2016 One-Year Seismic Hazard Forecast for the 

Central and Eastern United States from Induced 

and Natural Earthquakes

By Mark D. Petersen, Charles S. Mueller, Morgan P. Moschetti, Susan M. Hoover, Andrea L. Llenos, 

William L. Ellsworth, Andrew J. Michael, Justin L. Rubinstein, Arthur F. McGarr, and Kenneth S. Rukstales

Open-File Report 2016–1035

Version 1.1, June 2016

U.S. Department of the Interior

U.S. Geological Survey
ii

U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia

First release: 2016

Revised: June 2016 (ver. 1.1)

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

its natural and living resources, natural hazards, and the environment—visit

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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:

Petersen, M.D., Mueller, C.S., Moschetti, M.P., Hoover, S.M., Llenos, A.L., Ellsworth, W.L., Michael, A.J., 

Rubinstein, J.L., McGarr, A.F., and Rukstales, K.S., 2016, 2016 One-year seismic hazard forecast for the 

Central and Eastern United States from induced and natural earthquakes (ver. 1.1, June 2016): U.S. Geological 

Survey Open-File Report 2016–1035, 52 p., http://dx.doi.org/10.3133/ofr20161035.

ISSN 2331-1258 (online)

Cover: Map showing chance of damage from an earthquake in the Central and Eastern United States during 2016. 

Percent chances are represented as follows: pale yellow, less than 1 percent; dark yellow, 1 to 2 percent; orange, 

2 to 5 percent; red, 5 to 10 percent; dark red, 10 to 12 percent. 
iii

Acknowledgments

We wish to thank the November 2014 Induced Seismicity workshop participants, over 100 individuals who made 

valuable suggestions for revising the 2014 U.S. Geological Survey (USGS) National Seismic Hazard Modeling 

Project maps and models to incorporate induced seismicity. We thank State geological survey representatives and 

experts on induced seismicity from Alabama, Arkansas, Colorado, Kansas, New Mexico, Ohio, Oklahoma, and 

Texas for their guidance in delineating and discussing potential induced seismicity zones. In particular, we would 

like to thank Scott Ausbrooks, Karen Berry, Lisa Block, Jeremy Boak, Rex Buchanan, Amberlee Darold, Heather 

DeShon, Sandy Ebersole, Jeff Fox, Cliff Frohlich, Michael Hansen, Austin Holland, Matt Hornbach, Steve Horton, 

Dave Love, Thomas Serenko, Nick Tew, Scott Tinker, and Lynn Watney for providing extensive comments and 

review. We also thank members of the National Seismic Hazard and Risk Assessment Steering Committee—John 

Anderson (chair), Norm Abrahamson, Kenneth Campbell, Martin Chapman, Michael Hamburger, William Lettis, 

Nilesh Shome, Ray Weldon, and Chris Wills—who reviewed the documentation, data, methods, and models, and 

provided important feedback that helped us to improve the quality of this product. In addition, we thank Dan 

McNamara for reviewing the document. We thank the USGS Earthquake Hazard Program for funding the 

development of the maps. This work benefited from discussions with Robert Williams, Jill McCarthy, David Wald, 

Bruce Worden, Vince Quitoriano, Kishor Jaiswal, Michael Blanpied, and Harley Benz. We thank Melanie Parker 

and Mari Kauffmann for editing the manuscript and figures. We also thank Kristi Hartley, Tania Larson, and 

Janet Slate for facilitating the publication of this report.
iv

Contents

Abstract ...................................................................................................................................................................... 1

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

Classification of Induced and Natural Earthquakes .................................................................................................. 12

Seismic Hazard Models Including Induced and Natural Earthquakes in the CEUS.................................................. 14

Methodology ......................................................................................................................................................... 14

Model Development: Logic Tree for Sources Within Induced Zones (Figure 3A).................................................. 17

Level 1: Catalog and Earthquake Sources ........................................................................................................ 17

Level 2: Alternative Models for Classifying Induced and Natural Earthquakes.................................................. 17

Level 3: Earthquake Catalog Duration............................................................................................................... 18

Informed Model.............................................................................................................................................. 18

Adaptive Model.............................................................................................................................................. 18

Level 4: Smoothing Distances Applied in Predictive Model ............................................................................... 18

Level 5: Maximum Magnitude (Mmax)............................................................................................................... 19

Level 6: Ground Motion Models......................................................................................................................... 19

Model Development: Logic Tree for Sources Outside of Induced Zones (Figure 3B) ........................................... 20

Results...................................................................................................................................................................... 20

Seismic Hazard Maps ........................................................................................................................................... 20

Hazard Curves ...................................................................................................................................................... 32

Modified Mercalli Intensity Maps ........................................................................................................................... 34

Final Maps ............................................................................................................................................................ 37

Conclusions .............................................................................................................................................................. 40

References ............................................................................................................................................................... 41

Appendix 1. Likelihood Testing for Smoothed Seismicity Parameters, Oklahoma and Southern Kansas................. 48

Appendix 2. The Adaptive Model.............................................................................................................................. 50

Figures

1. Zones of induced seismicity defined in this report, information on oil and gas plays, sedimentary 

basins (U.S. Energy Information Administration, 2015), wells that are associated with earthquakes 

(Weingarten and others, 2015), and the earthquake rate from 1980 through 2015 in the Central and 

Eastern United States.................................................................................................................................... 6

2. Seismicity maps of the earthquake catalogs used to assess the 2016 hazard across the Central 

and Eastern United States............................................................................................................................. 8

3. Two logic trees applied in the 2016 one-year seismic hazard model........................................................... 15

4. Peak horizontal ground acceleration hazard maps showing sensitivity to input parameters relative to a 

base case .................................................................................................................................................... 22

5. Comparisons of the hazard maps 1-percent probability of exceedance in 1 year for adaptive and 

informed models and for the final model and the 2014 National Seismic Hazed Model .............................. 28

6. Peak horizontal ground acceleration hazard curves for towns and cities located near induced 

earthquakes for the 2016 one-year model and for the 2014 National Seismic Hazard Model. .................... 33
v

7. Modified Mercalli Intensity maps and chance of damage for the Western United States and the Central 

and Eastern United States based on peak horizontal ground acceleration and 1-hertz spectral 

acceleration ................................................................................................................................................. 35

8. Final hazard maps for Modified Mercalli Intensity (MMIs) and chance of damage for the Western United 

States and the Central and Eastern United States based on averages of MMIs converted from peak 

horizontal ground acceleration and 1-hertz spectral acceleration................................................................ 38

1–1. Information gains and optimized smoothing distances as a function of the catalog year of the events 

used to develop trial smoothed seismicity models....................................................................................... 49

2–1. Maps showing maximum earthquake rates from the 1-year, 2-year, 36-year, and 2014 National 

Seismic Hazard Model long-term catalogs, and a seismicity map ............................................................... 51

2–2. Maps comparing trimmed and untrimmed earthquake rates........................................................................ 52

Tables

1. Zones of induced seismicity considered in this assessment.......................................................................... 3

2. Regions of induced seismicity to consider in the future ............................................................................... 11
1

2016 One-Year Seismic Hazard Forecast for the Central 

and Eastern United States from Induced and Natural 

Earthquakes

By Mark D. Petersen, Charles S. Mueller, Morgan P. Moschetti, Susan M. Hoover, Andrea L. Llenos, 

William L. Ellsworth, Andrew J. Michael, Justin L. Rubinstein, Arthur F. McGarr, and Kenneth S. Rukstales

Abstract

The U.S. Geological Survey (USGS) has produced a 1-year seismic hazard forecast for 2016 for 

the Central and Eastern United States (CEUS) that includes contributions from both induced and natural 

earthquakes. The model assumes that earthquake rates calculated from several different time windows 

will remain relatively stationary and can be used to forecast earthquake hazard and damage intensity for 

the year 2016. This assessment is the first step in developing an operational earthquake forecast for the 

CEUS, and the analysis could be revised with updated seismicity and model parameters. Consensus 

input models consider alternative earthquake catalog durations, smoothing parameters, maximum 

magnitudes, and ground motion estimates, and represent uncertainties in earthquake occurrence and 

diversity of opinion in the science community. Ground shaking seismic hazard for 1-percent probability 

of exceedance in 1 year reaches 0.6 g (as a fraction of standard gravity [g]) in northern Oklahoma and 

southern Kansas, and about 0.2 g in the Raton Basin of Colorado and New Mexico, in central Arkansas, 

and in north-central Texas near Dallas. Near some areas of active induced earthquakes, hazard is higher 

than in the 2014 USGS National Seismic Hazard Model (NHSM) by more than a factor of 3; the 2014 

NHSM did not consider induced earthquakes. In some areas, previously observed induced earthquakes 

have stopped, so the seismic hazard reverts back to the 2014 NSHM. Increased seismic activity, whether 

defined as induced or natural, produces high hazard. Conversion of ground shaking to seismic intensity 

indicates that some places in Oklahoma, Kansas, Colorado, New Mexico, Texas, and Arkansas may 

experience damage if the induced seismicity continues unabated. The chance of having Modified 

Mercalli Intensity (MMI) VI or greater (damaging earthquake shaking) is 5–12 percent per year in 

north-central Oklahoma and southern Kansas, similar to the chance of damage caused by natural 

earthquakes at sites in parts of California.

