Topographic Slope as a Proxy for Seismic opographic Slope as a Proxy for Seismic 

Site-Conditions (V 30

S ) and Amplification ) and Amplification 

Around the Globe Around the Globe 

Open-File Report 2007-1357 Open-File Report 2007-1357
 

U.S. Department of the Interior U.S. Department of the Interior 

U.S. Geological Survey U.S. Geological Survey 
Topographic Slope as a Proxy for Seismic 

Site-Conditions (VS

30) and Amplification 

Around the Globe 

By Trevor I. Allen and David J. Wald 

Open-File Report 2007–1357 

U.S. Department of the Interior 

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

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia 2007 

For product and ordering information: 

World Wide Web: http://www.usgs.gov/pubprod 

Telephone: 1-888-ASK-USGS 

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

its natural and living resources, natural hazards, and the environment: 

World Wide Web: http://www.usgs.gov 

Telephone: 1-888-ASK-USGS 

Suggested citation: 

Allen, T.I., and Wald, D.J., 2007, Topographic slope as a proxy for global seismic site conditions 

(VS

30) and amplification around the globe: U.S. Geological Survey Open-File Report 2007-1357, 69 p. 

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

endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report. 

ii
Contents 

Contents ..............................................................................................................................................................................iii

Figures .................................................................................................................................................................................iv

Tables ..................................................................................................................................................................................vi

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

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

Data.......................................................................................................................................................................................4

Methodology........................................................................................................................................................................4

Application in Active Tectonic Regions .........................................................................................................................6

California ..........................................................................................................................................................................6

Taiwan ............................................................................................................................................................................13

Alaska .............................................................................................................................................................................14

Nevada............................................................................................................................................................................17

Salt Lake City, UT ..........................................................................................................................................................19

Seattle, WA....................................................................................................................................................................20

Indonesia........................................................................................................................................................................22

Iran ..................................................................................................................................................................................24

Italy..................................................................................................................................................................................26

Japan ..............................................................................................................................................................................28

Mexico ............................................................................................................................................................................31

New Zealand .................................................................................................................................................................34

The Philippines..............................................................................................................................................................38

Puerto Rico.....................................................................................................................................................................39

Turkey .............................................................................................................................................................................41

Southern Europe ...........................................................................................................................................................43

Application in Intraplate Regions ..................................................................................................................................45

Mississippi Embayment Region .................................................................................................................................45

Australia .........................................................................................................................................................................47

Scandinavia ...................................................................................................................................................................49

Application for the Continental U.S...............................................................................................................................50

Western U.S...................................................................................................................................................................50

Eastern U.S. ...................................................................................................................................................................53

Web Delivery of Global VS

30 Maps..................................................................................................................................54

USGS Topographic Slope VS

30 Model .......................................................................................................................55

Mosaic VS

30 Model.........................................................................................................................................................55

Custom VS

30 Model.........................................................................................................................................................55

Discussion..........................................................................................................................................................................57

iii
Why Topographic Slope Works as a Proxy for VS

30 ................................................................................................57

Comparison with Geologically-Based VS

30 Maps ....................................................................................................58

Conclusions .......................................................................................................................................................................59

Acknowledgments............................................................................................................................................................61

References Cited ..............................................................................................................................................................62

Glossary..............................................................................................................................................................................69

Figures 

1. A, The topographic relief map for the State of California. B, Site-condition map for California 

based on geology and VS observations. C, Site-condition map derived from topographic 

slope.. ......................................................................................................................................................................8

2. Correlations of measured VS

30 (m/s) versus topographic slope (m/m) for A, active tectonic, 

and B, stable continental regions.. ....................................................................................................................9

3. Histograms indicating logarithmic differences of measured Californian VS

30 values compared 

with A, values derived from topographic slope correlations and, B, values based on existing 

VS

30 site-condition maps. ......................................................................................................................................9

4. A, Topographic map of the San Francisco Bay Area. B, Site-condition map based on geology 

and VS

30 observations. C, Site-condition map derived from topographic slope. D, The ratio of 

the predicted amplification for the geologically- and topographically-based VS

30 maps.. ....................11

5. A, Topographic map of the Los Angeles region. B, site-condition map based on geology and 

VS

30 observations. C, Site-condition map derived from topographic slope. D, The ratio of the 

predicted amplification for the geologically- and topographically-based VS

30 maps. ............................12

6. A, Topographic map of Taiwan with elevation in meters. B, Site-condition map based on 

geology and VS

30 observations. C, Site-condition map derived from topographic slope. D, 

Population density map of Taiwan...................................................................................................................15

7. Histograms indicating logarithmic differences of measured Taiwanese VS

30 values compared 

with A, values derived from topographic slope correlations and, B, values based on existing 

VS

30 site-condition maps .....................................................................................................................................15

8. A, The topographic relief map for the State of Alaska. B, Site-condition map derived from 

topographic slope. ..............................................................................................................................................17

9. A, Topographic map of the Anchorage, Alaska region. B, Site-condition map derived from 

topographic slope ...............................................................................................................................................18

10. Histogram indicating logarithmic differences of combined Anchorage compared with values 

derived from topographic slope correlations. ...............................................................................................18

11. A, Topographic map of the Salt Lake City, Wasatch Front region of Utah. B, Site-condition 

map based on geology and VS observations. C, Site-condition map derived from topographic 

slope. .....................................................................................................................................................................20

iv
12. Histograms indicating logarithmic differences of measured Utah VS

30 values compared with 

A, values derived from topographic slope correlations and, B, values based on existing VS

30

site-condition maps.. ..........................................................................................................................................21

13. A, Topographic map of the Seattle, Washington, region. B, Site-condition map based on 

geology and VS observations. C, Site-condition map derived from topographic slope.........................22

14. Histograms indicating logarithmic differences of measured Seattle, Washington VS

30 values 

compared with A, values derived from topographic slope correlations and, B, values based 

on existing VS

30 site-condition maps.................................................................................................................23

15. A, Topographic map of Indonesia. B, Site-condition map derived from topographic slope. C, 

Population density map of Indonesia..............................................................................................................25

16. A, Topographic relief map for Iran, superimposed with the epicenters of recent deadly 

earthquakes. B, Site-condition map derived from topographic slope. ....................................................27

17. A, Topographic map of Italy. B, Site-condition map based on geology and VS observations. C, 

Site-condition map derived from topographic slope. D, Population density map of Italy ....................28

18. Histograms indicating logarithmic differences of Italian VS

30 values compared with A, values 

derived from topographic slope correlations and, B, values based on existing VS

30 sitecondition maps ....................................................................................................................................................29

19. A, Topographic map of Japan. B, Site-condition map based on geology and VS

30 observations. 

C, Site-condition map derived from topographic slope. D, Population density of Japan.....................32

20. Histograms indicating logarithmic differences of Japanese VS

30 values, compared with A, 

values derived from topographic slope correlations and, B, values based on existing VS

30 sitecondition maps ....................................................................................................................................................32

21. VS

30 map for Mexico employing the coefficients for active tectonic regions...........................................33

22. A, Topographic relief map of Mexico City. B, Site-condition map derived from topographic 

slope. C, Population density map of Mexico City. ........................................................................................34

23. A, Topographic relief map for New Zealand’s North Island. B, Site-condition map derived 

from topographic slope......................................................................................................................................36

24. A, Topographic relief map for New Zealand’s South Island. B, Site-condition map derived 

from topographic slope......................................................................................................................................37

25. A, Topographic map of the Greater Wellington region, New Zealand. B, Site-condition map 

derived from topographic slope .......................................................................................................................37

26. A, Topographic map of the Philippines. B, Site-condition map derived from topographic 

slope. C, Population density map of the Philippines....................................................................................39

27. A, Topographic map of Puerto Rico. B, Site-condition map based on geology and VS

30

observations. C, Site-condition map derived from topographic slope.....................................................40

v
vi

28. Histograms indicating logarithmic differences of Puerto Rican VS

30 values compared with A, 

values derived from topographic slope correlations and, B, values based on existing VS

30 sitecondition maps ....................................................................................................................................................41

29. A, Topographic map of Turkey. B, Site-condition map based on surficial geology from KOERI. 

C, Site-condition map derived from topographic slope ...............................................................................43

30. Histograms indicating logarithmic differences of Turkish VS

30 values compared with A, values 

derived from topographic slope correlations and, B, values based on existing VS

30 sitecondition maps ....................................................................................................................................................43

31. A, Topographic relief map for Southern Europe, Anatolia, and Northern Africa. B, Sitecondition map derived from topographic slope ............................................................................................44

32. A, Topographic map showing the Mississippi Embayment region of the central United States. 

B, Site-condition map based on geology and VS observations C, Site-condition map derived 

from topographic slope......................................................................................................................................46

33. Histograms indicating logarithmic differences of VS

30 values for the Mississippi Embayment 

region, compared with A, values derived from topographic slope correlations and, B, values 

based on existing VS

30 site-condition maps ....................................................................................................47

34. Site-condition map for Australia derived from topographic slope ............................................................48

35. Histogram indicating logarithmic differences of Australian VS

30 data compared with values 

derived from topographic slope correlations ................................................................................................49

36. A, Topographic relief map for the stable continental setting of Norway. B, Site-condition 

map derived from topographic slope ..............................................................................................................50

37. Estimated site-condition map for the continental United States, west of and including the 

Rocky Mountains ................................................................................................................................................52

38. Estimated site-condition map for the continental United States east of the Rocky Mountains...........54

39. Screenshot of the web page for the global topographically-based VS

30 grid delivery service. ............56

Tables 

1. Short-period (0.1 to 0.5 s) site amplification factors of Borcherdt (1994). ..................................................3

2. Summary of slope ranges for subdivided NEHRP VS

30 categories................................................................5
Topographic Slope as a Proxy for Seismic Site-Conditions 

(VS

30) and Amplification Around the Globe 

By Trevor I. Allen and David J. Wald1

Executive Summary 

It is well-known that large global earthquakes can have a dramatic effect on local communities 

and the built environment. Moreover, ground motions amplified by surficial materials can exacerbate 

the situation, often making the difference between minor and major damage. For a real-time earthquake 

impact alert system, such as Prompt Assessment of Global Earthquakes for Response (PAGER) (Wald 

and others, 2006), we seek to rapidly evaluate potential ground shaking in the source region and 

subsequently provide an estimate of the population exposure to potentially fatal levels of ground 

shaking in any region of the world. The contribution of surficial geology (particularly soft sediments) 

to the amplification of ground shaking is an important component in predicting the levels of ground 

motion observed at any site. Unfortunately, the availability of information regarding seismic siteconditions is only available at a few sites around the globe. 

Herein, we describe a methodology for deriving maps of seismic site-conditions anywhere in the 

world using topographic slope as a proxy. Average shear-velocity down to 30 m (or VS

30) measurements 

are correlated against topographic slope to develop two sets of coefficients for predicting VS

30: one for 

active tectonic regions that possess dynamic topographic relief, and one for stable continental regions 

where changes in topography are more subdued. These coefficients have been applied to the 

continental United States, in addition to other regions around the world. They are subsequently 

compared to existing site-condition maps based on geology and observed VS

30 measurements, where 

available. The application of the topographic slope method in regions with abundant VS

30 measurements 

(for example California, Memphis, and Taiwan) indicates that this method provides site condition-maps 

of similar quality, or in some cases, maps superior to those developed from more traditional techniques. 

Having a first-order assessment of seismic site-conditions anywhere in the world provides a 

valuable tool to rapidly estimate ground motions following any global earthquake, the primary 

motivation for this research. These VS

30 maps will enable us to better quantify possible ground shaking 

and rapidly deliver these predictions to emergency managers and responders. In addition, the VS

30 maps 

for the globe will also have practical applications for numerous related probabilistic- and scenario-based 

studies. To date, several researchers have requested maps or have used the approach outlined herein 

for their own applications (for example Cagnan and Kariptas, written commun., 2007; Harmandar and 

others, 2007). 

Given that we anticipate a significant demand for these products, we have developed an internet 

delivery service so that users can download maps and grids of seismic site-conditions for specified 

regions. To some extent, these grids can also be customized by the user if they disagree with the 

predefined correlations derived using the methodologies described within this report. 

Finally, this report represents a more comprehensive account of this technique and provides a 

more fully illustrated global description of results than that given in Wald and Allen (2007), which has 

been accepted for publication in the Bulletin of the Seismological Society of America. 

