ARRA-Funded VS30 Measurements Using 

Multi-Technique Approach at Strong-Motion 

Stations in California and 

Central-Eastern United States

Open-File Report 2013–1102

U.S. Department of the Interior 

U.S. Geological Survey 
This page intentionally left blank 
ARRA-Funded VS30 Measurements Using 

Multi-Technique Approach at Strong-Motion 

Stations in California and 

Central-Eastern United States

By Alan Yong, Antony Martin, Kenneth Stokoe, and John Diehl

Open-File Report 2013–1102

U.S. Department of the Interior 

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013 

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Suggested citation:

Yong, A., Martin, A., Stokoe, K., and Diehl, J., 2013, ARRA-funded VS30 measurements using 

multi-technique approach at strong-motion stations in California and central-eastern United 

States: U.S. Geological Survey Open-File Report 2013–1102, 59 p. and data files,

http://pubs.usgs.gov/of/2013/1102/. 

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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.
iii

Contents

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

1.0 Introduction .................................................................................................................................. 2

2.0 Methods ....................................................................................................................................... 3

2.1 Prerequisites to Field Surveys ................................................................................................. 3

2.2 Geophysical Techniques.......................................................................................................... 4

2.2.1 Active Surface-Wave Methods .......................................................................................... 4

2.2.1.1 SASW Technique ....................................................................................................... 5

2.2.1.2 MASW (MASRW and MASLW) Techniques ................................................................ 6

2.2.2 Passive Surface-Wave Methods ....................................................................................... 7

2.2.2.1 Horizontal-to-Vertical Spectral Ratio (HVSR) Technique ........................................... 7

2.2.2.2 Array Microtremor (AM) Technique ............................................................................ 8

2.2.2.3 ReMiTM Technique ...................................................................................................... 8

2.2.3 Active and Passive Surface-wave Modeling...................................................................... 9

2.2.4 Active Body-Wave Methods ............................................................................................ 12

2.2.4.1 Seismic P- and S-Wave Refraction Techniques....................................................... 12

2.3 Selection of Geophysical Techniques for Field Deployment and Site Characterization 

 Strategies................................................................................................................................ 13

2.4 Field Procedures.................................................................................................................... 16

2.4.1 Survey Control................................................................................................................. 16

2.4.2 HVSR............................................................................................................................... 16

2.4.3 SASW.............................................................................................................................. 17

2.4.4 MASRW and MASLW ....................................................................................................... 17

2.4.5 AM and ReMiTM ............................................................................................................... 18

2.4.6 P- and S-wave Seismic Refraction.................................................................................. 19

2.5 Data Reduction and Modeling................................................................................................ 20

2.5.1 HVSR Analysis ................................................................................................................ 20

2.5.2 Active and Passive Surface-wave Analyses.................................................................... 21

2.5.2.1 SASW Data Reduction ............................................................................................. 21

2.5.2.2 MASRW and MASLW Data Reduction....................................................................... 21

2.5.2.3 AM and ReMiTM Data Reduction............................................................................... 23

2.5.2.4 Active and Passive Surface Wave Data Modeling (VS Profile and VS30 Value) ........ 24

2.5.3 Seismic Refraction Data Modeling .................................................................................. 27

3.0 Results....................................................................................................................................... 29

4.0 Data and Resources .................................................................................................................. 33

5.0 Acknowledgments...................................................................................................................... 33

6.0 References Cited ....................................................................................................................... 33

7.0 Glossary (Common Terms) ....................................................................................................... 41

8.0 Figures....................................................................................................................................... 42

9.0 Tables ........................................................................................................................................ 53

10.0 Appendix A .............................................................................................................................. 59
iv

Figures

1. Maps of strong motion stations in California and in the Central-Eastern U.S. (CEUS). .............42

2. Samples of prerequisite maps: satellite- (left) and geologic-based (right)..................................43

3. Typical passive surface wave arrays. .........................................................................................44

4. Horizontal-to-vertical spectral ratio (HVSR) Instrumentation ......................................................45

5. SASW array geometry and instrumentation ...............................................................................46

6. MASW and seismic refraction survey design .............................................................................47

7. Histogram of 191 calculated VS30 values grouped into NEHRP site classes..............................48

8. Histograms of VS30 values (blue) and adjusted VS(Z, Z + 30) values (red) grouped into 

NEHRP site classes (overlapping areas shown as purple)......................................................49

9. Plot of VS30 values versus adjusted VS(Z, Z + 30) values, with velocity ranges of NEHRP site 

classes indicated......................................................................................................................50

10. Distribution of the MASRW technique and various combined methods deployed at 191 

strong motion stations..............................................................................................................51

11. Distribution of the MASRW technique and various combined methods used to calculate 

VS30 values for 191 strong motion stations ..............................................................................52

Tables

1. Summary of seismographic networks and the number of strong-motion (SM) stations 

selected from each network.....................................................................................................53

2. Summary of site conditions and applicable geophysical techniques. .........................................53

3. Summary of results on site conditions at 191 strong-motion (SM) stations in California and 

the Central-Eastern United States (CEUS)..............................................................................54

 4. Summary of NEHRP site classes and measured VS30 values for strong motion 

stations in California and the Central-eastern U.S. ....................................................54
ARRA-Funded VS30 Measurements Using MultiTechniques at Strong-Motion Stations in 

California and Central-Eastern United States

By Alan Yong1, Antony Martin2, Kenneth Stokoe3, and John Diehl2 

Abstract

Funded by the 2009 American Recovery and Reinvestment Act (ARRA), we 

conducted geophysical site characterizations at 191 strong-motion stations: 187 in 

California and 4 in the Central-Eastern United States (CEUS). The geophysical methods 

used at each site included passive and active surface-wave and body-wave techniques. 

Multiple techniques were used at most sites, with the goal of robustly determining VS

(shear-wave velocity) profiles and VS30 (the time-averaged shear-wave velocity in the 

upper 30 meters depth). These techniques included: horizontal-to-vertical spectral ratio 

(HVSR), two-dimensional (2-D) array microtremor (AM), refraction microtremor 

(ReMiTM), spectral analysis of surface wave (SASW), multi-channel analysis of surface 

waves (Rayleigh wave: MASRW; and Love wave: MASLW), and compressional- and 

shear-wave refraction. Of the selected sites, 47 percent have crystalline, volcanic, or 

sedimentary rock at the surface or at relatively shallow depth, and 53 percent are of 

Quaternary sediments located in either rural or urban environments. Calculated values of 

VS30 span almost the full range of the National Earthquake Hazards Reduction Program 

(NEHRP) Site Classes, from D (stiff soils) to B (rock). The NEHRP Site Classes based 

on VS30 range from being consistent with the Class expected from analysis of surficial 

geology, to being one or two Site Classes below expected. In a few cases where 

differences between the observed and expected Site Class occurred, it was the 

consequence of inaccurate or coarse geologic mapping, as well as considerable 

degradation of the near-surface rock. Additionally, several sites mapped as rock have Site 

Class D (stiff soil) velocities, which is due to the extensive weathering of the surficial 

rock. 

 

1

 U.S. Geological Survey 

2

 GEOVision, Inc. 

3

 University of Texas, Austin 
2 

1.0 Introduction

With funding from the 2009 American Recovery and Reinvestment Act (ARRA, 

http://www.recovery.gov/; accessed June 27, 2012), the U.S. Geological Survey solicited 

proposals to characterize geotechnical site conditions at 197 strong-motion (SM) stations 

in California and the central-eastern United States (fig. 1 and table 1). 

A consortium of academic researchers and commercial collaborators, consisting 

of principals from the University of Texas, Austin, and GEOVision©, Inc., competitively 

responded to the U.S. Geological Survey’s request for proposal and, in March 2010, the 

consortium was awarded a contract for 191 stations (table 1). 

The project had two objectives: (1) to acquire a range of site-specific geophysical 

data for the ground motion modeling community, and (2) to establish a pilot project for 

the Advanced National Seismic System to guide similar efforts in the future. The most 

important task in this site characterization project was the determination of the 

geotechnical parameter VS30, the time-averaged shear-wave velocity (VS) in the upper 30 

m. Despite its inherent limitations (Boore, 2004; Mucciarelli and Gallipoli, 2006; 

Bragato, 2008; Castellaro and others, 2008; Lee and Trifunac, 2010), VS30 is presently 

used as the single explanatory variable for describing the influence of local conditions on 

ground motions (Boore and others, 1993, 1994, 1997; Abrahamson and others, 2008). It 

is also widely used as the basis for establishing building codes (Borcherdt, 1994; Dobry 

and others, 2000; Building Seismic Safety Council, 2003; Eurocode 8, 2004), and for 

developing modern microzonation maps (Scott and others, 2006; Castellaro and 

Mulargia, 2009; Thompson and others, 2010; Yong and others, 2011). In addition to the 

estimates of VS30, the observations and experience gained from this project are expected 

to yield guidelines for using indirect or non-invasive geophysical techniques to 

characterize site conditions. 

The 187 stations investigated in California represent 6 different seismographic 

networks: 131 are operated by the Southern California Seismic Network (SCSN), 2 by 

the U.C. Berkeley Digital Seismic Network, 3 by the utility Pacific Gas and Electric, 25 

by the Northern California Seismic Network, 25 by the California Geological Survey, and 

1 by the University of California San Diego’s ANZA seismic network (fig. 1 and table 1). 

These sites were selected on the basis of their expected exceedance of ground motions 

(peak ground acceleration for 10 percent in 50 years) as estimated by Petersen and others 

(2008). Four Central-Eastern United States (CEUS) sites were included to demonstrate 

that these techniques could be used outside of California. 

Fieldwork and data analysis took more than 2 years to complete (March 1, 2010–

May 31, 2012). In this report, we describe the range of techniques that were used to 

investigate the sites and the strategies used to overcome problems encountered in the data 

acquisition and analysis components of the project (see “Methods” in Section 2.0). For 

results, we summarize our preliminary observations (see “Results” in Section 3.0) and 

present individual site reports (see appendix A) where details about the geophysical 

results and models that were developed for each SM station can be found. 
3 

2.0 Methods

Various non-invasive geophysical survey methods—consisting of passive and 

active surface- and body-wave techniques—were applied at each station (tables 2 and 3). 

The following sections describe the prerequisites to field survey, the individual 

techniques applied, the basis for applying one or more established techniques (table 2), 

and the procedures for field acquisition and data analysis. Details on site-specific 

applications, as well as any deviation(s) from standardized procedures, are described in 

the “Observations/Discussion” section of each site report (appendix A). 

2.1 Prerequisites to Field Surveys 

In addition to strict requirements specified by the ARRA legislation, three 

conditions were imposed on the contractors to address concerns about scientific rigor and 

issues relating to cost and environmental impact. These mandates included: prerequisites 

on site selection for the measurement arrays prior to each site visit, metrics on distances 

between the array(s) and the station, and restrictions on types of geophysical survey 

methods. 

The first requirement was designed to minimize the amount of time spent at each 

station. Details about a station, determined using map-based satellite imagery and 

geology, must first be approved by the U.S. Geological Survey prior to deployment of 

geophysical arrays at the site. For every station, maps (as shown in fig. 2) include a red 

dot marking the known (on record) location of the SM instrumentation, a green circle 

delineating the 150-m radial distance from the SM site, a blue circle indicating the 300-m 

maximum radial extent, and red line(s) describing the proposed location(s) of the 

recording array(s) to be deployed during the survey. 

To acquire the best possible interpretation of the velocity profile directly beneath 

each station, a second requirement restricted the locations of measurement arrays to 

within 150-m radial distance from the known locations (on record) of the SM instrument. 

However, when elements, such as man-made impediments (parked vehicles, temporary or 

permanent structures, permission-related access issues, etc.) or geophysical constraints 

(irregular surface topography, steep slope gradient, etc.), within the 150-m radial area, 

prohibited the effective deployment of the array, we followed the recommendation of 

Borcherdt (2002) to allow an extension of the radius to no more than 300 m from the SM 

instrument. A necessary condition for such an exception was that the surficial geologic 

materials surrounding the station should be verified as matching, or closely matching, the 

materials at the location of the proposed array. 

The third requirement restricts field survey methods to non-invasive geophysical 

techniques, either in combination (preferred) or as standalone. Non-invasive methods 

have become popular because, in contrast to traditional down-hole methods, they do not 

require expensive and time consuming drilling of boreholes. Given that almost all SM 

stations are hosted by public and private entities, non-invasive techniques were deemed 

best suited for characterizing site conditions in such sensitive settings. The major 

drawback for non-invasive methods, however, is that these techniques determine seismic 

properties indirectly by inversion of surface-wave phase velocity to estimate body-wave 

velocity, and hence tend to introduce more uncertainty into the estimated values than 

invasive approaches (Boore, 2006; Moss, 2008). Nevertheless, by comparing results from 
4 

multiple techniques the inherent uncertainties can be significantly reduced (Boore, 2006; 

Cornou and others, 2006; Foti and others, 2007; Cakir and Walsh, 2009; Odum and 

others, 2010; Comina and others, 2011; Cakir and Walsh, 2012). 

2.2 Geophysical Techniques

Eight geophysical techniques were used in this project (table 2). These included 

active surface-wave techniques (spectral analysis of surface waves [SASW] and multichannel analysis of surface waves [MASW] using both Rayleigh [MASRW] and Love 

[MASLW] waves), passive surface-wave techniques (horizontal-to-vertical spectral ratio 

[HVSR], array microtremor [AM], and refraction microtremor [ReMiTM]), and seismic 

refraction of both P- and S-waves. Each technique was used either as a standalone 

method or, whenever applicable, as part of a multi-method approach (tables 2 and 3) that 

combined complementary techniques. For each SM station, VS models and VS30 values are 

then determined on the basis of one, a subset, or all these techniques. 

In this report, we briefly describe each geophysical technique and highlight details 

that are pertinent to this project. References that fully explain the techniques are provided 

and, whenever possible, include the main developers of each approach. 

(Note: In the individual site reports, MASW and MALW refer to MASRW and MASLW, 

respectively; see Glossary for de-abbreviations). 

2.2.1 Active Surface-Wave Methods

Active surface-wave methods, such as SASW and MASW, are proven nondestructive geophysical techniques for determining the variation of VS with depth (Stokoe 

and others, 1994; Brown and others, 2000; Park and others, 1999; Foti and others, 2007). 

Measurements are made on the ground surface at strain levels in the elastic range 

(< 0.001 percent) (Holtz and others, 2010). Surface-wave surveys consist of recording 

Rayleigh-wave phase velocity (VR) and/or Love-wave phase velocity (VL) data in the 

field, generating a dispersion curve, and then using iterative forward- and/or inversemodeling techniques to determine the corresponding VS model and VS30 value. These 

techniques, particularly those utilizing Rayleigh waves, have undergone significant 

research and development over many decades (Stokoe and others, 1988; Park and others, 

1999; Zywicki, 1999; Park and others, 2005; Stokoe and others, 2006; Strobbia and Foti, 

2006; Cox and Wood, 2011) since the seminal works of Aki (1957, 1965). Although 

Love waves have been utilized in seismological studies for many years (for example, Lay 

and Wallace, 1995), the application of Love wave recordings to geotechnical 

investigations is only recently gaining traction. Consequently, the literature is sparse on 

topics such as spectral analysis (SASLW) and multi-channel analysis (MASLW) of Love 

waves. To our knowledge, one of the earliest applications of Love waves for 

characterizing near surface VS structure is by Mari (1984). Later, a computational basis 

for the spectral analysis of Love waves was presented in Guzina and Madyarov (2003). 

More recent publications describing the application of MASLW include Safani and others 

(2005), Eslick and others (2007), Pei (2007), Schuler (2008), Lane (2009), and Xia and 

others (2012). 
5 

2.2.1.1 SASW Technique

The seismic source for the SASW method is a vertical dynamic load that 

generates horizontally propagating Rayleigh waves. Ground motions are monitored by 

two vertical receivers and recorded by a data acquisition system capable of producing 

records in both time and frequency domains. Theoretical as well as practical 

considerations (attenuation, near-field effects, and spatial aliasing) necessitate the use of 

different receiver intervals to generate the dispersion curve over the wavelength range 

required to determine the VS profile. To develop a VS model to a 30-m depth, energy 

sources typically include: small hammers (rock hammer or 3-lb hammer) for short 

receiver intervals; 10- to 20-lb sledge hammers for intermediate separations, and 

accelerated weight drops (AWD) or an electromechanical shaker for larger spacings. 

More energetic sources, such as bulldozers or seismic vibrators (VibroseisTM), can be 

used to characterize velocity structures to depths of 100 m or more. Generally, high 

frequency (short wavelength) surface waves are recorded across receiver pairs spaced at

short intervals, whereas low frequency (long wavelength) surface waves require greater 

spacing between receivers. Dispersion data averaged across greater distances are often 

smoother because effects of localized heterogeneities are averaged. 

Two source-receiver configurations typically used for acquiring SASW data are 

the common-midpoint (fixed center point for expanding receiver array) and the commonsource (fixed source location, moving receivers) geometries. The common-receiver 

midpoint geometry has a distinct advantage at sites with lateral velocity variation because 

all receiver pairs sample an overlapping region of the array. In order to minimize nearfield effects associated with the cylindrical (as opposed to planar; see Section 2.2.1.2) 

wave-front, while also ensuring a good signal-to-noise ratio, the distance between the 

source and near receiver typically is set to be equal to the receiver spacing (SánchezSalinero, 1987; Nazarian and Desai, 1993). The source location is generally reversed 

(Stokoe and others, 1994) to assess the effects of small phase shifts between sensors and 

the lateral velocity variation. 

The time-domain records from the two receivers are converted to the frequencydomain by calculating the Fast Fourier Transform, the cross power spectrum, and the 

coherence. The frequency-dependent phase of the cross-power spectrum, φw (f), 

represents the phase differences (between -π and π) from the two receivers as the wave 

train passes through. Because the phase differences are in wrapped form, they must be 

unwrapped to form a continuous function of frequency versus phase angle. During the 

unwrapping process, near-field data (typically wavelengths longer than twice the distance 

from the source to near receiver) and low-coherence data are discarded. The experimental 

dispersion curve, as a function of the unwrapped phase angle and the distance between 

receivers, is calculated as: 

where, VR is Rayleigh wave phase velocity, f is frequency, d2 is the distance between 

receivers, and Δφ is the phase difference in degrees. This process is repeated for each 

receiver spacing and individual dispersion curves are combined to form a composite 

dispersion curve. This composite dispersion curve is typically resampled and smoothed to 

VR = f d2

Δφ

360°
6 

form a representative dispersion curve, which is utilized for forward and/or inverse 

modeling. A more detailed description of the SASW technique is provided in Joh (1996). 