Introduction

The 2014 U.S. Geological Survey (USGS) United States National Seismic Hazard Model

(NSHM) provides forecasts of locations, magnitudes, and rates of future natural (tectonic) earthquakes, 

as well as estimates of long-term ground shaking hazard that are applied in building codes and sitespecific structural designs, risk assessments, financial instruments, and other public policy applications 

(Petersen and others, 2014, 2015a). As in previous hazard models, nontectonic events were removed 

from consideration in the 2014 hazard assessment, so that model does not consider mining-related 

seismicity or earthquakes caused by wastewater injection or other human activities.
2

In this report, referring to earthquakes and seismicity as “induced” or “potentially induced” 

indicates that the seismicity in a given region has shown an increased earthquake rate that can be 

attributed to human activities, such as fluid injection or extraction (Segall, 1989; Segall and Lu, 2015). 

We acknowledge that this classification is based on circumstantial evidence and scientific judgment and 

that a lack of relevant technical information on the geological condition, state of stress, and human 

industrial activity (such as injection well pumping data) makes it difficult to assess seismic hazard. In 

this report, however, we do not explore the causes of the increased seismicity, but rather we try to find a 

way to quantify the associated hazard. Because regions of induced seismicity are identified by sharp 

increases in the rate of earthquakes in a particular area, we consider induced earthquakes to be 

earthquakes within a certain area and time window that have been provisionally identified by the 

science literature and local expertise.

Earthquake rates have recently increased markedly in multiple areas of the Central and Eastern 

United States (CEUS), especially since 2010, and scientific studies have linked the majority of this 

increased activity to wastewater injection in deep disposal wells (table 1) (Ellsworth, 2013; Keranen and 

others, 2014; Walsh and Zoback, 2015; Weingarten and others, 2015). Figure 1 shows the location of 

wells associated with earthquakes (Weingarten and others, 2015) and a timeline of earthquake rates, and 

figure 2 shows the seismicity maps for varying time intervals in the CEUS. Between 1980 and about 

2010, CEUS earthquake rates were relatively stable, but recent rates in some areas have increased by 

more than an order of magnitude. Such changes have caused concern to many, including residents, 

business owners, engineers, and public officials responsible for mitigating or responding to the effects 

of these earthquakes on nearby populations (for example, Ground Water Protection Council and 

Interstate Oil and Gas Compact Commission, 2015).
3

Table 1. Zones of induced seismicity considered in this assessment. For each zone, weight, earthquake rates, time windows, and data sources 

are shown.

[Spanner colors correspond with label colors used in figures 1 and 2. See text for details on unresolved zones. Weight given as decimal percentage. M, adjusted 

moment magnitude; dec., declustered earthquake catalog; full, non-declustered earthquake catalog]

Zones of induced 

seismicity

Weight based 

on scientific 

consensus—

considered 

induced

1-year count

M2.7+ through fall 

2015

2-year count

M2.7+ through fall 

2015

Start year–end 

year

(if applicable)

Largest 

earthquake

(M, date)

References

dec. full dec. full

Zones that have had M2.7 and greater earthquake activity in years 2014–2015

Greeley, Colorado 1.0—yes 0 0 1 1 2013–present 3.2, Jun. 2014 Yeck and others (2014),

Ground Water Protection Council 

and Interstate Oil and Gas 

Compact Commission (2015, 

appendix C)

Youngstown, Ohio 1.0—yes 0 0 1 2 2010–present 3.7, Dec. 2011 Kim (2013),

Skoumal and others (2015a, b)

Oklahoma-Kansas 1.0—yes 149 3,528 292 5,991 2006–present 5.6, Nov. 2011 Keranen and others (2013, 2014),

Sumy and others (2014),

Andrews and Holland (2015),

Buchanan (2015),

Walsh and Zoback (2015)

Cogdell, Texas 1.0—yes 1 1 3 4 1976–present 4.5, Jun. 1978 Davis and Pennington (1989),

Gan and Frohlich (2013)

Fashing, Texas 1.0—yes 1 1 2 2 1973–present 4.6, Oct. 2011 Pennington and others (1986),

Frohlich and Brunt (2013)

North Texas (Azle, 

Cleburne area, 

Dallas-Fort 

Worth Airport, 

Mineral Wells)

1.0—yes 0 0 1 1 2008–present 3.4, Nov. 2013 Frohlich and others (2011),

Frohlich (2012),

Justinic and others (2013),

Hornbach and others (2015)

Timpson, Texas 1.0—yes 0 0 1 1 2011–present 4.8, May 2012 Frohlich and others (2014)

Venus, Texas 1.0—yes 1 1 2 2 2008–present 4.0, May 2015 Frohlich (2012)
4

Table 1. Zones of induced seismicity considered in this assessment. For each zone, weight, earthquake rates, time windows, and data sources 

are shown.—Continued

[Spanner colors correspond with label colors used in figures 1 and 2. See text for details on unresolved zones. Weight given as decimal percentage. M, adjusted 

moment magnitude; dec., declustered earthquake catalog; full, non-declustered earthquake catalog]

Zones of induced 

seismicity

Weight based 

on scientific 

consensus—

considered 

induced

1-year count

M2.7+ through fall 

2015

2-year count

M2.7+ through fall 

2015

Start year–end 

year

(if applicable)

Largest 

earthquake

(M, date)

References

dec. full dec. full

Zones that have not had M2.7 and greater earthquake activity in years 2014–2015

El Dorado, 

Arkansas

1.0—yes 0 0 0 0 1983–present 2.9, Jun. 1994 Cox (1991)

Paradox Valley, 

Colorado

1.0—yes 0 0 0 0 1991–present 3.9, Jan. 2013 Ake and others (2005), 

King and others (2014),

Block and others (2014, 2015),

Yeck and others (2015) 

Rangely, ColoradoUtah1.0—yes 0 0 0 0 1957–present 4.3, Apr. 1970 Raleigh and others (1976),

Karen A. Berry, Colorado Geological 

Survey, written commun. (2015)

Rocky Mountain 

Arsenal, 

Colorado

1.0—yes 0 0 0 0 1962–1979 4.8, Aug. 1967 Evans (1966), 

Healy and others (1968),

Karen A. Berry, Colorado Geological 

Survey, written commun. (2015)

Dagger Draw, New 

Mexico

1.0—yes 0 0 0 0 1998–present 4.1, Dec. 2005 Sanford and others (2006),

Pursley and others (2013)

Ashtabula, Ohio 1.0—yes 0 0 0 0 1987–2007 3.9, Jan. 2001 Seeber and others (2004)

Perry, Ohio 1.0—yes 0 0 0 0 1975–present 4.6, Jan. 1986 Nicholson and others (1988),

Skoumal and others (2015a)

Alice, Texas 1.0—yes 0 0 0 0 1938–present 3.5, Apr. 2010 Frohlich and others (2012)
5

Table 1. Zones of induced seismicity considered in this assessment. For each zone, weight, earthquake rates, time windows, and data sources 

are shown.—Continued

[Spanner colors correspond with label colors used in figures 1 and 2. See text for details on unresolved zones. Weight given as decimal percentage. M, adjusted 

moment magnitude; dec., declustered earthquake catalog; full, non-declustered earthquake catalog]

Zones of induced 

seismicity

Weight based 

on scientific 

consensus—

considered 

induced

1-year count

M2.7+ through fall 

2015

2-year count

M2.7+ through fall 

2015

Start year–end 

year

(if applicable)

Largest 

earthquake

(M, date)

References

dec. full dec. full

Unresolved zones

Brewton, Alabama 0.5—partially 0 0 0 0 1997–present 4.7, Oct. 1997 Gomberg and Wolf (1999),

Berry H. (Nick) Tew, Jr., Geological 

Survey of Alabama, written 

commun. (2015)

North-central 

Arkansas

0.8—partially 4 5 10 11 2009–present 4.7, Feb. 2011 Horton (2012),

Scott Ausbrooks, Arkansas 

Geological Survey, written 

commun. (2015)

Raton Basin, 

Colorado-New 

Mexico

0.8—partially 6 10 11 18 2001–present 5.2, Aug. 2011 Meremonte and others (2001),

Rubinstein and others (2014),

Karen A. Berry, Colorado Geological 

Survey, written commun. (2015)

Sun City, Kansas 0.5—partially 2 3 2 3 2015–present 4.0, May 2015 Rex Buchanan and Lynn Watney,

Kansas Geological Survey, written 

commun. (2015)

Irving, Texas 0.0—pending 

relevant 

published 

study

5 8 6 9 2008–present 3.3, Jan. 2015 Research ongoing
6

Figure caption starts on the following page.
7

Figure 1. Zones of induced seismicity defined in this report, information on oil and gas plays, sedimentary 

basins, wells that are associated with earthquakes, and the earthquake rate from 1980 through 2015 in the Central 

and Eastern United States. A, Map showing locations of oil and gas plays and sedimentary basins in relation to 

wells that have been associated with induced seismicity (U.S. Energy Information Administration, 2015; Weingarten 

and others, 2015). Label colors correspond with colors used in table 1: black text identifies zones of induced 

seismicity that had moment magnitude (M) 2.7 and greater earthquake activity in years 2014–2015, gray text 

identifies zones that did not have M2.7 and greater earthquake activity in years 2014–2015, and red text identifies 

unresolved zones (see text for details). B, Cumulative (top) and annual counts (bottom) of M2.7 and greater 
8

earthquakes in the Central and Eastern United States (CEUS) and five select zones of induced seismicity since 

1980. The North Texas zone excludes Irving and Venus (fig. 1A, table 1). Earthquake rates are computed for this 

magnitude and time period (starting in 1980) because this is the earliest time for which data on M2.7 earthquakes

are estimated to be complete across the region (Petersen and others, 2014).

Figure caption on page 10.
9

Figure caption on page 10.
10

Figure 2. Seismicity maps of the earthquake catalogs used to assess the 2016 hazard across the Central and 

Eastern United States (CEUS; gray line indicates western boundary from 2014 National Seismic Hazard Model).

