AbstracT - The Nevada Seismological...

University of Nevada, Reno

Analysis of Shear Wave Velocity Measurements for Prediction Uncertainties

in Southern California

A thesis submitted in partial fulfillment of therequirements for the degree of Master of Science in

Geophysics

by

Mayo Thompson

Dr. John N. Louie/Thesis Advisor

May, 2010

UNIVERSITY

OF NEVADA

We recommend that the thesisprepared under our supervision by

MAYO THOMPSON

entitled

Analysis of Shear Wave Velocity Measurements for Prediction Uncertainties

in Southern California

be accepted in partial fulfillment of therequirements for the degree of

MASTER OF SCIENCE

John N. Louie, Ph.D., Advisor

Aasha Pancha, Ph.D., Committee Member

Gary M. Norris, Ph.D., Committee Member

Paula J. Noble, Ph.D., Graduate School Representative

Marsha H. Read, Ph.D., Associate Dean, Graduate School

May, 2010

THE GRADUATE SCHOOL

ABSTRACT

i

Table of Contents

ABSTRACT i

LIST OF TABLES iii

LIST OF FIGURES iv

INTRODUCTION 1

METHODS 3

RESULTS AND DISCUSSION 7

COMPARISON WITH SCEC CVM VERSION 4 7

COMPARISON WITH PROXY USING TOPOGRAPHIC SLOPE 9

VS30 MAPS 9

CONCLUSION 12

APPENDIX - VARIOGRAMS 50

APPENDIX-CUMULATIVE DISTRIBUTION PLOTS 59

BIBLIOGRAPHY 60

ii

LIST OF TABLES

Table 1 – Survey Methods 17

Table 2 – Site Characterizations 18

Table 3 – VSZ Values 34

Table 4 – ZV Calculations 38

iii

LIST OF FIGURES

Figure 1 – Location of all site characterizations used in this study 42

Figure 2 – Mean and Standard Deviation of the complete data set and subsets 43

Figure 3 – Plot of Topographic Slope estimations versus Direct Measurements 44

Figure 4 – VS30 maps created using natural log transformed data 45

Figure 5 – VS30 maps created using non-transformed data 46

Figure 6 – Hazard maps (VS30 < 360 m/s) 47

Figure 7 – Hazard maps (VS30 < 180 m/s) 48

Figure 8 – Contour of spatial standard deviation 49

Figure 9 – Variogram generated using transformed Complete Data Set 51

Figure 10 – Variogram generated using non-transformed Complete Data Set 52

Figure 11 – Variogram generated using transformed Basin Subset 53

Figure 12 – Variogram generated using non-transformed Basin Subset 54

Figure 13 – Variogram generated using transformed Rock Data Subset 55

Figure 14 – Variogram generated using non-transformed Rock Data Subset 56

Figure 15 – Variogram generated using transformed Soil Data Subset 57

Figure 16 – Variogram generated using non-transformed Soil Data Subset 58

iv

Figure 17 – Cumulative Distribution Plots 59

v

INTRODUCTION

In the U.S., the average shear wave velocity in the upper 30 m (VS30) is currently the

most popular predictor of ground motion and site amplification. Joyner (1981) suggested

the correlation between site amplification and near-surface shear wave velocity.

Borcherdt and Glassmoyer (1994), using strong motion data from the Loma Prieta, CA ,

M 7.1 earthquake on October 17 1989, found empirical evidence that VS30, selected

based on borehole depth achieved in a day (Tinsley, et al., 2004), could predict site

amplification. They further defined site-classes, which were then adopted by the

engineering community.

Site classifications are made using various methods, classified as either invasive or non-

invasive (Table 1), to determine VS30. The destructive nature of invasive methods and

their lack of repeatability (Moss, 2008) has made non-invasive methods more preferable.

Non-invasive methods tend to be less time and labor intensive, involve less equipment

and require less permitting, making data acquisition easier and faster. The volume-

averaging nature of non-invasive methods may be more informative where geology is

laterally variable compared to single point measurements of invasive method.

Site characterizations, even though much more data is available presently, are still

relatively sparse. Wills et al. (2000) uses over 500 site characterizations in their site

characterizations based on geology. The number increases to nearly 800 California sites

statewide in the regression curves computed using topographic slope as a proxy (Wald &

Allen, 2007). Understanding that some areas are much more sampled than others, such

1

as the San Gabriel River Valley (Thelen, et al., 2006), on average there is one site

characterization per 536 square kilometers or 2 site characterizations for the city of Los

Angeles.