2.2.1.2 MASW (MASRW and MASLW) Techniques

In MASW techniques, surface waves are recorded by a linear array of 24 or more 

geophones typically spaced 1- to 3-m apart. Vertical and horizontal energy sources range 

from small hammers to large weight drops and vibratory sources. When applying the 

MASW technique to develop a one-dimensional (1-D) VS model (see Subsection 2.2.3), 

surface-wave data are preferably acquired using multiple-source offsets at both ends of 

the array. A wave-field transform is applied to the time-history data to convert the 

seismic record from a time-distance space to a phase velocity-frequency space in which 

the surface-wave dispersion curve can be easily identified. Common wave-field 

transforms include the frequency-wavenumber (f-k) transform, the slant-stack transform 

(McMechan and Yedlin, 1981), and the phase-shift transform (Park and others, 1998). 

Another method for obtaining the surface-wave dispersion curve includes multi-offset 

phase analysis (Strobbia and Foti, 2006). 

The f-k transform, which is a two-dimensional (2-D) Fourier transform over the 

time and space domain, can be very easily implemented in computer programs, such as 

MATLABTM, and is therefore commonly utilized. The phase-shift transform is 

implemented in many commercially developed geophysical software packages designed 

for surface-wave analysis. The slant-stack (τ-p) transform is exclusively used in 

geophysical signal processing, but has the disadvantage of lower resolution compared to 

the phase-shift transform (Park and others, 1998). 

MASW and SASW data are subject to near-field effects resulting from the nonplanar wave-front (Lai and Rix, 1998; Scocco and others, 2002). Near-field effects in 

MASRW data have been the subject of significant research in recent years (O’Neill, 2003; 

Zywicki and Rix, 2005; Xu and Miller, 2006; Bodet and others, 2009; Yoon and Rix, 

2009; Li and Rosenblad, 2011). Although the results of these studies vary, they 

nonetheless demonstrate that near-field effects can result in significantly underestimated 

Rayleigh-wave phase velocities at wavelengths greater than one-half the receiver-spread 

length for linear arrays (Bodet and others, 2009), at wavelengths greater than the meansource to receiver distance (Yoon and Rix, 2009), or at wavelengths greater than twice 

the mean source to receiver distance (Li and Rosenblad, 2011). Of course, field logistics 

dictate that having a receiver geometry adequate for mitigating near-field effects has to 

be balanced against the disadvantages of longer receiver arrays (such as the loss of highfrequency dispersion data, the significant contribution of lateral-velocity variation, and 

increased signal attenuation). The cylindrical beam-forming method of Zywicki (1999) 

and Zywicki and Rix (2005) offers a means to address the inconsistency between the 

cylindrical- and plane-wave assumptions used in the wavefield transformation process; 

however, the routine is currently not readily available. Multi-offset phase analysis, as 

described by Strobbia and Foti (2006), can help distinguish between near-field effects, 

lateral velocity variation, and higher mode contamination in MASRW data. 

As discussed earlier, applications of Love-wave methods at the geotechnical scale 

have a much shorter history than Rayleigh-wave based methods. For this reason, much of 

the basic applied research (near-field effects; optimal survey design; source-frequency 

limitations, such as the low-frequency limits of a hammer and plank source; strengths and 
7 

limitations) has yet to be discussed in published literature. Many large, low-frequency 

sources utilized for deep SASW soundings (such as bulldozers and large, vertical weightdrops) are not applicable to Love-wave soundings. In fact, the only cost-effective sources 

for generating SH-wave energy known to us are a sledge hammer with horizontal traction 

plank, Hasbrouck “golf shoe” source (Hasbrouck, 1983), or hammer-impact aluminum 

shear wave seismic source (Haines, 2007). Horizontal weight-drop or pendulum-type 

sources can be deployed to generate lower frequency energy but are time consuming to 

set up. Weight-drop sources that strike the ground at a 30o angle are more portable but 

need more testing. Large, horizontal VibroseisTM energy sources are very well-suited for 

Love-wave testing but are very costly. Due to practical (such as source frequency 

limitations) and theoretical (such as lower frequency Love waves required to image to a 

specific depth than Rayleigh waves in many geologic conditions) considerations, active 

Love wave techniques are not particularly well suited to the imaging of VS structure to 

30-m depth at soft soil sites. Active Love wave techniques are better suited to the 

imaging of 30-m deep VS structure at stiff soil and rock sites, where a sledge hammer and 

horizontal-traction plank source can generate sufficient energy over the required 

frequency range. Passive Love-wave techniques, however, can be useful for 

characterizing VS structure of low-velocity sediment sites in urban environments. 

2.2.2 Passive Surface-Wave Methods

Unlike active surface-wave techniques, the microtremor method records 

background noise emanating from ocean wave activity, atmospheric conditions, wind 

effects, traffic, industrial activity, construction activities, etc., and collectively are 

referred to as microseisms. Typically, microseisms with frequencies below 1 Hz have 

natural origins, whereas those with frequencies above 1 Hz are largely due to human 

activities (Okada, 2003). The most common techniques used for analysis of microtremor 

data include: the single-station Nakamura Method (Nakamura, 1989); f-k methods, such 

as beam-forming (Lacoss and others, 1969) and maximum-likelihood (Capon, 1969); and 

the spatial-autocorrelation (SPAC) method, which was originally based on work by Aki 

(1957). The SPAC method has since been extended and modified (Ling and Okada, 1993; 

Ohori and others, 2002; Cho and others, 2004) to permit the use of noncircular arrays, 

and is now collectively referred to as extended spatial autocorrelation (ESPAC or ESAC). 

Separately, Bettig and others (2001) developed a modified SPAC routine specifically 

optimized for random arrays. 

2.2.2.1 Horizontal-to-Vertical Spectral Ratio (HVSR) Technique

The horizontal-to-vertical spectral ratio (HVSR) technique, also known as the 

Nakamura Method, was first introduced by Nogoshi and Igarashi (1971) and later revised 

by Nakamura (1989). This method utilizes single-station recordings of ambient vibrations 

(microtremor or noise) made with a three-component seismometer. In this method, the 

ratio of the Fourier amplitude spectra of the horizontal and vertical components is 

calculated to determine the frequency of the maximum HVSR response, commonly 

accepted as an approximation of the fundamental frequency (f0) of the sediment column 

overlying bedrock (see Section 2.5.1). The same method, but applied to earthquake 

records, is also used to determine site frequency (Lermo and Chávez-García, 1994. 
8 

However, the HVSR technique, as applied during this investigation, was based only on 

microtremors. 

2.2.2.2 Array Microtremor (AM) Technique

The array microtremor (AM) technique utilizes four or more receivers aligned in a 

2-D array. Although any 2-D arrangement of receivers will suffice—including randomly 

placed receivers—triangular, circular, semi-circular, and L-shaped arrays are commonly 

used. Receivers typically consist of 1- to 4.5-Hz geophones, although very deep (> 200 

m) soundings will necessitate the use of 5- to 20-s seismometers. The triangular array, 

which may consist of several embedded equilateral triangles, is often used, as it provides 

good results with a relatively small number of geophones (fig. 3). With this array, the 

length of the side of the outer triangle should be at least equal to the desired depth of 

investigation. The L-shaped array is useful at sites located at the corner of perpendicular 

intersecting roads. Typically, 20 or more records of noise data, each with duration of 30- 

to 60-s, are acquired to image VS structure in the upper 100 m. Some data acquisition 

systems may allow continuous recording for the duration of the measurement. Imaging 

the velocity structure to greater depths requires a significantly longer recording duration. 

As previously mentioned (Section 2.2.2), AM data are typically reduced using various f-k

or SPAC methods. 

2.2.2.3 ReMiTM Technique

Refraction microtremor (ReMiTM), also known as the passive MASRW technique, 

is a passive surface-wave technique developed by Louie (2001). The ReMiTM method 

differs from the more established array microtremor technique in that it uses a linearreceiver array rather than a 2-D array and assumes multi-directional noise sources. 

Linear-array passive surface-wave datasets also can be extracted from 2-D arrays, such as 

the L-, T-, or X-shaped arrays. Although 2-D arrays are much more robust than a linear 

array, there are often field conditions (sidewalk, alley, edge of a developed property) 

where only a linear array can be applied. 

ReMiTM field procedures typically consist of laying out a linear array of 24 4.5-Hz 

geophones and recording 20 or more noise records of 30–60-s duration. These noise 

records are processed using the software package SeisOpt® ReMiTM version 2.0 

developed by OptimTM Software and Data Services. This package is used to generate and 

combine the slowness (p) - frequency (f) transform of the noise records. In practice, the 

surface-wave dispersion curve is picked at the lower envelope (fig. 6 in Martin and 

others, 2006, and site report for CE.12076) of the surface-wave energy identified in the pf spectrum. Because the lower envelope of the surface-wave energy is picked rather than 

the peak, the resulting dispersion curve is somewhat subjective, particularly when records 

are noisy. 

Linear array passive surface-wave data can also be analyzed with ESAC routines, 

but as with the ReMiTM technique, results will only be accurate if there are multidirectional noise sources. Linear passive arrays are generally best suited for extending the 

depth of investigation of active surface-wave soundings in urban environments to a 

maximum depth on the order of 100 m. When using linear passive arrays to image VS

structure to depths greater than 100 m, the very low-frequency noise sources required are 

inherently more likely to have a directional noise bias. 
9 

2.2.3 Active and Passive Surface-Wave Modeling

Rayleigh and Love waves have multiple modes of propagation, which correspond 

to plane waves in 2-D space, when traveling in a vertically heterogeneous media. The 

SASW technique measures apparent-phase velocities (also referred to as effective- or 

average-phase velocities), which correspond to the superposed mode of fundamental and 

higher-mode surface waves, as well as the effects of body wave refraction/reflection. The 

phase velocities identified using MASRW or MASLW will correspond to either individual 

modes of surface-wave propagation, or an effective mode, depending upon the 

complexity of the velocity structure. Resolution in the wavenumber domain, which 

depends on the number of receivers and the array length, also affects the interpreted 

dispersion curve: a small number of receivers and a short array length results in poor 

phase velocity resolution, whereas a large number of receivers and a long array length 

improve resolution. In normally dispersive media, where VS increases with depth, the 

apparent Rayleigh- and Love-wave phase velocities generally correspond to the 

fundamental mode. There are, however, some cases of normally dispersive velocity 

structure where dominant higher modes and, thereby, modal superposition may occur in 

Rayleigh-wave data. These include: sites with a relatively thin low-velocity layer above a 

layer of much higher velocity; sites with a steep velocity gradient; or, sites with an abrupt 

increase in VS at depth. In the first case, higher-mode Rayleigh waves may have 

significantly higher amplitude than the fundamental mode over a wide frequency range. 

In the two latter cases, the fundamental-mode Rayleigh wave may jump to the first higher 

mode, or the fundamental and first higher modes may become superposed at low 

frequencies. At such sites, Love-wave techniques may be preferred because the 

fundamental-mode Love wave is expected to be dominant at all frequencies. 

In inversely-dispersive (velocity decreases with depth) and irregularly-dispersive 

(interspersed high- and low-velocity layers) media, higher-mode Rayleigh and Love 

waves may be dominant over some frequency ranges. In this case, the apparent phasevelocity data from SASW surveys will likely consist of superposed modes. In some 

cases, MASRW or MASLW data may be able to resolve multiple modes, albeit accurate 

identification of the modes is not always straightforward. However, in many cases the 

phase-velocity dispersion curves derived from either MASRW or MASLW data will 

consist of superposed modes. Lu and Zhang (2006) presented a method for automatic 

identification of higher modes based on the relative amplitude of Rayleigh-wave modes. 

Strategies that have been developed to estimate the effective Rayleigh wave mode, 

neglecting body wave refraction and reflection, for active and passive surface-wave data 

assume far-field plane Rayleigh wave propagation only (Tokimatsu and others, 1992), or 

incorporate source and receiver geometry using strategies outlined in O’Neill (2003) or 

Lai and Rix (1998, 1999). The Kausel and Röesset (1981) 3-D array-based solution, 

which is used to numerically simulate the effective mode for a SASW survey, can 

account for source-receiver geometry and body wave effects. Tokimatsu and others 

(1992) provided a technique for estimating the effective mode for a two-station SASW 

procedure that incorporates source-receiver geometry, but does not include body-wave 

effects.

The forward problem is typically solved using the Thomson-Haskell transfermatrix (Thomson, 1950; Haskell, 1953), dynamic stiffness matrix (Kausel and Roësset, 

1981), or reflection and transmission coefficient (Kennett, 1974) methods. All these 
10 

methods can determine fundamental- and higher-mode phase velocities, which 

correspond to plane waves in 2-D space. The transfer-matrix method is often used in 

MASRW and passive surface-wave software packages, whereas the dynamic stiffness 

matrix is utilized in many SASW software packages. MASRW and/or passive surfacewave modeling may involve modeling of the fundamental mode, some form of effective 

mode, or multiple individual modes (multi-mode). As outlined in Roësset and others 

(1991), several options exist for forward modeling of SASW data. One formulation takes 

into account only plane Rayleigh-wave motion (called the 2-D solution), whereas another 

includes all stress waves and incorporates a generalized receiver geometry (3-D global 

solution) or actual receiver geometry (3-D array solution). The 2-D solution effectively 

models the fundamental mode but can account for approximate modal superposition that 

may occur at high frequencies, due to a either high-velocity surface layer or shallow 

high-velocity layer. The 3-D array solution is the most accurate method of modeling 

SASW data. 

The particular technique preferred for modeling depends on the velocity structure, 

surface-wave methods used, resolution of the extracted phase-velocity data, and data 

reduction techniques. The fundamental mode is generally applicable to modeling 

Rayleigh-wave dispersion data collected at normally dispersive sites, providing there are 

not abrupt increases in velocity or steep velocity gradients; however, effective-mode or 

multi-mode approaches are often required for irregularly dispersive sites. If active and 

passive surface-wave data are combined, or MASRW data are combined, from multiple 

seismic records with different source offsets and receiver gathers, then effective-mode 

computations are limited to algorithms that assume far-field plane Rayleigh wave 

propagation. Linearized matrix inversion methods or global search methods (Monte Carlo 

approaches such as simulated annealing, generic algorithms and neighborhood algorithm) 

are typically used to solve the inverse problem. 

It may not always be possible to develop a coherent, fundamental mode 

dispersion curve over sufficient frequency range for modeling from MASRW data due to 

dominant higher modes. Furthermore, higher modes may not be clearly identified for 

multi-mode modeling. It may, however, be possible to identify the Rayleigh-wave phase 

velocity of the fundamental mode at 40-m wavelength (VR40), in which case VS30 can at 

least be estimated using the Brown and others (2000) relationship: 

VS30 = 1.045VR40,

which was established based on statistical analysis of a large number of surface-wave 

datasets from sites with control by velocities measured in nearby boreholes. 

There are little published data on the effects of lateral-velocity variation on the 

1-D velocity structure assumption made for surface-wave modeling. There are several 

methods outlined for identifying lateral velocity variation in MASRW data such as multioffset phase analysis (Strobbia and Foti, 2006) and another methodology developed by 

Boiero and Socco (2011). However, these schemes are best suited for Seismic-refraction 

surveys dentifying lateral-velocity variation beneath very long arrays, whereby, segments 

of the array satisfying the 1-D velocity structure assumption can be extracted for 

modeling. Seismic-refraction surveys may also be used to quantify lateral-velocity 

variation. Often, varying degrees of lateral variation occur beneath MASW arrays of the 

minimum length required to image to the desired depth of exploration. In such cases, use 

of only a small segment of the array for analysis is not an option. More research is needed 
11 

to assess the effects of common types of lateral-velocity variation (low-velocity surface 

layer of slightly variable thickness, shallow dip of velocity structure, undulating velocity 

structure, etc.) on VS models derived from surface-wave dispersion curves. Such lateralvelocity variation causes significant scatter in the surface-wave dispersion data extracted 

from a MASRW or MASLW dataset. This scatter may be used to quantify lateral-velocity 

variation; either by modeling the upper and lower envelopes of the dispersion data in 

addition to the smoothed-average dispersion curve, using the scatter to assign error bars 

to the dispersion data and modeling using global inversion routines, or by using the 

variation of VR40 to estimate the variation of VS30. While it is expected that reliable 

estimates of VS30 can be made if a coherent dispersion curve can be extracted from a 

MASRW dataset, the relationship of the VS model to the 2-D VS structure beneath the 

array may not be clear. In some cases, the short wavelength (high frequency) dispersion 

data may be selectively biased towards lower phase velocities associated with the 

sediments beneath a small segment of the array. In such cases, caution should be used 

when applying multi-mode or effective-mode modeling routines because lateral-velocity 

variation may be such that the fundamental mode was successfully recovered, while, the 

resulting 1-D VS model would be expected to excite dominant higher modes over some 

frequency range. 

The SASW and MASW techniques can generally be used interchangeably for 

characterizing the VS structure in the upper 30 m, particularly at normally dispersive sites 

where VS gradually increases with depth. SASW is the technique better suited for deep 

(40–100 m or greater) active surface-wave soundings because large energy sources, such 

as bulldozers or vibrators, can be more efficiently utilized. The SASW technique 

generally requires a shorter receiver array to image to a specific depth than the MASRW 

technique and may, therefore be better suited for characterizing sites with significant 

lateral velocity variation. However, the MASRW technique may be better suited for sites 

with complex velocity structure, such as inversely dispersive sites (near-surface velocities 

decrease with depth) or irregularly dispersive sites (velocity inversions, high velocity 

layers, etc.), because dominant higher-mode Rayleigh waves, if present, can be more 

readily identified. There are, however, SASW modeling routines (3-D global and array 

inversion) capable of addressing modal superposition and body-wave effects, which in 

some cases may be more easily implemented than effective- or multi-mode analysis of 

MASRW data. Complex wave propagation, associated with significant lateral-velocity 

variation and non-planar geologic structures can be clearly evident on MASRW seismic 

records but not in SASW data as only two to four receiver stations are typically utilized. 

MASRW seismic records can also be utilized for P-wave refraction analysis to identify 

the approximate depth and P-wave velocity (VP) of the saturated zone. Anchoring the 

depth to the VP of the saturated zone improves the accuracy of VS models developed from 

the inversion of Rayleigh-wave dispersion curves. 

Active and passive surface-wave techniques can be used as complementary 

techniques (table 3). For example, active surface-wave techniques, such as SASW, 

MASRW, and MASLW, image the shallow-velocity structure that cannot be accurately 

described by the passive AM techniques for estimating VS30. AM techniques, however, 

work best in noisy environments where depth penetration by active techniques can be 

limited. In noisy environments, the AM technique can be used to extend the depth of VS

profiles derived from SASW, MASRW, and/or MASLW data. The degree of fit in the 
12 

overlapping portions of the dispersion curves from more than one technique increases the 

level of confidence in the final results. 