Label colors correspond with colors used in table 1: black text identifies zones of induced seismicity that had 

moment magnitude (M) 2.7 and greater earthquake activity in years 2014–2015, gray text identifies zones that did 

not have M2.7 and greater earthquake activity in years 2014–2015, and red text identifies unresolved zones (see 

text for details). These maps show catalog durations that were applied in this assessment. A, Seismicity map 

showing the 2014 National Seismic Hazard Model's long-term catalog; earthquakes are shown for the full and 

declustered catalogs. B, Declustered seismicity map for the year 2015. C, Declustered seismicity map for the years 

2014 and 2015. D, Declustered seismicity map for the years 1980 through 2015.

Assessing hazard and potential damage from these events is difficult because induced 

earthquakes can vary rapidly in time and space based on changes in industrial activity, which can be 

caused by economic or policy decisions; this variability also makes induced earthquake hazard difficult 

to forecast. For this reason, hazard estimates from induced earthquakes are not compatible with 

estimates of long-term seismic hazard caused by tectonic processes. Building-code committees are 

reluctant to consider induced seismicity in their current design codes because the hazard from induced 

earthquakes will change before the building regulations are enacted, which causes confusion in the 

design process. Conversely, officials will not be able to rely on standard building codes when making 

decisions regarding the mitigation of damage from induced earthquakes.

Even though induced earthquakes are not considered in building-code maps, they create seismic 

hazard to buildings, bridges, pipelines, and other important structures and are a concern for about 

7.9 million people living in the vicinity of these events. Several damaging earthquakes have occurred 

recently near injection wells, for example, in 2011 near Prague, Oklahoma (moment magnitude [M] 5.6; 
11

Keranen and others, 2013); in 2011 near Trinidad, Colorado (M5.3; Rubinstein and others, 2014); in 2012

near Timpson, Texas (M4.8; Frohlich and others, 2014); and in 2011 near Guy, Arkansas (M4.7; Horton, 

2012). High peak accelerations have also been recorded in recent events located near active injection 

wells. For example, a 25 January 2013 local magnitude (mbLg) 4.1 earthquake near Timpson, Tex.,

caused 0.62 g (as a fraction of standard gravity [g]) peak horizontal ground acceleration (PGA) (Frohlich 

and others, 2014); a 10 October 2015 M4.3 earthquake near Cushing, Okla., produced 0.60 g PGA 

(McNamara and others, 2015b); and a 01 January 2016 M4.2 earthquake near Edmond, Okla., generated 

0.58 g PGA (U.S. Geological Survey, 2016a). While peak acceleration ground shaking values may not 

correlate as well as peak ground velocity or other measures with damage (Worden and others, 2012), these 

examples illustrate that high ground shaking is occurring at sites near wastewater disposal wells.

To ascertain the best data, methods, models, and products for assessing and communicating 

seismic hazard from these induced earthquakes, we held a workshop in Midwest City, Okla., in 

November 2014 attended by more than 150 representatives from academia, government, and industry. 

One key issue discussed at the workshop involved the appropriate duration of the hazard forecast. It was 

decided that because induced earthquakes can vary so rapidly in space and time, the hazard model

should only be used to forecast short intervals, such as one year, unlike the 2014 NSHM which applies a

50-year time window. A feature of a short-term forecast is that it allows for changes and could be 

updated annually, or even more often, if needed. Workshop participants also recommended research 

directions and products that would be helpful in making decisions about these earthquakes. The 

workshop results were summarized, research prioritized, and hazard evaluated in a report that shows 

sensitivity to the seismic hazard input parameters (Petersen and others, 2015b).

For this report, we built on the sensitivity study of Petersen and others (2015b) to develop a 

1-year hazard model for 2016 that includes both natural and induced earthquakes within the CEUS. The 

model is based on geological and seismological data (fig. 2) and considers the locations of oil and gas 

plays and sedimentary basins (U.S. Energy Information Administration, 2015) and wells (Weingarten 

and others, 2015) (fig. 1A). The seismic hazard assessment for induced seismicity considered seismicity 

patterns, earthquake rates, and ground shaking data. We have delineated zones of induced seismicity 

(referred to as zones in this report; see table 1) based on these data and scientific literature on places 

where induced earthquakes are occurring. We have identified several regions of induced earthquake 

activity that were not included in this analysis (table 2).

Table 2. Regions of induced seismicity to consider in the future.

Region Reference

Coso region, California Feng and Lees (1998)

The Geysers, California Eberhart-Phillips and Oppenheimer (1984), Oppenheimer (1986)

Salton Trough, California Brodsky and Lajoie (2013), Wei and others (2015)

Los Angeles Basin, California Hauksson and others (2015)

Sleepy Hollow, Nebraska Rothe and Lui (1983)

Rattlesnake Canyon, New Mexico Sanford and others (1993)

Bakken Shale, Montana-North Dakota Frohlich and others (2015)

Belmont/Guernsey County, Ohio Skoumal and others (2015a)

Harrison County, Ohio Friberg and others (2014), Skoumal and others (2015a)

Washington County, Ohio Skoumal and others (2015a)

Permian Basin, Texas-New Mexico Wesson and Nicholson (1987)
12

To generate this model, we have held discussions with many scientists and engineers from 

national, State, and local government agencies; industry; and academia to define data, methods, and 

models that are most applicable for assessing induced earthquake hazard. This model was also discussed 

and reviewed by our National Seismic Hazard and Risk Assessment Steering Committee, which is 

composed of hazard experts from across the United States. Results of these discussions led to the 

following considerations. We have included two methods (an informed model and an adaptive model) to 

account for induced and natural earthquakes in the 2016 one-year model. Zones of induced seismicity 

were identified and weighted according to scientific literature and expert opinion. Those zones for 

which the scientific research is not yet complete (such as the Irving, Tex., zone, table 1) were not 

included as induced earthquakes in the model at this time as advised by local experts.

We have not included an induced earthquake hazard component for the Western United States, 

though studies have been done in the area. Hauksson and others (2015) attempted to identify new 

induced earthquakes in the Los Angeles Basin, but though they did not find widespread evidence for 

significant earthquake rate changes in this region, they identified two areas which may have induced 

activity. Several researchers (Eberhart-Phillips and Oppenheimer, 1984; Oppenheimer, 1986; Smith and 

others, 2000) indicated that some seismicity near the Geysers, California, may be induced. Several 

researchers (Brodsky and Lajoie, 2013; Wei and others, 2015) discussed indirect relationships between 

fluid injection activities and the triggering of two strike-slip shocks with magnitudes larger than M5.3 

near Brawley, Calif., in the Salton Trough. These areas could be considered in future assessments but 

were not considered here. Because we consider the 2016 one-year hazard model only for the CEUS in 

this report, figures that include maps for the Western United States do so only for comparison. They use 

the 2014 NSHM based on natural earthquakes and are positioned alongside but separate from the CEUS 

maps.

Our assessment of induced earthquake hazard was dependent on the assumption that past 

earthquake rates will remain constant over the next year of the forecast. While this assumption will not 

hold for areas of injection over long periods, recent studies (Llenos and others, 2015; appendix 1)

indicate that assessing earthquake rates observed over short time windows of a year or less are currently 

the best method available for forecasting the next year’s rate of induced earthquakes. This model, 

however, does not account for increased, reduced, or new induced activity in 2016.

This assessment is the first step in developing an operational earthquake forecast for the CEUS, 

and the model could be updated every year, or even over a shorter time period (contingent on public 

interest and programmatic resources). Further research, however, is needed to ensure a robust 

forecasting model. For example, we considered alternative source and ground motion models for this 

assessment because the science of induced earthquakes is still evolving and large uncertainties 

characterize hazard forecasts involving induced activity. In addition, industrial activity is rapidly 

changing, and the sources of potentially induced earthquakes may change over the course of the next 

year.

Classification of Induced and Natural Earthquakes

Earthquake swarms, sequences of several earthquakes in a short period of time, are not unusual. 

We know that swarms of natural earthquakes can occur across the United States (such as the one near 

Sheldon, Nevada, that started in mid-2014), but nonetheless, it is widely accepted in the scientific 

community that swarms of induced earthquakes in the CEUS are quantitatively related to injection 

activities as measured by pumping volumes and rates (Ellsworth, 2013; McGarr and others, 2015; 

Weingarten and others, 2015). For this assessment, we classified earthquakes as induced or natural so 

that we could account for differences in earthquake source, frequency, propagation, and ground shaking 
13

characteristics. Induced and natural earthquakes are thought to differ in several ways, and further 

research is needed to determine how they differ, but enough research is available to forecast such 

events. For example, observations worldwide of induced earthquakes indicate that their maximum 

magnitudes may be smaller than those for natural earthquakes, but many scientists also suggest induced 

earthquakes can trigger larger earthquakes on known or unknown faults (McGarr, 2014; Petersen and 

others, 2015a, b). Also, induced earthquakes tend to exhibit more swarmlike behavior and shallower 

average depths of rupture than many natural earthquakes (Gomberg and Wolf, 1999; Seeber and others, 

2004; McNamara and others, 2015a; Skoumal and others, 2015a, b). Several models and observations

suggest that induced swarms may have more small earthquakes compared to the numbers of large 

earthquakes (greater b-value; Benz and others, 2015; Petersen and others, 2015b). Hough (2014) 

suggested that high intensities close to the induced earthquakes are consistent with shallow depths, and 

low intensities at regional distances are consistent with low stress drop. Further, Atkinson (2015)

compared empirical data from induced and natural earthquakes (NGA-West2 model) and determined 

that induced events may cause higher ground shaking close to the earthquake, possibly because of their 

shallow depths. Douglas and others (2013), however, indicated that ground shaking data from 

geothermally induced and natural earthquakes cannot be statistically distinguished; rather, they suggest 

that the ground motion distributions for natural and induced earthquakes may have different standard 

deviations. These discordant conclusions indicate that additional research is needed to better understand 

these differences.