 

1

 U.S. Geological Survey, Geologic Hazards Team, 1711 Illinois St., Golden, CO 80401 

1
Introduction 

Recognition of the importance of the ground motion amplification from regolith has lead to the 

development of systematic approaches to mapping seismic site-conditions (for example, Park and 

Elrick, 1998; Wills and others, 2000; Holzer and others, 2005) as well as to quantifying both amplitudeand frequency-dependent site amplifications (for example, Borcherdt, 1994). A now standardized 

approach is measuring or mapping the average shear-wave velocity in the upper 30 m, VS

30. Moreover, 

many U.S. Building Codes now require site characterization to be explicitly in terms of VS

30 (for 

example, Building Seismic Safety Council, 2000; Dobry and others, 2000). In addition, many of the 

ground motion prediction equations (for example, Boore and others, 1997; Chiou and Youngs, 2006) 

are calibrated against seismic station site-conditions described with VS

30 values. 

Maps of seismic site-conditions on regional scales are relatively rare since they require 

substantial investment in geological and geotechnical data acquisition and interpretation. In many 

seismically active regions of the world, information about surficial geology and shear velocity either 

does not exist, varies dramatically in quality, varies spatially, or is not easily accessible. Such maps are 

available for only a few regions, predominantly in seismically active urban areas of the world. 

Topographic elevation data, on the other hand, are available at uniform sampling for the globe. 

Intuitively, topographic variations should be an indicator of near-surface geomorphology and lithology 

to the first order, with steep mountains indicating rock, nearly-flat basins indicating soil, and a 

transition between the end members on intermediate slopes. Indeed, the similarity between the 

topography of California (fig. 1a) and the surficial site-condition map derived from geology (fig. 1b) is 

striking. In addition, recent studies have confirmed good correlations among VS

30 and both slope and 

geomorphic indicators in Japan (Matsuoka and others, 2005) and between and elevation VS

30 in Taiwan 

(Chiou and Youngs, 2006). Other geoscience disciplines have used similar topography-based 

techniques to characterize thickness of sediment deposits for hydrologic and geomorphic purposes (for 

example, Gallant and Dowling, 2003). 

Whether topography alone can routinely distinguish between more subtle variations in surficial 

geology and, in particular, shallow site-conditions (and thus ground motion amplification), is the 

subject of this analysis. Our primary hypothesis is that the similarity of geology and topography, or 

more specifically, the slope of topography, may be exploited to provide a first-order assessment of sitedependent features of seismic hazard. This is particularly important in regions that do not possess highquality surficial geology or regolith maps, or for areas where geology maps have not been sufficiently 

correlated with VS

30 measurements. 

Slope of topography, or gradient, should be diagnostic of VS

30, since more competent (highvelocity) materials are more likely to maintain a steep slope, whereas deep basin sediments are 

deposited primarily in environments with very low gradients. Furthermore, sediment fineness, itself a 

proxy for lower shear velocity (Park and Elrick, 1998), should relate to slope. For example, steep, 

coarse, mountain-front alluvial fan material typically grades to finer deposits with distance from the 

mountain front and is deposited at decreasing slopes by less energetic fluvial and pluvial processes. 

The motivation for deriving a relationship between topography and site-condition comes from a 

practical need to characterize approximate site amplification as part of an effort to rapidly predict 

ground shaking and earthquake impact globally. The prediction of reliable ground-motions is a key 

objective for the Prompt Assessment of Global Earthquakes for Response (PAGER) program of the 

U.S. Geological Survey (USGS) National Earthquake Information Center (Wald and others, 2006). For 

PAGER, we must compute empirically-based ShakeMaps (Wald and others, 1999b; Wald and others, 

2005) that incorporate our best estimate of seismic site-conditions in any region of the world. Relying 

on ground motion predictions on rock sites rather than considering potential modification of shaking 

from regolith can result in differences in ground motion of up to 250 percent (see table 1). This 

2
difference can be equivalent to more than a full unit in shaking intensity (Wald and others, 1999a). 

Consequently, we require at least a first-order approximation of seismic site-conditions to enable 

modifications to ground motion predictions. Beyond this specific application, we expect that such 

correlations may be useful for other seismological and geotechnical applications, including introducing 

site amplification to regional hazard maps. 

It should be stressed that VS

30 is not the only factor that dictates the ground motion response at a 

particular site. Deep basins can introduce amplified ground-motion at low frequencies as a result of 

resonance effects (for example, Campillo and others, 1989). Alternatively, deep low-velocity structures 

can attenuate much of the high-frequency energy before it reaches near-surface sediments; thus ground 

motions may be deamplified relative to surrounding regions on firmer sites where seismic waves are 

transmitted more efficiently. In addition, topographic effects on rock sites may act to enhance ground 

motions (for example, Dowrick and others, 1995). 

In our analysis, we first correlate 30 arc second topographic data with VS

30 measurements in 

areas of active tectonics to develop a set of coefficients to predict VS

30. We then produce VS30

 maps, 

effectively forward predictions of VS

30 from topographic slope, in areas where the VS30 measurements 

originate and compare our estimated VS

30 values to the observations, both visually and statistically. In 

addition, we compare our topographically-based maps with existing VS

30 site-condition maps used for 

ShakeMap and other applications that are based initially on geologic maps. These analyses are then 

repeated for VS

30 data aggregated in stable continental regions. Assuming these correlations can be used 

in other regions where abundant VS

30 are not available, we apply this technique, based primarily on 

United States and Taiwanese data, to estimate seismic site-conditions to many other regions around the 

world using the correlation. Finally, we use our active tectonic and stable-continent VS

30-topographic 

slope correlations to produce regional-scale site-condition maps for the continental United States. 

Some of the example regions given in this report may not have either measured VS

30 data associated to 

validate the model or a corresponding geologically-based VS

30 map. These examples can be considered 

as forward predictions and are included to illustrate the potential of this technique. 

The topographic slope method has several limitations, and we are currently considering various 

approaches that may improve this technique. For example, remote sensing techniques that estimate 

composition of surficial materials could be employed in certain geological conditions where slope is a 

relatively poor predictor (for example unweathered volcanic plateaus or flat-lying carbonates). Cases 

that we have observed are noted in this report, but overall, this technique does provide a robust firstorder assessment of global seismic site-conditions and will be a valuable tool in rapid assessment of 

ground motion or regional earthquake hazard estimation. 

Table 1. Short-period (0.1 to 0.5 s) site amplification factors from equation 7a, and mid-period (0.4 to 2.0 s) from equation 

7b of Borcherdt (1994). Class is NEHRP letter classification; VS is mean VS

30 velocity (m/s) from Wills and others (2000), and 

PGA is the cutoff input peak acceleration in cm/s2 (see Borcherdt, 1994, for more details). 

Class VS Short-period (PGA) Mid-period (PGV) 

PGA 150 250 350 150 250 350 

B 686 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

C 464 1.15 1.10 1.04 0.98 1.29 1.26 1.23 1.19 

D 301 1.33 1.23 1.09 0.96 1.71 1.64 1.55 1.45 

E 163 1.65 1.43 1.15 0.93 2.55 2.37 2.14 1.91 

3
Data 

Measured VS

30 data have been compiled from several sources. We note that VS30 “data” 

themselves require significant interpretation and that not all approaches for resolving VS

30 are equal, nor 

do they produce equivalent results. We do not appraise the quality of the VS

30 measurements herein. 

However, in our analyses, we do have the opportunity to compare the various data sets with one another 

within the framework of an independent parameter; namely, slope of topography. 

In California, we use some 767 shear-velocity measurements (provided by C. Wills, written 

commun., 2005). Many of these data were used to develop the current Californian Site-conditions Map 

(Wills and others, 2000). Values of VS

30 for Salt Lake City and the Utah ShakeMap VS30 site-condition 

map were provided by K. Pankow (written commun., 2006) and represent measurements gathered by 

the Utah Geological Survey from other sources (Ashland and McDonald, 2003). Central United States 

VS

30 data (432 sites in total) are obtained from R. Street (written commun., 2005). Many of these data 

were assembled by the work of Street and others (2001) and include sites in Tennessee, Missouri, 

Kentucky, and Arkansas. We also acquired VS

30 maps used for ShakeMap purposes from network 

operators in California, Utah, and Memphis. 

Outside the United States, we use observations from Taiwan (387 sites) and Italy (43 sites) 

compiled by the Pacific Earthquake Engineering Research Center (PEER) Next Generation Attenuation 

(NGA) project courtesy of W. Silva (written commun., 2006) and available online at 

http://peer.berkeley.edu /products/nga_project.html. Data for 436 sites across Australia were provided 

by Geoscience Australia, collected under the auspices of their National Risk Assessments Program for 

urban areas (for example, Dhu and Jones, 2002; Jones and others, 2005). Additional Australian data 

were obtained from recent Spectral Analysis of Surface Waves (SASW) surveys (Collins and others, 

2006) at several ground motion recording sites. We also obtained national site-condition maps for 

Taiwan (C.-T. Lee, written commun., 2007), Italy (A. Michelini, written commun., 2006), Puerto Rico 

(G. Cua, pers. Comm., 2007), and Turkey (Z. Cagnan, written commun., 2007) for comparative 

purposes. 

For topography, we employ the Shuttle Radar Topography Mission 30-sec (SRTM30) global 

topographic data set (Farr and Kobrick, 2000). The SRTM30 data are considered an upgrade from the 

commonly-used USGS 30-sec topographic data (GTOPO30). We use the 30-sec data in our analysis 

rather than some of the higher-resolution data sets available because those data are not available or 

complete on a global scale. Wald and others (2006) showed that higher-resolution details of siteconditions can indeed be recovered with 9 arc sec data in California, but that finer resolution is not yet 

uniformly available globally. It is also important to note that different resolution topographic data will 

result in varying slope values, particularly in areas of high relief, and may require alternative 

correlations with VS

30 than presented herein. It is worth noting that if the resolution of the topography 

data is lower than the ridge-to-valley wavelength in a particular region, it will tend to artificially flatten 

the perceived topography on average. 

Other data described in this paper include VS

30 values from Puerto Rico (J. Odum, written 

commun., 2006), Seattle, Washington (I. Wong, written commun., 2006), and New Zealand . A subset 

of VS

30 data for Japan was provided by H. Kawase (written commun., 2006) and the corresponding KNet strong-motion station locations were obtained online at http://www.kyoshin.bosai.go.jp/knet/pubdata/sitedb/sitepub_en.csv. 

Methodology 

We first correlate VS

30 (m/s) with topographic slope (m/m) at each VS30 measurement point for 

data in active tectonic areas (fig. 2a). Color-coded symbols represent data from different geographic 

4
regions: California, Taiwan, Italy, and Utah. Figure 2b represents the correlation between VS

30 and the 

slope for stable continental regions employing measurements from the Mississippi Embayment region 

in the central United States, and Australia. The overall trend in both figures illustrates increasing VS

30

with increasing slope, indicative of faster, more competent materials holding steeper slopes. There is 

significant scatter, yet we will show that the trend is sufficient to be used as a reliable predictor of VS

30

. 

However, there are likely biases in data sampling; in particular, the lack of VS

30 measurements at steeper 

gradients. Most of the VS

30 data are found to sample relatively low gradients of less than about 7 percent 

(percent grade is the vertical rise over horizontal distance traversed), or a slope of about 4 degrees. In 

general, VS

30 measurements are collected as an effort to characterize amplification at low VS30 sites rather 

than hard rock sites, and those data from rock VS

30 sites tend to show more scatter (Wills and Clahan, 

2006). 

One would not expect a direct, physical relationship between slope and VS

30, and in fact no 

simple analytic formula emerges from the data. Rather, we chose to characterize the relationship in 

terms of discrete steps in shear velocity values tied to National Earthquake Hazards Reduction Program 

(NEHRP) VS

30 boundaries (Building Seismic Safety Council, 1995). The NEHRP boundaries are further 

subdivided into narrower velocity windows to increase resolution where possible. Slope ranges for 

each bin were based on initial regression analysis, but final assignment of starting and ending slope bins 

required subjective modification where there were fewer data to constrain the regressions. Topographic 

slope at any site that falls within these windows is assigned a VS

30 by interpolating over the subdivided 

NEHRP boundaries based on that slope value (fig. 2; table 2). This approach differs slightly from the 

approach used initially by Wald and Allen (2007), who simply take the median velocity of the 

subdivided NEHRP classifications for those sites that have slope values within the specified range. It 

should be noted that we did not use the Utah VS

30 data in developing these correlations. It is observed 

that the Utah data possess systematically low shear velocity for a given slope as compared to mean 

values from the other regions. We discuss the implications of this omission later. 

