Scientific Investigations Report 2017–5013–A–H

Version 1.2, December 2018

U.S. Department of the Interior

U.S. Geological Survey

Prepared in cooperation with the California Geological Survey

The HayWired Earthquake Scenario—Earthquake Hazards
Cover. Oblique aerial image showing how the main active traces of the Hayward Fault (red lines) cut through the urban landscape of the 

east bay part of the San Francisco Bay area (north is to the upper right; fault is oriented northwest-southeast). The image is overlain on a 

“hillshade” (artificially illuminated from the northwest) to show fault-zone ground-surface features, such as a right-laterally offset stream 

channel in the foreground. The fault trace roughly bisects California Memorial Stadium at the University of California, Berkeley (O-shaped 

feature in the middle of the image). The stadium was renovated and seismically strengthened at an expense of $321 million (see http://news.

berkeley.edu/2011/09/01/memorial-stadium-renovated-with-help-of-berkeleys-own/) as an engineered countermeasure against fault-rupture 

hazard from a future earthquake such as that modeled in the HayWired scenario. Similarly, beneath the viewpoint of this image, the East 

Bay Municipal Utility District (EBMUD) mitigated the Hayward Fault crossing of the Claremont water-transmission tunnel by constructing a 

bypass tunnel at an expense of $38 million (see http://www.geoprac.net/geonews-mainmenu-63/64-project-related/333-the-claremont-tunneldesigned-to-survive-fault-rupture-on-the-hayward-fault/). Although notable risk-reduction measures such as these have already been taken 

along the Hayward Fault, issues with more than 300 buildings (mostly homes) that are situated directly on the fault, and dozens of major 

lifelines that cross it, have not yet been addressed. (Fault mapping by J. Lienkaemper, U.S. Geological Survey, 2008, see https://pubs.usgs.

gov/ds/2006/177/. Hillshade is based on EarthScope airborne light detection and ranging (lidar) data that covers the Hayward Fault and other 

important faults in northern California, http://opentopo.sdsc.edu/datasetMetadata?otCollectionID=OT.052008.32610.1.)
The HayWired Earthquake Scenario—

Earthquake Hazards

Edited by Shane T. Detweiler and Anne M. Wein

Prepared in cooperation with the California Geological Survey

Scientific Investigations Report 2017–5013–A–H 

Version 1.2, December 2018

U.S. Department of the Interior

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

RYAN K. ZINKE, Secretary

U.S. Geological Survey

William H. Werkheiser, Deputy Director 

 exercising the authority of the Director

U.S. Geological Survey, Reston, Virginia: 2017

First release: 2017

Revised: December 2018 (ver. 1.2)

For more information on the USGS—the Federal source for science about the Earth, its natural and living 

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(1–888–275–8747).

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visit https://store.usgs.gov.

Any use of trade, firm, or product names is for descriptive purposes only and does not imply 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:

Detweiler, S.T., and Wein, A.M., eds., 2017, The HayWired earthquake scenario—Earthquake hazards (ver. 1.2, 

December 2018): U.S. Geological Survey Scientific Investigations Report 2017–5013–A–H, 126 p., 

https://doi.org/10.3133/sir20175013v1.

ISSN 2328-0328 (online)
iii

The 1906 Great San Francisco earthquake (magnitude 7.8) and the 1989 Loma Prieta earthquake (magnitude 6.9) each 

motivated residents of the San Francisco Bay region to build countermeasures to earthquakes into the fabric of the region. 

Since Loma Prieta, bay-region communities, governments, and utilities have invested tens of billions of dollars in seismic 

upgrades and retrofits and replacements of older buildings and infrastructure. Innovation and state-of-the-art engineering, 

informed by science, including novel seismic-hazard assessments, have been applied to the challenge of increasing 

seismic resilience throughout the bay region. However, as long as people live and work in seismically vulnerable buildings 

or rely on seismically vulnerable transportation and utilities, more work remains to be done. 

With that in mind, the U.S. Geological Survey (USGS) and its partners developed the HayWired scenario as a tool to 

enable further actions that can change the outcome when the next major earthquake strikes. By illuminating the likely 

impacts to the present-day built environment, well-constructed scenarios can and have spurred officials and citizens 

to take steps that change the outcomes the scenario describes, whether used to guide more realistic response and 

recovery exercises or to launch mitigation measures that will reduce future risk.

The HayWired scenario is the latest in a series of like-minded efforts to bring a special focus onto potential impacts when the 

Hayward Fault again ruptures through the east side of the San Francisco Bay region as it last did in 1868. Cities in the east bay 

along the Richmond, Oakland, and Fremont corridor would be hit hardest by earthquake ground shaking, surface fault rupture, 

aftershocks, and fault afterslip, but the impacts would reach throughout the bay region and far beyond. The HayWired scenario 

name reflects our increased reliance on the Internet and telecommunications and also alludes to the interconnectedness 

of infrastructure, society, and our economy. How would this earthquake scenario, striking close to Silicon Valley, impact our 

interconnected world in ways and at a scale we have not experienced in any previous domestic earthquake?

The area of present-day Contra Costa, Alameda, and Santa Clara Counties contended with a magnitude-6.8 earthquake in 

1868 on the Hayward Fault. Although sparsely populated then, about 30 people were killed and extensive property damage 

resulted. The question of what an earthquake like that would do today has been examined before and is now revisited in the 

HayWired scenario. Scientists have documented a series of prehistoric earthquakes on the Hayward Fault and are confident 

that the threat of a future earthquake, like that modeled in the HayWired scenario, is real and could happen at any time. The 

team assembled to build this scenario has brought innovative new approaches to examining the natural hazards, impacts, 

and consequences of such an event. Such an earthquake would also be accompanied by widespread liquefaction and 

landslides, which are treated in greater detail than ever before. The team also considers how the now-prototype ShakeAlert 

earthquake early warning system could provide useful public alerts and automatic actions.

Scientific Investigations Report 2017–5013 and accompanying data releases are the products of an effort led by the USGS, 

but this body of work was created through the combined efforts of a large team including partners who have come together 

to form the HayWired Coalition (see chapter A). Use of the HayWired scenario has already begun. More than a full year of 

intensive partner engagement, beginning in April 2017, is being directed toward producing the most in-depth look ever at 

the impacts and consequences of a large earthquake on the Hayward Fault. With the HayWired scenario, our hope is to 

encourage and support the active ongoing engagement of the entire community of the San Francisco Bay region by providing 

the scientific, engineering, and economic and social science inputs for use in exercises and planning well into the future.

David Applegate

Associate Director for Natural Hazards, 

 exercising the authority of the Deputy Director

U.S. Geological Survey

Foreword

v

HayWired Review Panel

The HayWired Review Panel, a group whose expertise spans the scope of the HayWired 

scenario, assessed the overarching goals of the project along with the scientific approach and 

oversaw the reviews of each individual chapter in this volume. The panel consisted of Jack 

Boatwright (U.S. Geological Survey, USGS), Arrietta Chakos (Urban Resilience Strategies), 

Mary Comerio (University of California, Berkeley), Douglas Dreger (University of California, 

Berkeley), Erol Kalkan (USGS), Roberts McMullin (East Bay Municipal Utility District), 

Andrew Michael (chair, USGS), David Schwartz (USGS), and Mary Lou Zoback (Build 

Change, Stanford University).

HayWired Coalition Partners

Alameda County Mayors’ Conference

Alameda County Sheriff’s Office, Office of Emergency Services

American Red Cross

Art Center College of Design

ARUP—Design and Engineering Consultants

Association of Bay Area Governments—Metropolitan 

 Transportation Commission

Aurecon

Bay Area Center for Regional Disaster Resilience

Bay Area Council

Bay Area Rapid Transit Authority

Bay Area Urban Area Security Initiative

Bay Planning Coalition

Boston University

Business Recovery Managers Association

California Business, Consumer Services, and Housing Agency

California Department of Public Health

California Department of Transportation

California Earthquake Authority

California Earthquake Clearinghouse

California Geological Survey

California Governor’s Office of Business and 

 Economic Development

California Governor’s Office of Emergency Services

California Independent Oil Marketers Association

California ISO

California Public Utilities Commission

California Resiliency Alliance

California Seismic Safety Commission

Carnegie Melon University Silicon Valley

City and County of San Francisco

City of Berkeley

City of Fremont

City of Hayward

City of Oakland

City of Oakland, Fire Department

City of San Francisco, Department of Emergency 

 Management

City of Walnut Creek 

Contra Costa County Mayors’ Conference

Earthquake Country Alliance

Earthquake Engineering Research Institute

East Bay Municipal Utility District 

Federal Emergency Management Agency 

Joint Venture Silicon Valley 

Laurie Johnson Consulting|Research 

March Studios 

Marin Economic Consulting 

MMI Engineering 

Office of the Mayor, City and County of San Francisco 

Pacific Earthquake Engineering Research Center 

Pacific Gas and Electric 

Palo Alto University 

Price School of Public Policy and Center for Risk and 

 Economic Analysis of Terrorism Events, University of 

 Southern California

Rockefeller Foundation—100 Resilient Cities 

San Jose Water Company 

Southern California Earthquake Center 

SPA Risk LLC 

SPUR 

Strategic Economics 

Structural Engineers Association of Northern California 

The Brashear Group LLC 

University of California Berkeley Seismological Laboratory

University of Colorado Boulder 

University of Southern California

U.S. Department of Homeland Security 

U.S. Geological Survey 

Wells Fargo

vii

Contents

Foreword ........................................................................................................................................................iii

HayWired Review Panel...............................................................................................................................v

HayWired Coalition Partners .......................................................................................................................v

Chapters

A. The HayWired Scenario—How Can the San Francisco Bay Region Bounce Back from

 or Avert an Earthquake Disaster in an Interconnected World? ..............................................1

 By Kenneth W. Hudnut, Anne M. Wein, Dale A. Cox,Suzanne C. Perry, Keith A.

 Porter, Laurie A. Johnson, and Jennifer A. Strauss

Introduction...................................................................................................................................1

The Hayward Fault.......................................................................................................................2

The HayWired Scenario .............................................................................................................4

Motivation—Why Revisit a Hayward Fault Scenario Now? ................................................7

Objectives of the HayWired Scenario......................................................................................8

HayWired Scenario Process Will Tackle Urgent Questions.................................................8

HayWired Scenario Builds on the Success of Past SAFRR Scenarios ..............................9

The HayWired Coalition............................................................................................................11

A Call to Action for the Coming Year.......................................................................................11

References Cited .......................................................................................................................11

Appendix. History of San Francisco Bay Region Earthquake Scenarios and 

Seismic-Resilience Efforts..........................................................................................13

B. Overview of the HayWired Scenario Earthquake-Hazards Volume ......................................17

 By Ruth A. Harris

Introduction.................................................................................................................................17

Modeling the Mainshock..........................................................................................................17

Fault Slip......................................................................................................................................20

Liquefaction ................................................................................................................................20

Landslides ...................................................................................................................................20

Aftershocks.................................................................................................................................20

Conclusion...................................................................................................................................24

References Cited .......................................................................................................................24

C. HayWired Scenario Mainshock Ground Motions.....................................................................27

 By Brad T. Aagaard, John L. Boatwright, Jamie L. Jones, Tim G. MacDonald, 

 Keith A. Porter, and Anne M. Wein 

Abstract.......................................................................................................................................27

Introduction.................................................................................................................................27

HayWired Scenario Mainshock ..............................................................................................28

HayWired Scenario Mainshock Ground Motions ...............................................................28

Knowledge Gaps and Modeling Limitations..........................................................................34

Conclusion...................................................................................................................................34

Acknowledgments.....................................................................................................................34
viii

References Cited .......................................................................................................................34

Appendix......................................................................................................................................35

D. HayWired Scenario Mainshock Coseismic and Postseismic Surface Fault Slip................39

 By Brad T. Aagaard, David P. Schwartz, Anne M. Wein, Jamie L. Jones, and Kenneth

 W. Hudnut

Abstract.......................................................................................................................................39

Introduction.................................................................................................................................39

Mainshock Rupture and Coseismic Slip ................................................................................40

Distributed or Off-Fault Deformation ......................................................................................41

Afterslip .......................................................................................................................................42

Knowledge Gaps, Modeling Limitations, and Communication Needs..............................44

Conclusion...................................................................................................................................44

Acknowledgments.....................................................................................................................44

References Cited .......................................................................................................................45

E. HayWired Scenario Mainshock—Liquefaction Probability Mapping...................................49

 By Jamie L. Jones, Keith L. Knudsen, and Anne M. Wein

Abstract.......................................................................................................................................49

Introduction.................................................................................................................................49

Data.....................................................................................................................................50

Depth to Groundwater ............................................................................................54

Methods.......................................................................................................................................54

Results... ......................................................................................................................................58

Knowledge Gaps and Modeling Limitations..........................................................................64

Conclusion...................................................................................................................................64

Acknowledgments.....................................................................................................................65

References Cited .......................................................................................................................65

F. HayWired Scenario Mainshock—Earthquake-Induced Landslide Hazards .......................69

 By Timothy P. McCrink and Florante G. Perez

Abstract.......................................................................................................................................69

Introduction.................................................................................................................................69

Methodology and Data ............................................................................................................72

Geologic-Material-Strength Map .................................................................................72

Slope Gradient...................................................................................................................78

Earthquake Shaking .........................................................................................................78

Earthquake-Landslide Susceptibility.............................................................................78

Calculation of Newmark Displacements ......................................................................78

Calculation of Slope-Failure Probabilities ....................................................................82

Areas for Hazard-Model Improvement .........................................................................82

Summary......................................................................................................................................84

Acknowledgments.....................................................................................................................86

References Cited .......................................................................................................................86

Appendix 1. Seismic Hazard Zoning (SHZ) Reports in the San Francisco Bay Region..88

Appendix 2. Geologic Map Unit Names/Descriptions for California Geological Survey 

Seismic-Hazard-Zone (SHZ) Maps for the San Francisco Bay Region and Their 

Correlation to the Generalized Geologic-Compilation Map ..................................89
ix

G. HayWired Scenario Aftershock Sequence................................................................................91

 By Anne M. Wein, Karen R. Felzer, Jamie L. Jones, and Keith A. Porter

Abstract.......................................................................................................................................91

Introduction.................................................................................................................................91

Characteristics of Earthquake Sequences—Magnitudes, Frequencies, Location, 

and Decay.............................................................................................................92

Aftershock Simulations.............................................................................................................94

Description of the HayWired Aftershock Sequence ...........................................................96

Scenario Aftershock Forecasts.............................................................................................100

Scenario Aftershock Forecast 1—Soon After the Mainshock ...............................100

Scenario Aftershock Forecast 2—24 Hours After the Mainshock.........................102

Scenario Aftershock Forecast 3—48 Hours After the Mainshock.........................104

Scenario Aftershock Forecast 4—7 Days After the Mainshock ............................106

Scenario Aftershock Forecast 5—40 Days After the Mainshock ..........................108

Probability of Largest Aftershock .........................................................................................110

Limitations of Aftershock Sequence Simulation and Forecasting ..................................110

Conclusion.................................................................................................................................110

Acknowledgments...................................................................................................................111

References Cited .....................................................................................................................111

H. HayWired Scenario Three-Dimensional Numerical Ground-Motion Simulation Maps...115

 By Keith A. Porter

Abstract.....................................................................................................................................115

Introduction—Two Valid Methods to Create a Ground-Motion Map ............................115

Skewness in Ground-Motion Intensity.................................................................................117

Nonlinearity in Damage Aggravates the Damage Estimate .............................................120

Spatial Correlation...................................................................................................................120

Extreme Values in 3D Modeling.............................................................................................121

Uncertainties ............................................................................................................................125

Conclusion.................................................................................................................................125

References Cited .....................................................................................................................126
x

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

square foot (ft2) 0.09290 square meter (m2)

square mile (mi2) 2.590 square kilometer (km2) 

Cohesion

pound per square foot (lb/ft2) 0.04788 kilopascal (kPa) 

Conversion Factors

U.S. customary units to International System of Units

International System of Units to U.S. customary units

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 10.76 square foot (ft2) 

square kilometer (km2) 0.3861 square mile (mi2)

Velocity

centimeter per second (cm/s) 0.3937 inch per second (in./s)

centimeter per second (cm/s) 0.0223694 mile per hour (mi/hr)

meter per second (m/s) 3.281 foot per second (ft/s)

meter per second (m/s) 2.23694 mile per hour (mi/hr)

Datum

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).
xi

Abbreviations and Acronyms

3D three dimensional

ABAG Association of Bay Area Governments

BAREPP Bay Area Regional Earthquake Preparedness Project

BART Bay Area Rapid Transit

Cal OES California Governor’s Office of Emergency Services

Caltrans California Department of Transportation

CAPSS Citizens Advisory Panel on Seismic Safety

CDC Centers for Disease Control and Prevention

CGS California Geological Survey

CPT cone penetration test

CSSC California Seismic Safety Commission

DEM digital elevation model

EBMUD East Bay Municipal Utility District

EERI Earthquake Engineering Research Institute

ESIP Earthquake Safety Improvements Program

ETAS epidemic-type aftershock sequence

FEMA Federal Emergency Management Agency

Fv site coefficient

g acceleration due to gravity

GMPE ground-motion prediction equation

LPI liquefaction potential index

M magnitude

Ma mega-annum or millions of years ago

MMI Modified Mercalli Intensity

MW moment magnitude

NAD83 North American Datum of 1983

NED National Elevation Dataset

NGA-West2 Next Generation Attenuation Relationships for Western United States

NISEE National Information Service for Earthquake Engineering

NRC National Research Council

PEER Pacific Earthquake Engineering Research Center

PDT Pacific Daylight Time

PGA peak ground acceleration
xii

PGV peak ground velocity

PSA or pSa pseudo-spectral acceleration

PSA03 short-period (0.3-second) pseudo-spectral-acceleration response

PSA10 long-period (1-second) pseudo-spectral-acceleration response

PST Pacific Standard Time

RMS Risk Management Solutions

SA spectral acceleration

SAFRR U.S. Geological Survey Science Application for Risk Reduction project

SCEC Southern California Earthquake Center

SHZ seismic hazard zone

SPUR San Francisco Bay Area Planning and Urban Research Association

UCERF3 Uniform California Earthquake Rupture Forecast, version 3

USGS U.S. Geological Survey

VS30 time-averaged shear-wave velocity to a depth of 30 meters

WGS84 World Geodetic Survey 1984

Chapter A

The HayWired Scenario—How Can the San Francisco Bay 

Region Bounce Back from or Avert an Earthquake Disaster 

in an Interconnected World?

By Kenneth W. Hudnut,1 Anne M. Wein,1 Dale A. Cox,1 Suzanne C. Perry,1 Keith A. Porter,2 Laurie A. Johnson,3

and Jennifer A. Strauss4

The HayWired Earthquake Scenario—Earthquake Hazards

Edited by Shane T. Detweiler and Anne M. Wein

Scientific Investigations Report 2017–5013–A–H

[Also see https://doi.org/10.3133/sir20175013]

Introduction

The HayWired scenario is a hypothetical yet scientifically 

realistic and quantitative depiction of a moment magnitude (Mw) 

7.0 earthquake (mainshock) occurring on April 18, 2018, at 

4:18 p.m. on the Hayward Fault in the east bay part of the San 

Francisco Bay area, California. The hypothetical earthquake 

has its epicenter in Oakland, and strong ground shaking from 

the scenario causes a wide range of severe impacts throughout 

the greater bay region. In the scenario, the Hayward Fault is 

ruptured along its length for 83 kilometers (about 52 miles).

Building on a decades-long series of efforts to reduce 

earthquake risk in the San Francisco Bay region, the 

hypothetical HayWired earthquake is used to examine the 

well-known earthquake hazard of the Hayward Fault, with 

a focus on newly emerging vulnerabilities. After a major 

earthquake disaster, reestablishing water services and foodsupply chains are, of course, top priorities. However, problems 

associated with telecommunication outages or “network 

congestion” will increase and become more urgent as the bay 

region deepens its reliance on the “Internet of Things.”

Communications at all levels are crucial during incident 

response following an earthquake. Damage to critical facilities 

(such as power plants) from earthquake shaking and to 

electrical and telecommunications wires and fiber-optic cables 

that are severed where they cross a fault rupture can trigger 

cascading Internet and telecommunications outages, and 

restoring these services is crucially important for emergencyresponse coordination. Without good communications, 

emergency-response efficiency is reduced, and as a result, 

life-saving response functions can be compromised. For these 

reasons, the name HayWired was chosen for this scenario 

to emphasize the need to examine our interconnectedness 

and reliance on telecommunications and other lifelines (such 

as water and electricity) toward the goal of making the San 

Francisco Bay region more resilient in future earthquakes.

Earthquake risk in the San Francisco Bay region has been 

greatly reduced as a result of previous concerted efforts; for 

example, an as much as $50 billion investment in strengthening 

infrastructure was motivated in large part by the 1989 magnitude 

(M) 6.9 Loma Prieta earthquake (KQED, 2014). The earthquake

hazard from the Hayward Fault remains high, however, and much

work still needs to be done to ensure that the region is ready for

a major earthquake like that modeled in the HayWired scenario.

Already, there is a renewed commitment from the newly formed

HayWired Coalition—consisting of numerous government,

academic, utility-provider, and community stakeholders—to bring

new and varied perspectives to bear on the problems that remain.

Similar to previous U.S. Geological Survey (USGS) Science 

Application for Risk Reduction project (SAFRR) led scenarios, 

the HayWired scenario earthquake is a natural-hazards incident, 

with additional cascading hazards of fault rupture, aftershocks 

(subsequent earthquakes), afterslip (subsequent movement on a 

fault), landslides, liquefaction (soils becoming liquid-like during 

shaking), and fire following earthquake (potentially widespread fires 

triggered by an earthquake) that can be as destructive and disruptive 

as powerful ground shaking in the mainshock. For the earthquake 

scenario, damages are calculated using engineering best practices 

combined with new basic research into building-code performance 

objectives, urban search and rescue, interactions between lifelines, 

self-protective actions, and other topics. This research provides 

new insight into expected physical and environmental damages 

from an event such as the earthquake modeled in the HayWired 

scenario, as well as into the restoration of structures, infrastructure, 

and lifelines and the addressing of environmental effects. Social and 

economic consequences, as well as topics for discussion of policy 

considerations, are also addressed in the scenario.

1

U.S. Geological Survey.

2

University of Colorado Boulder.

3

Laurie Johnson Consulting.

4

University of California Berkeley Seismological Laboratory.
2 The HayWired Earthquake Scenario—Earthquake Hazards

The HayWired Coalition stakeholders are addressing implications of the scenario 

during the year from April 2017 through April 2018. The idea is that solutions will 

be devised and initial actions taken during this year to reduce the impacts of future 

damaging earthquakes in the San Francisco Bay region and elsewhere. Longer-term 

actions and outcomes also are hoped for and expected. Discussions, workshops, and 

exercises will focus on key themes not addressed as fully (or at all) in previous SAFRR 

earthquake scenarios—(1) the effects of aftershocks and afterslip, (2) performance 

objectives embedded in building codes, (3) complications of fire following earthquake, 

(4) anticipating environmental health issues, (5) urban search and rescue implications, 

(6) effects of lifeline interdependencies (including the Internet and digital economy), 

and (7) communities at risk for long-term recovery and rebuilding challenges. 

The scenario is intended to help communities improve their knowledge and use of 

earthquake-hazard, early warning, and aftershock and afterslip forecast information. 

Through community engagement, the HayWired scenario will help inform decisions 

about building-code-performance objectives, business-continuity planning, and 

empowering communities through capacity building.

USGS Scientific Investigations Report (SIR) 2017–5013, describing the 

HayWired scenario, is planned to be published as three volumes. As HayWired 

volumes are published, they will be made available at https://doi.org/10.3133/

sir20175013. Ideally, the HayWired scenario volumes will help readers collectively 

improve their own and their community’s resilience in future disasters.

The Hayward Fault

Most people living in the San Francisco Bay region understand that the threat 

of earthquakes is real and inescapable. Few other regions on Earth are as strongly 

associated with earthquakes. Photographers vividly depicted the aftermath of the 

1906 M7.8 Great San Francisco earthquake and the fire that followed (fig. 1). The 

San Andreas Fault, the source of this earthquake (Lawson, 1908), runs along the 

Pacific Ocean side of the San Francisco Peninsula and then continues offshore of the 

City of San Francisco and the Golden Gate Bridge.

Scientists recognize another earthquake threat in the bay region, rivaling that of 

the San Andreas Fault, that is posed by the Hayward Fault along the east side of San 

Francisco Bay (fig. 2). The most recent large earthquake on the Hayward Fault was 

Why the Name HayWired?—Internet and 

Interconnectedness

The scenario’s name, HayWired, 

refers to the rupture of the Hayward 

Fault and speaks to the potential 

disruption to our wired and wireless 

world. California has not experienced 

a large earthquake in an urban 

environment since our society, culture, 

and economy have become intertwined 

with the Internet. Experience in recent 

Japan (2011 magnitude-9.1 Tohoku) 

and New Zealand (2011 magnitude-6.2 

Christchurch) earthquakes suggest 

that Internet service outages tend to be 

localized and coinciding in duration 

with the loss of power service. Although 

designed to be robust because of 

its redundancy, the Internet is not 

immune to performance problems. 

More generally, “wired” represents 

interconnectedness at many levels—

the interconnectedness of seismicity 

evidenced by afterslip and aftershocks, 

interdependencies of lifelines, social 

connectivity through technology 

(including earthquake early warning), 

and ripple effects of damages and 

disruption throughout an economy, 

encompassing especially the modern 

digital economy. The HayWired theme 

is particularly appropriate for the San 

Francisco Bay region, which is home to 

Silicon Valley and to world leaders in 

technology and digital communications.

A B

Figure 1. Photographs of damage in San Francisco, California, after the 1906 Great San Francisco earthquake and subsequent fire. The ground shaking 

from this magnitude-7.8 earthquake caused destruction throughout the San Francisco Bay region. A, The remains of San Francisco City Hall following 

the earthquake; B, houses in San Francisco damaged by the earthquake. (Images from National Information Service for Earthquake Engineering-Pacific 

Earthquake Engineering Research Center (NISEE-PEER), University of California, Berkeley, used with permission.)
Chapter A. The HayWired Scenario—How Can the San Francisco Bay Region Bounce Back from or Avert an Earthquake Disaster? 3

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27

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26

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28

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2

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Wight Way

Collayami

Mysterious Ridge

Bennett Valley

West Napa

Trout Creek

Point Reyes

Gordon Valley

Midland

Franklin

Southampton

Los Medanos-Roe Island

Pittsburg-Kirby Hills

Clayton

Mt. Diablo North

Mt. Diablo South

Pilarcitos

Las Positas

Orestimba

Monte Vista-Shannon

Silver Creek

Ortigalita North

Ortigalita South

Sargent

Zayante-Vergeles

San Joaquin

Reliz

Quien Sabe

Monterey Bay-Tularcitos 

Mission

Butano

Dunnigan Hills

Faults

Monterey

Bay

PA C I F I C

OCEAN

San Francisco Bay

0 10 20 KILOMETERS

0 10 20 MILES

HAYWARD

FAULT

CONCORD FAULT

RODGERS CREEK FAULT

MAACAMA FAULTMAACAMA FAULT

HUNTING

CREEK

HAYWARD

CONCORD 

FAULT

GREEN VALLEY

BERRYESSA

 GREENVILLE FAULT GREENVILLE FAULT

SAN ANDREAS FAULT

PAICINES FAULT

SAN ANDREAS FAULT

PAICINES FAULT

CALAVERAS

FAULT

CALAVERAS

FAULT

RODGERS CREEK FAULT

HUNTING

CREEK

SAN GREGORIO FAULT

33%

8%

16%

6%

22%

26%

San Francisco Bay Region

San Jose

Monterey

Salinas

Santa

Cruz

Half Moon

Bay

Point

Reyes

Mill

Valley

Napa

Santa

Rosa

Fremont

Oakland

San

Francisco

122°30’

38°

37°30’

38°30’

37°

122°

72%

probability of one or more

M v6.7 earthquakes

from 2014 to 2043 in the 

San Francisco Bay Region

3

EXPLANATION

Major plate boundary faults

Lesser-known smaller faults

Urban areas

-

r

Figure 2. Map of known active geologic faults in the San Francisco Bay region, California, 

including the Hayward Fault. The 72 percent (%) probability of a magnitude (M) 6.7 or greater 

earthquake in the region includes well-known major plate-boundary faults, lesser-known faults, 

and unknown faults. The percentage shown within each colored circle is the probability that 

a M6.7 or greater earthquake will occur somewhere on that fault system by the year 2043. The 

probability that a M6.7 or greater earthquake will involve one of the lesser known faults is 13 

percent. ≥, greater than or equal. (From Aagaard and others, 2016.)
4 The HayWired Earthquake Scenario—Earthquake Hazards

a M6.8 earthquake in 1868 (fig. 3). Damage was limited in 

1868 because the region was sparsely populated and barely 

developed.

The Hayward Fault now threatens more than 7 million 

people, approximately 2 million buildings, the San Francisco 

Bay region’s and the Nation’s economies, and dozens 

of major infrastructure lifelines including freeways and 

tunnels, pipelines, aqueducts, electric substations, electrictransmission and distribution lines, phone lines and fiberoptic routes, and rail lines. The surface trace of the Hayward 

Fault runs beneath the foundations of more than 300 homes, 

as well as under other structures, including the football 

stadium at the University of California, Berkeley, which has 

been extensively seismically retrofitted.

Although the Hayward Fault regularly produces 

smaller earthquakes, such as a M4.0 event on August 

17, 2015, recent scientific investigations show that large 

earthquakes on the fault occur about once every 100 

to 220 years (Lienkaemper and others, 2002, 2010). 

Therefore, it is of concern that the 1868 earthquake was 

149 years ago because about one average “recurrence 

interval” has elapsed since the last large earthquake 

The HayWired Scenario is Not an Earthquake Prediction

The HayWired scenario is not an earthquake prediction. Scientists cannot predict the particular date and time of an 

earthquake. However, when (not if) the Hayward Fault next produces a large earthquake, it could happen in many different 

ways. The HayWired scenario is one of 39 scenario earthquakes with magnitudes ranging between magnitude (M) 6.6 

and 7.2 that have been simulated on various segments of the Hayward Fault. An effectively infinite variety of earthquake 

magnitudes, locations, and other fault-rupture details are possible. Real outcomes of the next earthquake someday in 

the future will differ from what is presented here, but visualizing, analyzing, and planning for this particular scenario 

earthquake can help reduce damage and enable communities to more quickly recover when an actual earthquake occurs. 

The M7.0 scenario earthquake described here is not a worst case. Larger earthquakes could occur on the Hayward Fault 

and elsewhere in the San Francisco Bay region. For example, the northern part of the Hayward Fault last experienced a 

large rupture about 300 years ago, so an earthquake of even larger magnitude than the 1868 M6.8 Hayward earthquake is a 

distinct possibility. Recent studies have suggested that a future rupture of the Hayward Fault could generate an earthquake 

as large as M7.4, if the rupture were to link up with the Rodgers Creek Fault to the north. However, a M7.0 earthquake 

like the HayWired scenario mainshock occurs frequently enough, and its consequences are serious enough, to make it an 

earthquake worth planning for.

• What is a scientifically reasonable size earthquake that people can 

expect and prepare for along the Hayward Fault?

• What could happen as a result of the HayWired scenario 

earthquake and its cascading effects (or in other future damaging 

events in the San Francisco Bay region), and what can we do 

about it?

• What can San Francisco Bay region residents, communities, and 

businesses do to prepare for an event like that in the HayWired 

scenario?

• How can we learn more about the effects of a disaster like that 

modeled in the HayWired scenario and take action and support 

actions by others to prepare for such an event?

5

The year 2018 corresponds with the sesquicentennial (150th anniversary) of the last large earthquake on the Hayward Fault. The date corresponds with the 

annual observation and activities surrounding the April 18 anniversary of the 1906 San Francisco earthquake on the nearby San Andreas Fault. The time of day 

is selected for easy recollection, because the time “4:18” resembles the date “4/18/2018.”

Figure 3. Photograph showing shaking damage from the 1868 magnitude-6.8 

Hayward Fault earthquake, which caused collapse of the Alameda County 

Courthouse in San Leandro, California (photograph courtesy of the Bancroft 

Library, University of California, Berkeley). Inset shows the same building before 

the earthquake (photograph courtesy of courtesy of San Leandro Public Library). 

on the fault. The next large Hayward Fault earthquake could 

happen at any time, and such an earthquake is certain to occur 

in the future.

The HayWired Scenario

What might the impacts and consequences be if another 

earthquake like the 1868 event were to happen today on 

the Hayward Fault? The HayWired scenario examines one 

such hypothetical earthquake—a M7.0 earthquake on April 

18, 2018, at 4:18 p.m.5 The HayWired scenario asks the 

following questions:
Chapter A. The HayWired Scenario—How Can the San Francisco Bay Region Bounce Back from or Avert an Earthquake Disaster? 5

In the HayWired scenario earthquake, the rupture of the 

Hayward Fault starts beneath southeast Oakland and, in less 

than a minute, travels along more than 83 kilometers (about 

52 miles) of its length, both northward toward Richmond 

and San Pablo Bay and southward toward Fremont, at speeds 

as great as 11,000 kilometers per hour (about 7,000 miles 

per hour). As the fault break reaches the Earth’s surface, it 

Figure 4. U.S. Geological Survey ShakeMap of the San Francisco Bay region, California, showing instrumental intensity (estimated Modified Mercalli 

Intensity) for the hypothetical magnitude-7.0 mainshock of the HayWired earthquake scenario on the Hayward Fault. The earthquake begins under the 

City of Oakland (star) and ruptures the Hayward Fault along more than 83 kilometers (about 52 miles) of its length in both directions. km, kilometer; peak 

acc., peak acceleration; peak vel., peak velocity; %g, amount of ground acceleration caused by the scenario mainshock, expressed in terms of the 

percentage of gravity’s acceleration at the Earth’s surface; cm/s, centimeter per second. (Modified from Aagaard, Boatwright, and others, this volume; 

modified from U.S. Geological Survey, 2014.)

damages roads and buried pipelines and electrical conduits 

that cross the fault north of Hayward. In Berkeley, for 

example, the ground shifts by as much as 1 to 1.5 meters 

(about 3 to 5 feet) in a matter of seconds. As the USGS map 

of expected ground shaking (called a scenario ShakeMap) 

for the HayWired mainshock shows (fig. 4), the earthquake 

produces severe shaking and moderate to heavy damage 

Chapter A. The HayWired Scenario—How Can the San Francisco Bay Region Bounce Back from or Avert an Earthquake Disaster? 17

San Jose

San Francisco

Sacramento

Stockton

Fremont

Berkeley

Richmond

Salinas

Merced

Rocklin

Santa Cruz

Soledad

Coalinga

Clearlake

-- Earthquake Planning Scenario --

Shakemap for haywiredm7.0 Scenario

Scenario Date: Apr 18, 2018 23:18:00 UTC M 7.0 N37.80 W122.18 Depth: 8.0km 

PLANNING SCENARIO ONLY -- Map Version 28 Processed 2015-05-13 22:46:39 UTC 

INSTRUMENTAL 

INTENSITY

PEAK VEL.(cm/s)

PEAK ACC.(%g)

POTENTIAL 

DAMAGE

PERCEIVED 

SHAKING

none none none Very light Light Moderate Mod./Heavy Heavy Very Heavy

Not felt Weak Light Moderate Strong Very Strong Severe Violent Extreme

Scale based upon Worden et al. (2012)

-124 ̊ -123 ̊ -122 ̊ -121 ̊ -120 ̊

36 ̊

36.5 ̊

37 ̊

37.5 ̊

38 ̊

38.5 ̊

39 ̊

0 50

km

Oakland Livermore

Basin

Evergreen

Basin

San Pablo

Bay

<0.05 0.3 2.8 6.2 12

<0.02 0.1

I II-III IV V VI VII VIII IX X+

1.4 4.7 9.6 20 41 86 >178

22 40 75 >139
6 The HayWired Earthquake Scenario—Earthquake Hazards

in the east bay and Silicon Valley (roughly the part of the 

bay area at the southern end of San Francisco Bay) and 

widespread strong shaking throughout the region. San 

Francisco Bay region residents feel ground shaking that 

lasts 30 seconds or longer, and many people have difficulty 

walking and standing. Effects and destruction from the fault 

rupture and ground shaking are severe (fig. 5), but adding to 

this are a cascade of other hazards, including liquefaction, 

landslides, and fire following earthquake.

The hypothetical HayWired mainshock disrupts lifelines, 

supply chains, and the economy not only in the San Francisco 

Bay region but also disrupts the U.S. economy because of the 

economic importance of the region, particularly Silicon Valley 

(see Joint Ventures Silicon Valley, 2017). Dozens of significant 

aftershocks and fault afterslip (the Hayward Fault continues to 

creep in the weeks and months after the mainshock) will cause 

additional damage, requiring repeated repairs. Water supplies 

could be impaired for months, hindering household and 

Hudnut_fig5

A

B C

Figure 5. Photographs 

showing examples of types 

of damage to lifelines and 

infrastructure expected to 

occur along the Hayward 

Fault in the San Francisco 

Bay region, California, in 

an earthquake like the 

magnitude-7 mainshock 

modeled in the HayWired 

scenario. A, Damage to a 

road from fault offset; B, 

column of water gushing 

from a damaged pipeline; 

and C, large electrical 

conduit broken by fault 

offset. (Photographs from 

National Information 

Service for Earthquake 

Engineering-Pacific 

Earthquake Engineering 

Research Center (NISEEPEER), University of 

California, Berkeley, used 

with permission.)
Chapter A. The HayWired Scenario—How Can the San Francisco Bay Region Bounce Back from or Avert an Earthquake Disaster? 7

business recovery even in undamaged buildings. The effects 

on the region’s and the Nation’s economy will continue for 

years and will be costly and wide reaching. Because relatively 

few buildings are insured for earthquakes, owners will face 

challenges financing repairs. Occupants will have to find 

alternative housing or business space, and some people might 

be forced to move away from the region for at least some 

period of time and possibly not return.

Because our lives and economy are now fully intertwined 

with the Internet, the hypothetical disruption from the 

HayWired scenario is compounded. Our society takes for 

granted that information, goods, and services are available at 

a moment’s notice through the Internet. A large earthquake on 

the Hayward Fault could be the first major U.S. earthquake 

for which much of our commerce (“e-commerce,” including 

shipping and distribution management) and our daily 

interactions happen online.

What happens if Internet services are disrupted following a 

natural-hazard event? What will such a disaster mean to you, your 

family, your job, and your community? Scientists, engineers, and 

social scientists created the HayWired scenario to help you answer 

these questions and prioritize actions you can take now to protect 

lives, businesses, neighborhoods, and homes.

Preparing for the next large, damaging earthquake in the 

San Francisco Bay region is not an insurmountable task, and 

governments, critical infrastructure managers and providers, 

businesses, and residents have already taken enormous strides 

toward that goal. The 1989 M6.9 Loma Prieta earthquake served 

as a “wake-up call” for the bay region, inciting continued efforts 

to prepare for earthquakes, including a better understanding 

of earthquakes and their hazards, active risk reduction, and 

improvement of societal resilience. The HayWired scenario may be 

especially informative to young people in the San Francisco Bay 

region, many of whom were not yet born or have no memory of 

the Loma Prieta earthquake, as well as newcomers to the region. It 

is hoped that the HayWired scenario will energize new efforts and 

a next generation of proponents and champions acting along a line 

of purposeful, thoughtful, and responsible action, similar to earlier 

efforts, to help the bay region prepare for its next large earthquake.

Motivation—Why Revisit a Hayward 

Fault Scenario Now?

The HayWired earthquake scenario is being done at a time 

when the economy of the San Francisco Bay region is healthy 

and thriving. Strong economic growth comes with strains on 

infrastructure, affordable housing shortages, and more pronounced 

income disparities. Although the strength of the bay region’s 

economy is an asset in building more resilient infrastructure and 

communities, emerging issues increase the region’s vulnerability 

to an earthquake, and these issues need attention. 

It is time to update earthquake scenarios for the San 

Francisco Bay region to leverage new knowledge, capabilities, 

and developments. Some new key themes extend previous 

scenario analyses (see Objectives of the HayWired Scenario 

discussed below) to enhance collaborations with traditional 

audiences and to target new audiences to help increase the 

region’s resilience in a natural disaster.

The HayWired scenario includes the following new or 

updated developments that were not included (or given as 

much attention) in previous disaster and earthquake scenarios 

or studies (see appendix):

• A comparison of the use of ground-motion-estimation 

methods.

• An estimate of landslide probability and liquefaction 

probability.

• Forecasting effects of fault afterslip and aftershocks.

• Public expectations for the performance of new buildings 

and potential benefits of enhancing building-code 

requirements for new buildings.

• New methods to estimate urban search-and-rescue needs in 

response to building collapse and stalled elevators.

• Expected complications of fire following earthquake 

and the interoperability of portable water-supply 

systems used for firefighting.

Extraordinary Investment in Earthquake Resilience 

Already Accomplished

Earthquake scenarios and seismic-resilience efforts are 

not new to the San Francisco Bay region (see the appendix 

for background on previous initiatives and accomplishments). 

All told, the bay region has invested at least $25 billion 

(Association of Bay Area Governments, 2014a), and 

reportedly as much as $50 billion (KQED, 2014), in 

earthquake countermeasures since the 1989 magnitude-6.9 

Loma Prieta earthquake, strengthening potential points of 

failure in buildings and infrastructure. Concurrently, local 

and regional organizations have started to confront many 

of the complex, interrelated societal issues that can limit 

resilience. An extraordinary amount has already been done in 

the San Francisco Bay region to reduce the earthquake risk to 

communities and critical facilities and lifelines and to increase 

societal resilience in response to natural hazards.

However, the HayWired earthquake scenario shows that 

much more needs to be done. Many reasonable mitigation 

options can take decades to complete, such as the costly 

replacement of brittle water-distribution pipelines. Time is of 

the essence if decision makers wish to complete these actions 

before the next large earthquake actually occurs in the bay 

region. Society has a strong interest in continuing to strengthen 

infrastructure to reduce damage and better sustain us during 

response and recovery from a natural disaster. It also has a 

strong interest in planning and preparedness activities, not only 

engineering approaches, to improve community resilience.
8 The HayWired Earthquake Scenario—Earthquake Hazards

• Anticipation of environmental-health issues.

• A new method to estimate water-supply damage and 

service-restoration time in an earthquake sequence, and 

quantifying lifeline interdependencies.

• An estimation of communication and infrastructure 

damage and service restoration time.

• Potential benefits of earthquake early warning.

• Identification of potential long-term recovery 

challenges for the region’s communities.

• Impacts to and resilience of the digital economy and 

strategies to bolster economic resilience.

Objectives of the HayWired Scenario

Objectives of the HayWired scenario are to (1) improve 

the communication and use of earthquake-hazard science in 

decision-making, (2) advance basic knowledge of earthquake 

risks and to inform actions to reduce earthquake risks, and (3) 

help build community capacity to respond to and recover from 

earthquakes. Each of these objectives is given in greater detail 

below: 

1. Improve the communication of earthquake-hazard science 

for use in decision-making:

A. Support the use of earthquake-hazard science in risk 

reduction with engineering, environmental science, 

and social sciences.

B. Improve understanding among Earth scientists, 

engineers, and others of numerical simulation of 

earthquake ground motion for earthquake scenarios, 

as opposed to the use of empirical ground-motionprediction equations.

C. Improve understanding of the benefits of earthquake 

early warning.

D. Educate about liquefaction and landslides.

E. Educate about aftershocks and afterslip.

F. Educate about operational earthquake forecasts.

2. Advance basic knowledge of earthquake risks and inform 

actions to reduce earthquake risks:

A. Evaluate the societal impacts of the seismicperformance objectives in building codes. 

Evaluate public preferences for the seismic 

performance of new buildings. Identify an option 

for the public to provide input into the seismic 

performance of new buildings. Evaluate its costs 

and benefits. Educate community leaders and 

structural engineers about that new knowledge. 

Formulate an option for a community to enhance 

new buildings with a simple enhancement to the 

language of a building-code adoption ordinance.

B. Develop a new method to estimate water-supplynetwork recovery considering lifeline interaction, 

resource limitations, aftershocks, and afterslip, 

without reliance on expert opinion, proprietary 

computer models, or “black-box” software (only 

inputs and outputs can be viewed, not underlying 

code). Educate and facilitate conversations about 

reducing damage and speeding recovery of watersupply systems.