We relied on published scientific research (table 1) and on consultations with State geological 

surveys and local experts (see Acknowledgements) to differentiate between induced and natural 

earthquakes (Davis and Frohlich, 1993). The scientific literature typically categorizes induced 

earthquakes by using one or more of the following criteria: (1) a statistically significant rate change of 

earthquakes (Llenos and Michael, 2013); (2) earthquakes located within a few kilometers of, and 

synchronous with, an active injection well and at depths consistent with injection (table 1); and 

(3) injection continuing or recently ceased at the time of the earthquake (table 1). The evidence for the 

activity being induced is especially compelling when reductions in the earthquake rate correlate with

reductions in wastewater injection (McNamara and others, 2015b). For those earthquakes where origins 

remain unresolved, we considered alternative models to accommodate this uncertainty.

To classify induced and natural earthquakes for this assessment, we used polygonal shapes

(in map view) to define 21 zones of induced seismicity that are thought to encompass areas of induced 

earthquakes occurring within a prescribed time interval (fig. 1, table 1). Any earthquakes located outside 

of these 21 zones are considered to be natural earthquakes for this informed model. The time interval

corresponds to the period when anomalously high earthquake rates were observed or to periods of 

industrial activity as defined in the scientific literature. We also weighted each zone according to the 

support among the scientific community for its classification as induced; most zones were weighted 

using published studies, but we allowed for alternative customized weights for five zones to account for 

comments from State geological surveys and local experts. The weights applied to each zone and the 

bases for those weights are as follows: (1) 100 percent weight on induced branch—based on scientific 

literature and discussions with local experts who are in support of the classification (16 zones), 

(2) 80 percent weight on induced branch—based on scientific literature and discussions with local 

experts who are mostly supportive of this classification but wish to acknowledge alternatives (Raton 

Basin and North-central Arkansas), (3) 50 percent weight on induced branch—based on discussions 

with local experts supported by evidence of notable naturally occurring earthquakes (Brewton, Ala., and 

Sun City, Kans.), and (4) 0 percent weight on induced branch—based on expert opinion and little or no 

published science indicating that earthquakes are induced (Irving, Tex.).
14

We recognize that within some of these zones are earthquakes that occurred before the recent 

spate of wastewater injection activities, and it is possible that natural earthquakes are ongoing and 

interspersed with the induced events. For example, from 1950 to 2005, Oklahoma recorded an average 

of 1.5 earthquakes with a magnitude (M) greater than 3.0 per year, though some of these earlier 

earthquakes may also have been induced (Hough and Page, 2015). Over the past few years, however, 

Oklahoma has recorded several hundred M3.0+ earthquakes per year, many of which are thought to be 

related to wastewater injection. Nevertheless, there is no reason to believe that the background rate of 

natural earthquakes has decreased during this recent flurry of activity. Other regions such as central 

Arkansas and the Raton Basin, Colo. and N. Mex., are also thought to experience both induced and 

natural earthquakes. We are likely including a few natural (tectonic) earthquakes in our zones of 

induced seismicity, but as observed in Oklahoma, the numbers of natural earthquakes within these zones 

are probably quite low compared to numbers of induced earthquakes, so we do not account for this 

complication in the current assessment.

Seismic Hazard Models Including Induced and Natural Earthquakes in the CEUS

In this paper, we forecast the CEUS ground shaking hazard by including contributions from both 

natural and induced earthquakes. We used earthquake information collected through the end of 2015 to 

assess the hazard for a 1-year period—the forecast expires at the end of 2016. 

We used two models to account for earthquake classification as induced or natural: the informed 

model considers the possibility that induced earthquakes differ from natural earthquakes, and the 

adaptive model does not differentiate between the two types of earthquakes. We also considered 

alternative earthquake source and ground motion models by using a logic-tree (or decision-tree) 

framework, in which weights were applied to each parameter based on the degree of acceptance in the 

scientific community regarding which is best for forecasting earthquakes and associated ground 

shaking.

Methodology

The seismic source and ground motion models we used are the most important components of 

this analysis; for details on faults and other parts of the hazard model, refer to the 2014 NSHM 

documentation (Petersen and others, 2014, 2015a, b). The seismicity rate model assumes that patterns 

and rates of smaller earthquakes (M2.7–4.7) can be used along with a truncated Gutenberg-Richter 

magnitude-frequency distribution (Gutenberg and Richter, 1944) from the minimum considered 

magnitude to the maximum magnitude (Mmax) to forecast the rates and locations of large earthquakes

(Frankel, 1995; Petersen and others, 2014). In this analysis, we incorporated catalogs with shorter 

duration than were applied in the 2014 NSHM because we are only forecasting earthquake rates for

2016. The ground motion models are primarily based on limited ground shaking recordings of global 

earthquakes in stable continental regions as well as ground shaking simulations using earthquake source 

and crustal properties consistent with this region. This is an active area of research to better define the 

differences between induced and natural earthquake ground shaking.

We used two logic trees for this assessment, one for sites located within zones of reported 

induced seismicity and another for sites located outside of these zones (fig. 3). We include both 

alternative logic trees because we want to be able to treat earthquakes within zones of induced 

seismicity differently from other earthquakes. We need both sets of logic trees to produce a seismic 

hazard map that includes influences from earthquakes within and outside of the zones. These 

components were combined to forecast the rates and locations of earthquakes across the CEUS.
15

Differences between the logic trees are primarily found in the parameters of the seismicity model 

(labeled “rate model” on fig. 3), which involves the catalog duration, smoothing distance, and maximum 

magnitude, and those of the ground motion model. Figure 3A shows the logic tree for sources within 

zones of induced seismicity, and figure 3B shows the logic tree for sources outside the zones.

Figure 3. Two logic trees applied in the 2016 one-year seismic hazard model. A, For sites within defined zones 

of induced seismicity. B, For sites outside of zones of induced seismicity. (NSHM, National Seismic Hazard Model; 

km, kilometer; M, moment magnitude; Mmax, maximum magnitude; GMM, ground motion model; hypo, 

hypocentral; CEUS, Central and Eastern United States. See table 1 and figure 1 for details on the zones of induced 

seismicity used in this assessment.)

1 In the informed model, unresolved zones are given special weight to acknowledge that (1) at this time, there is no scientific

consensus regarding the classification of earthquakes as potentially induced, as with Brewton, Irving, and Sun City, or (2) the 

zone is in a tectonically active area with some natural earthquakes, as with the North-central Arkansas and Raton Basin 

zones. These decisions were made with input from State geologists and local experts. Weights for these zones are shown in 

table 1, column 2.

2 See appendix 2 for more information on the adaptive model.

3 In the informed model, for sites inside the zones but outside of the corresponding time windows shown in table 1, 

column 7, the seismic hazard is equivalent to the 2014 NSHM. The informed model in figure 3B shows how the values 

are derived. [This footnote applies to figure 3A only.]

4 The long-term model will have similar treatment as the 2014 NSHM (Petersen and others, 2014) for the adaptive model, 

but a key difference for the informed model is that the catalog will now include earthquakes from 2013 through 2015.
16

Figure 3. Two logic trees applied in the 2016 one-year seismic hazard model. A, For sites within defined zones 

of induced seismicity. B, For sites outside of zones of induced seismicity. (NSHM, National Seismic Hazard Model; 

km, kilometer; M, moment magnitude; Mmax, maximum magnitude; GMM, ground motion model; hypo, 

hypocentral; CEUS, Central and Eastern United States. See table 1 and figure 1 for details on the zones of induced 

seismicity used in this assessment.)—Continued

1 In the informed model, unresolved zones are given special weight to acknowledge that (1) at this time, there is no scientific

consensus regarding the classification of earthquakes as potentially induced, as with Brewton, Irving, and Sun City, or (2) the 

zone is in a tectonically active area with some natural earthquakes, as with the North-central Arkansas and Raton Basin

zones. These decisions were made with input from State geologists and local experts. Weights for these zones are shown in 

table 1, column 2.

2 See appendix 2 for more information on the adaptive model.

3 In the informed model, for sites inside the zones but outside of the corresponding time windows shown in table 1, 

column 7, the seismic hazard is equivalent to the 2014 NSHM. The informed model in figure 3B shows how the values 

are derived. [This footnote applies to figure 3A only.]

4 The long-term model will have similar treatment as the 2014 NSHM (Petersen and others, 2014) for the adaptive model, 

but a key difference for the informed model is that the catalog will now include earthquakes from 2013 through 2015.
17

Model Development: Logic Tree for Sources Within Induced Zones (Figure 3A)

This section describes the levels of figure 3A, the logic tree for sites within induced zones, in 

detail. Level 1 describes an earthquake catalog that extends to the end of 2015 and fault and area 

sources from the 2014 NSHM (Petersen and others, 2014, 2015a). Level 2 describes the classification of

earthquakes as induced or natural and the estimation of earthquake rates. Level 3 describes the durations

of the earthquake catalogs that best predict earthquakes. Level 4 describes the smoothing parameters 

applied in the model. Level 5 describes the maximum magnitudes applied in the model. Level 6 

describes the alternative ground motion models.

Level 1: Catalog and Earthquake Sources

Level 1 of the logic tree describes the catalog, fault sources, and area sources. This level is the 

same for both logic trees, that is, for sites both within and outside of zones of induced seismicity (fig.