We have also performed multiple linear regression analyses on both slope and elevation, 

attempting to correlate them jointly with VS

30. Essentially, slope and elevation themselves correlate 

well, but elevation alone is, in general, a poorer predictor of VS

30 than slope. In essence, there are many 

areas of low slope over a wide range of possible elevations. Hence, the joint analysis proved weaker 

than using slope alone. 

Table 2. Summary of slope ranges for subdivided NEHRP VS

30 categories. 

Class VS

30 range 

(m/s) 

Slope range (m/m) – 

(active tectonic) 

Slope range (m/m) – 

(stable continent) 

E <180 <1.0E-4 <2.0E-5 

D 

180–240 

240–300 

300–360 

1.0E-4–2.2E-3 

2.2E-3–6.3E-3 

6.3E-3–0.018 

2.0E-5–2.0E-3 

2.0E-3–4.0E-3 

4.0E-3–7.2E-3 

C 

360–490 

490–620 

620–760 

0.018–0.050 

0.050–0.10 

0.10–0.138 

7.2E-3–0.013 

0.013–0.018 

0.018–0.025 

B >760 >0.138 >0.025 

5
Application in Active Tectonic Regions 

California 

We compute VS

30 for all of California, applying the topographic slope ranges shown in figure 2a 

(tectonic regions) to corresponding VS

30 values in order to produce the map shown in figure 1c. A 

direct comparison to the topographic slope-based VS

30 predictions can be made from the California 

statewide map, based on surface geology and VS

30 measurements (fig.1b; modified from Wills and 

others, 2000). One notable difference between the slope-derived map of VS

30 (fig. 1c) and the geologybased Wills and others (2000) map (fig. 1b) is that the former allows more continuous variations in 

VS

30, whereas the latter assigns VS30 values to all occurrences of that geological unit to a constant (mean 

VS

30) value. Consequently, the relative few colors of the Wills and others (2000) map are a 

consequence of the few discrete geologic units that were classified. Wills and Clahan (2006) present 

further subdivisions based on geological considerations that may provide a more precise assignment of 

VS

30 variations. 

To provide more quantitative validation for this technique, we present histograms that indicate 

the (log) ratio of measured VS

30 values and those estimated from topographic slope for the same sites in 

California (fig. 3a). Figure 3b shows the equivalent plot when we compare measured VS

30 to those 

velocities assigned by Wills and others (2000). Neither comparison have significant bias, and the slopebased and geology-based values have comparable scatter. 

To examine the topographic approach in more detail, figures 4 and 5 show more detailed maps 

centered on the high seismic-risk areas of the San Francisco Bay area and the Los Angeles region, 

respectively. Owing to the investment of intensive geophysical and geotechnical investigations, 

significant portions of the VS

30 data are obtained from these heavily-populated regions. Direct 

comparison of the measured VS

30 values (colored circles) on figures 4a and 5a with the Wills and others 

(2000) map (figs. 4b and 5b) and with corresponding slope-derived values (figs. 4c and 5c) proves 

informative. There is a favorable agreement between the Wills and others (2000) VS

30 map and the 

slope-derived VS

30 maps. However, the slope-derived maps predict wider ranges in VS30 that appear more 

spatially variable than the geology-based maps. Conversely, geology-based values are typically taken 

as constants within a specific geological unit, independent of any slope variations that may correlate 

with changing material properties (mostly particle size) and thus with VS

30 values. 

Even at this scale, in the San Francisco Bay area many of the details of the geology-based (fig. 

4b) and topography-based (fig. 4c) maps are recovered, and the automated assignment of velocities near 

the DE boundary to near sea-level elevations seems to mimic the mapped extent of this site class on the 

Wills and others (2000) map. Correspondence between classes C near the BC boundary are less well 

recovered, but fortunately the overall error in site amplification introduced by misclassifying C for BC, 

or vice versa, is only about 10 percent (see table 1). Similar correlation is seen in the maps for the Los 

Angeles region (fig. 5). Here, additional surficial materials near the DE boundary are present in the 

slope map (fig. 5c) that are not seen on the geology map (fig. 5b). The abundance of surficial material 

near the DE boundary on the topographically-based VS

30 map appears consistent with the measured data 

that are superimposed onto the elevation map (fig. 5a). Many of these areas on the Wills and others 

(Wills and others, 2000) map could likely be classified near the DE boundary, based on both border line 

low VS

30 (for class D) observations and small particle sizes (C. Wills, written commun., 2005). 

Next we compare the geologically- and topographically-based VS

30 maps for the San Francisco 

Bay area and Los Angeles (figs. 4d and 5d, respectively) more quantitatively. The ratio of the 

amplification for the VS

30 maps is calculated. Amplification at mid-periods is assigned to each grid cell 

by applying the amplification factors of Borcherdt (1994), assuming a uniform peak ground 

6
acceleration (PGA) of 250 cm/s2

. The ratio of amplifications for the San Francisco Bay area (fig. 4d) 

for the two maps indicates relatively neutral differences in amplification around the bay itself. 

However, the topographically-based VS

30 map predicts consistently higher amplification in the Central 

Valley in the northeast margin of the map. In the Los Angeles region the Wills et al. (2000) map tends 

to predict slightly higher amplifications over a larger area than does the topographically-based map (fig. 

5d). However, the topographically-based map seems to indicate higher seismic amplification potential 

in much of the densely populated Los Angeles Basin. 

It is interesting to note that the resolution of the topography (30 arc sec) allows for detailed 

maps of site-conditions. Many of these details come from small-scale topographic features that are 

likely to be manifestations of real site differences, such as basin edges and hills protruding into basins 

and valleys, and are thus easily visible due to their significant slope change signatures. Typically, these 

edges are important for predicting ground motion variations due to earthquakes. Again, because higher 

resolution topography is available for this area of the world, additional details could be resolved. 

7
Figure 1. A, The topographic relief map for the State of California with elevation in meters (see legend). B, Site-condition 

map for California based on geology and VS observations (modified from Wills and others, 2000). C, Site-condition map 

derived from topographic slope using the correlations indicated in table 2. 

8
Figure 2. Correlations of measured VS

30 (m/s) versus topographic slope (m/m) for A, active tectonic, and B, stable 

continental regions. Color-coded polygons represent VS

30 and slope ranges consistent with ranges given in table 2 and 

also consistent with the VS

30 legends for all geologic- and topographic-based maps throughout this paper. 

Figure 3. Histograms indicating logarithmic differences of measured Californian VS

30 values compared with A, values 

derived from topographic slope correlations and, B, values based on existing VS

30 site-condition maps. N is the number of 

VS

30 measurements. 

9
10
Figure 4. A, Topographic map of the San Francisco Bay Area. Circles indicate the location of measurements, color-coded 

by VS

30 in m/s (see left legend). B, Site-condition map based on geology and VS30 observations (modified from Wills and 

others, 2000). C, Site-condition map derived from topographic slope. D, The ratio of the predicted amplification for a 

uniform PGA of 250 cm/s2 for the geologically- and topographically-based VS

30 maps. Blues indicate that the Wills and 

others (2000) map predicts higher amplification, whereas reds indicate higher amplifications are predicted from the 

topographically-based map. White indicated where the two VS

30 maps predict the same amplification. 

11
Figure 5. A, Topographic map of the Los Angeles region. Circles indicate the location of measurements, color-coded by 

VS

30 in m/s. B, site-condition map based on geology and VS30 observations (modified from Wills and others, 2000). C, Sitecondition map derived from topographic slope. D, The ratio of the predicted amplification for a uniform PGA of 250 cm/s2

for the geologically- and topographically-based VS

30 maps. 

12
Taiwan 

The next question we address is whether the correlations hold in areas that have similar overall 

topographic expression but exhibit different geology, tectonics, and geomorphology. Taiwan was 

chosen to test this hypothesis, primarily because the site classes on a national scale are well-understood 

from prior studies (for example, Lee and others, 2001) and are available for direct comparison. The 

high seismicity in Taiwan results from the convergence of the Philippine Sea plate and the Eurasian 

plate. The Philippine plate is moving NW at a rate of about 7 cm/yr relative to the Eurasian plate. 

The abundance of shear-wave observations used in our correlations provides us with a valuable 

validation case study to ensure that the slope calibration with VS

30 is robust among our base data sets. 

Figure 6 shows the topographic map superimposed with color-coded VS

30 measurements (fig. 6a), the 

national site-condition map (fig. 6b; modified from Lee et al., 2001), and the topographic-gradient 

derived VS

30 map for the island of Taiwan (fig. 6c). Shallow shear-wave velocities around Taiwan vary 

widely, but they do so with rather systematic trends that are well-recovered using topographic slope. 

Figure 7a provides the ratio of measured versus slope-derived VS

30 values. The mean and 

standard deviation are comparable to those evaluated for California sites (fig. 3a). In presenting the 

Taiwanese site-condition map, we have assigned median shear velocities based on the NEHRP 

categories adopted by Lee and others (2001), the exception for this being site class E, where we 

assigned a VS

30 of 150 m/s. Statistically, the topographically-derived site-condition map for Taiwan 

performs very well (fig. 7a) relative to the geologically-based map (fig. 7b). The Taiwanese are 

currently working on a revised site classification scheme that should further improve comparisons to 

measured data (C.-T. Lee, written commun., 2007). The correlations derived jointly from California and 

Taiwan shear-wave observations and topographic slope seem to apply in areas of similar overall 

topographic expression independent of other factors that may be contributing. 

It is interesting to note that, in a significant number of the examples represented in this report, 

population exposure tends to correlate very well with slower VS

30 site-conditions calculated from the 

slope of topography. Intuitively, the correlation between population density and seismic site-condition 

is not surprising since steep hill slopes are not usually as desirable for building structures, while flat-togently sloping lands tend to consist of more fertile soils suitable for agricultural purposes and have 

traditionally been inhabited. Using Taiwan as an example, we compare seismic site-condition to 

population exposure as derived from the Oak Ridge National Laboratory’s LandScan 2005 database 

(Dobson and others, 2000; Bhaduri and others, 2002) (fig. 6d). We see that urban communities appear 

to be most dense in the flat-lying lands that correspond to lower VS

30. In particular, the region around 

Taipei possesses significant amplification potential owing to its location on primarily NEHRP site class 

D surficial material. Population becomes less dense as we grade from gently sloping to steeper terrains. 

This figure serves to remind urban planners, disaster mitigators, and emergency responders why we 

must consider seismic site-conditions as an important component in earthquake hazard assessment, 

since those areas with the densest populations are also likely to have the strongest site amplifications. 

13
14
Figure 6. A, Topographic map of Taiwan with elevation in meters. Circles indicate the location of measurements, colorcoded by VS

30 in m/s. B, Site-condition map based on geology and VS30 observations (modified from Lee and others, 

2001). C, Site-condition map derived from topographic slope. D, Population density map of Taiwan derived from the 

LandScan 2005 population database (for example, Dobson and others, 2000; Bhaduri and others, 2002). See figure 1 for 

topography and VS

30 legends. 

Figure 7. Histograms indicating logarithmic differences of measured Taiwanese VS

30 values compared with A, values 

derived from topographic slope correlations and, B, values based on existing VS

30 site-condition maps. N is the number of 

VS

30 measurements. Differences in N between A and B arise when the grid point referred to is not defined within the 

selected map grid. 

Alaska 

Alaska is the most seismically active state in the United States, largely because of the active 

subduction of the Pacific plate beneath the North American plate at a rate of about 5–7 cm/yr. The 

region has hosted two great-magnitude (M > 9.0) earthquakes since 1957 along this tectonic plate 

boundary (Wesson and others, 1999). In addition to earthquakes occurring along the subduction zone, 

significant seismicity also occurs throughout Alaska as a result of stresses imposed by interaction of the 

plates. Much of this relative motion is accommodated at the principal boundary between the two plates 

in the Gulf of Alaska, but there is also substantial crustal deformation and earthquake activity inland of 

the plate boundary in central Alaska. In addition to occurring on major mapped faults such as the 

Denali fault, shallow-focus earthquakes in central Alaska also occur on lesser, unmapped faults. 

