, Mexico, and Taiwan. However, it is 

necessary to understand how these data will translate into accurate 

representations of ShakeAlert performance by considering 

differences in station distributions, tectonic setting, ground-motion 

attenuation and amplification, and other factors. Furthermore, 

it remains an open question whether it is necessary for the 

magnitude distribution of such data to match Gutenberg-Richter 

statistics (Gutenberg and Richter, 1944) for an accurate assessment 

of system performance at different alert and damage thresholds.

Synthetic Waveforms for Performance Testing 

and Evaluation

Large earthquakes are few and far between. Thus, in 

order to assess the performance of the system we may need 

to consider the use of scenario events and waveforms. This 

is especially important for the geodetic components of the 

system, for which there are only limited data from historical 

events (Ruhl and others, 2017). The challenge with generating 

synthetic waveforms for EEW testing is that they must contain 

both high-frequency energy to trigger seismic algorithms and 

low-frequency energy for testing geodetic algorithms, as well as 

accurately producing both P and S waves. 

There are now standard community hybrid methods for generating broadband S waves, which combine forward modeling (as 

much as 1 Hertz, Hz) and stochastic signals at higher frequencies 

(for example, Boore, 2003). Methods have also been developed to 

produce synthetic displacement waveforms appropriate for testing 

geodetic algorithms (Melgar and others, 2016). However, existing 

geodetic algorithms are typically triggered by the seismic component of the system; ongoing research is focused on incorporating 

high frequencies into these synthetics (for example, Yamada and 

Heaton, 2008; Ruhl and others, 2017; Wirth and others, 2017). 

Ideally, we need thousands of realistic slip patterns for many magnitudes (M6–8 in California and M7–9.2 in Cascadia), and associated waveforms that trigger the seismic-based systems and provide 

realistic data to the geodetic ones. Through systematic testing 

with a large number of synthetic events, and including multiple 

realizations of the same scenario event, we will gain a thorough 

understanding of each EEW algorithm’s strengths and weaknesses.

Testing protocols for validating scenario earthquakes and 

their resulting synthetic waveforms must also be established. 

Scenario earthquakes must obey realistic moment-release patterns 

as inferred from observations (for example, Melgar and Hayes, 

2017; Ye and others, 2016). Whereas synthetic waveforms have 

been validated in terms of metrics useful to the engineering 

seismology community, such as peak motions (PGV, PGA, and 

spectral accelerations) (for example, Goulet and others, 2014), 

these metrics are predominantly sensitive to features of the S 

waves and not the P waves. Minimal effort has been devoted 

to validating techniques for generating broadband synthetic 

waveforms for their use in testing EEW algorithms. Validation 

of existing suites of synthetic waveforms will help guide 

development of additional suites of earthquake scenarios and new 

techniques for generating synthetic waveforms.

Expecting the Unexpected

We must identify ways to expose ShakeAlert to 

unanticipated events, or scenarios that do not happen with 
Challenge 4—Explore the Use of New Instrumentation 11

sufficient regularity to be part of our current or historical 

testing. There is no simple solution to this problem, but 

there are opportunities to test ShakeAlert performance for 

an expanded range of events. We must also accept potential 

failure of the system owing to an unexpected or unprecedented 

event, and plan how to communicate information about such 

failures as rapidly as possible. 

One approach is to expand the synthetic ground motions 

described above to simulate large events and complex 

sequences, including aftershock sequences, foreshockmainshock events, and large offshore events (including 

Cascadia subduction events). As mentioned above, synthetic 

data are required to have sufficient high-frequency content to 

be realistic for the seismic algorithms, while also having the 

low-frequency content (including permanent displacements) 

required for geodetic algorithms. One challenge to this 

approach is in defining, based on available computational 

resources or other considerations, a limited set of appropriate 

scenarios for which to produce deterministic simulations. 

One clear case is for tsunami earthquakes for which the 

high-frequency ground shaking may be relatively depleted 

compared to the longer period amplitudes. We must also 

ensure a sufficiently agile system for producing synthetics that 

can be altered to reflect the current station distribution (or even 

test future states).