As previously mentioned, the theoretical models used to interpret the surfacewave dispersion assume horizontally layered, laterally invariant, homogeneous-isotropic 

material, that is, subsurface structure with 1-D response. Although these conditions are 

seldom strictly met at a site, results from surface-wave surveys do provide a good 

“global” estimate of the material properties along the array hence may be more 

representative of the site than a borehole-based “point” estimate. 

2.2.4 Active Body-Wave Methods

2.2.4.1 Seismic P- and S-Wave Refraction Techniques

Detailed discussions of the seismic-refraction method are provided in Redpath 

(1973), Dobrin and Savit (1988), and Telford and others (1990). Of two basic types, 

compressional (P) wave and shear (S) wave, P-wave surveys are more commonly 

conducted. 

When conducting a refraction seismic survey, seismic waves are generated by an 

energy source such as a sledgehammer impacting a metallic plate (vertically for P-wave 

and horizontally for S-wave), a weight drop (horizontal, vertical or at an angle), vibratory 

source (vertical or horizontal), or an explosive charge. These seismic waves propagate 

into the subsurface at a velocity dependent on the elastic properties of the material 

through which they travel. When the waves reach an interface where the density or 

velocity changes significantly, a portion of the energy is reflected back to the surface and 

the remainder is transmitted into the lower layer. Where the velocity of the lower layer is 

higher than that of the upper layer, a portion of the energy is also critically refracted 

along the interface. Critically refracted waves travel along the interface at the velocity of 

the lower layer and continually refract energy back to the surface. Receivers (geophones) 

laid out in a linear array on the surface, record the incoming refracted and reflected 

waves. The seismic-refraction method involves analysis of the travel times of the first 

energy to arrive at the geophones. These first arrivals are from either the direct wave (at 

geophones close to the source), or critically refracted waves (at geophones further from 

the source). Where subsurface velocity structure is complex, converted waves may be 

erroneously interpreted as S-waves; therefore, it is important to validate S-wave 

refraction models with P-wave refraction models, at least when the saturated zone does 

not occur within the depth range of interest. 

Analysis of seismic-refraction data depends on the complexity of the subsurface 

velocity structure. If the subsurface target is planar in nature, then the slope intercept 

method (Telford and others, 1990) can be used to model multiple horizontal or dipping 

planar layers. A minimum of one end-shot is required to model horizontal layers and 

reverse end-shots are required to model dipping planar layers. If the subsurface target is 

undulating (buried topography of a bedrock valley), then layer-based analysis routines 

such as the delay-time method (Wyrobek, 1956; Gardner, 1967), time-term inversion 

method (Scheidegger and Willmore, 1957), Hales method (Hales, 1958), plus-minus 

method (Hagedoorn, 1959), reciprocal method (Hawkins, 1961) (also referred to as the 

ABC method), wave-front method (Rockwell, 1967), and generalized reciprocal method 

(GRM) (Palmer, 1980) are required to model subsurface velocity structure. These 
13 

methods generally require a minimum of five shot-points per spread (end shots, off-end 

shots, and a center shot). If the subsurface velocity structure is complex and cannot be 

adequately modeled using layer-based modeling techniques because of complex 

weathering profile in bedrock, numerous lateral velocity variations, etc., then Monte 

Carlo or tomographic inversion techniques (Schuster and Quintus-Bosz, 1993; Zhang and 

Toksöz, 1998) are required to model the seismic-refraction data. These techniques require 

a high shot density, for example, at every two to six stations (geophones). Additionally, 

most tomographic inversion techniques cannot effectively take advantage of off-end shots 

to extend the depth of investigation, so longer profiles are required. 

Velocity inversions, hidden layers, or lateral velocity variations can also cause 

errors in seismic-refraction models. A velocity inversion is a geologic layer with a lower 

seismic velocity than an overlying layer. Critical refraction does not occur along such a 

layer because velocity has to increase with depth for critical refraction to occur. This type 

of layer, therefore, cannot be recognized or modeled and depths to underlying layers will 

be overestimated. Velocity inversions may occasionally occur at soil sites and may be 

quite common at sedimentary rock sites, but are not expected in weathered crystalline 

rock. A hidden layer is a substrate where velocity increases, but does not give rise to a 

first arrival because it is of insufficient thickness relative to the contrast in velocity with

the bounding layers. Because the seismic-refraction method generally only involves the 

interpretation of first arrivals, a hidden layer cannot be recognized or modeled, hence 

depths to underlying layers would be underestimated. A subsurface velocity structure that 

increases continually as a function of depth rather than as discrete layers will also cause 

depths to subsurface refractors to be underestimated, in a manner very similar to that of 

the hidden layer problem. Lateral velocity variations that are not adequately addressed in 

the seismic models will also lead to depth errors. Tomographic imaging techniques can 

often resolve the complex velocity structures associated with hidden layers, velocity 

gradients and lateral velocity variations. At sites with steeply dipping or highly irregular 

bedrock surfaces, or complex velocity structure, out of plane refractions (refractions from 

structures to the side of the line rather than from beneath the line) may add significant 

errors to the model. 

2.3 Selection of Geophysical Techniques for Field Deployment and Site 

Characterization Strategies

The approach to deploying the aforementioned geophysical techniques was 

primarily based on: (1) the known capabilities of the standalone technique or methods 

(groups of techniques) as previously outlined; (2) map information and on-site 

reconnaissance for assessing the geologic and noise conditions at each station; and (3) 

lessons learned as the investigation progressed. 

Stations were summarily categorized as rural (r), suburban (s), or urban (u) to 

account for noise conditions, and as soil or rock to estimate geologic conditions (table 3). 

Urban, and possibly suburban, sites were expected to have sufficient noise (energy) to 

apply passive surface-wave techniques. For the purpose of this project, shallow-rock sites 

were defined as soil sites with expected high-velocity sedimentary or crystalline rock at 

depths less than the exploration depth of the surface-wave techniques utilized. Rock sites 

were defined (using geologic maps) as being Tertiary or older sediments, sedimentary 
14 

rock or crystalline rock (volcanic, intrusive, and metamorphic) at or immediately beneath 

the surface. 

The HVSR technique was used at all sites. HVSR measurements typically were 

made at three locations of each array for two main purposes: to demonstrate whether or 

not modeling the subsurface velocity structure as being 1-D is appropriate, and to 

estimate the fundamental resonance frequency of the site. The HVSR measurement 

locations were generally distributed at the end- and mid-points of each survey array 

segment. Frequently, one of the HVSR records was obtained adjacent to the 

seismographic station. 

Rural soil sites were always characterized by the MASRW method. In addition, 

SASW techniques were often used at rural soil sites, particularly those that were expected 

to have relatively low VS. The first arrival refraction data from the MASRW records were 

always reviewed to determine if the water table was located at shallow depth and, if so, 

the depth to and the P-wave velocity of the saturated zone was estimated. Occasionally, 

an inferred water table seismic reflector was used to estimate the water table depth. 

Urban soil sites were typically characterized using the combination of MASRW, 

AM, and ReMiTM techniques. The MASRW technique was applied to characterize 

shallow velocity structure, whereas the AM and ReMiTM techniques ensured that a depth 

of investigation of at least 30 m was achieved. The 2-D AM geometries typically applied 

included a 7- or 10-channel triangular array with 1-Hz geophones and/or a 48-channel Lshaped array with 4.5 Hz geophones. The L-shaped array was most commonly deployed 

because there was insufficient space for the triangular arrays at most sites, for example, 

an L-shaped array could be deployed along property boundaries or at street intersections. 

Additionally, data from either or both of the two linear segments of the arrays could be 

later extracted from an L-shaped array for ReMiTM analysis. In this project, both 2-D and 

linear array passive surface-wave data were acquired at each site to compare results in a 

variety of noise conditions. 

Shallow rock sites in rural environments were typically characterized using the 

MASRW technique, supplemented by P-wave seismic-refraction data. In urban 

environments, passive surface-wave data were also acquired in addition to the 

aforementioned methods. During the early phases of the investigation, it was recognized 

that the MASRW technique was not effective at some sites; in particular, sites where a 

thin sediment layer (about 10-m thick or less) overlying high-velocity rock excites 

dominant higher Rayleigh-wave modes at lower frequencies. Therefore, and although 

being outside the scope of work, attempts were made to characterize such sites using the 

S-wave seismic refraction and/or MASLW (Love wave) techniques. The MASLW 

technique was generally quite effective at sites with shallow rock. As depth to rock 

increased, Rayleigh-wave techniques were also effective; however, when interpreting 

Rayleigh-wave dispersion data, the possibility of mode jumping (fundamental to first 

higher mode) or modal superposition (mode mixing) had to be considered. Surface-wave 

techniques are not particularly well-suited for accurately characterizing the VS of relative 

high velocity rock, even when rock is quite shallow (surface-wave wavelengths much 

longer than typically recorded are required to accurately constrain the VS of shallow 

rock). Hence, S-wave seismic refraction was applied (whenever possible) to help 

constrain and validate the VS of rock. 
15 

Rock sites were generally the most difficult sites to characterize due to limited 

accessibility, lateral velocity variation and occasional dominant higher mode surface 

waves. Topographic variation at rock sites often limited testing to a ridge top or toe of 

slope of an outcrop and occasionally required that testing be conducted further away from 

the seismic station than desired. 

During the early phases of field investigation, lateral velocity variation was 

significant at many rock sites. Therefore, the P-wave seismic-refraction technique—

anticipated for use only at about 30 to 40 percent of rock and shallow rock sites—was 

eventually used at almost all rock sites. Typically, P-wave seismic-refraction data were 

acquired concurrently with MASRW data by adding multiple interior shot locations along 

the MASRW array. These interior shot locations also proved useful for MASRW analysis 

at many sites, particularly those with significant lateral velocity variation or dominant 

higher modes at high frequencies. Occasionally, P-wave seismic-refraction data were 

acquired using longer arrays to image to the 30-m depth without the use of far-offset shot 

locations. In many cases, seismic-refraction models were used to quantify lateral velocity 

variation beneath the portion of the seismic line where the depth of investigation was 

greatest. However, this process was not initiated until a number of sites were encountered 

that required the use of seismic-refraction models to assess VS30 or validate surface wave 

VS models. The P-wave refraction technique was not always effective at rock sites due to 

weak P-wave signals, out-of-plane refractors, scattering, etc. 

When lateral velocity variation was too extreme, a coherent surface-wave 

dispersion curve could not be developed and surface-wave techniques were ruled 

ineffective. When a coherent dispersion curve could be developed for a site with 

significant lateral velocity variation, large scatter was observed in the dispersion data 

resulting in a VS model that may not be representative of the average velocity structure. 

VS30, however, likely falls within the range beneath the test area and is still useful for the 

purpose of site characterization. A small (5°) dip in velocity structure at depth can result 

in long wavelength divergence of surface-wave dispersion data reduced from forward and 

reverse source locations. Near-surface lateral velocity variation, which was very 

common, results in significant scatter in short wavelength dispersion data. Lateral 

velocity variation can also severely complicate attempts at multi-mode or effective-mode 

inversion, particularly if the short wavelength (high frequency) segment of the dispersion 

curve is biased to a portion of the array with lower VS. Some rock sites had dominant 

higher Rayleigh-wave modes over a wide frequency range, often associated with a very 

thin low velocity residual soil layer over stiffer sediments or rock. The MASLW 

technique, which again was not within the original scope of work, was effectively used at 

many such sites. Additionally, rock sites with relatively abrupt increases in velocity at 

depth or steep velocity gradients (such as weathered crystalline rock sites) often include 

superposition of Rayleigh wave modes at long wavelengths (low frequencies). The 

MASLW technique was not typically applied to this type of site but should be considered 

for future investigations, as modal superposition or mode jumping is not expected to be 

an issue with Love wave dispersion data. 

A number of seismic stations sited on rock were located at the toe of bedrock 

outcrops (mountain or hill). These sites could not generally be characterized with surfacewave methods, as the bedrock surface dips beneath an array aligned along the toe of 

slope. S-wave seismic-refraction surveys, which were not part of the original scope of 
16 

work, were conducted in an attempt to characterize such sites. S-wave seismic-refraction 

models were typically only included in the site reports if S-wave first arrivals could be 

reliably interpreted. Strong P-wave energy and converted waves were prevalent in some 

S-wave refraction datasets and limited the use of the data. 

Other seismic stations were located on dipping sedimentary rock. Ideally in such 

conditions, the seismic arrays are aligned parallel to strike and also, when possible, 

perpendicular to strike to assess the variations in lateral velocity. However, local surface 

topography often limit ideal survey locations, hence it was not usually possible to place 

arrays parallel or perpendicular to strike while also minimizing topographic variation 

along the array. Often, dipping sedimentary rock sites had relatively low velocity, thereby 

behaving more like a soil site, or near-surface velocity was controlled predominately by 

weathering rather than lithology. Here, surface-wave techniques were relatively effective 

even when arrays were aligned at an oblique angle to strike of the geologic bedding. 

2.4 Field Procedures

2.4.1 Survey Control

For control on the surveys, the end- and mid-points of each geophysical array 

were surveyed with a Magellan Professional MobileMapperTM CX GPS system or 

Trimble Pro XRS GPS system with OmniStar differential corrections. When accessible, 

the seismic station also was surveyed. The assigned datum for all coordinate values is the 

World Geodetic System 1984 (WGS84). Tree cover at several sites required geophysical 

arrays to be surveyed with a Nikon NPL-362 total-station surveying system and tied to 

GPS control points. All geophone and shot point locations were based on 100-m tape 

measure(s). Relative elevations of seismic-refraction lines were surveyed using a Sokkia 

C300 automatic level or total-station system. 

2.4.2 HVSR

The primary seismic system used to acquire HVSR data was a Nanometrics 

Trillium Compact 120-s seismometer (herein referred to as Trillium Compact) and a 

Kinemetrics Quanterra Q330 data recorder (fig. 4). HVSR measurements were also made 

at some sites with a Micromed Tromino ENGY (herein referred to as Tromino) (fig. 4). A 

minimum of three HVSR recordings was made at each site. The measurement locations 

were either distributed along the measurement array(s), or often, one HVSR location was 

placed near the seismic station. The HVSR data acquisition procedures generally 

followed user guidelines developed under the Site EffectS assessment using Ambient 

Excitations (SESAME) research project (SESAME, 2004). 

It was necessary to take an appreciable amount of time to determine the optimal 

method of deploying the Trillium Compact for HVSR measurements. The best quality 

data were generally acquired when the sensor was placed in a shallow hole and coupled 

to the ground by a small aluminum cradle with spikes, provided by the manufacturer. The 

sensor was always covered with a bucket or plastic tub to keep direct wind off the sensor 

and, more importantly, to keep the wind from moving the sensor cable. In gravelly soils, 

the sensor was placed in a small hole with the aluminum cradle coupled to the ground 

using gypsum plaster (plaster of Paris). On hard substrate, the sensor was either placed 

directly on the ground and coupled by leveling legs or was placed on the aluminum 
17 

cradle without legs. Microtremor (noise) data were recorded at a 200-Hz sample rate and 

output to 3 column ASCII files, each containing 1-hr blocks of data, with columns 1 

through 3 comprised of the vertical, north, and east components, respectively. Typically, 

the Trillium Compact was set up and recording for at least 15 min (settling time) before 

other field activities were suspended for HVSR recording. 

The Tromino is a higher frequency seismometer, optimally designed for HVSR 

measurements in shallow sedimentary basins. The Tromino was typically only used at 

sites expected to have relatively shallow bedrock. The Tromino was coupled to the 

ground using three spikes provided by the manufacturer or geophone spikes adapted for 

the instrument. Microtremor measurements were typically made for between 15 and 45 

min, depending on site conditions, with data recorded at 128 samples per second. 

Recordings were stored in the instrument’s internal memory, downloaded to a laptop 

computer, viewed in the software package (Grilla) provided by Micromed, and 

reformatted to an ASCII file for further analysis. 

2.4.3 SASW

SASW equipment consisted of Oyo Geospace 1-Hz and 4.5-Hz geophones, a 

Hewlett Packard (HP 35670A) dynamic signal analyzer, hammers of various sizes, and a 

90-kg accelerated weight drop (AWD) as shown in figure 5. 

SASW data were generally acquired using a common-receiver-midpoint 

geometry, where the midpoint of the receivers remained fixed during data acquisition 

(fig. 5). An alternate acquisition geometry, which was not typically applied during this 

investigation, is the common-source geometry where the source location remains fixed 

during data acquisition. The common-source geometry is typically applied when large 

energy sources (VibroseisTM) that are inefficient to move are used. 

SASW data typically were collected by spacing base receivers 2, 4, 8, 12, 16, 24, 

and 32 m apart; spacing of additional receivers were used as necessary. The spacing of 

receivers generally provided adequate overlap of dispersion data over a range of 

wavelengths between 1 and 64 m. For each receiver spacing, reversed source locations 

were occupied with a common-receiver midpoint, where possible. The distance between 

the source and near receiver was typically set equal to the receiver spacing. A rock 

hammer, a 3-lb hammer, 10- to 20-lb sledgehammers, and an AWD were used as energy 

sources—larger energy sources were used as receiver spacing increased. Data from the 

transient impacts (hammers) were averaged 10–20 times to improve the signal-to-noise 

ratio. Data were saved to floppy disk and file names and field parameters were 

documented. 

2.4.4 MASRW and MASLW 

The MASRW and MASLW seismic data acquisition system consisted of two 24-

channel Geometrics Geode signal enhancement seismographs combined to form a 48-

channel system and a laptop computer running the Geometrics Seismodule Controller. 

Equipment used for the MASRW surveys consisted of Geospace 4.5-Hz vertical 

geophones, seismic cable with 3 m-take-outs, a truck mounted AWD, a 3-lb hammer, 10- 

and 20-lb sledgehammers, an aluminum plate, Geometrics hammer switches and a trigger 

cable. Equipment for MASLW surveys consisted of Geospace 4.5 or 10-Hz horizontal 

geophones and a 10- or 20-lb sledgehammer with a horizontal traction plank (wood beam 
18 

with metallic cleats), Hasbrouck “golf shoe” source (Hasbrouck, 1983), or hammerimpact aluminum shear wave seismic source (Haines, 2007). Photographs of the 

aforementioned equipment are shown in figure 6. 