To compensate for this lack of measurements, researchers have created various proxies to

predict VS30 where there are few to no direct measurements. Petersen et al (1997)

differentiated site conditions from Tinsley and Fumal (1985) into three categories:

alluvium, soft-rock, and hard-rock and extended the shear wave velocity maps into

surrounding areas using geologic maps. Park and Elrick (1998) created a larger, similar

map of VS30 based on units differentiated by age (Quarternary (Q), Tertiary (T), and

Mesozoic (M)) for southern California. Increasing the number of units, Wills et al (2000)

created a site conditions map for California grouping geologic units into seven units of

similar VS30. Wills and Clahan (2006) further divided the seven units into 19

geologically defined categories for the Los Angeles area to increase the precision of site

condition predictions in ground-motion calculations. The topographic slope proxy (Wald

& Allen, 2007) uses 8 units based on the NEHRP values with its predictions applicable to

various locations globally.

These proxies, as they are prediction of site characterizations, therefore have an inherent

uncertainty associated with estimates. Some site characterizations report an uncertainty

(Thelen, et al., 2006), but generally, if mentioned, it is the data uncertainty. The

epistemic uncertainty (uncertainty in measurement technique), when reported, is not

enough to describe the difference. The aleatory uncertainty (uncertainty spatially or true

natural randomness) associated with these proxies is not reported. Since many data

2

measurements are commercial property, it is not easy to ascertain the related

uncertainties. Using a publicly available data set, we seek to evaluate various

fundamental uncertainties of VS30 predictions as compared to VS30 measurements.

METHODS

In Southern California there are nearly 400 publicly available site characterizations using

direct measurement of VS30 (Table 2). Nearly 75% of those, all performed using

refraction microtremor (Louie, 2001), are available online from the Nevada

Seismological Laboratory at (www.seismo.unr.edu/hazsurv). These consist of dense,

high-resolution surveys along the San Gabriel River (Thelen, et al., 2006) as well as point

measurements at several California Intergrated Seismic Network sites in the Inland

Empire of Southern California, in the Shavers and Maniobara Valleys, and in The Joshua

Tree National Park collected from 2003 to 2007. Data analysis was done using the

commercial SeisOptReMi software (2007, Optim Inc.).

The remaining site characterizations are taken from the Next Generation Attenuation

Project database (Power, Chiou, Abrahamson, Bozorgnia, Shantz, & Roblee, 2008). The

NGA database includes thousands of recorded ground motions and station site conditions

for California, measured (Borcherdt & Fumal, 2002), (Kayen, Thompson, Minasian, &

Carkin, 2005) or inferred from Wills & Clahan (2006). Of these global measurements,

this study only uses the few measurements classified as measured and performed in

Southern California (Figure 1.). The NGA direct measurements are from surveys

performed using various invasive and non-invasive geophysical methods (Moss, 2008).

3

http://www.seismo.unr.edu/hazsurv

The refraction microtremor method uses microtremor noise from sources such as traffic

on streets and freeways excites Rayleigh waves, which are recorded for a total of five

minutes by a linear array of 12 or 24 vertical refraction geophones. The noise records are

transformed into slowness–frequency (p-f) space, and a dispersion curve picked along a

minimum-velocity envelope. Modeling the dispersion curve produces a depth–velocity

sounding, which can be vertically averaged to the single Vs30 value used by the NEHRP-

UBC code.

For the 82 unpublished site characterizations presented in this paper, we computed

summary values for VSZ, (the average shear wave velocity to depth Z) for depths 10, 30,

50, and 100 meters (Table 3) by arithmetic slowness averaging with the formula below:

where Z is the total depth and zi is the thickness of the ith layer having a shear velocity Vi.

We have also calculated several values in addition to the VSZ. We have picked Z500, Z1000

and Z1500, which are the depths where the shear velocity first exceeds, or nearly exceeds,

500 m/s, 1000 m/s and 1500 m/s respectively (Table 4). The ZV values are used as

interface depths, i.e. depth to bedrock. The velocities selected were the closest to the

target values possible, with the corresponding “error” relating to the velocity exceeded in

the profile, and not depth.