C. Educate and facilitate conversations about 

enhancing firefighting capabilities to resist fire 

following earthquake.

D. Develop methods to estimate the number of people 

trapped in collapsed buildings and inside stalled 

elevators. Educate and facilitate conversations about 

reducing the number of people potentially trapped 

inside stalled elevators.

E. Help anticipate environmental health issues.

F. Engage stakeholders in discussions about the 

vulnerabilities and resilience of cyber infrastructure 

and the Internet economy.

3. Help build community capacity to respond to and recover 

from earthquakes:

A. Facilitate conversations about lifeline-restoration 

interdependencies.

B. Help inform and stimulate the development of 

predisaster plans and policy interventions in 

emergency management, hazard mitigation, and 

recovery management that can work toward 

keeping residents and businesses in their 

communities.

C. Provide materials for emergency response, business 

continuity, and recovery exercises, as well as for 

many other uses.

HayWired Scenario Process Will 

Tackle Urgent Questions

From April 2017 through April 2018, the HayWired 

Coalition and the authors of the HayWired scenario plan to 

continue the process of working together on developing it. 

One goal is to improve the transfer of scientific information to 

those who need it and to expand the application of science and 

earthquake engineering to earthquake-risk reduction. We plan 
Chapter A. The HayWired Scenario—How Can the San Francisco Bay Region Bounce Back from or Avert an Earthquake Disaster? 9

to advance the discussion of key earthquake issues in the San 

Francisco Bay region, staging new conversations that update 

resilience strategies and boost ongoing decision-making and 

policy-making on seismic issues. We seek to reach audiences 

beyond emergency responders (who use scenarios extensively) 

to directly engage communities and businesses in earthquake 

response and recovery planning.

Using the HayWired scenario as a topic for focus during 

the coming year of workshops and meetings presents an 

opportunity to ask and address new, urgent questions about 

disaster resilience following an earthquake. In addition to 

the objectives listed in the preceding section, the following 

questions may also help to provoke thoughts for discussion in 

the upcoming year:

• Why might one want to use numerical simulation 

of earthquake ground motion rather than empirical 

ground-motion-prediction equations to better 

understand and prepare for earthquakes?

• How well do building codes intend new buildings to 

perform in a large metropolitan earthquake, and how 

well do those performance objectives align with the 

public’s preferences?

• How would a simple increase in the design strength of 

new buildings reduce building impairment following 

an earthquake, and how much would such strength 

increases cost? 

• How many people should fire departments anticipate 

having to rescue from collapsed buildings and stalled 

elevators?

• How can fire departments better prepare for fire 

following earthquake with portable water-supply 

systems, and how can fire departments that already 

have such systems coordinate their use?

• How do lifelines—water, power, communication, 

roads, and transit—interact during and following an 

earthquake, and how do they interact during damage 

restoration?

• How can a water utility estimate earthquake damage, 

restoration time, and the macroeconomic losses 

resulting from water-service interruption without 

relying on expert opinion, proprietary computer 

models, or black-box software? 

• To what extent do shaking, liquefaction, landslide, fault 

slip, aftershocks, and afterslip contribute to watersystem damage and hinder recovery?

• What can a water utility do to speed restoration and 

reduce its dependence on other lifelines?

• How many injuries can be avoided by combining 

earthquake early warning with “drop-cover-andhold-on” actions taken by people to protect themselves 

when ground shaking starts?

• What will happen to an Internet-dependent society 

when a large earthquake occurs?

• How can resilience be further improved to ensure 

that supply chains for many basic needs such as 

food, water, fuel, and pharmaceuticals will not be 

disrupted?

• What are the environmental-health concerns following 

a large earthquake?

• How will earthquake early warning, fault afterslip, 

aftershocks, and fire following earthquake affect 

disaster management and recovery?

• What types of long-term outcomes could communities 

and neighborhoods face as a result of earthquake 

damage, impacts, and cascading effects, including the 

potential displacement of people and businesses?

• Long-term community recovery will be at risk; how 

will people collectively respond?

HayWired Scenario Builds on the 

Success of Past SAFRR Scenarios

The USGS Science Application for Risk Reduction project, as did its predecessor the Multi-Hazards Demonstration 

project, uses the latest Earth-science advances in novel ways 

to inform decisions on how to improve disaster resilience. 

SAFRR has created detailed, science- and natural-hazardevent-based scenarios of (1) a M7.8 San Andreas Fault earthquake in southern California (ShakeOut—Jones and others, 

2008), (2) atmospheric-river storms rivaling those that caused 

California’s Great Flood of 1862 (ARkStorm—Porter and 

others, 2010), (3) an earthquake the size of the 2011 Tohoku, 

Japan, earthquake (M9.0), and (4) a tsunami generated off 

the Alaska Peninsula that severely impacts the Ports of Los 

Angeles and Long Beach (SAFRR Tsunami—Ross and others, 

2013) and now HayWired. Each of these scenarios was developed to provide science for decision-making and to engage 

potential users of the information throughout the scenario 

development process.

Each SAFRR scenario depicts a scientifically plausible 

natural-hazard source and its cascading effects, with impacts and 

consequences severe enough to demand attention but not so severe 

as to be unbelievable. The scenarios address interacting hazards 

and involve multiple science, engineering, and other disciplines 

and user communities. The scenarios explore previously unknown 

or less-studied vulnerabilities and cascading effects that only 

come into focus when one looks beyond impacts and stakeholders 

considered in isolation. They provide decision makers with clear 
10 The HayWired Earthquake Scenario—Earthquake Hazards

HayWired Coalition Partners

Alameda County Mayors’ Conference

Alameda County Sheriff’s Office, Office of Emergency Services

American Red Cross

Art Center College of Design

ARUP—Design and Engineering Consultants

Association of Bay Area Governments—Metropolitan 

 Transportation Commission

Aurecon

Bay Area Center for Regional Disaster Resilience

Bay Area Council

Bay Area Rapid Transit Authority

Bay Area Urban Area Security Initiative

Bay Planning Coalition

Boston University

Business Recovery Managers Association

California Business, Consumer Services, and Housing Agency

California Department of Public Health

California Department of Transportation

California Earthquake Authority

California Earthquake Clearinghouse

California Geological Survey

California Governor’s Office of Business and Economic

 Development

California Governor’s Office of Emergency Services

California Independent Oil Marketers Association

California ISO

California Public Utilities Commission

California Resiliency Alliance

California Seismic Safety Commission

Carnegie Melon University Silicon Valley

City and County of San Francisco

City of Berkeley

City of Fremont

City of Hayward

City of Oakland 

City of Oakland, Fire Department

City of San Francisco, Department of Emergency Management

City of Walnut Creek 

Contra Costa County Mayors’ Conference

Earthquake Country Alliance

Earthquake Engineering Research Institute

East Bay Municipal Utility District

Federal Emergency Management Agency

Joint Venture Silicon Valley

Laurie Johnson Consulting|Research

March Studios

Marin Economic Consulting

MMI Engineering

Office of the Mayor, City and County of San Francisco

Pacific Earthquake Engineering Research Center

Pacific Gas and Electric

Palo Alto University

Price School of Public Policy and Center for Risk and 

 Economic Analysis of Terrorism Events, University of 

 Southern California

Rockefeller Foundation—100 Resilient Cities

San Jose Water Company

Southern California Earthquake Center

SPA Risk LLC

SPUR

Strategic Economics

Structural Engineers Association of Northern California

The Brashear Group LLC

University of California Berkeley Seismological Laboratory

University of Colorado Boulder

University of Southern California 

U.S. Department of Homeland Security

U.S. Geological Survey

Wells Fargo

explanations and information for mitigation and preparedness 

actions and have been used for national-to-local disaster-response 

exercises and planning.

As with previous SAFRR-led scenarios, the initiating 

event of the HayWired scenario is described (the mainshock 

fault rupture and strong ground motion) and then expanded 

to cover secondary hazards, in particular liquefaction, 

landslides, aftershocks, and afterslip, which are described 

in this volume, USGS SIR 2017–5013–A–H ( https://doi.

org/10.3133/sir20175013v1). Physical damage to structures, 

critical facilities, and infrastructure, environmental health 

concerns, and social and economic consequences for policy 

consideration are planned to be described in two following 

volumes of SIR 2017–5013. This broad-based approach is 

intended to inform decisions about how to enhance earthquake 

resilience.

A combined approach is used that involves working as 

a team to describe not only the natural-hazard event and its 

cascading effects but also the impacts and consequences. The 

combined team includes the following:

• Physical scientists working to understand the natural

hazard.

• Engineers working to understand the earthquake effects

and solving problems of how to improve the performance

and recovery of critical facilities and infrastructure.

• Biogeochemists shedding light on the human health

aspects of building materials exposed during a disaster.

• Social scientists working to understand the

consequences to communities—economic, health, and

quality of life.

Through new and innovative methods of crafting disaster 

scenarios, developed in each case by a USGS SAFRR-led team, 

and beginning with ShakeOut, scenarios and exercises have 

been used as effective methods to engage the public and elected 

officials. One goal of the HayWired scenario is to expand efforts 

to coordinate with decision makers who can use the scenario. 

Another goal is to engage residents of the San Francisco Bay 

region, especially to update and refresh similar work (for example, 
Chapter A. The HayWired Scenario—How Can the San Francisco Bay Region Bounce Back from or Avert an Earthquake Disaster? 11

Algermissen and others, 1972; Steinbrugge and others, 1987; and 

Earthquake Engineering Research Institute, 1996, among others). 

In terms of culture and community, the HayWired scenario builds 

on previous efforts in the bay region to form a new coalition to 

identify and address remaining and evolving vulnerabilities to 

earthquakes and other disasters and to further reduce risk.

The HayWired Coalition

SAFRR has partnered with a number of organizations to 

form a group named the HayWired Coalition to disseminate 

and make use of the HayWired scenario (see sidebar, HayWired 

Coalition Partners). The HayWired Coalition aims to identify 

the scenario’s potential impacts on their constituents and 

to align the scenario with the concerns of their respective 

communities. The coalition began forming in mid-2016 to 

help with the process of receiving input and organizing the 

interactions of a broad range of stakeholders. The coalition has 

assisted the scenario-development team in helping to identify 

previously unrecognized vulnerabilities of communities, 

lifelines, infrastructure, and supply chains. In the development 

of previous disaster scenarios, much of this interaction took 

place organically after the scenario was finalized and published. 

Instead, for the HayWired scenario, a deliberate effort is 

underway to engage partners, help them identify mitigation 

actions, and begin to use the scenario to reduce risk even before 

full publication of SIR 2017–5013.

A Call to Action for the Coming Year

The release of SIR 2017–5013–A–H, detailing the 

hazards of the HayWired scenario earthquake, comes about 1 

year before the hypothetical date of the HayWired mainshock 

of April 18, 2018. To better understand the potential impacts 

and consequences of the HayWired scenario, Earth scientists, 

engineers, and social scientists plan to use the upcoming year 

(April 2017 through April 2018) to share information and to 

work with State of California and San Francisco Bay region 

partners in the HayWired Coalition, representing agencies and 

experts in lifelines, engineering, local government, publichealth, business, and emergency management to further 

enhance earthquake- and disaster-resilience planning, policy, 

and action across the region.

Through a collaborative and intensive series of 

workshops and user-engagement activities, scenario 

researchers and potential users of the information plan to 

build on the San Francisco Bay region’s already strong 

foundation of earthquake preparedness, to further strengthen 

infrastructure, and to help communities become even more 

resilient. The HayWired scenario is expected to serve as an 

earthquake-planning resource for many years to come, to 

help heighten and sustain attention, to focus action on the 

San Francisco Bay region’s remaining vulnerabilities, and to 

continue to reach out to communities and the public.

References Cited

Aagaard, B.T., Blair, J.L., Boatwright, J., Garcia, S.H., Harris, 

R.A., Michael, A.J., Schwartz, D.P., and DiLeo, J.S., 2016,

Earthquake outlook for the San Francisco Bay region 2014–

2043 (ver. 1.1, August 2016): U.S. Geological Survey Fact

Sheet 2016–3020, 6 p., accessed April 10, 2017, at http://

dx.doi.org/10.3133/fs20163020.

Algermissen, S.T., Rinehart, W.A., Dewey, J., Steinbrugge, 

K.V., Degenkolb, H.J., Cluff, L.S., McClure, F.E.,

Gordon, R.F., Scott, S., and Lagorio, H.J., 1972, A study

of earthquake losses in the San Francisco Bay area:

Washington, D.C., National Oceanic and Atmospheric

Administration, Office of Emergency Preparedness, 220 p.

Association of Bay Area Governments, 2014a, LP25—Policy 

Actions, Loma Prieta 25 Symposium: Association of Bay 

Area Governments, accessed March 31, 2017, at http://

resilience.abag.ca.gov/wp-content/documents/LP25/LP25_

PolicyActions.pdf.

Association of Bay Area Governments, 2014b, Cascading 

failures—Earthquake threats to transportation and utilities: 

Association of Bay Area Governments, 47 p., accessed April 12, 

2017, at http://resilience.abag.ca.gov/wp-content/documents/

Cascading_Failures/InfrastructureReport_2014.pdf.

California Governor’s Office of Emergency Services and 

Federal Emergency Management Agency, 2016, Bay 

Area earthquake plan: California Governor’s Office of 

Emergency Services and Federal Emergency Management 

Agency, 46 p., accessed April 12, 2017, at http://www.

caloes.ca.gov/PlanningPreparednessSite/Documents/

BayAreaEQConops(Pub_Version)_2016.pdf.

California Seismic Safety Commission, 1991, Loma Prieta’s 

call to action—Report on the Loma Prieta earthquake of 

1989: California Seismic Safety Commission, 97 p., accessed 

April 12, 2017, at https://ia801402.us.archive.org/33/items/

lomaprietascallt1991cali/lomaprietascallt1991cali.pdf.

City and County of San Francisco, 2016, Resilient San Francisco, 

stronger today, stronger tomorrow: City and County of San 

Francisco, 137 p., accessed April 12, 2017, at http://sfgov.

org/orr/sites/default/files/documents/Resilient%20San%20

Francisco_0.pdf.

Earthquake Engineering Research Institute, 1996, Scenario for 

a magnitude 7.0 earthquake on the Hayward Fault: Oakland, 

Calif., Earthquake Engineering Research Institute, 109 p.

Earthquake Engineering Research Institute, 2006, Managing 

risk in earthquake country—Estimated losses for a repeat 

of the 1906 San Francisco earthquake and the earthquake 

professionals’ action agenda for northern California: 

Earthquake Engineering Research Institute, 24 p., accessed 

April 12, 2017, at http://www.1906eqconf.org/mediadocs/

managingrisk.pdf.
12 The HayWired Earthquake Scenario—Earthquake Hazards

Johnson, L.A., 2014, Lifelines interdependency study|report: 

City and County of San Francisco Lifelines Council, 47 

p., accessed April 12, 2017, at http://sfgov.org/esip/sites/

default/files/Documents/homepage/LifelineCouncil%20

Interdependency%20Study_FINAL.pdf.

Joint Ventures Silicon Valley, 2017, Silicon Valley index: 

Joint Ventures Silicon Valley web page, accessed February 

28, 2017, at http://www.siliconvalleyindicators.org/.

Jones, L.M., Bernknopf, R., Cox, D., Goltz, J., Hudnut, K., 

Mileti, D., Perry, S., Ponti, D., Porter, K., Reichle, M., 

Seligson, H., Shoaf, K., Treiman, J., and Wein, A., 2008, 

The ShakeOut scenario: U.S. Geological Survey OpenFile Report 2008–1150 and California Geological Survey 

Preliminary Report 25, 312 p. and appendixes, accessed 

April 12, 2017, at https://pubs.usgs.gov/of/2008/1150/.

Kircher, C.A., Seligson, H.A., Bouabid, J., and Morrow, 

G.C., 2006, When the big one strikes again—Estimated 

losses due to a repeat of the 1906 San Francisco 

earthquake: Earthquake Spectra, v. 22, no. S2, p. 

S297–S339, accessed April 12, 2017, at http://www.

nehrpscenario.org/wp-content/uploads/2009/03/kircher_

etal.pdf.

KQED, 2014, 25 years after the Loma Prieta earthquake, 

are we safer?: KQED Science web page, accessed April 

11, 2017, at https://ww2.kqed.org/science/2014/10/13/25-

years-after-the-loma-prieta-earthquake-are-we-safer/.

Lawson, A.C., chairman, 1908, The California earthquake 

of April 18, 1906—Report of the State Earthquake 

Investigation Commission: Washington, D.C., Carnegie 

Institution of Washington Publication 87, 2 vols.

Lienkaemper, J.J., Williams, P.L., and Guilderson, T.P., 2010, 

Evidence for a twelfth large earthquake on the southern 

Hayward Fault in the past 1900 years: Bulletin of the 

Seismological Society of America, v. 100, no. 5A, p. 

2024–2034, doi: 10.1785/0120090129.

Lienkaemper, J.J., Dawson, T.E., Personius, S.F., Seitz, 

G.G., Reidy, L.M., and Schwartz, D.P., 2002, A record of 

large earthquakes on the southern Hayward Fault for the 

past 500 years: Bulletin of the Seismological Society of 

America, v. 92, no. 7, p. 2637–2658.

Maffei, Janiele, 2010, The coming Bay Area earthquake—2010 update of scenario for a magnitude 7.0 

earthquake on the Hayward Fault: Earthquake Engineering Research Institute Northern California Chapter, 115 

p., accessed April 12, 2017, at http://www.eerinc.org/

wp-content/uploads/2009/06/Building_Earthquake_Resiliency_in_SF_Bay_Area_V17-2.pdf.

National Research Council, 1994, Practical lessons from 

the Loma Prieta earthquake: Washington, D.C., National 

Academy Press, 288 p. doi: 10.17226/2269.

Northridge 20 Symposium, 2014, Northridge 20 Symposium 

summary report—The 1994 Northridge earthquake—Impacts, 

outcomes, and next steps: Northridge 20 Symposium, January 

16-17, 2014, Los Angeles, California, 30 p., accessed 

April 12, 2017, at http://www.northridge20.org/wp-content/

uploads/2014/05/Northridge20_Summary_Report.pdf.

Porter, K., Wein, A., Alpers, C., Baez, A., Barnard, P., Carter, J., 

Corsi, A., Costner, J., Cox, D., Das, T., Dettinger, M., Done, J., 

Eadie, C., Eymann, M., Ferris, J., Gunturi, P., Hughes, M., Jarrett, 

R., Johnson, L., Dam Le-Griffin, H., Mitchell, D., Morman, S., 

Neiman, P., Olsen, A., Perry, S., Plumlee, G., Ralph, M., Reynolds, 

D., Rose, A., Schaefer, K., Serakos, J., Siembieda, W., Stock, J., 

Strong, D., Sue Wing, I., Tang, A., Thomas, P., Topping, K., and 

Wills, C.; Jones, L., chief scientist, Cox, D., project manager, 2011, 

Overview of the ARkStorm scenario: U.S. Geological Survey 

Open-File Report 2010–1312, 183 p. and appendixes, accessed 

April 10, 2017, at https://pubs.usgs.gov/of/2010/1312/.

Risk Management Solutions, Inc., 2008, 1868 Hayward 

earthquake—140-year retrospective: Risk Management 

Solutions, Inc., special report, revised 2013, 26 p., accessed April 

12, 2017, at http://forms2.rms.com/rs/729-DJX-565/images/

eq_1868_hayward_eq_retrospective.pdf.

Ross, S., and Jones, L., eds., 2013, The SAFRR (Science 

Application for Risk Reduction) tsunami scenario: U.S. 

Geological Survey Open-File Report 2013–1170 and California 

Geological Survey Preliminary Report 229, accessed April 11, 

2017, at https://pubs.usgs.gov/of/2013/1170/.

San Francisco Bay Area Planning and Urban Research Association, 

2010, After the disaster—Rebuilding our transportation 

infrastructure: San Francisco Bay Area Planning and Urban 

Research Association, 30 p., accessed April 12, 2017, at http://

www.spur.org/publications/spur-report/2010-07-06/after-disaster.

Steinbrugge, K., Lagorio, H.J., Davis, J.F., Bennett, J.H., Borchardt, 

G., and Toppozada, T.R., 1987, Earthquake planning scenario 

for a magnitude 7.5 earthquake on the Hayward Fault in the 

San Francisco Bay area: California Department of Mines and 

Geology, Special Publication 78, 235 p.

U.S. Geological Survey, 2014, Earthquake planning scenario—

ShakeMap for Haywired M7.05-scenario: U.S. Geological 

Survey web page, accessed April 11, 2017, at https://earthquake.

usgs.gov/scenarios/eventpage/ushaywiredm7.05_se#shakemap?

source=us&code=gllegacyhaywiredm7p05_se.

U.S. Geological Survey and cooperators, 1990, The next big 

earthquake in the Bay Area may come sooner than you think—

Are you prepared?: U.S. Geological Survey, newspaper insert, 

23 p., accessed April 18, 2017, at https://pubs.er.usgs.gov/

publication/70186988.

Working Group on California Earthquake Probabilities, 1990, 

Probabilities of large earthquakes in the San Francisco Bay 

region, California: U.S. Geological Survey Circular 1053, 

51 p., accessed April 11, 2017, at https://pubs.usgs.gov/

circ/1990/1053/report.pdf.
Chapter A. The HayWired Scenario—How Can the San Francisco Bay Region Bounce Back from or Avert an Earthquake Disaster? 13

Appendix. History of San Francisco Bay Region Earthquake Scenarios and 

Seismic-Resilience Efforts

Figure 6. Image of the cover of “The Next 

Big Earthquake in the Bay Area May Come 

Sooner Than You Think—Are You Prepared?” 

(U.S. Geological Survey and cooperators, 

1990). This 1990 newspaper insert, updated and 

reissued again in 1994, was widely distributed 

in newspapers in the San Francisco Bay region. 

Specifically designed for the general public, 

it is one of the first documents with a holistic 

view of the region’s earthquake risk and needed 

preparedness actions.

Algermissen and others (1972) and Steinbrugge and 

others (1987) provided early and comprehensive analyses of 

a hypothetical Hayward Fault earthquake. They focused on 

lifelines in their planning scenario for a M7.5 earthquake on the 

fault. The M6.9 Loma Prieta earthquake of October 17, 1989, 

helped to generate a focus and a more regional perspective 

on seismic vulnerabilities in the San Francisco Bay region. 

Documents such as the California Seismic Safety Commission’s 

(CSSC) 1991 report, “Loma Prieta’s Call to Action” (California 

Seismic Safety Commission, 1991), and the National Research 

Council’s (NRC) 1994 report, “Practical Lessons from the 

Loma Prieta Earthquake” (National Research Council, 1994), 

established a baseline for assessing the region’s major seismic 

vulnerabilities and policy needs at the time, as well as a baseline 

for considering the region’s progress on these issues over the 

past 26 years. However, as these reports aptly cautioned, the 

1989 earthquake was not a rigorous test of the region’s built 

environment, preparedness, or hazard mitigation.

The combination of the 1989 Loma Prieta earthquake and 

follow-on assessments also helped to create a more integrated, 

regional perspective on the earthquake vulnerability of the San 

Francisco Bay region and encouraged people to work together as 

a region on earthquake-hazard mitigation, response, and recovery 

planning. The newspaper insert, “The Next Big Earthquake 

in the Bay Area May Come Sooner Than You Think. Are You 

Prepared?” (U.S. Geological Survey and cooperators, 1990), 

first published in 1990 and updated and reissued in 1994, is one 

of the first widely disseminated public-education documents 

with a holistic view of the region’s earthquake risk and needed 

preparedness actions (fig. 6). It gave greater public visibility 

to the Working Group on California Earthquake Probabilities’ 

1990 report, “Probabilities of Large Earthquakes in the San 

Francisco Bay Region, California” (Working Group on California 

Earthquake Probabilities, 1990), which showed that the region’s 

high-risk geologic faults had a combined 67-percent likelihood of 

generating another M7 earthquake in the next 30 years. This was 
14 The HayWired Earthquake Scenario—Earthquake Hazards

a substantial increase in risk over the group’s 1988 estimation of a 

50-percent likelihood of such an event.

It was also in the 1980s and 1990s that the Bay Area 

Regional Earthquake Preparedness Project (BAREPP) existed and 

helped to develop plans, model policies and ordinances for seismic 

safety, and build coalitions across local governments, lifeline 

providers, and in the housing and business sectors. Originally 

formed by the CSSC, BAREPP became part of the California 

Governor’s Office of Emergency Services (Cal OES) and was 

co-housed with the Association of Bay Area Governments 

(ABAG) for a number of years before it closed in about 2000, and 

its staff were mostly absorbed into Cal OES.

At the 1995 Earthquake Engineering Research Institute 

(EERI) annual meeting, a multidisciplinary panel of speakers 

laid out the potential impacts and issues associated with a M7.2 

earthquake on the northern end of the Hayward Fault with ground 

motions directed primarily southward along the fault system. 

Again, this was fundamental work to pull together a regional 

perspective on seismic vulnerabilities and policy needs with 

an emphasis on the heavily urbanized east bay corridor of San 

Francisco Bay. The scenario report was recapped and updated in 

a 2010 report by Janiele Maffei and the EERI Northern California 

Chapter, “The Coming Bay Area Earthquake—2010 Update 

of Scenario for a Magnitude 7.0 Earthquake on the Hayward 

Fault” (Maffei, 2010). It is one of the most recent assessments 

of mitigation activities across the region, as well as of the 

likely effects of a Hayward Fault earthquake. Advances made 

in mitigating the earthquake risk for critical infrastructure and 

facilities, improving emergency-response planning, and initial 

steps taken to address vulnerable commercial and residential 

building stock are compiled throughout the report. It was 

developed as part of regional activities surrounding the 140th 

anniversary of the 1868 Hayward Earthquake. It also draws on 

damage and loss estimates in a 2008 Risk Management Solutions 

(RMS) special report, “1868 Hayward Earthquake: 140-Year 

Retrospective” (Risk Management Solutions, Inc., 2008). Using 

its proprietary software, RMS estimated total economic losses 

of $174.3 billion from shaking and fire following earthquake 

damages to residential and commercial properties for a M7 

earthquake on the Hayward Fault. Maffei (2010) concludes with: 

Ten years into the new century, we have not yet reached 

an acceptable level of seismic safety, and it is clearly 

necessary for earthquake professionals to continue their 

advocacy. But they must take their message beyond 

enclosed spaces out to the stakeholders in whatever 

arena they can be found. Every private property owner 

in the Bay Area must come to understand the losses 

each and all of us will face, and how those impacts will 

harm the quality of life we enjoy in this unique part of 

the world. 

The economic recession in 2010 was considered to constrain the 

capacity to invest in earthquake mitigation.

Another major multidisciplinary scenario and regional 

seismic-safety-policy assessment occurred in 2006 for the 100th 

anniversary of the 1906 Great San Francisco earthquake on 

the San Andreas Fault. The EERI Northern California Chapter 

undertook an effort to integrate seismic-safety progress and 

needs in the San Francisco Bay region that are contained in 

the 2006 anniversary conference briefing packet, “Managing 

Risk in Earthquake Country—Estimated Losses for a Repeat 

of the 1906 San Francisco Earthquake and the Earthquake 

Professionals’ Action Agenda for Northern California” 

(Earthquake Engineering Research Institute, 2006). Cal OES, 

the Federal Emergency Management Agency (FEMA) and 

other organizations also helped to fund the development of the 

scenario presented at the 2006 conference, and it was used as 

the basis for a Golden Guardian exercise led by Cal OES (see 

http://cdphready.org/cal-oes-annual-exercise-series-goldenguardian/). The scenario results are summarized in a 2006 paper 

by Kircher and others, “When the Big One Strikes Again—

Estimated Losses due to a Repeat of the 1906 San Francisco 

Earthquake” (Kircher and others, 2006). A key feature of the 

modeling effort was the detailed update of the building stock for 

the 19-county northern California region. The loss estimation 

results focused primarily on casualties, building damage, and 

economic losses.

Other regional emergency-planning work in the San 

Francisco Bay region has tended to look at either a very large 

M7.9 San Andreas Fault earthquake or a M7.0 Hayward 

Fault earthquake, for which the 2006 and 2008 efforts, 

respectively, have served as key sources of information. Some 

additional noteworthy resources with a regional perspective on 

immediate earthquake impacts and needs in the San Francisco 

Bay region are:

• The San Francisco Bay Area Planning and Urban 

Research Association (SPUR) 2010 report, “After the 

Disaster: Rebuilding our Transportation Infrastructure” 

(San Francisco Bay Area Planning and Urban Research 

Association, 2010). This report looks at transportation 

system redundancy on a corridor-by-corridor basis and 

the effects of failures in each of these corridors, as well 

as regionwide.

• The Bay Area Urban Areas Security Initiative regional 

catastrophic earthquake plans (see http://www.

bayareauasi.org/catastrophicplans/). Many of these plans 

were developed in about 2010–2011 and use a M7.9 

San Andreas Fault earthquake scenario. The plans cover 

debris removal, donations management, interim housing, 

logistics, mass care and sheltering, mass fatality, mass 

transportation and evacuation, and volunteer management. 

• The ABAG 2014 report, “Cascading Failures: Earthquake 

Threats to Transportation and Utilities” (Association of 

Bay Area Governments, 2014b). This report considers 

impacts to airports, transportation, fuel, electricity, and 

water, as well as system interdependencies considering 

M7.0 Hayward Fault, M7.9 San Andreas Fault, and M6.8 

Concord Fault earthquake scenarios. 

• The ABAG 2015 reports, “Stronger Housing, Safer 

Communities” (see http://resilience.abag.ca.gov/projects/

stronger_housing_safer_communities_2015/). These 
Chapter A. The HayWired Scenario—How Can the San Francisco Bay Region Bounce Back from or Avert an Earthquake Disaster? 15

reports evaluate the seismic and flood vulnerability of 

the region’s communities and housing stock and provide 

strategies for local governments to mitigate those risks. 

For seismic vulnerability, it looks at areas where strong 

ground shaking is expected from M7.8 (San Andreas 

Fault) and M6.9 (Hayward Fault) earthquakes. Data 

developed for community-vulnerability assessments 

will inform the communities-at-risk assessment in the 

HayWired scenario. 

• The Cal OES-FEMA 2016 report, “Bay Area 

Earthquake Plan,” (California Governor’s Office 

of Emergency Services and Federal Emergency 

Management Agency, 2016). This is a catastrophic 

planning document that looks at both M7.0 Hayward 

Fault and M7.9 San Andreas Fault earthquake 

scenarios. The M7.0 Hayward Fault scenario 

has an epicenter in north Richmond and ruptures 

southward. There is a general-audience video for the 

plan at https://www.fema.gov/media-library/assets/

videos/118139/.

The year 2014 marked the 25th and 20th anniversaries, 

respectively, of two significant California earthquakes—

the 1989 M6.9 Loma Prieta and the 1994 M6.7 Northridge 

earthquakes. Commemorative symposiums were held both in 

Los Angeles in January and Oakland in October 2014. Both 

of these symposia acknowledged the tremendous seismicresilience work that has been accomplished in California over 

the past decades (Association of Bay Area Governments, 

2014a; Northridge 20 Symposium, 2014). 

Resilience strategies (developed with funding from the 

Rockefeller Foundation—100 Resilient Cities initiative) 

in the cities of Berkeley (see http://www.ci.berkeley.ca.us/

Resilience/), San Francisco (see http://sfgsa.org/resilient-sf/), 

and Oakland (see https://beta.oaklandca.gov/issues/resilientoakland/) provide more city-specific information on previous 

seismic-risk assessments, past resilience investments, and 

future strategies and plans. Additional detailed seismic-risk 

assessments for San Francisco include:

• The SPUR Resilient City initiative that has a series 

of policy papers on needed improvements to San 

Francisco’s lifelines, new buildings, and existing 

buildings, particularly housing, to ensure that residents 

can safely stay in the city following a major earthquake 

(see http://www.spur.org/featured-project/resilientcity).

• The Citizens Advisory Panel on Seismic Safety 

(CAPSS) studies assessed the seismic vulnerability 

of San Francisco’s private building stock and looked 

at potential mitigation needs based on a series of 

scenarios. This was then turned into the 50-task, 

30-year Earthquake Safety Improvement Program 

(ESIP). CAPSS and ESIP documents are available at 

http://sfgov.org/esip/program/. 

• The San Francisco Lifelines Council’s lifelines 

interdependency study (Johnson, L.A., 2014), 

which resulted in a 5-year collaborate work 

program for lifeline operators (see http://sfgov.

org/esip/sites/default/files/Documents/homepage/

LifelineCouncil%20Interdependency%20Study_

FINAL.pdf).

• The City of San Francisco’s resilience strategy (City 

and County of San Francisco, 2016), which provides 

an integrated approach to hazard planning (see http://

sfgov.org/orr/resilient-san-francisco/).

The ABAG Loma Prieta (LP) 25 report (Association of Bay 

Area Governments, 2014a), identifies four key seismic-safety 

issues where significant policy gaps remain and encourages 

the San Francisco Bay region’s more than 100 cities to work 

together on a regional legislative agenda:

• Update building codes.—Adopt building-code standards 

to improve the seismic performance of new and existing 

buildings and ensure that building codes meet community 

performance expectations.

• Upgrade vulnerable apartments and condominiums.—

Enact statewide guidelines for the identification, 

evaluation, and retrofit of seismically unsafe apartment 

and condominium buildings.

• Develop financial incentives.—Establish regional financialincentive programs for improving the seismic safety of 

apartments and condominium buildings.

• Convene lifeline providers and cities.—Establish a State 

lifelines council and convene regional lifeline councils in 

the San Francisco Bay region and in southern California.

Chapter B

Overview of the HayWired Scenario Earthquake-Hazards 

Volume

By Ruth A. Harris

The HayWired Earthquake Scenario—Earthquake Hazards

Edited by Shane T. Detweiler and Anne M. Wein

Scientific Investigations Report 2017–5013–A–H

[Also see https://doi.org/10.3133/sir20175013]

Introduction

The HayWired scenario is a hypothetical earthquake 

sequence that is being used to better understand hazards for 

the San Francisco Bay region during and after an earthquake 

of magnitude 7 on the Hayward Fault. The Working Group 

on California Earthquake Probabilities calculates that

there is a 33-percent likelihood of a large (magnitude 6.7 

or greater) earthquake occurring on the Hayward Fault 

in the next few decades (Aagaard and others, 2016). 

A large Hayward Fault earthquake will produce strong 

ground shaking, permanent displacement of the Earth’s 

surface, landslides, liquefaction (soils becoming liquid-like 

during shaking), and subsequent earthquakes, known as 

aftershocks.

The most recent large earthquake on the Hayward 

Fault occurred on October 21, 1868, and it ruptured 

the southern part of the fault. The 1868 magnitude-6.8 

earthquake occurred when the San Francisco Bay region 

had far fewer people, buildings, and infrastructure (roads, 

communication lines, and utilities) than it does today, yet 

the strong ground shaking from the earthquake still caused 

significant building damage and loss of life (fig. 1). Because 

the region is now densely populated and has many more 

homes and buildings and much more critical infrastructure 

than it did in 1868, the next large Hayward Fault 

earthquake is anticipated to affect thousands of structures 

and disrupt the lives of millions of people. To help provide 

the crucial scientific information that the San Francisco Bay 

region will need to prepare for the next large earthquake, 

The HayWired Earthquake Scenario—Earthquake Hazards 

volume describes the strong ground shaking modeled in 

the scenario and the hazardous movements of the Earth’s 

surface that the shaking will activate.

Modeling the Mainshock

The HayWired scenario mainshock consists of a threedimensional (3D) computer simulation of a hypothetical 

magnitude 7.0 earthquake that begins on April 18, 2018, at 

4:18 p.m. on the Hayward Fault at 8 kilometers (about 5 miles) 

depth beneath Oakland (see Aagaard, Boatwright, and others, 

this volume). In less than a minute, the Hayward Fault ruptures 

along more than 83 kilometers (about 52 miles) of its length, both 

northward toward San Pablo Bay and southward toward Fremont. 

The scenario mainshock generates ground offset along both the 

northern and southern parts of the fault. The HayWired mainshock 

is larger than the 1868 Hayward earthquake and produces strong 

ground shaking over a larger part of the San Francisco Bay region.

Determining and understanding the impacts of expected 

future large earthquakes requires realistic estimates of the ground 

shaking. The 3D computer simulations of strong ground shaking 

caused by the HayWired mainshock are an improvement over 

those based solely on ground-motion prediction equations that do 

not include the details of how the geology of the San Francisco 

Bay region affects earthquakes (see Porter, this volume). Strong 

ground shaking in general is primarily affected by three factors—

(1) earthquake magnitude, (2) proximity of a site (such as a 

building) to an earthquake, and (3) the geologic conditions under a 

site. Additional important parts of the simulations of the HayWired 

mainshock are the 3D structure of the Earth’s crust below the bay 

region, particularly the basins, and the incorporation of a factor to 

account for the fact that the Hayward Fault slowly moves (creeps) 

between large earthquakes. In the HayWired ground-shaking 

simulations, more than 40 percent of the San Francisco Bay region 

experiences shaking stronger than Modified Mercalli Intensity VII, 

a level of shaking that typically causes slight to moderate damage 

to better built structures and considerable damage to poorly built 

structures (fig. 2).
18 The HayWired Earthquake Scenario—Earthquake Hazards

Figure 1. U.S. Geological Survey ShakeMap of the San Francisco Bay region, California, showing the inferred intensity of ground shaking in 

the 1868 magnitude-6.8 Hayward earthquake (compare to fig. 2, which is for the larger, HayWired scenario mainshock) (figure modified from 

Boatwright and Bundock, 2008). Red lines are geologic faults; black line shows the part of the Hayward Fault that ruptured in 1868; diamonds 

show locations of 1868 damage reports used to infer the Modified Mercalli Intensity, MMI. The 1868 earthquake damaged or destroyed 

many buildings throughout the region and caused the deaths of about 30 people. The inset photograph shows one wrecked building in what 

is now San Francisco’s Financial District (courtesy of the Karl V. Steinbrugge Collection, Earthquake Engineering Research Center, University 

of California, Berkeley). km, kilometer.

-122 ̊

37 ̊

37.5 ̊

38 ̊

0 10 20 30

km

MMI

POTENTIAL 

DAMAGE

PERCEIVED 

SHAKING

I II-III IV V VI VII VIII IX X+

none none none Very light Light Moderate Moderate/Heavy Heavy Very Heavy

Not felt Weak Light Moderate Strong Very strong Severe Violent Extreme
Chapter B. Overview of the HayWired Scenario Earthquake-Hazards Volume 19

Figure 2. Map of the San Francisco Bay region, California, showing simulated ground shaking caused by the hypothetical 

magnitude-7 mainshock of the HayWired earthquake scenario on the Hayward Fault. Ground shaking is colored to show 

how strong it is from a number of perspectives. These include the perceived shaking, the potential damage, the peak ground 

acceleration (peak acc.; relative to the percentage of the acceleration of gravity at the Earth surface, %g), the peak ground 

velocity (peak vel.; cm/s, centimeters per second), and the Instrumental Intensity (estimated Modified Mercalli Intensity). 

The Modified Mercalli Intensity describes how the earthquake is felt and its effects. The mainshock begins beneath the City 

of Oakland (star) and causes the Hayward Fault to rupture along 83 kilometers (about 52 miles; thick black line) of its length. 

Strong ground shaking occurs throughout the region. (Figure modified from Aagaard, Boatwright, and others, this volume.)

 Area 

of map

 123 ̊

0

0 10 20 30 MILES

10 20 30 40 50 KILOMETERS

 122 ̊ 121 ̊

37 ̊

38 ̊

I

II

III

IV

V

VI

VII

VIII

IX

X

Modified Mercalli Intensity

San Francisco

EXPLANATION

San Jose

Santa Rosa

Livermore

Fremont

Napa

Concord

Gilroy

Sacramento

Stockton

SAN FRANCISCO BAY

CALIF

PACIFIC 

OCEAN

 Lake 

Berryessa

 San Pablo

 Bay

 San Luis

Reservoir

INSTRUMENTAL 

INTENSITY

PEAK VEL.(cm/s)

PEAK ACC.(%g)

POTENTIAL 

DAMAGE

PERCEIVED 

SHAKING

I II-III IV V VI VII VIII IX X+

<0.02 0.1

0.3

1.4

2.8

4.7

6.2

9.6

12

20

22

41

40

86

<0.05 75 >139

>178

none none none Very light Light Moderate Mod./Heavy Heavy Very Heavy

Not felt Weak Light Moderate Strong Very strong Severe Violent Extreme

Base from U.S. Geological Survey 2009

National Elevation Dataset 30-meter 

digital elevation model.
20 The HayWired Earthquake Scenario—Earthquake Hazards

Fault Slip

The HayWired scenario mainshock causes slip (relative 

movement between opposite sides of a fault) along both the 

southern and northern parts of the Hayward Fault (see Aagaard, 

Schwartz, and others, this volume). The offset distance along the 

fault where the fault intersects the Earth’s surface (called the fault 

trace) is of concern, because lifelines (for example, transportation 

infrastructure and buried utilities) and other structures that cross 

the fault need to be able to accommodate fault movement, both 

during and after a large earthquake. To examine fault-trace offset 

in the HayWired scenario, a variation of the HayWired scenario 

mainshock is used that assumes that sudden offset occurs along 

the Hayward Fault trace from San Pablo Bay to Fremont, with a 

maximum of more than 2 meters (m; about 7 feet) of offset in the 

San Pablo area. Longer duration slip on the fault, called afterslip, 

following the HayWired mainshock and its strong shaking is also 

calculated. Additional fault-trace offset, called afterslip, of as much 

as 0.5 to 1.5 m might occur along the Hayward Fault, with most of 

it occurring during the first months after the mainshock.

Liquefaction

A large earthquake on the Hayward Fault will cause 

liquefaction, especially in soft, water-saturated soils around 

the margins of San Francisco Bay (see Jones and others, this 

volume). Liquefaction is a type of earthquake-induced ground 

failure in which solid ground temporarily transforms into a 

softened or liquefied state during strong shaking. Liquefaction 

can cause severe damage to lifelines, as well as to structures 

and their foundations, as a result of settlement, spreading or 

lateral motions of the shallow soils, and damage to engineered 

structures such as embankments, levees, and dams.

For the HayWired scenario, the strong ground shaking 

during the hypothetical mainshock is used to estimate and 

map the likelihood of liquefaction in western Alameda and 

northern Santa Clara Counties (fig. 3). Information about how 

soils respond to strong ground shaking, data on the depth to 

the water table from the California Geological Survey, and 

geologic maps of the San Francisco Bay region showing 

deposits younger than 2.6 million years (Quaternary and 

younger) are used for the liquefaction estimates. In areas 

where insufficient soil-response information is available, the 

Federal Emergency Management Agency’s (FEMA) Hazus 

computer program is used to identify areas susceptible to 

liquefaction hazards. 

For the HayWired scenario, the probability of 

liquefaction in western Alameda County is as much as 0.75 

for some areas along major creeks and for areas of artificial 

fill near the margins of San Francisco Bay. The HayWired 

scenario also generates high liquefaction probabilities in the 

Quarry Lakes Regional Recreation Area in Fremont and at 

several places along Alameda Creek, Arroyo de la Laguna, 

and Arroyo Valle between Fremont and Livermore. The 

northern part of the western Alameda County shoreline, 

where most of the shoreline area is developed, such as the 

city shorelines of Alameda, Oakland, and Hayward, also 

has a probability of liquefaction that exceeds 0.4 to 0.5. 

In the Santa Clara Valley, liquefaction probability for the 

HayWired scenario reaches a high of about 0.5, with 0.4 to 

0.5 liquefaction probability around the southernmost part 

of San Francisco Bay and along the banks of the Guadalupe 

River and Coyote Creek.

Landslides

In the HayWired scenario, the hypothetical mainshock 

triggers landslides throughout the San Francisco Bay region 

(see McCrink and Perez, this volume). Calculations are made 

of the likely slope failures (landslides) generated by strong 

ground shaking in the 10-county region surrounding San 

Francisco Bay. Information is incorporated from geologic 

maps of the region, geologic-material strength parameters 

compiled by the California Geological Survey, and slopegradient data at 10-m (about 33 feet) resolution calculated 

from the U.S. Geological Survey 2009 National Elevation 

Dataset. The analyses assume that slopes of hillsides are not 

water saturated.