Each MASRW array typically consisted of a single spread of 48 geophones, 

spaced 1.5 m apart. With this survey design, source locations were most commonly offset 

at 1.5, 5, 10, 20, and 30 m from the near geophone at each end of the array (fig. 6). 

Occasionally, the farthest offset shot locations were not included as a result of 

insufficient space. The basis of this survey design was to allow extraction of Rayleigh 

wave dispersion data to a sufficiently long wavelength to develop a 30-m deep VS model, 

while abiding by the near-field criteria presented by Yoon and Rix (2009). At sites with a 

lack of sufficient space, 1-m geophone spacing was used. One-meter geophone spacing 

was also used at most sites where passive surface-wave data were acquired. Additionally, 

MASW data were occasionally acquired along longer arrays deployed to extend the 

seismic-refraction depth of investigation. 

When permitted, the AWD was used as an energy source at all off-end shot 

locations; otherwise, a 20-lb sledgehammer was used as the primary source. However, 

most sites without vehicle access for the AWD had relatively high S-wave velocity, and 

therefore a 20-lb hammer was sufficient to image to 30-m depth. In low noise 

environments, the AWD and 20-lb hammer sources generally provided sufficient energy 

to about 4-5 and 7-8 Hz, respectively. Large vibratory energy sources are expected to 

perform better at low frequencies, but were not used during this investigation. A 3-lb 

hammer and light blows with a 10-lb hammer were also used near the shot location at 

1.5-m offset from the near geophone in an attempt to generate higher frequency surface 

waves. Multiple energy sources were also used at a center shot location during standard 

MASRW data acquisition. At rock sites, seismic-refraction data were also acquired along 

MASRW arrays by adding interior shot locations at intervals of 4 to 6 geophones. The 

additional refraction shots often proved very useful for MASRW analysis at sites with 

significant lateral velocity variation. Seismic data were acquired with a record length of 1 

s or longer and a 0.125 ms sample rate to allow both surface-wave and seismic-refraction 

analysis. Generally, the final seismic record at each shot point was the result of stacking 

5–15 shots to increase the signal-to-noise ratio. Data were saved to an internal hard drive 

of a laptop computer. Data file names and important acquisition parameters were 

documented. 

The design of the MASLW survey was similar to MASRW design. For this 

technique, 4.5- or 10-Hz horizontal geophones and a hammer and wooden traction plank 

with a vehicle as hold-down weight, or hammer and portable aluminum S-wave source, 

were used as energy sources. Lower frequency energy sources were not available for 

MASLW surveys, primarily because the technique was not originally part of the scope of 

work and was typically only applied at relative high velocity sites. MASLW data were 

generally acquired in conjunction with S-wave seismic-refraction data along the same 

array. Both forward and reverse polarity seismic records (hammer blows to opposite sides 

of the source) were acquired at each shot location. 

2.4.5 AM and ReMiTM

Four array configurations were used for acquisition of data from the array 

microtremor method (AM) (fig. 3): 
19 

• 7-channel triangular array with 1-Hz geophones 

• 10-channel triangular array with 1-Hz, geophones 

• 48-channel L-shaped array with 4.5-Hz geophones 

• 48-channel T-shaped array with 4.5-Hz geophones 

Triangular arrays typically have a minimum of four sensors: a sensor at each 

corner of an equilateral triangle and a sensor at the center of the triangle. In addition to 

the aforementioned geometry, the 7-channel triangular array has a smaller triangle array 

rotated 180° and embedded within the outer triangle with a maximum dimension of half 

the outer triangle (fig. 3). In the case of the 10-channel triangular array, a third triangular 

array is embedded in the inner triangle. The maximum length of the sides of the 

triangular arrays used in this project ranges from 50 to 80 m. L- and T-shaped arrays 

typically had 24 geophones on each leg of the array, with geophone spacing varying 

between 3 and 7.5 m. In order to verify that, under some noise conditions, passive 

surface-wave data at frequencies lower than 4.5 Hz can be extracted from arrays using 

4.5-Hz geophones, a 9-channel L-shaped array was co-located with a 48-channel Lshaped array at one site (CE.14395). 

ReMiTM data was also collected at all sites where passive surface-wave 

measurements were made in order to compare dispersion data extracted from linear 

arrays to that extracted from the 2-D arrays. Passive ReMiTM data were either acquired 

along a separate array (at sites where triangular arrays were utilized) or extracted from 48 

channel “L” or “T” array datasets. 

A Geometrics Geode 24-channel seismic-recording system was used to record the 

passive surface-wave data. Data were typically acquired during HVSR recordings when 

there was sufficient time to record 30–50 30-s records. Data were stored on a laptop 

computer and data acquisition parameters, array geometry and file names were 

documented.

2.4.6 P- and S-wave Seismic Refraction

Both the data acquisition system and the seismic sources used for the seismicrefraction surveys are the same as those used for the MASRW and MASLW surveys 

(Section 2.4.4). Similarly, the P-wave refraction surveys utilized 4.5-Hz vertical 

geophones, but for the S-wave surveys, 4.5-, 10-, and 28-Hz geophones were used. 

Each seismic line consisted of a single spread of 24 or 48 geophones spaced 1.5–3 

m apart. Seismic-refraction data were generally acquired in conjunction with MASRW or 

MASLW data from arrays with 1.5-m geophone spacing. About 15–23 seismic-refraction 

shot-point locations were occupied on each line: multiple off-end shots where necessary 

and possible, end shots and multiple interior shot-points were nominally located between 

every fourth or sixth geophone. 

The AWD and 20-lb sledgehammer were typically used as energy sources for Pwave seismic-refraction data acquisition. A wooden traction plank or aluminum source 

was typically used for S-wave seismic-refraction data acquisition. Both forward and 

reverse (hammer blows to opposite sides of the source) polarity seismic records were 

acquired at each S-wave refraction shot location. Generally, the final seismic record at 

each shot point was the result of stacking 5–15 shots to increase the signal-to-noise ratio. 

Data were saved to computer with data file names and important acquisition parameters 

were documented. 
20 

2.5 Data Reduction and Modeling

2.5.1 HVSR Analysis

HVSR data were reduced using the Geopsy Version 2.7.0 software package 

(http://www.geopsy.org; last accessed June 27, 2012) developed by Marc Wathelet for 

the SESAME project. In Geopsy, the reduction of HVSR data generally adheres to the 

H/V user guidelines developed under the SESAME project (SESAME, 2004). 

Microtremor data recorded by the Trillium Compact seismometer and Q330 data 

recorder were stored in files containing 1-h blocks of data. These data files were 

appended into a single file containing the vertical, north, and east components of 

recorded noise data, respectively. Although not necessary for HVSR analysis, the data 

were converted from counts to velocity units. Microtremor data recorded by the 

Tromino® were exported to an ASCII file using the software package Grilla, provided 

with the instrument. Upon export, a 0.3-Hz low-cut filter was automatically applied. Data 

files were then loaded into the Geopsy software package, where data file columns 

containing the vertical and horizontal (north and east) components and sample rate (200 

Hz for Trillium Compact and 128 Hz for Tromino®) were specified. HVSR was typically 

calculated over a frequency range dependent on the observed site response and using a 

time window length of 100–200 s. Time windows containing transients (nearby foot or 

vehicular traffic) or segments yielding poor quality results were not utilized for analysis. 

Time windows were either: automatically picked and then manually edited; or, had an 

anti-triggering algorithm applied to avoid transients. For every selected time window, 

Fourier amplitude spectra were calculated and smoothed by the Konno and Ohmachi 

(1998) filter with a smoothing coefficient value between 30 and 40. The vertical 

amplitude spectra were divided by the root-mean-square (RMS) of the horizontal 

amplitude spectra to calculate the HVSR for each time window and the average HVSR. 

After calculating the standard deviation of the HVSR amplitudes for all windows, the 

average response is divided and multiplied by the standard deviation to produce the 

minimum and maximum HVSR spectra, respectively (SESAME, 2004). 

Because HVSR measurements were primarily used to verify that a site’s velocity 

structure was generally 1-D in nature and to determine the approximate fundamental 

resonance frequency, HVSR data were not generally modeled and no attempt was made 

to jointly invert HVSR and surface-wave dispersion data. Occasionally, and particularly 

for shallow rock sites, the HVSR modeling routines available in the software packages 

Grilla v. 6.1 (Micromed) and SeisImager/SW (Geometrics, Inc.) were used to calculate 

the theoretical HVSR response for the VS models developed by inversion of the surfacewave dispersion data, and then these responses were compared to the measured HVSR 

data. The quarter-wavelength approximation method, first introduced by Joyner and 

others (1981) and adapted (Boore and Brown, 1998; Ibs-von Seth and Wohlenberg, 1999; 

Delgado and others, 2000) as: 

where f0 is the site fundamental frequency, is the average shear-wave velocity of the 

soil column overlying bedrock at depth z, was also used on occasion to determine if the 

f0 = VS

4z

VS
21 

VS model developed from the surface-wave dispersion data for shallow rock sites was 

consistent with the HVSR data.

2.5.2 Active and Passive Surface-Wave Analyses

Several software packages were used for reduction and modeling of active and 

passive surface-wave data. These commercial software packages included: 

• Geometrics/Oyo Corporation SeisImager/SW v. 4.305—active and passive 

surface-wave reduction and analysis including fundamental-, effective- and multimode Rayleigh-wave inversion and fundamental-mode Love-wave inversion. 

• Geogiga Seismic Pro 6.1 or Pro 7.1 Surface Plus and Surface—active and passive 

surface-wave reduction and analysis (fundamental-mode and multi-mode 

Rayleigh-wave inversion). 

• Kansas Geological Survey SurfSeis 3—surface-wave analysis (fundamental- and 

multi-mode Rayleigh-wave inversion). 

• SeisOpt ReMiTM v. 5.0 by OptimTM Software and Data Solutions (reduction and 

analysis of linear array passive surface-wave data). 

• WinSASW v. 1.23 written by Sung-Ho Joh, University of Texas at Austin—

SASW data reduction and viewing of surface-wave dispersion data. 

• WinSASW v. 3.1.2 and v. 3.2.6 written by Sung-Ho Joh (Soil Dynamics Lab, 

Chung-Ang University, Korea)—SASW data reduction and modeling, generation 

of representative dispersion curve and fundamental-mode (2-D solution) 

Rayleigh-wave inversion and effective-mode (3D-global and 3D-array solutions) 

inversion of Rayleigh-wave dispersion data acquired using SASW method. 

2.5.2.1 SASW Data Reduction

SASW data were reduced using the WinSASW software packages as follows: 

• Input forward- and reverse-direction phase spectrum and coherence for a 

particular receiver spacing. 

• Enter receiver spacing, geometry, and wavelength restrictions (maximum 

wavelength typically set to twice the receiver spacing). 

• Mask phase data (either the forward and reverse directions individually or the 

average). 

• Generate dispersion curve. 

• Repeat for all receiver spacings and merge all dispersion curves. 

• Document workflow and output files in spreadsheet. 

2.5.2.2 MASRW and MASLW Data Reduction

MASRW and MASLW data were reduced using the Geogiga Surface Plus 

software package as follows:

• Input all seismic records into software.

• Enter receiver spacing, geometry, and wavelength restrictions, as necessary. 

• Select receiver offset range for processing and mute noise at late times. 

• Apply wavefield transform to seismic record to convert data to phase velocity—

frequency space. 
22 

• Identify and pick dispersion curve (fundamental mode and/or effective mode). 

• Repeat for all shot records and various receiver offset ranges. 

• Enter all input and output data file names, shot locations, receiver geometry, and 

receiver-offset range into spreadsheet and calculate maximum wavelength by 

which to minimize near-field effects. 

• Apply maximum wavelength criteria, reformat, and merge all data files.

• Load merged dispersion curve into WinSASW software package, review scatter 

in dispersion data, delete outliers and save final dispersion data for modeling. 

MASRW and MASLW data-reduction strategies evolved as challenging datasets 

were encountered. MASRW data collected at soil sites typically involved the acquisition 

of multiple off-end and mid-point shot locations. Multiple energy sources (3-lb hammer, 

10-lb hammer, 20-lb hammer, and/or AWD) were used at off-end and center-shot 

locations. Generally, no single seismic record yielded a dispersion curve covering a 

sufficient range of wavelength (or frequency) to develop a VS model to a depth of 30 m 

with acceptable accuracy. It was, therefore, necessary to acquire dispersion data using 

multiple-source locations, source types, and different receiver gathers. As an example, to 

obtain short-wavelength (high-frequency) dispersion data, it was not uncommon to apply 

the wavefield transform to receiver gathers consisting of only the nearest 6 to 12 

geophones when the energy source consisted of a 3-lb hammer. Occasionally, SASW 

analysis was applied to selected receiver pairs from MASRW seismic records to obtain 

shorter wavelength dispersion data or to supplement MASRW dispersion data at complex 

sites. 

At rock sites, the multiple interior source locations that were acquired to complete 

a seismic-refraction survey were also available for surface-wave analysis. Lateral 

velocity variation was the single largest problem when characterizing rock sites. 

Variations of 20 percent or more in the average VP of the upper 30 m (VP30) were often 

observed over relatively short distances in seismic-refraction models at such sites. When 

lateral velocity variation was too severe, it was not possible to develop a coherent 

composite dispersion curve over a sufficient range of wavelengths for modeling. When it 

is possible to develop a coherent composite dispersion curve, it is often assumed that the 

resulting VS model, or at least the estimate of VS30, is representative of average site 

conditions. The degree of scatter in the dispersion data may be useful for assessing lateral 

velocity variation. It, however, could be used to quantitatively assign errors bars 

whenever a global inversion routine was used to thoroughly search the model space. 

Nevertheless, we did not apply global inversion routines for inversion of the surfacewave dispersion curves during this project. At more complex sites, it was not unusual for 

the composite dispersion curve to be comprised of more than 100 dispersion curves 

derived from multiple shot locations and different receiver gathers. Little is published on 

the effects of lateral velocity variation on the VS models and VS30 estimates developed 

from various surface-wave testing configurations. Information is also scarce on the effect 

of lateral velocity variation on the amplitude of higher modes. It is possible that certain 

types of lateral velocity variation may severely limit the application of effective- and 

multi-mode surface-wave modeling techniques. 

The Rayleigh wave dispersion curves were generally assumed to associate with 

the fundamental or effective mode. Sites with large increases in velocity at relatively 
23 

shallow depth or steep velocity gradients were often observed to have a jump from the 

fundamental first higher Rayleigh mode at intermediate to low frequencies. For sites with 

dominant higher Rayleigh-wave modes at intermediate to high frequencies (except for 

sites with a high velocity surface layer), it was often possible to take advantage of the 

dependence of source offset on the amplitude of the higher modes to extract the 

fundamental-mode data over a wide range of frequencies. In some cases, lateral velocity 

variation was such that it was likely that the fundamental mode was recovered, albeit 

with significant scatter, even though dominant higher modes were expected. The 

selective picking of dispersion data to avoid obvious higher modes and the large number 

of dispersion curves from multiple shot offsets and receiver gathers limited the use of 

effective-mode routines that incorporate source offset or source and receiver geometry. 

Therefore, whenever potential modal superposition or mode jumping at long wavelengths 

(low frequencies) were observed, only effective-mode routines based on far-field 

Rayleigh-wave propagation were considered for modeling. The fundamental Love-wave 

mode appeared to be dominant on all MASLW datasets used for site characterization. 

2.5.2.3 AM and ReMiTM Data Reduction

AM data, including 2-D and linear-array data, were reduced using the extendedspatial-autocorrelation (ESPAC or ESAC) routines in software application programs such 

as SeisImager/SW and/or Geogiga Surface Plus through the following steps: 

• Input all seismic records into software. 

• Enter receiver spacing, geometry, and wavelength restrictions, as necessary. 

• Calculate the SPAC function for each seismic record and average. 

• For each frequency calculate the degree of fit of a first-order Bessel function to 

the SPAC function for a multitude of phase velocities. 

• Identify and pick dispersion curve as the best fit of the Bessel function for each 

frequency. 

• Convert dispersion curves to WinSASW format for modeling with maximum 

wavelength typically set to about twice the average length of the sides of the 2-D 

array or the length of the linear array. 

• Document input and output data files and critical analysis steps in a spreadsheet. 

Passive ReMiTM data were reduced using the SeisOpt® ReMiTM data analysis 

package. Data reduction steps included the following: 

• Extract linear arrays from L- and T- shaped arrays using the computer program 

seg2_edit (Ellefsen, 2003). 

• Convert SEG-2 format field files to SEG-Y format. 

• Preprocess data, including trace-equalization gaining and DC offset removal. 

• Erase receiver geometry present in the file header. 

• Compute the velocity spectrum of each record by p-f transformation. 

• Combine the individual p-f transforms into one image. 

• Pick and save the velocity spectrum image. 

• Convert dispersion curve to WinSASW format with maximum wavelength 

typically set to the length of the array. 

• Document input and output data files and critical analysis steps to spreadsheet. 
24 

Under optimal conditions, AM and ReMiTM data originating from the same array 

typically yielded similar dispersion curves. In cases where data differed, the AM data 

were assumed to be most representative of site conditions, assuming all datasets agreed 

with the MASRW data over the frequency range where the dispersion data overlapped. 

There was, however, at least one site (CI.SBC) where lateral velocity variation was the 

source of the difference between dispersion curves obtained by the two methods. In this 

case, the ReMiTM array co-located with the MASRW array yielded dispersion data that 

best agreed with the MASRW dispersion data. 

2.5.2.4 Active and Passive Surface-Wave Data Modeling (VS Profile and VS30 Value)

Prior to data modeling, it is necessary to develop a regularly sampled 

representative dispersion curve. Numerous schemes exist for generating a representative 

dispersion curve including manual picking, direct averaging, and more sophisticated 

averaging schemes. The moving average-curve fitting routine outlined in Joh (1996) 

(implemented in WinSASW v. 3.1.2) was used to generate representative dispersion 

curves for the SASW, MASRW, MASLW, and passive surface-wave datasets. These 

representative dispersion curves were typically sampled at equal logarithmic wavelength 

increments. When multiple surface-wave datasets were acquired at a site, individual 

representative dispersion curves were combined (weighted average) and resampled 

yielding a final representative dispersion curve for the combined dataset. Occasionally, 

lateral velocity variation was such that there was significant scatter in the dispersion data 

from co-located SASW and MASRW arrays, or from the forward- and reverse-source 

locations of an MASRW array. In such cases, it was common to develop independent 

representative dispersion curves for the SASW and MASRW datasets or two 

representative dispersion curves for the MASRW dataset. 