Many studies have been undertaken that examine site response within Anchorage, given its high 

seismic hazard, relatively high exposure, and geographic setting within a sedimentary basin (for 

example, Nath and others, 1997; Dutta and others, 2000; Combellick, 2001; Dutta and others, 2001; 

Martirosyan and others, 2002; Dutta and others, 2003; Badal and others, 2004). Indeed, ground failure 

in Anchorage following the 1964 Prince William Sound earthquake was widely reported (Martirosyan 

and others, 2002; Dutta and others, 2003). Such studies, however, are only available on a very 

localized scale. A state wide site-condition map for Alaska has been developed by Martirosyan and 

Hansen (2007) for use in ShakeMap applications. However, this map was unavailable for comparison 

at the time this report was written. 

15
We generate a VS

30 map for the State of Alaska using our topographically based approach. In 

general, we observe that much of the southern margin of the state, adjacent to the subduction zone, 

indicates relatively fast material, consistent with the dynamic topographic relief (fig. 8). Another 

feature that can clearly be identified in our VS

30 map of Alaska is the Yukon delta in western Alaska. 

This region indicates slower-velocity surficial material consistent with fluvial processes of the Yukon 

River. Also of note in figure 8 is the low velocity of the braided river systems throughout the 

Mackenzie River Basin in northwestern Canada. 

Next we investigate the seismic site-conditions of Anchorage in more detail and superimpose 

combined VS

30 values from Dutta and others (2000) and the PEER NGA database (fig. 9a). Despite the 

limited resolution, much of Anchorage indicates lower-velocity surficial materials (fig. 9b) consistent 

with the city’s location within a sedimentary basin. In general, our result is consistent with the more 

detailed studies in Anchorage mentioned above, with mapped VS

30 grading from fast to slower velocities 

westward towards the harbor. Statistically, our predictions of VS

30 compare very well with observed VS30

values at strong-motion sites across Alaska and with measurements taken in Anchorage (fig. 10). 

16
Figure 8. A, The topographic relief map for the State of Alaska. B, Site-condition map derived from topographic slope. 

17
Figure 9. A, Topographic map of the Anchorage, Alaska region. Circles indicate the location of VS

30 measurements, colorcoded by velocity in m/s. B, Site-condition map derived from topographic slope. See figure 1 for figure legends. 

Figure 10. Histogram indicating logarithmic differences of combined Anchorage (Dutta and others (2000) and NGA VS

30

values compared with values derived from topographic slope correlations. 

18
Nevada 

Scott and others (2004) describe the collection of VS

30 data on a transect along the Truckee River 

in Reno, Nevada. These results, based on microtremor analysis, suggest that VS

30 is higher than that 

expected from alluvium-filled basins, based on NEHRP previsions. Scott and others, (2004) concluded 

that mapped surficial geology is not necessarily a reliable predictor of VS

30, owing to stiffer underlying 

Tertiary sediments. The location of this survey, closely following an erosive river environment, may be 

a possible explanation for these results. Consequently, we observed that the Nevada data indicated 

systematically higher velocities with topographic slope than our reference dataset for California and 

Taiwan. We use no data from Nevada in our correlations between slope and VS

30. A local adjustment 

factor may have to be applied to the VS

30 map based on topographic slope. 

We have not developed a separate topographic slope-based map for the State of Nevada. 

However, the State is largely represented in the northeast quadrant of the California map (fig. 1). Site 

classifications for Nevada appear to be highly variable, with spatially periodic changes in site class that 

are associated with the Basin and Range tectonic province (fig. 1c). 

Salt Lake City, Utah 

The next example within active tectonic regions is that of the Salt Lake City, Wasatch Front 

region of Utah. This site-condition map can also be considered a forward prediction since, unlike 

California and Taiwan, no VS

30 data for this region were used in our calibration analysis. The data, 

which were obtained from the Utah Geological Survey, appeared to have VS

30 values systematically 

lower than mean values from other active regions with similar slope (fig. 2a). The geologically- and 

topographic slope-based maps (figs. 11b and 11c, respectively) demonstrate similar trends. However, 

there appears to be a significant trend towards lower velocities in the geologically-based site map, west 

of the Wasatch Front. This observation is consistent with the measured data. On average, the geologybased map (fig. 12b) represents the measured data better than does the slope-derived map (fig. 12a). 

The latter shows an overall bias indicating that VS

30 predictions in the Salt Lake City region are on 

average over-predicted by the topographic-slope approach employing the current correlations. It is 

possible that near-surface shear velocities in the region are lower for a given slope angle than in 

California and Taiwan, and thus require slight modification to the slope versus VS

30 correlation. 

Alternatively, the VS

30 measurements underestimate actual in situ velocities for some other reason, not 

yet established. 

While we observe that the slope-derived map (fig. 11c) indicates a more natural progression of 

VS

30 grading towards higher values in steeper topographic relief, it is also possible that Lake Bonneville 

deposits that abut the mountain front, rather than sloping, may violate the basic assumption on which 

our correlations are based (K. Pankow, written commun., 2007). The small overall bias in the Salt Lake 

City region could be removed with an overall shift of about 25 percent in predicted VS

30 values, though it 

is possible that these biases are concentrated in particular geological units. 

19
Figure 11. A, Topographic map of the Salt Lake City, Wasatch Front region of Utah. Circles indicate the location of 

measurements, color-coded by VS

30 in m/s. B, Site-condition map based on geology and VS observations. C, Sitecondition map derived from topographic slope. 

20
Figure 12. Histograms indicating logarithmic differences of measured Utah VS

30 values compared with A, values derived 

from topographic slope correlations and, B, values based on existing VS

30 site-condition maps. N is the number of VS30

measurements. 

Seattle, Washington 

The Cascadia subduction zone lies just off the coast of Oregon and Washington. This 

subduction zone is the main tectonic feature in the geologically active Pacific Northwest. On shore, the 

(continental) North American plate overlays the subducting (oceanic) Juan de Fuca plate. Assessment 

of the regional shaking impact of an anticipated great Cascadia subduction zone earthquake requires at 

least a first-order approximation of site-conditions. 

The contribution of site response to the observed ground shaking is well recognized in the 

Seattle region (Frankel and others, 1999). Assessment of site characteristics has found that sites on 

artificial fill are likely to experience significant response from an input ground motion. Given that no 

Seattle data were used in the model development, our topographically-based assessment of VS

30 can be 

considered as forward prediction of seismic site-conditions. Visually, the topographically-based map 

appears to recover many of the key features in the geology-based map (figs. 13b and 13c). The former 

does indicate greater variability in predicted VS

30 than the latter, particularly in the lower-lying regions 

to the southwest of downtown Seattle, where a higher proportion of slower D class material is 

predicted. However, the geologically-based map predicts more slow E class material. The topographic 

approach also predicts more higher-velocity B class material within the mountains that bound the city. 

Statistically, the topographically-based map represents the observed values relatively well (fig. 14a) 

however, there is not a strong correlation. The geologically-based map has a strong peak about the 

median value consistent with the fact that it was based on these observed VS

30 values (fig. 14b). These 

results suggest that VS

30 maps based on topography alone can serve as a suitable proxy in the absence of 

more detailed geologically-based site-condition maps. 

21
Figure 13. A, Topographic map of the Seattle, Washington, region. Circles indicate the location of measurements, colorcoded by VS

30 in m/s. B, Site-condition map based on geology and VS observations. C, Site-condition map derived from 

topographic slope. 

22
Figure 14. Histograms indicating logarithmic differences of measured Seattle, Washington VS

30 values compared with A, 

values derived from topographic slope correlations and, B, values based on existing VS

30 site-condition maps. N is the 

number of VS

30 measurements. 

Indonesia 

The origin of the Indonesian Archipelago about the Sunda Arc results from the active 

subduction of the Indo-Australian tectonic plate beneath the Eurasian plate. The high rate of destructive 

seismicity that accompanies this plate motion has been recently demonstrated by several catastrophic 

earthquakes including the 2004 MW 9.0 and 2005 MW 8.7 Sumatra-Andaman Islands earthquakes, and 

the 2006 MW 6.3 Yogyakarta earthquake. Little information regarding the response of near-surface 

geology to strong ground shaking in Indonesia is available. However, a reconnaissance report 

following the 2006 Yogyakarta earthquake notes that the most heavily damaged areas were underlain by 

young volcanic deposits consisting of undifferentiated tuff, ash, breccia, agglomerates, and lava flows 

(Earthquake Engineering Research Institute, 2006). Moreover, the report explicitly states that the most 

pronounced effects were associated with directivity and soil amplification. Consequently, first-order 

estimates of seismic site-conditions for Indonesia will be important to assess earthquake hazard in 

future studies. A pilot hazard study performed in the Sulawesi Province also suggested significant 

potential for ground motion amplification owing to effects of surficial geology (Thenhaus and others, 

1993). 

Figure 15a shows the topographic relief of Indonesia. Because of the active subduction of the 

Indo-Australian plate beneath the island arc, the topographic signature of the islands is dominated by a 

chain of active volcanoes adjacent to the subduction zone. The margin most distant from the active 

subduction indicates more subdued topography grading to relatively flat slopes near the coastlines. This 

subdued topography is particularly apparent for the island of Sumatra. The corresponding VS

30 map 

derived from the slope of topography (fig. 15b) indicates broad regions of low-velocity surficial 

materials (NEHRP site class D). The steeper volcanic chain is indicated by faster BC to B surficial 

materials. We also note narrow zones of lower VS

30 along the margins nearest the Sunda Arc. 

Figure 15c shows the population density map for Indonesia, one of the most densely populated 

nations on Earth. The island of Java is particularly heavily populated, with much of the exposure 

correlating to NEHRP site classes C and D. High exposure rates are also observed in southeastern 

Sumatra, which again is largely mapped as site class D. 

23
24
Figure 15. (Facing page) A, Topographic map of Indonesia. B, Site-condition map derived from topographic slope. C, 

Population density map of Indonesia. See figure 6 for map legends. 

Iran 

This highly seismic region forms the north-west trending boundary between the Arabian and 

Eurasian tectonic plates. The Arabian plate is a small plate split from the African plate by rifting along 

the Red Sea. As it underthrusts the Eurasian Plate it causes uplift of the Zagros Mountains and 

numerous devastating earthquakes. Iran has suffered many deadly earthquakes in the recent past with 

death tolls in excess of 1000 each; for example 1962 MS 7.3 Buyin-Zara; 1968 MS 7.3 Dasht-e Bay z; 

1972 MS 6.9 Ghir; 1976 MW 7.0 Iran-Turkey border; 1978 MW 7.3 Tabas; 1981 MW 7.2 Sirch; 1990 MW

7.4 Manjil; 1997 MW 7.2 Ardakul; and 2003 MW 6.6 Bam. The combination of high hazard and 

primarily poorly constructed adobe structures makes Iran an extremely high-risk region. 

In almost all of the earthquakes noted above, it was identified that adobe houses located on 

alluvium suffered greater damage than those located on firm gravels or rock sites (Ambraseys, 1963; 

Bayer and others, 1969; Dewey and Grantz, 1973; Berberian, 1979; Berberian and others, 1992; Jafari 

and others, 2005; Building and Housing Research Center, 2007). Consequently, first-order estimates of 

seismic site-conditions are fundamental for quantifying regions at risk for strong ground shaking from 

surficial effects. 

We superimpose some significant Iranian earthquakes listed above onto the topographic map 

(fig. 16a). From the seismic site-condition map developed from the slope of topography, we observe 

that many of these deadly earthquakes have occurred near sites of relatively low VS

30 (fig. 16b), 

consistent with our earlier observation that populations tend to concentrate on flat-lying sedimentary 

basins. In particular, the deadly Manjil earthquake near the Caspian Sea, which resulted in 

approximately 35,000 deaths (Utsu, 2002), is located in close proximity to large Quaternary 

sedimentary deposits, indicated by low VS

30 in figure 16b (for example, Niazi and Bozorgnia, 1992). 

The Dasht-e Bay z and Bam earthquakes, which resulted in very high casualties (15,000 and 26,000 

deaths, respectively), also occurred near regions of significant amplification potential, and this is likely 

to have contributed to the extreme number of fatalities observed. Unfortunately, no VS

30 measurements 

are available from Iran for comparison. 

25
26
Figure 16. (Facing page) A, Topographic relief map for Iran, superimposed with the epicenters of recent deadly 

earthquakes (red stars). B, Site-condition map derived from topographic slope. 

Italy 

Primary tectonic activity in Italy results from the collision of the Eurasian and African Plates. 