The highest probabilities of landsliding in the HayWired 

scenario, greater than 32 percent, occur on steep to very 

steep slopes and on existing landslides (fig. 4). Lower 

probabilities of landsliding occur on moderate to gentle 

slopes in areas near the Hayward Fault that are subject to 

the strongest ground shaking and on steeper slopes with 

increasing distance from the fault that experience weaker 

ground shaking. For the HayWired scenario, the area of 

significant landsliding for loss-estimation purposes is where 

peak ground shaking during the magnitude-7 mainshock 

is expected to be greater than 20 centimeters per second 

(cm/s; about 8 inches per second). Most models that assess 

earthquake-triggering of landslides are primarily studies of 

landslide initiation and are not yet able to predict landslide 

runout, which is how far a landslide travels down a hillside.

Aftershocks

The magnitude-7 mainshock of the HayWired scenario 

is followed by aftershocks (Wein and others, this volume). 

Large earthquakes are sometimes viewed as standalone 

events; however, this is rarely, if ever, the case. Instead, 

large earthquakes are accompanied over the course of the 

subsequent minutes to years by additional nearby earthquakes 

(aftershocks), along with occasional, more distant “triggered” 

earthquakes. Aftershocks occur as a result of readjustments of 

the Earth’s crust following an initial large earthquake.
Chapter B. Overview of the HayWired Scenario Earthquake-Hazards Volume 21

Figure 3. Map of the San Francisco Bay region, California, showing liquefaction (soils becoming liquid-like during shaking) probabilities for 

western Alameda and northern Santa Clara Counties as a result of strong ground shaking during the hypothetical magnitude-7 mainshock of the 

HayWired earthquake scenario on the Hayward Fault. <, less than; >, greater than. (From Jones and others, this volume.)

"

"

"

"

"

"

"

SAN

MATEO

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

SAN

FRANCISCO

MARIN

SAN

JOAQUIN

SANTA

CRUZ

Oakland

San Jose

Alameda

Hayward

Fremont

Livermore

Hydrology from U.S. Geological Survay National Hydrography Dataset, 2016. 

Boundary data from U.S. Census Bureau TIGER data, 2016.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N.

Area

of map

CALIF

0 7 14 KILOMETERS

0 7 14 MILES

EXPLANATION

Probability of liquefaction in

 San Francisco Bay area

Not assessed

>0.50

0.40–0.50

0.30–0.40

0.20–0.30

0.10–0.20

0.05–0.10

<0.05

County boundary

Area where Holzer and

others' (2008, 2010) methods

are used to map liquefaction

probability

PA

CIFIC

OCEAN

SAN

FRANCISCO

BAY

122.5° 122° 121.5°

38°

37.5°

San Pablo 

 Bay
22 The HayWired Earthquake Scenario—Earthquake Hazards

Harris_fig4

EXPLANATION

0 30

20 40

10

0

20 40

10 30 KILOMETERS

MILES

SONOMA

HAYWARD FAULT

NAPA

MARIN

SOLANO

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

SANTA CRUZ

SAN

MATEO

SAN

FRANCISCO

Base from U.S. Geological Survey 2009 National Elevation Dataset 10-meter digital elevation model showing land-surface elevations as shaded-relief. County boundaries from California Department of Forestry and 

Fire Protection, 2009.

20 cm/s PGV contour

L

M

H

VH

0–2

Landslide probability

Category Percent

2–15

15–32

>32

CALIF

Area of map

121° 122°

38°

37°

For the HayWired scenario, it is estimated that there is at 

least a 1-in-5 chance of a magnitude 6.4 or larger aftershock 

occurring in the months and years after the mainshock. Statistical 

methods based on decades of aftershock observations from around 

the world are used to simulate aftershock sequences during the first 

2 years following the HayWired scenario mainshock. Although 

aftershocks generated by a Hayward Fault earthquake are expected 

to continue for much longer than 2 years, the 2-year aftershock 

timeline was chosen to match the recovery horizon of FEMA’s 

San Francisco Bay Area Catastrophic Earthquake Plan.

Figure 4. Map of landslide probabilities for the 10-county San Francisco Bay region, California, resulting from shaking during the 

hypothetical magnitude-7 mainshock of the HayWired earthquake scenario on the Hayward Fault. The red contours outline areas 

where shaking (peak ground velocity, PGV) exceeds 20 centimeters per second (cm/s; about 8 inches per second). Landslide 

probability: L, low; M, medium; H, high; VH, very high. (Modified from McCrink and Perez, this volume.)
Chapter B. Overview of the HayWired Scenario Earthquake-Hazards Volume 23

Aftershocks are modeled to occur after the HayWired 

scenario magnitude-7 mainshock. One particular feature of 

the HayWired aftershock estimation model is that it does 

not necessarily place the aftershocks on known geologic 

faults, instead they are placed based using statistical 

calculations alone. Because geologists think that large 

earthquakes do not randomly occur in space but instead 

occur on geologic faults, the largest HayWired scenario 

aftershocks, those greater than magnitude 6, are moved to 

nearby known faults (fig. 5).

Figure 5. Map of the San Francisco Bay region, California, showing larger aftershocks of magnitude (M) 5 or greater 

following the hypothetical magnitude-7 mainshock of the HayWired earthquake scenario on the Hayward Fault. Note 

that many aftershocks are not shown, including those smaller than M5 that occur on the Hayward Fault itself (see 

chapter G, figs. 5 and 6). >, greater than. (From Wein and others, this volume.)

PACIFIC 

^_

OCEAN

Oakland Union City San Francisco

San Jose

Santa Clara

Fremont

Sunnyvale

Cupertino

Palo 

Alto

Menlo 

 Park

Fairfield

Napa

Vallejo

San Pablo

San Rafael

Santa Rosa

Petaluma

0 10 20 30 MILES

0 10 20 30 KILOMETERS

Quaternary faults modified from U.S. Geological Survey and California Geological Survey, 2006.

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N.

EXPLANATION

Epicenters for earthquakes

M≥5 in aftershock sequence

^_ M7.05 mainshock

M5.0–M6.0

> M6.0

Hayward Fault rupture

Faults

 Area 

of map

-123° -122° -121°

39°

38°

37°

CALIF
24 The HayWired Earthquake Scenario—Earthquake Hazards

As expected based on assumptions of aftershock locations 

in general, most of the simulated HayWired aftershocks 

occur near the Hayward Fault. This is because the fault is the 

location of the HayWired scenario mainshock. However, the 

HayWired scenario does include some aftershock locations 

away from the Hayward Fault, including in the furthest 

reaches of the greater San Francisco Bay region. Many of the 

HayWired scenario aftershocks, especially the larger ones, 

would be potentially damaging and felt throughout the San 

Francisco Bay region.

Conclusion

This overview of The HayWired Earthquake Scenario—

Earthquake Hazards volume summarizes the major findings 

by the authors of the individual chapters. Each set of authors 

has recommendations for future work. The recommendations 

range from follow-on studies of how creeping faults, such as 

the Hayward Fault, produce large earthquakes and subsequent 

fault offset to investigations of the response of soils to 

sudden ground shaking. For further, detailed information, 

see the individual chapters within this volume. As additional 

HayWired volumes are published, they will be made available 

at https://doi.org/10.3133/sir20175013.

References Cited

Aagaard, B.T., Blair, J.L., Boatwright, J., Garcia, S.H., Harris, 

R.A., Michael, A.J., Schwartz, D.P., and DiLeo, J.S., 2016, 

Earthquake outlook for the San Francisco Bay region 2014–

2043 (ver. 1.1, August 2016): U.S. Geological Survey Fact 

Sheet 2016–3020, 6 p., accessed April 10, 2017, at http://

dx.doi.org/10.3133/fs20163020.

Boatwright, John, and Bundock, Howard, 2008, Modified 

Mercalli Intensity Maps for the 1868 Hayward earthquake 

plotted in ShakeMap format: U.S. Geological Survey OpenFile Report 2008–1121, accessed April 11, 2017, at https://

pubs.usgs.gov/of/2008/1121/.


The HayWired Earthquake Scenario—Earthquake Hazards

Edited by Shane T. Detweiler and Anne M. Wein

Scientific Investigations Report 2017–5013–A–H

[Also see https://doi.org/10.3133/sir20175013]

Abstract

The HayWired scenario examines a hypothetical earthquake 

(mainshock) with a moment magnitude (Mw) of 7.0 occurring 

on April 18, 2018, at 4:18 p.m. on the Hayward Fault in the east 

bay part of California’s San Francisco Bay area. Determining and 

understanding the impacts of expected future large earthquakes 

requires realistic estimates of the ground shaking. The ground 

motions for the HayWired scenario mainshock come from a 

three-dimensional computer simulation of a magnitude 7.0 

earthquake that incorporates (1) variability in fault slip over the 

complex geometry of the Hayward Fault, (2) the effects of starting 

the rupture at a specific point below the city of Oakland, and (3) 

propagation of the seismic waves through the complex geologic 

structure of the San Francisco Bay region. These effects give rise 

to a complex pattern of shaking. Some features are specific to the 

choice of parameters for this scenario, such as the starting location 

of the rupture and the distribution of slip over the fault surface. 

Others are more general as a result of known variations in the 

geologic structure that cause some areas to consistently have more 

intense shaking than others.

Introduction

The sudden slip of one side of a fault past the other releases 

strain energy by radiating seismic waves. When the waves reach 

the Earth’s surface, we feel the passage of the seismic waves in 

the form of earthquake ground shaking. Strong shaking can throw 

people to the ground, cause ground failure (in the forms of cracks, 

liquefaction, landslides, and lateral spreading), and generates most 

of the earthquake damage to buildings, roads, pipelines and other 

structures. Thus, estimating the damage and other impacts of a 

large earthquake relies on accurate estimates of the ground motion. 

Ground motions depend on three primary effects, as well as 

on additional smaller, but still significant, secondary effects. The 

first factor is magnitude—a bigger earthquake releases more strain 

Chapter C

HayWired Scenario Mainshock Ground Motions

By Brad T. Aagaard,1 John L. Boatwright,1 Jamie L. Jones,1 Tim G. MacDonald,1 Keith A. Porter,2 and

Anne M. Wein1

1

U.S. Geological Survey.

2

University of Colorado, Boulder.

energy, which means more energy is carried in the seismic waves. 

The earthquake magnitude depends on the length and depth of the 

fault that moves as well as the amount of slip. The second factor is 

distance from the fault. Because the radiated energy spreads over 

a greater volume as it propagates outward from the fault rupture, 

the amplitudes of the seismic waves decrease as they travel away 

from the fault. As a result, sites farther from the fault rupture tend to 

experience less intense shaking. The third factor is soil conditions—

the characteristics of the soil or rock at a particular location affect 

the amplitude and duration of the shaking at that site. Softer soils, 

such as alluvial sediments that accumulate in basins and river 

valleys, generally cause stronger shaking than nearby rock, all other 

things being equal. The secondary factors include (1) directivity, in 

which ground motions are most focused in the direction of rupture 

propagation along the fault, (2) radiation pattern, variations in 

energy distribution that depend on the orientation of the rupturing 

fault and the direction of the slip, and (3) the spatial variability in 

the amount of slip, with stronger shaking surrounding parts of the 

fault where slip is greater.

To determine how structures respond to earthquake shaking, 

engineers quantify ground motion by several measures—peak 

horizontal ground acceleration (PGA), peak horizontal ground 

velocity (PGV), and, most commonly, 5-percent-damped spectral 

acceleration (SA) response at several periods of vibration, especially 

0.3 second (s), 1.0 s, and 3.0s. SA response can be thought of as 

the PGA measured at the roof of a building as a result of the ground 

shaking at its base. The period of vibration depends on the height 

of the building—approximately 0.3 s for a low-rise building, 1.0 s 

for a 12-story building, and 3.0 s for a 40-story building. In other 

words, a given ground motion having a SA of 0.5 the acceleration 

due to gravity (g) at a period of 1.0 s corresponds to measuring an 

acceleration of 0.5 g at the roof a 12-story building with the ground 

motion input at the building’s base.

It is also common to express ground motion in the subjective 

terminology of the Modified Mercalli Intensity (MMI) scale. 

MMI is quantified based on damage and human response in 

an earthquake, but it can be estimated from the instrumental 

measurements of PGV and PGA. ShakeMaps, for example, depict 

MMI that are estimated from PGA and PGV and using groundmotion-to-intensity conversion equations (for example, Wald and 

others, 2005; Worden and others, 2012).
28 The HayWired Earthquake Scenario—Earthquake Hazards

HayWired Scenario Mainshock

The HayWired scenario examines a hypothetical earthquake 

(mainshock) with a moment magnitude (Mw) of 7.0 occurring 

on April 18, 2018, at 4:18 p.m. on the Hayward Fault in the east 

bay part of California’s San Francisco Bay area. This scenario 

mainshock is a specific scenario (HS+HN G04 HypoO) from a 

suite of 39 scenario earthquakes for the Hayward Fault developed 

by Aagaard and others (2010a). The scenario parameters make 

use of the considerable geologic and geophysical data that have 

been collected over the past several decades. The scenario includes 

the three-dimensional (3D) geometry of the Hayward Fault based 

on mapping of its surface trace and location of microearthquakes 

at depth. The scenario earthquake rupture starts under the city of 

Oakland, with fault slip progressing north into San Pablo Bay and 

south to the city of Fremont (fig. 1). The slip varies over the fault 

with patches of high and low slip, consistent with observations 

from earthquakes on other faults. Additionally, the slip tapers in 

areas where other studies (for example, Funning and others, 2007) 

have imaged interseismic creep (slow, more or less continuous 

or episodic slip). Aagaard, Schwartz, and others (this volume) 

discusses the coseismic slip at the surface and an estimate of creep 

(afterslip) triggered by the mainshock rupture.

The 3D computer simulation solves the wave equation in a 

250-kilometer (km)-long, 100-km-wide, and 40-km-deep region 

given the imposed slip on the fault. The U.S. Geological Survey 

(USGS) Bay Area Seismic Velocity Model 08.3.0 (Aagaard and 

others, 2010b) describes the properties in the 3D volume. The 

properties include the different geologic units and how the density 

and stiffness of those units vary from one another, as well as how 

they vary with depth. This complex geologic structure affects the 

seismic waves as they propagate. The amplitude of the motion 

increases in softer materials, and reflections and refractions occur 

as the waves encounter interfaces between different geologic units. 

HayWired Scenario Mainshock Ground 

Motions 

The ground motions from the simulation are characterized 

in terms of PGA, PGV, SA, and MMI in the form of a ShakeMap, 

which is available from the USGS ShakeMap website (U.S. 

Geological Survey, 2014). The simulation domain from Aagaard 

and others (2010a) does not span the entire 16-county region 

participating in the San Francisco Bay Area Earthquake Plan 

(California Governor’s Office of Emergency Services, 2016), so 

ground-motion prediction equations (GMPEs) of Wald and others 

(2005) are used to extend the ShakeMap an additional 20 km to 

the north, 80 km to the south, 65 km to the west, and 45 km to the 

east (see appendix for details).

Figure 2 shows the Instrumental Intensity (an estimate 

of MMI) throughout the study region. Table 1 presents an 

abbreviated description of the effects of shaking for each level 

of intensity. More than 40 percent of the San Francisco Bay 

urban area experiences shaking stronger than MMI VII, which 

corresponds to slight to moderate damage in ordinary structures 

and considerable damage in poorly built or badly designed 

structures. The strongest shaking occurs near the fault rupture 

and decreases with distance. Additional features include (1) 

stronger shaking along the strike of the Hayward Fault both to the 

northwest and southeast away from the epicenter in Oakland as a 

result of rupture directivity, (2) amplification of motions in several 

sedimentary basins scattered about the San Francisco Bay region 

(especially the Livermore Basin, the Evergreen Basin east of San 

Jose, and the sedimentary basin underneath San Pablo Bay), and 

(3) variability in shaking at sites along the fault corresponding 

to the spatial variation in slip for this scenario (for example, the 

high intensities just north of the epicenter associated with a large 

slip patch at depth and lower intensities a little southeast of the 

epicenter in an area with lower than average slip at depth). Over 

the past 100,000 years, the region just east of the Hayward Fault 

has undergone substantially more deformation than the region 

just west of the fault. As a result, the material is less rigid and this 

leads to greater amplification east of the Hayward Fault compared 

to west of the Hayward Fault, despite having a much thinner 

sediment cover (Aagaard and others, 2010a).

We also expect local amplification of ground motions in the 

very thin, very soft bay mud and artificial fills around the margins 

of San Francisco Bay. The discretization of the simulation domain 

does not have sufficient resolution to capture these small-scale 

features that are less than a grid size. They are captured in an 

approximate way through site amplification factors (Graves 

and Pitarka, 2010) in postprocessing of the simulation ground 

motions using the same approach that is used in many groundmotion prediction equations. Furthermore, with a focus on 

capturing the variability in ground motion at the regional scale, the 

simulation output was captured on a 1/60-degree grid (spacing of 

approximately 2 km), so features smaller than 2 km in size are not 

resolved very well. Thus, the simulations capture site amplification 

associated with features larger than about 2 km in size but have 

poor resolution of amplification at smaller length scales.

Some details of the scenario rupture, such as the hypocenter 

and slip distribution, will likely be different in the next large 

Hayward Fault earthquake. To explore the associated uncertainty 

in ground shaking, Aagaard and others (2010a) considered 

variations in a variety of scenario parameters, such as hypocenter, 

slip distribution, rupture length, and magnitude, in their suite of 39 

scenarios. Shorter rupture lengths resulting in smaller magnitude 

earthquakes reduce the amplitude of the ground motions. For 

example, in one of the Mw 6.8 scenarios, only 10 percent of the San 

Francisco Bay urban area is subjected to shaking as strong as MMI 

VII, compared to more than 40 percent for the HayWired scenario 

mainshock. Shifting the hypocenter away from the middle of the 

rupture toward either end increases the directivity and asymmetry 

in the shaking distribution, with larger motions in the direction the 

rupture propagates away from the hypocenter and smaller motions 

near the hypocenter (fig. 3). The distribution of slip also strongly 

affects the intensity of shaking along the length of the rupture. On 

the other hand, some ground-shaking patterns persist across the 

scenarios, most notably, the intense shaking and extended duration 
Chapter C. HayWired Scenario Mainshock Ground Motions 29

Boatwright_fig1

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Slip, in meters

N

Figure 1. Map image of of the San Francisco Bay region, California, showing a three-dimensional perspective view of the slip 

distribution and rupture propagation on the Hayward Fault for the magnitude-7 mainshock of the HayWired earthquake scenario. The 

white-yellow-red-black colors show the distribution of slip on the Hayward Fault, and the gray contours show the leading edge of the 

fault slip propagation away from the hypocenter (rupture initiation point) at 1-second intervals. The thick blue line delineates the surface 

trace and the blue sphere identifies the epicenter.

Base map © OpenStreetMap contributors, and the GIS User Community.

Table 1. Effects of earthquake shaking intensity (reproduced from U.S. Geological Survey, 2015).

Intensity Shaking Description/Damage

I Not felt Not felt except by a very few under especially favorable conditions.

II Weak Felt only by a few persons at rest, especially on upper floors of buildings.

III Weak Felt quite noticeably by persons indoors, especially on upper floors of buildings. Many people do not 

recognize it as an earthquake. Standing motor cars may rock slightly. Vibrations similar to the passing of a 

truck. Duration estimated.

IV Light Felt indoors by many, outdoors by few during the day. At night, some awakened. Dishes, windows, doors 

disturbed; walls make cracking sound. Sensation like heavy truck striking building. Standing motor cars 

rocked noticeably.

V Moderate Felt by nearly everyone; many awakened. Some dishes, windows broken. Unstable objects overturned. 

Pendulum clocks may stop.

VI Strong Felt by all, many frightened. Some heavy furniture moved; a few instances of fallen plaster. Damage slight.

VII Very strong Damage negligible in buildings of good design and construction; slight to moderate in well-built ordinary 

structures; considerable damage in poorly built or badly designed structures; some chimneys broken.

VIII Severe Damage slight in specially designed structures; considerable damage in ordinary substantial buildings 

with partial collapse. Damage great in poorly built structures. Fall of chimneys, factory stacks, columns, 

monuments, walls. Heavy furniture overturned.

IX Violent Damage considerable in specially designed structures; well-designed frame structures thrown out of plumb. 

Damage great in substantial buildings, with partial collapse. Buildings shifted off foundations.

X Extreme Some well-built wooden structures destroyed; most masonry and frame structures destroyed with foundations. 

Rails bent.
30 The HayWired Earthquake Scenario—Earthquake Hazards

Figure 2. ShakeMap of the San Francisco Bay region, California, showing instrumental intensity (estimated Modified 

Mercalli Intensity) for the magnitude-7 mainshock of the HayWired earthquake scenario. The earthquake ruptures the 

Hayward Fault in both directions from the hypocenter (star) under the City of Oakland. Shaking is amplified in the Livermore 

and Evergreen sedimentary basins. Figure modified (roadways removed) from U.S. Geological Survey (2014). See table 1 

for explanation of intensity. km, kilometer; %g, percentage of acceleration due to gravity; cm/s, centimeter per second.

San Jose

San Francisco

Sacramento

Stockton

Fremont

Salinas

Faireld

Merced

Rocklin

Santa Cruz

Soledad

Coalinga

Clearlake

-- Earthquake Planning Scenario --

ShakeMap for haywiredm7.0 Scenario

Scenario Date: Apr 18, 2018 23:18:00 UTC M 7.0 N37.80 W122.18 Depth: 8.0km

PLANNING SCENARIO ONLY -- Map Version 28 Processed 2015-05-13 22:46:39 UTC 

INSTRUMENTAL 

INTENSITY

PEAK VEL.(cm/s)

PEAK ACC.(%g)

POTENTIAL 

DAMAGE

PERCEIVED 

SHAKING

I II-III IV V V I VII VIII IX X+

<0.02 0.1 1.4 4.7 9.6 20 41 86 >178

<0.05 0.3 2.8 6.2 12 22 40 75 >139

none none none Very light Light Moderate Mod./Heavy Heavy Very Heavy

Not felt Weak Light Moderate Strong Very strong Severe Violent Extreme

Scale based upon Worden et al. (2012)

-124 ̊ -123 ̊ -122 ̊ -121 ̊ -120 ̊

36 ̊

36.5 ̊

37 ̊

37.5 ̊

38 ̊

38.5 ̊

39 ̊

0 50

km

Oakland

Livermore

Basin

Evergreen

Basin

San Pablo

Bay

Scale based on Worden and others, 2012.
Chapter C. HayWired Scenario Mainshock Ground Motions 31

Boatwright_fig3A

 Area 

of map

 123 ̊

0

0 10 20 30 MILES

10 20 30 40 50 KILOMETERS

 122 ̊ 121 ̊

37 ̊

38 ̊

I

II

III

IV

V

VI

VII

VIII

IX

X

Modified Mercalli Intensity

San Francisco

EXPLANATION

San Jose

Santa Rosa

Livermore

Fremont

Napa

Concord

Gilroy

Sacramento

Stockton

A

SAN FRANCISCO BAY

CALIF

PACIFIC 

OCEAN

 Lake 

Berryessa

 San Pablo

 Bay

 San Luis

Reservoir

Base from U.S. Geological Survey 2009 

National Elevation Dataset 30-meter 

digital elevation model.

Figure 3. Maps of the San Francisco Bay region, California, showing instrumental intensity (estimated Modified 

Mercalli Intensity; see table 1) for the magnitude-7 mainshock of the HayWired earthquake scenario (A) and two 

alternative scenarios with different epicenters (indicated by the black stars) from Aagaard and others (2010b). The 

scenario in B has an epicenter in San Pablo Bay and directs more energy southeast along the fault, whereas the 

scenario in C with the Fremont epicenter directs more energy northwest along the Hayward Fault. The colors in A

do not precisely match those in figure 2 due to slight differences in the rendering workflow. Hayward Fault rupture 

shown as black line.
32 The HayWired Earthquake Scenario—Earthquake Hazards

Figure 3.—Continued

Boatwright_fig3B

 Area 

of map

 123 ̊ 122° 121 ̊

37 ̊

38 ̊

San Francisco

EXPLANATION

San Jose

Santa Rosa

Livermore

Fremont

Napa

Concord

Gilroy

Sacramento

Stockton

0

0 10 20 30 MILES

10 20 30 40 50 KILOMETERS

B

I

II

III

IV

V

VI

VII

VIII

IX

X

Modified Mercalli Intensity

SAN FRANCISCO BAY

PACIFIC 

OCEAN

 Lake 

Berryessa

 San Pablo

 Bay

 San Luis

Reservoir

CALIF

Base from U.S. Geological Survey 2009 

National Elevation Dataset 30-meter 

digital elevation model.
Chapter C. HayWired Scenario Mainshock Ground Motions 33

Boatwright_fig3C

 Area 

of map

 123 ̊ 122° 121 ̊

37 ̊

38 ̊

San Francisco

EXPLANATION

San Jose

Santa Rosa

Livermore

Fremont

Napa

Concord

Gilroy

Sacramento

Stockton

0

0 10 20 30 MILES

10 20 30 40 50 KILOMETERS

C

I

II

III

IV

V

VI

VII

VIII

IX

X

Modified Mercalli Intensity

SAN FRANCISCO BAY

CALIF

PACIFIC 

OCEAN

 Lake 

Berryessa

 San Pablo

 Bay

 San Luis

Reservoir

Base from U.S. Geological Survey 2009 

National Elevation Dataset 30-meter 

digital elevation model.

Figure 3.—Continued
34 The HayWired Earthquake Scenario—Earthquake Hazards

of shaking in several sedimentary basins across the region, as 

mentioned earlier. See Aagaard and others (2010b) for a more 

detailed discussion of the potential variability in ground motions for 

large Hayward Fault earthquakes.

Knowledge Gaps and Modeling 

Limitations

To more fully understand the effects of future large 

earthquakes of the Hayward Fault, the following knowledge 

gaps and modeling limitations need to be addressed:

• We need additional scientific studies of the Hayward 

Fault to better understanding how observations of 

interseismic creep (slow, more or less continuous 

or episodic slip) can be used to improve forecasts 

of future large earthquakes and the resulting ground 

motions for the Hayward Fault.

• The HayWired scenario mainshock ground-motion 

simulation uses a coarse and generic representation of 

the soil properties based on the time-averaged shearwave velocity to a depth of 30 meters (VS30) mapping 

of Wills and others (2000). For example, very soft 

soil translates to a VS30 of 180 meters per second in 

this mapping. The ground-motion simulation methods 

may not accurately capture the nonlinear seismic 

response of very soft soils, and the nonlinear effects 

depend on many factors including the amplitude and 

frequency of the motion. As a result, it is difficult 

to predict whether the simulations underestimate or 

overestimate the amplitude of the ground motion 

in very soft soils. Site-specific investigations with 

detailed local geologic information need to be used to 

evaluate the seismic hazard in these regions.

• The realistic distribution of shaking for the HayWired 

scenario mainshock computed using a 3D computer 

simulation is fundamentally different from those 

based solely on median values from GMPEs. The 

GMPEs correspond to the median expected value 

of ground motion based on the collected history 

of observed earthquake ground motions. As a 

result, GMPEs include an average of the effects of 

the rupture propagation, variability in the spatial 

distribution of slip, and complex interactions with the 

local geology. These median-based distributions of 

shaking lack the spatial variability produced in real 

earthquakes and sophisticated scenarios, such as the 

HayWired mainshock, that are needed to accurately 

forecast damage (see Porter, this volume).

• A single scenario, such as HayWired, is not a basis 

for probabilistic design. Future studies may provide 

additional realizations of specific scenarios within a 

probabilistic framework to provide localized groundmotion models for design (for example, Graves and 

others, 2010).

Conclusion

The ground motions for the mainshock of the HayWired 

earthquake scenario are a product of a sophisticated 3D 

computer simulation that includes a realistic distribution 

of slip on the complex geometry of the Hayward Fault and 

propagation of the seismic waves through the 3D geologic 

structure surrounding the fault. The resulting ground motions 

exhibit significant variability across the San Francisco 

Bay region and highlight the ability to capture complex 

interactions between the propagating fault rupture and 

the 3D geologic structure, especially sedimentary basins. 

Detailed ground-motion simulations like the HayWired 

scenario mainshock play an important role by providing 

the essential information for assessing and mitigating the 

risk to buildings, bridges, pipelines, and other infrastructure 

associated with damaging earthquakes. They also provide 

crucial inputs for estimating ground failure (liquefaction, 

landslides, and lateral spreading) used in making decisions 

about land use and land remediation.

Acknowledgments

We thank the USGS National Earthquake Information 

Center for making the HayWired mainshock ShakeMap 

available in the ShakeMap Scenario Archive. Sarah Minson 

and Peggy Hellweg provided helpful feedback on an early 

version of the manuscript.

References Cited

Aagaard, B.T., Graves, R.W., Schwartz, D.P., Ponce, D.A., 

and Graymer, R.W., 2010a, Ground-motion modeling of 

Hayward Fault scenario earthquakes, part I—Construction 

of the suite of scenarios: Bulletin of the Seismological 

Society of America, v. 100, no. 6, p. 2927–2944.

Aagaard, B.T., Graves, R.W., Rodgers, A., Brocher, T.M., 

Simpson, R.W., Dreger, D., Petersson, N.A., Larsen, 

S.C., Ma, S., and Jachens, R.C., 2010b, ground-motion 

modeling of Hayward Fault scenario earthquakes, part II—

Simulation of long-period and broadband ground motions: 

Bulletin of the Seismological Society of America, v. 100, 

no. 6, p. 2945–2977.
Chapter C. HayWired Scenario Mainshock Ground Motions 35

Boore, D.M., and Atkinson, G.M., 2008, Ground-motion 

prediction equations for the average horizontal component of 

PGA, PGV, and 5%-damped PSA at spectral periods between 

0.01 s and 10.0 s: Earthquake Spectra, v. 24, no. 1, p. 99–138.

California Governor’s Office of Emergency Services, 2016, Bay 

Area earthquake plan—July 2016: California Governor’s 

Office of Emergency Services, accessed August 25, 2016, 

at http://www.caloes.ca.gov/for-individuals-families/

catastrophic-planning.

Funning, G.J., Bürgmann, R., Ferretti, A., and Novali, 

F., 2007, Asperities on the Hayward fault resolved by 

PS-InSAR, GPS and boundary element modeling: Eos 

(Transactions of the American Geophysical Union), v. 88, 

no. 52, S23C-04. 

Graves, R.W., and Pitarka, A. 2010, Broadband groundmotion simulation using a hybrid approach: Bulletin of the 

Seismological Society of America, v. 100, no. 5A, 

p. 2095–2123, doi: 10.1785/0120100057.

Graves, R., Jordan, T.H., Callaghan, S., Deelman, E., Field, E., 

Juve, G., Kesselman, C., Maechling, P., Mehta, G., Milner, 

K., Okaya, D., Small, P., and Vahi, K., 2010, CyberShake—A 

physics-based seismic hazard model for southern California: 

Pure Applied Geophysics, v. 168, no. 3–4, p. 367–381, 

doi: 10.1007/s00024-010-0161-6.

U.S. Geological Survey, 2014, Earthquake planning scenario—

ShakeMap for Haywired M7.05-scenario: U.S. Geological 

Survey web page, accessed August 25, 2016, at https://

earthquake.usgs.gov/scenarios/eventpage/ushaywiredm7.05_

se#shakemap?source=us&code=gllegacyhaywiredm7p05_se.

U.S. Geological Survey, 2015, The Modified Mercalli 

Intensity scale: U.S. Geological Survey web page, accessed 

August 25, 2016, at https://earthquake.usgs.gov/learn/

topics/mercalli.php.

Wald, D., Worden, B., Quitoriano, V., and Pankow, K., 2005, 

ShakeMap Manual: technical manual, users guide, and software 

guide: U.S. Geological Survey Techniques and Methods 12–

A1, ver. 1.0, accessed August 25, 2016, at https://pubs.usgs.gov/

tm/2005/12A01/.

Wills, C., Petersen, J.M., Bryant, W.A., Reichle, M., Saucedo, 

G.J.,Tan, S., Taylor, G., and Treiman, J., 2000, A site-conditions 

map for California based on geology and shear-wave velocity: 

Bulletin of the Seismological Society of America, v. 90, no. 6B, 

p. S187–S208, doi 10.1785/0120000503.

Worden, C.B., Gerstenberger, M.C., Rhoades, D.A., and Wald, 

D.J., 2012, Probabilistic relationships between ground-motion 

parameters and Modified Mercalli Intensity in California: 

Bulletin of the Seismological Society of America, v. 201, no. 1, 

p 204–221, doi: 10.1785/0120110156.

This appendix provides details on extrapolating the 

simulated ground motions for the M7.0 mainshock of the 

HayWired earthquake scenario to a larger region. The groundmotion parameters are extrapolated for a grid of points (spaced 

at about 1.6-km intervals) outside of the simulation region using 

median expected ground motions according to the following 

procedure:

1. For a point outside the simulation model extent, a 

transect line is drawn between it and the earthquake 

epicenter (fig. 4); each point has a unique transect.

2. Along the transect a 20×20-km area is constructed on the 

border of the inside edge of the simulated model extent, such 

that the transect bisects that area (green square in fig. 4);

3. Within the 20×20-km area, the average difference between 

the simulated ground-motion value (for example, PGA, 

PGV, SA03) and the median motions estimated by the 

Boore and Atkinson (2008) ground-motion prediction 

equation is calculated; 

4. The median ground motion value for the point outside the 

model extent is adjusted by the difference calculated in step 

3 for the 20×20-km area.

This results in estimates of the ground motions outside the 

simulation domain having the expected decrease in amplitude 

with distance and local site effects captured in the ground-motion 

prediction equation while retaining consistency with the ground 

motions predicted by the simulation at its edges.

Appendix
36 The HayWired Earthquake Scenario—Earthquake Hazards

Figure 4. Map of the San Francisco Bay region, California, showing earthquake-simulation extent, median ground-motion extent, and 

one transect from the mainshock epicenter of the HayWired earthquake scenario to a point outside the simulation extent. The median 

ground-motion values for the point outside the simulation extent are adjusted by adding the average difference in ground-motion values 

in the square area to dovetail the median ground-motion data with the simulated data.

BoatwrightfigA1

 Area 

of map

^_

0 4 KILOMETERS

0 4 MILES Hydrology from the U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016.

North American Datum of 1983 UTM 10N projection.

Central Meridian, 123° W., latitude of origin, 0.0° N.

EXPLANATION

Simulation model extent

Median ground-motion model extent

Transect

Area for estimating difference

PACIFIC

OCEAN

Soledad

Salinas

Santa Cruz

San Jose

San Francisco

Fremont

Stockton

Fairfield

Rocklin

Sacramento

Merced

Coalinga

37°

36°

124° 123° 122° 121° 120°

38°

39°

CALIF


Chapter D

HayWired Scenario Mainshock Coseismic and 

Postseismic Surface Fault Slip

By Brad T. Aagaard, David P. Schwartz, Anne M. Wein, Jamie L. Jones, and Kenneth W. Hudnut

The HayWired Earthquake Scenario—Earthquake Hazards

Edited by Shane T. Detweiler and Anne Wein

Scientific Investigations Report 2017–5013–A–H

[Also see https://doi.org/10.3133/sir20175013]

Abstract

The HayWired scenario examines a hypothetical earthquake 

(mainshock) with a moment magnitude (Mw) of 7.0 occurring on 

April 18, 2018, at 4:18 p.m. on the Hayward Fault in the east bay 

part of California’s San Francisco Bay area. Earthquake shaking 

produces damage, and so can fault slip at the ground surface. 

The HayWired scenario mainshock includes slip on the Hayward 

Fault during the earthquake rupture, as well as longer duration 

slip on the fault, called afterslip. When fault slip occurs suddenly 

during an earthquake rupture, or over a longer duration after the 

earthquake rupture, it can damage facilities and systems straddling 

the fault, such as buildings, pipelines, electrical transmission 

lines, roads, bridges, and tunnels. Because much of the length of 

the HayWired scenario mainshock fault rupture breaks through 

a highly developed area, we give special consideration to the 

amount, extent, and severity of the fault slip during dynamic 

earthquake rupture along the Hayward Fault, as well as to the 

amount and duration of subsequent fault afterslip.

Introduction

When a fault ruptures in a large shallow crustal earthquake, 

the two sides of the fault slip relative to each other, and this 

slip rupture commonly propagates to the surface during the 

earthquake. Surface slip is the measurement of the slip (relative 

displacement of formerly adjacent points on opposite sides of a 

fault) at the ground surface. Fault slip may occur suddenly during 

an earthquake (coseismic slip); continue to occur gradually in the 

days, weeks, months, and even years that follow an earthquake 

(postseismic slip or afterslip); or occur very slowly between 

earthquake events (interseismic fault creep). Afterslip (postseismic 

slip) is an accelerated form of fault creep. All three types of 

fault slip (coseismic slip, afterslip, and interseismic slip) are an 

engineering concern for manmade structures that cross the surface 

traces of active faults, although in most cases postseismic and 

interseismic slip are negligible. 

The HayWired scenario examines a hypothetical earthquake 

(mainshock) with a moment magnitude (Mw) of 7.0 occurring on 

April 18, 2018, at 4:18 p.m. on the Hayward Fault in the east bay 

part of California’s San Francisco Bay area. The Hayward Fault 

behaves differently from many other faults; it accommodates part 

of its long-term surface slip through interseismic fault creep. Many 

cultural features along the fault, such as roadways, curbs, utility 

pipelines, and buildings, have been deformed or offset by creep 

at the surface (McFarland and others, 2009). This is in contrast 

to a locked fault that lacks interseismic surface creep. On most 

locked faults the long-term geologic surface slip occurs almost 

entirely as coseismic slip. Because the Hayward Fault creeps, and 

accelerated surface creep on the Hayward Fault was observed in 

the town of Haywards (now called Hayward) following the 1868 

magnitude (M) 6.8 earthquake (Lawson, 1908; Lienkaemper 

and others, 1991) and near Oakland following a 2007 M4.2 

earthquake (Lienkaemper and others, 2012), we expect that 

afterslip will also occur following for the next large earthquake 

on the Hayward Fault. On faults with interseismic surface creep, 

it is not uncommon for afterslip to account for more than half the 

total fault offset; afterslip of this nature was observed from the 

1976 M7.5 Guatemala (Bucknam and others, 1978), the 1987 

M6.6 Superstition Hills, California (Sharp and others, 1989); the 

2004 M6.0 Parkfield, California (Lienkaemper and others, 2006); 

the 2009 M6.3 L’Aquila, Italy (Wilkinson and others, 2010); and 

the 2014 M6.0 South Napa, California, (Lienkaemper and others, 

2016; Hudnut and others, 2014) earthquakes. 

Like aftershocks, the longer duration phenomenon of afterslip 

is associated with relaxation of the Earth’s crust following the 

strong shaking and rapid release of energy in the earthquake 

mainshock. Afterslip is related to coseismic slip in much of the 

same way that aftershocks are related to the mainshock. For a 

large Hayward Fault earthquake, we expect that utilities and 

transportation infrastructure, as well as commercial buildings and 

homes, that straddle the fault will be damaged by coseismic slip 

and will continue to be seriously affected by afterslip, especially 

in areas where afterslip fills in deficits of coseismic slip (Aagaard 

and others, 2012). Afterslip interferes with efforts to make needed 

repairs in the hours and days following a large earthquake and 
40 The HayWired Earthquake Scenario—Earthquake Hazards

during longer-term reconstruction. For example, afterslip following 

the 2014 South Napa earthquake continued to twist roads, 

foundations, and curbs as much as a year after the mainshock 

(Hudnut and others, 2014; Lienkaemper and others, 2016). 

Water distribution lines were broken and remained threatened by 

afterslip; the City of Napa reported a complex pattern of ongoing 

breaks over a period that lasted several weeks after the earthquake 

(Buehrer, 2015). After the California Department of Transportation 

(Caltrans) initially patched the Highway 12 road surface across 

the coseismic surface rupture on the south section of the fault, they 

needed to repatch it within 5 days (Lienkaemper, written commun., 

2016). The South Napa afterslip also occurred near homes; faulting 

ruptured directly through the foundations of about 100 homes in 

the Browns Valley section of the City of Napa. The homeowners 

faced the dual dilemma of replacing their slab foundation and 

possibly having to replace it again in the future. We expect similar 

situations along the Hayward Fault, with a larger earthquake 

resulting in a longer duration of afterslip and creating longer term 

effects on repair and recovery of cross-fault infrastructure than the 

1-year effects of the rapid slip that occurred after the South Napa 

earthquake (Lienkaemper and others, 2016).

Mainshock Rupture and Coseismic Slip

The distribution of slip for the HayWired scenario mainshock 

rupture accounts for the various places along the Hayward Fault 

where interseismic fault creep has been observed. The creeping 

regions and their spatial distribution over the fault likely influence 

the partitioning of the long-term geologic slip into interseismic 

creep, coseismic slip, and afterslip. Consequently, estimating 

the slip along the Hayward Fault involves understanding how 

the creeping regions affect the coseismic-slip distribution and 

how much afterslip occurs at a site. Aagaard and others (2010) 

developed a simple model for estimating reduced coseismic slip 

in areas with interseismic creep imaged by Funning and others 

(2007). Because the interseismic creep rate in these areas is less 

than the long-term geologic slip rate for the Hayward Fault, the 

HayWired scenario mainshock (scenario HS+HN G04 HypoO in 

Aagaard and others, 2010) has some coseismic slip occurring in 

these areas with interseismic creep.

The HayWired scenario mainshock has fault rupture for 

a distance of 83 kilometers (km) from San Pablo Bay in the 

north extending south through the City of Fremont (fig. 1). 

In the scenario, coseismic slip exceeds 2 meters (m) in the 

San Pablo area, it decreases through Richmond and Berkeley 

with some sections of the rupture having less than 0.25 m 

of slip, and the amount of coseismic surface offset increases 

to more than 1 m from Berkeley to Oakland and again from 

just south of the epicenter to Hayward. At Hayward, the slip 

drops to less than 0.5 m, and there is no coseismic surface 

faulting along the southern 22 km of the fault rupture. 

Approximately 63 km of the rupture is on land. In contrast 

to the ShakeOut scenario on the southern San Andreas Fault 

(Jones and others, 2008), which ruptured through mostly rural 

Figure 1. Map image of of the San Francisco Bay region, California, showing a three-dimensional perspective view of coseismic surface slip along 

the fault rupture for the magnitude-7 mainshock of the HayWired earthquake scenario. The colors and height show the amount of slip. The thick blue 

line denotes the length of the fault rupture (including subsurface slip) and the green sphere in Oakland identifies the scenario mainshock epicenter.

0.0 0.5 1.0 1.5 2.0 2.5

Slip, in meters

N

SAN FRANCISCO BAY

Base map © OpenStreetMap contributors, and the GIS User Community.
Chapter D. HayWired Scenario Mainshock Coseismic and Postseismic Surface Fault Slip 41

Table 1. Land cover in the San Francisco Bay region, California, 

disturbed by coseismic slip along the Hayward Fault during the 

magnitude-7 mainshock of the HayWired earthquake scenario.

[%, percent]

Land-cover class

Percentage of land cover along 

mainshock fault rupture with 

measurable coseismic slip

Open water 25

Developed, open space 24

Developed, low density1 19

Developed, medium density2 22

Developed, high density3 2

Forest, scrub, grassland, wetlands 8

1

Low-density developed often corresponds with single-family residential 

areas and is a mix of impervious surface and vegetation (20–49% impervious).

2

Medium-density developed is commonly single-family residential with 

50–79% impervious surface. 

3

High-density developed includes multifamily housing and commercial 

areas; 80–100% impervious surface is found in these areas.

areas, the HayWired scenario mainshock ruptures through 

populated areas. Referring to the 2011 National Land Cover 

Database (Homer and others, 2015), approximately 43 percent 

of the land cover disturbed by the Hayward Fault rupture is 

classified as developed (table 1); nearly half of the surface 

rupture is passing through residential or commercial areas. 

The percentage of open water (table 1) reflects the extension 

of surface fault rupture into San Pablo Bay.

The slip distribution for the HayWired scenario mainshock 

is one possible realization for a large earthquake rupture on 

the Hayward Fault. The spatial distribution of slip could be 

significantly different in the next large Hayward Fault earthquake 

and likely varies from earthquake to earthquake. For example, 

such an event might have greater slip along the southern Hayward 

Fault compared to the northern Hayward Fault. This slip would be 

consistent with paleoseismic interpretations of past coseismic slip 

on the fault in the Fremont area (Lienkaemper and others, 1999; 

Lienkaemper and others, 2002; Lienkaemper and others, 2010). 

The initial distribution of coseismic surface slip plays an important 

role in determining the amount of afterslip that is likely to occur at 

any location along the fault.