As testing datasets grow in size, it is desirable to develop 

the capability to test both long, continuous waveform records 

as well as hundreds or thousands of earthquake records rapidly. 

Current capabilities are limited because events must be run at 

real-time speeds (for example, 7-minute-long event waveforms 

take 7 minutes to run through the test system). More efficient 

testing mechanisms will allow more tests to be run to determine, 

for example, the effects of noise and network topology changes. 

In order to evaluate the performance of updated versions of 

algorithms, or to test algorithms with new datasets, new replay 

tools have been developed. These programs make it possible 

to replay anything from a few recordings of a single event to 

several days of waveform data from an entire network at a 

time. As in the case of synthetic ground motions, being able to 

“tweak” replayed data would add value. For example, the ability 

to add certain types of noise (spikes, square wave pulses, and so 

forth) to test system response is desirable and should be pursued 

as a research avenue. 

We also must explore different potential failure scenarios 

for ShakeAlert. How will the system handle cases of highaftershock activity during which the communications 

infrastructure for delivering alerts may become progressively 

degraded? What if an earthquake happens during or soon 

after another natural-disaster emergency, such as a wildfire 

or flood? How will the system respond to potential security 

threats? These types of events must be considered and guarded 

against as well, while also taking into consideration their 

relative likelihood of occurrence to ensure appropriate priority 

is given to understanding and addressing the most likely 

scenarios.

Challenge 4—Explore the Use of New 

Instrumentation

EEW is possible today owing to the combination of 

geophysical observational systems with processing algorithms 

that can characterize the expected ground shaking in real 

time. We have so far focused on the use of “traditional”, that 

is, scientific-grade, seismic and geodetic instrumentation that 

has been the basis of most of the EEW development to date. 

The quality of ShakeAlert performance could be improved 

by adding more instrumentation of this traditional type; 

however, there is also the potential to make use of other and 

new types of instrumentation. The instrumentation and the 

associated methods described below are in various stages of 

design, development, and proof-of-concept. Thus, these do 

not provide an alternative to ShakeAlert’s use of traditional 

networks. However, they do provide the potential to contribute 

to ShakeAlert in the future. We focus on several areas of 

interest:

• Low-cost instrumentation;

• Offshore instrumentation; and

• Strainmeters.

Low-cost instrumentation

One approach to improving the speed and accuracy of an 

EEW system in order to increase the area receiving actionable 

alerts and decrease the likelihood of false alarms is to increase 

the number of instruments close to an earthquake rupture. 

Given limited funds available for building and maintaining 

instrument networks, low-cost instruments may be an effective 

way of supplementing our higher quality traditional networks 

of instruments. Their efficacy for EEW depends on signaldetection characteristics and operational stability.

Microelectromechanical system (MEMS) accelerometers 

are the best studied and operationally proven type of low-cost 

sensor. The Quake Catcher Network (Cochran and others, 

2009) and Community Seismic Network (Clayton and others, 

2011) are operating networks of MEMS seismometers for 

seismic and engineering studies. These sensors have been 

benchmarked against traditional seismometers (Cochran, 

and others, 2011; Evans, and others, 2014) and have been 

successfully used for event detection (for example, Chung and 

others, 2011; Lawrence and others, 2014; Chung and others, 

2015). Mini-arrays of low-cost sensors may be able to extract 

higher order earthquake-source information than is available 

from a single 3-component instrument. By combining these 

low-cost devices with traditional seismometers, it may be 

possible to improve confidence in initial detections at modest 

cost, but much research is needed to explore the utility of 

these approaches. Further research is needed to understand the 
12 Research to Improve ShakeAlert Earthquake Early Warning Products and Utility

degree to which these low-cost networks may contribute to 

the ShakeAlert system in providing fast and reliable groundmotion alerts. 

MEMS accelerometers are integrated into smartphones, 

allowing for the possibility of supplementing ShakeAlert 

with crowd-sourced observations (Finazzi and Fassò, 2017; 

Kong and others, 2016a). In the case of smartphones, the 

challenge is in harnessing the millions of privately owned 

phones. However, it has been proposed that crowd-sourced 

observations from smartphone MEMS accelerometers alone 

could be used to provide EEW. Two large-scale efforts are 

underway. The Earthquake Network application has been 

downloaded by several million users and has identified more 

than 780 earthquakes based on observing clusters of triggers 

on user’s phones (Finazzi and Fassò, 2017; Finazzi, 2016). 