4

Shear wave velocities were also predicted at all of our measurement sites using two other

predictive methods. One set of shear-wave velocities utilized the Southern California

Earthquake Center (SCEC) Community Velocity Model (CVM) (Magistrale, Day,

Clayton, & Graves, 2000) version 4. The output consisted of predictions at depth

increments of 1 meter from 0 meters to 29 meters. The VS30 from the CVM was then

calculated by the arithmetic slowness averaging formula above.

We extracted a second set of shear wave predictions using the topographic slope model

(Wald & Allen, 2007). We imported a grid of VS30 predictions, obtained from the

USGS Global Vs30 server (http://earthquake.usgs.gov/hazards/apps/vs30), into Surfer,

and extracted site characterization predictions for our entire data set.

Data was broken into various subsets to distinguish any trends in the methods or subsets.

We initially separated the site characterizations into three subsets: “rock”, “soil”, and

“basin”. Initially, “rock” sites consisted of site characterizations that had a CVM VS30 of

>800 m/s, all others were “basin”. Further geographic differentiation placed the sites in

the Los Angeles basin into the “soil” category.

As many of the sites characterizations were outside of the Los Angeles Basin Model of

the CVM and realizing that not all sites outside this area are rock, we then differentiated

the sets based upon the site class map of Wills et al(2000). We placed site

characterizations in regions designated NEHRP B-BC in the “rock” category. Similarly,

for the “basin” and “soil” categories, we separated them based on NEHRP C-CD and D-

DE respectively.

5

We calculated several statistical values for site characterizations using the SCEC CVM

and topographic slope prediction methods and the direct measurements from borehole,

refraction microtremor, and SASW. We calculated the mean and standard deviation or

sigma for the complete data set and all subsets. We computed the log of

predictions/direct measurement and evaluated histograms of their frequency. The

predictions were also plotted against the direct predictions to check for data correlation.

Kriging was used on all categories to create discretized grids of VS30 and spatial standard

deviation at each node. The software Surfer version 8 was used to compute the kriging

variance, or the squared spatial standard deviation, which is a function of the sampling of

an area (Carr, 1995). The sill and covariance, variables used to compute the kriging

variance, are estimated from a theoretical variogram model. The sill is the sum of scale

and nugget effect (figure 9). The theoretical variogram is based upon an experimental

variogram computed from the following equation:

where N is the number of measurements, h is the distance between measurement xi and

xi+h or lag width and Z, for this study, is VS30.

Before executing the kriging algorithm, we evaluate the data’s statistics. The observed

versus expected cumulative distribution function was plotted to ascertain which

probability density function best matched the distribution of the data (figure 17). Data

with a Gaussian probability density function or normal distribution is ideal for kriging

6

(Carr, 1995). If the data does not conform to a normal distribution then some data

transformation should be performed before and after using the kriging algorithm. Chilés

& Delfiner (1999) and Carr (1995) outline a method of data transformation using the

natural logarithm.

The variogram is also used to evaluate the fractal characteristics of the sampling area.

The variogram is computed for some lag width and maximum lag distance. The lag

width and gamma values are then converted to log values. The log(lag width) is plotted

versus log(gamma) and a linear trend line is found that approximates the data to the

length or the lag distance where the sill is reached. The slope of the line is then used in

the following equation:

where S is the slope of the trend line and D is the fractal dimension of the data (Mela &

Louie, 2001).

RESULTS AND DISCUSSION

Comparison with SCEC CVM Version 4

A comparison of our survey measurements and the SCEC CVM show large residuals. A

first order look at the data reveals that the bulk of measurements with large differences lie

on the edge or outside of the Los Angeles Basin model defined in the CVM. These sites

tend to have a VS30 > 800 m/s. The CVM presumes that any geology outside of the

7

modeled basins has the velocity of hard rock (Magistrale et al., 2000, SCEC CVM v4

website) although there are no hard rock sites in California (Field, 2000). Our direct

measurements of these sites reveal Vs30 values that are much lower than those

considered hard rock shear velocities in the CVM.