Distributed or Off-Fault Deformation

In addition to estimating the amount of expected 

coseismic surface slip, another aspect of slip is the degree 

to which it is concentrated along the main fault trace (or in 

a narrow zone about it) or is partially distributed across a 

zone that could be many tens to hundreds of meters (or even 

kilometers) wide at various points along the rupture. This 

distributed deformation can occur as warping, within a few 

to a few tens of meters of the main fault trace, or as discrete 

fractures. Discrete fractures are usually located within several 

hundred meters of the main trace and have a smaller amount 

of slip. Some controls on the amount of deformation that may 

occur include: changes in fault geometry, the thickness of the 

sedimentary fill that the rupture propagates through, and the 

degree of smoothness (maturity) of the fault. 

Distributed deformation has long been recognized. Lawson 

(1908) noted locations along the San Andreas Fault rupture in the 

1906 San Francisco earthquake where fence lines across the fault 

were displaced by multiple fault traces across zones as much as 

115 m wide. Petersen and others (2011) summarize distributed 

faulting from eight well-mapped strike-slip surface ruptures that 

include the 1968 Borrego Mountain (California), 1979 Imperial 

Valley (California), 1987 Superstition Hills (California), 1992 

Landers (California), 1995 Kobe (Japan), 1999 Hector Mine 

(California), 1999 Izmit (Turkey), and 1999 Duzce (Turkey) 

earthquakes. They show that for these ruptures, distributed 

faulting occurs primarily within a couple of hundred meters to 

as far as 2 km from the main fault trace and that the potential for 

the occurrence of distributed-fault displacement decreases as a 

function of distance from the main trace.

Analysis of the 200-km surface rupture from the 2013 M7.7 

Balochsitan, Pakistan, earthquake provides a recent example of 

the along-strike variation of off-fault deformation. Using pre- and 

post-event satellite optical images of the rupture, Gold and others 

(2015) measured on-fault (within10m), midfield (<350 m), and 

far-field (distances >350 m)deformation. Gold and others (2015) 

conclude that, on average, 28 percent of the surface slip occurred 

off-fault with significant along-strike variability, although nearly 

100 percent of the total slip occurs within a few hundred meters of 

the main fault trace.

These noted examples are for large events on essentially 

locked faults. There are no mapped examples of a large 

rupture on a creeping fault, such as the Hayward Fault, for 

comparison. In the HayWired scenario mainshock, it is 

expected that surface rupture will occur primarily where the 

fault is currently creeping at the surface. This is expressed as 

a zone averaging from ~2 to 15 m in width along most of the 

fault. This does not preclude the occurrence of some off-fault 

coseismic displacement on previously identified parallel, 

subparallel, or branching fault traces that are not creeping 

(Radbruch, 1969; Lienkaemper, 1992) or on unmapped locked 

fault traces, as was the case for the 2004 M6.0 Parkfield 

earthquake with coseismic slip on the “Southwest Fracture 

Zone” and afterslip on the main trace (Rymer and others, 

2006). Although the potential for distributed deformation 

has not been quantified for the Hayward Fault, and is not a 

component of the HayWired scenario, in planning construction 

of long baseline structures that must cross the fault (pipelines, 

tunnels), the potential width of the fault zone needs to be taken 

into account. The lifeline exposure to coseismic slip along the 

Hayward Fault is summarized in table 2 (Jamie L. Jones, U.S. 

Geological Survey, written commun., 2016). 
42 The HayWired Earthquake Scenario—Earthquake Hazards

Afterslip

As explained in the introduction, coseismic slip would 

account for only some of the slip associated with this large 

scenario earthquake. In particular, afterslip is expected along 

the creeping regions of the Hayward Fault, which extend over 

the entire length of the rupture. 

Aagaard and others (2012) used Monte-Carlo methods to 

develop probabilistic estimates of coseismic slip and afterslip 

at four sites along the Hayward Fault for earthquake rupture 

lengths ranging from 35 to 90 km. Here we show results from 

that study for one site (alinement array site HTEM near the 

southern edge of a large, deep creeping region beneath Berkeley, California) for 90-km-long earthquake ruptures with magnitudes of approximately 7.1, to illustrate the progression in 

time, of the amount of afterslip and the associated uncertainty. 

The 500,000 realizations in the Monte Carlo analysis were 

constructed using nearly identical techniques as those used to 

construct the HayWired scenario mainshock rupture.

Empirical studies of historical afterslip data have firmly 

established that afterslip accumulates as a logarithmic progression in time (Smith and Wyss, 1968; Boatwright and others, 

1989; Savage and Langbein, 2008). Therefore, the afterslip 

occurs most rapidly immediately after the earthquake and 

slows down (or decays) over time. Aagaard and others (2012) 

explicitly include both the coseismic slip and afterslip in the 

total slip estimates by modifying the power-law expression 

for afterslip from Boatwright and others (1989) to match the 

coseismic-slip value at 1 second (s) and end at the total slip 

value (coseismic+afterslip). This leads to a temporal evolution 

for the total slip given by:

 (1)

 (2)

Table 2. Lifeline exposure to coseismic slip in the San Francisco Bay region, California, for the magnitude-7 

mainshock of the HayWired earthquake scenario (Jamie L. Jones, U.S. Geological Survey, written commun., 2016).

Lifeline Number of fault rupture 

crossings

Number with surface 

offsets

Range of offset, 

in meters

Roadways, highway 37 27 0.3–2.0

Roadways, secondary 127 80 0.0–2.1

Roadways, surface streets 424 270 0.0–2.1

Railways 8 4 0.9–1.6

Water supply conveyance systems 13 3 0.0–0.9

Oil/gas pipelines 25 15 0.7–2.1

Electric transmission lines 37 8 0.1–1.7

Fiber-optic telecommunications 241 132 0.0–2.0

 (3)

 (4)

 

where the accumulated surface slip, D, grows from the coseismic slip, Dcoseismic, at a decreasing rate and approaches a final 

value, Dtotal asymptotically, and t is time. The time constant, T, 

tends to be poorly determined, but in most cases it is about 1 

year or more. The power-law exponent, C, tends to vary with 

the earthquake magnitude; for consistency with increasing 

coseismic slip with earthquake magnitude (6.0≤Mw≤7.5) and 

theoretical formulations for afterslip and empirical results, 

C=0.881–0.111Mw.

Figure 2 shows the temporal evolution of the afterslip 

across three time scales (24 hours, 4 weeks, and 12 months), 

starting from the coseismic-slip value. The thick solid orange 

lines display the median trajectory, and the thick dashed 

orange lines display the trajectory for the median plus one 

standard deviation from the Monte Carlo analysis. The powerlaw temporal evolution results in a rapid onset that decays 

with time. For example, 10 percent of the afterslip occurs in 

the first minute, 25 percent in the first hour, 35 percent in the 

first 6 hours, 40 percent in the first 24 hours, 70 percent in the 

first 30 days, and 85 percent in the first 6 months. The median 

trajectory grows from a coseismic slip of 0.1 m to a total slip 

value of 1.1 m. The median plus one standard deviation trajectory grows from a coseismic slip of 1.1 m to a total slip value 

of 2.4 m. The median trajectory has a very small amount of 

coseismic surface slip, so much of the damage due to slip on 

the fault would not happen immediately but would occur in 

the hours, days, and weeks following the earthquake as a result 

of afterslip. At one standard deviation above the median, the 

coseismic slip and afterslip are comparable, so damage would 

likely occur at the time of the earthquake as well as in the 

hours, days, and weeks following the earthquake.

= ( ) −

− B − a

a

D D

1 , total coseismic

a

T

s

1

1 ,

C

= +

= ( ) − A − aD aD 1

1 , total coseismic

( ) ( )

= +

+

D t A B

T t

1

1 / , C
Chapter D. HayWired Scenario Mainshock Coseismic and Postseismic Surface Fault Slip 43

Figure 2. Graphs showing accumulation of fault afterslip on time scales of hours (A), weeks (B), and 

months (C) after an earthquake. The thick solid orange line shows the median trajectory and the thick 

dashed orange line shows the median plus one standard deviation trajectory. The thin red, blue, and green 

dashed lines show three realizations illustrating the variability in behavior associated with the uncertainty 

in the coseismic slip and afterslip. The yellow background shows the time span of the first day, the green 

background shading shows the time span from 1 day to 1 month, and the blue background shows the time 

span from 1 month to 1 year.

First day A

B

C

First month

Time after earthquake, in hours

Time after earthquake, in weeks

Time after earthquake, in months

First year

Slip, in meters Slip, in meters Slip, in meters

Slip, in feet Slip, in feet Slip, in feet

0 1 2 3 4

0 1 2 3

0

0.5

0.0

1.5

1.0

2.5

2.0

3.5

3.0

0.5

0.0

1.5

1.0

2.5

2.0

3.5

3.0

0.5

0.0

1.5

1.0

2.5

2.0

3.5

3.0

2

0

6

4

10

8

2

0

6

4

10

8

2

0

6

4

10

8

4 8 12 16 20 24

4

5 6 7 8 9 10 11 12

Figure 2 also illustrates the wide range of behavior associated 

with the uncertainty in the model parameters and expected spatial 

variation of coseismic slip. It does not show the uncertainty for 

the form of the mathematical model itself. The blue (lowest thin 

dashed curve) delineates a progression that is about one half of 

the median trajectory, consistent with a coseismic-slip value of 

one half of the median coseismic slip and total slip of one half of 

the median total slip. In other realizations, the coseismic-slip and 

total-slip values may not be proportional to the median values. 

For example, the green (highest thin dashed curve) illustrates a 

trajectory with a coseismic slip near the median plus one standard 

deviation but with a total slip only about half of a standard 

deviation above the median value. The red (middle thin dashed 

curve) shows another example with coseismic slip slightly above 
44 The HayWired Earthquake Scenario—Earthquake Hazards

the median value, but a larger relative total slip. Other realizations 

exhibit similar behavior with values above or below the median.

The afterslip behavior can also vary significantly from 

location to location along the length of the rupture. For example, 

in the 1987 M6.6 Superstition Hills earthquake, the local variation 

in the proportion of coseismic slip to total slip ranged from about 

2 to 18 percent (Harsh, 1982), and we expect similar variability 

for comparable magnitude earthquakes on the Hayward Fault. 

The amount of coseismic surface slip will affect the amount of 

total slip (coseismic slip plus afterslip) along the fault. Where 

coseismic slip is large, as in the San Pablo area at the north end of 

the scenario rupture (fig. 1), the amount of afterslip will likely be 

small. In contrast, locations where the amount of coseismic slip is 

small will likely experience larger amounts of afterslip. Small or 

negligible values of coseismic slip, as modeled for the HaywardFremont section of the fault in the scenario (fig. 1), could lead to 

overly optimistic estimates of the effect of a large earthquake on 

utility, transportation, and building infrastructure immediately 

following such an earthquake, because the infrastructure could 

be significantly affected by up to 0.5–1.5 m (median plus 

one standard deviation) of afterslip in the hours following the 

earthquake. Due to the large uncertainty in the coseismic and 

afterslip estimates, real-time geodetic and rapid postearthquake 

response measurements of coseismic and early afterslip are crucial 

for accurate forecasts of the total slip that will develop. Therefore, 

following a large Hayward Fault earthquake, observations made 

along the fault of the amount of coseismic slip and rate of afterslip 

will provide a clearer forecast of the total slip expected to develop. 

These efforts would likely need to be much more extensive than 

those of Hudnut and others (2014) following the 2016 M6.0 South 

Napa earthquake, in order to accurately forecast afterslip along the 

length of a much longer Hayward Fault rupture.

Knowledge Gaps, Modeling 

Limitations, and Communication Needs

To better prepare for the effects of coseismic slip and afterslip 

from future large earthquakes of the Hayward Fault, the following 

knowledge gaps, modeling limitations, and communications issues 

need to be addressed:

• The details of the physical relations among interseismic 

fault creep, coseismic fault slip, and afterslip are not 

well known. To reduce the uncertainty in forecasting 

coseismic slip and afterslip for large earthquakes on the 

Hayward Fault and on other faults with interseismic 

creep, additional research is necessary to better understand 

the physics involved in fault creep and sudden slip in 

earthquake ruptures.

• Given the current state of knowledge of fault afterslip, 

near real-time measurements of coseismic fault 

slip and the progression of afterslip following a 

large Hayward Fault earthquake will be crucial for 

developing accurate and precise estimates of the 

amount and ultimate duration of afterslip.

• Operational procedures, similar to those used for 

forecasting aftershock behavior, need to be developed 

for forecasting afterslip. These forecasts will provide 

important scientific guidance to building owners, 

utility and infrastructure operators, and municipalities 

as they repair damage, rebuild, and recover from a 

large earthquake.

• Afterslip of 0.5–1.5 m on the Hayward Fault following 

a large earthquake will complicate repairs of structures 

straddling, and infrastructure crossing, the fault. This 

hazard needs to be communicated to the associated 

stakeholders so that they can incorporate these 

complications into their earthquake mitigation and 

response plans.

• There is potential for the occurrence of distributed 

faulting many tens to hundreds of meters from the 

main creeping fault trace during a large Hayward 

Fault rupture. This has importance in planning long 

baseline structures such as pipelines and transportation 

infrastructure that cross or are astride the fault. 

Conclusion

In the HayWired scenario some slip occurs coseismically at 

the surface as the Hayward Fault ruptures in the large mainshock 

earthquake, but it is expected to be only part of the total surface 

slip associated with the earthquake (Aagaard and others, 2012). 

The remaining surface slip occurs postseismically, that is after 

the shaking has stopped, which is a seismic fault movement 

called afterslip. This afterslip is expected to make a substantial 

contribution to the total slip and may be responsible for up to 

0.5–1.5 m of additional slip along the fault. In this particular 

HayWired scenario, the coseismic slip peaks at 2.1 m, and it will 

have immediate impacts at some locations. However, utilities, 

transportation infrastructure, and structures straddling the Hayward 

Fault where little coseismic slip occurred will likely be affected by 

significant afterslip following a large Hayward earthquake.

Acknowledgments

We thank Ben Brooks and Jessica Murray (U.S. 

Geological Survey) for their constructive reviews, which 

helped improve the manuscript.
Chapter D. HayWired Scenario Mainshock Coseismic and Postseismic Surface Fault Slip 45

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Abstract

The HayWired scenario examines a hypothetical 

earthquake (mainshock) with a moment magnitude of 7.0 

occurring on April 18, 2018, at 4:18 p.m. on the Hayward 

Fault in the east bay part of California’s San Francisco Bay 

area. For the HayWired scenario mainshock, we provide 

maps of earthquake-induced liquefaction probability 

(the likelihood of surface manifestations of liquefaction) 

using liquefaction probability estimates from the Federal 

Emergency Management Agency’s loss estimation tool 

Hazus-MH 2.1. We also produce more detailed liquefaction 

probability maps of northern Santa Clara County and western 

Alameda County by using the approach of Holzer, Noce, and 

Bennett (U.S. Geological Survey), who used the liquefaction 

potential index parameter as an index for liquefaction 

hazard in their mapping of a smaller part of northern Santa 

Clara County and western Alameda County. We apply their 

methods and extend their mapping as far from the area over 

which they collected subsurface (cone penetration test) 

data as may be practicable. The more detailed analysis of 

liquefaction probability, where available, is used to update 

liquefaction probabilities from Hazus in these areas. To 

assess liquefaction hazards in the remainder of the area 

impacted by the HayWired scenario earthquake (that is, not 

western Alameda County and northern Santa Clara County), 

we rely on the approach used in Hazus, which makes use of 

liquefaction susceptibility mapping.

Results of our liquefaction probability mapping of 

northern Santa Clara County and western Alameda County, 

and the Hazus mapping, show that areas of artificial fill 

along the San Francisco Bay margin and youthful alluvial 

deposits along the larger streams feeding into the bay have 

the highest likelihood of producing liquefaction-related 

ground-surface damage. The range and variability of the 

mapped ground motions (peak ground accelerations) in the 

HayWired mainshock contribute to a more variable depiction 

of liquefaction hazard, and in some cases a higher probability 

of liquefaction, than has been shown previously.

Chapter E

HayWired Scenario Mainshock—Liquefaction Probability 

Mapping

By Jamie L. Jones, Keith L. Knudsen, and Anne M. Wein

Introduction

The HayWired scenario examines a hypothetical earthquake 

(mainshock) with a moment magnitude (Mw) of 7.0 occurring 

on April 18, 2018, at 4:18 p.m. on the Hayward Fault in the east 

bay part of California’s San Francisco Bay area. The purpose of 

this earthquake-induced liquefaction assessment is to characterize 

liquefaction hazards and map their probability for the HayWired 

scenario mainshock. Liquefaction is a form of earthquakeinduced ground failure in which formerly solid ground transforms 

temporarily to a softened or liquefied state. Liquefaction occurs 

where strong ground motions produce a rise in pore-water pressure 

that in turn causes granular material to briefly lose strength and 

liquefy (Youd, 1973). The occurrence of liquefaction is restricted 

to certain geologic and hydrologic settings that experience 

relatively high levels of ground shaking. Areas susceptible to 

liquefaction are underlain by water-saturated, cohesionless, 

granular sediment. These conditions are typically found in areas 

of geologically youthful (Knudsen and others, 2009; Knudsen and 

Bott, 2011), sandy and silty deposits near water bodies, including 

artificial fill placed around the margin of San Francisco Bay 

(for example, Witter and others, 2006). Liquefaction probability 

estimates can be developed to describe two ways of thinking about 

the hazard—(1) the likelihood of a specific location experiencing 

liquefaction (for example, at the location of a cone penetration 

test, CPT, sounding) and (2) the percent of the ground surface area 

likely to experience liquefaction (for example, Holzer and others, 

2008, 2010, 2011).

Several types of liquefaction-induced ground failure can 

occur in larger earthquakes, including lateral spread, ground 

oscillation, flow failure, and loss of bearing strength. Each of these 

liquefaction-induced ground-failure phenomena affect the built 

environment differently. Lateral spreading occurs on gentle slopes 

when subsurface liquefaction allows upper strata and surface soils 

to displace downslope or toward a steep slope or free face (for 

example, a stream bank). Pipelines, roads, utilities, bridge piers, 

and structures with shallow foundations can be severely damaged 

by lateral spread (Rausch, 1997; Cubrinovski and others, 2014). 

Flow failure generally occurs on steeper slopes when liquefied 

The HayWired Earthquake Scenario—Earthquake Hazards

Edited by Shane T. Detweiler and Anne M. Wein

Scientific Investigations Report 2017–5013–A–H

[Also see https://doi.org/10.3133/sir20175013]
50 The HayWired Earthquake Scenario—Earthquake Hazards

material flows downslope at high velocity (Rausch, 1997; Youd, 

1973). Flow failures, due to their high speed and potential for the 

liquefied material to flow and spread out over the landscape, have 

the potential to cause the greatest amount of damage to buildings 

and other infrastructure but are relatively rare. Ground oscillation 

is similar to lateral spread but occurs in areas where the slope is 

too gentle to permit permanent lateral movement (Holzer and 

Youd, 2007). Ground oscillations may affect structures and utilities 

buried underground. Finally, loss of bearing strength results from 

soils losing their ability to support structures when deeper soils 

lose shear strength and weaken due to liquefaction. Infrastructure 

resting either on top of or within soils losing bearing strength 

can settle unevenly and be damaged (Tinsley and Ponti, 2008). 

Buoyant objects, such as partially empty fuel or water tanks and 

sewers, can “float” to the surface through the more dense liquefied 

material. 

An example of liquefaction catastrophically impacting a 

community occurred in Christchurch, New Zealand, during the 

2010 to 2012 earthquake sequence, and most extensively after the 

February 22, 2011, Mw 6.2 earthquake. This earthquake sequence 

caused properties and streets to be buried in thick layers of sand, 

silt, and water ejected from the subsurface, as well as by sewage 

from sewer lines broken by the ground deformation (McSaveney, 

2014; van Ballegooy and others, 2014; Green and others, 2014). 

House foundations cracked and buckled, wrecking many homes. 

Despite the damage to homes, there were few serious injuries 

that resulted from liquefaction. Several thousand homes have 

been demolished and some sections of Christchurch will never 

be reoccupied as the result of the damage and permanent loss 

of elevation due to ground settlement (Scott and Carville, 2016; 

Tonkin and Taylor, Ltd., 2013).

Liquefaction occurred in the San Francisco Bay area as a 

result of several early historical earthquakes, including the 1868 

Hayward and the 1906 San Francisco earthquakes; liquefaction 

also was one cause of damage in the 1989 Loma Prieta earthquake 

(Youd and Hoose, 1978; Tinsley and others, 1998). The most 

extensive and damaging liquefaction in the bay area has occurred 

in areas of poorly engineered artificial fill in San Francisco in 1906 

and along the east bay shoreline in 1989. In the 1906 earthquake, 

liquefaction-related ground deformation in San Francisco damaged 

water pipelines and this disruption to the water supply contributed 

significantly to the extent of the fire following the earthquake. 

A digital compilation of past occurrences of liquefaction in the 

nine-county bay area is presented in Knudsen and others (2000); 

these data have been used to review results of the analyses in this 

assessment. We have filtered the historical occurrences shown 

in our figures and attempted to include only those occurrences 

where the historical accounts allowed for confident location of the 

occurrences by Knudsen and others (2000).

Data

To compute liquefaction probabilities using the Holzer and 

others (2008, 2010, 2011) methodology, several types of data 

are needed—geologic maps, scenario estimates of peak ground 

acceleration (PGA), and depth to groundwater. Detailed 1:24,000-

scale Quaternary geologic mapping of the area is available from 

Witter and others (2006) (fig. 1). Shaking information is from the 

Aagaard, Boatwright, and others (this volume) scenario shaking 

map being used in the HayWired scenario. Depth to groundwater 

is available from the California Geological Survey (CGS) Seismic 

Hazard Zonation Program (Tim McCrink, CGS, written commun., 

2014).

Holzer and others (2008, 2010) grouped geologic 

map units from Witter and others (2006) into just a few 

categories based on comparable liquefaction susceptibilities. 

For example, the geologic map units most susceptible to 

liquefaction based on Holzer and others’ (2008, 2010) CPT 

and liquefaction potential index (LPI) analyses include latest 

Holocene alluvial-fan levee deposits (Qhly) in Santa Clara 

Valley and artificial fill (af) in the greater Oakland area. These 

geologic units, due to the similarities in their liquefaction 

probability curves, are grouped into one category, which we 

refer to as group 1 (see table 1). A second group of geologic 

map units with similarities in their liquefaction probability 

curves showing lower liquefaction potential, which we refer 

to as group 2, was broken out by Holzer and others (2008, 

2010) and treated separately in the liquefaction probability 

analysis. Holzer and others (2008, 2010) did not compute 

liquefaction potential for a number of geologic map units, 

including Holocene San Francisco Bay mud (Qhbm) and 

early Quaternary and older (>1.4 million years ago or megaannum, Ma) deposits and bedrock (br); these geologic units 

with no computed liquefaction potential are referred to as “not 

assessed” in this analysis.

Figure 2 shows the shaking (PGA) distribution for the 

HayWired scenario study area along with the boundaries for 

the areas where liquefaction probability was modeled using 

the methods of Holzer and others (2008, 2010). The shaking 

depiction is from the work of Aagaard, Boatwright, and 

others (this volume); it is based on three-dimensional physical 

modeling. In such modeling, accounting for site conditions, 

like the likely amplifications of longer period motions by soft 

soils around the margin of San Francisco Bay, is not easily 

accomplished. It is possible that the shaking model used 

underpredicts shaking in these areas. Because liquefaction 

is most likely to occur around the margins of the bay and 

near larger active creeks (Witter and others, 2006) where 

there are softer soils, we may be underpredicting the extent 

of liquefaction by using this shaking model. Note that most 

of the area of intense shaking is contained within the area 

where Holzer and others’ (2008, 2010) liquefaction probability 

modeling is applied. To estimate liquefaction probabilities 

for tracts in areas outside the black line, we use the Federal 

Emergency Management Agency’s (FEMA) loss estimation 

tool Hazus-MH 2.1 (Federal Emergency Management Agency, 

2012; Hope Seligson, Seligson Consulting, written commun., 

2016) and the liquefaction susceptibility mapping of Witter 

and others (2006) and Knudsen and others (2000). 
Chapter E. HayWired Scenario Mainshock—Liquefaction Probability Mapping 51

Jones_fig1

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

SAN

MATEO

San Jose

Oakland

Fremont

Hayward

Alameda

Livermore

4 8 KILOMETERS

0

0

4 8 MILES Surficial geology data from Witter and others, 2006.

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N.

EXPLANATION

Quaternary geology (from Witter and others, 2006)

Historical

af

afem

alf

acf

adf

gq

ac

Qhc

Qhfy

Qhly

Qhty

Qhay

Qhbs

Qhds

Qhbm

Qhed

Qhb

Qhfe

Qhf

Qhff

Qhl

Qds

Qb

Qf

Qt

Qa

Qpf

Qpt

Qpa

Qmt

Qbt

Qop

Qof

Qot

Qoa

br

Latest Holocene

Holocene

Holocene to

latest Pleistocene

Latest Pleistocene

Pleistocene

Early to late

Pleistocene

Early Quaternary

and older

Qht

Qha

Area where Holzer and others' (2008, 2010) 

methods are used to map liquefaction 

probability

Water

 Area 

of map

SAN FRANCISCO BAY

San Luis 

Reservoir

CALIF

37.5°

37.25°

37.75°

122.25° 122° 121.75° 121.5°

Figure 1. Map of the southeastern San Francisco Bay area, California, showing Quaternary geologic mapping (from Witter and others, 

2006) used to estimate liquefaction probabilities in the HayWired earthquake scenario using Holzer and others’ (2008, 2010, 2011) 

methodology. Liquefaction probabilities for the remainder of the area affected by shaking in this scenario were calculated using the 

Hazus-MH 2.1 loss estimation tool (Federal Emergency Management Agency, 2012) and existing liquefaction-susceptibility mapping. 

Black line shows areas in which Holzer and others’ (2008, 2010, 2011) methodology is applied. See table 1 for map unit descriptions.
52 The HayWired Earthquake Scenario—Earthquake Hazards

Table 1. San Francisco Bay area, California, Quaternary geologic map units (from Witter and others, 2006) and assigned liquefaction 

probability group for the HayWired earthquake scenario.

[Ma, mega-annum or millions of years ago]

Geologic unit name Geologic map unit 

abbreviation

Holzer and others 

(2008, 2010, 2011) 

liquefaction group

Liquefaction group1

Artificial fill af Group 1 Group 1 and group 22

Artificial fill over estuarine mud afem Not assessed Group 1

Artificial fill, levee alf Not assessed Group 2

Artificial fill, channel acf Not assessed Group 2

Artificial fill, dams adf Not assessed Group 2

Gravel quarries and percolation ponds gq Not assessed Not assessed

Artificial stream channel ac Not assessed Group 2

Modern stream-channel deposits Qhc Not assessed Group 1

Latest Holocene alluvial-fan deposits Qhfy Group 2 Group 2

Latest Holocene alluvial-fan levee deposits Qhly Group 1 Group 1

Latest Holocene stream-terrace deposits Qhty Group 1 Group 1

Latest Holocene alluvial deposits, undifferentiated Qhay Not assessed Group 1

Latest Holocene beach sand Qhbs Not assessed Group 1

Holocene dune sand Qhds Not assessed Group 2

Holocene San Francisco Bay mud Qhbm Not assessed Group 2

Holocene estuarine delta deposits Qhed Not assessed Not assessed

Holocene basin deposits Qhb Not assessed Group 2

Holocene fine grained alluvial-fan/estuarine complex deposits Qhfe Not assessed Group 2

Holocene alluvial-fan deposits Qhf Group 2 Group 2

Holocene alluvial-fan deposits, fine-grained facies Qhff Group 2 Group 2

Holocene alluvial fan levee deposits Qhl Group 2 Group 2

Holocene stream terrace deposits Qht Not assessed Group 2

Holocene alluvium, undifferentiated Qha Not assessed Group 2

Late Pleistocene to Holocene dune sand Qds Not assessed Group 2

Late Pleistocene to Holocene basin deposits Qb Not assessed Not assessed

Late Pleistocene to Holocene alluvial-fan deposits Qf Not assessed Group 2

Late Pleistocene to Holocene stream-terrace deposits Qt Not assessed Group 2

Late Pleistocene to Holocene alluvium, undifferentiated Qa Not assessed Group 2

Late Pleistocene alluvial-fan deposits Qpf Not assessed Group 2

Late Pleistocene stream-terrace deposits Qpt Not assessed Group 2

Late Pleistocene alluvium, undifferentiated Qpa Not assessed Group 2

Pleistocene marine-terrace deposits Qmt Not assessed Not assessed

Pleistocene bay-terrace deposits Qbt Not assessed Not assessed

Early to late Pleistocene pediment deposits Qop Not assessed Not assessed

Early to middle Pleistocene alluvial-fan deposits Qof Not assessed Not assessed

Early to middle Pleistocene stream-terrace deposits Qot Not assessed Not assessed

Early to middle Pleistocene undifferentiated alluvial deposits Qoa Not assessed Not assessed

Early Quaternary and older (>1.4 Ma) deposits and bedrock br Not assessed Not assessed

1

Two liquefaction curves/groups are available for use in assessing liquefaction probability for this area (from Holzer and others, 2008, 2010, 2011); see tables 2 and 3, as 

well as Methods section. “Not assessed” means that we judged the liquefaction hazard for these units to be very low to negligible, and the probability of liquefaction was 

not calculated for polygons with these map unit designations.

2

Assignment of liquefaction grouping to artificial fill depends on the nature of geologic map unit thought to underlie the fill.
Chapter E. HayWired Scenario Mainshock—Liquefaction Probability Mapping 53

Jonesfig2

Solano

MARIN

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

SAN

FRANCISCO

SAN

JOAQUIN

STANISLAUS

San Jose

Oakland

Stockton

Fremont

Hayward

Alameda

Livermore

SAN

FRANCISCO BAY

San Pablo 

 Bay

PACIFIC

OCEAN

0 6.5 13 KILOMETERS

0 6 12 MILES

EXPLANATION

Peak ground acceleration (%g)

< 0.15

Holzer and others (2008, 2010)

mapping extent

> 1.05

Area where Holzer and others'

(2008, 2010) methods are used

to map liquefaction probability

0.9–1.05

0.15–0.3

0.3–0.45

0.45–0.6

0.6–0.75

0.75–0.9

Peak ground acceleration data from U.S. Geological Survey, 2014.

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N. Area 

of map

38°

37.5°

122.5° 122° 121.5°

CALIF

Figure 2. Map of the San Francisco Bay region, California, showing peak ground acceleration (PGA) used for characterizing 

liquefaction hazard in the HayWired earthquake scenario (from Aagaard, Boatwright, and others, this volume). Areas outlined in black 

are the areas where the Holzer and others’ (2008, 2010) methods are used to estimate liquefaction probability. <, less than; >, greater 

than; %g, percentage of acceleration due to gravity.
54 The HayWired Earthquake Scenario—Earthquake Hazards

Depth to Groundwater

Contours of 3 meters (m) depth to groundwater in Santa 

Clara Valley and western Alameda County were provided by 

the California Geological Survey (CGS) (Tim McCrink, CGS, 

written commun., 2014). The depth to groundwater for the areas 

of interest is shown on figure 3. For Santa Clara Valley, the 3-m 

water-table depth line divides the region of interest into two areas 

we treated differently following the approach of Holzer and others 

(2008, 2010, 2011). Areas on the San Francisco Bay side of the 

line are generally considered to have water-table depths less than 

3 m, and areas on the inland side are believed to have water-table 

depths greater than 3 m. Parts of the 3-m groundwater contour 

have been omitted from this analysis because they were either not 

complete contours (they could not be closed off to definitively say 

whether the groundwater level was above or below 3 m) or they 

were not included in Holzer and others’ (2008, 2010) work. 

Methods

In this liquefaction assessment, which will be used in Hazus 

(Federal Emergency Management Agency, 2012) loss estimation 

and other earthquake consequences modeling in the HayWired 

scenario project, we supplement the Hazus liquefaction probability 

mapping being done by Hope Seligson (Seligson Consulting, 

written commun., 2016) by expanding the probabilistic 

liquefaction hazard mapping of Holzer and others (2008, 2010) to 

cover broader areas. 

A probabilistic assessment of liquefaction potential 

includes evaluation of both the susceptibility of surficial deposits 

to liquefaction and the probability that earthquake ground 

motions will exceed a specified threshold level, or “liquefaction 

opportunity.” A liquefaction susceptibility map reflects the 

distribution of surficial deposits with different physical properties 

and variations in hydrologic conditions (for example, Witter 

and others, 2006; Knudsen and others, 2000). A liquefaction 

potential map is the product of a liquefaction susceptibility map 

and a liquefaction opportunity map depicting ground motions. 

In this project, we use the HayWired ground motions described 

by Aagaard, Boatwright, and others (this volume) and geologic 

mapping by Witter and others (2006) in a relatively new approach 

to liquefaction probability mapping developed by Holzer and 

others (2008, 2010) to provide liquefaction probability maps for 

the HayWired scenario. 

Holzer and others (2008, 2010) map liquefaction 

probabilities in western Alameda County and northern Santa Clara 

County for magnitude 6.7 and 7.0 earthquakes on the Hayward 

Fault. They collected CPT data in both of these areas in sufficient 

density to characterize the liquefaction susceptibility of many 

of the more extensive and likely more hazardous Quaternary 

geologic map units. They computed distributions of the LPI 

from CPT data for surficial geologic map units. Geologic map 

units are then grouped together if their LPI distributions are 

similar. These distributions are then used to develop liquefaction 

probability curves for each of the groupings and to develop 

probabilistic liquefaction hazard maps for different earthquake 

scenarios. We follow their procedure, except we use the simulated 

ground motions for the HayWired scenario mainshock (Aagaard, 

Boatwright, and others, this volume). We have also expanded the 

area to map liquefaction probabilities in areas outside of the areas 

characterized by Holzer and others (2008, 2010).

The liquefaction probability curves of Holzer and others 

(2008, 2010, 2011) are based on the following functional 

form:

(1)

where PGA is the peak ground acceleration, MSF is the 

liquefaction magnitude scaling factor, and A, B, and C are 

liquefaction probability constants empirically derived by 

Holzer and others (2008, 2010, 2011) based on probabilities 

inferred from LPI distributions. Holzer and others (2008, 

2010, 2011) adopted the definition of MSF described by Youd 

and others (2001) where:

 

 (2)

and M is the earthquake moment magnitude, which is Mw 7.05 

for the HayWired scenario (magnitude reported to two decimal 

places based on original data from Aagaard and others, 2010). 

For the HayWired scenario, MSF equals 1.17.

Toprak and Holzer (2003) showed that areas in Monterey 

County with calculated LPI values of more than 5 showed 

surface manifestation of liquefaction 58 percent of the time 

during recent earthquakes, agreeing with earlier research 

that an LPI of 5 or greater is a good indicator of liquefaction 

occurrence (Iwasaki and others, 1982). In this case, following 

the approach of Holzer and others (2008, 2010, 2011), 

liquefaction probability at a location is the likelihood that an 

area will have an LPI greater than 5. For further discussion of 

their methodology, see Holzer and others (2011).

According to Holzer and others (2008, 2010), their 

liquefaction probability estimates describe both how likely 

liquefaction is to occur at a random point in a geologic map 

unit polygon, as well as the percent of ground surface area 

in that geologic map unit polygon likely to be affected by 

liquefaction.

Tables 2 and 3 show the liquefaction probability constants 

A, B, and C from Holzer and others (2011) used to calculate 

liquefaction probability in equation 1 for Santa Clara Valley (table 

2) and the greater Oakland area (table 3). Holzer and others (2011) 

developed unique constants for geological units in each of the 

two areas they studied. We used the geologic map unit groupings 

of Holzer and others (2008, 2010, 2011), and added geologic 

map units that weren’t included in their analysis to the groups, 

based on our understanding of the nature of the liquefaction 

susceptibility of each of these units, as well as their similarity in 

age and depositional environment to map units in groups 1 and 2 

P A

PGA MSF

B

1 / H C =

+

MSF M 10 / 2.24 2.56 =
Chapter E. HayWired Scenario Mainshock—Liquefaction Probability Mapping 55

Figure 3. Map of 

the southeastern San 

Francisco Bay area, 

California, showing 

3-meter (m) contour 

from historical high 

groundwater data for 

northern Santa Clara and 

western Alameda Counties 

(from Tim McCrink, 

California Geological 

Survey, written commun., 

2014). We use a standard 

groundwater depth of 1.5 

m throughout western 

Alameda County for 

calculating liquefaction 

probability in the HayWired 

earthquake scenario, 

which is consistent with 

the approach of Holzer 

and others (2010, 2011). 

For areas in Santa Clara 

County shown with 

depths to historical high 

groundwater of less than 

3 m, to be consistent 

with Holzer and others 

(2008, 2010, 2011), we use 

the constants for 1.5-m 

depth shown in table 2 for 

calculating liquefaction 

probability. For Santa Clara 

County areas with depth 

to groundwater greater 

than 3 m, to be consistent 

with Holzer and others 

(2008, 2010, 2011), we use 

the constants associated 

with the 5-m depth to 

groundwater shown in 

table 2. Parts of the 3-m 

groundwater contour 

have been omitted from 

this analysis because they 

were either not complete 

contours (they could not 

be closed off) or they were 

not included in Holzer and 

others’ (2008, 2010) work.

122.25° 122° 121.75°

37.75°

37.50°

CONTRA

COSTA

ALAMEDA

Santa

Clara

SAN

MATEO

San Luis

Reservoir

SANTA

CLARA

San Jose

Oakland

Fremont

Hayward

Alameda

Livermore

0 4 8 KILOMETERS

0 4 8 MILES

EXPLANATION

Area where Holzer and others' (2008, 2010) methods are 

used to map liquefaction probability

3-meter depth to groundwater contour

Depth to groundwater contour data from California Geological Survey, 2016.

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N.

SAN FRANCISCO BAY

 Area 

of map CALIF

37.25°
56 The HayWired Earthquake Scenario—Earthquake Hazards

(see table 1 and fig. 4). In the map database of Witter and others 

(2006), artificial fill polygons are tagged with the geologic map 

unit likely to underlie the manmade artificial fill. Polygons mapped 

as artificial fill by Witter and others (2006) were assigned to group 

1 or 2 based primarily on which geologic map unit the artificial 

fill bodies rest. If the artificial fill was placed over Holocene 

San Francisco Bay mud, then that area is assigned to group 1, 

the more susceptible deposits, because a significant fraction of 

all liquefaction occurrences in past bay area earthquakes have 

occurred in this setting.

The need for two unique sets of constants in the two 

different areas suggests that the liquefaction potential of 

Quaternary geologic map units used by Witter and others (2006) 

(fig. 1, table 1) may vary from region to region within the bay 

area. Holzer and others (2008, 2010) did not collect CPT data 

in areas outside of the greater Oakland area and Santa Clara 

Valley—without CPT data available to develop the liquefaction 

probability constants, expanding the analysis outside of 

Alameda and Santa Clara Counties is not appropriate.

For areas outside the extent of Holzer and others’ (2008, 

2010) mapping, we use liquefaction probabilities estimated by 

Hope Seligson (Seligson Consulting, written commun., 2016), 

who applies the Hazus (Federal Emergency Management Agency, 

2012) methodology. When using FEMA’s nationally applicable 

geographic information systems (GIS)-based Hazus software, 

one can start with a geologic map, produce a liquefaction 

susceptibility map based on recommendations by Youd and 

Perkins (1978), and then Hazus uses that map, along with 

scenario or probabilistic ground motions, to estimate liquefaction 

probability and permanent ground deformations. However, when 

suitable liquefaction susceptibility mapping is available, it is 

best to start with that, and Hope Seligson (Seligson Consulting, 

written commun., 2016) has used the Knudsen and others (2000) 

liquefaction susceptibility maps for the bay area. Because the 

extent of liquefaction in future earthquakes is not expected to 

cover 100 percent of even the most hazardous areas, Hazus uses 

a factor referred to as “the proportion of map unit susceptible 

to liquefaction” (that is, the likelihood of susceptible conditions 

existing at any given location within the unit) to limit the areal 

extent of liquefaction. Hazus allows a maximum of 25 percent 

of an area to liquefy when the area has been assigned very high 

liquefaction susceptibility and assigns lower “proportions” to less 

susceptible areas. The liquefaction probability is then estimated by 

the following equation:

 (3)

where P[LSC | PGA=a] is the conditional liquefaction probability 

for the liquefaction susceptibility category given a specific 

PGA (calculated within Hazus based on empirical procedures 

and statistical modeling by Liao and others, 1988), KM is the 

moment magnitude correction factor (this corrects from a baseline 

magnitude of 7.5; for the HayWired scenario mainshock this is 

P L P L PGA a

K K [ ] P [ | ] SC

SC

M W

= ml

=

×

×

1.09), KW is the groundwater correction factor (this corrects from a 

baseline depth of 5 feet), and Pml is the proportion of the geologic 

map unit assumed susceptible to liquefaction, described above. 

Hazus identifies the centroid of each census tract and calculates 

the values for each parameter in equation 3 at the centroid. Hazus 

makes use of these liquefaction probabilities and then calculates 

expected permanent ground deformation. These grounddeformation values are then used in the Hazus loss calculations.

Holzer and others (2008, 2010) identified map units 

Qhly in Santa Clara Valley and af in the greater Oakland area 

as having a high likelihood of experiencing liquefaction; 

we grouped these into one set of similar map units, which 

we refer to as group 1. Holzer and others (2008, 2010) 

grouped Qhf, Qhff, and Qhl into another group of similar 

map units, which we refer to as group 2. Group 1 is more 

susceptible to liquefaction than is group 2. See table 1 for 

assignments to the different liquefaction hazard groupings 

for the Quaternary geologic map units identified by Witter 

and others (2006) in our study area. For northern Santa Clara 

Valley, Holzer and others (2008, 2010) developed curves for 

two water-table depths. The main difference between our 

approach and that used by Holzer and others (2008, 2010) 

is that we use the HayWired ground motions (Aagaard, 

Boatwright, and others, this volume), whereas Holzer and 

others (2008, 2010) estimated ground motions using the Next 

Generation Attenuation of Ground Motions (NGA) project 

ground-motion prediction equation (GMPE) developed by 

Boore and Atkinson (2008) for two southern Hayward Fault 

earthquake scenarios. An additional difference between our 

approach and Holzer and others’ (2008, 2010) approach is 

that the HayWired scenario ground motions used data for 

VS30, the time-averaged shear-wave velocity (VS) to a depth 

of 30 m, from Wills and others (2000) to account for site 

conditions (Aagaard, Boatwright, and others, this volume), 

whereas the Holzer and others (2008, 2010) analysis used 

VS30 generated using site-specific CPT soundings. Both the 

Holzer and others’ (2008, 2010) and Aagaard, Boatwright, 

and others’ (this volume) PGA data were corrected to 

account for local site conditions based on the site correction 

in Boore and Atkinson (2008).

Our analysis was conducted using GIS applications and a 

grid composed of approximately 50-m cells to represent the area. 

Liquefaction probabilities were calculated by first assigning each 

grid cell a Quaternary geologic map unit based on the Witter and 

others (2006) mapping (fig. 1), a groundwater depth (Santa Clara 

Valley only), and the associated liquefaction probability constants 

from table 2 for Santa Clara Valley or table 3 for western Alameda 

County. PGA values are also assigned to each cell. Equation 1 is 

then used to calculate the liquefaction probability for each grid 

cell. Data are available in Jones and Knudsen (2017).

For further discussion of the methods used to map 

liquefaction probabilities, and the associated uncertainties, 

please refer to Holzer and others (2008, 2010, 2011).
Chapter E. HayWired Scenario Mainshock—Liquefaction Probability Mapping 57

Figure 4. Map of 

the southeastern San 

Francisco Bay area, 

California, showing 

grouping of Quaternary 

geologic map units in 

liquefaction-hazard 

groups 1 (higher hazard) 

and 2 (lower hazard) 

for the HayWired 

earthquake scenario 

(see table 1) using the 

methods of Holzer and 

others (2008, 2010).

Jones_fig4

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

SAN

MATEO

San Jose

Oakland

Fremont

Hayward

Alameda

Livermore

SAN FRANCISCO BAY

122° 121.75°

37.75°

38°

37.5°

37.25°

122.25°

San Luis

Reservoir

0 4 8 KILOMETERS

0 4 8 MILES Surficial geology data modified from Witter and others, 2006.

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N.