The MyShake application has been downloaded by more than 

200,000 users worldwide and collects on-phone triggers and 

also waveform data (Kong and others, 2016b). MyShake is 

not yet issuing alerts, but it detects earthquakes and estimates 

earthquake locations and magnitudes in real time.

Low-cost, consumer-grade global positioning system 

(GPS) chips, including those found in smartphones, are 

capable of recording displacements as small as those observed 

in a M6 earthquake (Minson and others, 2015). While these 

consumer-grade GPS solutions are noisier than their scientificgrade counterparts, they have several attractive characteristics 

besides their low cost. Specifically, they use on-board 

positioning and can obtain real-time correction information 

from satellite-based augmentation systems, both of which only 

the most advanced (and typically most expensive) scientific 

GPS receivers can do. Consumer GPS sensors eliminate 

significant latencies and vulnerabilities found in many 

scientific GPS network precise-point positioning operations. 

For example, correction information can be obtained from a 

satellite constellation rather than an Internet connection (that 

may be unavailable during and after a significant earthquake), 

and a sensor’s position can be computed on the GPS chip 

instead of sending the raw observations to a processing center.

Offshore Instrumentation

The plate boundary along western North America 

includes convergent systems offshore northern California, 

Oregon, Washington, and Alaska. Subduction zones present 

a different set of challenges compared to the strike-slip San 

Andreas Fault System. In subduction environments, the 

source region for large earthquakes is often located outside 

the footprint of regional seismic networks. It takes time 

for the first seismic waves from offshore earthquakes to 

arrive onshore, delaying characterization of the earthquake, 

although the arrival of strong shaking is likewise delayed. It 

is also critical to correctly discriminate outer-rise and in-slab 

earthquakes from megathrust events, which carry significantly 

greater seismic and tsunami hazard.

For Cascadia, the offshore environment is currently 

monitored by the Regional Scale Nodes offshore Oregon and 

the NEPTUNE Ocean Observatory offshore Vancouver Island. 

Continuous seismic data are telemetered in near real time to 

the Pacific Northwest Seismic Network from 14 ocean-bottom 

seismometers (OBS) offshore Oregon in two dense clusters 

and the data are ingested into ShakeAlert. However, currently 

these data typically do not contribute to ShakeAlert owing 

to telemetry latency. Ocean Networks Canada is currently 

exploring how they might adapt their existing offshore 

instrumentation to provide early warning for British Columbia.

Ideally, a real-time, offshore network would be based 

on deploying a seafloor cable that would provide power and 

telemetry to seafloor instruments. A range of instruments could 

be deployed on the ocean floor for real-time monitoring. The 

most useful instruments would be OBS that record passing 

seismic waves and bottom-pressure recorders that record 

changes in water pressure related to vertical movement of 

the seafloor. GPS-acoustic systems (wherein a sea-floor 

benchmark is acoustically coupled to a floating GPS-enabled 

buoy or robotic sea-surface vessel) would be the best means to 

measure horizontal displacement. However, the wide spacing 

of seafloor transponders and continuously operating seasurface platform needed make this approach more challenging 

for real-time applications, although seafloor-based geodetic 

measurements to rapidly estimate seafloor deformation would 

be particularly useful for EEW coupled to tsunami early 

warning. 

Seafloor instrumentation is an active area of development 

from which ShakeAlert could benefit in the future. It is 

possible that flow meters measuring changes in fluid flux may 

provide information about changes in shallow strain. Other 

emergent technologies include fiber-optic strainmeters or 

acoustic-mesh networks. However, these new data sources 

will require new, or modified, detection methods as they will 

contain different types of noise than is typical for land-based 

seismic or GNSS stations. 