There is some correlation between our measured Vs30 and SCEC CVM Vs30 at basin

and soil sites (Figure 2). Basin site predictions have a mean value that is within 5% of

direct measurements with 87% ±20% of measurements. Mean value and standard

deviation of soil sites predictions are within 15% of measurements. Less than half of soil

site predictions are ±20% of measurements.

The Los Angeles Basin Proper has a relatively homogenous geologic makeup and

geotechnical borehole coverage is dense (Magistrale, Day, Clayton, & Graves, 2000).

Also, there are 43 active oil fields in the Los Angeles Basin Proper (Bilodeau, Bilodeau,

Gath, Oborne, & Proctor, 2007). For these reasons, the soil and basin site statistics are

contrary to what we expect for these subsets. Direct measurements in this fairly

homogenous area should have a small variability thereby needing a relatively small

sampling. The small difference in the average values in the basin sites may reflect a need

for more sampling.

The rock site predictions have a poor correlation with measurements. The mean of

predictions is more than the five times the mean of measurements. Most site predictions

are more than 4 times the direct measurement, with several almost an order of magnitude

8

larger. Furthermore, the SCEC CVM overpredicts measurements at all rock sites except

for one location.

Comparison with proxy using topographic slope

The statistical calculations comparing the topographic slope predictions and direct

measurements are somewhat ambiguous in their result. Figure 2 shows the topographic

slope mean for the complete data set is within ±20% of direct measurements while the

standard deviation is 68%. These values are similar for the subsets although more

correlation was expected for the basin and soil subsets as they are more heavily sampled.

We see similar features, lesser mean and standard deviation, when comparing a subset to

the complete data set. A lesser mean can arise from a smoothing of the data.

These values of the statistical moments of the data seem to suggest agreement with direct

measurements. Looking at figure 3, we see that most points lie outside a ±20% window.

As a matter of fact, less than half of the predictions based on topographic slope plot

within this error window. Many rock site characterizations presented in this study are

recent. The number of rock site characterizations is relatively small which may explain

the disagreements at these locations. Although in the basin and soil subsets, where we

expect more homogeneity of geological deposits and higher sampling, there is only ~40%

and ~50% agreement respectively.

VS30 Maps

We created several VS30 maps for the southern California study area, each based on a

subset or the complete dataset of shear wave velocities, to evaluate the spatial uncertainty

9

associated with estimations. We compared maps generated from the dataset and with

transformations applied. These maps are first order VS30 maps and are made with minor

correlation to geologic maps. The subjective nature of correlating raster images to those

discretized from a set of data may introduce more uncertainty.

The rock subset based VS30 maps, with (figure 4) and without (figure 5) data

transformation, have several artifacts due to the scatter and minimal amount of data. The

amount of artifacts as well as kriging variance in figure 8 decreases with soil subset VS30

map. Basin features become evident in the Los Angeles basins model but there is no real

difference between transformed and non-transformed data.

With the basin subset maps in figures 4 & 5, we begin to see more basin structure due to

the increase in associated points and coverage. The Coachella and Imperial valleys are

evident as the Los Angeles, San Fernando, and Ventura basins. There is a shift to higher

velocities with the transformed map, which appears more reasonable and comparable to

other maps. Note that the kriging variance in figure 8 is much less than the rock subset

map.

The VS30 map and kriging variance is one of our goals for this study. The Chino basin

and San Bernadino Valley are minor but noticeable on both maps in figures 4 & 5

utilizing the complete data set as well as the San Gabriel Mountains. The white area in

the Mojave Desert depicts erroneously low VS30 predictions. This major artifact, due to

the kriging algorithm compensating for the high velocity to the south, does not appear in

10

the transformed data map. Since the same features are visible without the artifact, we

have more confidence in the transformed data VS30 map.

Hazard maps were created to look at the probability of a certain VS30 or less. We created

these maps for two threshold values, < 360 m/s in figure 6 and < 180 m/s in figure 7,

NEHRP D and E respectively for all datasets except the rock subset. Using the complete

dataset, even though with many artifacts in the areas of low population, renders the best

map. The basins are highlighted using this technique. Unlike in other methods, NEHRP

E is seen in the Los Angeles basin and the Imperial Valley.