Rayleigh-wave dispersion data (SASW, MASRW, AM, and ReMiTM) were 

typically modeled using the 2-D solution in the WinSASW software program or effective 

(average) Rayleigh-wave mode routine in SeisImager/SW. The use of the multi-mode 

modeling routines available in SeisImager/SW, Surfeis 3, and Geogiga Surface/Surface 

Plus were explored in several datasets. In some datasets, the effective-mode solution in 

SeisImager/SW was often successful at modeling jumps from fundamental mode to firsthigher mode at low frequencies without the need to use a multi-mode modeling routine. 

Love-wave dispersion data (MASLW) were modeled using the fundamental mode Lovewave solution available in SeisImager/SW. 

All software packages used for analysis of the surface-wave dispersion data 

utilize linear inversion routines and require starting models. Typically, layer thickness 

was manually defined such that the first layer was no thinner than one-third to one-half 

the minimum wavelength with layer thicknesses generally increasing with depth. At some 

sites, seismic-refraction models were used to support an upper-layer thickness that was 

less than would be normally estimated based on the minimum wavelength. 

Mass density was assumed in the models and allowed to increase with increasing 

velocity. Where VP exceeded about 1,500 m/s, assumed density was loosely tied to the 

Nafe-Drake empirical relationship between VP and density for saturated sediments and 

sedimentary rocks (Ludwig and others, 1970). Within the normal range encountered in 

geotechnical engineering, variation in mass density has a very small effect on surface-
25 

wave dispersion. During Rayleigh-wave data modeling, the VP of unsaturated soils and 

rock was most commonly estimated using a Poisson’s ratio (v) of 0.3 and the following 

relationship (Sheriff, 2002): 

. 

The VP of the saturated zone—the depth of which was typically estimated from 

seismic refraction first arrival data or occasional identification of a possible water table 

reflector—was set to greater than 1,500 m/s or the actual velocity generally estimated 

from seismic-refraction data. Poisson's ratio is not required as an input parameter for 

inversion of Love-wave dispersion data. 

Depending on the inversion routine utilized, a starting model was either 

developed based on the methodology outlined in Joh (1996) (Rayleigh wave only), 

defined by the user based on general characteristics of the dispersion curve and through 

several iterations of forward modeling (Rayleigh and Love wave), or estimated by 

mapping λ/3 (λ is wavelength) to depth and the following relationship (Rayleigh wave 

only): 

VS =1.1VR . 

Starting models were modified (whenever necessary) throughout the inversion process to 

improve the fit between the calculated and experimental dispersion curves. 

Most dispersion curves were modeled based on the fundamental-mode 

assumption (2-D solution in WinSASW for Rayleigh wave and SeisImager/SW for Love 

wave). In some cases, superposition of the Rayleigh fundamental and first-higher modes 

was suspected at low frequencies due to steep velocity gradients or abrupt increases in 

velocity at depth. At some sites, insufficient resolution in the wavenumber domain 

associated with relatively high velocities and inadequate array length also may have 

contributed to apparent modal superposition in the Rayleigh-wave dispersion data. In 

these cases, the far-field effective-mode (generally referred to as average-mode in the 

data reports) solution in SeisImager/SW was used in an attempt to more accurately 

constrain VS at depth, and in particular, the velocity of the half-space. At several sites, 

there was a strong possibility that the Rayleigh fundamental mode jumped to the first 

higher mode at intermediate to low frequencies due to a significant increase in VS at 

depth. The effective mode solution in SeisImager/SW and 3-D global solution in 

WinSASW v. 3.1.2 (not applicable to MASW and passive SW data) often adequately 

estimated the mode jump without having to resort to multi-mode modeling routines. 

Effective-mode and fundamental-mode VS models were often compared to seismicrefraction models to determine which model was most likely representative of the 

subsurface velocity structure. Effective-mode modeling was avoided in many cases when 

a wide wavelength or frequency range of the dispersion curve was affected by the 

effective-mode assumption because (when possible) the fundamental mode was 

specifically targeted at higher frequencies during data reduction. 

VS models resulting from inversion of the surface-wave dispersion data should be 

considered to be a representative rather than an accurate velocity profile due to the nonuniqueness inherent in any inversion process. Global inversion routines, which generate 

an ensemble of velocity models fitting the dispersion curve, were not used in this project. 

VP =VS

2(1− v)

1− 2v
26 

In some cases, several VS-versus-depth models were developed by perturbing the starting 

model (layer thickness and/or VS) to demonstrate non-uniqueness (model equivalence) of 

the solution and the insensitivity of VS30 to model equivalence. At some sites with 

significant scatter in the dispersion data, both the upper and lower envelopes of the 

dispersion data were modeled to determine the approximate variation in VS and VS30. 

Plots of the composite-experimental dispersion data, representative-dispersion 

curve and theoretical-dispersion curves, and associated VS-versus-depth models resulting 

from inverse modeling of the dispersion data were developed for each site. Depth of 

investigation was typically assumed to be less than one-half of the maximum Rayleighwave wavelength and less than one-third of the maximum Love-wave wavelength. 

Typically, the dispersion data were presented as plots of the log of wavelength versus 

phase velocity rather than the more conventional frequency versus phase velocity. 

Wavelength is critical to the data modeling process and is too often not considered. For 

example, minimum wavelength is used to determine the minimum thickness of the 

surface layer (one-third to one-half the minimum wavelength) and depth of investigation 

(one-half to one-third the maximum wavelength). Additionally, criteria applied to 

minimize near-field effects will determine the maximum wavelength based on survey 

design. Review of data in wavelength-phase velocity space can be useful for designing 

the layering in the initial model rather than relying on automatic layer generation 

routines, which can over parameterize the inversion. Finally, a logarithmic wavelength 

sampling of the dispersion curve gives sufficient weight to the segment of the dispersion 

curve associated with deeper sediments. Linear-frequency sampling of the dispersion 

curve gives too much weight to near surface velocity structure. Log of frequency, equal 

period and other alternative sampling routines may also be viable. 

Based on the model of the VS profile developed for each station, the resultant VS30

value, defined as the ratio of 30 m to the shear-wave travel-time, is calculated by the 

following relationship (Boore, 2006): 

where, the travel-time, Δti, is defined as the ratio of the depth interval, Δzi, to the shearwave velocity of the interval, (VS)i: 

Δti = Δzi / (VS)i . 

In most seismographic networks, SM sensors are typically installed at the ground 

level; hence, the depth intervals (∆zi) for these sites represent layers as measured from the 

ground surface to the 30-m depth. At CI network stations, however, sensors are housed 

inside insulated vaults, which are commonly buried 1–3 m below the surface. To account 

for the actual emplacement depths of these sensors, an adjusted VS30 value is calculated 

for each station and denoted as VS(Z, Z +30), where Z is the reference location in the VS

profile representing the emplacement depth and where the 30-m interval is determined. 

Using the same VS30 (Boore, 2006) relation and Z values reported by the network, VS(Z, Z 

+30) values are (instead) calculated using depth intervals estimated from the reported 

emplacement depths (Z > 0 m) to the 30-m depth (Z + 30) in each model of the VS profile. 

VS30 = 30

Δti

i

∑
27 

Occasionally, alternative strategies, as outlined in the site reports, were utilized to adjust 

VS30 to the inferred properties beneath the seismic station.

2.5.3 Seismic Refraction Data Modeling

Seismic-refraction data were typically modeled using the tomographic analysis 

technique available in the SeisImagerTM/2D software package developed by Oyo 

Corporation/Geometrics, Inc. Refraction tomography techniques are often capable of 

resolving complex velocity structures (velocity gradients) that were observed in bedrock 

weathering profiles. Layer-based modeling techniques, such as the GRM, or time-term 

method, were incapable of accurately modeling the velocity gradients that often occurred 

in weathered bedrock. The GRM method, as implemented in the software package 

Seismic Pro 6.1 Refractor (Geogiga, Inc.) was, however, used to model seismic-refraction 

data at several shallow bedrock sites. 

The first step in data processing is picking the arrival time of the first energy 

received at each geophone (first-arrival) for each shot point. The first arrivals on each 

seismic record are either a direct arrival from a compressional (P) or shear (S) wave 

traveling in the uppermost layer, or a refracted arrival from a subsurface interface where 

there is an abrupt increase in velocity. First arrival times were selected using the 

automatic- and manual-picking routines in the software package SeisImagerTM/2D. These 

first-arrival times were saved in an ASCII file containing shot location, geophone 

locations, and associated first-arrival time. Uncertainty in the first-arrival times generally 

increases with distance from the shot point. S-wave seismic-refraction records from 

normal and reverse polarity shots were combined into a single seismic record for the 

picking of first-arrival data. An S-wave stack (reverse-polarity record subtracted from 

normal-polarity record to enhance S-wave energy and cancel P-wave energy) was also 

applied to facilitate picking of the S-wave first-arrival data. 

Data quality was affected by factors such as: wind, surface conditions, traffic 

noise, and geologic conditions. No attempt was made to interpret seismic-refraction data 

when it was believed that a reliable interpretation could not be made due to noise or other 

factors (it was often much more difficult to pick S-wave seismic-refraction first-arrival 

data than P-wave first-arrival data due to P-wave interference, poor polarity reversal, 

potential converted waves). P-wave energy does not generally exhibit polarity reversal 

and could often be eliminated (to a certain extent) by using an S-wave stack. When clear 

reversed S-wave waveforms were not observed on normal- and reverse-polarity seismic 

records, error can be significantly larger in the first arrival data. At sites with complex 

velocity structure or even shallow dipping velocity structure, converted waves, which can 

exhibit polarity reversal, can occur as first-arrivals resulting in significant error in the Swave velocity model. However, very high quality S-wave refraction data were generally 

acquired at sites where the velocity structure is generally 1D. Unless shallow rock or 

sediments were saturated, S-wave seismic-refraction models were typically correlated to 

P-wave seismic-refraction models to confirm that converted waves or first arrival picking 

errors did not seriously degrade the velocity model. 

GRM was only applied to selected shallow bedrock sites, primarily to model the 

depth to, and seismic velocity of, the bedrock unit. First-arrival and elevation data were 

entered into the software package Seismic Pro 6.1 Refractor where time-distance plots for 

the forward- and reverse-shots were generated. Forward-shots were shot-points where 
28 

energy travels from geophone 1–48. Energy travels in the opposite direction for reverseand interior-shots that have both forward and reverse components. The first-arrival data 

for all shot-points were then assigned to the layer from which they were refracted. 

Typically, two to three layers were assigned to the travel-time data. The travel-time data 

associated with the lower layers were then shifted (in time) to line up with the travel-time 

data associated with the zero-offset end-shot, thereby forming a single travel-time curve 

for each refractor along the line. This method was used for both forward- and reverseshots according to the procedures outlined in Lankston and Lankston (1986) and Redpath 

(1973). GRM analysis was then conducted to develop a velocity model. 

Almost all seismic-refraction data were modeled using the tomographic inversion 

routines available in SeisImagerTM/2D. After loading the seismic-refraction first-arrival 

and elevation data into the software package, travel-time data were inspected to 

determine if the velocity structure was consistent with a layered or smooth velocity 

gradient earth model. When the velocity structure appeared to exhibit layering, a simple 

two- to three-layer velocity model was developed using the time-term method 

(Scheidegger and Willmore, 1957) and used as the starting model for the tomographic 

inversion. If the velocity structure appeared to be exhibiting smooth velocity gradients, 

then a smooth velocity gradient starting model with 20 layers was generated by inserting 

parametric information such as the velocity of the top layer, maximum velocity at a user 

defined depth, and depth to the bottom of the model. Velocity models were generally 

extended to far-offset shot locations to increase the depth of investigation. A longer 

geophone array without far-offset shot locations is preferred for tomographic inversion of 

seismic-refraction data. However, for the purpose of efficiency, seismic-refraction data 

were generally acquired in conjunction with MASRW/MASLW data along the 70.5-mlong arrays with maximum source-offset of 30 m. At selected sites, seismic refraction 

was acquired along longer arrays with lengths up to 141 m to extend the depth of 

investigation. A minimum of 20–50 iterations of non-linear ray-path inversions was

implemented to develop a velocity model with acceptable RMS error between the 

observed and calculated travel-time data. Tomographic inversion techniques will model a 

smooth velocity gradient even if a sharp-velocity boundary exists, particularly when a 

smooth-velocity gradient starting-model is used. The use of layered starting models can 

result in sharper velocity gradients over geologic contacts. Final tomographic velocity 

models for each seismic line were exported as ASCII files and imported into Surfer® v. 9, 

developed by Golden Software, where the velocity models were gridded, contoured and 

annotated for presentation. 

At some sites, the seismic-refraction velocity models were analyzed to quantify 

lateral velocity variation beneath the array and to determine if a surface-wave VS model is 

consistent with the P- or S-wave velocity structure. In the case of a P-wave seismicrefraction model, the segment of the model from the central portion of the line with the 

greatest depth of investigation was loaded into a Microsoft® Excel Spreadsheet developed 

for analysis. VP30, the average VP of the upper 30 m, was estimated along this interval by 

projecting the velocity at the base of the model to 30-m depth. The variation in VP30 along 

the central portion of the line provided an estimate of the degree of lateral velocity 

variation beneath the line. An average VP model was developed over the central portion 

of the line by horizontally averaging the traveltime of each model cell and cell thickness, 

whereby the average VP30 for the central portion of the line could be determined. The VP
29 

model was converted to a VS model for various realistic constant values of Poisson’s 

ratio. A similar analysis was applied to S-wave seismic-refraction models for more direct 

comparison to the surface-wave VS model. 

A number of sites, typically seismic stations located at the toe of a bedrock 

outcrop, could only be characterized using S-wave seismic-refraction models, and one or 

two sites could only be characterized using P-wave seismic-refraction models. At other 

sites, VS models and VS30 estimates from S-wave refraction models or estimated from Pwave refraction models—based on assumed constant Poisson’s ratio—were used to 

corroborate surface-wave VS models, particularly at sites with significant lateral velocity 

variation.

Complete seismic-refraction surveys were not typically conducted at soil sites; 

however, the reversed off-end and center-shot locations used for MASRW data 

acquisition provided sufficient seismic-refraction data for simple horizontal layer-based 

analysis. Seismic-refraction analytic utilities available in SeisImagerTM/2D and Geogiga 

SS Imager were used to interactively fit horizontal layer-based models to the seismicrefraction records to approximate P-wave velocity structure. The primary purpose of the 

refraction analysis was to estimate the depth to the water table and P-wave velocity of the 

saturated zone because constraining the saturated zone when modeling Rayleigh-wave 

dispersion data improves the accuracy of the resulting VS model. At several sites, it was 

possible to estimate the depth of the saturated zone by analyzing a possible water-table 

reflector. 

3.0 Results

At the end of this project, VS profiles and VS30 estimates were produced for 187 

stations in California and 4 in the CEUS (fig. 1). These results represent a significant 

increase (approximately doubling) in the number of measurement-based VS30 values for 

SM stations in California (Chiou and others, 2008; Yong and others, 2012). As a pilot 

project, the experience provided the Advanced National Seismic System with information 

necessary to begin evaluating measurement techniques and to develop guidelines for 

characterizing site conditions at other SM stations. Detailed geotechnical information 

specific to each station is provided as individual site reports in appendix A. The 

repository for raw data is linked to each report and can be accessed by double clicking the 

title (network-code.station-code) of each report. Another link to simultaneously 

download all site reports and raw data can be found on the U.S. Geological Survey 

Publications webpage describing this report (http://pubs.usgs.gov/of/2013/1102/). The 

following is an overview of results from this project. 

Table 3 summarizes the stations included in this study by distinguishing each 

station through the combination of its network and station codes. This scheme serves as a 

unique identifier of each station in subsequent discussions and in the accompanying data 

reports (see appendix A). Following the station coordinates (provided by the respective 

network operators) are the abbreviations (see footnotes for definitions in table 3) of the 

type(s) of techniques (standalone or combination of techniques) used near each station, 

the site coordinates of the measurement array(s), and the maximum calculated distance 

(m) based on the coordinates of the station and of the measurement array(s). Next, 

estimates of VS30 and the adjusted VS30 (denoted as VS(Z, Z +30), unless an alternative 

approach was applied; see Section 2.5.2.4 and fig. 9), as well as the method(s) (using the 
30 

same abbreviation scheme) that are the basis for determining VS models and VS30 values 

are provided. Other attributes of table 3 include the National Earthquake Hazards 

Reduction Program (NEHRP) and Geomatrix 3-letter (GMX) Site Classes as defined by 

the Building Seismic Safety Council (BSSC, 2003) and described by Chiou and others 

(2008), respectively. Noise, geologic, and lithologic conditions provide additional site 

information, which were mostly used for logistics when preparing for fieldwork. 

To demonstrate that our measurement locations meet the Borcherdt (2002) 

recommendation of 300 m or less, the coordinates of the measurement array are defined 

as the farthest endpoint of the array from the station location. Most (84 percent) of the 

measurement arrays fall within the preferred 150-m radius; of these, six sites are at 

(CI.JEM) or significantly close (CE.13922, CI.CIA, CI.FHO, CI.CGO, and CI.IDO) to 

this limit. Twenty-seven sites (14 percent) are in the 150- and 300-m range. Sites for 

stations CI.TUQ, CE.14560, and NM.BLO exceed the 300-m threshold, having 

respective values of 303, 380, and 660 m. As anticipated (see Section 2.1), the proximity 

of a recording location to a site often was controlled by manmade impediments or 

geophysical constraints. In the case of NM.BLO, the field crew acquired seismic data in 

much closer proximity to the seismic-station site, but data quality was such that 

additional data acquisition was required at an alternate location farther away from the 

seismic station. To this end, better quality seismic data were acquired along an array that 

was positioned more than 600 m away from NM.BLO. 

The distribution of the VS30 values among the NEHRP Site Classes is shown in the 

histogram of figure 7. The mean, median, and range of measured VS30 values within each 

NEHRP Site Class, as well as for the entire dataset, are reported in table 4. Stations 

CI.DRE (Desert Research Extension Center) and CI.CLC (China Lake Naval Weapons 

Center) have, respectively, the lowest (196 m/s) and highest (1,464 m/s) VS30 values in 

this project. Most (104) of the measurements fall within NEHRP Site Class C (VS30

values between 360 and 760 m/s), similar to previous studies (Wills and Silva, 1998; 

Chiou and others, 2008; Yong and others, 2012). This likely reflects purposeful siting of 

earthquake monitoring stations in densely population regions where enclaves are 

typically located on sedimentary basins consisting of relatively softer materials. Site 

Class D (VS30 = 180–360 m/s) has 58 measurements, and Site Class B (VS30 = 760–1,500 

m/s), with the fewest, has 29 measurements. No measurements were obtained that fall in 

Site Class E (VS30 < 180 m/s) or Class A (VS30 > 1,500 m/s). 