EXPLANATION

Quaternary geologic mapping groupings for probabilistic liquefaction assessment

using Holzer and others' (2008, 2010) methods

Liquefaction hazard not assessed

Area where Holzer and others' (2008, 2010)

methods are used to map liquefaction probability

Group 2 —Lower liquefaction-hazard group

Group 1—Higher liquefaction-hazard group

Water

CALIF

 Area 

of map
58 The HayWired Earthquake Scenario—Earthquake Hazards

Table 2. Santa Clara Valley, California, liquefaction probability constants for equation 1 (summarized from Holzer and others, 2011) for 

the HayWired earthquake scenario.

Results

In the western Alameda County study area (fig. 5), 

calculated probabilities of liquefaction are as much as 75 

percent for some areas along major creeks and for areas of 

artificial fill near the San Francisco Bay margin. According 

to Holzer and others (2008, 2010), the probability estimates 

describe both how likely liquefaction is to occur at the point 

where the calculation is conducted (at the location of the 

CPT sounding), as well as the percentage of ground surface 

area in that geologic map unit polygon likely to be affected 

by liquefaction (assuming sufficient CPT sampling for the 

geologic map unit). High liquefaction probabilities are mapped 

in the Quarry Lakes Regional Recreation Area in Fremont and 

at several places along Alameda Creek, Arroyo de la Laguna, 

and Arroyo Valle between Fremont and Livermore (fig. 5). 

Much of the northern part of the western Alameda County 

shoreline also has a relatively high probability of liquefaction 

(40–50 percent) where the majority of the shoreline area is 

developed (for example, shorelines of the cities of Alameda, 

Oakland, and Hayward). Comparison of the locations of 

past occurrences of liquefaction shown in figure 5 with the 

liquefaction probability mapping shows that, in general, sites 

of liquefaction in past earthquakes lie in areas mapped as 

having a high probability of liquefaction.

In the Santa Clara Valley (fig. 6), liquefaction probability 

reaches a high of about 50 percent. Those areas that are 

mapped with 40–50 percent liquefaction probability are along 

the southernmost part of San Francisco Bay and along the 

banks of Guadalupe River and Coyote Creek. These areas are 

generally occupied by commercial development (for example, 

office buildings) rather than residential development. The 

Area Water table 

depth, in meters

Group 1, map units Group 2, map units

Qhly, Qhty Qhf/Qhfy, Qhff, Qhl

A B C A B C

Santa Clara Valley 1.5 0.6503 0.2981 –3.7789 1.8336 1.2479 –2.5577

5.0 0.5886 0.4586 –3.5751 0.2268 0.6571 –3.4305

Table 3. Greater Oakland, California, area liquefaction probability constants for equation 1 (summarized from Holzer and others, 2011) 

for the HayWired earthquake scenario.

Area Water table 

depth, in meters

Group 1, map units Group 2, map units

af Qhf, Qhff, Qhl

A B C A B C

Oakland 1.5 0.7826 0.2315 –4.6645 0.0645 0.3366 –6.2881

majority of developed land in Santa Clara Valley lies in areas 

with lower liquefaction probability (5–10 percent). Sites of 

liquefaction occurrences in past earthquakes in Santa Clara 

Valley are mostly located in or near areas mapped with higher 

probabilities of liquefaction (fig. 6).

The two liquefaction probability modeling approaches 

yield slightly different probability estimates for census tracts. 

Hazus probabilities (fig. 7) are calculated using existing 

liquefaction susceptibility maps (Knudsen and others, 2000; 

Witter and others, 2006) and water-table depths (as used in 

Holzer and others, 2008, 2010, 2011) at the centroid of the 

census tract. Probabilities for the Holzer and others’ (2008, 

2010, 2011) method shown in figure 8 are averages of the 

liquefaction probabilities in developed areas of the census 

tract. The average for each census tract is calculated by (1) 

selecting the 50-m grid cells classified as developed (low-, 

medium-, or high-intensity) in the 2011 National Land 

Cover Database (Homer and others, 2015) with nonzero 

liquefaction probabilities and (2) summing the probabilities 

and dividing by the count of the selected grid cells. Figure 9 

compares liquefaction probabilities by census tract derived 

using the Holzer and others (2008, 2010, 2011) approach 

with liquefaction probabilities calculated using Hazus. Hazus 

estimates larger probabilities of liquefaction in most census 

tracts (figs. 8, 9). Liquefaction probabilities derived using 

Holzer and others’ (2008, 2010, 2011) approach are larger than 

60 percent and exceed Hazus liquefaction probabilities for those 

census tracts. Where the liquefaction probabilities in Hazus 

are capped at 25 percent (for example, in areas of artificial fill 

around the margins of San Francisco Bay), the liquefaction 

probabilities derived using the Holzer and others’ (2008, 2010, 

2011) methods are mostly in the range of 40–60 percent.
Chapter E. HayWired Scenario Mainshock—Liquefaction Probability Mapping 59

Figure 5. Map of the southeastern San Francisco Bay area, California, showing probability of liquefaction in the HayWired earthquake 

scenario for western Alameda County. Historical liquefaction occurrences are from the compilation by Knudsen and others (2000). We 

have chosen to show lateral spread, ground settlement, or sand boil points for which Knudsen and others (2000) assigned a moderate to 

high degree of location certainty (“S” events, defined as having indefinite or approximate locations by Knudsen and others, 2000, were 

excluded due to higher uncertainty in positional accuracy). Note the good correlation between past occurrences of liquefaction and the 

mapping of higher liquefaction probabilities. <, less than; >, greater than.

Jones_fig5

SAN

MATEO

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

QUARRY LAKES REGIONAL

RECREATION AREA

Alameda Creek

Arroyo de la Laguna

Arroyo Valle

SAN FRANCISCO BAY

Alameda

Hayward

Fremont

Oakland

Livermore

Calaveras

Reservoir

San Antonio

Reservoir

0 3 6 KILOMETERS

Historical liquefaction data from Knudsen and others, 2000. 0 3 6 MILES

Depth to groundwater contour data from California Geological Survey, 2016.

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N.

EXPLANATION

Probability of liquefaction in western Alameda County

< 0.05

> 0.5

Not assessed

Historical liquefaction area

Historical liquefaction line

Historical liquefaction point

0.4–0.5

0.3–0.4

0.2–0.3

0.1–0.2

0.05–0.1

Area where Holzer and others' (2008, 2010)

methods are used to map liquefaction probability

3-meter depth to groundwater contour

Inland waterways

 Area 

of map

122.25° 122° 121.75°

37.75°

37.5°

CALIF
60 The HayWired Earthquake Scenario—Earthquake Hazards

Figure 6. Map of the southeastern San Francisco Bay area, California, showing probability of liquefaction in the HayWired earthquake 

scenario for the Santa Clara Valley. Historical liquefaction occurrences are from the compilation by Knudsen and others (2000). We have 

chosen to show lateral spread, ground settlement, or sand boil points for which Knudsen and others (2000) assigned a moderate to high 

degree of location (“S” events, defined as having indefinite or approximate locations by Knudsen and others, 2000, were excluded due 

to higher uncertainty in positional accuracy). Note the good correlation between past occurrences of liquefaction and the mapping of 

higher liquefaction probabilities. <, less than; >, greater than.

Jones_fig6

37.5°

37.25°

122.2° 122° 121.8°

ALAMEDA

SANTA

CLARA

SAN

MATEO

SANTA

CRUZ

Guadalupe

River

Coyote Creek

Los Gatos Cre e k

San Jose

Calaveras

Reservoir

0 2 4 KILOMETERS

0 2 4 MILES

Historical liquefaction data from Knudsen and others, 2000.

Depth to groundwater contour data from California Geological Survey, 2016.

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N.

EXPLANATION

Probability of liquefaction in Santa Clara Valley

< 0.05

> 0.5

Not assessed

Historical liquefaction point

Historical liquefaction line

Historical liquefaction area

0.4–0.5

0.3–0.4

0.2–0.3

0.1–0.2

0.05–0.1

3-meter depth to groundwater contour

Area where Holzer and others' 

(2008, 2010) methods are used to 

map liquefaction probability

Inland waterways

 Area 

of map CALIF

SAN FRANCISCO BAY
Chapter E. HayWired Scenario Mainshock—Liquefaction Probability Mapping 61

Figure 7. Map of the San Francisco Bay region, California, showing tract-level liquefaction probabilities for the HayWired earthquake 

scenario as estimated using Hazus-MH 2.1 (Federal Emergency Management Agency, 2012). Tract-level probabilities are estimated by 

Hazus at the tract centroid using liquefaction susceptibility, peak ground acceleration, magnitude, depth to groundwater, and the geologic 

map unit’s liquefaction susceptibility proportion. Historical liquefaction occurrences are from the compilation by Knudsen and others (2000). 

We have chosen to show lateral spread, ground settlement, or sand boil points for which Knudsen and others (2000) assigned a moderate to 

high degree of location. <, less than; >, greater than.

Jones_fig7

SONOMA NAPA

SOLANO

MARIN

ALAMEDA

STANISLAUS

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

SAN

JOAQUIN

SAN

FRANCISCO

CONTRA

COSTA

SAN

BENITO

San Luis

Reservoir

0 7 14 KILOMETERS

0 7 14 MILES

EXPLANATION

Liquefaction probability—Derived 

using Hazus

< 0.05

Not measured

> 0.25

Historical liquefaction area

Historical liquefaction line

Historical liquefaction point

0.2–0.25

0.15–0.2

0.1–0.15

0.05–0.1

Area where Holzer

and others' (2008, 2010)

methods are used to map

liquefaction probability

Historical liquefaction data from Knudsen and others, 2000.

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016, and Federal Emergency 

Management Agency Hazus-MH 2.1, 2014.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N.

PACIFIC

OCEAN

SAN

FRANCISCO BAY

San Pablo 

 Bay

 Area 

of map CALIF

38°

122.5° 122° 121.5°

37.5°

37°
62 The HayWired Earthquake Scenario—Earthquake Hazards

Figure 8. Map of the San Francisco Bay region, California, showing tract-level liquefaction probabilities in the HayWired earthquake 

scenario for Alameda and Santa Clara Counties, as estimated based on Holzer and others (2008, 2010, 2011). Tract-level probabilities 

are estimated by taking the sum of all non-zero 50-meter-resolution grid-cell-based probabilities in 2011 National Land Cover Database 

(Homer and others, 2015) developed (low-, medium-, or high-intensity) areas in a tract and dividing by the count of summed grid cells in 

the tract. <, less than; >, greater than.

SONOMA NAPA

SOLANO

MARIN

ALAMEDA

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

SAN

JOAQUIN

STANISLAUS

SAN

FRANCISCO

CONTRA

COSTA

SAN

BENITO

0 7 14 KILOMETERS

0 7 14 MILES

EXPLANATION

Liquefaction probability—Derived using Holzer 

and others'(2008, 2010, 2011) method

< 0.05

Not measured

> 0.25

0.2–0.25

0.15–0.2

0.1–0.15

0.05–0.1

Area where Holzer and others' 

(2008, 2010) methods are used to map

liquefaction probability

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016, and Federal Emergency 

Management Agency Hazus-MH 2.1, 2014.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N.

PACIFIC

OCEAN

 Area 

of map

SAN

FRANCISCO BAY

San Pablo 

 Bay

 San Luis 

Reservoir

122.5° 122° 121.5°

38°

37°

37.5°

CALIF
Chapter E. HayWired Scenario Mainshock—Liquefaction Probability Mapping 63

Jones_fig9

SONOMA NAPA

Solano

MARIN

ALAMEDA

SANTA

CLARA

SANTA

CRUZ

SAN

MATEO

SAN

JOAQUIN

STANISLAUS

SAN

FRANCISCO

CONTRA

COSTA

SAN

BENITO

0 7 14 KILOMETERS

0 7 14 MILES

EXPLANATION

Difference between liquefaction probabilities—Derived using Hazus and Holzer 

and others'(2008, 2010, 2011) methods

Difference > 0.1—Holzer and others' method larger

Difference < 0.1—Holzer and others' method larger

Difference < 0.1—Hazus larger

Difference > 0.1—Hazus larger

Holzer and others' methods = Hazus

Area where Holzer and others' (2008, 2010) methods

are used to map liquefaction probability

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016, and Federal Emergency 

Management Agency Hazus-MH 2.1, 2014.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N. Area 

of map

PACIFIC

OCEAN

SAN

FRANCISCO BAY

San Pablo 

 Bay

 San Luis 

Reservoir

122.5° 122° 121.5°

38°

37°

37.5°

CALIF

Figure 9. Map of the San Francisco Bay region, California, showing the difference between Hazus-MH 2.1 (Federal Emergency Management 

Agency, 2012) derived liquefaction probability values and liquefaction probability derived using the methods of Holzer and others (2008, 2010, 2011) 

for Alameda and Santa Clara Counties in the HayWired earthquake scenario. Probabilities are represented at the census-tract level; Hazus uses 

census tract centroid data, whereas values derived using the Holzer and others’ (2008, 2010, 2011) methods are averages (the sum of all non-zero 

50 meter-resolution grid-cell-based liquefaction probabilities in 2011 National Land Cover Database (Homer and others, 2015) developed (low-, 

medium-, or high-intensity) areas in a tract divided by the count of summed grid cells in the tract). <, less than; >, greater than; =, equal to.
64 The HayWired Earthquake Scenario—Earthquake Hazards

Knowledge Gaps and Modeling 

Limitations

Holzer and others (2008, 2010) did not collect sufficient 

CPT data to characterize all Quaternary geologic map units in 

their study area. Thus, Holzer and others (2008, 2010) simplified 

the Quaternary geologic mapping from Witter and others (2006) 

by grouping map units into one of three groups with high, lower, 

and negligible potential for liquefaction; we did the same. To fully 

explore the range in liquefaction probability and provide more 

detailed mapping, a much greater number of CPT soundings 

would need to be collected for each geologic map unit mapped in 

the area of interest.

In earlier work, Toprak and Holzer (2003) concluded that, 

in general, areas with CPT soundings with calculated LPI values 

of 5 or greater are assumed to experience surface manifestations 

of liquefaction. Holzer and others’ (2008, 2010) subsequent 

liquefaction-probability mapping is based on this conclusion, 

and the calculated “probabilities of liquefaction” are actually 

probabilities of calculated LPI being greater than 5. Further, 

Holzer and others (2008, 2010) are able to both calculate a 

probability of liquefaction at a point on the land surface where 

their CPT data are collected and use that same probability to 

represent an extent of the area likely to liquefy, based on the 

assumption that they have adequately sampled the extent and 

variability of the map unit polygons with their CPT soundings. 

This assumption may or may not be valid for all of the Quaternary 

geologic map units.

Holocene San Francisco Bay mud (Qhbm) around the 

southern margin of San Francisco Bay was not evaluated by 

Holzer and others (2008, 2010) because it was mostly inaccessible 

to their CPT truck. This area is sparsely developed, but there 

may be implications for the lifeline infrastructure located in areas 

mapped as Qhbm. Infrastructure found on Holocene San Francisco 

Bay mud deposits around the southern margin of the bay includes 

substations, electric-power-generation plants, transmission lines, 

railways, and pipelines. We assigned Qhbm to group 2, the 

moderate liquefaction-hazard group.

Existing regional geologic mapping has been conducted 

and compiled at a 1:24,000 scale. Site-specific conditions may 

be different than those portrayed, particularly in areas of sandy 

artificial fill, where mitigation of soil to preclude liquefaction and 

(or) related damage to structures may have been conducted. The 

mapping by Witter and others (2006) and Knudsen and others 

(2000) is based on the most recent 7.5-minute topographic maps, 

and land-surface topography has been altered in many places since 

publication of the most recent version of the topographic maps. 

Where large-scale grading/earth movement for development has 

occurred, the Quaternary geologic maps may not capture the finer 

resolution nature of geologic materials.

In Holzer and others (2008, 2010), the calculation of 

liquefaction probabilities includes use of historical highgroundwater data from CGS, as well as depths to groundwater 

collected at the time of CPT soundings. The methodology used 

by Holzer and others (2008, 2010), and this study, does not 

account for seasonal fluctuations in depth to groundwater nor 

for differences between the historical high groundwater and the 

depth to groundwater at the time of a future earthquake. Drought 

conditions in California will increase the depth to groundwater 

in some parts of the San Francisco Bay area. At the time of this 

report, California had been experiencing drought conditions for 

several years, and groundwater levels can start to show measurable 

changes (increasing depth to groundwater) after at least one 

year of drought (U.S. Geological Survey, 2015). As the depth to 

groundwater increases, liquefaction potential decreases. However, 

around the bay margins, where most of the high liquefaction 

probability deposits have been identified, the depth to groundwater 

is typically controlled by water levels in the bay. Furthermore, sealevel rise may have increased the liquefaction hazard in some Bay 

margin areas, like the City of San Francisco (Thomas Holzer, U.S. 

Geological Survey, written commun., 2015).

In assessing liquefaction hazards, earthquake magnitude 

is generally used as a surrogate for earthquake duration (for 

example, Youd and others, 2001). The Holzer and others (2008, 

2010, 2011) methodology uses earthquake magnitude to calculate 

the liquefaction factor of safety, which is the basis for estimating 

liquefaction potential. Earthquake duration has a direct impact 

on the occurrence, extent and intensity of liquefaction surface 

manifestations, with longer durations increasing the damage from 

liquefaction. Earthquake durations longer or shorter than the 

average for a given magnitude may lead to underpredictions or 

overpredictions of liquefaction probability, respectively.

The calculation of liquefaction probability is highly 

dependent on the ground motions used. For the HayWired scenario 

mainshock, the PGA estimates we use were simulated by Aagaard, 

Boatwright, and others (this volume) using a three-dimensional 

physics-based model. The limitations of that model in areas with 

soft soil, such as around the margins of San Francisco Bay, are 

described by Aagaard, Boatwright, and others (this volume). Also, 

because our analysis makes use of a relatively fine 50-m grid, and 

the PGA values are provided by Aagaard, Boatwright, and others 

(this volume) as a much coarser grid, we interpolated between 

the provided PGA values. Use of shaking maps generated by 

other Hayward Fault earthquake scenarios would likely result in a 

different depiction of liquefaction hazard; for example, shaking in 

San Francisco might be greater for other scenario earthquakes.

Improved hazard-mapping approaches by geologic and 

geotechnical communities would help to better assess liquefaction 

hazards. For example, relatively detailed topographic information 

is increasingly available for many areas, and the identification of 

scarps and gentle slopes could be used, along with better methods 

of predicting liquefaction-related ground-surface displacement. 

Finer-resolution input data and improved models for predicting 

liquefaction-related ground-surface displacement would be useful 

to a wide range of interested parties.

Conclusion

The liquefaction-probability mapping based on the 

methods of Holzer and others (2008, 2010, 2011) for western 

Alameda County and Santa Clara Valley provides insight into 

the liquefaction hazard for a large earthquake on the Hayward 
Chapter E. HayWired Scenario Mainshock—Liquefaction Probability Mapping 65

Fault. For the Mw 7.0 HayWired scenario mainshock, a high 

likelihood of liquefaction is calculated for several areas, and thus 

liquefaction may be a major source of significant damage. Impacts 

from liquefaction-induced ground failure are expected to occur 

in residential areas, commercial areas, and especially around the 

San Francisco Bay margin, where much of the region’s extensive 

network of lifelines are located. Liquefaction is expected to be 

less extensive in the northern Santa Clara Valley than in western 

Alameda County and the areas affected are expected to be mostly 

commercial. Note that our liquefaction probability estimates 

do not account for site-specific engineering solutions that have 

been implemented to reduce either the extent and severity of 

liquefaction or the resulting damage to existing structures. 

Liquefaction probabilities for western Alameda County 

and the Santa Clara Valley differ (in some cases are higher) from 

Holzer and others’ (2008, 2010) results, because we use the 

Aagaard, Boatwright, and others (this volume) HayWired shaking 

depiction, which takes advantage of earthquake simulations. 

Therefore, there are higher (and lower) shaking levels than the 

estimates used in the Holzer and others’ (2008, 2010) studies 

where PGA was derived from the NGA ground-motion prediction 

equation by Boore and Atkinson (2008).

The liquefaction-probability mapping using the Holzer and 

others’ (2008, 2010) methodology is being used in other analyses 

of the HayWired scenario project, including in calculating loss 

estimates using Hazus (Hope Seligson, Seligson Consulting, 

written commun., 2016). However, for other areas shaken by 

the scenario earthquake (including in San Francisco, where 

liquefaction has occurred in the past), we only have liquefactionprobability estimates based on the Hazus approach. Results of our 

analyses are being incorporated into analyses of infrastructure/

lifeline exposure, damages, and potential service disruptions. 

Acknowledgments

Thomas Holzer and Thomas Noce (U.S. Geological Survey, 

USGS) were consulted on application of their methods to the 

HayWired scenario mainshock and expanded geographical area. 

Thomas Holzer contributed to the knowledge gaps analyses. 

Reviewers Thomas Holzer and Eric Thompson (USGS) provided 

thoughtful reviews of the material. Amandine Dhellemmes 

(USGS) ran the preliminary analysis, explained differences in 

results from using the HayWired scenario compared to previouslyused scenarios, and helped to identify geologic units that were not 

accounted for in the extended region.

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Chapter F

HayWired Scenario Mainshock—Earthquake-Induced 

Landslide Hazards

By Timothy P. McCrink1 and Florante G. Perez1

The HayWired Earthquake Scenario—Earthquake Hazards

Edited by Shane T. Detweiler and Anne M. Wein

Scientific Investigations Report 2017–5013–A–H

[Also see https://doi.org/10.3133/sir20175013]

Abstract

The HayWired scenario examines a hypothetical 

earthquake (mainshock) with a moment magnitude (Mw) of 

7.0 occurring on April 18, 2018, at 4:18 p.m. on the Hayward 

Fault in the east bay part of California’s San Francisco Bay 

area. The probability of widespread slope failures (landslides) 

triggered by the scenario earthquake has been estimated for 

a 10-county area surrounding San Francisco Bay. The four 

primary datasets essential to this estimation are a regionalscale geologic map of the area, geologic-strength parameters 

compiled as a part of the California Geological Survey Seismic 

Hazard Mapping Program, earthquake-shaking data from the 

U.S. Geological Survey (USGS) ShakeMap developed for this 

scenario, and 10-meter digital elevation data from the USGS 

2009 National Elevation Dataset (NED). A map of earthquaketriggered landslide susceptibility was prepared in terms of yield 

acceleration, which incorporates geologic-material strength and 

slope gradient. A simplified Newmark rigid sliding-block slopestability model was used to estimate cumulative downslope 

displacement of hillslopes for the scenario ground shaking, 

and probability of landslide failure as a function of predicted 

Newmark displacement was calculated. Known limitations to the 

methodology used and areas for improvement are discussed. In 

a later section of this report, calculated landslide displacements 

and probabilities are used to identify vulnerable highways and 

utility infrastructure and to estimate economic loss to structures 

in developed areas of the San Francisco Bay region. 

Introduction

The HayWired scenario examines a hypothetical earthquake 

(mainshock) with a moment magnitude (Mw) of 7.0 occurring 

on April 18, 2018, at 4:18 p.m. on the Hayward Fault in the east 

bay part of California’s San Francisco Bay area. One component 

of the scenario is to examine infrastructural impacts in the 

San Francisco Bay area from the earthquake. Slope failures 

(landslides) triggered by such a seismic event can be expected to 

cause extensive damage affecting major transportation corridors, 

communications networks, and lifeline infrastructure, as well as 

numerous structures in densely populated hilly areas of the San 

Francisco Bay region. This study estimates the probability of 

earthquake-triggered slope failure in a 10-county area surrounding 

San Francisco Bay (fig. 1) to assist in the estimation of damages 

and losses for this scenario mainshock event.

A number of methods to predict the occurrence of 

earthquake-triggered landslides have been developed over the 

past 15 years (for example, Jibson and others, 2000; Jibson, 2007; 

Bray and Travasarou, 2007; Saygili and Rathje, 2008; Rathje 

and Saygili, 2008; Rathje and Antonakos, 2011). Generally, 

these methods require estimates of one or more ground-shaking 

parameters, slope gradient, and geologic-material strength. 

More recently, and similar to this project, attempts have been 

made to develop near-real-time assessments of the likelihood of 

earthquake-triggered slope failures as a part of the U.S. Geological 

Survey (USGS) Prompt Assessment of Global Earthquakes for 

Response (PAGER) system using ground-motion estimates as 

input (Godt and others, 2009; Nowicki and others, 2014). A major 

goal of this study was the development of a procedure that would 

lead to the development of a statewide earthquake-triggered 

landslide-susceptibility map of California using the methodologies 

listed above along with maps and data being compiled at the 

California Geological Survey (CGS). If available, such a map 

could be used to rapidly identify areas of concentrated earthquakeinduced slope failures given only ShakeMap ground-motion input. 

This product would be valuable to Earth scientists and emergency 

responders following a large urban earthquake in California.

The methodology used to estimate landslide hazards 

for the HayWired scenario uses the Jibson (2007) Newmark 

rigid sliding-block displacement analysis regression equation, 

which has been adopted by the CGS Seismic Hazard Mapping 

Program. The Seismic Hazard Mapping Program, initiated after 

the Mw 6.9 1989 Loma Prieta earthquake, produces regulatory 

“zones of required investigation.” The purpose of the zone 

maps for earthquake-triggered landslides created by CGS is 

to identify areas with a high potential for slope failure during 1

California Geological Survey.
70 The HayWired Earthquake Scenario—Earthquake Hazards

5

580

80

SANTA CRUZ

SANTA 

CLARA

SAN FRANCISCO

SAN 

 MATEO

ALAMEDA

CONTRA

COSTA

MARIN

SOLANO

SONOMA

NAPA

Yolo

Santa

 Clara

Sunnyvale

Fremont

Hayward

Stockton

Berkeley

Concord

Vallejo

Santa Rosa

Sacramento

Oakland

San Jose

San Francisco

37 ̊

38 ̊

123° 122°

0

0 75 KILOMETERS

25 50 MILES

25 50

PACIFIC

OCEAN

SAN FRANCISCO BAY

San Luis

Reservoir

 Lake 

Berryessa

101 XX

101 XX

Base map modified from Map Resources © 2014.

San Pablo

 Bay

CALIF

Area of map

Hayward Fault

EXPLANATION

Figure 1. Generalized map of the San Francisco Bay region, California, showing the 10-county HayWired 

earthquake scenario study region.
Chapter F. HayWired Scenario Mainshock—Earthquake-Induced Landslide Hazards 71

earthquake shaking so that local governmental agencies can 

require site-specific geologic and geotechnical investigations to 

assess the presence of, and if necessary, require mitigations for 

seismic slope-instability hazards. Soon after the Loma Prieta 

earthquake, CGS conducted a pilot study in the southern Santa 

Cruz Mountains, where a methodology developed by the USGS 

(Wieczorek and others, 1985) in San Mateo County was applied 

to slope failures triggered by the 1989 earthquake (McCrink and 

Real, 1996; McCrink, 2001). The purpose of the CGS study was 

to not only evaluate the ability of the USGS model to identify 

the Loma Prieta slope failures but also to find ways to improve 

it. The USGS approach in San Mateo County used conservative 

estimates of best-available geologic-material strength for broad 

groups of rock types, upper and lower bound earthquake strongmotion records, and saturated and unsaturated slope conditions 

in a Newmark rigid sliding-block analysis. When applied 

directly to the Loma Prieta earthquake slope failures, the CGS 

pilot study found that roughly 50 percent of the triggered slope 

failures were included in the higher levels of hazard potential, 

and the other 50 percent fell in the lowest level of hazard 

potential. The advisory committee established to provide 

input to CGS on zone-map preparation had recommended that 

regulatory zone maps should strive to include 80 to 90 percent 

of the hazard, and the pilot study went on to find improvements 

to the USGS methodology. 

Although the basic Newmark sliding-block model 

was retained, the CGS pilot study applied several data 

improvements to the methodology—a new 10-meter 

(m) digital elevation model (DEM) was used to prepare 

a slope-gradient map, a detailed digital map of existing 

landslides was prepared, and a map identifying areas 

underlain by adverse bedding conditions was prepared. In 

addition, a considerable number of geotechnical laboratory 

strength tests were compiled for most of the geologic 

formations that experienced earthquake-triggered slope 

failures in the Loma Prieta earthquake. Statistical analyses of 

these laboratory tests were performed and used to categorize 

geologic formations into geologic material strength groups. A 

number of stability analyses were run using a representative 

strong-motion recording from the earthquake with varying 

strength parameters, thickness of the failure mass, and slopesaturation conditions. An optimal parameter set for preparing 

zone maps was selected by comparing the percentage of 

mapped ground failures captured by each parameter set with 

the percentage of the map area that would need to be included 

in a hazard zone. For the purpose of preparing regulatory 

zones, a conservative parameter set was selected, where 

strength parameter cohesion is set to zero. However, for 

this study, we selected a set of parameters that was termed 

“optimum” in the CGS pilot study (fig. 2). The parameter 

set selected for the HayWired scenario uses mean values of 

effective internal angle of friction (φ’) and median values of 

effective cohesion in pounds per square foot (c’), a landslide 

thickness of 50 feet, and unsaturated slope conditions. It was 

called optimum in the pilot study, because it performed the 

best in capturing the slope failures triggered by the scenario 

earthquake (78 percent), while simultaneously minimizing the 

amount of area considered hazardous (40 percent). 

Figure 2. Graph of the parameter-set efficiency from a California Geological Survey pilot study in the Santa Cruz 

Mountains, California, using ground failures from the moment magnitude (Mw) 6.9 1989 Loma Prieta earthquake 

(modified from McCrink, 2001). The point labeled “optimum parameter set” reflects the parameter set used in this 

study—mean effective internal angle of friction (φ’), median values of effective cohesion (c’), unsaturated slopes, 

and landslide thickness of 50 feet. GFC, ground-failure capture; QC, quadrangle area covered.
72 The HayWired Earthquake Scenario—Earthquake Hazards

The challenge in conducting the present study was applying 

a procedure developed for 1:24,000-scale mapping, where 

scale-appropriate details could be incorporated, to the whole San 

Francisco Bay region at a much smaller map scale. Certain details 

that improve zone-mapping results, such as identifying areas with 

adverse bedding conditions, could not be included at the smaller 

scale required in this project. More significantly, rock strength 

values correlated with detailed geologic mapping at 1:24,000 

scale had to be applied to more generalized mapping covering the 

entire study region. The primary effect of significant reductions in 

map scale is the aggregation of many similar-age rock units into 

a few map units. These generalizations undoubtedly decrease the 

accuracy of the maps by some unknown amount, but the overall 

approach in this study, using locally derived geologic-material 

strengths and higher resolution digital-terrain data, is expected to 

perform better than earlier regional hazard-mapping methods (for 

example, Wieczorek and others, 1985) or the methodologies for 

global application at much coarser resolution under development 

for Prompt Assessment of Global Earthquakes for Response 

(PAGER) (Godt and others, 2009; Nowicki and others, 2014). 

Methodology and Data 

Estimation of cumulative permanent slope displacements 

requires three general data types. A geologic-material-strength 

map and slope-gradient data are required to calculate yield 

acceleration (ay), the key earthquake-landslide susceptibility 

parameter (described below), and ground shaking parameters 

from the HayWired scenario ShakeMap are combined with 

ay to estimate displacements. The following sections describe 

sources of these data and processing performed to prepare 

them for analyses. Also see data in McCrink and Perez (2017).

Geologic-Material-Strength Map 

The determination of appropriate dynamic geologic-material 

strengths to use over a large map area is simultaneously the most 

important and the most difficult task. Material-strength properties 

can have wide ranges of values within mapped geologic formations 

depending on interformational stratigraphy, along-strike facies 

changes, and variations in structural-deformation history. In addition, 

it has been shown that even the best laboratory testing procedures 

performed on small diameter samples of failed-slope materials 

can do a poor job of providing reliable strength values (Leighton 

and Associates and William Cotton and Associates, 1991). CGS 

landslide zone-mapping procedures address some of the uncertainty 

associated with characterizing material strength within geologic map 

units by limiting statistical analyses of laboratory shear tests to a 

7.5-minute quadrangle area (~180 square kilometers, km2). Limiting 

the analyses to this relatively smaller area is intended to partially 

account for spatial variability within geologic formations due to 

stratigraphic, facies, and deformation variability. Other strategies 

employed in zone-map preparation include grouping geologic units 

with similar strength characteristics, mapping preexisting landslides 

and treating them separately from the geologic unit in which they 

occur, and identifying potential adverse bedding conditions and 

treating those areas as distinct geologic units. For the HayWired 

scenario mainshock, because of the large area considered, most of 

these strategies could not be employed. 

Characterization of geologic-material strength in the study 

region requires two components: laboratory shear-strength testing 

of the various geologic units and a scale-appropriate geologic 

map so the strength parameters can be applied spatially. Strength 

data were available for the 29 7.5-minute quadrangles where 

CGS seismic-hazard-zone (SHZ) maps have been prepared 

(fig. 3). More than 2,000 laboratory shear tests, primarily direct 

shear and a lesser number of triaxial shear tests, were available 

to characterize the geologic units in the zoned quadrangles, and 

these data were extrapolated to the rest of the study region, an area 

approximately five times larger (~150 quadrangles) as described 

below. The geologic mapping available for seismic-hazard zoning 

was from a variety of 1:24,000-scale sources (see appendix 1 for 

a list of seismic-hazard zoning reports) and had differing geologic 

nomenclature in many cases.

Because uniform 1:24,000-scale geologic mapping does 

not yet exist for the entire HayWired scenario study region, 

we used an unpublished, statewide, generalized geologiccompilation map (C. Gutierrez, CGS, 2014, written commun.), 

from which the 10-county study region was extracted. The 

geologic data sources for the extracted San Francisco Bay 

region map include bedrock geology from Graymer and 

others (2006), and Quaternary geology from Knudsen and 

others (2000) and Witter and others (2006). This unpublished 

compilation of bay region geology was an intermediate product 

in the preparation of the site-conditions map of California (Wills 

and others, 2015), which may be the map of choice to use as 

the primary input for a future statewide ay map. The Wills and 

others (2015) site-conditions map is intended to show potential 

seismic amplification due to near-surface soils, so it provides 

added detail to Quaternary geologic map units compared to 

the CGS SHZ maps, and generalizes and combines bedrock 

units where shear-wave velocity is expected to be similar. The 

unpublished generalized geologic-compilation map includes 

data on existing landslides where available, as well as data 

for artificial fill and colluvium. The existing landslide data 

was considered important for the HayWired scenario. The 

generalized geologic-compilation map is composed of 66 

unique geologic map units. In contrast, the 1:24,000-scale maps 

in the 29 7.5-minute quadrangle study region for which shearstrength data exist contain 240 geologic units.

The approach taken to correlate the SHZ maps and 

generalized geologic-compilation map was to evaluate the 

geologic-map explanations and background literature of the 

maps to determine which geologic units stratigraphically and 

lithologically belong together within the 10-county study region. 

Table 1 shows the 66 geologic map units from the generalized 

geologic-compilation map correlated with the 240 geologic 

map units found in CGS SHZ maps that cover the HayWired 

study region (map unit symbols only). Some units, such as Kgr 

(Cretaceous granitic rocks), are directly correlated between maps, 
Chapter F. HayWired Scenario Mainshock—Earthquake-Induced Landslide Hazards 73

Figure 3. Map of the San Francisco Bay region, California, showing the 10-county HayWired earthquake scenario study 

region with an inset of the 29 7.5-minute quadrangles covered by California Geological Survey seismic hazard zone maps.

McCrink_fig2

0 30

20 40 KILOMETERS

10

0

20 40 MILES

10 30

SONOMA

NAPA

MARIN

SOLANO

CONTRA

COSTA

ALAMEDA

SANTA 

CLARA

SANTA CRUZ

SAN

MATEO

SAN

FRANCISCO

Base from U.S. Geological Survey 2009 National Elevation Dataset 10-meter digital elevation model showing land-surface elevations as shaded-relief. County boundaries from California Department of Forestry and 

Fire Protection, 2009.

1

29

7.5-minute quadrangles covered by Seismic Hazard Zoning

HAYWARD FAULT

CALIF

Area of map

121° 122°

38°

37°

whereas others, such as map unit Tms (Sedimentary rocks—

Miocene) on the generalized geologic-compilation map, represents 

as many as 36 distinct geologic units on the SHZ maps. Five 

map units, TKs, TKss, Tss, Tst, and Tv, that cover a considerable 

area on the compilation map do not have equivalents on the SHZ 

geologic maps because either they are map units that occur outside 

the SHZ map area or represent units within the SHZ map area for 

which no strength data are available.
74 The HayWired Earthquake Scenario—Earthquake Hazards

Table 1. The 66 geologic map units from the unpublished California Geological Survey (CGS) generalized geologic-compilation map (GGCM) for 

the San Francisco Bay region (C. Gutierrez, CGS, 2014, written commun.) correlated with the 240 geologic map units found in CGS seismic-hazard 

maps that cover the 29 7.5-minute quadrangles in the HayWired study region (map unit symbols only; see appendix 2 for map unit descriptions).

GGCM CGS seismic-hazard maps that cover the 29 7.5-minute quadrangles in the HayWired study region

Quaternary sediments

af ac af afbm alf PP pp Qaf Qaf/tf Qafs Qhbr

Qal-deep Qal-deep

Qal-thin Qal-thin

Qb Qhbs Qs

Qha gq Qal Qha Qhaf Qhay Qhb Qhf Qhf1 Qhf2 Qhff Qhfp Qht Qht1 Qht2 Qhl Qhl1 Qhl2

Qal Qa Qf Ql Qt

Qhy Qhasc Qhc Qhfy Qhsc Qhty Qhly

Qhym Qhbm Qhfe

Qls Qls

Qoa Qc Qcl Qot1 Qot2

Qpa Qoa Qoa1 Qoa2 Qof Qop Qot Qpa Qpaf Qpaf1 Qpaf2 Qpf Qpoaf Qu Qpt

Qs Qd Qds Qob

Qsl Qsr

Qt Qbt Qmt

Sedimentary rocks

Kfs fb1 fh fms Kfn Kfs Kfsh Kfss KJfa

Ks Kau Kc Kcg Kcm Kcs Kcu Kcus Kcusm Kcv Kd Kkh Ko Kp Kr Ksc Ksh Kshu Kslt

Kslto Kss Ksu Ksuh Ksus KsVII Ku Kuc KuII Kull Kus

KJf fsp gi KJg KJsu

KJfs fss KJf KJfs

KJs Kbc Kbs KJk Kjk KJkc KJkv KJms spFR spFr

QTs QTgt QTi QTl Qtl QTm QTp QTsc QTsc? QTsl QTu

Tepas Tgsm Tshs

Tes Tb Tbm Tbmw Tbs Tcm Tcm? Tcml Tcs Tdi Tes Tls Tmru Tshc Tsrm Tstw Ttls Ttss Tu

Tvb? Tw Tws

TKfs TKfs

TKs

TKss

Tmoes Tlsl

Tmos Tbms Tla Tsl Tsm Tsms Tts Ttv

Tms Tbd Tbe Tbg Tbh Tbi Tbl Tbr Tbu Tc Tcc Tccs TccsI Th Tld Tlo Tm Tms Tmsu

Tn To Tor Tord Tr Tro Ts Tsa Tso Tss Tssc Tsso Tst Tt Tte Tul Tus Tush

Toes dbm Tsr

Tos Tz

Tpas Ta Tpsu Tvh Tvhl Tvhu

Tpms Tcgl Tcglt Tmll Tmlu Tol? Tp Tptm Tsg
Chapter F. HayWired Scenario Mainshock—Earthquake-Induced Landslide Hazards 75

Table 1.—Continued

GGCM CGS seismic-hazard maps that cover the 29 7.5-minute quadrangles in the HayWired study region

Tps Tps

Tsh Tsh

Tss

Tst

Volcanic rocks

Qv Qv

QTv QTv

Tmov Tmb Tv Tvq

Tmv Tbp Torv Tpm Tuss Tusv Tvo

Tov Tov

Tpmv Tgvt Tgvtt Tub

Tpv Tba br

Tv

fcv fcv

Tkfv Tkfv

Kfv fpv md v

KJfv fg fmv fu KJu

KJfvc KJfvc

KJfvs KJfvs

KJv KJv

Jfv Jfv

Jv db Jb Jbk Jdb Jpb Jsv Jt

Intrusive rocks

Jhg Jhg

Ji gb Jgb Jic Jog Joi Jou Jov

Kgr Kgr

Metamorphic rocks

fcm fcm

fsr am bi bl bs cg ch fpl fs fsr fws gs

Jsp Jfg Jfgs Jos Jsp Jspm Jssp KJfsp KJfy sc scm sp

Kfc Kfc

Kfm Kfm

KJfc fc fmc Kfgwy KJc KJfch KJflg KJs KJsk

KJfm fl fm fmm fy1 fy2 fys KJfe KJfm KJfmw Kjm KJm

MzPzm MzPzm

Mzv Mzv

Serp no data/no equivalent
76 The HayWired Earthquake Scenario—Earthquake Hazards

Half of the units in the generalized geologic-compilation map 

had corresponding SHZ geologic map units with geologic material 

strength data. For these units, φ’, c’, and moist unit weight (γ) data 

were compiled and the appropriate average (mean or median) 

values were derived for each. The other half of the units that had 

no geologic-material strength data were grouped with those that 

had data, based on whether or not they had correlative units within 

the SHZ geologic maps. If the units in the generalized geologiccompilation map had correlative SHZ units but the SHZ units had 

no data, we referred back to the seismic hazard zoning reports 

for the San Francisco Bay region (appendix 1) to see how these 

units were grouped. Then, based on the geologic-material strength 

Table 2. The 66 geologic map units from the unpublished California Geological Survey (CGS) generalized geologic-compilation map for 

the San Francisco Bay region (C. Gutierrez, CGS, 2014, written commun.) with material-strength values used in the landslide analyses for 

the HayWired earthquake scenario. 

[Strength values in blue indicate that laboratory data were not available and values were assigned based on age, lithology, and geologic judgement. The bedrock geologicdata source for the generalized geologic-compilation map (Graymer and others, 2006) does not distinguish between Great Valley sequence and Great Valley complex rocks 

as do other geologic maps in the San Francisco Bay region, and we have adopted their terminology that uses only Great Valley complex. phi’, mean values of effective 

internal angle of friction; c’, median values of effective cohesion; lb/ft2, pound per square foot]

Map symbol Generalized geologic-compilation map units Mean phi’, in degrees Median c’, in lb/ft2

Quaternary sediments 

af Artificial fill 25 450

Qal-deep Quaternary alluvium - deep 25 500

Qal-thin Quaternary alluvium - thin 25 500

Qb Quaternary beach deposits 27 500

Qha Alluvium (Holocene) 23 500

Qal Quaternary alluvium 25 250

Qhy Alluvium (late Holocene) 25 550

Qhym Mud deposits (late Holocene) 19 250

Qls Quaternary landslide deposits 13 650

Qoa Alluvium (early Pleistocene) 36 300

Qpa Alluvium (Pleistocene) 26 550

Qs Beach and dune sand (Quaternary) 33 100

Qsl Hillslope deposits (Quaternary) 30 575

Qt Marine terrace deposits (Pleistocene) 21 400

Sedimentary rocks

Kfs Franciscan Complex sedimentary rocks (Cretaceous) 33 800

Ks Great Valley complex sedimentary rocks (Cretaceous) 31 485

KJf Franciscan Complex sedimentary rocks (Early Cretaceous and (or) Late Jurassic) 36 580

KJfs Franciscan Complex sedimentary rocks (Early Cretaceous and (or) Late Jurassic) 36 950

KJs Great Valley complex sedimentary rocks (Early Cretaceous and (or) Late Jurassic) 28 740

QTs Sediments (early Pleistocene and (or) Pliocene) 26 800

Tepas Sedimentary rocks (Eocene and (or) Paleocene) 26 200

Tes Sedimentary rocks (Eocene) 29 646

TKfs Franciscan Complex sedimentary rocks (Eocene, Paleocene, and (or) Late Cretaceous) 31 650

TKs Sedimentary rocks (Paleocene and (or) Late Cretaceous) 31 650

TKss Sedimentary rocks (Paleocene and (or) Late Cretaceous) 31 650

Tmoes Sedimentary rocks (Miocene, Oligocene, and (or) Eocene) 31 650

values assigned for zone map production, that unit was assigned to 

a generalized geologic-compilation map unit with similar strength 

characteristics. The remaining units that did not have equivalent 

units from the SHZ geologic maps were assigned the strengths of 

other units on the basis of age, lithology and geologic judgement. 