The high-topography zone of the central Apennines, which trends NW-SE within peninsular Italy, is 

dissected by a Quaternary fault system that has hosted several moderate-to-large historical earthquakes 

(Cello and others, 1997), most recently 1980 MW 6.9 Iripinia, and 1997 MW 6.0 Umbria-Marche. Site 

response effects were documented following the 1997 Umbria-Marche seismic sequence (Caserta and 

others, 2000; Tertulliani, 2000). In 2002, the Molise and Puglia regions of southern Italy were struck 

by two moderate-magnitude earthquakes (Cara and others, 2005). The first and largest of the two (MW

5.7) resulted in 30 casualties in the town of San Giuliano di Puglia. The high intensities observed in 

this town are largely attributed to local site-conditions (Cara and others, 2005). 

The Istituto Nazionale di Geofisica e Vulcanologia (INGV), Italy, has recently installed the 

ShakeMap system as a routine process of its national earthquake monitoring program. We obtained the 

national site-condition map that is used in this system (fig. 17b) courtesy of A. Michelini (written 

commun., 2006). In general, the topographically-derived map (fig. 17c) demonstrates many of the same 

features as the geologically-derived map. However, there are some discrepancies over the absolute 

value of VS

30 assignments. We observe that the geologically-based map (fig. 17b) has systematically 

higher velocities than our topographically-based map (fig. 17c). This difference is reflected in figure 18 

when we compare VS

30 values at Italian strong-motion stations, available from the PEER NGA project, 

with the respective site-condition maps. Despite a larger spread in the comparisons, the 

topographically-based map tends to recover individual VS

30 values better than the geologically-based 

map, which is biased towards higher velocities. Again, population density correlates well with flatlying, low-VS

30 areas (fig. 17d). 

Although the site-condition maps generally indicate similar trends, one obvious disparity 

between them occurs in the Puglia region of southeastern Italy, where figure 17b indicates significantly 

faster velocities than are predicted by the topographic approach. The predominant geology comprises 

flat-lying Mesozoic shallow-water carbonate and evaporite rocks (de Alteriis and Aiello, 1993). 

Relative to much of Italy and other Mediterranean regions, the seismicity of Puglia is quite low. This 

observation is subsequently reflected in the absence of significant topographic relief. Consequently, we 

can conclude that, in geological conditions dominated by these flat-lying carbonates, the automated 

topographic approach may not recover VS

30 as well as a more rigorous assessment of local surficial 

geology. 

27
Figure 17. A, Topographic map of Italy. Circles indicate the location of measurements, color-coded by V 30 S . B, Sitecondition map based on geology and VS observations. C, Site-condition map derived from topographic slope. D, 

Population density map of Italy. See figures 1 and 6 for map legends. 

28
Figure 18. Histograms indicating logarithmic differences of Italian VS

30 values from the NGA dataset, compared with A, 

values derived from topographic slope correlations and, B, values based on existing VS

30 site-condition maps. N is the 

number of VS

30 measurements. 

Japan 

Japan is a densely populated nation that is high in seismic hazard. Much of Japan’s population 

resides on thick sedimentary basins that have significant potential to amplify ground shaking. 

Tectonically, Japan is located near the junction of the Pacific, Philippine Sea, and Okhotsk tectonic 

plates. The Pacific plate is moving northwest at a rate of about 8–9 cm/yr relative to, and subducting 

beneath, the Philippine Sea and Okhotsk plates. In addition, the Philippine Sea plate is subducting 

northward beneath Japan and the Okhotsk and Amurian plates (Miller and Kennett, 2006). The 

complex tectonics drive the high rates of seismicity observed in the region, and the subducting slabs 

provide the source for the many active volcanoes that occupy the island. 

Figure 19a shows the topographic map of Japan. We have obtained a geologically-based VS

30

map (fig. 19b) of Japan courtesy of M. Matsuoka (written commun., 2006). This map was developed 

analyzing geomorphic characteristics tied to VS

30 measurements (Matsuoka and others, 2005). They 

further refine this model using multiple linear regression analysis on additional geomorphic indicators; 

i.e., correlating VS

30 with elevation, topographic slope, and the distance from mountain ranges. Our 

purely topographically-based map (fig. 19c) exhibits many of the same trends as the Matsuoka and 

others (2005) map. However, the topographic approach does not predict as much slow-velocity 

material (equivalent to NEHRP site class E) as indicated in figure 19b. As with Taiwan, we also plot 

the population exposure (fig. 19d) and compare this with the site-condition map. Again we note a 

strong correlation between population density and assignments of low VS

30

. 

Shallow shear-wave velocities have been determined for a number of sites within the KiK-Net 

strong-motion network (Kawase and Matsuo, 2004; H. Kawase, written commun., 2006). Kawase and 

Matsuo (2004) made these VS

30 estimates by first using a spectral inversion of ground motion recordings 

to determine site factors, and then inverting for the best fit 1-D shear-velocity model using a genetic 

algorithm. Unlike most other regions studied, many of these sites were located on rock, so the 

distribution is biased towards higher-velocity VS

30 data. The ratio of measured VS30 to predicted VS30 is 

29
compared for both site-condition maps in figure 20. We observe no significant difference between the 

geologic- and geomorphic-based map of Matsuoka et al. (2005) and our site-condition map based on 

topographic slope. However, it appears that both maps tend to underestimate VS

30, relative to observed 

data. We note that there is not uniform sampling of VS

30 data across all possible site classes, with only a 

limited number of C and D site classes available for comparison. Furthermore, this strong-motion 

network was probably designed so that the accelerometers were located at “hard rock” sites. 

Consequently, it is expected that the location of these sites will be more biased towards faster velocities 

than average sites of similar topographic slope. Since these VS

30 values were not derived from more 

widely used measurement techniques, we are as yet unsure of the consistency of this approach 

compared with more standard VS

30 measurements; however, the strong correlation of the two VS30

 maps 

(figs. 19B, C) with each other provides some level of confidence in the overall results. Also, as noted 

earlier, the differences between observed and topographically-based VS

30 may not affect amplification 

potential significantly since most of the data are of higher velocity, which occurs near or above the BC 

boundary and where site effects are not as pronounced. 

30
31
Figure 19. A, Topographic map of Japan. Circles indicate the location of VS

30 measurements, color-coded by velocity in 

m/s. B, Site-condition map based on geology and VS

30 observations (modified from Matsuoka and others, 2005). C, Sitecondition map derived from topographic slope. D, Population density of Japan.

Figure 20. Histograms indicating logarithmic differences of Japanese VS

30 values, compared with A, values derived from 

topographic slope correlations and, B, values based on existing VS

30 site-condition maps. N is the number of VS30

measurements. The difference in N between the two histograms is because some of the VS

30 measurement points are 

plotted outside the grid defined by the geologic- and geomorphic-based map of Matsuoka and others (2005). 

Mexico 

One of the most obvious and most well-documented occurrences of seismic site amplification 

resulted from the great 1985 MW 8.1 Michoacan earthquake that devastated Mexico City, some 350 km 

from the epicenter (for example, Campillo and others, 1989; Glass, 1989; Ordaz and Singh, 1992). In 

this case, a number of factors contributed to the high damage rates observed, including complex 

heterogeneous rupture (Campillo and others, 1989), enhanced transmission path (Furumura and 

Kennett, 1998), basin resonance effects at long periods (2–3 sec), and local site response from lakebed 

sediments (Campillo and others, 1989). 

Much of Mexico’s seismicity is driven by the subduction of the Rivera and Cocos microplates 

beneath the Pacific coast of Mexico (part of the North American plate). The slower subducting Rivera 

Plate is moving northeast at about 2 cm/yr relative to the North American plate, and the faster Cocos 

plate is moving in a similar direction at a rate of about 4.5 cm/yr. These microplates interact with the 

larger Pacific plate via a combination of transform faults and spreading ridges (DeMets and Stein, 

1990). Further north, the Gulf of California represents a developing transform-rift margin (Umhoefer 

and others, 1994). 

We calculate a VS

30 map for the country of Mexico from the topographic slope employing the 

coefficients for active tectonic regions (fig. 21). We observe that relatively high VS

30 values are 

associated with the Sierra Madre Occidental and Oriental mountain ranges along the western and 

eastern margins of the Mexican Plateau, respectively. The Mexican Plateau itself is represented by 

more complex VS

30 patterns, but in general is indicated by slower surficial materials relative to the 

mountain ranges that bound the plateau. The surficial materials that make up the Mexican Plateau are 

32
most likely weathering products derived from the Sierra Madre Mountains via fluvial processes. 

Slower velocity materials are also predicted along the western coast of Mexico along the Gulf of 

California and Pacific coastlines. Again, these surficial materials are most likely derived from 

weathering processes within the Sierra Madre Occidental Mountains. 

Figure 22 shows a more detailed view of the seismic site-conditions about Mexico City. Much 

of the city is built upon lakebed sediments, which are known to have contributed to seismic wave 

amplification following the devastating 1985 Michoacan earthquake (Ordaz and Singh, 1992). The 

topographically-based VS

30 map indicates that much of Mexico City is built upon NEHRP site class D. 

The extent of site class D also corresponds to the regions of highest population density (fig. 22c). 

Figure 21. VS

30 map for Mexico employing the coefficients for active tectonic regions. See figure 22 for a more detailed 

view of Mexico City. 

33
Figure 22. A, Topographic relief map of Mexico City. B, Site-condition map derived from topographic slope. C, Population 

density map of Mexico City. 

New Zealand 

New Zealand straddles the boundary between the Indo-Australian and Pacific tectonic plates. 

The relative plate motion in New Zealand is expressed by a complex assemblage of active faults, 

coupled with a moderately high rate of seismicity (Stirling and others, 2002). The plate boundary 

beneath New Zealand comprises the Hikurangi (North Island) and Fiordland (far south of South Island) 

subduction zones, which dip in the opposite sense relative to each other. Active tectonic motion is 

accommodated by the faults of the axial tectonic belt that traverse much of the South Island between the 

subducting slabs (Stirling and others, 2002). The Alpine Fault in the central South Island 

accommodates the release of much of the strain energy produced by the relative plate motion. 

34
Significant site response effects have been observed from several of New Zealand’s large 

earthquakes. Furthermore, microzoning was clearly recognized following the deadly M 7.8 1931 

Hawke’s Bay earthquake. Dowrick and others (1995) note that the structures most affected by this 

earthquake were located on rock sites and the least damage occurred on the soft alluvium sites. They 

conclude that topographic effects may have enhanced the ground shaking in rock locations, and strong 

ground shaking at high frequencies was attenuated on the deep basin sediments. In contrast to these 

observations, Abercrombie and Benites (1998) noted that weak-motion data recorded in the cities of 

Hastings and Napier, both of which were affected by the 1931 quake, exhibited prolonged duration and 

amplification of shaking owing to the existence of thin soil layers above a Miocene-age sedimentary 

basin. These data suggest that there may be strong nonlinear behavior of the soils in this region when 

subjected to increasing levels of ground shaking. 

More recently, a reconnaissance report following the MW 6.5 1987 Edgecumbe, Bay of Plenty 

earthquake indicated that most of the damage occurred on drained swampland consisting of soft, 

unconsolidated soils (EQE Incorporated, 1987; Franks, 1988). Based on these observations, it is 

important that the effects of site response should be considered for hazard and risk assessments in 

regions such as New Zealand. Based on our topographic approach for defining seismic site-conditions, 

we observe that many of New Zealand’s most developed urban centers appear to be located in regions 

of significant amplification potential, including Greater Wellington, Christchurch, Hastings, Napier, 

Dunedin, and Invercargill (figs. 23 and 24). 

Limited work in defining VS

30 in the Greater Wellington region has been performed through 

collaborations among the University of Nevada, Reno; Victoria University, Wellington; and the 

Institute of Geological and Nuclear Sciences (see http://www.seismo.unr.edu/ hazsurv/#wnlh). This 

work culminated in approximately 90 VS

30 measurements across the local area, from the city of 

Wellington to Upper Hutt. We compare our topographically-based map of the Wellington region with 

these measured values (fig. 25). Despite limitations in the resolution of the topographic data and the 

tight spatial clustering of the VS

30 measurements, we observe the same general trends are recovered by 

the topographic-based site-condition map. This dataset, however, comprises more E site class material 

than is predicted with the topographic approach. The availability of higher-resolution topography data 

in this region may yield site-conditions more comparable to the observed data. 

35
Figure 23. A, Topographic relief map for New Zealand’s North Island. B, Site-condition map derived from 

topographic slope. 

36
Figure 24. A, Topographic relief map for New Zealand’s South Island. B, Site-condition map derived from 

topographic slope. See figure 23 for map legends. 