Strainmeters

An accurate estimate of an earthquake’s moment release 

naturally depends on detection of the associated static and 

dynamic deformation, preferably as close to the fault as 

possible. Although traditional seismometers measure inertial 

quantities, like acceleration, and continuous-GPS stations 

measure ground displacement very well, these quantities alone 

do not capture the full broadband-frequency content of ground 

deformation except when combined appropriately (Melgar 

and others, 2013). Geodetic-grade strainmeters can measure 

elastodynamic deformation associated with earthquakes 

over a broad frequency band, ranging from static values to 

a high-frequency limit imposed by their baseline lengths; 

their seismic detection characteristics complement traditional 

instrumentation (Barbour and Agnew, 2012). 

Currently, 33 of the 82 borehole strainmeters (BSM) in 

the Plate Boundary Observatory (PBO) are located within 

275 km of the Cascadia subduction zone trench axis, where 

increased station density (whether seismic or geodetic) is 
References Cited 13

critical to fully characterizing large megathrust events. Recent 

developments in fiber-optic strain-sensing systems suggest 

that a dense network of direct measurements of strain can 

be made efficiently and affordably, either in boreholes (for 

example, DeWolf and others, 2015) or by utilizing existing 

telecommunications infrastructure (for example, Lindsey and 

others, 2017). Other BSM networks exist in the United States 

outside of PBO—most notably the USGS network in the San 

Francisco Bay area and elsewhere along the North AmericanPacific Plate margin.

Barbour and Crowell (2017) have investigated the 

feasibility of using BSM observations from the PBO network 

to rapidly determine earthquake magnitude. They found a 

simple log-log scaling between earthquake magnitude, peak 

root-mean-squared strain, and hypocentral distance that one 

could use to estimate magnitude rapidly, with a standard error 

of 0.3 magnitude units. This strain scaling is also in agreement 

with GPS-derived strains in triangulated subnetworks for the 

2011 moment magnitude (MW) 9.0 Tohoku-oki earthquake 

when spatial aliasing is taken into account. Furthermore, the 

greatest source of bias in the regression is found to be event 

source and seismic-wave-propagation path terms owing 

to large-scale geologic heterogeneities, and rock materialvelocity contrasts or fault-frictional variations, rather than 

local site or station terms, owing to variability in installation 

techniques or local geology. 

The results of Barbour and Crowell (2017) for the PBO 

BSMs were found using raw, uncalibrated, instrumental strain 

observations, indicating the ease of potential inclusion into 

EEW systems. Moreover, accurate estimates of magnitude 

can be made even when a single strain channel (out of four) is 

functional because their observation is not based on calibrated 

tensor-strain quantities that require at least three operational 

channels; this extends the potential lifespan of the PBO BSMs 

well beyond their nominal lifespan for tensor-strain estimation 

(for example, detection of slow slip). Strain observations, 

particularly attenuation with distance, are consistent with 

the peak ground-velocity data used to create the USGS 

ShakeMap, and show no systematic differences between the 

types of strainmeters: Sacks-Evertson dilatometers, SacksEvertson-Sakata 3-component extensometers, and Gladwin 

4-component extensometers (Barbour and Langbein, 2018). 

Additional research is needed to quantify the contribution of 

strainmeter data to improving the timeliness and (or) accuracy 

of ground-motion predictions by the ShakeAlert system. 

Conclusions

This document attempts to capture the range of research 

needs and opportunities that should be addressed over 

the next 5 years to improve the accuracy, timeliness, and 

usefulness of ShakeAlert products. The many wide-ranging 

approaches documented here are testament to the creativity 

of the large numbers of researchers now actively engaged in 

the development effort. The United States is poised to launch 

a public warning system to alert communities of incoming 

earthquake shaking that is a product of vigorous and diverse 

research efforts at multiple public and private institutions 

and conducted within a collaborative framework. It is key 

that we maintain this diversity of research and collaborative 

team effort to ensure the success of the first earthquake early 

warning system in the United States.

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Menlo Park Publishing Service Center, California

Manuscript approved August 10, 2018

Edited by Monica Erdman

Layout and design by Cory Hurd
Cochran and others—Research to Improve ShakeAlert Earthquake Early Warning Products and Utility—Open-File Report 2018–1131

ISSN 2331-1258 (online)

https://doi.org/10.3133/ofr20181131