It is, however, possible to reassign the NEHRP Site Classes for several stations by 

adjusting VS30 to account for the estimated emplacement depth of the SM sensor at each 

station (see Section 2.5.2.4). These adjusted values are listed in table 3 and provided in 

each site report. Inherently, an adjusted VS30 value will be larger than a measured VS30

value, as illustrated in figure 9 (denoted as VS(Z, Z +30)). In proportion to a VS30 value, 

adjustments tend to increase with increasing values of VS30, particularly for VS30 values 

larger than about 400 m/s. The largest adjustment (an increase of 93 percent) was for 

NM.BLO, which has a measured VS30 of 1,202 m/s, because the testing location had 

several meters of sediment overlying bedrock, whereas the seismic station is located 

directly on rock. On the basis of the adjusted VS30 values, the Site Classes of 14 stations 

would be reassigned: 4 stations would be reclassified from Site Class B to A, and 10 

stations from Site Class C to B. A comparison of the distributions of VS30 and adjusted 

VS30 values is shown in figure 8. 
31 

For planning purposes, local geological conditions at each station were described 

using the binary system of “rock” or “soil” (see Section 2.0). Here (table 3), the “rock” 

category also includes many stations considered as “shallow-rock” sites, where the 

seismic station was previously expected to be directly located on rock. This classification 

scheme produces a near equal split of stations, where 52 percent are categorized as 

“rock” sites (table 3). This statistic, however, appears to be inconsistent with the 

observation that the distribution of VS30 values is skewed towards lower velocities (fig. 7), 

an observation that warrants further investigation. For the time being, two key factors 

also should be considered. First, the “rock” or “soil” categories are too broad to fully 

describe the complex geological conditions encountered in this project (rock sites include 

sediments and sedimentary rock of Tertiary age, or older crystalline rock, and do not 

consider extent of weathering). Nevertheless, as a logistical tool for planning purposes, it 

was the most convenient method for generalizing geological site conditions. Second, 

these classes typically were derived from geological maps or were assigned during onsite 

reconnaissance, where both sources are known to have critical limitations. For example, 

map-based assessments often cannot be well-constrained due to poor accuracy, low detail 

and (again) do not consider weathering. Field-based approaches for assessing local 

material properties can also be limited by extensive soil cover, such that depth to rock 

and rock type are not clear. An examination of the data in table 3 reveals a few stations 

(CI.GATR, CI.LJR, CI.SCZ2, NC.BBGB, NC.PMPB and PG.VPD) that initially were 

classified as “rock”, but later were determined to have relatively lower than expected VS30

values corresponding to Site Class D. Four of these sites (CI.GATR, CI.SCZ2, 

NC.BBGB and PG.VPD) have Tertiary shale units at or near the surface, which could 

potentially weather to low velocity clay, hence the low VS30. The other two sites were 

mapped as Mesozoic Franciscan Assemblage (NC.PMPB) and granitic rocks (CI.LJR) 

and are expected to have much higher VS30. This result emphasizes the importance of 

acquiring measurement-based VS30 data whenever possible.

Cultural settings, the basis for establishing a classification system to categorize 

local noise conditions, were also used for planning purposes. Table 3 describes each 

station in terms of its assigned cultural class: rural (r), suburban (s), or urban (u). 

Regardless of the classification schemes used in this project, the MASRW technique was 

used at all stations. 

The rural setting is most common, and includes 147 stations. In this low noise 

environment, active source surface- and body-wave techniques dominated the methods 

deployed. These methods, with the exception of the MASRW technique as the standalone 

approach at 12 soil sites, typically consisted of combinations of multiple techniques such 

as SASW, MASLW, MASRW, and seismic refraction (P- and S-wave). It is important to 

note that seismic-refraction models were only used to estimate VS30 when surface-wave 

techniques were not effective. The seismic-refraction technique was primarily used in a 

support role to identify depth to groundwater, depth to bedrock, quantify lateral velocity 

variation, or support VS30 estimates resulting from surface-wave data. As a result, VS30

values for 25 sites could not be estimated from MASRW data and instead were estimated 

from SASW, MASLW, and P- and/or S-wave seismic-refraction data. Passive surfacewave techniques (AM and/or ReMiTM) were used at 10 rural stations and eight of these 

deployments contributed data for estimating VS30 values. 
32 

In moderate to low noise environments, 17 stations were categorized as situated in 

suburban settings. Although it is an intermediate category between the rural and urban 

classes, the suburban sites relied heavily on the same active surface-wave techniques 

applied at the rural sites. Interestingly, MASRW, MASLW, and P- and S-wave seismicrefraction techniques were used at seven suburban sites, primarily due to a thin sediment 

cover overlying rock. VS30 could only be estimated from MASRW at one of these sites. At 

the remaining sites, four sites were based on MASLW and two on S-wave refraction. 

Passive surface-wave techniques (AM and ReMiTM) were used at only five suburban sites 

but always contributed data for estimating VS30 values (AM and ReMiTM at three sites and 

AM only at two sites). At station CI.WNS, the MASRW and seismic P-wave refraction 

techniques were inadequate for modeling shallow VS profile. Instead, the CI.WNS model 

was based on the PS-suspension logging technique (abbreviated as P in table 3) 

conducted by GEOVision©, Inc., for an earlier study. 

At the 27 urban sites, passive surface-wave techniques (AM and ReMiTM) were 

universally deployed, always in conjunction with MASRW, with the expectation that high 

levels of ambient noise generated by cultural sources would produce robust data for 

analysis. SASW data were also acquired at one urban site (CI.BAK) and P-wave seismicrefraction data at another urban site (CI.LAF). The seismic-refraction technique typically 

was not applied at urban sites because geologic conditions typically consist of soil rather 

than rock and the seismic-refraction technique was not expected to be effective in 

developed (asphalt or concrete surfaces), high ambient noise environments. VS30 values at 

all urban sites relied on VS models from at least one of the two passive surface-wave 

techniques in combination with an active surface-wave technique. 

The station VS30 values reported in table 3 (with the exception of CI.WNS, as 

previously noted) were calculated from VS models derived from the various measurement 

techniques applied at each site. Techniques were either individually employed or multiple 

techniques were applied, in which case, and whenever appropriate, the same array 

configurations were shared (see Section 2.0). As previously mentioned, preference was 

always given to VS30 values estimated from surface-wave data—VS30 values estimated 

from seismic-refraction models were only utilized at 12 station sites where surface-wave 

methods were ineffective. Details on how particular methods were selected (or excluded) 

are provided in Section 2.3 and in individual station reports in appendix A. For example, 

the MASLW, MASRW, and seismic P- and S-wave refraction (collectively abbreviated as 

the MlMrSrpSrs method in table 3) techniques were used at station NC.MMX1, but only 

data from the P- and S-wave seismic-refraction models could be used to estimate the 

station VS30 value—coherent dispersion curves were not recoverable from the Rayleigh 

and Love data at this site. Station CE.24706 is another example where AM, MASRW, 

ReMiTM, and P-wave seismic-refraction techniques (collectively abbreviated as the 

AMrRSrp method in table 3) were used; after careful consideration, data from the 

ReMiTM technique were not included for developing a VS model and estimating VS30

solution because the dispersion data derived from the array microtremor measurements 

were in better agreement with that derived from the MASRW measurements. As with all 

other sites, seismic-refraction models were not used to estimate VS30, unless surface-wave 

methods were ineffective. 

Figure 10 shows the number of, and in what proportion, various combinations of 

measurement techniques, including cases of when only one technique was used at the 
33 

sites. Although MASRW (abbreviated as Mr in table 3) was used at all sites, it was the 

only technique applied at 12 soil sites and the only surface-wave technique applied at 73 

rock sites. The SASW (abbreviated as S in table 3) technique, used in conjunction with 

the MASRW technique at 29 sites, was applied almost exclusively on soil sites. Although 

not part of the scope of work, MASLW (abbreviated as Ml in table 3) was used in an 

attempt to characterize 38 sites. Passive surface-wave techniques, including array 

microtremor (abbreviated as A in table 3), and the ReMiTM technique (abbreviated as R in 

table 3), were applied at 42 sites, primarily urban and suburban sites although 

occasionally at rural sites if there was a busy road near the site. The seismic-refraction 

technique (primarily P-wave and occasionally S-wave) was used at 122 sites, primarily in 

a support role to identify depth to groundwater or bedrock and quantify lateral velocity 

variation. 

Figure 11 shows the number and proportion of various combinations of 

techniques (again including single techniques) that were selected from all surveys to 

derive the VS models from which the VS30 values are calculated. There are five cases 

where all of the same techniques that were used in the field also were used to derive VS30; 

in each case, the corresponding wedges in figures 10 and 11 have the same (non-gray) 

color. Note that while the MASRW technique (abbreviated as Mr in table 3) was deployed 

as a standalone technique at 12 out of 191 stations (fig. 10), MASRW models alone were 

used as the basis to estimate VS30 at 94 (nearly one-half) of all stations surveyed, 

primarily because seismic-refraction surveys were typically used in a supporting role and 

only used to estimate VS30 when various surface-wave techniques were ineffective. 

SASW data were combined with MASRW data or used alone to characterize 21 sites. 

MASLW models were used to characterize 17 sites and combined with MASRW models 

to characterize an additional 5 sites. Active and passive surface-wave data were 

combined and used to estimate VS30 at 40 sites. A total of 12 sites could only be 

characterized using P- and/or S-wave seismic-refraction techniques.

4.0 Data and Resources

All data are available from the U.S. Geological Survey publication site for this 

report (http://pubs.usgs.gov/of/2013/1102/). 

5.0 Acknowledgments

We are grateful to William Leith for support of the project through the 2009 

American Recovery and Reinvestment Act (ARRA). Special thanks to: Jean Paul 

Ampuero, Dominic Assimaki, Rufus Catchings, Silvia Castellaro, Arthur Frankel, Susan 

Hough, Swaminathan Krishnan, and Walter Silva; who provided guidance in the 

development of the project. Beneficial remarks from John Boatwright and Nancy E. 

King, in addition to constructive comments by reviewers Christopher Stevens and Robert 

Graves greatly improved this report. 

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41 

7.0 Glossary (Common Terms)

AM: Array Microtremor. 

HVSR: Horizontal-to-Vertical-Spectral-Ratio. 

MASW: Multi-channel-Analysis-of-Surface Waves (generic); however, generally assumed 

as Rayleigh-wave based. 

MASLW: Multi-channel-Analysis-of-Surface Waves (Love-wave). Referred to as MALW 

in individual site reports. 

MASRW: Multi-channel-Analysis-of-Surface-Waves (Rayleigh-wave). Referred to as 

MASW in individual site reports. 

ReMiTM: Refraction Microtremor. 

SASW: Spectral-Analysis-of-Surface-Waves. 

SASLW: Spectral-Analysis-of-Surface-Waves (Love-wave). 

SM: strong motion. 

VL: Love-wave phase velocity 

VP: p-wave velocity. 

VR: Rayleigh-wave phase velocity. 

VR40: Rayleigh-wave phase velocity of the fundamental mode at 40-m wavelength. 

VS: shear-wave velocity. 

VS30: time-averaged shear-wave velocity in the upper 30 m from the surface. 
42 

8.0 Figures

Figure 1. Maps of strong-motion stations in California and in the central-eastern U.S. 

Seismographic networks include: ANZA Seismic Network (AZ), U.C. Berkeley Digital Seismic 

Network (BK), California Geological Survey Seismic Network (CE), Southern California Seismic 

Network (CI), Northern California Seismic Network (NC), Pacific Gas and Electric Seismic Network 

(PG), and Central-Eastern United States (CEUS) Seismic Networks (NM and US). 

6/17/13 12:29 PM 92 Figure01.DRFT44.docx

California 

Missouri 

Illinois Indiana 

Ohio 

U.S.A 

Δ BK, NC, PG

Δ CI (including AZ)

Δ CE

Δ NM and US (CEUS)

Figure 1. Maps of strong motion stations in California and in the Central-eastern U.S. (CEUS).
43 

Figure 2. Samples of prerequisite maps: satellite-based (left) and geologic-based (right). The location (on record) of the station (red dot), proposed 

array site (red line), 150-meter radial threshold (green circle), and 300-meter threshold (blue circle) are shown on each map. Note that the maps are 

not at the same scale. 

6/17/13 11:31 AM 93 Figure02.DRFT44.docx

MBS1

118.945° W

118.945° W

37.76° N37.76° N

SELECTED SEISMOGRAPHIC 

STRONG MOTION STATIONS

PREPARED FOR

UNITED STATES GEOLOGICAL SURVEY File Name: 10115-MBS1-I.MXD

Approved by: A Martin

Drawn by: T Rodriguez

Developed by: A. Martin

Date: 7-15-10

GV Project: 10115

NOTES:

1. WGS 1984 COORDINATE SYSTEM

2. Image Source: ESRI, i-cubed, USDA FSA,

ESRI, AEX, GeoEye, Getmapping, Aerogrid, IGP

Legend

Approximate Seismometer Location

Possible Surface Wave Testing Location(s) / Area

150m radius from station

300m radius from station p 0 75 150 225

Meters

SITE MAP

AND POSSIBLE TEST LOCATION

STATION NC-MBS1

Tbm

Tba

Qbt

MBS1

118.94° W

118.94° W

118.945° W

118.945° W

118.95° W

118.95° W

37.765° N37.765° N

37.76° N37.76° N

37.755° N37.755° N

SELECTED SEISMOGRAPHIC 

STRONG MOTION STATIONS

PREPARED FOR

UNITED STATES GEOLOGICAL SURVEY File Name: 10115-MBS1.MXD

Approved by: A Martin

Drawn by: T Rodriguez

Developed by: A. Martin

Date: 7-29-10

GV Project: 10115

Legend

Approximate Seismometer Location

Possible Surface Wave Testing Location(s) / Area

150m radius from station

300m radius from station p 0 150 300 450

Meters

SITE GEOLOGY

AND POSSIBLE TEST LOCATION

STATION NC-MBS1

NOTES:

1. WGS 1984 COORDINATE SYSTEM

2. Geologic Map of Long Valley Caldera, 

Mono-Inyo Craters Volcanic Chain, and Vicinity, Eastern California

by Roy A. Bailey, 1989

Description of Geologic Map Units

Jg = Granitic rocks mapped as granite of Casa Diablo Mountain

Pzms = Metasedimentary rocks

Qal = Younger alluvium, unconsolidated silt, sand and gravel

Qbt = bishop Tuff nonwelded to densely welded ash-flow tuff

Qog = Older gravel deposits, coarse gravels

Tba = Basalt and andesite

Tbm = Quartz latite of Bald Mountain

Figure 2. Samples of prerequisite maps: satellite- (left) and geologic-based (right). The known (on record) location of the 

station (red dot), proposed array site (red line), 150-meter radial threshold (green circle), and 300-meter threshold (blue circle) 

are shown on each map. Note that the maps are not at the same scale.
44 

Figure 3. Typical passive surface-wave arrays. 

6/17/13 11:31 AM 94 Figure03.DRFT44.docx

Figure 3. Typical passive surface wave arrays.

24 channel linear array

48 channel “L” shaped array

Acquisition of passive surface wave data 

along 48 channel “L” shaped array

Acquisition of passive surface wave 

data along 24 channel linear array

Acquisition of passive surface wave data 

along 10 channel triangle array

10 channel triangle array
45 

Figure 4. Horizontal-to-vertical spectral ratio (HVSR) instrumentation.

6/17/13 11:31 AM 95 Figure04.DRFT44.docx

Figure 4. Horizontal-to-vertical spectral ratio (HVSR) Instrumentation. 

Nanometrics Trillium Compact 

seismometer (used for HVSR 

measurements at almost all sites). 

HVSR measurements (seismometer is 

covered to keep wind off sensor and 

sensor cable).

Micromed Tromino ENGY (used for 

HVSR measurements at selected 

remote and/or shallow rock sites). 

Nanometrics Trillium Compact 

deployed in shallow hole. 
46 

Figure 5. SASW-array geometry and instrumentation. 

6/17/13 11:32 AM 96 Figure05.DRFT44.docx

Typical Common Center Point SASW Array Geometry

Figure 5. SASW array geometry and instrumentation. 

HP Dynamic Signal Analyzer

Various hammer energy sources

SASW array and 90 kg accelerated 

weight drop energy source (AWD)

Geospace 1 Hz vertical geophones 
47 

Figure 6. MASW and seismic-refraction survey design. 

6/17/13 11:32 AM 97 Figure06.DRFT44.docx

Figure 6. MASW and seismic refraction survey design. 

Typical MASW and seismic refraction array geometry. 

Geometrics Geode seismograph. 

MASRW array with 4.5 Hz vertical geophones. 

90 kg accelerated weight drop 

(AWD) energy source.

Horizontal traction plank and hammer for 

MASLW and S-wave refraction acquisition. Hammer impact aluminum S-wave seismic 

source and 4.5 Hz horizontal geophones. 

10 and 20 lb sledgehammer 

energy sources. 

3 lb hammer energy source 

used for MASRW acquisition. 
48 

Figure 7. Histogram of 191 calculated VS30 values grouped into NEHRP site classes. 

17/13 11:33 AM 98 Figure07.DRFT4

Distribution of Calculated VS30 (Surface)

VS30 (m/s)

Frequency

0 500 1000 1500 2000 2500

0 20 40 60 80 100

0

58

104

29

0

250 750 1250 1750 2250 2500

CDE Site Class B Site Class A 

Figure 7. Histogram of 191 calculated VS30 values grouped into NEHRP site classes. 
49 

Figure 8. Histograms of VS30 values (blue) and adjusted VS(Z, Z + 30) values (red) grouped into NEHRP site 

classes (overlapping areas shown as purple). Each type of data has 191 observations. 

17/13 11:32 AM 99 Figure08.DRFT4

Distribution of Calculated VS30 (Surface & Edepth)

VS30 (m/s) & VS(Z, Z + 30) (m/s)

Frequency

0 500 1000 1500 2000 2500

0 20 40 60 80 100

0

58

104

29

0

47

105

35

4

250 750 1250 1750 2250 2500

105 

104 

DE C B Site Class A 

Figure 8. Histograms of VS30 values (blue) and adjusted VS(Z, Z + 30) values (red) grouped 

into NEHRP site classes (overlapping areas shown as purple). Each type of data has 

191 observations.
50 

Figure 9. Plot of VS30 values versus adjusted VS(Z, Z + 30) values, with velocity ranges of NEHRP site classes indicated. Blue line indicates the trend for 

a one-to-one relation and red line is the linear least-squares fit to the data. 