Figure 25. A, Topographic map of the Greater Wellington region, New Zealand. Circles indicate the location of 

measurements, color-coded by VS

30 (see figure 23). Data were measured through collaborations between the 

University of Nevada, Reno; Victoria University, Wellington; and the Institute of Geological and Nuclear 

Sciences. B, Site-condition map derived from topographic slope. See figure 23 for map legends. 

37
The Philippines 

The Philippine archipelago represents a complex system of microplates that are being 

compressed between two convergent plate margins that bound the nation: the Philippine Sea and 

Eurasian plates. Because of its tectonic setting, the Philippines experiences frequent damaging seismic 

activity. It was reported following the MW 7.7 1990 Central Luzon earthquake that the most serious 

damage occurred on soft soil sites (EQE Engineering, 1990). 

The seismic site-condition map for the Philippines, derived from topographic slope, indicates 

several broad regions of low VS

30 (fig. 26b). Slower VS30 values generally tend to mimic the regions of 

highest population exposure (fig. 26c). Consequently, site response will be an important factor when 

providing rapid estimates of ground shaking following a large earthquake in the region. 

38
Figure 26. A, Topographic map of the Philippines. B, Site-condition map derived from topographic slope. C, Population 

density map of the Philippines. 

Puerto Rico 

The island of Puerto Rico, in the northwest Caribbean, lies within a broad deformation zone 

between the Caribbean and North American tectonic plates (Masson and Scanlon, 1991). In 1999, the 

USGS held a workshop to develop strategies to mitigate against earthquake and tsunami hazards in the 

region (Brink and others, 1999; Mueller and others, 2003). One of the key outcomes from this 

workshop was the need to develop a site response map for Puerto Rico. Puerto Rico has recently 

installed the ShakeMap system (Clinton and others, 2006) that incorporates a national site-condition 

map (fig. 27b). This map is based upon VS

30 measurements conducted by Odum and others (2007). 

We compare the topographically-derived site-condition map (fig. 27c) with the map based on 

geology and VS

30 measurements (fig. 27b). The first thing that is apparent from figure 27b is the 

absence of any significant lateral variability in VS

30 assignments. Except for minor variations along 

coastlines and tributaries, existing ShakeMap VS

30 site-conditions are mapped almost entirely between 

360–490 m/s. The topographically-derived map, however, demonstrates significant variability, and 

39
mapped extents appear consistent with the mapped geology where harder plutonic and volcanic rocks 

are recovered with fast VS

30, while Quaternary alluvial deposits are recovered with slower VS30 (see Fig. 2 

in Odum and others, 2007). 

The number of VS

30 measurements are limited for the island of Puerto Rico. However, we use 

data from two recent studies (Pérez-Marcial, 2005; Odum and others, 2007) to evaluate both sitecondition maps. We find a good correlation between measured VS

30 and predicted VS30 from the 

topographic slope method (fig. 28a). The geologically-based map, however, appears to be a very poor 

predictor of VS

30 (fig. 28b). 

Figure 27. A, Topographic map of Puerto Rico. Circles indicate the location of measurements, color-coded by VS

30 in m/s. 

B, Site-condition map based on geology and VS observations. C, Site-condition map derived from topographic slope. 

40
Figure 28. Histograms indicating logarithmic differences of Puerto Rican VS

30 values from Pérez-Marcial (2005) and Odum

and others (2007), compared with A, values derived from topographic slope correlations and, B, values based on existing 

VS

30 site-condition maps. N is the number of VS30 measurements. 

Turkey 

Turkey is a tectonically active region that experiences frequent destructive earthquakes. Firstorder tectonics are controlled by the collision of the Arabian plate and the Eurasian plate. A large piece 

of continental crust almost the size of Turkey, called the Anatolian block, is being compressed in a 

westerly direction. The block is bounded to the north by the North Anatolian Fault and to the southeast 

by the East Anatolian Fault. It was observed following the deadly 1999 zmit earthquake that site 

effects were a significant contributor to the high human losses (Bray and Stewart, 2000; Rathje, 2000; 

Bakır and others, 2002). 

We compare the national site-conditions map for Turkey compiled by Department of 

Earthquake Engineering, Kandilli Observatory and Earthquake Research Institute (KOERI; Z. Cagnan, 

written commun., 2007), derived from geologically-based data (fig. 29b), with our topographicallyderived map (fig. 29c). The former was prepared using the correlations of NEHRP site classifications 

as a function of lithologic age, as described by Wills and Silva (1998). From this work, Turkey was 

assigned three different site classes: B, C, and DE. We assign VS

30 to the Turkish geologically-based 

map by taking the median NEHRP value for classes B and C, while the DE site class was assigned on 

the basis of the range given by Wills et al. (2000). The topographic-slope technique recovers most of 

the features indicated by the geology-based approach. Shear-wave values obtained from the NGA 

dataset are compared to the topographically-based approach (fig. 30a). In general, the topographic 

slope method tends to overestimate VS

30 with respect to the values given in the NGA database. 

However, there are reservations over the quality of these assignments since they were not derived from 

in situ measurement techniques. The same comparison using the Turkish national site-condition map 

appears to overestimate VS

30 as well, but yields a better overall median value with a slightly higher 

standard deviation (fig. 30b). 

41
42
Figure 29. A, Topographic map of Turkey. Circles indicate the location of measurements, color-coded by VS

30 in m/s. B, 

Site-condition map based on surficial geology from KOERI (Z. Cagnan, written commun., 2007). C, Site-condition map 

derived from topographic slope. See figure 1 for map legends. 

Figure 30. Histograms indicating logarithmic differences of Turkish VS

30 values taken from the NGA database, compared 

with A, values derived from topographic slope correlations and, B, values based on existing VS

30 site-condition maps. N is 

the number of VS

30 measurements. 

Southern Europe 

The moderate-to-high rate of seismicity in the Mediterranean region originates from the 

convergence of the African and Eurasian tectonic plates at a rate of about 6 mm/year. To date, little 

work available to the public has been done to constrain parameters for regional site-conditions. 

Using the active tectonic VS

30-slope relations (fig. 31), we observe that many features of the 

southern European and northern African geologic landscape are well recovered using our technique. 

Major mountain belts are clearly identified as high velocity zones, including the Alps, Pyrenees (France 

and Spain), Apennines (Italy), Dinaride (Greece), and Atlas (Morocco, Algeria, and Tunisia) mountain 

ranges. Other features such as the Po Basin (Italy) and Nile River Delta (Egypt) are clearly identified as 

low velocity zones. Less obvious at this scale, the Rhône (France) and Rhine (France and Germany) 

River Valleys can also be identified as a low seismic velocity zone. 

43
Figure 31. A, Topographic relief map for Southern Europe, Anatolia, and Northern Africa. B, Site-condition map derived 

from topographic slope. See figure 1 for map legends. 

44
Application in Intraplate Regions 

Mississippi Embayment Region 

As expected from basic geomorphology, in areas of significant relief, mountains correlate well 

with rock and basins correlate well with sediments. Will this approach work in areas of lower overall 

relief? While a similar range of shear-velocity measurements, from hard rock to soft sediments, exist in 

the Mississippi Embayment, the associated topography as captured by the 30 arc sec (approximately 1 

km resolution) SRTM30 data is much more subdued, as indicated by the narrow range in elevation (fig. 

32a). Hence, there is less variation in slope and consequently, it might be expected that it would be 

more difficult to assign slope ranges that define the VS

30 categories. Furthermore, as in the active 

tectonic regions, few VS

30 measurements are available for high-velocity, hard rock sites. Nonetheless, 

there does appear to be a natural progression among VS

30 values plotted against slope for both the central 

Australia and United States (fig. 2b), and we use these limits to produce the slope-based site-condition 

map for the Mississippi Embayment region shown in figure 32. We compare the 432 VS

30 measurements 

with topography (fig. 29a), the VS

30 site-condition map (fig. 32b) used for the regional ShakeMap 

installation (Bauer and others, 2001; Brackman, 2005), and the slope-derived VS

30 map (fig. 32c) at sites 

in the Mississippi Embayment. 

We find excellent correspondence of measured and slope-estimated VS

30 values. The lowest VS30

regions, particularly those along river channels and in the Mississippi Embayment itself, are recovered, 

as are the few relatively high-velocity VS

30 values in the southwestern corner of the map. On average 

there is very little bias to the estimates (fig. 33a). The same cannot be said of the geology-based sitecondition map (fig. 32c), which shows an overall bias, having significantly lower VS

30 values with 

respect to those measured (fig. 33b). Likewise, there is a more continuous progression of varying VS

30

in the topography-based map (fig. 32c) than in the geology-based map (fig. 32b). Furthermore, the 

current map shown in figure 32b, used by the Central United States Earthquake Consortium (CUSEC), 

itself shows significant inconsistencies across state borders because they were mapped independently 

by different researchers. These inconsistencies are mostly observed outside the embayment itself. 

Consequently, it appears that the slope versus VS

30 categories for this region of low topographic 

relief can successfully be used as a proxy for basic site-conditions, just as for tectonically active 

regions. Another redeeming feature is that either the slope range or the mean slope values for a given 

region can provide simple quantitative diagnostics for the nature of the appropriate VS

30-slope 

coefficients applied to the region. For example, mean slope values for the active tectonic regions are 

about 0.07; for the Mississippi Embayment region the mean slope is much lower, at 0.01, and over the 

entire continental United States east of the Rocky Mountains, the slope mean is about 0.04. Hence, we 

can use the mean slope to establish which slope correlations should be employed: active tectonic or 

stable continent. Alternatively, simplified characterizations of tectonically active versus stable 

continent domains could suffice in choosing between the coefficients employed for slope-based VS

30

assignments. 

45
Figure 32. A, Topographic map showing the Mississippi Embayment region of the central United States. Circles indicate 

the location of measurements, color-coded by VS

30 in m/s. B, Site-condition map based on geology and VS observations 

(modified from Bauer and others, 2001). C, Site-condition map derived from topographic slope. 

46
Figure 33. Histograms indicating logarithmic differences of VS

30 values for the Mississippi Embayment region, compared 

with A, values derived from topographic slope correlations and, B, values based on existing VS

30 site-condition maps. N is 

the number of VS

30 measurements. 

Australia 

Despite Australia’s stable continental setting, it has hosted several large earthquakes in the past 

50 yrs (for example, McCue, 1990; Clark and McCue, 2003). In contrast to most stable continental 

regions, where the largest magnitude earthquakes (M > 6.0) are concentrated in failed rifts or passive 

margins, Australia’s largest onshore earthquakes have occurred largely in the ancient Proterozoic and 

Archean terranes of western and central Australia (Johnston and Kanter, 1990). 

We estimate seismic site-conditions for Australia (fig. 34) using the stable continental 

relationships employed for the Mississippi Embayment region. Many geological features are wellrecovered, including the high-velocity Eastern Highlands; Adelaide Fold Belt (South Australia); and the 

adjacent low-velocity playa lakes–Eyre, Frome, Torrens and Gairdner–which largely remain dry for 

almost the whole year. The Kimberly, Pilbra, and Yilgarn Cratons (Western Australia) are also 

recovered as faster velocity material. Visually, this map compares quite well with the national site 

condition map of A. McPherson and L. Hall (unpub. manuscript, 2007). However, their map grid was 

unavailable for direct comparison at the time this report was written. One region where these two maps 

disagree is in the Nullarbor Plain region of southeastern Western Australia and southwestern South 

Australia. This zone comprises very flat-lying carbonate-dominated sediments that A. McPherson and 

L. Hall (unpub. manuscript, 2007) have classified as NEHRP class C. In contrast, the topographicallybased approach classifies this region near the DE boundary. Nevertheless, the topographically-based 

approach appears to be a good predictor of VS

30 relative to the map of A. McPherson and L. Hall (unpub. 

manuscript, 2007). 

Comparing measured VS

30 data to the Australian topographically-based site-condition map, we 

find a good correlation between observed and predicted values within moderate confidence limits (fig. 

35). Most of these data were obtained from the urban regions of Sydney, Newcastle, and Perth; 

however, some scattered SASW data from ground motion recording sites across the continent were also 

included (Collins and others, 2006). This analysis suggests that the approach does recover first-order 

site-conditions relatively well within the stable continental setting of Australia. 

47
Figure 34. Site-condition map for Australia derived from topographic slope and the stable continent relations. 

48
Figure 35. Histogram indicating logarithmic differences of Australian VS

30 data compared with values derived from 

topographic slope correlations. N is the number of VS

30 measurements. 