The generalized geologic-compilation map became a map of 

geologic-materials strength by assigning the recompiled laboratory 

strength values (φ’, c’, γ) to the map database. Table 2 provides a 

list of the units in the generalized geologic-compilation map with φ’

and c’ values that were used in the analyses. Appendix 2 (available 

as a .csv or .xlsx file) contains a detailed listing of all SHZ geologic 

map units, shows which 7.5-minute quadrangles contain strength 
Chapter F. HayWired Scenario Mainshock—Earthquake-Induced Landslide Hazards 77

Map symbol Generalized geologic-compilation map units Mean phi’, in degrees Median c’, in lb/ft2

Tmos Sedimentary rocks (Miocene and Oligocene) 34 200

Tms Sedimentary rocks (Miocene) 31 850

Toes Sedimentary rocks (Oligocene and (or) Eocene) 28 800

Tos Sedimentary rocks (Oligocene) 29 620

Tpas Sedimentary rocks (Paleocene) 31 260

Tpms Sedimentary rocks (Pliocene and early Miocene) 31 740

Tps Sedimentary rocks (Pliocene) 31 700

Tsh Merced sedimentary rocks (Miocene) 29 620

Tss Sedimentary rocks (Miocene) 29 620

Tst Sedimentary rocks (Miocene) 29 620

Volcanic rocks

Qv Volcanic rocks (Pleistocene) 27 650

QTv Volcanic rocks (early Pleistocene and (or) Pliocene) 27 650

Tmov Volcanic rocks (Miocene and (or) Oligocene) 32 400

Tmv Volcanic rocks (Miocene) 26 700

Tov Volcanic rocks (Oligocene) 32 760

Tpmv Volcanic rocks (Pliocene and early Miocene) 27 725

Tpv Volcanic rocks (Pliocene) 27 725

Tv Volcanic rocks (Miocene) 33 400

fcv Franciscan Complex volcanic rocks (Eocene and (or) Paleocene) 33 700

Tkfv Franciscan Complex volcanic rocks (Paleocene, and (or) Late Cretaceous) 31 740

Kfv Franciscan Complex volcanic rocks (Cretaceous) 31 550

KJfv Franciscan Complex volcanic rocks (Early Cretaceous and (or) Late Jurassic) 31 375

KJfvc Franciscan Complex volcanic rocks and chert (Early Cretaceous and (or) Jurassic) 33 760

KJfvs Franciscan Complex volcanic and sedimentary rocks (Early Cretaceous and (or) Jurassic) 31 700

KJv Franciscan Complex or Great Valley complex volcanic rocks (Early Cretaceous and (or) Jurassic) 30 760

Jfv Franciscan Complex volcanic rocks (Jurassic) 30 700

Jv Great Valley complex volcanic rocks (Jurassic) 30 700

Intrusive rocks

Jhg Salinian complex plutonic rocks (Jurassic) 32 760

Ji Great Valley complex plutonic rocks (Jurassic) 32 350

Kgr Salinian complex plutonic (granite) rocks (Cretaceous) 32 760

Metamorphic rocks

fcm Franciscan Complex metamorphic rocks (Eocene and (or) Paleocene 28 500

fsr Franciscan Complex mélange (Eocene, Paleocene, and (or) Late Cretaceous) 30 575

Jsp Great Valley complex serpentinite (Jurassic) 29 750

Kfc Metamorphic rocks (Cretaceous and (or) Jurassic) 33 760

Kfm Franciscan Complex metamorphic rocks (Cretaceous) 28 700

KJfc Franciscan Complex chert (Early Cretaceous and (or) Late Jurassic) 33 740

KJfm Franciscan Complex metamorphic rocks (Early Cretaceous and (or) Late Jurassic) 28 500

MzPzm Salinian complex metamorphic rocks (Mesozoic and (or) Paleozoic) 30 600

Mzv Metamorphic rocks (Mesozoic) 30 600

Serp Franciscan Complex serpentinite (Jurassic) 26 550

Table 2.—Continued
78 The HayWired Earthquake Scenario—Earthquake Hazards

data, how they were correlated with the compilation map units, and 

provides the average strength values that were used.

Slope Gradient

Slope gradient was derived from the 2009 National 

Elevation Dataset (NED) produced and distributed by the 

USGS (2009) with a cell size of 1/3 arc-second (approximately 

10 meters, m). The elevation of the 10-county study region 

ranges from −10 to 1,372 m. The original NED, which is in 

geographic coordinates (latitude and longitude), was projected 

to Universal Transverse Mercator, North American Datum 

of 1983 (NAD83). A slope-gradient map was made from 

the projected DEM, using the average maximum technique 

(also known as neighborhood-slope algorithm), where the 

maximum rate of change for each cell is calculated over the 

distance between the cell and its eight neighbors to identify the 

steepest downhill descent (Burrough and McDonnell, 1998). 

The resulting slope-gradient map is shown in figure 4.

Earthquake Shaking

Raster versions of the HayWired scenario mainshock shaking 

data used in this study were derived from the USGS ShakeMap 

(U.S. Geological Survey, 2014). These data represent one of the 

ground-motion simulations from a suite of earthquake scenarios 

that Aagaard and others (2010) developed for the HaywardRodgers Creek Fault system. The simulation is based on a 

three-dimensional (3D) ground-motion model that captures the 

variability in the earthquake-source parameters consistent with 

geophysical and seismic observations, as well as the uncertainty 

in how creep may affect the distribution of coseismic slip on the 

Hayward Fault. The model also identifies the important features in 

the ground motions, such as effects from the 3D geologic structure 

and fault-rupture directivity, and characterizes the distribution of 

shaking and its sensitivity to variations in the earthquake source 

(Aagaard and others, 2010). These data, originally with a 1-minute 

(approximately 1.6 kilometer, km) resolution, were converted 

from geographic coordinates, World Geodetic Survey 1984 

(WGS84) datum, into Universal Transverse Mercator, NAD83, 

and resampled to a 10-m-pixel resolution to have the same 

resolution as the other raster data. The simulated ground motion 

for the Mw 7.05 HayWired earthquake scenario has a maximum 

peak ground acceleration (PGA) of 2.16 g (the acceleration due to 

gravity) (fig. 5) centered on the northern end of the Hayward Fault. 

Peak ground velocity (PGV) was used to provide a 

practical limit to where earthquake-triggered landslides are 

considered significant. HayWired scenario PGV raster data 

were converted into contour line features representing 10, 

20, and 40 centimeters per second (cm/s) PGV (fig. 6), and, 

as explained below, slope-failure probabilities were only 

considered in areas where PGV was greater than 20 cm/s.

Earthquake-Landslide Susceptibility

The susceptibility to earthquake-induced landsliding 

is defined by the yield acceleration (also referred to as 

critical acceleration). Yield acceleration (ay) is defined as the 

earthquake inertial force required to initiate slope movement, 

that is, when the factor of safety equals 1.0. Newmark (1965) 

defined yield acceleration as:

(1)

where FS is the Factor of Safety, g is the acceleration due to 

gravity, and α is the angle from the horizontal that the sliding 

mass initially moves. Physically what Newmark’s equation 

means is that every time earthquake ground motion beneath 

a slope exceeds the ay for that slope, the upper part of the 

slope, the “sliding block,” detaches and slides downslope. For 

the duration of an entire earthquake, the sliding block will 

accumulate displacement from all the occurrences of ground 

motion that exceed ay. For natural slopes, the total displacement 

from the earthquake shaking can be related to an overall hazard 

index for an area (Jibson and others, 2000). 

We assume the landslide failure surface is relatively 

shallow and can be approximated by an infinite slope model, 

where the failure surface is parallel to the ground surface. 

We also assume that the earth materials are unsaturated at the 

time they are subject to the earthquake shaking. With these 

assumptions, the FS is determined by:

(2)

where c’ is effective cohesion, γ is the unit weight of the slide 

mass material and h is its thickness, α is the slope gradient of the 

ground surface, and φ’ is the effective friction angle. Slide mass h

was set to 50 feet and the geologic-materials map data (φ’, c’, γ) 

were combined with the slope-gradient map to calculate FS and ay. 

Calculation of Newmark Displacements

Calculation of Newmark displacement (Newmark, 1965), 

representing earthquake-induced landslide-hazard potential, uses 

a regression equation developed by Jibson (2007). The particular 

regression selected for this study uses yield acceleration ratio 

(ay/PGA) and earthquake magnitude (M) to estimate Newmark 

displacement (DN): 

. (3)

Although Jibson (2007) developed equations based on 

several ground motion parameters, this equation was chosen 

primarily because the ground-motion input parameter PGA is

readily available from the USGS ShakeMap for the HayWired 

scenario (U.S. Geological Survey, 2014). 

a F = − ( 1 S g) sinα, y

FS = + c h ' s γ α in cot t α φ an ',

= − + − + − log D a 2.71 log[(1 / ) PGA a( / PGA M ) ] 0.424 . N y y

2.335 1.478
Chapter F. HayWired Scenario Mainshock—Earthquake-Induced Landslide Hazards 79

Figure 4. Map showing slope-gradient for the 10-county San Francisco Bay region, California, studied in the HayWired scenario.

McCrink_fig4

Low : 0

High : 74.65

0 30

20 40

10

0

20 40

10 30 KILOMETERS

MILES

SONOMA

HAYWARD FAULT

NAPA

MARIN

SOLANO

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

SANTA CRUZ

SAN

MATEO

SAN

FRANCISCO

Slope gradient from U.S. Geological Survey 2009 National Elevation Dataset 10-meter digital elevation model. County boundaries from California Department of Forestry and Fire Protection, 2009.

Slope gradient, in degrees

EXPLANATION

CALIF

Area of map

121° 122°

38°

37°
80 The HayWired Earthquake Scenario—Earthquake Hazards

Figure 5. Map of the San Francisco Bay region, California, showing peak ground acceleration (PGA) from the HayWired scenario 

ShakeMap (U.S. Geological Survey, 2014). g, acceleration due to gravity.

McCrink_fig5

EXPLANATION

PGA ( )

0 30

20 40

10

0

20 40

10 30 KILOMETERS

MILES

SONOMA

NAPA

MARIN

SOLANO

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

SANTA CRUZ

SAN

MATEO

SAN

FRANCISCO

Base from U.S. Geological Survey 2009 National Elevation Dataset 10-meter digital elevation model showing land-surface elevations as shaded-relief. County boundaries from California Department of Forestry and 

Fire Protection, 2009.

0.0061–0.0485

0.0486–0.1164

0.1165–0.2013

0.2014–0.3032

0.3033–0.4391

0.4392–0.6004

0.6005–0.7872

0.7873–1.0589

1.0590–1.4920

1.4921–2.1628

g

Hayward Fault

CALIF

Area of map

121° 122°

38°

37°
Chapter F. HayWired Scenario Mainshock—Earthquake-Induced Landslide Hazards 81

Figure 6. Map of the San Francisco Bay region, California, showing peak ground velocity (PGV) contours from the HayWired scenario 

ShakeMap (U.S. Geological Survey, 2014). cm/s, centimeters per second.

McCrink_fig6

EXPLANATION

PGV contour

0 30

20 40

10

0

20 40

10 30 KILOMETERS

MILES

SONOMA

NAPA

MARIN

SOLANO

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

SANTA CRUZ

SAN

MATEO

SAN

FRANCISCO

Base from U.S. Geological Survey 2009 National Elevation Dataset 10-meter digital elevation model showing land-surface elevations as shaded-relief. County boundaries from California Department of Forestry 

and Fire Protection, 2009.

10 cm/s

20 cm/s

40 cm/s

HAYWARD FAULT

CALIF

Area of map

121° 122°

38°

37°
82 The HayWired Earthquake Scenario—Earthquake Hazards

These calculations were implemented within the geographic 

information system (GIS) and carried out for each 10-m grid cell 

throughout the study region. Moderate (15 to 30 centimeters, 

cm) to large (>100 cm) displacements are predicted in the upland 

areas of Marin, Contra Costa, Alameda, Santa Clara, and Santa 

Cruz Counties, where ground motions are predicted to be high 

(fig. 7). However, large displacements are also predicted in the 

northern reaches of Sonoma and Napa Counties where ground 

motions are expected to be considerably lower. Limiting the area 

where landslide occurrence and related damage are significant is 

necessary to address model limitations, primarily created by our 

inability to adequately define dynamic-strength values. A review 

of the landslides triggered by three recent California earthquakes 

found that for the 2003 Mw 6.6 San Simeon earthquake, 96 percent 

of the mapped landslides (L. Rosenberg, San Luis Obispo County 

Geologist, written commun., 2004), not including liquefactionrelated features, occurred in areas where ShakeMap (http://

earthquake.usgs.gov/earthquakes/shakemap/) indicates PGV 

shaking intensities greater than 20 cm/s. For the Mw 6.7 1994 

Northridge earthquake, we found that 91 percent of the landslides 

mapped by Harp and Jibson (1995, 1996) occurred in that same 

range of PGV. In the case of the Mw 6.9 1989 Loma Prieta 

earthquake, 100 percent of the earthquake-triggered landslides 

mapped by Keefer and Manson (1998) in the epicentral area were 

in areas with greater than 20 cm/s PGV as indicated by ShakeMap, 

although there were a few notable landslides as far north as 

Stinson Beach where shaking was much lower. On the basis of 

these observations, we believe that the 20 cm/s PGV contour 

provides a reasonable limit to where damage from earthquaketriggered landsliding should be considered significant.

Calculation of Slope-Failure Probabilities

Slope failure probability (Pf) is based on predicted Newmark displacement (DN) and is calculated using the relationship of Jibson and others (2000): 

 (4)

Like the calculation of DN, Pf was calculated at a 10-m 

resolution throughout the study region and then trimmed back to 

the 20 cm/s PGV contour. The values produced by this relation 

represent the proportion of area expected to fail for a given DN. 

It should be recognized that this equation was derived from 

examination of landslides triggered by the 1994 Northridge 

earthquake and has not been shown to be valid in other areas. 

However, to our knowledge there are no other equations available. 

Figure 8 shows the slope failure probability map for the study 

region, categorized to match the hazard-potential categories related 

to DN and trimmed to the 20 cm/s PGV contour. 

Failure probabilities of 2 to 15 percent, corresponding to 

displacements of 1–5 cm, are most prevalent in southernmost 

Sonoma County and the western parts of Contra Costa and 

Alameda Counties along the trace of the Hayward Fault. A 

scattered pattern of these displacements is also evident on 

P D = − 0.335[1 exp(−0.048 )] f N

1.565

gentle hills in the central to eastern areas of Contra Costa and 

Alameda Counties east of the fault. Pf in the range of 15 to 

32 percent (DN 5 to 15 cm) cover only the moderately steep 

hills of Contra Costa, Alameda, and Santa Clara Counties 

east of the fault. Pf greater than 32 percent (DN>15 cm) are 

extensively mapped on steep to very steep slopes of Sonoma, 

Napa, Marin, Contra Costa, Alameda, San Mateo, Santa Clara, 

and Santa Cruz Counties.

Areas for Hazard-Model Improvement

One of the goals of our study was to evaluate the 

feasibility of preparing a statewide earthquake-induced landslide 

susceptibility map. On the basis of this study, we believe there 

are several issues to resolve before that goal can be achieved. The 

selection of representative geologic-material strength parameters 

is a key source of uncertainty in preparing landslide-hazard maps, 

including those prepared in this study. One drawback to using 

laboratory derived strength values is that they provide estimates 

of static strength within relatively intact materials. Earthquake 

loading of natural slopes can be expected to exploit discontinuities 

and other weak parts of weathered materials that are difficult to 

locate, sample, and test in typical geotechnical investigations. 

This is compounded by the difficulty in accounting for spatial 

variability within mapped geologic formations—there are just 

not enough test measurements to do so effectively. There are two 

notable implications of using the laboratory derived strength data 

in a regional hazard assessment such as this. First, to maximize 

the capture of the hazard identified by slope failures in the Loma 

Prieta earthquake, the modeled landslide thickness needs to be 

greater than originally used in the regression model for Newmark 

displacement. One way to view this is that the landslide mass 

must be thicker, and the driving forces greater, because the 

static strength data are too high relative to the actual dynamic 

strength. Second, as a consequence of the thicker slide mass in 

the model, the failure probabilities are high out into areas that 

are anticipated to have relatively low shaking intensities, and 

therefore a threshold, here a 20 cm/s PGV cutoff was used to limit 

where landslide losses should be considered significant. Recently, 

Saade and others (2016) demonstrated the value of replacing the 

rigid sliding-block model and infinite-slope failure with a model 

incorporating a limit equilibrium analysis and circular slip failure. 

They performed a parametric study to identify the relations 

among ay, slope angle, and shear strength and showed a significant 

improvement in regional seismic-landslide-hazard assessment 

for an area damaged by the Northridge earthquake. This new 

methodology is currently being evaluated by CGS as a means to 

improve mapping of landslide-hazard zones in California and, if 

successful, might be useful for developing a statewide earthquakelandslide susceptibility map.

There also are geologic field observational methods that 

may improve our ability to address model limitations. Detailed 

geologic-material characterization in the San Francisco Bay 

region was done by Ellen and Wentworth (1995) in which 

they compiled detailed descriptions of unit composition, 
Chapter F. HayWired Scenario Mainshock—Earthquake-Induced Landslide Hazards 83

Figure 7. Map of the San Francisco Bay region, California, showing landslide-hazard potential in terms of Newmark displacement (Newmark, 

1965), which is a function of yield acceleration ratio and earthquake magnitude for the HayWired earthquake scenario. >, greater than.

McCrink_fig7

EXPLANATION

Average Newmark displacement,

in centimeters

0 30

20 40

10

0

20 40

10 30 KILOMETERS

MILES

SONOMA

HAYWARD FAULT

NAPA

MARIN

SOLANO

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

SANTA CRUZ

SAN

MATEO

SAN

FRANCISCO

Base from U.S. Geological Survey 2009 National Elevation Dataset 10-meter digital elevation model showing land-surface elevations as shaded-relief. County boundaries from California Department of Forestry and

Fire Protection, 2009.

0–1

1–5

5–15

15–30

30–100

>100

CALIF

Area of map

121° 122°

38°

37°
84 The HayWired Earthquake Scenario—Earthquake Hazards

bedding and fracture characteristics, topographic expression, 

weathering, and other properties relevant to seismic slope 

stability. In addition, the geologic strength index (GSI) 

developed by Hoek and Brown (1980; see also Marinos and 

others, 2005) has successfully been used for engineering 

projects in a variety of rock types and terrains and was used 

by Saade and others (2016) for regional mapping. CGS is 

currently evaluating GSI in the San Francisco Bay region 

to assess its applicability in assigning strength values where 

laboratory data do not exist. An obvious limitation to these 

observational techniques is the time and cost involved in 

visiting numerous sites to collect observations. However, it 

might be possible to use modern digital-terrain data along 

with Ellen and Wentworth’s (1995) “hillside materials units” 

and relate these parameters to GSI to automate the process. 

Alternatively, remote-sensing technologies such as NASA’s 

new hyperspectral thermal emission spectrometer (HyTES) 

may provide insight on the spatial distribution of geologic 

material strength properties (http://hytes.jpl.nasa.gov/). 

Existing landslides were treated as distinct geologic 

units in our study, with strength properties that approximate 

a residual strength on an existing landslide slip surface. 

There are several limiting aspects to incorporation of existing 

landslides in this manner. First, landslide inventories are not 

uniformly available throughout the study region and thus are 

inconsistently handled. A prime example of this is Contra 

Costa County where CGS is currently preparing detailed 

inventory maps for seismic-hazard zoning. Because this work 

is still in progress and was largely unavailable for HayWired, 

relatively lower displacements and failure probabilities (fig. 8) 

are found in this county when compared to Alameda County, 

despite its close proximity to the Hayward Fault. This effect 

can also be seen in central Marin County where detailed 

inventories are available—larger areas of higher DN were 

observed (figs. 7 and 8) despite having lower modeled ground 

motions than western Contra Costa County.

A second limitation lies in the treatment of all existing 

landslides as having the same strength parameters and a 

uniform thickness. Landslides formed in different rock 

materials have different strengths, but this simplification in 

the model is necessary in the absence of laboratory tests or 

other methods to determine landslide slip surface residual 

strengths. Applying a uniform slide thickness to existing 

landslides is, like in other areas, a simplification used to make 

the model work. The approach is not realistic, particularly for 

deep-seated, large landslides whose natural periods can be 

very different from that of the earthquake shaking to which 

they are subject in the near field, and are distinctly different 

from the shallow landslides that are best modeled by the 

Newmark method. All landslide inventories produced by CGS 

in the past 20 years capture an observational judgement on 

ranges of landslide thickness. We suggest that this information 

combined with a hybrid displacement model that considers 

both rigid and flexible sliding behaviors developed by Rathje 

and Antonakos (2011) may offer a path forward for addressing 

the hazards associated with existing landslides.

Another limitation to earthquake-landslide hazard maps is 

that not all seismic loading is accounted for in either probabilistic 

seismic-hazard analysis (PSHA) maps or in ShakeMap. 

Topographic amplification is a phenomenon shown through 

theoretical studies and postearthquake field observations (for 

example, Boore, 1972; Çelebi, 1987; Geli and others, 1988; 

Assimaki and others, 2005) to have significant effects at the tops 

of bluffs and ridges. Topographic effects can act in combination 

with site-response amplification caused by low velocity layers, 

such as deeply weathered rock underlying ridges (Bard and 

Tucker, 1985; Assimaki and Jeong, 2013). Incorporating 

amplified shaking, preferentially in ShakeMap, would remove 

some uncertainty from earthquake-landslide hazard models. 

Maufroy and others (2015) have introduced a method to estimate 

topographic amplification based on surface curvature, which 

is easily calculated from a DEM. This model needs to be 

evaluated in areas where damaging topographic effects have been 

documented (Çelebi, 1987; McCrink and others, 2010; Hough and 

others, 2010; Assimaki and Jeong, 2013) to test its validity.

Beyond the modeling for earthquake-triggered landslides, 

there are two issues that relate to the application of our results 

into loss modeling that warrant mention. First is the fact 

that landslide models estimate locations that are susceptible 

to slope-failure initiation and do not indicate any type of 

landslide runout, especially for shallow disrupted landslides 

that can travel large distances. Thus, vulnerability of various 

infrastructure elements and use of economic-loss models 

may have difficulty identifying a significant part of landslide 

damages. There currently are no methods to reliably estimate 

landslide runout, and cartographic/GIS techniques should be 

evaluated. Second, the landslide models used in this study 

produce displacement and failure probability maps at a 10-m 

resolution, whereas the Hazus loss-modeling software uses 

U.S. Census tracts as its minimum mapping unit. Aggregating 

the landslide-hazard data into the much coarser census tracts 

requires considerable knowledge and judgement, and creates 

some ambiguity in the resulting loss estimates.

Summary

In this analysis, we used a regional-scale geologiccompilation map, geologic-strength parameters collected 

as part of the CGS Seismic Hazard Mapping Program, 

earthquake shaking parameters from ShakeMap generated 

for the HayWired scenario mainshock, and digital terrain 

data from NED to produce earthquake-triggered landslide 

susceptibility (ay), hazard potential (DN), and probability of 

failure (Pf) maps for a 10-county region around San Francisco 

Bay. The ay map (not shown) was prepared using Newmark’s 

(1965) equation after applying some simplifying assumptions. 

The DN map (fig. 7) was prepared using a regression equation 

developed by Jibson (2007) that uses, in addition to ay, PGA

and earthquake magnitude as ground shaking input. Pf was 

determined from an algorithm developed by Jibson and others 

(2000) that uses DN as the sole input. The Pf map (fig. 8) was 
Chapter F. HayWired Scenario Mainshock—Earthquake-Induced Landslide Hazards 85

Figure 8. Map of seismic-landslide hazards for the San Francisco Bay region, California, for the HayWired earthquake scenario, 

showing Newmark displacement (DN, Newmark, 1965) and landslide probability (Pf) as a function of Newmark displacement trimmed to 

areas where peak ground velocity (PGV) exceeds 20 centimeters per second (cm/s). Categories of displacement and failure probability 

have been made to match only for display purposes. L, low; M, medium; H, high; VH, very high; cm, centimeter.

McCrink_fig8

EXPLANATION

Seismic landslide hazards

0 30

20 40

10

0

20 40

10 30 KILOMETERS

MILES

SONOMA

HAYWARD FAULT

NAPA

MARIN

SOLANO

CONTRA

COSTA

ALAMEDA

SANTA

CLARA

SANTA CRUZ

SAN

MATEO

SAN

FRANCISCO

Base from U.S. Geological Survey 2009 National Elevation Dataset 10-meter digital elevation model showing land-surface elevations as shaded-relief. County boundaries from California Department of Forestry and 

Fire Protection, 2009.

20 cm/s PGV contour

0–1

1–5

5–15

>15

L

M

H

VH

0–2

Newmark

displacement ( DN) probability Pf

Landslide

cm Category Percent

2–15

15–32

>32

CALIF

Area of map

121° 122°

38°

37°
86 The HayWired Earthquake Scenario—Earthquake Hazards

trimmed to the 20 cm/s PGV contour in an attempt to put 

a practical limit to the area likely to experience significant 

landsliding in the HayWired scenario earthquake. 

Acknowledgments

The authors thank their colleagues at CGS— Carlos 

Gutierrez, Dave Branum, Mike Silva, Teri McGuire, Rick 

Wilson, Mark Wiegers, Rui Chen, and Badie Rowshandel for 

their assistance in the collation and interpretation of the hazard 

data. Thanks are also due to the USGS for providing the modeled 

earthquake shaking data. Jamie Jones (USGS) prepared the 

landslide input for Hazus that was run by Hope Seligson (Seligson 

Consulting). This report benefited from thoughtful reviews 

by Mike Silva, Chris Wills, and Kate Allstadt. Special thanks 

to Dale Cox and Anne Wein (USGS) for their conscientious 

management of the HayWired project and to Steve Hickman, 

Shane Detweiler, Andy Michael, and Ken Hudnut (all USGS), as 

well as the rest of the HayWired technical review panel, for their 

thorough evaluation of this study. The authors also would like to 

acknowledge the conscientious work of James Hendley and Claire 

Landowski, USGS technical editors, as well as the work of Cory 

Hurd, USGS graphics/layout specialist.

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Zone Report 044, section 2, p. 25–44. 

Wiegers, M.O., Aue, K., and McCrink, T.P., 2001, Evaluation 

report for earthquake-induced landslide hazard in the 

Calaveras Reservoir 7.5-Minute Quadrangle, Santa Clara 

County, California: California Geological Survey Seismic 

Hazard Zone Report 048, section 2, p. 23–43.

Wiegers, M.O., Aue, K., and McCrink, T.P., 2001, Evaluation 

report for earthquake-induced landslide hazard in the Milpitas 

7.5-Minute Quadrangle, Santa Clara and Alameda Counties, 

California: California Geological Survey Seismic Hazard 

Zone Report 051, section 2, p. 27–44.

Slater, C.F., and Aue, K., 2002, Evaluation report for 

earthquake-induced landslide hazard in the San Jose West 

7.5-Minute Quadrangle, Santa. Clara County, California: 

California Geological Survey Seismic Hazard Zone Report 

058, section 2, p. 25–40.

Slater, C.F., Wiegers, M.O., and McCrink, T.P., 2002, 

Evaluation report for earthquake-induced landslide hazard 

in the Cupertino 7.5-Minute Quadrangle, Santa Clara 

County, California: California Geological Survey Seismic 

Hazard Zone Report 068, section 2, p. 23–42. 

Wiegers, M.O., and Clahan, K.B., 2002, Evaluation report 

for earthquake-induced landslide hazard in the Los Gatos 

7.5-Minute Quadrangle, Santa Clara County, California: 

California Geological Survey Seismic Hazard Zone Report 

069, section 2, p. 25–47.

McCrink, T.P., and Wiegers, M.O., 2003, Evaluation report 

for earthquake-induced landslide hazard in the Richmond 

7.5-Minute Quadrangle, Alameda County, California: 

California Geological Survey Seismic Hazard Zone Report 

070, section 2, p. 23–45. 

McCrink, T.P., Bott, J.D.J., Wiegers, M.O., Wilson, R.I., 

McMillan, J.R., and Haydon, W.D., 2003, Evaluation report 

for earthquake-induced landslide hazard in the San Leandro 

7.5-Minute Quadrangle, Alameda County, California: California 

Geological Survey Seismic Hazard Zone Report 078, section 2, 

p. 25–44. 

Wilson, R.I., Wiegers, M.O., McCrink, T.P., Haydon, W.D., and 

McMillan, J.R., 2003, Evaluation report for earthquake-induced 

landslide hazard in the Oakland East 7.5-Minute Quadrangle, 

Alameda County, California: California Geological Survey 

Seismic Hazard Zone Report 080, section 2, p. 25–47.

McCrink, T.P., Wilson, R.I., Haydon, W.D., McMillan, J.R., and 

Wiegers, M.O., 2003, Evaluation report for earthquake-induced 

landslide hazard in the Oakland West 7.5-Minute Quadrangle, 

Alameda County, California: California Geological Survey 

Seismic Hazard Zone Report 081, section 2, p. 25–44. 

McCrink, T.P., Wiegers, M.O., Wilson, R.I., Haydon, W.D., and 

McMillan, J.R., 2003, Evaluation report for earthquake-induced 

landslide hazard in the Briones Valley 7.5-Minute Quadrangle, 

Alameda County, California: California Geological Survey 

Seismic Hazard Zone Report 084, section 2, p. 5–22. 

Wiegers, M.O., and Bott, J.D.J., 2003, Evaluation report for 

earthquake-induced landslide hazard in the Newark 7.5-Minute 

Quadrangle, Alameda County, California: California Geological 

Survey Seismic Hazard Zone Report 090, section 2, p. 25–43. 

Wiegers, M.O., Rosinski, A.M., and Bott, J.D.J., 2003, Evaluation 

report for earthquake-induced landslide hazard in the Hayward 

7.5-Minute Quadrangle, Alameda County, California: California 

Geological Survey Seismic Hazard Zone Report 091, section 2, 

p. 25–46. 

Slater, C.F., and Wiegers, M.O., 2003, Evaluation report for 

earthquake-induced landslide hazard in the Santa Teresa Hills 

7.5-Minute Quadrangle, Santa Clara County, California: 

California Geological Survey Seismic Hazard Zone Report 097, 

section 2, p. 29–47. 

Slater, C.F., and Wiegers, M.O., 2004, Evaluation report for 

earthquake-induced landslide hazard in the Morgan Hill 

7.5-Minute Quadrangle, Santa Clara County, California: 

California Geological Survey Seismic Hazard Zone Report 096, 

section 2, p. 29–48. 

Appendix 1. Seismic Hazard Zoning (SHZ) Reports in the San Francisco Bay Region

[Chronological order]
Chapter F. HayWired Scenario Mainshock—Earthquake-Induced Landslide Hazards 89

Wiegers, M.O., 2004, Evaluation report for earthquake-induced 

landslide hazard in the Niles Quad 7.5-Minute Quadrangle, 

Alameda County, California: California Geological Survey 

Seismic Hazard Zone Report 098, section 2, p. 29–50. 

Wiegers, M.O., and Bott, J.D.J., 2005, Evaluation report for 

earthquake-induced landslide hazard in the Castle Rock Ridge 

7.5-Minute Quadrangle, Santa Clara County, California: 

California Geological Survey Seismic Hazard Zone Report 108, 

section 2, p. 19–39. 

Slater, C.F., and Wiegers, M.O., 2006, Evaluation report for 

earthquake-induced landslide hazard in the Mountain View 

7.5-Minute Quadrangle, San Mateo, Santa Clara and Alameda 

Counties, California: California Geological Survey Seismic 

Hazard Zone Report 060, section 2, p. 25–38. 

Wilson, R.I., Thornburg, J., and Rosinski, A.M., 2006, Evaluation 

report for earthquake-induced landslide hazard in the Palo Alto 

7.5-Minute Quadrangle, San Mateo and Santa Clara Counties, 

California: California Geological Survey Seismic Hazard Zone 

Report 111, section 2, p. 23–45.

Slater, C.F., Wiegers, M.O., Bott, J.D.J., and McCrink, T.P., 2006, 

Evaluation report for earthquake-induced landslide hazard in 

the Mt. Sizer 7.5-Minute Quadrangle, Santa Clara County, 

California: California Geological Survey Seismic Hazard Zone 

Report 118, section 2, p. 21–38. 

Wilson, R.I., and Rosinski, A.M., 2008, Evaluation report 

for earthquake-induced landslide hazard in the Mindego 

Hill 7.5-Minute Quadrangle, Santa Clara and San Mateo 

Counties, California: California Geological Survey Seismic 

Hazard Zone Report 109, section 2, p. 21–43.

Wiegers, M.O., and Perez, F.G., 2008, Evaluation report 

for earthquake-induced landslide hazard in the Dublin 

7.5-Minute Quadrangle, Alameda County, California: 

California Geological Survey Seismic Hazard Zone Report 

112, section 2, p. 23–42. 

Perez, F.G., 2008, Evaluation report for earthquake-induced 

landslide hazard in the Livermore 7.5-Minute Quadrangle, 

Alameda County, California: California Geological Survey 

Seismic Hazard Zone Report 114, section 2, p. 23–40. 

Perez, F.G., and Haydon, W.D., 2009, Evaluation report 

for earthquake-induced landslide hazard in the Altamont 

7.5-Minute Quadrangle, Alameda County, California: 

California Geological Survey Seismic Hazard Zone Report 

119, section 2, p. 23–43. 

Silva, M.A., Haydon, W.D., and McCrink, T.P., 2012, 

Evaluation report for earthquake-induced landslide hazard 

in the Lick Observatory 7.5-Minute Quadrangle, Santa 

Clara County, California: California Geological Survey 

Seismic Hazard Zone Report 110, section 2, p. 17–34.

Appendix 2 is available online only as comma separated 

value (.csv) files and a .xlsx file at https://doi.org/10.3133/

sir20175013v1. The appendix is a detailed listing of the 240 

geologic map units shown on the California Geological Survey 

(CGS) seismic-hazard-zone maps for the San Francisco Bay 

region that cover the 29 7.5-minute quadrangles in the 10-county 

study region for the HayWired earthquake scenario. The appendix 

shows how the 240 units were correlated with the 66 geologic map 

Appendix 2. Geologic Map Unit Names/Descriptions for California Geological 

Survey Seismic-Hazard-Zone (SHZ) Maps for the San Francisco Bay Region and 

Their Correlation to the Generalized Geologic-Compilation Map

units from the unpublished CGS generalized geologic-compilation 

map for the San Francisco Bay region (C. Gutierrez, CGS, 2014, 

written commun.), derived from bedrock geology from Graymer 

and others (2006) and Quaternary units from Knudsen and others 

(2000) and Witter and others (2006). The appendix also shows 

which of the map quadrangles contain strength data and provides 

the average strength values that were used in the landslide analyses 

for the HayWired earthquake scenario. 

Chapter G

HayWired Scenario Aftershock Sequence

By Anne M. Wein,1 Karen R. Felzer,1 Jamie L. Jones,1 and Keith A. Porter2

The HayWired Earthquake Scenario—Earthquake Hazards

Edited by Shane T. Detweiler and Anne M. Wein

Scientific Investigations Report 2017–5013–A–H

[Also see https://doi.org/10.3133/sir20175013]

1

U.S. Geological Survey.

2

University of Colorado Boulder.

Abstract

The HayWired scenario examines a hypothetical earthquake 

(mainshock) with a moment magnitude (Mw) of 7.0 occurring on 

April 18, 2018, at 4:18 p.m. on the Hayward Fault in the east bay 

part of California’s San Francisco Bay area. Earthquakes are rarely 

isolated events. Instead, earthquakes usually occur in clusters within 

an area and over days, months, or even years. In the HayWired 

scenario, the mainshock is followed by a series of aftershocks that 

occur over the course of 2 years. The aftershock sequence includes 

175 Mw 4 or larger earthquakes that occur in the vicinity of the 

mainshock fault rupture, as well as in the south end and northeast 

of the San Francisco Bay area. The largest scenario aftershock, an 

Mw 6.4 earthquake, is located in Cupertino. We estimate that the 

occurrence of an Mw 6.4 or larger aftershock following an Mw 7.0 

mainshock has at least a 1 in 5 chance of occurring. 

Scientists cannot currently predict earthquakes, but the 

occurrence of large earthquakes increases the chance that more 

earthquakes (their aftershocks) will occur. Aftershock forecasts 

provide an estimate of the frequency and probability of earthquakes 

for magnitude ranges during periods within confidence intervals. 

For example, an aftershock forecast after the HayWired mainshock 

states that “within the next week, the chance of an earthquake of 

magnitude 5 or greater is 99 percent, and it is most likely that 1 to 

9 such earthquakes may occur.” To illustrate, scenario forecasts 

are provided at points in time throughout the HayWired aftershock 

sequence—approximately 20 minutes, 1 day, 2 days, 1 week, and 40 

days after the mainshock. 

This HayWired aftershock scenario applies existing methods 

to simulate and forecast aftershock sequences for the purpose 

of improving the communication of aftershock forecasts by (1) 

informing stakeholders about aftershock sequences and forecasts; 

(2) allowing time for potential users to provide feedback 

on aftershock forecasts and consider their use in mitigation, 

preparedness, response, and recovery decisions; and (3) providing 

a working example for estimating cumulative damages and 

exercising disaster management during an earthquake sequence. 

Introduction

Earthquakes are rarely isolated events. Instead, they 

usually cluster in time and space. Most commonly, the largest 

earthquake of the cluster, called the mainshock, is followed by a 

sequence of smaller earthquakes called aftershocks. Aftershocks 

generally occur near the mainshock and result from stresses that 

are redistributed in the Earth’s crust following the mainshock. 

The majority of aftershocks occur within tens of miles 

corresponding to one or two rupture lengths of the mainshock, 

but if the mainshock is very large, the aftershocks could occur 

thousands of miles away. Aftershocks can continue over a 

period of weeks, months, years, or even decades, depending 

on the geologic setting in which they occur. In general, the 

larger the mainshock, the more numerous the aftershocks 

and the longer it will take to return to the background rate of 

earthquakes. (The background rate of earthquakes is the rate at 

which they were occurring immediately before the mainshock, 

assuming there was no other large earthquake in the area in the 

few years before the mainshock.)

In northern California, well-studied aftershock sequences 

include those that followed the 1989 moment magnitude (Mw) 

6.9 Loma Prieta and 2014 Mw 6.0 South Napa earthquakes. 

The largest aftershocks for each sequence were an Mw 5.2 

earthquake a few minutes after the Loma Prieta mainshock 

(Dietz and Ellsworth, 1990) and an Mw 3.9 earthquake 2 

days after the South Napa mainshock (Brocher and others, 

2015). Thousands of aftershocks, including 20 aftershocks of 

magnitude (M) 4 or greater, were recorded in the 1989 Loma 

Prieta aftershock zone that spanned 25 miles (between Los 

Gatos to the north and Watsonville to the south). In the San 

Francisco Bay area, Meltzner and Wald (2003) retrospectively 

estimated that following the April 18, 1906, Mw 7.8 San 

Francisco earthquake, the largest aftershock within 20 months 

had a magnitude of ~6.7 and occurred ~100 kilometers (km) 

west of Eureka on April 23, 1906. They deduced that the largest 

aftershocks occurred at the ends of the 1906 rupture or away 

from the rupture entirely; very few significant aftershocks 

occurred along the mainshock rupture itself. This aftershock 

pattern following the 1906 San Francisco Mw 7.8 mainshock has 

been explained to be the result of the stress shadow (reduction 
92 The HayWired Earthquake Scenario—Earthquake Hazards

in the Earth’s stress available to produce earthquakes) (Simpson 

and Reasenberg, 1994; Harris and Simpson, 1998). Further 

back in time, the 1868 Mw.7 earthquake on the Hayward Fault 

reportedly had strong aftershocks into the next month (Stover 

and Coffman, 1993; Toppozada and Real, 1981) but was 

preceded by a seismically active period with 12 earthquakes of 

M>5.5 from 1855 to 1866 within 60 km of the Hayward Fault 

and was followed by 13 relatively quiet years (Toppozada and 

others, 2002).

Scientists cannot currently predict the exact location, 

magnitude, or occurrence time of any earthquake, and do not 

expect to be able to do so in the foreseeable future. However, 

using statistical relations, they can estimate the probability of 

earthquakes of specific magnitudes during specific periods. The 

U.S. Geological Survey (USGS) reports aftershock forecasts after 

sizeable earthquakes (for example, Reasenberg and Jones, 1989). 

These forecasts change over time because of the expected decay 

in the aftershock frequency over time. The forecasts also change 

after a large aftershock triggers more aftershocks. Furthermore, 

forecasts are updated because aftershock data allow the forecaster 

to improve and better tailor the forecast to the specific earthquake 

sequence and particular geologic setting. 

Aftershock locations can be unexpected, and their 

magnitudes can be large enough to cause or aggravate damage 

and complicate emergency response and recovery. Therefore, 

emergency managers, community leaders, and others who respond 

to and recover from earthquakes can benefit from understanding 

aftershocks and earthquake forecast capabilities to better inform 

plans and procedures. 

For example, a study of the communication of aftershock 

forecasts during the recent Canterbury, New Zealand, 

earthquake sequence of 2010–2012 (for example, Becker and 

others, 2015; Wein and others, 2016) reveals the following 

potential benefits: 

5. Reassurance to the public and insurers when recorded 

earthquake frequencies are within the bounds of uncertainty of the scientific forecast; 

6. Motivation for the public and community leaders to 

maintain preparedness, further develop emergency plans, 

and establish relationships with emergency managers; 

7. Guidance for government, insurers, and businesses about 

the timing of restoration and recovery decisions that may 

be affected by aftershocks; and

8. Information for development of safer building standards 

and land-use policies. 

The HayWired scenario is a hypothetical earthquake 

sequence that can be used to better inform potential users 

of aftershock forecast information. The HayWired scenario 

mainshock is a Mw 7.0 earthquake on the Hayward Fault with 

a hypocenter in Oakland, California, in the San Francisco 

Bay area (fig. 1; U.S. Geological Survey, 2014). The scenario 

planning assumes that the scenario Hayward Fault mainshock 

occurs at 4:18 p.m. Pacific Daylight Time (PDT) on April 18, 

2018. The simulated ground shaking caused by the scenario 

Hayward Fault mainshock was produced by Aagaard and 

others (2010a) and Aagaard, Boatwright, and others (this 

volume). We reiterate that scientists cannot predict the exact 

location, timing, or magnitude of future earthquakes, so our 

hypothetical scenario is just for educational purposes.

This chapter describes characteristics of earthquake 

sequences and refers to the method used to simulate 2 years 

of aftershocks for the HayWired scenario mainshock. The 

description of the selected earthquake-sequence scenario includes 

a time series of aftershock magnitudes; maps of the aftershock 

locations; and details for the times, locations, and depths of 

the larger aftershocks. This is followed by a series of scenario 

aftershock forecasts that could be transmitted soon after the 

HayWired scenario mainshock and updated forecasts that could 

be transmitted 24 hours, 48 hours, 7 days, and 40 days later. The 

likelihood of the largest simulated aftershock of magnitude (M) 6.4 

occurring after a M7.0 mainshock in any earthquake sequence is 

calculated. The limitations of the statistical aftershock simulation 

and forecast methods are identified before suggesting potential 

uses of the analysis. 

Characteristics of Earthquake Sequences—

Magnitudes, Frequencies, Location, and Decay

Methods to simulate aftershock sequences and forecast 

earthquakes are based on the following empirical relations: 

1. The occurrence rates of aftershocks. The frequency of 

aftershocks decays with time after the mainshock. This 

relation is called the Omori-Utsu law (table 1; Omori, 

1894; Utsu, 1961).

2. The relation between the magnitude of the mainshock 

and the number of aftershocks. Utsu-scaling estimates 

that on average for each unit increase in mainshock 

magnitude approximately 10 times as many aftershocks 

are produced (table 2; Utsu, 1971, p. 420–427).

3. The distribution of the number of aftershocks across 

magnitude. The Gutenberg-Richter law holds that, 

on average, there are 10 times more aftershocks with 

magnitude M X than aftershocks with one unit greater 

magnitude, M (X+1); for example, 10 times as many M5 

aftershocks than M6 aftershocks (table 3; Gutenberg and 

Richter, 1944).

4. The relation between spatial density of aftershocks and 

distance from the mainshock. Fewer aftershocks occur 

farther from the mainshock than occur closer to the 

mainshock. The Felzer and Brodsky (2006) hypothesis 

states that the decay of aftershocks between 0.2–50 km 

is well approximated by a basic inverse-power law (for 

every unit increase in distance, there is an exponential 

decrease in the number of aftershocks) (table 4). 