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Figure09.DRFT44.docx

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0 500 1000 1500 2000 2500

0 500 1000 1500 2000 2500

Surface & Adjusted VS30

VS(Z, Z + 30) (m/s); Z is sensor emplacement depth (m)

VS30 (m/s); surface to 30 m depth

250 750 1250 1750 2250

250 750 1250 1750 2250

NEHRP 

Site Class A 

NEHRP 

Site Class B 

NEHRP 

Site Class C 

Site Class D 

Site Class E 

CDE Site Class B Site Class A 

Figure 9. Plot of VS30 values versus adjusted VS(Z, Z + 30) values, with velocity ranges of NEHRP 

site classes indicated. Blue line indicates the trend for a one-to-one relation and red line is the 

linear least-squares fit to the data.
51 

Figure 10. Distribution of the MASRW technique and various combined methods deployed at 191 strong-motion stations. The color scheme used in 

this and the next figure (fig. 11) is the same. Categories include the standalone technique of MASRW (red) or methods that combine: AM, MASRW, 

SASW and ReMiTM (green) aka AMrSR*; AM, MASRW and ReMiTM (orange) aka AMrR*; MASRW, Seis-RefracP (P-wave seismic refraction), and SeisRefracS (Swave seismic refraction) (yellow) aka MrSrpSrs*; MASRW and SASW (blue) aka MrS*. The remaining categories (gray) in this figure differ 

in proportion to the gray color categories used to estimate VS30 values in figure 11. 

6/17/13 11:33 AM 101 

Figure10.DRFT44.docx

Distribution of Methods Deployed at Sites

32 (17%) 

AM, 

MASRW, & 

ReMi 

12 (6%) 

MASRW 

37 (19%) 

MASLW, 

MASRW, 

Seis-Refracp, & 

Seis-Refracs

3 (2%) 

MASRW, ReMi, & 

Seis-Refracp

2 (1%) 

MASRW, 

Seis-Refracp, & 

Seis-Refracs

21 (11%) 

MASRW, & SASW

71 (37%) 

MASRW, & 

Seis-Refracp

5 (3%) 

MASRW, 

SASW, &

Seis-Refracp

1 (1%) 

MASLW, 

MASRW, & 

Seis-Refracp

4 (2%) 

AM, 

MASRW, 

SASW, & 

ReMi 

3 (2%) 

AM, 

MASRW, 

ReMi, & 

Seis-Refracp

Figure 10. Distribution of the MASRW technique and various combined methods deployed at 191 strong motion 

stations. The color scheme used in this and the next figure (Figure 7) is the same. Categories include the standalone 

technique of MASRW (red) or methods that combine: AM, MASRW, SASW and ReMiTM (green) aka AMrSR*; AM, 

MASRW and ReMiTM (orange) aka AMrR*; MASRW, Seis-RefracP (P-wave seismic refraction), and Seis-RefracS (Swave seismic refraction) (yellow) aka MrSrpSrs*; MASRW and SASW (blue) aka MrS*. The remaining categories 

(gray) in this figure differ in proportion to the gray color categories used to estimate VS30 values in Figure 7.

*See references 1-2 in Table 3.
52 

Figure 11. Distribution of the MASRW technique and various combined methods used to calculate VS30 values for 191 strong motion stations. The 

color scheme used to designate the various categories in this and the previous figure (fig. 10) is the same. Categories include the standalone 

technique of MASRW (red), or methods that combine: AM,MASRW, SASW, and ReMiTM (green) aka AMrSR*; AM, MASRW, and ReMiTM (orange) aka 

AMrR*; MASRW, Seis-RefracP (P-wave seismic refraction), and Seis-RefracS (S-wave seismic refraction) (yellow) aka MrSrpSrs*; MASRW, and SASW 

(blue) aka MrS*. The remaining categories (gray) in this figure are not proportionate to the gray color categories deployed in the field (fig. 10).

6/17/13 11:33 AM 102 Figure11.DRFT44.docx

Distribution of Site Methods for Calculating VS30

94 (49%) 

MASRW 

18 (9%) 

AM, 

MASRW, & 

ReMi 16 (8%) 

AM, & 

MASRW 

5 (3%) 

MASLW & 

MASRW

17 (9%) 

MASLW 

3 (2%) 

MASRW & 

ReMi 

1 (1%) MASRW, 

Seis-Refracp, & 

Seis-Refracs

19 (10%) 

MASRW & 

SASW

2 (1%) SASW 

5 (3%) 

Seis-Refracp

1 (1%) 

Seis-Refracp & 

Seis-Refracs

6 (3%) 

Seis-Refracs

3 (2%) 

AM, 

MASRW, 

SASW, & 

ReMi 

1 (1%) PS-log 

Figure 11. Distribution of the MASRW technique and various combined methods used to calculate VS30 values for 191 

strong motion stations. The color scheme used to designate the various categories in this and the previous figure 

(Figure 6) is the same. Categories include the standalone technique of MASRW (red), or methods that combine: AM, 

MASRW, SASW and ReMiTM (green) aka AMrSR*; AM, MASRW and ReMiTM (orange) aka AMrR*; MASRW, SeisRefracP (P-wave seismic refraction), and Seis-RefracS (S-wave seismic refraction) (yellow) aka MrSrpSrs*; MASRW 

and SASW (blue) aka MrS*. The remaining categories (gray) in this figure are not proportion to the gray color

categories deployed in the field (Figure 6).

*See references 1-2 in Table 3.
53 

9.0 Tables

Table 1. Summary of seismographic networks and the number of strong-motion (SM) stations selected from each network. 

Table 2. Summary of site conditions and applicable geophysical techniques. 

Network

Number of 

Stations 

Proposed

Number of 

Stations 

Surveyed

Regional Seismographic Networks

CI 130 131 Southern California Seismographic Network (SCSN)

BK, NC, PG 30 30 Northern California Seismographic Network (NCSN)

CE 25 25 California Strong Motion Instrumentation Program (CSMIP), Statewide

NM, US 12 4 Center for Earthquake Research Institute (CERI), Central-eastern U.S. (CEUS)

AZ 0 1 ANZA, southern California

Types of Site

Conditions P-wave S-wave

MASRW MASLW Seismic Seismic

Refraction Refraction

Rural Rock Sites      

Suburban Rock Sites      

Urban Rock Sites      

Rural Soil Sites      

Suburban Soil Sites        

Urban Soil Sites      

Active

Surface Wave Methods Surface Wave Methods Body Wave Methods

Geophysical Techniques

HVSR AM ReMiTM SASW

Passive Active
54 

Table 3. Summary of results on site conditions at 191 strong-motion (SM) stations in California and the Central-Eastern United States (CEUS).

Network Station

Latitude 

(°N) of 

Station

Longtitude 

(°E) of 

Station

Method(s)1

Deployed at 

Site

Latitude 

(°N) of 

Site

Longtitude 

(°E) of 

Site

Distance2 (m) 

from Station 

to Site

VS (m/s) 

by 

Method(s)

Adjusted 

VS (m/s)

Method(s)3

for VS NEHRP4 GMX5

Noise 

Condition6

Geologic 

Condition Lithologic Condition7

AZ PFO 33.61167 -116.45943 MlMrSrpSrs 33.61158 -116.45631 289 763 848 MlMr B/C IGA r rock qd

BK KCC 37.32363 -119.3187 MrSrp 37.32554 -119.32096 292 1252 2250 Mr B/A FGA r rock Kmg

BK RAMR 35.63604 -120.86978 MrSrp 35.63638 -120.86894 85 300 328 Mr D IMD r rock Tml

CE 11023 33.2389 -115.513 AMrR 33.23892 -115.51188 104 212 212 AMrR D AHD r soil Ql

CE 11625 33.56409 -115.98763 MrS 33.56452 -115.98786 52 318 318 MrS D IHD r soil Qa

CE 11684 33.39799 -115.65859 MrS 33.3978 -115.65711 139 202 202 MrS D IHD r soil Ql

CE 12076 33.6781 -116.178 AMrR 33.67716 -116.18063 265 263 263 AMrR D AHD u soil Qa-Qc

CE 12108 33.8149 -116.4612 AMrR 33.81686 -116.46087 220 301 301 AMrR D AHD u soil Qs

CE 12149 33.9617 -116.5105 AMrR 33.96058 -116.50927 168 359 385 AMr D/C AHD u soil Qa

CE 12952 33.7577 -116.374 AMrR 33.75711 -116.37476 96 274 274 AMr D AHD u soil Qs

CE 13096 33.69884 -117.2664 MrSrp 33.69986 -117.26691 123 646 699 Mr C ATA r rock Mzp/Mzq

CE 13099 33.8759 -117.5928 AMrR 33.87752 -117.59272 180 420 420 AMrR C AHD u soil Qyfg

CE 13100 33.87559 -117.63549 AMrR 33.8753 -117.63428 116 341 357 AMrR D/C AQD s soil Qoa

CE 13922 33.6691 -117.33225 MlMrSrpSrs 33.66783 -117.33272 148 581 675 Ml C AHB/AQB s soil Qyva

CE 13929 33.64911 -117.20525 MlMrSrpSrs 33.64952 -117.20593 78 565 565 Ml C AQB s soil Qof

CE 14241 33.77725 -118.13308 AMrR 33.77612 -118.13458 187 282 282 AMr D IQD u soil Qops

CE 14395 33.75513 -118.20115 AMrR 33.75569 -118.20177 85 230 230 AMr D IHD u soil Qal

CE 14560 33.76856 -118.19654 AMrR 33.77036 -118.20004 380 322 322 AMrR D IQD/IHD u soil Qt

CE 23063 34.5025 -117.897 AMrRSrp 34.50277 -117.89898 184 506 506 AMr C AQD r soil Qo

CE 23897 34.13322 -117.25281 MlMrSrpSrs 34.13281 -117.25429 144 584 584 Mr C AHB/AKB s soil Qa/ps

CE 23958 34.43942 -117.64644 AMrR 34.43891 -117.64823 174 423 443 AMr C AHD r soil Qa

CE 24029 34.10353 -118.10011 AMrR 34.10445 -118.10065 114 369 377 AMr C/D AQD u soil Qae

CE 24644 34.74335 -118.72493 MrSrp 34.74374 -118.72481 45 421 421 Mr C IGB r rock grd

CE 24706 34.546507 -118.129614 AMrRSrp 34.54856 -118.12929 230 468 501 AMr C IHB/GHB s soil Qa

CE 24775 34.86637 -118.88359 Mr 34.86419 -118.88294 250 437 437 Mr C IHC/GHB/GHC r soil Qa

CE 24965 34.5702 -118.02999 AMrR 34.5717 -118.02852 214 326 326 AMrR D AHD u soil Qa

CE 24967 34.60335 -118.18212 AMrR 34.60481 -118.18229 163 330 330 AMrR D AHD u soil Qa

CE 65097 38.51455 -119.47973 AMrSR 38.5133 -119.48065 160 415 415 AMrSR C AHD/AQD r soil Qf3-2

CI ADO 34.55048 -117.43399 MrS 34.55152 -117.434 116 375 400 Mr C IHD r soil Qa

CI ALP 34.68702 -118.29947 Mr 34.68673 -118.30004 61 347 362 Mr D/C IHD r soil Qa

CI ARV 35.1269 -118.83009 MrSrp 35.12636 -118.8305 71 348 395 Mr D/C IMD r rock Tsm/Tbe

CI BAK 35.34444 -119.10445 AMrSR 35.34326 -119.10489 137 275 299 AMrSR D IHD u soil Qf

CI BAR 32.68005 -116.67215 MrSrp 32.67949 -116.67264 77 511 559 Mr C AGB/AGA r rock gr

CI BBR 34.26215 -116.92084 Mr 34.26223 -116.92141 53 356 380 Mr D/C IHD r soil Qyf3

CI BBS 33.92139 -116.98058 AMrR 33.92053 -116.98124 113 341 352 AMrR D IQD u soil Qoa

CI BC3 33.65513 -115.45364 MrSrp 33.65578 -115.45368 72 763 862 Mr B IGA r rock gr

CI BCW 34.9401 -119.41314 MrSrp 34.93946 -119.41227 107 305 335 Mr D IOC r rock Tsi

CI BEL 34.00057 -115.99816 MrSrp 33.99932 -115.99776 144 796 796 Mr B INA r rock gn

CI BFS 34.23883 -117.65853 MlMrSrpSrs 34.23827 -117.65841 63 500 731 Mr C IHB/IHA r soil Qg

CI BHP 33.99053 -118.36171 AMrR 33.98983 -118.3615 80 300 323 AMr D IQD u soil Qoa

CI BLA 34.06948 -116.38896 MrSrp 34.0693 -116.39028 123 524 613 Mr C IQB r soil Qoa

CI BLA2 34.06931 -116.38993 MrSrp 34.0693 -116.39028 32 524 613 Mr C IQB r soil Qoa

CI BLY 33.7503 -114.52373 MrSrp 33.7515 -114.52389 134 1029 1197 Mr B IZA r rock PlPs

CI BOM 33.36465 -115.72963 MrS 33.36488 -115.7291 55 202 214 MrS D IHD/IQD r soil Ql/Tl

CI BOR 33.2682 -116.41716 MrSrp 33.26748 -116.41608 128 619 962 Mr C/B IHA/IHB r soil Qf

CI BTP 34.68218 -118.57406 MrSrp 34.68258 -118.57461 67 461 526 Mr C INB r rock gn

CI CAC 34.13678 -118.12199 AMrR 34.13608 -118.12136 97 399 433 AMr C CQD u soil Qof

CI CAR 35.30819 -119.84583 MrSSrp 35.3083 -119.84672 82 310 339 Mr D IHD r soil Qa

CI CBC 34.13972 -118.12715 AMrR 34.13885 -118.12787 117 361 437 AMrR C/D GQD u soil Qof

CI CCC 35.52475 -117.36459 Mr 35.52513 -117.36339 117 432 461 Mr C IQD r soil sa
55 

Table 3. Summary of results on site conditions at 191 strong-motion (SM) stations in California and the Central-Eastern United States (CEUS) 

—continued. 

Network Station

Latitude 

(°N) of 

Station

Longtitude 

(°E) of 

Station

Method(s)1

Deployed at 

Site

Latitude 

(°N) of 

Site

Longtitude 

(°E) of 

Site

Distance2 (m) 

from Station 

to Site

VS (m/s) 

by 

Method(s)

Adjusted 

VS (m/s)

Method(s)3

for VS NEHRP4 GMX5

Noise 

Condition6

Geologic 

Condition Lithologic Condition7

CI CGO 36.54998 -117.80294 MrSrp 36.54883 -117.80201 152 715 739 Mr C IZA r rock Mr

CI CHF 34.33338 -118.02588 MrSrp 34.33273 -118.02647 90 927 1032 Mr B IGA r rock Igd

CI CHN 33.9988 -117.68026 AMrR 33.99775 -117.68087 130 292 307 AMrR D IHD u soil Qyf3a

CI CIA 33.40184 -118.41496 MrSrp 33.40283 -118.41602 148 505 526 Mr C INB r rock m/ub

CI CLC 35.81574 -117.59751 MlMrSrpSrs 35.81487 -117.59603 165 1464 1792 Srp A/B IGA r rock gr

CI CRP 34.13624 -118.12705 AMrR 34.13603 -118.12887 169 340 474 AMrR D/C FQD u soil Qof

CI CTC 33.65511 -115.99004 Mr 33.65474 -115.9892 88 370 396 Mr C IQD r soil Qo-u

CI CTD 33.35486 -118.44436 MrSrp 33.3559 -118.44457 117 756 881 Mr C/B IFA r rock KJf

CI CWC 36.43907 -118.08048 MrSrp 36.43903 -118.08106 52 580 580 Mr C AHB r soil Qal

CI DAN 34.63725 -115.3801 MrS 34.63784 -115.38094 101 477 528 MrS C IHD r soil Qya

CI DNR 33.56667 -116.63056 MrRSrp 33.56715 -116.63082 59 361 411 MrR C IJC/IZC r rock Qa/ms

CI DPP 32.99886 -116.94188 MlMrSrpSrs 32.99878 -116.94074 107 611 830 Ml C/B IGA/IGB r rock Kg-gr

CI DRE 32.80535 -115.44677 MrS 32.80502 -115.4463 57 196 216 Mr D IHD r soil Ql

CI DSC 35.14255 -116.10395 MlMrSrpSrs 35.14202 -116.10419 63 1329 1484 Srs B AHA/AZA r rock Qal/C

CI DVT 32.65877 -116.09984 MrRSrp 32.65902 -116.10019 43 625 625 MrR C AGA/AGB r rock qd

CI EDW2 34.8811 -117.99388 MlMrSrpSrs 34.88215 -117.99309 137 1242 1445 Ml B IGA r rock qm

CI EML 32.89083 -116.84566 MlMrSrpSrs 32.89049 -116.84699 130 805 1188 Srs B IVA r rock Kmv

CI ERR 33.11645 -115.82271 MrS 33.11615 -115.82256 36 238 253 MrS D IHD r soil Qc

CI FHO 34.09355 -116.93588 MrSrp 34.09277 -116.93719 149 416 460 Mr C IQB/IQC r soil Qot

CI FIG 34.72832 -119.98803 MlMrSrpSrs 34.72804 -119.98779 38 489 630 Ml C IFB/IFA r rock fg

CI FUR 36.46717 -116.86322 MrSSrp 36.46713 -116.8643 97 433 456 Mr C IQD/IPD r soil Tft/Qai

CI GATR 34.55266 -120.50235 MrSrp 34.55314 -120.50194 65 338 345 Mr D/C IMB/IMD r rock Tml

CI GLA 33.05149 -114.82706 MrSrp 33.05048 -114.82716 113 743 834 Mr C/B IVA r rock Tvb

CI GMR 34.78457 -115.65994 MlMrSrpSrs 34.78493 -115.65962 50 943 1270 Srs B IHA/IGA r rock Qaag-Qyag/Qha/fpg/Qhs

CI GOR 33.1537 -117.22921 MrSrp 33.15448 -117.22935 88 559 623 Mr C IGA/IGB s rock Kt

CI GRA 36.99606 -117.36615 MrSSrp 36.99644 -117.36605 43 386 407 S C IHD r soil Qal

CI GSC 35.30177 -116.80574 MrSrp 35.30142 -116.80642 73 679 735 Mr C AGA r rock gr

CI HEC 34.82954 -116.33637 MrSrp 34.82984 -116.33694 62 726 781 Mr C/B IGA r rock gqm

CI IDO 33.79673 -116.22153 MrSrp 33.79789 -116.22243 153 557 597 Mr C IQD r soil Qo

CI IKP 32.65012 -116.10948 MrSrp 32.65055 -116.1094 48 1058 1295 Mr B IGA r rock qd