Scandinavia 

With the (geologically) recent deglaciation of the Fennoscandian Shield, the Scandinavian 

region is now considered to have potential for significant tectonic activity (for example from postglacial 

rebound), despite its stable continental setting (Bungum and others, 2005). This potential hazard is 

further exacerbated by its proximity to both a rifted passive continental margin and to failed rift zones. 

Although the region has not observed any large magnitude earthquakes (M > 5.8) in the past century, 

there is growing neotectonic evidence for several large earthquakes (M > 7.0) in recent prehistoric times 

(Olesen and others, 2000; Bungum and others, 2005). 

We derive a site-condition map for Norway employing the stable continent relations (fig. 36). 

Owing to its significant topographic relief, we observe that much of the country can be classified as 

NEHRP site class B. Intuitively, this makes sense given that the recent glaciation scoured much of the 

preexisting surficial materials, exposing fresh unweathered rock. In contrast, interspersed glacial 

valleys, composed of glacial till eroded from adjacent mountains, are low-lying and are marked by low 

VS

30. 

49
Figure 36. A, Topographic relief map for the stable continental setting of Norway. B, Site-condition map derived from 

topographic slope. See figure 1 for map legends. 

Application for the Continental United States 

Equipped with these correlations between topographic slope and VS

30, and assuming either stable 

continent or tectonic coefficients for slope versus VS

30 apply, we can readily generate maps of estimated 

VS

30 velocities for any region around the globe. As an example, we describe regional evaluation of site 

classes for the continental United States below. At present, VS

30 maps for the entire continental United 

States are not available. Consequently, these maps, despite inherent limitations, will be an important 

starting point for many regional and national hazards analyses if extended beyond hazard on bedrock. 

Western United States 

 We now apply the slope and VS

30 relations developed for active tectonic regions for the western 

margin of the continental United States, west of the front range of the Rocky Mountains (fig. 37). The 

regional topographically-based site-condition map indicates broad regions of contrasting site-conditions 

throughout the western United States, with faster material associated with much of the Rocky and 

Cascade Mountain ranges, and slower material interspersed in the lower-lying basins. In Nevada, in 

particular, we observe highly variable and spatially periodic changes in site class associated with the 

Basin and Range Province (also see Fig. 1c). It is also noteworthy that this map has excellent 

correspondence with the United States national surficial materials map (Soller and Reheis, 2004). We 

observe that regions of different surficial material tend to produce different site class signatures. For 

example, lacustrine sediments that cover much of western Utah are also well recovered. 

50
As noted in northeast California (fig. 1), regions of recent volcanism are interpreted as having 

relatively slow velocities on our topographically-based site-condition map. This is because the 

associated lava flows have relatively low topographic gradient. In addition to areas of northern 

California, areas of low VS

30 due to recent volcanism is particularly apparent in southern Idaho, central 

Oregon, and southeastern Washington, east of the Cascades. This observation highlights one of the 

limitations in using this technique in broad scale applications. One must be aware of existing 

geological conditions within the region of interest that may affect the reliability of this approach. 

51
Figure 37. Estimated site-condition map for the continental United States, west of and including the Rocky Mountains, 

derived from topographic slope and slope-VS

30 correlations for active tectonic regions. 

52
Eastern United States 

We also apply our approach using the stable-shield slope-VS

30 correlations to produce a seismic 

site-condition map of the entire continental United States, east of the Rocky Mountains (fig. 38). Again, 

the seismic site-condition map produced recovers many of the surficial features described by Soller and 

Reheis (2004). In particular, the Appalachians indicate relatively fast velocity material, consistent with 

their steeper terrain and relatively high topographic relief. The Atlantic and Gulf coasts indicate slower 

material, corresponding to Coastal Plain sediments. Glacial deposits adjacent to the Great Lakes region, 

in addition to Minnesota, North Dakota and South Dakota, are also well-recovered (Soller and Reheis, 

2004). It is likely that our topographic slope correlations under-predict VS

30 in areas where flat-lying 

carbonate rocks dominate (for example, southern Florida), but the absence of VS

30 measurements or sitecondition maps precludes direct comparison. These carbonates may have varying degrees of 

lithification or weathering and surficial deposits that preclude regional, broad-brush VS

30 classification 

(for example, A. McPherson and L. Hall, unpub. manuscript, 2007). It is worth noting that had 

southern Florida ranked high for earthquake hazards, such information would likely be more readily 

available; its low-hazard status warrants a more regional approach at this time. 

 While some aspects of these maps may be very approximate, they do provide first-order VS

30

site-condition maps for the continental United States, with very little effort. One obvious side-benefit 

of this approach is that this map requires only our correlation, a digital 30-sec topographic map, and a 

few seconds of computer time to produce. Greater detail, and perhaps greater accuracy, might be 

obtained with higher-resolution elevation data. 

53
Figure 38. Estimated site-condition map for the continental United States east of the Rocky Mountains, derived from 

topographic slope and slope-VS

30 correlations for stable continental regions. 

Web Delivery of Global VS

30 Maps 

Given that we expect a significant demand for these simplified topographically-based VS

30

 maps 

for regional as well as global hazard and risk studies, we have chosen to make the VS

30 grids available 

via a web delivery service. Motivation for providing this service is also to avoid any possible 

misinterpretation in duplicating our method; while duplication should be straight-forward, differences 

may ensue when processing large topographic data sets. Users of this service will be able to choose 

from three styles of global VS

30 maps: USGS topographic slope, a compiled mosaic, and user-specified 

customized maps. Maps of specified dimensions (lon1/lon2/lat1/lat2) will be individually computed 

and will subsequently be available for download from the web server (fig. 39). 

The three categories of site-condition maps are briefly described below. Access to all maps is 

provided at: 

 http://earthquake.usgs.gov/ vs30/

54
USGS Topographic Slope VS

30 Model 

The USGS topographic slope-based VS

30 model is currently the default site-condition map being 

used in global ShakeMap applications. It comprises the maps indicated in this report using the 

coefficients described in table 2. Users can access the predefined maps and grids (those presented in 

this report) or generate a VS

30 grid for selected map extents anywhere around the globe, specifying either 

active tectonic or stable continent coefficients on the interactive user interface (fig. 39). 

Mosaic VS

30 Model 

Several regional seismic site-condition maps already exist that have been developed by 

independent researchers (for example, Wills and others, 2000; Bauer and others, 2001; Lee and others, 

2001; Matsuoka and others, 2005; Martirosyan and Hansen, 2007). Furthermore, the use of higherresolution topographic data than the SRTM30 data presently used may provide significant 

improvements to our current site-condition maps. Where we have confidence that these site-condition 

maps are better than what we can achieve employing the topographic-based approach with SRTM30 

data, we stitch together these grids within our global grid above, thus creating a mosaic of 

topographically- and geologically-based VS

30 maps. These maps will represent our preferred global sitecondition model and will be the model that will be employed for Global ShakeMap applications. At 

present we do not have the capability to provide the mosaic VS

30 model online, and this product will be 

delivered at a later date. Consequently our preferred site-condition model is presently based purely 

upon the topographically-derived model described above. However, this will be rectified once we have 

the resources and permissions to develop the mosaic model further. 

Custom VS

30 Model 

We also enable users to customize site-condition maps. If users derive alternative VS

30-slope 

correlations from those described in table 2, they may customize a seismic site-condition map for a 

specified region. Users will have the option to specify the topographic slope ranges that correspond to 

their predefined VS

30 ranges. Once this request is submitted, the Web Service will calculate and return a 

customized VS

30 grid that can subsequently be downloaded from the ftp server. 

55
Figure 39. Screenshot of the web page for the global topographically-based VS

30 grid delivery service. 

56
Discussion 

Why would topographic slope provide such a good proxy for the average shear velocity in the 

top 30 meters of the ground? A discussion of a wide range of geological materials and erosional and 

depositional domains, and their influence on the physical properties controlling shear velocity, is 

beyond the scope of this discussion. However, some limited examples of widespread geomorphic 

domains are warranted. We consider the physical properties that most influence shallow shear 

velocities in soil and rock separately. 

Why Topographic Slope Works as a Proxy for VS

30

Of the physical properties of soils, those that have a strong effect on shear modulus are most 

pertinent to shear velocity. In general, void space and effective mean stress dominate shear modulus 

changes, since density variations tend to be rather small in soils (Fumal and Tinsley, 1985). When 

considering only shallow (top 30 m) conditions, mean principal effective stresses do not vary 

dramatically. Hence, among physical parameters, void ratio is one of the most important affecting 

shear modulus. Fumal and Tinsley (1985) found that the soil texture and the relative grain-size 

distribution can be a good measure of void ratio. For the San Francisco Bay area, they divided the soils 

into four textural categories based on grain-size distribution and found that shear-velocity generally 

increases as mean grain size increases. That shear velocity increases with increasing grain size goes a 

long way in explaining why lower shear velocity and lower topographic slope correlate so well: particle 

size decreases as the available energy in the depositional environment decreases at lower slopes. 

In rock, Fumal and Tinsley (1985) show that the two dominant physical properties determining 

shear velocity are hardness and fracture spacing, with greater hardness and spacing resulting in higher 

velocities. Here too, we would expect that these parameters would be correlative with topographic 

slope as hard rock and coarse fracture spacing both resist weathering, allowing rocks with higher shear 

velocities to hold a steeper slope. 

In semiarid alluvial fan systems, found in much of the western United States, mountain fronts 

typically grade from steep bedrock slopes to the outer fan, where channels are very shallow and braided 

(for example, Blatt and others, 1980). Generally, there is a decrease in grain size down-fan as the 

importance of stream-flow deposits dominates that of debris-flow deposition. With increasing distance 

from the mountain front, floodplain deposits continue to decrease in particle size as deposited at 

decreasing slopes by less energetic fluvial and pluvial processes. 

Naturally, our above generalization applies only to the overall trend of fining particle size with 

lower gradients, and hence lower VS

30 with lower slope within a particular depositional setting. There 

are also several reasons why topographic slope should be limited in its ability to recover VS

30 by a 

number of known geologic processes and overall variations in geologic materials. Clearly, other 

processes can modify or control particle size and other factors that determine VS

30 in any depositional 

environment, such as variable source material, sorting, cementing, channeling, etc. These processes and 

material properties will presumably lead to substantial variation on the overall trend we observe. For 

example, in many western United States soils, the age of the soil development and weathering will 

influence VS

30, with perhaps little change in topographic slope. Soil aging, particularly calcite 

cementation of soils (a.k.a. caliche) in the Las Vegas Basin, Nevada, has been shown to elevate VS

30

values to 500–600 m/s (Scott and others, 2004), despite relatively low slopes. Fortunately, such rigid 

soil should be expected to hold considerable slope under erosional (stream cutting) influences, so the 

overall trend may still be consistent with our simple assumptions. 

57
Thelen and others (2006), based on VS

30 profiles in the Los Angeles Basin, suggest that slope 

also controls the distribution of clay minerals in the basin, which they describe as key in the designation 

of mappable soil units, and that slope also controls texture, which in turn affects porosity of the type of 

soil formed. Thelen et al. (2006) further conclude that the best surface indicator of VS

30 may be the 

hydraulic gradient of the San Gabriel River, another manifestation of the influence of slope. Yet, they 

rightly caution that to the extent that soils are predictors of hydraulic gradient, they may also be 

considered only rough predictors of VS

30

. 

Our simple assumption on the correlation of slope with VS

30 will break down for some obvious 

topographic and geomorphic combinations. For example, in continental glaciated terrains, topography 

alone cannot distinguish between topographically similar depositional (glacial till) drumlins and 

erosional (bedrock) roche moutonnées. Likewise, and more extensive in area, nominally flat volcanic 

plateaus may not be recognized as rock since they can have low overall slope. The latter case happens 

to be quite common for much of northeastern California, where significant areas of hard rock (fig. 1c) 

are assigned to soft rock or soil based on our procedure because of regions of low slope (fig. 1a). Since 

our goal is to quantify shaking in populated areas, and glacial formations and unweathered volcanic 

plateaus tend to be sparsely populated, particularly in comparison to the many urbanized low-sloping 

alluvial basins, this misclassification may not lead to significant uncertainties in loss or damage 

assessments. 