The square root of the kriging variance or spatial standard deviation, similar to other

seismic data, is reported in natural log units. In some locations in the Los Angeles area

basins model, the spatial standard deviation reaches 0.3 natural log units, which translates

to a percent change of -26% to +35%. This can result in a NEHRP site characterization

being under or over predicted by a site class. The gray area in figure 8, in which there are

several predictions, reflects >0.38 natural log units change in value or percent change -

32% to 46%. The spatial standard deviation does not reflect the epistemic or data

uncertainty.

The variogram, used to compute the kriging variance, may give insight to another method

of computing seismic velocities. The fractal dimension is used to extrapolate between

known data points in the petroleum industry and hydrology. This is effective because

porosity and permeability, which are related to the shear modulus and thereby to VS30,

exhibit fractal characteristics.

11

CONCLUSION

Predictions based on topographic slope currently do not address geology in certain areas.

Predictions based on topographic slope do not address values of Vs at lower depths.

Predictive methods of Vs30 need additional measurements in order to better estimate

seismic hazard.

The data uncertainty is mainly reported and, when the survey method is reported,

epistemic can be computed also. It is known that the further the distance from a

measurement, the likelihood of that measurement decreases and that value is not reported.

The spatial variance needs to be considered as well as other uncertainties. This

uncertainty needs to be considered in probabilistic models that utilize predictive methods.

Data transformation is seldom used in predictive methods. Most error related to

earthquake seismology is reported in natural log units. A lognormal distribution best fits

VS30 data and using the proper distribution may allow for a better match of the

predictions based on direct measurements.

The fractal dimension has been used in the private sector successfully for more than a

decade to limit risk. It is a function of sampling and magnitude at a location. Using the

fractal dimension may provide a new method to better estimate Vs30 at various

resolutions.

The amount of earthquake related seismic data is a magnitude larger than when the Loma

Prieta earthquake of October 17, 1989 occurred. Where a borehole was once used,

12

several other geophysical methods can achieve results in a fraction of the time. With

methods currently available, more dense direct measurements are feasible and can be

done in a timely manner to decrease prediction uncertainties.