CI IRM 34.15734 -115.14513 MrSrp 34.15773 -115.14488 49 981 1055 Mr B IGA r rock Kii

CI ISA 35.66278 -118.47403 MrSrp 35.66223 -118.47436 68 644 823 Mr C/B FGA r rock Kma

CI JEM 33.08098 -116.59755 MrSrp 33.07979 -116.59831 150 501 559 Mr C IGB/INB r rock Js

CI JRC2 35.98249 -117.80885 MrSSrp 35.98221 -117.80835 55 623 729 S C IVB r rock Qtu

CI JVA 34.36622 -116.61266 MrS 34.36608 -116.61227 39 383 417 Mr C IHD r soil Qa

CI LAF 33.86889 -118.33143 AMrRSrp 33.86838 -118.3323 98 257 278 AMrR D IHD u soil Qae

CI LCP 34.73547 -120.27984 MrSSrp 34.73601 -120.27922 83 259 267 MrS D IHD r soil Qa/QTp

CI LDF 35.13066 -115.18416 Mr 35.13094 -115.18452 45 453 487 Mr C IHD r soil Qal

CI LDR 34.9906 -118.34156 Mr 34.99089 -118.34027 122 378 415 Mr C IQD r soil Qoa

CI LEV 34.61404 -118.28929 MlMrSrpSrs 34.61343 -118.28877 83 505 505 Ml C AHB s soil Qa

CI LJR 34.80762 -118.86775 MlMrSrpSrs 34.8079 -118.86784 32 303 325 Ml D IGB/IGC r rock qm

CI LPC 34.31478 -117.54642 MrSrp 34.3149 -117.54511 121 506 542 Mr C IHB/IHC r soil Qa

CI LRL 35.47954 -117.68212 MrSrp 35.4792 -117.68304 91 511 596 Mr C IGA/IGB r rock qd

CI LUG 34.3656 -117.36683 MrS 34.3651 -117.36696 57 365 381 MrS C/D IQD r soil Qof

CI MCT 34.22645 -116.04073 MlMrSrpSrs 34.22641 -116.03938 124 1188 1188 Srp B IGA r rock bqm

CI MIK 34.13688 -118.12601 AMrR 34.13505 -118.12419 264 333 376 AMrR D/C DQD/EQD/GQD u soil Qof

CI MLAC 37.63019 -118.83605 AMrSR 37.6304 -118.83596 25 308 322 MrS D IQD r soil Qoa

CI MPI 34.8126 -119.14523 MrSrp 34.81301 -119.14641 117 452 536 Mr C IGB r rock gr

CI MPP 34.88848 -119.81362 MrSrp 34.88795 -119.81378 61 522 558 Mr C IEB/ILB r rock Tss

CI MSC 34.03852 -116.64795 MlMrSrpSrs 34.03978 -116.64845 147 377 434 Ml C INB r rock gn
56 

Table 3. Summary of results on site conditions at 191 strong-motion (SM) stations in California and the Central-Eastern United States (CEUS) 

—continued. 

Network Station

Latitude 

(°N) of 

Station

Longtitude 

(°E) of 

Station

Method(s)1

Deployed at 

Site

Latitude 

(°N) of 

Site

Longtitude 

(°E) of 

Site

Distance2 (m) 

from Station 

to Site

VS (m/s) 

by 

Method(s)

Adjusted 

VS (m/s)

Method(s)3

for VS NEHRP4 GMX5

Noise 

Condition6

Geologic 

Condition Lithologic Condition7

CI MSJ 33.80801 -116.96789 AMrR 33.80941 -116.96769 157 244 255 AMrR D IHD s soil Qa

CI MTP 35.48434 -115.5532 MrSrp 35.48552 -115.5534 132 956 1079 Mr B IZA r rock epC

CI NBS 34.78035 -116.55798 MrS 34.78127 -116.55733 118 419 461 MrS C IHD r soil Qa

CI NEE2 34.76754 -114.61883 MrS 34.76713 -114.6194 69 401 427 MrS C IQD/IHD r soil Qayi

CI NJQ 34.53412 -120.17737 MrS 34.53489 -120.17646 119 297 314 MrS D IHD r soil Qa

CI NSS2 33.55553 -115.94586 MrS 33.55453 -115.94624 117 352 377 MrS D/C IHD r soil Qa

CI OLP 32.60778 -116.93036 MrSrp 32.60781 -116.93083 44 489 548 Mr C IVB r rock KJmv

CI PASC 34.17141 -118.18523 MrSrp 34.17198 -118.18704 178 714 988 Mr C/B IGA s rock qd

CI PDM 34.3033 -114.14218 MrSrp 34.30269 -114.14411 190 1312 1560 Mr B/A INA r rock pCc

CI PHL 35.40773 -120.54556 MlMrSrpSrs 35.40828 -120.5459 68 384 428 Mr C IHB r soil Qg

CI PLC 33.82436 -116.51195 AMrR 33.82408 -116.51238 50 346 371 AMr D/C IHD u soil Qa

CI PLM 33.35361 -116.86265 MrSrp 33.35398 -116.86302 54 465 540 Mr C AGB r rock gr

CI PLS 33.7953 -117.60906 MrSrp 33.79505 -117.6088 37 699 699 Mr C AJA r rock Jbc

CI PMD 33.64785 -116.37769 MrSrp 33.64695 -116.37871 138 786 978 Mr B IGA r rock qd

CI RCT 36.30523 -119.24384 AMrSR 36.30629 -119.2439 118 282 301 AMrSR D IHD r soil Qf

CI RRX 34.87499 -116.99683 MrS 34.87498 -116.99575 99 372 396 MrS C IQD/IHD r soil Qa/Qof

CI RSB 33.97327 -117.32755 AMrR 33.97343 -117.32638 109 360 663 AMr C/D FHC u soil Qya

CI RSS 33.97327 -117.32755 AMrR 33.97425 -117.32795 115 360 360 AMr C/D LHC/BHC u soil Qya

CI RXH 33.18313 -115.62257 MrSrp 33.18379 -115.62283 77 309 324 Mr D IVD r rock Qrv-r

CI SAL 33.2801 -115.98585 Mr 33.28034 -115.98654 69 253 264 Mr D IQD r soil Qoa

CI SAN 33.70442 -117.88669 AMrR 33.70636 -117.88731 223 222 222 AMr D BHD/GHD u soil Qal

CI SBB2 34.68844 -117.8242 MrSrp 34.68803 -117.82466 62 1131 1319 Mr B IGA r rock qm

CI SBC 34.44076 -119.71492 AMrR 34.44143 -119.71379 128 485 507 MrR C CHD u soil Qa

CI SBI 33.48046 -119.02986 MrSrp 33.48005 -119.02961 51 854 1043 Mr B KQA/KVA r soil Qoa/Tv

CI SBPX 34.23246 -117.2349 MrSrp 34.23242 -117.23545 51 393 441 Mr C IGB r rock gr

CI SCI2 32.9799 -118.54697 MlMrSrpSrs 32.98019 -118.54774 79 442 442 Mr C AQB/AVB r soil Qso/Tr

CI SCZ2 33.99549 -119.63514 Mr 33.99512 -119.63417 98 313 325 Mr D IMD r rock Tm

CI SDG 32.784 -117.13805 MrSrp 32.78417 -117.13655 141 439 439 Mr C IQD s soil Qoa

CI SDR 32.73561 -116.94241 MlMrSrpSrs 32.73557 -116.94374 124 827 861 Srs B/C KGA/IGA s rock Kgt

CI SHO 35.89964 -116.27518 MrS 35.89972 -116.27479 36 356 377 MrS D/C IQD r soil Qalo

CI SIO 34.29224 -119.16694 AMrR 34.29197 -119.16707 32 249 249 AMr D AHD s soil Qhf

CI SLA 35.89095 -117.28332 MrSrp 35.89184 -117.28312 101 482 521 Mr C IVA r rock Tv

CI SLB 33.48519 -115.86643 MrS 33.48492 -115.86669 39 336 377 MrS D/C IHD r soil Qcb/Qa-Qc

CI SLR 33.83359 -116.79737 MlMrSrpSrs 33.83317 -116.79764 53 503 617 MlMr C IGB r rock qdi

CI SMM 35.31402 -119.99579 MrS 35.3146 -119.99565 66 225 235 MrS D IHD r soil Qa

CI SMR 35.37688 -120.61236 MrSrp 35.37665 -120.61286 52 847 1054 Mr B IKA r rock Kas/Ka

CI SMV 34.27091 -118.74407 AMrR 34.2692 -118.74286 220 260 278 AMr D AHD u soil Qa

CI SMW 35.01108 -120.40997 MrSrp 35.01016 -120.40962 107 676 742 Mr C IVA r rock Tov

CI SNCC 33.24786 -119.52437 MrSrp 33.2476 -119.52353 83 541 668 Mr C FQB r soil Qs/Qt

CI SNO 34.03515 -116.80778 MrSrp 34.03563 -116.80879 107 503 553 Mr C INB r rock gn

CI SPG2 36.20057 -118.76624 MrSrp 36.20012 -118.76663 61 910 987 Mr B IGA r rock gr

CI SWS 32.94503 -115.7999 MrSrp 32.94516 -115.80059 66 525 587 Mr C IGA/IGB r rock gd

CI SYP 34.52775 -119.97834 MrSrp 34.52646 -119.9791 159 440 470 Mr C IEB r rock Tma

CI TA2 34.38206 -117.67819 MrSrp 34.38287 -117.67908 122 565 565 Mr C ANB r rock ml/gn

CI TEH 35.2913 -118.42079 MrSrpSrs 35.29032 -118.42094 110 895 1076 MrSrpSrs B IGA r rock gr

CI TFT 35.1458 -119.41941 MrS 35.14518 -119.41871 94 332 367 MrS D AHD/KHD r soil Qa

CI THM 33.65066 -116.07734 Mr 33.65043 -116.07634 96 452 480 Mr C IQD r soil Qo-u

CI TIN 37.05422 -118.23009 MrRSrp 37.05652 -118.23132 278 396 396 Mr C AVD/APD r rock Tb/Ts

CI TOR 33.57526 -116.22584 MrSrpSrs 33.57571 -116.22679 101 1100 1100 Srp B IGA r rock qdi

CI TUQ 35.43584 -115.92389 MrSrp 35.43644 -115.92715 303 819 853 Mr B IGA r rock gr
57 

Table 3. Summary of results on site conditions at 191 strong-motion (SM) stations in California and the Central-Eastern United States (CEUS) 

—continued. 

1,3Abbreviations for methods with the following techniques:

 A for array microtremor (AM);

 Mr for multi-channel analysis of surface (Raleigh) waves (MASRW);

 Ml for multi-channel analysis of surface (Love) waves (MASLW);

 P for PS-suspension logging;

 R for refraction microtremor (ReMiTM);

 S for spectral analysis of surface waves (SASW);

 Srp for seismic refraction, P-wave; and

 Srs for seismic refraction, S-wave. 2

Distance is measured from station coordinates (on record) to coordinate of farthest end-point of array. 4

NEHRP Site Class exceptions: Whenever an estimated VS30 value falls close to a Site Class boundary, the assigned Site Class is 

denoted by “/” such that the first letter is the actual Site Class according to the calculated VS30 value and second letter is the 

recommended Site Class based on the proximity of the VS30 value to the adjacent Site Class boundary. 5

GMX for Geomatrix code; the same basis (see NEHRP Site Class exceptions) for the usage of “/” is also applied here. 6

Noise conditions are based on the cultural settings of rural (r), suburban (s) and urban (u).

7

See the individual site reports for lithologic units.

Network Station

Latitude 

(°N) of 

Station

Longtitude 

(°E) of 

Station

Method(s)1

Deployed at 

Site

Latitude 

(°N) of 

Site

Longtitude 

(°E) of 

Site

Distance2 (m) 

from Station 

to Site

VS (m/s) 

by 

Method(s)

Adjusted 

VS (m/s)

Method(s)3

for VS NEHRP4 GMX5

Noise 

Condition6

Geologic 

Condition Lithologic Condition7

CI USB 34.41357 -119.84368 AMrR 34.41463 -119.84549 204 300 300 AMrR D IQD/FQD u soil Qoa

CI VCS 34.48372 -118.11781 AMrR 34.48447 -118.11749 88 405 433 AMrR C IQC r soil Qoa

CI VES 35.84088 -119.08489 MrS 35.84067 -119.08428 60 393 456 Mr C IQD r soil Qoa3/QTkr

CI VOG 36.32097 -119.38224 AMrR 36.32044 -119.38314 100 341 358 AMrR D/C IHD s soil Qf

CI WBS 35.53664 -118.14035 MrSrp 35.53716 -118.14091 77 402 426 Mr C IGA r rock gr

CI WES 32.75905 -115.73152 MrS 32.75945 -115.73273 122 242 269 MrS D IHD r soil Qa-Qc

CI WGR 34.51085 -119.27407 MlMrSrpSrs 34.51104 -119.27369 41 660 757 Srs C/B IKA r rock Qg/Kush

CI WNS 34.11395 -118.37967 MrSrp 34.11352 -118.37966 48 1043 1043 P B BGA s rock qd

CI WWC 33.94068 -116.40876 MrSrp 33.94134 -116.40834 83 587 727 Mr C INB r rock sg

NC BBGB 36.57849 -121.03956 MlMrSrpSrs 36.57752 -121.03955 108 285 312 Ml D IED r rock Tk

NC BJOB 36.61087 -121.31467 MrSrp 36.61041 -121.31403 77 486 528 Mr C IGB r rock Kqd

NC CADB 37.16346 -121.62645 MlMrSrp 37.16371 -121.62737 86 600 600 Ml C IFA r rock fg

NC CAL 37.45035 -121.80037 MrSrp 37.45063 -121.79928 101 519 557 Mr C IHB/IFB r rock Qls/fs

NC CCOB 37.25898 -121.67311 MlMrSrpSrs 37.25796 -121.67343 117 400 414 Mr C IKD r rock Kp

NC CHR 37.37289 -121.76887 MrSrp 37.37268 -121.76826 59 471 471 Mr C IMB r rock Tbr

NC CNI 37.60791 -121.96442 MlMrSrpSrs 37.60754 -121.96465 46 517 584 MlMr C IKB r rock Ko

NC CSL 37.7241 -122.11927 MrSrp 37.72349 -122.11865 87 378 391 Mr C IVB/IVC r rock Jsv

NC HCAB 37.02533 -121.48476 MrSrp 37.02599 -121.48514 81 646 671 Mr C IEB/ILB r rock Tsh

NC JBMB 37.31861 -122.15314 MlMrSrpSrs 37.31792 -122.15347 82 584 637 Ml C IFB r rock fs/fg

NC JBNB 37.12043 -122.1552 MlMrSrpSrs 37.12014 -122.1564 111 760 942 Ml B IGA r rock qd

NC JELB 36.92742 -121.82765 Mr 36.92684 -121.82651 120 323 323 Mr D IQD r soil Qar

NC JFP 37.36045 -122.18986 MlMrSrpSrs 37.36001 -122.19081 97 437 456 Ml C IFB r rock fg

NC JLAB 37.15432 -121.73467 MlMrSrpSrs 37.15422 -121.73517 46 477 559 Mr C IFB r rock fm

NC JSFB 37.40372 -122.17596 MrSrp 37.40333 -122.17617 47 432 463 Mr C IMB r rock Tm

NC JSGB 37.28395 -122.05026 Mr 37.28362 -122.05012 39 376 400 Mr C IQD r soil Qoa/Qsc

NC MBS1 37.76099 -118.94467 MlMrSrpSrs 37.7604 -118.94436 71 740 833 Srp C/B IGA r rock Jg

NC MDPB 37.63227 -119.07935 MrSrp 37.63188 -119.0792 45 580 706 Mr C IVA r rock Qad

NC MMLB 37.65909 -118.97907 MrSrp 37.65924 -118.97949 41 317 317 Mr D IVD r rock Qmrm

NC MMX1 37.61711 -118.95976 MlMrSrpSrs 37.61685 -118.96114 125 565 687 SrpSrs C IZA r rock Pzms

NC PAGB 35.73065 -120.24986 MlMrSrpSrs 35.73016 -120.24907 90 473 510 Ml C IJB r rock sp

NC PHOB 35.86661 -120.4796 MrS 35.86724 -120.48005 81 376 404 MrS C IQD/IPD r soil QTp

NC PHSB 35.82402 -121.05396 MrSrp 35.82379 -121.05484 83 422 422 Mr C IMB r rock Tvq

NC PMPB 36.2159 -120.80126 MlMrSrpSrs 36.21634 -120.80196 80 308 308 Ml D IFD r rock fm

NC PSM 36.0688 -120.59618 MrSrp 36.06897 -120.59556 59 560 620 Srp C IMB/IMA r rock Tm

NM BLO 39.1719 -86.5222 MlMrSrpSrs 39.17766 -86.52406 660 1202 2317 Srs B/A DZA s rock M2

NM JCMO 38.25749 -90.55847 MlMrSrpSrs 38.25676 -90.55864 83 768 902 Ml B LZA s rock Ojc

NM PIOH 40.15802 -84.21151 MrSrp 40.15879 -84.21123 89 416 447 Mr C IZB s rock Oc

PG DCD 35.22132 -120.84669 MlMrSrpSrs 35.22184 -120.84633 66 517 764 Ml C/B IVA r rock Tov

PG SHD 35.16898 -120.76319 MlMrSrpSrs 35.16931 -120.76366 56 818 935 MlMr B IKA r rock Kas

PG VPD 35.23979 -120.86951 MrSrp 35.23998 -120.87017 64 262 262 Mr D IMD r rock Tm

US ACSO 40.23228 -82.98202 MlMrSrpSrs 40.23205 -82.9813 66 417 467 MlMr C IZB r rock Doh
58 

Table 4. Summary of National Earthquake Hazards Reduction Program site classes and measured VS30 values for strong motion stations in 

California and the central-eastern U.S. 

NEHRP NEHRP Std. Deviation

Site VS30 (m/s) Number of Max. Min. Mean Median of

Class Ranges Stations VS30 (m/s) VS30 (m/s) VS30 (m/s) VS30 (m/s) VS30 (m/s)

A > 1500 0 NA NA NA NA NA

B 760 - 1500 29 1464 763 994 943 199

C 360 - 760 104 760 361 502 485 105

D 180 - 360 58 360 196 296 304 46

E < 180 0 NA NA NA NA NA
59 

10.0 Appendix A 

The complete collection of site reports is appended alphabetically based on the 

station's network code followed by the station code. These reports can be found as 

of2013-1102_appendix_a.pdf at http://pubs.usgs.gov/of/2013/1102/.