3A, B); we did not allow for alternatives at this level. For this assessment, we produced a declustered 

catalog (Gardner and Knopoff, 1974) with a minimum magnitude of M2.7. The declustered catalog for 

induced seismicity is consistent with a b-value of 1.0 and a catalog completeness level of M2.7 since 

about 1980 (Petersen and others, 2015b). Tests show that a larger b-value may be appropriate if the full, 

non-declustered catalog is applied in the analysis. We tested the sensitivity of hazard when applying 

other catalogs with other minimum magnitude thresholds, without declustering, and with varying bvalues and determined that the catalogs and parameters we have chosen provide a reasonable estimate of 

earthquake rates with sufficient detail for this application (Petersen and others, 2015b). We decided to 

apply the declustered catalog in this model for the following reasons: (1) the probabilistic method as 

defined by Cornell (1968) requires a catalog with independent earthquakes, (2) the 2014 NSHM and 

previous versions all applied declustered catalogs, and (3) it allows us to compare hazard in the CEUS 

with other places across the United States (for example, California). Because the CEUS induced 

seismicity sequences are swarmlike, the Gardner and Knopoff (1974) declustering methodology may 

underestimate the hazard. In some cases, such as following the 2011 Prague, Okla., earthquake, this 

declustering methodology may remove some independent earthquakes. It is important to point out that if 

we were to apply the full catalog rather than a declustered catalog, the hazard may be significantly 

higher than what we show in this model (Petersen and others, 2015b). Assessing the best methods for 

declustering the catalog or determining if the full catalog should be included in the hazard assessment

will help to determine the hazard.

Level 2: Alternative Models for Classifying Induced and Natural Earthquakes

The second level considers two alternative models to classify earthquakes in the CEUS: (1) the 

informed model, which assumes that earthquakes can be broadly classified as induced or natural based 

on scientific information and literature, and (2) the adaptive model, which assumes that we cannot 

distinguish between induced and natural earthquakes (appendix 2). The informed model treats induced 

and natural earthquakes differently, allowing for the application of recent research on induced 

earthquakes. This model also allows for earthquake rate decay. The adaptive model uses a uniform 

approach across the CEUS and does not distinguish between induced and natural earthquakes. It does 

not depend on judgments about areas and time windows of induced seismicity. The adaptive model does 

not allow for a short-term decay of earthquake rates because it uses the maximum earthquake rate 

determined from alternative catalog time windows. Both informed and adaptive models are useful for 

this analysis and we have weighted them equally.
18

Level 3: Earthquake Catalog Duration

The third level defines the catalog durations used to forecast earthquake rates: 1-year (2015), 

2-year (2014–2015), or 36-year (1980–2015) durations or the long-term duration applied in the 2014 

NSHM (1700–2012) extending through 2015 for the informed model (fig. 2). It is important to note that 

the 2014 NSHM and its long-term catalog consider variable completeness levels across the CEUS 

(Petersen and others, 2014), but this study uses a uniform CEUS-wide completeness threshold of M2.7

for the 1-year, 2-year, and 36-year catalogs.

There are several reasons for considering varying catalog lengths: (1) it is likely that earthquakes

that occurred during the most recent time will best predict the next year’s activity; (2) sometimes the 

induced seismicity goes through alternating periods of heightened activity and dormancy that are not 

predictable without other industry information on fluid injection and extraction; and (3) sometimes, 

though injection has stopped, earthquakes can occur months or years later in the vicinity of the injection 

well (such as the Rocky Mountain Arsenal zone; Evans, 1966; Healy and others, 1968). Though all four 

catalog durations used in this assessment provide important earthquake rate information, the informed 

and adaptive models use different durations and assign them different weights (compare the “Catalog 

Duration” level in fig. 3A to the same level in fig. 3B).

Informed Model

For calculating the hazard for an earthquake source within a zone, the informed model for 

induced earthquakes assumes that a 1-year catalog is the most effective predictor of the earthquake rate 

for the following year. We gave this catalog the most weight because statistical studies indicate that the 

shorter time window is better than longer periods of earthquake observations in predicting the next 

year’s rate (appendix 1). Nevertheless, we also allowed for a catalog that uses a 2-year duration, but we 

gave it less weight. This emphasis on short-term catalog durations was suggested by local experts who 

attended our workshop. In addition to being better for forecasting earthquake rates for the following 

year, application of short-term durations have the added benefit that hazard is reduced in places where 

earthquake rates are decreasing in response to regulation of wastewater disposal or for other reasons.

To calculate the informed model hazard for sources inside of induced zones, we applied a

seismicity rate floor based on the 2014 NSHM (Petersen and others, 2014; fig. 3 footnotes). As 

discussed, we considered 21 zones as potentially encompassing induced seismicity. A total of 12 zones 

had activity during the 2-year catalog, and of those, 8 zones had potentially induced seismicity during 

the 1-year catalog. The remaining 9 zones, which were inactive during the past two years, are modeled 

with the 2014 NSHM hazard inputs. Additionally, 5 of the 21 zones were considered with partial weight

(see section “Classification of Induced and Natural Earthquakes”).

Adaptive Model

For the adaptive model, catalog durations were applied the same way for earthquake sources 

inside and outside of the zones. This model considers the maximum earthquake rate from among the 

1-year, 2-year, 36-year, and 2014 NSHM catalogs to assess the hazard (appendix 2). This alternative 

model accounts for hazard in areas that may be experiencing an increase in seismicity over a longer time 

scale.

Level 4: Smoothing Distances Applied in Predictive Model

The fourth level involves the smoothing distance applied in calculating the gridded rates 

(Frankel, 1995). Earthquakes above M2.7 are counted in each grid cell, the Gutenberg-Richter model is 
19

applied to get rates (10a

), and the rates are then smoothed spatially using a Gaussian operator. This 

branch is constrained by a series of likelihood tests to determine the optimal smoothing distance needed 

to forecast earthquakes (appendix 1). Level 4 of figure 3A outlines the smoothing distances that 

correspond to the four catalog durations used in level 3. Results of the statistical study for induced 

earthquakes indicate that shorter smoothing distances and recent, short-term catalogs provide better 

forecasts than larger smoothing distances and longer catalogs. These smoothing distances are also more 

compatible with the distances observed between induced earthquakes and the causative deep disposal 

wells. Because we are making a 1-year hazard forecast instead of a longer 50-year forecast, and because 

we are able to utilize recent earthquakes that are more accurately located, scientists at our workshop felt

that we should use a smaller smoothing distance than the 50-kilometer (km) to 75-km smoothing kernel 

applied in the 2014 NSHM to avoid over-smoothing the hazard. For sources inside the zones, the 

informed model allows for Gaussian smoothing kernels of 10-km and 20-km fixed widths, and the 

adaptive model allows for a broad range of distances including 10-km, 20-km, 50-km, and 2014 NSHM

widths (Petersen and others, 2014).

Level 5: Maximum Magnitude (Mmax)

The fifth level considers a maximum magnitude (Mmax) for the largest size of earthquake that 

can occur in the region. The Mmax we applied for natural earthquakes is the same as that applied in the 

2014 model, with a mean of M7.1 and a broad distribution (Petersen and others, 2014). The Mmax 

distribution is based on earthquake sizes observed in similar tectonic regions across the globe (Petersen 

and others, 2014). For the informed model, we consider an Mmax of M6 or a distribution with a mean 

of M7.1 for induced earthquakes. Many of the participants at our workshops felt that the lower Mmax 

branch should receive the most weight for sites within zones because we have not observed earthquakes

greater than M6 that were thought to be caused by wastewater injection. For the adaptive model, we 

only consider the 2014 NSHM Mmax model.

Level 6: Ground Motion Models

The sixth level involves the ground motion models. In addition to the set of ground motion 

models used for computing CEUS hazard for the NSHM (Petersen and others, 2014), we also 

considered a model developed by Atkinson (2015) for the informed model. This Atkinson (2015) model 

was compared to induced earthquakes in Canada and is thought to represent a reasonable alternative for 

induced earthquake shaking. Nevertheless, this model was developed using NGA-West2 ground motion 

models (for example, from data derived from the Western United States and other active tectonic 

regions) instead of data from the stable continental regions, so the model was down-weighted compared 

to the 2014 NSHM ground motion models applied for the CEUS region (fig. 3A). Earthquake depth is 

an important factor in calculating ground shaking. According to McNamara and others (2015a, b), most 

induced earthquakes in Oklahoma occur in bedrock with depths around 5 km. Consistent with the 2014 

NSHM, we used a 5-km depth for the eight CEUS ground motion models for calculating distance from 

the rupture source (there is no depth-dependent ground motion term in any of these CEUS ground 

motion models). For the Atkinson (2015) model, we applied 2-km and 5-km hypocentral depth 

alternatives in our analysis because these changes make differences in areas very close to the earthquake

source and we cannot rule out shallower depths. The induced ground motion model of Atkinson was

only considered for the informed model for sites within the zones and was only applied for M≤6 

earthquakes; the model is applied within 100 km of an earthquake hypocenter, unlike the suite of eight 

CEUS ground motion models that are applied out to 1,000 km in the 2014 NSHM. For the adaptive 
20

model, we only applied the suite of CEUS ground motion models that were applied in the 2014 NSHM 

(Petersen and others, 2014).

Model Development: Logic Tree for Sources Outside of Induced Zones (Figure 3B)

The logic tree for sources outside of induced zones is shown in figure 3B. Levels 1 and 2 are 

identical for sources located within and outside of the induced zones. The adaptive model also is the 

same for sources located inside and outside of the zones. This discussion, therefore, only pertains to 

how the informed model for sources outside of the zones (fig. 3B) differs from the informed model for 

sources within the zones (fig. 3A).