These four statistical relations are described in tables 1–4. 
Chapter G. HayWired Scenario Aftershock Sequence 93

Figure 1. ShakeMap of the San Francisco Bay region, California, showing instrumental intensity of the HayWired 

earthquake scenario magnitude-7 mainshock (modified from U.S. Geological Survey, 2014). km, kilometer; %g, percentage of 

acceleration due to gravity; cm/s, centimeter per second.

4

5

5

5

5

6

6

6

6

6

6

6

7

7

7

7

7

7

8

8

8

8

9

9

San Jose

San Francisco

Sacramento

Stockton

Fremont

Salinas

Fairfield

Merced

Rocklin

Santa Cruz

Soledad

Coalinga

Clearlake

-- Earthquake Planning Scenario --

ShakeMap for haywiredm7.0 Scenario

Scenario Date: Apr 18, 2018 23:18:00 UTC M 7.0 N37.80 W122.18 Depth: 8.0km

PLANNING SCENARIO ONLY -- Map Version 33 Processed 2017-01-11 01:59:06 UTC

INSTRUMENTAL

INTENSITY

PEAK VEL.(cm/s)

PEAK ACC.(%g)

POTENTIAL

DAMAGE

PERCEIVED

SHAKING

I II-III IV V VI VII VIII IX X+

<0.02 0.1 1.4 4.7 9.6 20 41 86 >178

<0.05 0.3 2.8 6.2 12 22 40 75 >139

none none none Very light Light Moderate Mod./Heavy Heavy Very Heavy

Not felt Weak Light Moderate Strong Very strong Severe Violent Extreme

-124 ̊ -123 ̊ -122 ̊ -121 ̊ -120 ̊

36 ̊

36.5 ̊

37 ̊

37.5 ̊

38 ̊

38.5 ̊

39 ̊

0 50

km

Scale based on Worden and others, 2012.
94 The HayWired Earthquake Scenario—Earthquake Hazards

Table 1. Numeric illustration of the Omori-Utsu law (Omori, 1894; 

Utsu, 1961) regarding the rate of aftershock decay over time.

When Observed 

number1

Expected 

number2

1st day (day 0.5–1.5) 420 733

10th day 44 70

100th day 4 7.1

1,000th day 0 0.71

10,000th day Unknown until 2021 0.071

1

Observed numbers of aftershocks for each day are the 1994 magnitude-6.7 

Northridge earthquake aftershocks (magnitude 2 or larger, within 30 kilometers) 

(Northern California Earthquake Data Center, 2014).

2

Expected numbers come from Reasenberg and Jones (1989) using the 

parameters from Michael (2012): a=–1.85, b=1, p=1, c=0.05 days.

Table 2. Numeric illustration of Utsu scaling (Utsu, 1971) regarding 

the number of aftershocks relative to the size of a mainshock.

[M, magnitude]

Magnitude of 

mainshock

Expected number of aftershocks M3 or larger 

in the first week1

5 6.7

6 67

7 670

8 6,700

1

Expected numbers come from Reasenberg and Jones (1989) using the 

parameters from Michael (2012): a=–1.85, b=1, p=1, c=0.05 days.

Table 3. Numeric illustration of the Gutenberg-Richter law 

(Gutenberg and Richter, 1944) regarding the size of aftershocks 

relative to the size of a mainshock.

Minimum 

magnitude

Observed number in 

the first week1

Expected number in 

the first week2

3 310 340

4 42 34

5 6 3.4

6 0 0.34

7 0 0.034

Table 4. Numeric illustration of the Felzer and Brodsky 

hypothesis (Felzer and Brodsky, 2006) regarding the distance of 

aftershocks relative to the location of a mainshock.

[km, kilometer; km2, square kilometer]

Distance from 

mainshock 

epicenter (km)

Observed 

number of 

aftershocks

Area within 

distance range 

(km2)

Number of 

aftershocks per 

km2

0 to 10 689 314 2.2

10 to 20 1,649 942 1.7

20 to 30 340 1,571 0.22

30 to 40 30 2,199 0.014

40 to 50 15 2,827 0.0053

1

Observed numbers of aftershocks for each day are the 1994 magnitude-6.7 

Northridge earthquake aftershocks (magnitude 2 or larger, within 50 kilometers) 

(Northern California Earthquake Data Center, 2014).

1

Observed numbers of aftershocks for each day are the 1994 magnitude-6.7 

Northridge earthquake aftershocks (magnitude 3 or larger, within 30 

kilometers) (Northern California Earthquake Data Center, 2014).

2

Expected numbers come from Reasenberg and Jones (1989) using the 

parameters from Michael (2012): a=–1.85, b=1, p=1, c=0.05 days.

Aftershock Simulations

Aftershock sequences for the HayWired scenario are 

simulated based on these aforementioned statistical relations 

and could therefore unfold in an infinite variety of ways after 

any given earthquake. We generated 13 hypothetical aftershock 

sequences that could follow the HayWired Mw 7.0 mainshock 

rupture. Each sequence depicts one possible outcome during the 

2 years following the scenario mainshock. The 2-year period was 

chosen to match the recovery horizon of the Federal Emergency 

Management Agency’s (FEMA) San Francisco Bay Area 

Catastrophic Earthquake Plan, under development at the same 

time as the HayWired scenario. The method used to generate 

earthquake sequences is the same as was used to simulate the 

1-week aftershock sequence for the ShakeOut scenario in southern 

California (Felzer, 2008).

The statistical model that we used to generate an aftershock 

sequence (a time series of earthquake magnitudes) is known as 

the epidemic-type aftershock sequence (ETAS) model (Ogata, 

1988). The version of the ETAS model used for the ShakeOut 

and HayWired scenarios also distributes the aftershocks in 

space (Felzer and others, 2002). Therefore, the model generates 

aftershocks stochastically using established empirical relations for 

aftershock magnitude, time, and location distributions (described 

above). The model also simulates secondary aftershocks—

aftershocks caused by other aftershocks—which is an important 

process found in real aftershock sequences (Felzer and others, 

2003). Because the ETAS model uses statistics for determining 

earthquake locations, without regard to existing geologic structures, 

expert opinion is used to move earthquakes on to known nearby 

faults. In particular, aftershocks with magnitudes greater than 6 are 

relocated to the closest faults capable of producing earthquakes of 

that size. This shift displaces these larger events away from their 

own aftershocks, so statistical relation 4 (see above) may not hold 

for the modeled secondary aftershock sequences.

The map series in figure 2 shows 13 statistical aftershock 

sequence simulations for the HayWired scenario mainshock, 

specifically including the aftershocks greater than M5. As expected, 

the earthquake sequences primarily produce aftershocks near 

the Hayward Fault (for example, HayWiredCatalog5). Some 
Chapter G. HayWired Scenario Aftershock Sequence 95

Wein_fig2

HayWiredCatalog1 HayWiredCatalog2 HayWiredCatalog3

HayWiredCatalog4 HayWiredCatalog5 HayWiredCatalog6

HayWiredCatalog7 HayWiredCatalog8 HayWiredCatalog9

HayWiredCatalog10 HayWiredCatalog11 HayWiredCatalog12

HayWiredCatalog13 (Selected)

EXPLANATION

Epicenters for earthquakes

M=5 in aftershock sequences

M>.5

Hayward Fault rupture

Counties

0 50 100 150 MILES

0 50 100 150 KLOMETERS

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N.

N

 Area 

of map CALIF.

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN

San Francisco San Francisco San Francisco

San Francisco San Francisco San Francisco

San Francisco San Francisco San Francisco

San Francisco

San Francisco

San Francisco San Francisco

Figure 2. Maps of the San Francisco Bay region, California, showing different sets (catalogs) of earthquake aftershock sequences 

generated by the epidemic-type aftershock sequence (ETAS) model for the HayWired earthquake scenario mainshock. Aftershocks 

of magnitude (M) 5 or greater are shown in each map as purple dots.
96 The HayWired Earthquake Scenario—Earthquake Hazards

sequences are more active than others; the number of aftershocks 

greater than M4 ranges from 149 (HayWiredCatalog1) to 678 

(HayWiredCatalog5), and the number of aftershocks greater than 

M5 ranges from 11 (HayWiredCatalog7) to 66 (HayWiredCatalog5). 

The largest aftershock magnitude in a sequence ranges from 

M5.8 (HayWiredCatalog7) to M7.3 (HayWiredCatalog9). The 

spatial distribution of the simulated aftershock sequences are also 

variable; some include triggered earthquakes farther to the north 

on the Rodgers Creek Fault (for example, HayWiredCatalog11), 

and other sequences include triggered activity east of the Hayward 

Fault (for example, HayWiredCatalog9 and HayWiredCatalog11) 

or in the south end of the San Francisco Bay area (for example, 

HayWiredCatalog13). 

The selected HayWired scenario aftershock sequence 

(HayWiredCatalog13) produces 175 M4 or larger earthquakes and a 

largest aftershock of M6.4. This sequence has a diversity of features, 

and it includes aftershocks near Palo Alto, Cupertino, and San Jose, 

California, at the south end of the San Francisco Bay area, along 

with a significant aftershock northeast of the San Francisco Bay 

area, affecting the Sacramento-San Joaquin Delta and its system of 

potentially fragile flood-control levees. The two largest aftershocks 

are relocated to the closest known faults capable of producing 

M6 earthquakes. One thing this sequence lacks is having as many 

aftershocks along the Hayward Fault as many of the other sequences. 

Although the diversity of locations illustrates the importance of 

preparing for aftershocks throughout the region, we should not forget 

the possibility of more activity along the primary fault

Description of the HayWired Aftershock 

Sequence

The HayWired scenario aftershock sequence is a cluster of 

earthquakes in time and space. The M≥2.5 scenario aftershock 

temporal distribution during the first 2 years after the HayWired 

scenario mainshock is shown in figures 3 and 4. The statistical 

relations of decay in earthquake frequency over time and fewer 

earthquakes of larger magnitudes are evident in the figures. The 

figures also show the larger aftershocks triggering aftershocks of 

their own within the periods indicated by different colored dots. 

The map in figure 5 shows the locations of all aftershocks 

of M≥2.5 in the HayWired aftershock sequence (data for 

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

0 90 180 270 360 450 540 630 720

Cumulative number of earthquakes

Number of days after mainshock

Figure 3. Graph showing the 2-year time series for cumulative number of HayWired earthquake scenario 

aftershocks of magnitude 2.5 or greater.

Figure 4. Graph showing the 2-year time series for HayWired earthquake scenario aftershocks of magnitude 2.5 or greater. The 

colored (red, orange, yellow, and green) dots mark periods in the sequence when a larger aftershock triggers more aftershocks. 

Color-coded time periods of the sequence are shown in figure 6.

2

3

4

5

6

7

0 90 180 270 360 450 540 630 720

Magnitude

Number of days after mainshock
Chapter G. HayWired Scenario Aftershock Sequence 97

point locations are available in Jones and Felzer, 2017). 

This sequence is one realization of the aftershock sequence 

simulation, and expert opinion found it reasonable. Note the 

density of activity in the Green Valley Fault Zone near Vallejo, 

near the Monte Vista-Shannon Fault Zone in the Palo AltoSunnyvale area, as well as along the Hayward Fault. A map 

view of the HayWired scenario mainshock and aftershock 

sequence, with color-coded time periods as in figure 4, is 

shown in figure 6. 

The larger aftershocks (M>5) are located on the map in 

figure 7. The HayWired scenario earthquake sequence includes 

2 aftershocks of M6 in Santa Clara County and 14 aftershocks 

of M5 near the Hayward Fault, near Vallejo, and in Santa Clara 

County. (The two M6 earthquakes are visibly shifted southwest 

Figure 5. Map of the San Francisco Bay region, California, showing 2 years of HayWired earthquake scenario aftershocks of magnitude (M) 2.5 

or greater (note that the Hayward Fault is mostly obscured by earthquake symbols).

^_

123° 122° 121°

39°

38°

37°

Oakland

Fremont

San Francisco

San Rafael

Petaluma

Santa Rosa

Napa

Fairfield

Vallejo

San Pablo

Union City

San Jose

Santa Clara

Sunnyvale

Cupertino

Palo Alto

Menlo Park

PACIFIC 

OCEAN

Quaternary faults modified from U.S. Geological Survey and California Geological Survey, 2006.

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N.

Epicenters for earthquakes in 

aftershock sequence

EXPLANATION

^_ M7.05 mainshock

Earthquake—M2.5 or greater 

Hayward Fault rupture

Local Quaternary faults

0 10 20 30 MILES

0 10 20 30 KILOMETERS

 Area 

of map

CALIF
98 The HayWired Earthquake Scenario—Earthquake Hazards

Figure 6. Maps of the San Francisco Bay region, California, showing the HayWired earthquake scenario aftershock 

sequence within periods between larger aftershocks that retrigger the sequence (see fig. 4). <, less than.

Wein_fig6

$+^_

$+

$+

$+

^_

San Francisco

Oakland

Napa

San Jose

124° 123° 122°

39°

38°

37°

121°

124° 123° 122°

39°

38°

37°

121°

124° 123° 122°

39°

38°

37°

121° 124° 123° 122° 121°

124° 123° 122° 121°

^_

^_

San Francisco

Oakland

Napa

San Jose

^_

San Francisco

Oakland

Napa

San Jose

Oakland

San Francisco

Napa

Oakland

San Francisco

San Jose

Napa

San Jose

Quaternary faults modified from U.S. Geological Survey and California Geological Survey, 2006.

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N.

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

EXPLANATION

Aftershock sequence broken into

periods between larger aftershocks Magnitude

< 4

4.0–5.0

5.0–6.0

_ Major earthquake event

Aftershocks from April 18 to 29, 2018

Aftershocks from April 29 to May 27, 2018

Aftershocks from May 27 to September 30, 2018

Aftershocks from September 30, 2018, to August 22, 2019

Aftershocks from August 22, 2019, to April 17, 2020

Hayward Fault rupture

Faults

 Area 

of map CALIF.

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN

PACIFIC

OCEAN
Chapter G. HayWired Scenario Aftershock Sequence 99

Figure 7. Map of the San Francisco Bay region, California, showing larger aftershocks of magnitude (M) 5 

or greater in the HayWired earthquake scenario. >, greater than.

Wein_fig7

PACIFIC 

^_

OCEAN

Oakland Union City San Francisco

San Jose

Santa Clara

Fremont

Sunnyvale

Cupertino

Palo 

Alto

Menlo 

 Park

Fairfield Napa

Vallejo

San Pablo San Rafael

Santa Rosa

Petaluma

0 10 20 30 MILES

0 10 20 30 KILOMETERS

Quaternary faults modified from U.S. Geological Survey and California Geological Survey, 2006.

Hydrology from U.S. Geological Survey National Hydrography Dataset, 2016.

Boundary data from U.S. Census Bureau TIGER data, 2016.

North American Datum of 1983 UTM 10N projection.

Central meridian, 123° W., latitude of origin, 0.0° N.

EXPLANATION

Epicenters for earthquakes

M≥5 in aftershock sequence

^_ M7.05 mainshock

M5.0–M6.0

> M6.0

Hayward Fault rupture

Faults

 Area 

of map

-123° -122° -121°

39°

38°

37°

CALIF

relative to their M5 aftershocks to place them on known faults. In 

a natural earthquake sequence, the M5 and M6 aftershocks would 

not be spatially separated, and most M5 events would be clustered 

around the M6 events on the known faults.) Attributes of the 14 

aftershocks with M≥5 are listed in table 5. The aftershock depths 

are constrained within the 5th and 95th percentiles of the observed 

and expected depths of earthquakes in the San Francisco Bay area. 

For example, the 1989 Mw 6.9 Loma Prieta earthquake nucleated 

at a depth of 18 km, which is among the deepest recorded 

earthquakes in the San Francisco Bay area (for example, Hill and 

others, 1990). The two scenario aftershocks in table 5 with depths 

of 2.65 km are considered very shallow for moderately sized 

earthquakes, although on some young faults shallow nucleation 

may theoretically occur.

ShakeMaps for each of the M≥5 aftershocks can be accessed 

at http://escweb.wr.usgs.gov/share/shake2/haywired/archive/

scenario.html (U.S. Geological Survey, 2015). They are labeled 

consistently with the table of aftershock attributes in terms of 

abbreviated city and magnitude. For example, Uc523 is the M5.23 

earthquake with a hypocenter in Union City.
100 The HayWired Earthquake Scenario—Earthquake Hazards

Table 5. Time, location, depth, and magnitude of aftershocks in the San Francisco Bay region, California, of magnitude 5 or greater in 

the HayWired earthquake scenario.

[Day is relative to the day the HayWired mainshock event occurred, with April 18, 2018, counted as day 1. All locations are in California. Latitude is in degrees 

north; longitude is in degrees west. Depth is how far below the Earth’s surface the aftershock hypocenter is located. PDT, Pacific Daylight Time; km, kilometers] 

Date Day Time (PDT) Latitude Longitude Location Depth (km) Magnitude

April 18, 2018 1 4:49 p.m. 37.6008 122.0172 Union City 2.65 5.23

April 19, 2018 2 4:16 a.m. 37.9630 122.3473 San Pablo 2.65 5.04

April 29, 2018 12 11:13 p.m. 38.1916 122.1483 Fairfield 11.05 5.58

May 2, 2018 15 8:44 p.m. 37.4829 121.9146 Fremont 7.15 5.10

May 20, 2018 33 8:37 a.m. 37.7561 122.1508 Oakland 8.45 5.42

May 28, 2018 41 4:47 a.m. 37.3867 122.1780 Palo Alto 18.97 6.21

May 28, 2018 41 8:11 a.m. 37.4528 122.1671 Menlo Park 7.26 5.52

May 28, 2018 41 6:22 p.m. 37.4604 122.1753 Atherton 7.91 5.11

May 28, 2018 41 11:53 p.m. 37.4099 122.1184 Palo Alto 8.36 5.69

June 23, 2018 67 8:27 p.m. 37.4391 122.1511 Palo Alto 2.85 5.22

July 1, 2018 75 11:19 a.m. 37.4435 122.1561 Palo Alto 8.69 5.26

September 30, 2018 166 8:16 p.m. 37.4386 122.0770 Mountain View 11.29 5.98

October 1, 2018 167 12:33 a.m. 37.3068 122.0592 Cupertino 15.45 6.40

October 1, 2018 167 2:24 a.m. 37.3835 122.0153 Sunnyvale 18.89 5.35

October 1, 2018 167 6:10 a.m. 37.3334 121.9541 Santa Clara 7.00 5.09

August 22, 2019 492 10:45 p.m. 37.4145 122.1235 Palo Alto 11.98 5.01

Scenario Aftershock Forecasts

The first 40 days of the HayWired scenario aftershock 

sequence represent average seismicity rates for California 

aftershocks; the sequence is closely aligned with the expected 

numbers of aftershocks forecasted. We provide scenario aftershock 

forecasts for time windows after the HayWired scenario 

mainshock. In the HayWired scenario, these time-sensitive 

forecasts would be released soon after the scenario mainshock 

and updated over the days and weeks afterwards. The frequency 

of providing these aftershock forecasts would be reset if a larger 

aftershock occurs. In the HayWired scenario, another immediate 

forecast is released when a M6 aftershock occurs, so as to update 

the forecast information.

Scenario Aftershock Forecast 1—Soon After the Mainshock

The next page shows a sample advisory message that would be publicly issued shortly after the occurrence of the HayWired 

scenario mainshock at 4:18 p.m. on April 18, 2018. The forecast is based on the rapidly estimated scenario mainshock magnitude of 7, a 

value that would be confirmed as Mw 7.0. The information used to create this scenario aftershock forecast is shown in table 6. (Note that 

the following abbreviations are used in the advisory messages on the following pages: PDT, Pacific Daylight Time; %, percent. Because 

these messages are intended for the media and the public, distances in the messages are provided in miles—1 mile=1.61 km.)

Magnitude analysis would be performed on the Oakland earthquake, indicating that the most precise magnitude value is Mw 7.0. 

Within 24 hours after the mainshock, the Northern California Seismic Network would have recorded 32 aftershocks larger than M4, 2 

aftershocks larger than M5, and no aftershocks larger than M6. This is close to the initial forecast (see table 6), so the scientists would 

continue to use generic mean California aftershock rates to forecast the expected number of aftershocks in this sequence.
Chapter G. HayWired Scenario Aftershock Sequence 101

 HayWired Scenario Earthquake Aftershock Forecast 

 SCENARIO----SCENARIO----SCENARIO----SCENARIO----SCENARIO

THIS IS NOT A REAL EARTHQUAKE OR REAL AFTERSHOCK FORECAST

Aftershock advisory from the United States Geological Survey (USGS) 

• An earthquake of magnitude 7 occurred at 4:18 p.m. PDT on April 18, 2018, near Oakland, California. More earthquakes than usual will 

continue to occur in the mainshock area.

• Be ready for more earthquakes: Find the California Governor’s Office of Emergency Services (Cal OES) event page at http://www.caloes.ca.gov/. 

See Earthquake Country Alliance (ECA, http://www.earthquakecountry.org/sevensteps/) for advice on earthquake preparedness. See the Centers for 

Disease Control and Prevention (CDC, https://emergency.cdc.gov/coping/index.asp) for advice on coping with a disaster. 

• During the next week, there are likely to be 437 to 508 aftershocks large enough to be felt, and there is a 99% chance of one or more 

aftershocks large enough to potentially cause damage.

What to Expect

It is normal for an earthquake of this size to cause an increase in the number of earthquakes (called aftershocks) in the area. The number of aftershocks 

will drop off over time, but a large aftershock can increase the numbers of earthquakes again, temporarily.

The aftershocks will occur mostly in the area affected by the magnitude-7 Oakland earthquake, within approximately 35 miles of Oakland, with some as 

far as 75 miles away.

When there are more earthquakes, the chance of a large earthquake is greater and the chance of damage is greater. The USGS advises everyone to 

remain aware of the possibility of aftershocks, especially when in or around vulnerable structures (such as unreinforced masonry buildings) and 

landslide areas.

No one can predict the exact time or place of any earthquake, including aftershocks. The USGS can forecast how many earthquakes to expect or the 

chance of having an earthquake within a given time period.

Current USGS Aftershock Forecast

The USGS estimates the chance of more aftershocks within the next week until April 25, 2018, 4:18 p.m. PDT as follows:

• The chance of an earthquake large enough to feel (magnitude 3 or greater) is greater than 99%, and it is most likely that 437 to 508 such earthquakes may 

occur. This chance is 1,500 times greater than it was before the magnitude-7 Oakland earthquake occurred.

• The chance of an earthquake of magnitude 5 or greater is 99%, and it is most likely that 1 to 9 such earthquakes may occur.

• The chance of an earthquake of magnitude 6 or greater is 2 in 5 (39%), and it is most likely that 0 to 2 such earthquakes may occur.

• The chance of an earthquake of magnitude 7 or greater is 1 in 20 (5%), such an earthquake is possible but with a low probability.

• The chance of any damaging earthquake is 1,500 times greater than it was before the magnitude-7 Oakland earthquake occurred.

The chance of earthquakes large enough to be felt or to cause damage remains elevated for the next month and the next year. The table provides forecasts for other time periods. 

The USGS calculates this earthquake forecast using a statistical analysis based on past earthquakes and the aftershocks recorded for this sequence. The 

forecast changes as time passes due to the decay in the frequency of aftershocks, larger aftershocks that may trigger further earthquakes, and changes in 

forecast modeling based on the earthquake data collected.

This advisory was issued: April 18, 2018, at 4:18 p.m.

This advisory will be updated on or before: April 19, 2018, at 4:18 p.m.
102 The HayWired Earthquake Scenario—Earthquake Hazards

Scenario Aftershock Forecast 2—24 Hours After the Mainshock

The next page shows a sample advisory message that would be publicly issued 24 hours after the occurrence of the HayWired 

scenario mainshock, that is, at about 4:18 p.m. on April 19, 2018. The scenario forecast is based on the slightly revised Mw 7.0 for the 

mainshock. The information used to create this scenario aftershock forecast is shown in table 7.

In the scenario, within the past 24 hours, seven aftershocks larger than or equal to M4 and no aftershocks larger than M5 would be 

recorded. This is consistent with the forecast advisory issued 24 hours after the Oakland earthquake (see table 7).

Table 7. Aftershock forecast 24 hours after the HayWired earthquake scenario mainshock (moment magnitude 7, April 18, 2018, at 4:18 

p.m.) for the next day, week, month, and year. 

[The number of aftershocks is the expected or average value. The numbers in parentheses are lower- and upper-bound values associated with 5-percent and 

95-percent probability of not being exceeded. The aftershock probability is the probability of at least one aftershock at the given magnitudes at the time of the 

forecast. M, magnitude; %, percent; ≥, greater than or equal] 

Forecast period Expected number of aftershocks Aftershock probability Increase in the seismicity rate relative 

M≥4 M≥5 M≥6 M≥7 to the rate before the mainshock 

Next 24 hours 7 (2–11) 1 (0–2) 7% 0.7% 2,500

Next week 19 (14–32) 2 (1–4) 19% 2% 1,000

Next month 33 (20–87) 3 (0–10) 30% 3% 400

Next year 52 (43–157) 6 (4–16) 34% 3% 50

Table 6. Aftershock forecast immediately after the HayWired earthquake scenario mainshock (moment magnitude 7, April 18, 2018, at 

4:18 p.m.) for the next day, week, month, and year.

[The number of aftershocks is the expected or average value. The numbers in parentheses are lower- and upper-bound values associated with 5-percent and 

95-percent probability of not being exceeded. The aftershock probability is the probability of at least one aftershock at the given magnitudes at the time of the 

forecast. M, magnitude; %, percent; ≥, greater than or equal] 

Forecast period Expected number of aftershocks Aftershock probability Increase in the seismicity rate relative 

M≥4 M≥5 M≥6 M≥7 to the rate before the mainshock

Next 24 hours 33 (24–43) 3 (1–6) 30% 3% 10,000

Next week 50 (39–62) 5 (1–9) 39% 5% 1,500

Next month 57 (45–70) 5 (2–10) 43% 6% 430

Next year 71 (57–85) 7 (3–13) 47% 6% 45
Chapter G. HayWired Scenario Aftershock Sequence 103

Aftershock advisory from the United States Geological Survey (USGS)

• An earthquake of magnitude 7 occurred at 4:18 p.m. PDT on April 18, 2018, near Oakland, California. More earthquakes than usual will 

continue to occur in the mainshock area.

• Be ready for more earthquakes: Find the California Governor’s Office of Emergency Services (Cal OES) event page at http://www.caloes.

ca.gov/. See Earthquake Country Alliance (ECA, http://www.earthquakecountry.org/sevensteps/) for advice on earthquake preparedness. 

See the Centers for Disease Control and Prevention (CDC, https://emergency.cdc.gov/coping/index.asp) for advice on coping with a disaster. 

• During the next week, there are likely to be 140 to 320 aftershocks large enough to be felt, and there is a 95% chance of one or more aftershocks large enough to potentially cause damage.

What to Expect

It is normal for a magnitude-7 earthquake to cause an increase in the number of earthquakes (called aftershocks) in the area. The number of 

aftershocks will drop off over time, but a large aftershock can increase the numbers of earthquakes again, temporarily.

The aftershocks will occur mostly in the area affected by the magnitude-7 Oakland earthquake, within approximately 35 miles of Oakland, 

with some as far as 75 miles away.

When there are more earthquakes, the chance of a large earthquake is greater and the chance of damage is greater. The USGS advises everyone 

to remain aware of the possibility of aftershocks, especially when in or around vulnerable structures (such as unreinforced masonry buildings) 

and landslide areas.

No one can predict the exact time or place of any earthquake, including aftershocks. The USGS can forecast how many earthquakes to expect 

or the chance of having an earthquake within a given time period.

Current USGS Aftershock Forecast

The USGS estimates the chance of more aftershocks within the next week until April 26, 2018, 4:18 p.m. PDT as follows:

• The chance of an earthquake large enough to feel (magnitude 3 or greater) is greater than 99%, and it is most likely that 140 to 320 such 

earthquakes may occur. This chance is about 1,000 times greater than it was before the magnitude-7 Oakland earthquake occurred.

• The chance of an earthquake of magnitude 5 or greater is 95%, and it is most likely that 1 to 4 such earthquakes may occur.

• The chance of an earthquake of magnitude 6 or greater is 1 in 5 (19%).

• The chance of an earthquake of magnitude 7 or greater is 1 in 50 (2%); such an earthquake is possible but with a low probability.

• The chance of any damaging earthquake is about 1,000 times greater than it was before the magnitude-7 Oakland earthquake occurred. 

The chance of earthquakes large enough to be felt or to cause damage remains elevated for the next month and the next year. The table provides forecasts for other time periods. 

The USGS calculates this earthquake forecast using a statistical analysis based on past earthquakes and the aftershocks recorded for this 

sequence. The forecast changes as time passes due to the decay in the frequency of aftershocks, larger aftershocks that may trigger further 

earthquakes, and changes in forecast modeling based on the earthquake data collected.

This advisory was issued: April 19, 2018, at 4:18 p.m.

This advisory will be updated on or before: April 20, 2018, at 4:18 p.m. 

 HayWired Scenario Earthquake Aftershock Forecast—24-Hour Update

 SCENARIO----SCENARIO----SCENARIO----SCENARIO----SCENARIO

THIS IS NOT A REAL EARTHQUAKE OR REAL AFTERSHOCK FORECAST
104 The HayWired Earthquake Scenario—Earthquake Hazards

Scenario Aftershock Forecast 3—48 Hours After the Mainshock

The next page shows a sample advisory message that will be publicly issued by the USGS 48 hours after the occurrence 

of the HayWired scenario mainshock, that is, at about 4:18 p.m. on April 20, 2018. The information used to create this scenario 

aftershock forecast is shown in table 8. 

Table 8. Aftershock forecast 48 hours after the HayWired earthquake scenario mainshock (moment magnitude 7, April 18, 2018, at 4:18 

p.m.) for the next day, week, month, and year. 

[The number of aftershocks is the expected or average value. The numbers in parentheses are lower- and upper-bound values associated with 5-percent and 

95-percent probability of not being exceeded. The aftershock probability is the probability of at least one aftershock at the given magnitudes at the time of the 

forecast. M, magnitude; %, percent; ≥, greater than or equal] 

Forecast period Expected number of aftershocks Aftershock probability Increase in the seismicity rate relative 

M≥4 M≥5 M≥6 M≥7 to the rate before the mainshock 

Next week 12 (8–29) 1 (1–4) 10% 1% 600

Next month 18 (11–42) 2 (0–6) 16% 1.6% 200

Next year 42 (35–120) 4–5 (3–13) 30% 3% 40
Chapter G. HayWired Scenario Aftershock Sequence 105

Aftershock advisory from the United States Geological Survey (USGS) 

• An earthquake of magnitude 7 occurred at 4:18 p.m. PDT on April 18, 2018, near Oakland, California. More earthquakes than usual will 

continue to occur in the mainshock area.

• Be ready for more earthquakes: Find the California Governor’s Office of Emergency Services (Cal OES) event page at http://www.caloes.

ca.gov/. See Earthquake Country Alliance (ECA, http://www.earthquakecountry.org/sevensteps/) for advice on earthquake preparedness. 

See the Centers for Disease Control and Prevention (CDC, https://emergency.cdc.gov/coping/index.asp) for advice on coping with a disaster. 

• During the next week, there are likely to be 80 to 290 aftershocks large enough to be felt, and there is a 64% chance of one or more aftershocks large enough to potentially cause damage.

What to Expect

It is normal for a magnitude-7 earthquake to cause an increase in the number of earthquakes (called aftershocks) in the area. The number of 

aftershocks will drop off over time, but a large aftershock can increase the numbers of earthquakes again, temporarily.

The aftershocks will occur mostly in the area affected by the magnitude-7 Oakland earthquake, within approximately 35 miles of Oakland, 

with some as far as 75 miles away.

When there are more earthquakes, the chance of a large earthquake is greater and the chance of damage is greater. The USGS advises everyone 

to remain aware of the possibility of aftershocks, especially when in or around vulnerable structures (such as unreinforced masonry buildings) 

and landslide areas.

No one can predict the exact time or place of any earthquake, including aftershocks. The USGS can forecast how many earthquakes to expect 

or the chance of having an earthquake within a given time period.

Current USGS Aftershock Forecast

The USGS estimates the chance of more aftershocks within the next week until April 27, 2018, 4:18 p.m. PDT as follows:

• The chance of an earthquake large enough to feel (magnitude 3 or greater) is greater than 99%, and it is most likely that 80 to 290 such 

earthquakes may occur. This chance is 600 times greater than it was before the magnitude-7 Oakland earthquake occurred.

• The chance of an earthquake of magnitude 5 or greater is 64%, and it is most likely that 1 to 3 such earthquakes may occur.

• The chance of an earthquake of magnitude 6 or greater is 1 in 10 (10%).

• The chance of an earthquake of magnitude 7 or greater is 1 in 100 (1%); such an earthquake is possible but with a low probability.

• The chance of any damaging earthquake is 600 times greater than it was before the magnitude-7 Oakland earthquake occurred. 

The chance of earthquakes large enough to be felt or to cause damage remains elevated for the next month and the next year. The table 

provides forecasts for other time periods. 

The USGS calculates this earthquake forecast using a statistical analysis based on past earthquakes and the aftershocks recorded for this 

sequence. The forecast changes as time passes due to the decay in the frequency of aftershocks, larger aftershocks that may trigger further 

earthquakes, and changes in forecast modeling based on the earthquake data collected.

This advisory was issued: April 20, 2018, at 4:18 p.m.

This advisory will be updated on or before: April 25, 2018, at 4:18 p.m. 

 SCENARIO----SCENARIO----SCENARIO----SCENARIO----SCENARIO

THIS IS NOT A REAL EARTHQUAKE OR REAL AFTERSHOCK FORECAST

 HayWired Scenario Earthquake Aftershock Forecast—48 Hour Update 
106 The HayWired Earthquake Scenario—Earthquake Hazards

Scenario Aftershock Forecast 4—7 Days After the Mainshock

Within the week after the Oakland earthquake, 47 aftershocks larger than or equal to M4 and no aftershocks larger than M5 

would be recorded. This is consistent with the initial forecast advisory issued (see table 6). 

The next page shows a sample advisory message that the USGS would publically issue 1 week after the occurrence of the 

HayWired scenario mainshock, that is, at about 4:20 p.m. on April 25, 2018. The information used to create this scenario aftershock 

forecast is shown in table 9. 

[The number of aftershocks is the expected or average value. The numbers in parentheses are lower- and upper-bound values associated with 5-percent and 

95-percent probability of not being exceeded. The aftershock probability is the probability of at least one aftershock at the given magnitudes at the time of the 

forecast. M, magnitude; %, percent; ≥, greater than or equal] 

Table 9. Aftershock forecast 7 days after the HayWired earthquake scenario mainshock (moment magnitude 7, April 18, 2018, at 4:18 p.m.) for 

the next day, week, month, and year.

Forecast period Expected number of aftershocks Aftershock probability Increase in the seismicity rate relative 

M≥4 M≥5 M≥6 M≥7 to the rate before the mainshock

Next week 4 (0–15) 0 (0–3) 4% 0.4% 210

Next month 10 (8–18) 1 (1–3) 10% 1% 120

Next year 31 (26–127) 3 (2–13) 20% 2% 30
Chapter G. HayWired Scenario Aftershock Sequence 107

 SCENARIO----SCENARIO----SCENARIO----SCENARIO----SCENARIO

THIS IS NOT A REAL EARTHQUAKE OR REAL AFTERSHOCK FORECAST

Aftershock advisory from the United States Geological Survey (USGS)

• An earthquake of magnitude 7 occurred at 4:18 p.m. PDT on April 18, 2018, near Oakland, California. More earthquakes than usual will continue to 

occur in the mainshock area.

• Be ready for more earthquakes: Find the California Governor’s Office of Emergency Services (Cal OES) event page at http://www.caloes.ca.gov/. 

See Earthquake Country Alliance (ECA, http://www.earthquakecountry.org/sevensteps/) for advice on earthquake preparedness. See the Centers for 

Disease Control and Prevention (CDC, https://emergency.cdc.gov/coping/index.asp) for advice on coping with a disaster. 

• During the next week, there are likely to be as many as 150 aftershocks large enough to be felt, and there is a 40% chance of one or more aftershocks 

large enough to potentially cause damage.

What to Expect

It is normal for a magnitude-7 earthquake to cause an increase in the number of earthquakes (called aftershocks) in the area. The number of aftershocks 

will drop off over time, but a large aftershock can increase the numbers of earthquakes again, temporarily.

The aftershocks will occur mostly in the area affected by the magnitude-7 Oakland earthquake, within approximately 35 miles of Oakland, with some as 

far as 75 miles away.

When there are more earthquakes, the chance of a large earthquake is greater and the chance of damage is greater. The USGS advises everyone to 

remain aware of the possibility of aftershocks, especially when in or around vulnerable structures (such as unreinforced masonry buildings) and landslide areas.

No one can predict the exact time or place of any earthquake, including aftershocks. The USGS can forecast how many earthquakes to expect or the 

chance of having an earthquake within a given time period.

Current USGS Aftershock Forecast

The USGS estimates the chance of more aftershocks within the next week until April 27, 2018, 4:18 p.m. PDT as follows:

• The chance of an earthquake large enough to feel (magnitude 3 or greater) is 98%, and it is most likely that as many as 150 such earthquakes may 

occur. This chance is 210 times greater than it was before the magnitude-7 Oakland earthquake occurred.

• The chance of an earthquake of magnitude 5 or greater is 40%, and it is most likely that 0 to 3 such earthquakes may occur.

• The chance of an earthquake of magnitude 6 or greater is 1 in 25 (4%), and it is most likely that 0 to 1 such earthquakes may occur.

• The chance of an earthquake of magnitude 7 or greater is 1 in 250 (0.4%); such an earthquake is possible but with a low probability.

• The chance of any damaging earthquake is 210 times greater than it was before the magnitude-7 Oakland earthquake occurred. 

The chance of earthquakes large enough to be felt or to cause damage remains elevated for the next month and the next year. The table provides 

forecasts for other time periods. 

The USGS calculates this earthquake forecast using a statistical analysis based on past earthquakes and the aftershocks recorded for this sequence. The 

forecast changes as time passes due to the decay in the frequency of aftershocks, larger aftershocks that may trigger further earthquakes, and changes in 

forecast modeling based on the earthquake data collected.

This advisory was issued: April 25, 2018, at 4:18 p.m.

This advisory will be updated on or before: May 2, 2018, at 4:18 p.m. 

HayWired Scenario Earthquake Aftershock Forecast—7-Day Update
108 The HayWired Earthquake Scenario—Earthquake Hazards

Scenario Aftershock Forecast 5—40 Days After the Mainshock

Forty days after the Haywired scenario mainshock, the expected number of aftershocks larger than M4 within the next week 

is one with a range of zero to two, and the seismicity over the next week is 30 times greater than what it would have been without 

the mainshock. There is a 0.8 percent (1 in 125) chance of an M6 or larger earthquake within the next week. In the scenario, a M6.2 

earthquake occurs in Palo Alto and triggers more aftershocks. The next page shows a sample a special aftershock advisory message 

following the M6.2 aftershock. Supporting information is summarized in table 10.

The HayWired scenario aftershock sequence continues as described above. The aftershock forecast would again be reset after the 

largest aftershock, a M6.4 earthquake in Cupertino, and an immediate forecast would be released. The HayWired scenario earthquake 

sequence does not contain the largest or most aftershocks of the simulated sequences. There are many more possible sequences, but we 

can estimate the likelihood that an aftershock sequence has a largest aftershock of M6.4.

Table 10. Aftershock forecast 40 days after the HayWired earthquake scenario mainshock (moment magnitude 7, April 18, 2018, at 4:18 

p.m.) for the next day, week, month, and year.

[The number of aftershocks is the expected or average value. The numbers in parentheses are lower- and upper-bound values associated with 5 percent and 

95 percent probability of not being exceeded. The aftershock probability is the probability of at least one aftershock at the given magnitudes at the time of the 

forecast. M, magnitude; %, percent; ≥, greater than or equal]

Forecast period Expected number of aftershocks Aftershock probability Increase in the seismicity rate relative 

M≥4 M≥5 M≥6 M≥7 to the rate before the mainshock

Next 24 hours 6 (4–9) 0–1 (0–2) 6% 0.06% 1,350

Next week 8 (7–18) 1 (0–2) 9% 0.9% 265

Next month 10 (8–30) 1 (0–4) 10% 1% 70

Next year 19 (17–40) 2 (1–5) 10% 1% 12.5
Chapter G. HayWired Scenario Aftershock Sequence 109

 SCENARIO----SCENARIO----SCENARIO----SCENARIO----SCENARIO

THIS IS NOT A REAL EARTHQUAKE OR REAL AFTERSHOCK FORECAST

Aftershock advisory from the United States Geological Survey (USGS) 

• An earthquake of magnitude 6.2 occurred at 4:57 p.m. PDT on May 28, 2018, near Palo Alto, California. The earthquake is a large aftershock 

following the magnitude-7 Oakland earthquake that occurred at 4:18 p.m. PDT on April 18, 2018. More earthquakes than usual will continue to 

occur in the mainshock and the Palo Alto areas. 

• Be ready for more earthquakes: Find the California Governor’s Office of Emergency Services (Cal OES) event page at http://www.caloes.ca.gov/). 

See Earthquake Country Alliance (ECA, http://www.earthquakecountry.org/sevensteps/) for advice on earthquake preparedness. See the Centers for 

Disease Control and Prevention (CDC, https://emergency.cdc.gov/coping/index.asp) for advice on coping with a disaster. 

• During the next week, there are likely to be 68 to 98 aftershocks large enough to be felt, and there is a 3 in 5 (56%) chance of one or more 

aftershocks large enough to potentially cause damage.

What to Expect

It is normal for a magnitude-7 earthquake of this size to cause an increase in the number of earthquakes (called aftershocks) in the area. The 

number of aftershocks will drop off over time, but a large aftershock can increase the numbers of earthquakes again, temporarily.

The aftershocks will occur mostly in the area affected by the magnitude-6.2 Palo Alto earthquake, within approximately 25 miles of Palo Alto, 

California, in addition to the area affected by the magnitude-7 Oakland earthquake, within approximately 35 miles of Oakland, California with 

some as far as 75 miles away.

When there are more earthquakes, the chance of a large earthquake is greater and the chance of damage is greater. The USGS advises everyone 

to remain aware of the possibility of aftershocks, especially when in or around vulnerable structures (such as unreinforced masonry buildings) 

and landslide areas.

No one can predict the exact time or place of any earthquake, including aftershocks. The USGS can forecast how many earthquakes to expect 

or the chance of having an earthquake within a given time period.

Current USGS Aftershock Forecast

The USGS estimates the chance of more aftershocks within the next week until June 4, 2018, 4:57 p.m. PDT as follows.

• The chance of an earthquake large enough to feel (magnitude 3 or greater) is greater than 99%, and it is most likely that 68 to 98 such earthquakes 

may occur. This chance is 265 times greater than it was before the magnitude-7 Oakland earthquake occurred.

• The chance of an earthquake of magnitude 5 or greater is 3 in 5 (56%), and it is most likely that 0 to 3 such earthquakes may occur.

• The chance of an earthquake of magnitude 6 or greater is 9 in 100 (9%).

• The chance of an earthquake of magnitude 7 or greater is 9 in 1,000 (1%); such an earthquake is possible but with a low probability.

• The chance of any damaging earthquake is 265 times greater than it was before the magnitude-7 Oakland earthquake occurred. 

The chance of earthquakes large enough to be felt or to cause damage remains elevated for the next month and the next year. The table provides 

forecasts for other time periods. 

The USGS calculates this earthquake forecast using a statistical analysis based on past earthquakes and the aftershocks recorded for this sequence. 

The forecast changes as time passes due to the decay in the frequency of aftershocks, larger aftershocks that may trigger further earthquakes, and 

changes in forecast modeling based on the earthquake data collected.

This advisory was issued: May 28, 2018, at 4:57 p.m.

This advisory will be updated on or before: June 4, 2018, at 4:57 p.m. 