Alternatively, it is likely that other readily-available characteristics of topography may further 

elucidate the difference, even between low gradient soils and rocks. For example, spatial roughness 

determined at high resolution may allow one to distinguish between smooth depositional sediments and 

rougher volcanic rock despite similar slopes. Additional digital geographic and (or) geomorphic data 

may also be exploited to this end as well; in particular, land use data may distinguish among 

comparable slopes of varying materials in many cases. For example, Matsuoka et al. (2005) found a 

good correlation between slope, along with geomorphic indicators (for example, man-made fill versus 

natural fill, distance to mountain front, etc.), and VS

30 in Japan. However, we have purposely limited our 

study to easily exploited topography data; further analysis is underway to provide additional constraints 

on VS

30 in areas that may violate our simple topographic slope versus VS30 assumptions. 

Comparison with Geologically-Based VS

30 Maps 

We should emphasize that our direct comparison with other VS

30 maps derived from maps of 

regolith and basement geology does not imply that we have full confidence in the details of either. 

Rather, consistencies and inconsistencies become more apparent with direct side-by-side comparison. 

Only on very local scales with dense VS

30 sampling are VS30 maps fully constrained, and then typically 

only along profiles (for example, Scott and others, 2004; Thelen and others, 2006). 

Since geology-based maps are typically developed with completely different goals in mind than 

constraining seismic site amplification, there are some obvious drawbacks to using them as a starting 

point for mapping site-conditions. Standard geologic maps ordinarily contain little information about 

the hardness or fracture spacing of bedrock units, so estimating shear-velocity is difficult (Fumal and 

Tinsley, 1985). Since bedrock VS

30 values are sparse, assignments to mapped bedrock units from 

observational VS

30 measurements are often uncertain. In soils, geotechnical properties (including cone 

penetration test results, thickness, and grain size) beneficial for detailed VS

30 assignments (for example, 

Holzer and others, 2005) are often lacking and VS

30 measurement localities are often poorly dispersed. 

 In our analyses we have shown to some degree that geologically-based VS

30 maps can have 

deficiencies with respect to predicting VS

30 measurements. These deficiencies are in part because 

assignments of single VS

30 values to an individual geological unit do not capture the potential variability 

of shear-wave velocity within the unit. One clear limitation is the lack of information on the depth 

58
variations of particular units; these thickness variations result in variable VS

30 values that are not 

accommodated. Furthermore, geology maps often ignore thin veneers of regolith that are important for 

constraining ground motion amplification, where the underlying bedrock is well-known. Another 

concern with geology-based maps is that variations in grain size within a unit (often associated with 

variable weathering profiles) can alter wave speeds, yet geological units are assigned single VS

30 values. 

Topographic slope, however, does correlate with grain size, so aspects of this variability are captured 

with our approach to mapping VS

30. At the very least, slope-based VS30 maps allow more continuous 

changes in VS

30 within single mapped geological units if the unit exhibits measurable variations in slope. 

Most existing site-condition maps have been derived primarily from existing or reinterpreted 

geological maps (for example, Fumal and Tinsley, 1985; Park and Elrick, 1998; Wills and others, 2000; 

Wills and Clahan, 2006). Fumal and Tinsley (1985) predicted shear wave velocities across southern 

California from geology based on 84 velocity borehole profiles. Their approach involved interpretation 

of different Quaternary alluvial units along with their lithologic characteristics. Such an approach 

precludes assigning VS

30 values without assigning such characteristics, usually from borehole logs, so 

substantial geotechnical information is required. Alternatively, Park and Elrick (1998) also predicted 

VS

30 values in southern California from more detailed geologic maps and found that their more 

numerous (196) VS

30 measurements warranted only three age-designated subdivisions (Quaternary, 

Tertiary, Mesozoic) to fully separate the measured VS

30 ranges. 

Wills et al. (2000) used VS

30 measurements from 556 profiles in conjunction with California 

geologic maps to produce a California statewide VS

30 site-condition map by grouping geologic units with 

similar physical properties into categories that were expected to have comparable shear-velocity values. 

Like Park and Elrick’s approach, no additional geotechnical information is required for units once their 

geologic versus VS

30 correspondence is ascertained, making their approach tractable for a statewide 

application. The Wills et al. (2000) VS

30 maps have been widely used for hazard studies and form the 

basis for site correction in ShakeMap in California (Wald and others, 2005). 

More recently, Wills and Clahan (2006) further distinguished between geologic units by 

grouping geologic units by age and then splitting units by texture and thickness of alluvial deposits. 

While this approach may reduce the number of misclassified sites, it also requires additional effort and 

more geotechnical information than simply sorting geologic units from existing geologic maps. A full 

map for California using this approach is under development according to Wills and Clahan, (2006), a 

substantial effort that is certainly warranted given the earthquake hazard and risk to major urban centers 

in California. In comparison, our approach is readily available for our global ShakeMap efforts. We 

expect to supplant our topographic-based VS

30 maps with more detailed regional VS30 maps as they are 

further developed. 

Conclusions 

We have developed a simple, inexpensive method for delivering first-order seismic sitecondition maps that can be used, in the absence of detailed geologically-based maps, to rapidly estimate 

potential ground shaking following large global earthquakes. This process has been developed 

primarily for ShakeMap and PAGER applications. However, the technique has potential to be used 

more widely in scenario and probabilistic earthquake hazard and risk assessments for disaster response 

and mitigation programs anywhere in the world. 

We exploit the simple and natural correlation between topography and surficial geology to 

derive topographic slope bounds that allow automatic mapping of VS

30 suitable on a regional scale 

anywhere on the globe. Since we are concerned with earthquake ground motions, and earthquakes 

occur predominantly in regions with significant tectonic relief, the VS

30 versus topographic slope 

59
correlation for tectonic regions (dominated by data from California and Taiwan) should hold under 

most circumstances. In stable continental regions that tend to exhibit more subdued topography (like 

the central United States), VS

30 values can also be recovered, but the correlation with slope is modified to 

accommodate the general observation that rock sites occupy lower slopes than in the active tectonic 

regions. Despite the overall lower range of slopes, the correspondence of VS

30 and slope in stable 

continental areas suggests that the results in these regions will also be beneficial for site-condition 

mapping. Analysis of any additional VS

30 measurements in stable continental areas that become 

available will allow us to better quantify the uncertainty as well as establish over what types of geologic 

and geomorphic regimes this method applies and where it is most limited. 

While these relationships for estimating VS

30 are calibrated against a particular resolution 

topography (30 arc sec global), they hold approximately for both lower- and higher-resolution maps. 

Beneficial attributes of the topographic-based site-condition maps, in addition to the obvious ease with 

which they can be produced, include both consistency and spatial continuity of resolution when making 

VS

30 assignments. Unlike geology-based maps, which typically assign a constant velocity to a particular 

geologic unit or units, the topographic-slope approach allows for variable VS

30 across a geologic unit, 

characterizing the presumed change in particle size with topographic gradient (alluvial fans or plains, 

for example). At the same time, with sharp, well-defined topographic features, there is also the ability 

to show discrete boundaries, for example, at mountain/basin interfaces. 

While the topographic slope approach provides adequate first-order estimates of regional site 

amplification for the entire globe, there are noted discrepancies. For example, we note a difference in 

geologically and topographically derived VS

30 values between soft and hard rock (NEHRP classes BC 

and C), and the correlation is made difficult for these units by the lack of shear-wave measurements. 

Fortunately, corresponding differences in site amplification for these site classes are relatively small 

(approximately 10 percent amplification in PGV for an input PGA of 200 cm/s2; see table 1), so 

distinguishing between them is not as critical as it is for other site classes. Again, additional VS

30 data 

for rock sites should improve our ability to recover VS

30 from slope for areas with fast VS30 values. We 

have also identified some specific geologic terrains and processes for which topographic slope and VS

30

are unlikely to correlate, and caution is urged in applying our approach without consideration of the 

geological units and environment. Although we have not made a systematic effort to establish over 

which geology and erosional and depositional environments our approach is applicable, we anticipate 

that additional VS

30 data acquisition over time will allow us and others to do so. In the mean time, for 

larger-scale site-condition mapping using higher-resolution topography, additional analysis is required 

and refined slope ranges will be needed to assign VS

30 values. 

Empirically-based global ShakeMaps benefit from the amplification assigned with this 

approach. Interestingly, Wald and others (1999b) originally settled on using topography as the base 

layer for ShakeMap since topography tends to highlight areas of amplified shaking in basins from those 

less amplified mountainous areas. They had not anticipated the additional benefit of these base maps 

for constraining the site factors directly. 

Topographic gradients can be easily converted to NEHRP site amplification factors for 

estimating ground motions in direct conjunction with standard ground motion prediction equations. In 

summary, our simple recipe for computing site amplification is: 

1. Calculate the maximum slope of topography using GMT command “grdgradient.” 

2. Determine map extent and compute mean slope over the domain (conveniently, GMT “grdinfo –L2” 

returns slope mean and standard deviation). For mean slopes less than 0.05, use the stable continent 

slope ranges for site class assignments; otherwise use the active tectonic slope ranges for site class 

assignments (table 2). Alternatively, simply assign table 2 coefficients based on knowledge of the 

tectonic regime. 

60
3. Assign VS

30 to all sites using the slope and VS30 ranges tabulated in table 2. 

4. For ShakeMap, amplify empirically-based ground motions based on the combinations of site class, 

ground motion period, and input amplitude based on the Borcherdt (1994) amplification factors 

given in table 1 (see Wald and others, 2005, for detailed use in ShakeMap). 

In addition to the shallowest site-conditions, seismic waves are also known to be strongly 

influenced by sediment thickness and basin structure (for example, Frankel and others, 1991; Field, 

2000). In order to automatically derive an estimate of soil thickness as well as its shallow velocity, we 

are investigating the potential for topography to characterize basin structures and their depth. It may 

also be possible to characterize basins in low, slow regions, by fitting simple functions or shapes (for 

example ellipses) whose aspect ratios should provide an estimate of basin location and orientation, in 

addition to depth. In the process of analyzing global earthquakes using ShakeMap, we are examining 

the effects of basin amplification while looking for topographic signatures that might be exploited with 

routine, uniform processing of globally available data. 

We have not fully exploited this topographic slope-based approach for mapping VS

30 by using the 

highest resolution topographic data available, which could be done for many areas. In addition, 

geomorphic, land-use, and other data sets could be brought to bear for some areas where such data 

exist. Local or regional-scale modifications to the correlations may provide improved VS

30 maps with 

little additional effort. In areas where numerous VS

30 measurements are available, or become available, 

minor modifications in the form of an overall static shift to slope-based VS

30 predictions or adjustments 

to the slope versus VS

30 correspondence ranges may be warranted. 

Finally, to avoid confusion in the duplication of our method, we have provided a web service to 

deliver specified global site-condition maps uniformly. Users can choose from three styles of sitecondition mapping: topography-based only, a mosaic of topography- and geologically-based maps, and 

maps customized to user specifications (that is, VS

30-slope coefficients). We envisage that such a service 

will be of significant value at all levels of government and private industry for many applications of 

global earthquake hazard and risk assessments. 

Acknowledgments 

Chris Wills of the California Geological Survey kindly provided his database of VS

30 values for 

California, Ron Street generously compiled and provided his collection of VS

30 values for the central 

United States, Walt Silva provided an early copy of the NGA database, Kris Pankow provided the Utah 

VS

30 data and ShakeMap VS30 grid, Tom Brackman provided Mississippi Embayment regional ShakeMap 

VS

30 grid, and Lisa Hall and Andrew McPherson provided Australian VS30 data. Discussions with 

Vincent Quitoriano, Chris Wills, Kris Pankow, Lisa Hall and Andrew McPherson were very beneficial. 

Internal reviews by Chuck Mueller, Rus Wheeler, and Steve Harmsen improved this manuscript. The 

maps produced in this analysis were made using Generic Mapping Tools (GMT; Wessel and Smith, 

1991). We also appreciate the constructive reviews of our more concise BSSA manuscript by Kris 

Pankow, Ivan Wong, and Chris Wills that led to significant improvements to the manuscript. Special 

thanks also goes to Eric Martinez and Vincent Quitoriano, who built the interactive VS

30 webpage and 

underlying PERL script to calculate the VS

30 maps on the fly. 

61
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Glossary 

Amplification factor Numerical factor to which bedrock ground motion is scaled owing to surficial 

materials. Factor is dependent on both amplitude and period of input bedrock motion.

NEHRP National Earthquake Hazards Reduction Program 

PGA Peak Ground Acceleration 

PGV Peak Ground Velocity 

Site class Classification of surficial material used to assign amplification factors.

SRTM Shuttle Radar Topography Mission 

VS

30 Average shear-velocity down to 30 m from the surface.