For Level 3, “Catalog Durations,” additional catalogs were considered. To calculate the 

informed model hazard for sources outside of induced zones, we considered 1-year and 2-year catalogs 

and the long-term catalog applied in the 2014 NSHM (1700–2012) extending through 2015 for the 

informed model. Also, we used the 36-year catalog in lieu of the long-term catalog for the unresolved 

zones (shown in red in figure 1A and table 1). In this way, the potentially induced earthquakes that are 

several decades old will be excluded from the model. Jordan and Jones (2010) indicated that short-term 

models demonstrate a probability gain in forecasting earthquakes relative to the long-term, timeindependent models typically used in seismic hazard analysis. Therefore, for these short-term hazard 

assessments, we applied equal weighting to the various catalog durations.

For Level 4, “Smoothing Distance,” we applied 10-km and 20-km smoothing distances for the 

1- and 2-year catalogs (appendix 1) and the 50- to 75-km smoothing options from the 2014 NSHM 

(Petersen and others, 2014). Smoothing distances for the branch that applies to the NSHM long-term 

catalogs are the same as in the 2014 NSHM (Petersen and others, 2014). The unresolved zones (shown 

in red in figure 1A and table 1) were modeled using the 20-km smoothing for the 36-year catalog.

For levels 5 and 6, we only applied the M7.1 Mmax with distribution option and the suite of 

CEUS ground motion models that were applied in the 2014 NSHM (Petersen and others, 2014).

Results

We developed 1-year seismic hazard maps, hazard curves, and Modified Mercalli Intensity 

(MMI) maps to illustrate the 2016 hazard forecast for the CEUS. Each of these different products 

portrays seismic hazard information useful for understanding potential ground shaking and earthquake

effects. In this section, we show the hazard for the alternative branches used in this assessment to 

illustrate the broad uncertainties therein.

Seismic Hazard Maps

We have produced seismic hazard maps based on 1-percent probability of exceedance in 1 year 

for the year 2016 that include induced and natural earthquakes (generally consistent with an annual 

1/100 probability or rate of exceedance). We calculate the 1-percent probability of exceedance in 1 year 

hazard because (1) we only want to forecast one year of seismicity, (2) we want to consider relatively 

small chances of shaking in 1 year, and (3) these probability levels were suggested at our workshop. The 

maps are generated for peak horizontal ground acceleration (PGA) and spectral acceleration at 1-hertz 

(Hz; 1-second) and 5-Hz (0.2-second) frequencies. They are made for a uniform site condition with a 

Vs30 (shear-wave velocity in the upper 30 meters) of 760 meters per second—although some of the 

intensity maps are modified for site condition. We calculate the hazard at a grid of sites separated by 

about 0.1 degree latitude and longitude across the United States.
21

Some of the input parameters contribute more to the overall hazard uncertainty than other 

parameters. We produced PGA comparison maps to illustrate the sensitivity of the ground shaking when 

selecting the 1-year or 2-year catalogs, the 10-km or 20-km Gaussian smoothing parameters, the M6 or 

M7.1 Mmax, and the Atkinson (2015) ground motion model with 2-km or 5-km depths or the CEUS 

suite of ground motion models. (The Atkinson [2015] model only applies for M6 earthquakes and 

smaller.) This sensitivity study shows the basic elements of an uncertainty analysis. For the purpose of 

this comparison, we assume a base case defined by the 1-year catalog, 10-km smoothing applied to the 

Gaussian kernel, an M7.1 Mmax, and the CEUS suite of ground motion models. Figure 4 shows 

difference and ratio comparisons for PGA between alternative catalog durations (fig. 4A), smoothing 

distances (fig. 4B), Mmax (fig. 4C), and ground motion models (fig. 4D).
22

Figure caption on page 26.
23

Figure caption on page 26.
24

Figure caption on page 26.
25

Figure caption on page 26.
26

Figure 4. Peak horizontal ground acceleration hazard maps showing sensitivity to input parameters relative to a 

base case (the base case, shown in the upper left, is the same for all parts of the figure). For A–D, the difference 

(lower left) is the base case (upper left) minus the model with the varied parameter (upper right), and the ratio (lower 

right) is the base case divided by the model with the varied parameter. Gray dashed line indicates western boundary 

of the Central and Eastern United States (CEUS). A, Sensitivity for catalog duration. Base map: 1-year catalog. 

Varied parameter: includes a 2-year catalog. B, Sensitivity for smoothing distances. Base map: 10-kilometer (km) 

smoothing parameter. Varied parameter: incorporates a 20-km smoothing parameter. C, Sensitivity for maximum 

magnitude (Mmax). Base map: moment magnitude (M) 7.1 Mmax. Varied parameter: M6.0 Mmax (within zones). 

D, Sensitivity for ground motion model (GMM). Base map: M7.1 Mmax and CEUS GMM. Varied parameters: 

incorporates an M6 Mmax—the equation does not apply for earthquakes above M6—and incorporates the Atkinson 

(2015) GMM for 2-km rupture depth.

Figure 4A shows the PGA ground motions from the 1-year and 2-year catalogs. The differences,

which are generally within ±0.05 g with ratios mostly between 10 and 50 percent, reflect the variability 

in the earthquake locations between the two catalogs.

Figure 4B shows the PGA hazard changes caused by varying the smoothing distances (10 and 

20 km). Changes to the hazard are mostly within ±0.02 g but in localized areas can be up to ±0.05 g. 

This is consistent with ratios of about ±25 percent. Hazard is more centrally focused with the smaller 

kernel.

Figure 4C shows the PGA hazard changes at sites within the zones caused by varying the Mmax.

(There are no Mmax differences outside of the zones.) The Mmax used in the 2014 NSHM causes 

increased ground motion up to 0.1 g in northern Oklahoma and ratio increases up to 50 percent in the 

seismic hazard over a broad area, compared to the model which applies the lower M6.0 Mmax. This 

result seems reasonable because the mean Mmax of M7.1 used in the 2014 NSHM allows for 

earthquakes larger than an Mmax of M6.0. The increases are even greater for the 1-Hz (1-s) spectral 

acceleration ground motions when applying the 2014 model. This result shows that it is important for 

the science community to better understand the sizes of induced and natural earthquakes. This Mmax 

parameter may be better defined by examining faults at or beneath the surface and applying scaling 

relationships between moment magnitude and fault length or area (McNamara and others, 2015b).

Figure 4D shows the PGA hazard changes caused by varying the ground motion model and 

Mmax. The reason we change these simultaneously is because we cannot calculate ground shaking 

using the Atkinson (2015) model for M≥6 earthquakes and we do not use this ground motion model 

outside of the zones. For ground motions outside of the zones, we only apply the 2014 NSHM CEUS 

ground motion models and the 2014 Mmax distribution centered at M7.1. Therefore, the hazard changes 

for varying ground motion models are only applicable within the zones and are associated with the 

smaller Mmax of M6.0. The hazard changes are large and widespread when varying the models. The 

2014 NSHM ground motion model causes increases of 0.25 g compared to the Atkinson (2015) model. 

The 2014 ratios show higher ground shaking levels (between a factor of 2 and 3) over a broad area of 

Oklahoma, Kansas, Colorado, New Mexico, Texas, Missouri, and Arkansas compared to ground 

motions from the Atkinson (2015) model. Directly over the areas of very active seismicity in Oklahoma,

ground motions are only about 10–25 percent higher. This comparison also seems reasonable because 

the Atkinson (2015) model indicates particularly high ground motions near the earthquake but fall off 

faster than typical CEUS models—because it is based on models for the Western United States. This 

result also points out the importance of improving our understanding of ground motion from induced 

and natural earthquakes.
27

Figure 5 shows comparisons of the adaptive and informed models (fig. 5A) and the final models

for PGA (fig. 5B), as well as comparisons of 1-Hz (1-s; fig. 5C) and 5-Hz (0.2-s; fig. 5D) spectral 

accelerations obtained by equally weighting the adaptive and informed models. The differences between 

the informed and adaptive models show the effect of characterizing induced and natural earthquakes. 

The final model plots show our best estimate for three ground motion frequencies. These final maps are 

compared with the 2014 NSHM to show the changes in hazard due to the inclusion of induced 

earthquakes and earthquake rate changes.
28

Figure caption on page 32.
29

Figure caption on page 32.
30

Figure caption on page 32.
31

Figure caption on page 32.
32

Figure 5. Comparisons of the hazard maps 1-percent probability of exceedance in 1 year for adaptive and 

informed models and for the final model and the 2014 National Seismic Hazed Model (NSHM). For A–D, the 

difference (lower left) is the upper left map minus the upper right map, and the ratio (lower right) is the upper left 

map divided by the upper right map. Gray dashed line indicates western boundary of the Central and Eastern 

United States (CEUS). The New Madrid Seismic Zone, referenced in the text, is located in southeastern Missouri, 

northeastern Arkansas, and adjacent parts of Tennessee and Kentucky. A, Peak horizontal ground acceleration 

(PGA) ground motion hazard: upper left shows hazard based on the adaptive model, upper right shows hazard 

based on the informed model. B, PGA hazard: upper left shows the final hazard for the 2016 one-year model, 

upper right shows the hazard for the 2014 NSHM. C, 1-hertz (Hz; 1-second) spectral acceleration hazard: upper left 

shows the final hazard for the 2016 one-year model, upper right shows the hazard for the 2014 NSHM. D, 5-hz 

(0.2-second) spectral acceleration hazard: upper left shows the final hazard for the 2016 one-year model, upper 

right shows the hazard for the 2014 NSHM.