HayWired Scenario Earthquake—Special Aftershock Advisory due to Magnitude-6.2 Aftershock Scenario
110 The HayWired Earthquake Scenario—Earthquake Hazards

Probability of Largest Aftershock

The likelihood of an aftershock sequence having a largest 

aftershock of M6.4 or larger for an M7.0 mainshock can be 

estimated from Lombardi’s (2002) statistical study of the 

difference (D1) between the magnitude of the mainshock (M=7.0) 

and the largest aftershock (M1=6.4)—that is, D1=M–M1. Lombardi 

reports that D1 has been observed not to be exponential and 

in one dataset was observed to be adequately fit by a normal 

distribution. Using probability theory, Lombardi shows how the 

expected value of D1 can be anywhere from 0.5 to 1.2, and how 

D1 approaches the exponential distribution in theory only under 

conditions that do not seem to apply to the HayWired earthquake 

sequence. Lombardi examines an earthquake catalog compiled by 

the Southern California Earthquake Center (SCEC) of earthquake 

clusters between 1990 and 2001. For a subset of the data with 

mainshock magnitude greater than or equal to 4.0 and including 

aftershocks of magnitude at least 2.0, Lombardi finds an expected 

value of D1, denoted here by m, of 1.2, as Richter (1958) had 

previously pointed out from work by Båth (1965), with a standard 

deviation of s=0.65. Here, D1=7.0–6.4=0.6, which is within the 

m±1s bounds (that is, 0.6 is within 1.2±0.65 or 0.55 to 1.85). If we 

assume m=1.2 as Båth suggested, s=0.65 as in the SCEC subset 

with M0≥4.0, and normally distributed D1, we can estimate the 

probability that the largest aftershock would be at least M6.4 (that 

is, that D1 would be at most 0.65) as in the selected HayWired 

aftershock simulation (see equation 1):

 

 

 (1)

In equation 1, F denotes the cumulative standard normal 

cumulative distribution function. Equation 1 produces a 

probability Pnormal=18 percent, that is, one can estimate that 1 in 

5 earthquakes of M7.0 would produce an aftershock larger than 

the largest one hypothesized here. If instead we assume D1 is 

exponentially distributed with expected value 1.2, we can estimate 

the nonexceedance probability Pexp as shown in equation 2:

 

 (2)

Equation 2 produces a probability Pexp=0.39, that is, 2 in 5 

earthquakes of M7.0 would produce an aftershock larger 

than the largest one hypothesized here. In either case, the 

HayWired scenario aftershock sequence does not seem to be 

an extreme example or anywhere near a worst case.

Limitations of Aftershock Sequence 

Simulation and Forecasting

Simulations and forecasts of aftershock sequences are 

based on spatial and temporal earthquake sequence statistics. The 

results are affected by the simulation parameters (for example, 

average aftershock rates). Previous earthquake sequences and 

a comparison of the number of aftershocks for M5.5 or larger 

earthquakes in California suggests that average aftershock rates 

may be lower in northern California than in southern California, 

although rates can vary widely in both regions (Llenos and 

Michael, 2015). Rates at which aftershocks occur can even vary 

quite widely for closely colocated mainshocks. More scientific 

investigation is required to explain this substantial variability. 

Further research and modeling may improve both our ability to 

make more accurate forecasts immediately after a large earthquake 

has occurred and our ability to make forecasts when early data for 

an earthquake sequence are unreliable.

More sophisticated aftershock modeling is needed to mesh 

aftershock forecasting with traditional fault and stress-renewalbased seismic-hazard models. The Uniform California Earthquake 

Rupture Forecast, version 3 (UCERF3) epidemic-type aftershock 

sequence (ETAS) is developing this capability (Field and others, 

2013). For instance, the working group is attempting to include 

spatiotemporal earthquake clustering by merging a point process 

ETAS model (Ogata, 1988) into the finite-fault based framework. 

The ultimate aim is to deploy UCERF3 as part of an operational 

earthquake forecast, although doing so will require additional 

work with respect to real-time network interoperability. 

The forecasts provided here convey the type of information 

that is available in an earthquake forecast, but the message 

template used above is under development. Specifically, 

communication of aftershock forecasts with multiple intended 

audiences uses (1) communication guidelines from social and 

behavioral science, (2) studies of implemented aftershock forecast 

communications, and (3) message testing.

Conclusion

Although it is not possible to predict the location, 

magnitude, and timing of earthquakes, scientists can forecast 

frequency and probability of aftershock magnitudes within 

confidence intervals for time windows. Statistical relations can 

be used to simulate earthquake sequences over time and space 

as an approach to describing elevated levels of earthquake 

risk. The devastating outcomes of recent earthquake sequences 

in New Zealand and Italy have demonstrated the need for 

scientific and transparent information about earthquake 

risk (Jordan and others, 2014). The HayWired scenario 

earthquake sequence offers an opportunity to investigate the 

possible uses of earthquake forecasts and consequences of 

aftershocks before a large earthquake occurs. A description 

P D

0.6 1.2

μ

0.65

0.18

normal

1

σ

= Φ −

= Φ −

=

P D 1 exp

1 exp

0.6

1.2

0.39

exp

1 = − −

= − −

=

μ
Chapter G. HayWired Scenario Aftershock Sequence 111

of one simulated aftershock sequence and sample aftershock 

forecasts are provided to expose stakeholders and decision 

makers to information that will be produced and communicated. 

This allows future recipients of earthquake forecasts the 

time for understanding the information and its limitations; 

considering its use in mitigation, preparedness, response, and 

recovery decisions; and providing feedback on how to improve 

communication products and processes. Finally, the aftershock 

scenario timeline and ShakeMaps can be used to investigate 

the implications of a series of damaging earthquakes and to 

hold exercises that practice resetting response and recovery 

throughout the duration of an earthquake sequence.

Acknowledgments

USGS Earthquake Science Center (ESC) scientists 

Jeanne Hardebeck, Ruth Harris, Morgan Page, Andrew 

Michael, Jack Boatwright, and Shane Detweiler provided 

crucial input on various aspects of the HayWired scenario 

aftershock sequence and earthquake forecast statistics. 

We are indebted to Ruth Harris and Morgan Page for their 

constructive review of the manuscript. Tim MacDonald 

(USGS Earthquake Science Center IT Specialist) generated 

the aftershock ShakeMaps. This construction of the aftershock 

sequence was funded by the following USGS components: 

Land Change Science, Science Application for Risk Reduction 

(SAFRR), and ESC. 

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Chapter H

HayWired Scenario Three-Dimensional Numerical 

Ground-Motion Simulation Maps

By Keith A. Porter1

The HayWired Earthquake Scenario—Earthquake Hazards

Edited by Shane T. Detweiler and Anne M. Wein

Scientific Investigations Report 2017–5013–A–H

[Also see https://doi.org/10.3133/sir20175013]

Abstract

The HayWired scenario examines a hypothetical 

earthquake (mainshock) with a moment magnitude (Mw) of 

7.0 occurring on April 18, 2018, at 4:18 p.m. on the Hayward 

Fault in the east bay part of California’s San Francisco Bay 

area. The HayWired scenario uses a three-dimensional (3D) 

numerical simulation for its ground-motion map to produce 

a more realistic estimate of damage and loss at both the local 

scale and in the aggregate. There are two common ways to 

create a ground-motion map for a hypothetical earthquake—(1) 

use ground-motion prediction equations (the more common 

of the two) or (2) use 3D ground-motion simulation. Each 

has advantages and disadvantages. The one based on groundmotion prediction equations (referred to here as a median map) 

usually only shows median shaking, that is, the 50th percentile. 

The median is a measure only of central value in a suite of 

many earthquakes, so a median map provides no information 

about real-world variability expected in a specific earthquake. 

Reflecting that variability can make a big difference in estimates 

of damage and loss, commonly increasing it over what one 

would estimate solely from the median. The reasons have to do 

with the skewed real-world distribution of shaking and with the 

upward-curving relation between shaking and damage at lower 

levels of shaking. It also has to do with spatial correlation in 

ground motion. 

For the HayWired scenario, the 3D model deals with 

uncertainty by conditioning on one particular hypocenter, one 

realization of slip distribution, and one particular simulation of 

high-frequency motion using a semistochastic approach without 

averaging over many possible outcomes. The reader will see 

how using just one outcome actually does capture two important 

aspects of uncertainty. There are other ways to construct 

and use a ground-motion map for a hypothetical earthquake. 

This work does not address other uses, such as probabilistic 

seismic hazard analysis, probabilistic seismic risk analysis, or 

1

University of Colorado Boulder.

structural design, nor does it deal with assessing hazard in the 

sense of a probabilistic relation between ground motion and 

exceedance frequency. Rather, the HayWired scenario aims to 

provide a single outcome in terms of shaking, damage, and loss 

that nonetheless realistically depicts uncertainty and avoids 

a low bias in estimated outcomes. It is intended to explain to 

engineers, planners, and other non-Earth-scientist readers who 

may be more familiar with median maps why the 3D maps 

look different and why the HayWired scenario has used the 3D 

method rather than a median map. 

Introduction—Two Valid Methods to 

Create a Ground-Motion Map 

The HayWired scenario examines a hypothetical earthquake 

(mainshock) with a moment magnitude (Mw) of 7.0 occurring on 

April 18, 2018, at 4:18 p.m. on the Hayward Fault in the east bay 

part of California’s San Francisco Bay area. The developers of 

the HayWired scenario have found that there are some unfamiliar 

features of three-dimensional (3D) numerical simulation that could 

alarm engineers and emergency managers who are familiar with a 

more frequently used approach.

This chapter is intended to inform engineers, emergency 

planners, and other consumers of earthquake-scenario groundmotion maps about the value and validity of 3D numerical 

simulation of ground motion for use in depicting realistic losses 

in a scenario earthquake. Bear in mind that a scenario (as the 

term is used here) depicts one realistic outcome of earthquake 

rupture, shaking, structural response, physical damage, and 

social and economic loss. It generally does not quantify and 

propagate uncertainties. In this way, a scenario differs from 

probabilistic seismic-hazard analysis or probabilistic seismicrisk analysis. As this work will show, a scenario developer still 

needs to account for some aspects of uncertainty, for at least two 

reasons—(1) to depict ground motion in a way that resembles 

maps of shaking in real earthquakes and (2) to minimize bias in 

the estimate of damage and loss. 
116 The HayWired Earthquake Scenario—Earthquake Hazards

One way to create an earthquake-scenario groundmotion map is to use three-dimensional (3D) numerical 

simulations (as in Aagaard and others 2010a, b). This 

approach uses a computer model to simulate how seismic 

waves travel from a fault rupture through the Earth’s 

crust and up through the surface soil to shake the base of 

buildings. It accounts for earthquake magnitude, the details 

of the rupture, the distance and path seismic waves must 

travel, the characteristics of the Earth’s crust along that path, 

and the soil conditions near the surface of the Earth. Let 

us refer to this as the 3D method. The 3D method reflects 

spatial variability in ground motion, which is a real, observed 

effect in which some places shake much more strongly and 

others much more weakly than the average site at the same 

distance from a fault. A 3D model uses a mathematical model 

of the fault, the Earth’s crust, and the soil near the surface of 

the Earth, along with laws of mechanics having to do with 

stress and strain, mass and acceleration, and the way waves 

behave to estimate earthquake shaking. 

The technical reader may be curious about how the 

3D model actually accounts for variability. Modelers use 

several approaches to address variability in ground motion 

conditioned on the fault segment that is assumed to rupture 

in an earthquake of a given magnitude. In the present case, 

Aagaard and others (2010a, b) produced stochastic (random) 

simulations of the fault-slip distribution and simulated 

ground motion for each of a few hypocentral locations along 

a fault. (Slip distribution can vary over the fault surface for 

a given segment and magnitude.) They simulated rupture 

speed and rise time, which are important parameters that 

affect ground motion and which vary for a given magnitude 

and location. The low-frequency content of ground motion 

(less than 1 hertz, Hz) at each grid point on a map is then a 

deterministic (that is, nonrandom) outcome of the simulated 

parameters and of the velocity model of the crust in the region. 

High-frequency motion is simulated using a semistochastic 

method proposed by Graves and Pitarka (2010) in which a 

source spectrum is assumed, white noise is filtered to match 

that spectrum with random phase, and simplified Green’s 

functions are used to propagate the high-frequency motion 

from subfault segments to the ground surface at each grid 

point. The numerical simulation used in Aagaard and others 

(2010a, b) and adopted here does treat nonlinearity in site 

amplification; see Graves and Pitarka (2010) for details. The 

two processes produce a low- and a high-frequency time series 

at each grid point, which are then summed. The time series 

can then be analyzed to produce the various ground-motion 

parameters at each grid point—peak ground acceleration, 

spectral-acceleration response at any period and damping 

ratio of interest, and so on. Aagaard and others (2010a, b) 

simulated ground motion at each of 52,118 grid points spaced 

approximately 1.85 kilometers (km) apart. 

Note that there is more than one approach to numerical 

simulation. The simulation used in HayWired uses a 

kinematic rather than dynamic calculation of the earthquake 

rupture. The time history of the rupture is not constrained 

to be physically self-consistent, so the simulation used here 

cannot be properly called completely physics-based. Let 

us henceforth refer to a map generated by 3D numerical 

simulation with semistochastic high-frequency motion, 

whether using a kinematic or dynamic rupture model, as a 

3D numerical-simulation map. 

Another way to create a ground-motion map 

is to use a mathematical model variously called an 

empirical ground-motion prediction equation (GMPE) 

or an attenuation relation. A GMPE is produced by 

fitting a smooth curve to ground-motion data observed 

in earthquakes of various magnitudes, rupture types, 

propagations distances, and site conditions. It accounts 

for magnitude, distance from a fault rupture to each site 

of interest, certain other features of a fault rupture and 

path from the rupture to the site, and the soil conditions 

near the surface of the Earth. It smooths out the variability 

by fitting a curve to past observations, typically to the 

natural logarithm of observed motion. Developers of 

GMPEs also commonly offer estimates of variability in 

the form of the standard deviation of the natural logarithm 

of the residual, usually in two parts—inter-event and 

intra-event variability. See for example, Abrahamson and 

others (2014) for a recent example of the Next Generation 

Attenuation Relationships for Western United States 

(NGA-West2) GMPEs. That uncertainty can be easy to 

overlook. Most scenario ground-motion maps (that is, 

ground-motion maps for hypothetical earthquakes) only 

show the median shaking. Let us refer to a map of median 

ground motion as a median map. Variability data are often 

available in addition to the median map but in a lessaccessible form. For example, the U.S. Geological Survey 

(USGS) provides a raw grid-data download file for the Mw

7.7 1999 Chi-Chi, Taiwan, earthquake. The file contains 

comma-separated-values (.csv) data of estimated ground 

motion at 5,900 grid points. Each line in the text file 

contains latitude, longitude, various measures of estimated 

ground motion—including peak ground acceleration 

(PGA), peak ground velocity (PGV), Modified Mercalli 

Intensity (MMI)— and two measures of uncertainty—the 

standard deviation of PGA and the standard deviation 

of PGV. The uncertainty information is not presented 

graphically. There is no map that quantifies uncertainty in 

the same units as the median map, so it is less accessible 

than the median map. For brevity, let us refer to a scenario 

loss estimate that assumes shaking uniformly takes on 

its median ground value as estimated by ground-motion 

prediction equations as the median method. 
Chapter H. HayWired Scenario Three-Dimensional Numerical Ground-Motion Simulation Maps 117

The median of any variable, whether it is an estimate of 

the ground motion at a site or some other variable, such as 

the household income of an arbitrary household in the United 

States, measures central tendency. It refers to the value that 

has a 50-percent chance of being exceeded—half of the 

observations are above it, half below.

In this chapter, let us compare the use of a 3D 

numerical-simulation map with a median map generated by 

ground-motion prediction equations, focusing on their use in 

scenario loss estimation, and in particular their treatment of 

variability. The median by definition provides no information 

about variability—for example, the median map does not tell 

one how much higher the ground motion is with 90-percent 

nonexceedance probability or how much lower is the 10th 

percentile of motion. Average is another measure of central 

tendency, distinct from the median. In the case of earthquake 

motion, the median is generally lower than the average. Like 

the median, the average by definition contains no information 

about variability. 

In the case of ground motion shown on a median map, 

the quantity shown at any arbitrary location is the value 

that has 50-percent probability of being exceeded, given 

an earthquake’s magnitude, distance, site conditions, and 

various attributes of the fault rupture and direction from the 

rupture to the site. The quantity shown at the same location 

on a 3D map is not a fixed percentile—it could be near 

the median or it could be lower or higher, according to the 

probability distribution of ground motion at that site. That 

probability distribution is more constrained—it tends to be 

narrower—than if one used a GMPE. It is constrained by the 

additional information that a 3D simulation entails but that 

a GMPE does not—slip distribution, hypocenter, and other 

fault-rupture propagation features, along with the mechanical 

properties of the Earth’s crust and soil along the path 

between rupture and the ground surface. 

In a scenario, it is not particularly important to show the 

ground motion with a specified percentile of exceedance. A 

3D map shows variability in ground motion—in some places 

motion is higher than the median; in others, near the median; 

and in still others, lower than the median. As a result, the 3D 

map looks blotchy, irregular, and asymmetric. Contours on 

median maps tend to look like concentric hot dogs aligned 

around a fault rupture, as shown in figure 1A. Contours in 

ground-motion maps that use 3D models tend to be more 

irregularly shaped, as shown in figure 1B, and more closely 

resemble observations in real earthquakes.

Both 3D and median approaches to creating earthquakescenario maps are valid and well respected among Earth 

scientists. ShakeMaps (the USGS product) are intended 

for “post-earthquake response and recovery, public and 

scientific information, as well as for preparedness exercises 

and disaster planning,” (U.S. Geological Survey, 2015). The 

developers of 3D maps offer them for use “in estimating the 

seismic hazard and the ground motions they can generate,” 

as Aagaard and others (2010) explain in their introduction. 

In short, both approaches can and have been used to 

inform emergency preparedness. Each has advantages 

and disadvantages. Three-dimensional models account for 

knowledge of a fault’s characteristics and the local geology 

but are computationally very demanding and have difficulty 

modeling high-frequency motion (the part of the earthquake 

motion that resonates with shorter buildings) with the same 

rigor as low-frequency motion (the part of the earthquake 

motion that resonates with taller buildings).

The median approach is based on past observations. 

In a sense, it considers the physics of the seismic setting, 

partitioning data by focal mechanism (for example, strikeslip, normal, reverse, and unspecified) and presetting some 

parameters such as site and anelastic coefficients, but 

the GMPEs on which the median approach is based are 

essentially regression analyses of past observations, albeit 

with modest constraints. The reader might assume that the 

closer resemblance of a 3D map to real earthquakes results 

from the use of site-specific averaged shear-wave velocity 

to a depth of 30 meters, VS30, alone, which median maps can 

(and do) also consider. However, recall that the hypocentral 

location, heterogeneous slip distribution, rise time, rupture 

speed, and crustal velocity model that are crucial to the 3D 

model have nothing to do with VS30, are not parameters of 

GMPEs, and therefore are not reflected in median maps 

created using GMPEs. 

Skewness in Ground-Motion Intensity

Frequent users of ground-motion maps may be surprised 

by the difference between the outputs of the two methods. 

The difference is largely the result of how the 3D model 

reveals spatial variability, whereas the median maps do not. 

Maps of shaking in real earthquakes tend to look more like 

the 3D model. It is largely for that reason that the HayWired 

and ShakeOut scenarios use the 3D model (see Jones 

and others, 2008, for more information on the ShakeOut 

scenario). 

There is a subtler but equally important reason for using 

a 3D ground-motion map for a disaster scenario—it will 

tend to produce estimates of damage and loss that are more 

realistic and higher in aggregate than if one only considers 

median shaking. This occurs for two reasons. First, as 

previously noted the average shaking tends to be higher than 

the median shaking. This is because the observed distribution 

of shaking tends to be skewed toward higher values (like 

a lognormal distribution) compared to the symmetric 
118 The HayWired Earthquake Scenario—Earthquake Hazards

Porter_fig1a

0 10 20

10 20 30 40

30 40 50 MILES

0 50 KILOMETERS

A

38°

37°

123° 122°

Base map from Google Earth; image Landsat/Copernicus; data LDEO-Columbia, NSF, NOAA; data SIO, NOAA, U.S., Navy, NGA, GEBCO, 2015.

Figure 1. Comparison of ground-motion maps of the San Francisco Bay region, California, for a hypothetical moment magnitude (Mw) 

7.0 Hayward Fault earthquake, like that in the HayWired earthquake scenario, produced using different approaches. A, ground-motion 

map of median 0.3-second, 5-percent damped elastic spectral-acceleration response calculated using ground-motion prediction 

equations. B, ground-motion map of median 0.3-second, 5-percent damped elastic spectral-acceleration response created using a 

three-dimensional ground-motion simulation. Red line, HayWard Fault rupture; Sa, spectral acceleration; sec, second; %, percent; g, 

acceleration due to gravity. [A was calculated by Open Source Seismic Hazard Analysis (OpenSHA, http://www.opensha.org) Scenario 

ShakeMap calculator version 1.3.1, using the Uniform California Earthquake Rupture Forecast, version 3 (UCERF3, http://www.wgcep.

org/ucerf3), Hayward-all rupture index 0; NGAWest2 2014 averaged attenuation relation; 50-percent exceedance probability; average 

horizontal motion; and the Wills and Clahan, 2006, time-averaged shear-wave velocity to a depth of 30 meters, VS30, model.] Groundmotion images created by Keith A. Porter, University of Colorado Boulder.)
Chapter H. HayWired Scenario Three-Dimensional Numerical Ground-Motion Simulation Maps 119

Figure 1.—Continued

Porter_fig1b

B

0 10 20

10 20 30 40

30 40 50 MILES

0 50 KILOMETERS

38°

37°

123° 122°

Base map from Google Earth; data SIO, NOAA, U.S., Navy, NGA, GEBCO; image Landsat; data MBARI; data LDEO-Columbia, NSF, NOAA, 2015.
120 The HayWired Earthquake Scenario—Earthquake Hazards

Figure 2. Graph of probability density versus earthquake-shaking 

intensity showing an example of normal and lognormal distributions. 

Both curves have the same average and standard deviation, with a 

ratio of standard deviation to average (the ratio is called the coefficient 

of variation) that is characteristic of ground-motion prediction 

equations. Notice how the normal distribution is symmetrical—it 

looks the same on the left side as on the right—but the lognormal is 

skewed to the right. The height of each curve measures how likely it is 

to produce a given shaking intensity value. Note that shaking intensity 

has a lower limit of zero, so the figure truncates the normal distribution 

below at zero, otherwise its symmetry would be even more obvious. 

bell-shaped curve (called the normal distribution). That 

means that the probability of high shaking intensity is higher 

in the skewed distribution and that the probability of low 

shaking is smaller (fig. 2).

Nonlinearity in Damage Aggravates the 

Damage Estimate

The second reason that 3D maps yield higher and more 

realistic loss estimates is that the skewed distribution of shaking 

is compounded with another common feature of damageability 

called nonlinear fragility. Together, the skewed distribution and 

nonlinear fragility aggravate damage above what one would 

expect based solely on median shaking. By “nonlinear fragility” 

I mean that failure probability increases nonlinearly with shaking 

intensity—the failure probability curves upward when plotted 

against shaking intensity. Figure 3 illustrates this point, for 

example, where a 50-percent increase in shaking intensity results 

FIgure 3. Graph showing that fragility functions commonly curve 

upward at lower values of earthquake-shaking intensity. As a result, 

a little higher shaking can produce a much higher failure probability, 

compared with how a little lower shaking reduces a somewhat lower 

failure probability. The lower and higher values of shaking intensity, 

0.67x and 1.5x, are about equally likely if the median shaking is x, which 

means the failure probabilities of 0.2y and 4y are equally likely, and the 

average or expected failure probability will tend to be higher than y. Porter_fig2

Shaking intensity

0

0

Probability density

Normal

Lognormal

in a four-fold increase in failure probability.) Of course, there 

are more-fragile buildings and less-fragile buildings—this is not 

meant to imply that shaking is all that matters to damage—but 

aside from the degree of fragility, most buildings and components 

are modeled as having a fragility function that resembles figure 3, 

with its attendant nonlinearity.

Spatial Correlation

Spatial correlation in ground motion may further aggravate 

the damage where development is concentrated in areas of more 

intense shaking. Spatial correlation in ground motion means 

that two nearby sites tend to experience similar shaking, which 

means that if a building experiences shaking that is somewhat 

higher than average, so will a nearby building. On average, 

both can be expected to experience greater damage than if they 

both experienced average shaking, so the sum of the damage 

to the two buildings can be expected to be greater than if they 

both experienced average shaking. Similarly, if one building 

Porter_fig3

0.67x x

0.2y

4y

y

1.5x

Shaking intensity

Failure probability
Chapter H. HayWired Scenario Three-Dimensional Numerical Ground-Motion Simulation Maps 121

experiences shaking that is somewhat lower than average, so will 

the other building, and both can be expected to experience lower 

damage (individually and in total) than if both had experienced 

average shaking. The closer together the two buildings are, the 

more similar their ground motion, and the more likely that both 

will experience higher than average shaking and damage or lower 

than average shaking and damage. 

Suppose by contrast there were no spatial correlation in 

shaking (that is, conditioned on the median and logarithmic 

standard deviation of shaking at two buildings); suppose 

the damage at one building were independent of damage at 

another. If one building experienced higher shaking than 

average, the other could experience average shaking, higher 

than average shaking, or lower than average shaking. On 

average, with a large number of buildings, there would be 

as many buildings experiencing higher than average shaking 

and damage as buildings experiencing lower than average 

shaking and damage. The differences from average tend 

to cancel out as the number of buildings increases. This 

general phenomenon is called the law of large numbers. In 

the absence of spatially correlated ground motion, the law 

of large numbers guarantees that the more buildings there 

are, the closer and closer to average will be the total damage. 

However, because spatial correlation does exist, the law of 

large numbers does not hold. As a consequence, even with 

large numbers of buildings, the uncertainty in the aggregate 

loss does not vanish. The greater the spatial correlation, the 

less the aggregate loss will tend to approach the average. 

Spatial correlation reaches tens of kilometers in real 

earthquakes, so buildings in an area larger than the City of 

San Francisco can all experience above-average shaking in the 

same earthquake. Because of spatial correlation, a community 

can expect to either “win” big (if one can be said to win at all 

in a powerful earthquake), with generally lower than expected 

ground motion throughout the community, or “lose” big, 

with generally higher than expected motion throughout the 

community.

Extreme Values in 3D Modeling

Three-dimensional models in general and the HayWired 

ground-motion map in particular can exhibit features that differ 

significantly from median maps. Long-period (1-second) pseudospectral-acceleration response (denoted PSA10 here) can exceed 

short-period (0.3-second) pseudo-spectral-acceleration response 

(denoted PSA03 here), which is contrary to what is typically 

observed in earthquakes. PSA10 is commonly about 40 percent 

as strong as PSA03, but it is possible for PSA10 to be greater than 

PSA03. This is a real, observable phenomenon. For example, in 

the 1994 Northridge, California, earthquake, that happened at 13 

of the 180 ground-motion recording stations listed in a USGS 

ground-motion data archive for the earthquake (U.S. Geological 

Survey, 2009a). In the 1989 Loma Prieta, California, earthquake, 

it happened at 20 of the 84 stations listed at that event’s ShakeMap 

archive (U.S. Geological Survey, 2009b). Sometimes the ratio 

PSA10 to PSA03 can greatly exceed 1.0. In two of the stations 

in the Northridge earthquake, stations LPU and PAR, the ratio 

reached 1.8:1, and in one station in the Loma Prieta earthquake, 

station A02, the ratio reached 3:1.

In the August 24, 2014, South Napa, California, earthquake 

(U.S. Geological Survey, 2014), station NP.1765 component 

HNE recorded PSA10=1.02 of the acceleration due to gravity 

(g) and PSA03=0.58 g, a ratio of 1.8:1. This appears to have 

been the highest recorded value of PSA10 of all the stations in 

the epicentral region. Its component HNN similarly recorded 

PSA10=0.99 g and PSA03=0.61 g, a ratio of 1.6:1. (Other stations 

in the epicentral region recorded more familiar-looking ratios; for 

example, the station that recorded the highest value of PSA03, 

station NC.N019B component 01.HNE, recorded PSA10=0.57 g

and PSA03=1.17 g, a ratio of 0.49:1. The next-highest PSA03 

value in the epicentral region, recorded by NC.HNC component 

HNN, was PSA03=1.08 g and PSA10=0.35 g, a ratio of 

0.32:1. However, the point is that, although it less typical for 

PSA10>PSA03, it is still not very rare.)

Some of these effects may be due to a long-period pulse or 

another explainable phenomenon. In fact, it is likely that none of 

the observed motion is truly inherently random if one could know 

and model the focal location and mechanism, slip distribution, 

rupture velocity, rise time, crustal-velocity structure, surficial 

geology, and other physical quantities of the fault rupture and 

the region. That is the point of the numerical ground-motion 

simulation—to explicitly model and to provide insight into these 

issues rather than to treat them as hidden variables outside the 

frame of a model that uses GMPEs and treats these effects with 

catch-all residual variability parameters.

In addition to high ratios of PSA10 to PSA03, the absolute 

amplitudes of PSA10 and PSA03 can sometimes be very high, 

both in 3D simulations and in real earthquakes. In the HayWired 

3D map, one location is estimated to experience PSA10=4.1 g. 

This is very unusual but not impossible. The site in HayWired 

has very soft soil, characterized by a 180 meter per second 

(m/s) average shear-wave velocity in the top 30 meters (m) of 

soil (denoted by VS30). Two stations in the September 20, 1999, 

moment magnitude (Mw) 7.6 Chi-Chi, Taiwan, earthquake, 

CHY080 and TCU084, experienced 5-percent damped 

PSA response near 1.0-second (s) period of 4.0 g and 3.7 g, 

respectively, both on VS30=665 m/s soil, according to Pacific 

Earthquake Engineering Research Center (2015). (At exactly 

1.0-s period, their 5-percent damped pseudo spectral acceleration 

response values were 2.94 g and 2.70 g, respectively.)

It is informative to estimate what would have been the ground 

motion at these stations had they been on VS30 =180 m/s soil, as at the 

HayWired site just mentioned. A national standard design document 

used by engineers in the United States, ASCE 7-10 (American 

Society of Civil Engineers, 2010), offers a model to estimate the 

amplification. It offers a parameter called “site coefficient Fv,” 

which relates 1-second spectral acceleration response shaking on 

soil with one value of VS30 to shaking on soil with a different value 

of VS30. Fv varies nonlinearly with shaking but saturates at high 

levels of shaking—meaning that above a specified level of shaking 
122 The HayWired Earthquake Scenario—Earthquake Hazards

(PSA10≥0.65 g in the case of VS30=600 m/s), Fv does not change. 

The data on which that model is based are entirely from lower levels 

of shaking, so to use Fv at the PSA10 values under consideration 

here requires one to assume that the model also applies at much 

higher levels of shaking. Let us assume for the moment that one 

can use that model (there being no apparently viable alternative). 

Using that model, the ASCE 7-10 model says that one would expect 

the softer soil to have experienced greater amplification that would 

have increased the motion on a site with VS30=180 m/s between 15 

and 85 percent compared with a site with VS30=600 m/s. (The reason 

for the range is that ASCE 7-10 offers one amplification value for 

soil with 180≤VS30≤360 m/s and another for soil with VS30≤180 m/s, 

so the HayWired site of interest is on the boundary.) Taking the 

average of the two suggests an expected value of amplification of 

about 50 percent compared with 600 m/s. That suggests that had the 

two instruments been on soil with VS30=180 m/s, an engineer using 

ASCE 7-10 would estimate that the peaks of the response spectra 

of CHY080 and TCU084 near 1.0-s period would have been 6.0 g

and 4.4 g, respectively. At exactly 1.0 s, one would estimate spectral 

acceleration response value of 4.4 g and 4.0 g, respectively. The 

point is that, with a caveat arising from extrapolating Fv, the estimate 

of PSA10=4.1 g from 3D numerical simulation is consistent with 

two Chi-Chi earthquake records of 4 g and 3.7 g near 1.0-s period, 

which were recorded on stiffer soil.

Graves (written commun., July 24, 2015) offers another 

caveat for the HayWired ground motion map. He warns “that 

the value simulated in the 3D calculation is based on a very 

simple model for non-linear soil response, which is not well 

calibrated for soft soil sites at such large motions.”

Close to the rupture, the South Napa earthquake produced 

short-period (0.3-s) spectral-acceleration response values that 

were much higher than the median calculated value. Farther 

away, motions were much lower than the median (calculated 

using the average of the four 2008 next-generation attenuation 

relations). Figure4A shows that some locations close to the 

rupture approached or reached life-safety design-level shaking 

for ordinary low-rise buildings (SDS in the language of ASCE 

7-10, which defines minimum design loads for buildings and 

other structures; American Society of Civil Engineers, 2010). 

Figure4B shows that within 20 km of the fault, observed 0.3-s 

motions were on average 1.25 to 2.25 higher than the median, and 

in some locations observed 0.3-s motions were as high as 5 times 

the median calculated value for an earthquake of this magnitude 

at the given distance. Farther from the fault, motions were below 

half the median. According to one seismologist who examined the 

USGS’s “Did You Feel It” data, the South Napa records were not 

very unusual (Susan Hough, USGS, written commun., September 

28, 2014). Thus, the South Napa earthquake highlights that it is 

not very unusual for some locations in an earthquake to experience 

shaking much higher than the median (fig. 4). All this is to show 

that real, not-very-unusual earthquakes can produce shaking more 

like 3D simulations than like ShakeMap medians.

These effects—high PSA10 and high ratios of PSA10 to 

PSA03—are unusual but as shown in figure 5 not improbable. 

They can be caused by constructive interference. For example, 

when waves from one part of the fault rupture far from the site 

arrive at the site and waves generated by a closer part of the 

rupture reach the same site at the same time. The two waves 

add, increasing the amplitude of ground motion at the site.

Very strong motion can also be caused by basin effects, 

where seismic waves traveling at high velocity through the 

rock of the Earth’s crust refract and slow when entering a 

basin of soft, sedimentary soil. This increases the amplitude 

of the waves in the same way that water waves slow and grow 

to great height when they reach the shore. Furthermore, the 

abrupt change in density between the rock and basin soil at the 

basin boundary can cause seismic waves to reflect, trapping 

energy in the basin for a longer time and extending the 

duration of shaking.

The reader should also consider the nature of extreme values 

in large samples. Even if we do see one or two records with very 

strong motion, the records are very rare, which might seem to 

suggest that the strong motions observed in the ground-motion 

map are very unlikely. However, this is not necessarily the case. In 

any given earthquake, the records available to us number at most 

in the low hundreds. Close to a fault the number is even lower—

the Pacific Earthquake Engineering Research Center (PEER) 

ground-motion database contains observed ground-motion 

records from only 59 stations in 8 earthquakes of M7 or greater 

and distance less than 10 km (Pacific Earthquake Engineering 

Research Center, 2015). By contrast, the HayWired groundmotion map contains simulated ground-motion records at 52,000 

locations centered about the 83-km-long rupture and spaced every 

1.8 km. That means that from this single scenario earthquake 

alone, the numerical ground-motion simulation provides 103×20/

(1.8×1.8)=635 simulated ground-motion records within 10 km of 

the fault, more than 10 times the available observations from all 

stations within 10 km of a fault in all earthquakes of magnitude 7 

or greater in the PEER database. In any population of uncertain 

quantities, the fewer samples or specimens one observes, the 

smaller the maximum value is likely to be, so it should not be 

surprising that the maximum ground motion in the PEER database 

is smaller than the maximum ground motion in the HayWired 3D 

ground-motion map. 

More real, observed ground-motion records would be 

welcomed, but it is more important to note that the extreme values 

in the existing database of real observations will tend to give a 

false impression of the maximum shaking that an earthquake of 

M7 can produce. That false impression is a necessary outcome 

of the size of the existing database. That there is no real physical 

limit on the upper bound of ground motion is also a necessary 

outcome of the size of the existing database. However, with only 

59 recordings, there is no guarantee that the maximum observed 

motion in the PEER database is the maximum that is physically 

possible; most likely the contrary is true.

Here is an analogy to illustrate the problem of judging 

extreme values from a limited data set. Pick 10 U.S. residents 

at random. Chances are the oldest will be about 72 years old. 

Pick 100 U.S. residents, and the oldest will be about 86 years 

old. Among 6,000 U.S. residents, one would find someone 100 

years old. Among 6 million people one would find someone 

110 years old. The same basic principle applies here—having 
Chapter H. HayWired Scenario Three-Dimensional Numerical Ground-Motion Simulation Maps 123

Figure 4. Graphs showing observed 

and calculated short-period 

(0.3-second) spectral-acceleration 

response (the part of the earthquake 

motion that a low-rise building would 

experience) in the August 2014 

South Napa, California, earthquake. 

Triangles are observations of shaking 

at 315 strong-motion instruments. A, 

Observed motions were much higher 

than median where it matters most, 

close to the epicenter. Triangles 

above the diagonal line are higher 

than would be calculated. Some 

motions reached design-level 

shaking of 1.3 of the acceleration 

due to gravity (g), where the median, 

maximum-component calculated 

motion would have been 0.28 g. B, 

The ratio of observed to median 

motions plotted against distance 

from the rupture. Motions within 

20 kilometers (km) of the epicenter 

were 1.25 to 2.25 times higher than 

the median calculated value and in 

some cases 3 to 5 times higher. Red 

dots and error bars show averages 

and ±1 standard deviations in 5-km 

distance bins. [Calculations use 

observations at 315 strong-motion 

instruments within 80 km of the fault 

rupture as reported by the Center 

for Engineering Strong Motion 

Data (2014), site soil information 

for instrument locations from 

the California Geological Survey 

(Wills and Clahan, 2006; averaged 

shear-wave velocity to a depth 

of 30 meters in American Society 

of Civil Engineers, 2010), and the 

equally weighted average of the four 

2008 next-generation attenuation 

relations (Stewart and others, 2008; 

calculations were performed before 

the release of the 2014 relations 

(Bozorgnia and others, 2014)].

Porter_fig4a

Life-safety design, downtown Napa

Actual motion 4.8 times median

1.5

1.0

0.5

0.0

0.0 0.5 1.0 1.5

Calculated spectral acceleration (0.3 second), in g

Observed spectral acceleration (0.3 second), in g

Perfect agreement

A

B

0.00

1.00

2.00

3.00

4.00

5.00

0 25 50 75

Distance, in kilometers

Ratio of observed to median spectral acceleration (0.3 second)
124 The HayWired Earthquake Scenario—Earthquake Hazards

Figure 5. Graphs showing pseudo spectral acceleration (pSa) response of stations CHY080 (A and B) and TCU084 (C and D) in the 1999 Chi 

Chi, Taiwan, earthquake. SRSS, square root of the sum of the squares; g, acceleration due to gravity; sec, second. (From Pacific Earthquake 

Engineering Research Center, 2015; used with permission.)

Porter_fig5a

A

Porter_fig5b

B

Porter_fig5c

C

Porter_fig5d

D
Chapter H. HayWired Scenario Three-Dimensional Numerical Ground-Motion Simulation Maps 125

values at each grid point in the HayWired scenario is like 

having a list of everybody’s age in the United States. Lacking 

the values at each grid point is like having the ages of only 59 

people. Even if the HayWired 3D ground-motion map exactly 

matched some past earthquake of the exact same magnitude, 

the maximum value on the map would almost certainly exceed 

the maximum we have observed by a strong-motion instrument in the same size earthquake because the number of 

observations by strong-motion instruments is so much smaller 

than the number of grid points. Comparing the maximum 

shaking from strong-motion instruments and the maximum 

shaking from a gridded 3D ground-motion map like that for 

the HayWired scenario is like comparing the maximum age 

from a sample of 100 people and the maximum age from a 

sample of 6,000. The maximum from the latter will generally 

be higher than that of the former, so if one assumes that what 

we have seen from strong-motion instruments is some sort of 

natural upper bound, one is likely to underestimate what shaking is possible. Also because the distribution of ground motion 

(as measured here in terms of PSA03 or PSA10) does not 

have an accepted, well-known upper limit (unlike human age), 

the maximum shaking that actually occurs (or appears on a 

gridded map with thousands or tens of thousands of points) is 

likely to be larger than the maximum observed from available 

strong-motion instruments.

In short, one would expect the highest values of ground 

motion in the HayWired 3D ground-motion map to be larger than 

the maximum values observed by strong-motion instruments in 

past earthquakes, just because we have so few actual records of 

strong motion near the fault in large earthquakes compared with 

the number of grid points in the 3D simulation.

Uncertainties

As previously noted, the HayWired scenario is not intended 

as a probabilistic seismic risk analysis. It depicts a single outcome 

of fault rupture, ground motion, and other parameters, so that 

emergency planners and others can more clearly see how one 

earthquake might turn out. Planning for that one event helps people 

to better prepare for the earthquake that will actually happen, but that 

will certainly happen differently. Why then worry about uncertainty 

at all? Which uncertainties should be quantified and how should they 

be propagated? The 3D numerical-simulation approach can capture 

uncertainty in fault rupture location, hypocenter, slip distribution, 

high-frequency motion, and other parameters. By selecting and 

depicting only one outcome, HayWired ignores how the groundmotion map would differ if the particular fault segments that rupture 

or the hypocenter, magnitude, slip distribution, or other factors 

were different. (Note that Aagaard and others (2010a,b), offer 39 

scenarios that vary these and other parameters; HayWired uses only 

one for its mainshock.)

Despite that these uncertainties are ignored, I feel that the 

ground motion in a 3D numerical simulation map, such as that used 

for the HayWired scenario, more closely resembles that in a real 

earthquake. Contours of shaking are blotchy, asymmetrical, irregular, 

with locations that experience motion that is much higher than the 

median, near the median, and lower than the median. Its asymmetry 

serves an educational purpose by showing people what real maps 

of shaking actually look like. It also tends to reduce the bias in the 

damage and loss estimate that would otherwise result from the 

combination of nonlinear fragility or vulnerability relationships with 

median shaking values from a GMPE. 

Make no mistake, 3D numerical simulation offers these two 

advantages in scenario development, relative to a median map 

from GMPEs. That is not to say that 3D numerical simulation 

is preferable in other situations. It is still fairly computationally 

costly, requires crustal velocity information that has not been 

compiled for much of the world, and lacks the built-in empirical 

validation that GMPEs offer. 

Conclusion

Most scenario ground-motion maps depict median shaking; 

that is, the shaking with 50-percent exceedance probability. 

They are created using empirical ground-motion-prediction 

equations, which themselves (oversimplifying somewhat) are 

created by fitting smooth curves to median values of historical 

shaking data. Maps of median motion do not depict variability 

in ground motion; it is the very definition of a median that it 

only depicts a central value and provides no information about 

variability. Median maps do not resemble real earthquakes in 

which motion varies greatly about the median, as a consequence 

of natural geological heterogeneity in fault rupture and in the 

surrounding crust. More importantly for the purposes of the 

HayWired scenario, such a median map would have resulted in 

an underestimate of average shaking and aggregate damage (as 

illustrated by fig. 3). In contrast, the 3D model actually used in 

HayWired estimates greater damages than the median model 

because the distribution of shaking intensity is skewed. Abovemedian shaking causes disproportionately more damage than 

below-median shaking takes away. (GMPEs also use lognormal 

distributions, but the point is that a median map does not reflect 

a GMPE’s distribution, only its median value.) The differences 

between the two models will be exacerbated where concentrations 

of manmade structures and development are exposed to the 

most intense shaking according to the 3D model. An emergencyplanning scenario that uses only median shaking will tend to 

leave a community underprepared. For this reason, the USGS 

has opted to use a 3D numerical ground-motion simulation map 

for the mainshock of the HayWired scenario. (HayWired uses 

median maps for aftershocks out of necessity.) Three-dimensional 

modeling is more costly and computationally demanding than 

median maps but more realistically depicts real-world variability 

in ground motion and helps to avoid underestimation of average 

ground motion and aggregate damage. There are some very 

high values of shaking in the HayWired scenario, but they are 

to be expected and are consistent both with past observations 

of maximum shaking in the 1999 Mw 7.6 Chi-Chi, Taiwan, 

earthquake and with extreme-value theory when trying to draw 

conclusions about large populations from small datasets.
126 The HayWired Earthquake Scenario—Earthquake Hazards

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Menlo Park Publishing Service Center, California

Manuscript approval date April 14, 2017

Edited by James W. Hendley II

Design and layout by Cory Hurd
Detweiler and Wein, editors—The HayWired Earthquake Scenario—Earthquake Hazards—Scientific Investigations Report 2017–5013–A–H, ver. 1.2

ISSN 2328-0328 (online)

https://doi.org/10.3133/sir20175013v1