Figure 5A shows the PGA hazard for the adaptive and informed models. The adaptive model is 

generally similar to the informed model within zones of induced seismicity. In general, in places where 

the adaptive model hazard is higher than the hazard from the informed model, the difference is caused 

by the methodology of selecting the maximum earthquake rate. Alternatively, in places where the 

informed model hazard is higher than the adaptive model hazard, the difference is caused by the 

trimming of earthquakes applied in the adaptive model (appendix 2). Nevertheless, both models 

(adaptive and informed) produce large hazard in active zones of induced seismicity, despite fundamental 

differences in how the earthquakes are classified (natural or induced). The ground motion ratio between 

the adaptive and informed models is ±50-percent for most of the CEUS; the difference is up to 0.25 g. 

The hazard at the New Madrid Seismic Zone (southeastern Missouri, northeastern Arkansas, and

adjacent parts of Tennessee and Kentucky) is higher in the adaptive model than the informed model 

because of the following differences: (1) earthquake rates from short-term 1-year and 2-year catalogs 

are higher than rates from long-term catalogs, (2) the adaptive model uses the maximum earthquake 

rates from alternative catalog durations, (3) the informed model uses earthquake rates from the 2014 

NSHM long-term catalog for New Madrid, and (4) the adaptive model applies only the 10-km

smoothing distance for the 1-year catalog, which increases rates over the earthquake locations (fig. 3).

Figure 5B shows the PGA hazard for the final 2016 one-year model, which weights the adaptive 

and informed models equally. We compare the final model to the 2014 model that does not consider 

induced earthquakes. The 2016 seismic hazard is higher by more than a factor of 3 almost everywhere in 

the CEUS compared to the 2014 model, mostly because of increases in the potentially induced 

seismicity throughout the CEUS over the past decade. The ±50-percent ratios between the adaptive and 

informed models are smaller in most areas compared to ratios between the 2016 one-year model and the 

2014 NSHM, which are more than a factor of 3 in areas of induced activity.

Hazard Curves

Hazard curves show ground motion levels on the x-axis and annual frequency of exceedance on 

the y-axis. Figure 6 shows hazard curves for several populated places near induced earthquakes: 

Oklahoma City and Waynoka, Okla.; Anthony, Kans.; Greenbrier, Ark.; Dallas, Tex.; and Trinidad, 

Colo. The highest curves, for the Oklahoma and Kansas sites, are due to high rates of induced 

earthquakes over the past decade. Curves for Greenbrier and Trinidad are lower than the Oklahoma 

curves by about a factor of 2 or more. The Dallas curve shows more variability in shape because it is 

influenced by the distant Oklahoma seismicity but is very close to the north Texas activity. Most of the 

2016 hazard curves are higher than the 2014 model in these locations by an order of magnitude.
33

Figure 6. Peak horizontal ground acceleration hazard curves for towns and cities located near induced 

earthquakes for the 2016 one-year model and for the 2014 National Seismic Hazard Model.
34

Modified Mercalli Intensity Maps

Many users prefer models that portray earthquake shaking intensity (MMI) or earthquake effects 

(see, for example, the USGS ShakeMap products, http://earthquake.usgs.gov/earthquakes/shakemap;

Worden and others, 2010) rather than more quantitative measures of ground shaking presented in our 

previously described hazard maps. The MMI scale uses Roman numerals, typically ranging from I to X, 

to describe how people feel the earthquake and how much damage occurs at a site. For example, 

shaking at intensities of MMI I, II, or III is generally light and either not felt or felt very weakly. 

Intensities of MMI IV and V are felt by many, but the earthquake shaking typically causes only limited 

damage to windows or dishes. An intensity of MMI VI indicates minor damage such as fallen plaster or 

cracks in the walls, and intensities of MMI VII, VIII, IX, and X are related to heavier damage. For the 

purposes of this forecast, we use MMI VI as the threshold of damage.

Figures 7A and 7B show MMI maps for the United States that include the 2014 NSHM for the 

Western United States and the 2016 one-year model for the CEUS (the 2014 NSHM is a long-term 

model displayed for 1-percent chance of exceedance in 1 year, and the 2016 model is a short-term 

model). We have included the 2014 NSHM long-term tectonic hazard in these plots so that we can 

compare it with CEUS earthquake rates. We used the Wald and Allen (2007) topographic-based site 

classification to assess the soils at each grid point applied in the calculations. We applied the equations 

of Worden and others (2012) to translate the PGA and 1-Hz (1-s) spectral acceleration ground motions 

into MMI. The ground motions were then amplified based on the Stewart and Seyhan (2013) 

amplification factors. The maps using PGA to calculate MMI (fig. 7A) show quite high MMI (VIII or 

more) and larger areas of potential damage than the maps that apply 1-Hz (1-s) spectral acceleration 

(fig. 7B). The maps that use 1-Hz (1-s) spectral acceleration to obtain MMI (fig. 7B) only reach about 

MMI VI; they do not allow for the larger intensities observed during the 2011 Prague, Okla., earthquake

(MMI VII–VIII; Hough, 2014). Nevertheless, the maps based on 1-Hz (1-s) spectral acceleration may 

give results that are more similar to peak velocities, which are often thought to be a better predictor of 

damage than other frequencies. Worden and others (2012) discussed combining various measures to 

obtain more robust MMI estimates, so we have averaged the PGA-based and 1-Hz (1-s) spectral 

acceleration-based MMI maps to obtain the final map for this report (see next section, “Final Maps”).
35

Figure caption on page 37.
36

Figure caption on page 37.
37

Figure 7. Modified Mercalli Intensity maps and chance of damage for the Western United States and the Central 

and Eastern United States (CEUS) based on peak horizontal ground acceleration and 1-hertz spectral acceleration. 

For A–D, the map of the Western United States shows data based on the long-term 2014 National Seismic Hazard 

Model, and the map of the CEUS shows data based on the 2016 one-year model. A–B, Modified Mercalli Intensity 

(MMI) maps at 1-percent probability of exceedance in 1 year for the United States obtained (A) by converting peak 

horizontal ground acceleration (PGA) to MMI and (B) by converting 1-hertz (Hz; 1-second) spectral acceleration to 

MMI. These maps (A and B) are site amplified. C–D, Chance of damage (MMI greater than or equal to VI) from an 

earthquake in 2016, obtained from the rate of occurrence of (C) PGA ground motions and (D) 1-Hz (1-second) 

ground motions that are correlated with MMI VI for a uniform alluvial soil (National Earthquake Hazards Reduction 

Program site class D).

Figures 7C and 7D show maps of the United States that also include the 2014 NSHM long-term 

tectonic hazard for the Western United States for comparison with the 2016 model hazard for the CEUS. 

In these maps, we consider the chances of experiencing damaging earthquakes for a fixed ground 

shaking level that corresponds with MMI VI (0.12 g PGA and 0.10 g 1-Hz [1-s] spectral acceleration). 

As in figures 7A and 7B, figures 7C and 7D show higher hazard for the PGA-based MMI compared to 

MMI obtained from converting 1-Hz (1-s) spectral acceleration. A weighted combination (average) of 

these maps may provide a more robust estimate of the potential earthquake intensity (MMI).

Final Maps

The final hazard maps show forecasts for MMI values and the chance of damaging earthquakes

(MMI of VI or more). These parameters may be more comprehensible for many people than 

unconverted PGA or spectral acceleration values (fig. 8). Figure 8A presents an MMI map with a 

1-percent probability of exceedance in 1 year for the United States obtained by averaging figures 7A

and 7B. Sites in north-central Oklahoma, southernmost Kansas, central Arkansas, the Dallas-Fort Worth 

area, and the Raton Basin near the border of Colorado and New Mexico show MMI values that are 

greater than VI, VII, or VIII. Forecasted ground shaking in the north-central Oklahoma and Dallas, 

Tex., regions are consistent with damage levels observed over the past decade (Wald and others, 2011).
38

Figure caption on page 40.
39

Figure caption on page 40.
40

Figure 8. Final hazard maps for Modified Mercalli Intensity (MMI) and chance of damage for the Western United 

States and the Central and Eastern United States (CEUS) based on averages of MMIs converted from peak 

horizontal ground acceleration and 1-hertz spectral acceleration. For A–B, the map of the Western United States 

shows data based on the long-term 2014 National Seismic Hazard Model, and the map of the CEUS shows data 

based on the 2016 one-year model. A, Modified Mercalli Intensity (MMI) map at 1-percent probability of 

exceedance in 1 year for the United States obtained by averaging figures 7A and 7B. B, Chance of damage from 

an earthquake during 2016 obtained by averaging figures 7C and 7D.

Many of the places where damage is likely in our 2016 model have experienced damage in the 

past decade. The USGS operates a “Did You Feel It?” Web tool to archive and analyze responses from 

individuals who have felt an earthquake. These responses are then assigned a shaking intensity (Wald 

and others, 2011). Since 2010, responses from earthquakes have been associated with MMI VI or 

greater in states experiencing induced earthquakes. About 1,500 individual citizen reports greater than 

MMI VI have been archived by “Did You Feel It?”: 1,300 in Oklahoma, 102 in Kansas, 50 in Arkansas, 

42 in Texas, and 6 in Colorado and New Mexico (V. Quitoriano and D. Wald, USGS, written commun., 

2016). This information, however, includes responses for all earthquakes, so it is possible that some of 

these events (especially from eastern Arkansas) may not correspond with induced earthquake activity. 

Nevertheless, the recent induced activity in these states is probably creating many or most of the 

responses collected with the “Did You Feel It?” tool.

The second version of the map (fig. 8B) shows the chance of damage from an earthquake during 

2016 using the same results as applied in figure 8A. The chance of having an event with MMI VI or 

greater is over 10 percent per year in north-central Oklahoma and southernmost Kansas. This chance of 

damage in parts of Oklahoma is similar to the chance of damage at high-hazard sites in California. More 

research is needed, however, to better quantify how recorded ground motions relate to observed damage 

from induced earthquakes. Figure 8B shows the chance of damage from an earthquake in 2016 by 

averaging the exceedance probabilities obtained from PGA at 0.12 g and 1-Hz (1-s) spectral 

acceleration at 0.10 g, which are considered the threshold of damaging ground shaking levels. Again, 

we have averaged both of these ground motion types because that provides a more robust estimate of the 

potential damage. The potential damage probabilities from an earthquake in 2016 are particularly high 

in parts of north-central Oklahoma, northern Texas, southern Colorado/northern New Mexico, and 

north-central Arkansas. Some locations, such as small areas of Alabama and Mississippi, also show 

increased chance of damage from an earthquake in 2016 based on increased earthquake rates over the 

last few years. The hazard is also high over the New Madrid seismic zone, which experiences 

earthquakes at regular intervals (Petersen and others, 2014). These high chances of damage are similar 

to those found in parts of California (Petersen and others, 2014). The potential for damage from 

earthquakes near M6 (applied in this model) is consistent with other recent earthquakes: the Napa,

Calif., M6.0 earthquake caused MMI of VIII (Did You Feel It?; U.S. Geological Survey, 2016d) or IX

(ShakeMap; U.S. Geological Survey, 2016e); the Mineral, Virginia, M5.8 earthquake caused MMI of 

VII (Did You Feel It?; U.S. Geological Survey, 2016c); and the Prague, Okla., M5.6 earthquake caused 

MMI of VII (Did You Feel It?; U.S. Geological Survey, 2016b).

Conclusions

This report describes a 1-year seismic hazard assessment for the CEUS that includes induced and 

natural earthquakes. We developed two models for this purpose: (1) an informed model that depends 

mostly on the 1-year and 2-year earthquake catalogs as well as a discrimination between induced and 

natural earthquakes and (2) an adaptive model that considers the maximum earthquake rate in 1-year, 
41

2-year, 36-year, and longer-term intervals and that does not discriminate between induced and natural 

earthquakes. Forecasts from these two hazard models are significantly higher than the 2014 NSHM by a 

factor of 3 or more. Generally, the two models agree within 50 percent or less from one another. The 

higher hazard levels in active injection areas could lead to potential damage across Oklahoma, Kansas, 

Colorado, New Mexico, Texas, and Arkansas. High hazard levels in some of these zones of induced 

seismicity are comparable with those in California and New Madrid, which also have high earthquake

rates. Over the past decade, damage has already been observed at several locations in these states.

However, some areas that have previously experienced induced earthquakes have quieted down over the 

past years, and the resulting hazard reverts back to what is portrayed in the 2014 National Seismic 

Hazard Model.

Further research from the science community on induced and natural earthquakes and their 

related ground shaking will improve these models. Future hazard models could be updated every year,

or even over a shorter time period, to improve the model’s predictability and its usefulness to policy 

makers. Varying disposal activities, policy changes, and further scientific research will change this 

model. The analysis could also be expanded to include induced earthquakes in the Western United 

States. Uncertainties are high in this analysis, and an important topic for future research is to try to 

quantify and reduce these uncertainties. Such efforts would be in line with Operational Earthquake 

Forecasting objectives that have been proposed for use by policy makers in decision making (Jordan and 

Jones, 2010; Field and others, 2016). Policy makers may consider these maps in formulating risk

mitigation strategies.

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Appendix 1. Likelihood Testing for Smoothed Seismicity Parameters, Oklahoma 

and Southern Kansas

Smoothing parameters for the earthquake rate models are informed by likelihood testing of the 

smoothing distance and catalog duration and timing. Details about the likelihood calculations are 

provided elsewhere (Moschetti, 2015); here, we only summarize those results. Likelihood testing 

employs trial earthquake rate models developed by varying the smoothing distance and time period for 

computing the smoothed seismicity models. All smoothed seismicity models were developed with 

isotropic, Gaussian smoothing kernels, where the smoothing distance represents the kernel width. 

Likelihood testing was only carried out for the earthquakes occurring in the Oklahoma-Kansas zone of 

induced seismicity (fig. 1, table 1) because other zones contained too few events for the statistical 

analysis.

Likelihood testing follows previous work (for example, Werner and others, 2010; Moschetti, 

2015) by comparing trial smoothed seismicity models with independent sets of later-occurring 

earthquakes (“testing catalogs”). Likelihood values derive from Poisson probabilities and follow a ratenormalized formulation that provides information about the spatial distribution of seismicity only 

(Werner and others, 2010). Trial rate models were developed from smoothing distances of 5 kilometers

(km), 10 km, 15 km, 20 km, 25 km, 35 km, 50 km, 75 km, and 100 km, and from events occurring in 

1-year time periods from calendar years 2009 through 2014. Likelihood of the smoothed seismicity 

models was computed from moment magnitude (M) ≥2.5 and M≥3.5 earthquakes from the period 

January–June 2015. Likelihood values are presented as the per-earthquake information gain achieved 

from the use of the smoothed seismicity model compared to the use of a uniform-rate model with the 

zone of induced seismicity.

Information gains from the likelihood testing are summarized in figure 1–1. Maximum 

information gains are achieved from the use of earthquakes occurring in 2014. The corresponding 

smoothing distance for this smoothed seismicity model is developed from a 10-km smoothing distance. 

Because of error bounds on the information gain estimates, we characterize the 10- and 20-km 

smoothing distances as optimized smoothing distances (Rhoades and others, 2011).

Development of the induced-earthquake rate models employs the main scientific findings of this 

likelihood testing. For the informed model (see the text of this report for details), most weight (0.90) 

within zones of induced seismicity is applied to the logic tree branch for the 1-year earthquake catalog. 

Equal weight (0.50) is applied to smoothing distances of 10 km and 20 km within the induced seismicity 

zones.
49

Figure 1–1. Information gains (A) and optimized smoothing distances (B) as a function of the catalog year of the 

events used to develop trial smoothed seismicity models. Results from moment magnitude (M) ≥2.5 and M≥3.5 

earthquakes are identified by the explanation in fig. 1–1A.

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50

Appendix 2. The Adaptive Model

In this appendix, we provide further details about the adaptive model, an alternative earthquake

rate model in which we assume that we cannot distinguish between natural and induced earthquakes.

Rather than basing the 2016 one-year forecast on earthquake rates estimated over a single time window, 

the adaptive model compares earthquake rates over multiple time windows (1-year, 2-year, and 36-year

durations) and uses the maximum of these rates only if it is significantly greater than the rates of the 

long-term 2014 National Seismic Hazard Model (NSHM). Otherwise, the long-term 2014 NSHM rate is 

used. In each spatial grid cell, we computed the rates of earthquakes of moment magnitude (M) 2.7 or 

greater in the declustered catalog over the three different time windows. The rates were obtained by 

spatially smoothing the earthquake counts over each time period with a Gaussian kernel using a 

10-kilometer (km) correlation distance for the 1-year rate, a 20-km distance for the 2-year rate, and a 

50-km distance for the 36-year rate. However, single mainshocks that happen to occur in these smaller 

time windows may raise the short-term rates far above the long-term 2014 NSHM rate and yet may not 

be unexpected given the long-term rate model. To minimize the effect of this type of fluctuation, we 

have developed the following trimming strategy. If the number of earthquakes within some specified 

trimming distance of a grid cell over either the 1-year, 2-year, or 36-year time period does not exceed 

the expected number given the long-term 2014 NSHM at the 95-percent confidence level, then we 

simply use the 2014 NSHM rate in that cell. At present, the trimming distance is set to match the 

changes we implement in the spatial smoothing kernel over time (for example, the trimming distance is 

50 km for the 36-year window, 20 km for the 2-year window, and 10 km for the 1-year window) and in 

general should reflect the improvement in earthquake location accuracy over time. Figure 2–1

demonstrates the effect of this trimming strategy. When trimming is not applied, there are many small 

areas that have a high maximum rate simply because of isolated mainshocks temporarily increasing the 

short-term rate above the long-term (fig. 2–1A). Trimming masks out these areas, limiting the higher 

maximum rates to the areas with rates that are significantly greater than the long-term 2014 NSHM rate 

(fig. 2–1B). Figure 2–1C identifies the earthquakes that remain after the trimming. Figure 2–2 compares 

the trimmed and untrimmed rates using difference and ratio maps. The adaptive model therefore 

essentially consists of the maximum rate from among the trimmed earthquake rates from the three time 

windows and the earthquake rate from the 2014 NSHM (fig. 2–1B).
51

Figure 2–1. Maps showing maximum earthquake rates from the 

1-year, 2-year, 36-year, and 2014 National Seismic Hazard Model 

long-term catalogs, and a seismicity map. A, Rates without 

trimming. Many hotspots of high maximum rates, the result of 

isolated mainshocks, are evident. B, Rates with trimming. The 

effect of isolated mainshocks is greatly reduced. C, Earthquakes of 

magnitude 2.7 or greater in the declustered catalog for 1980–2015. 

Gray earthquakes are masked by trimming; only the black 

earthquakes contribute to the maximum rates.
52

Figure 2–2. Maps comparing trimmed and untrimmed earthquake rates. A, Difference of trimmed rates minus 

untrimmed rates. B, Ratio of trimmed rates over untrimmed rates.
ISSN 2331-1258 (online)

http://dx.doi.org/10.3133/ofr20161035