13

Table 1 - Survey Methods

17

Method Classification Method Type

Invasive Downhole

Crosshole

Suspension Log

Seismic Cone Measurements

Non-Invasive ReMi

SASW

MASW

Spatial Autocorrelation

Reflection/Refraction

Horizontal-Vertical Spectral Ratio

Frequency-Wavenumber

Table 2 - Site Characterizations

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

1 AGO* 34.146 -118.766 409 529.1 327.2

2 BRE* 33.810 -117.982 238 250.0 287.9

3 BTC* 33.013 -115.221 273 232.3 831.5

4 CFS* 34.106 -117.283 320 317.4 273.2

5 CLT* 34.093 -117.316 304 318.4 362.0

6 Cottonwood* 33.709 -115.802 823 612.8 2787.6

7 CPP* 34.060 -117.809 279 419.2 314.2

8 CRN* 33.876 -117.560 340 399.5 370.3

9 CSMIP-1646* 33.741 -115.819 863 394.4 2681.0

10 DEC* 34.253 -118.333 450 568.2 483.4

11 DEV* 33.935 -116.580 499 443.9 822.5

12 DGR* 33.650 -117.010 554 368.6 3196.8

13 DJJ* 34.104 -118.454 336 493.0 443.2

14 DLA* 33.847 -118.097 233 225.5 272.4

15 FMP* 33.712 -118.292 350 430.3 2857.7

16 FON* 34.099 -117.439 509 349.4 370.3

17 FUL* 33.871 -117.923 306 320.4 364.0

18 GR2* 34.123 -118.296 414 587.1 456.2

19 GSA* 34.137 -118.128 337 354.7 412.5

20 HLL* 34.174 -118.360 302 310.2 328.1

21 HLN* 34.121 -117.219 347 348.7 273.2

22 JTNP_Line1* 33.643 -115.802 517 504.8 2950.9

23 JTNP_Line2* 33.654 -115.802 482 382.2 2915.8

24 JTNP_Line3* 33.669 -115.802 433 441.5 2881.1

25 JTNP_Line4* 33.683 -115.802 387 494.3 2849.4

18

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

26 JTNP_Line5* 33.693 -115.802 363 510.4 2826.2

27 KIK* 34.150 -118.102 393 385.0 381.0

28 KNW1* 33.714 -116.712 700 673.9 3387.8

29 KNW2* 33.714 -116.712 726 673.9 3387.8

30 LAF* 33.870 -118.334 281 265.9 342.7

31 LBW2* 33.798 -118.088 278 244.4 290.3

32 LCG* 34.000 -118.377 330 464.9 286.1

33 LFP* 34.305 -118.481 466 366.6 318.0

34 LGB* 33.975 -118.149 350 250.8 281.2

35 LGU* 34.109 -119.065 563 735.0 443.6

36 LKL* 34.616 -117.825 330 332.8 3384.4

37 LLS* 33.687 -117.943 218 267.2 276.2

38 LTP* 33.879 -118.176 258 239.8 275.4

39 MGE* 33.817 -116.368 277 316.9 822.5

40 MIS* 33.740 -118.336 363 746.5 2889.9

41 MLS* 34.005 -117.559 336 313.3 273.2

42 MOP* 34.281 -118.901 246 351.3 293.3

43 MUR* 33.601 -117.195 630 537.1 3439.1

44 MWC* 34.223 -118.063 557 728.5 2892.7

45 NOT* 34.229 -118.558 312 319.2 311.7

46 OGC* 33.788 -117.843 372 307.5 297.3

47 OLI* 33.945 -117.923 312 483.7 332.9

48 OrocopiaMts* 33.631 -115.810 793 525.7 2978.1

49 OSI* 34.613 -118.725 420 366.4 356.7

50 PAS* 34.148 -118.171 633 455.7 829.8

19

51 PASC* 34.172 -118.186 807 466.7 743.6

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

52 PDE* 34.443 -118.581 257 410.0 483.4

53 PDR* 33.963 -118.437 321 382.0 292.9

54 PDU* 34.121 -117.638 503 414.0 370.3

55 PER* 33.862 -117.206 556 423.2 3315.3

56 PFO1* 33.612 -116.459 932 544.6 1479.1

57 PFO2* 33.612 -116.459 676 544.6 1479.1

58 PSR* 34.093 -117.806 765 386.1 373.4

59 QUG* 34.396 -118.498 442 480.0 483.4

60 RDM1* 33.630 -116.848 662 668.0 2827.2

61 RDM2* 33.630 -116.848 610 668.0 2827.2

62 RIN* 34.282 -118.479 331 389.7 543.5

63 RIO* 34.106 -117.981 473 373.0 313.1

64 RPV* 33.744 -118.403 567 536.1 2982.4

65 RSS* 33.978 -117.327 387 394.7 370.3

66 RUS* 34.052 -118.080 300 348.5 302.2

67 RVR* 33.993 -117.374 634 328.2 273.2

68 SDD* 33.553 -117.662 412 381.0 2329.1

69 SES* 34.437 -119.138 447 391.8 443.8

70 SGRiv1 34.159 -117.905 619 659.9 733.5

71 SGRiv2 34.159 -117.908 619 664.9 733.5

72 SGRiv3 34.159 -117.911 417 674.4 733.5

73 SGRiv4 34.158 -117.914 520 648.3 403.3

74 SGRiv5 34.156 -117.917 590 623.9 403.3

75 SGRiv6 34.155 -117.919 589 590.2 421.0

76 SGRiv7 34.153 -117.921 696 568.2 421.0

20

77 SGRiv8 34.150 -117.923 696 544.8 421.0

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

78 SGRiv9 34.148 -117.925 539 524.3 421.0

79 SGRiv10 34.147 -117.927 578 505.0 421.0

80 SGRiv11 34.145 -117.930 574 472.3 421.0

81 SGRiv12 34.143 -117.932 540 444.9 421.0

82 SGRiv13 34.141 -117.935 669 419.7 421.0

83 SGRiv14 34.140 -117.937 665 388.9 421.0

84 SGRiv15 34.138 -117.940 653 365.0 421.0

85 SGRiv16 34.136 -117.942 665 353.9 429.5

86 SGRiv17 34.134 -117.944 642 346.2 429.5

87 SGRiv18 34.133 -117.946 630 340.3 398.9

88 SGRiv19 34.131 -117.949 650 332.6 398.9

89 SGRiv20 34.128 -117.949 642 329.7 398.9

90 SGRiv21 34.126 -117.950 650 329.6 398.9

91 SGRiv22 34.124 -117.948 642 326.8 398.9

92 SGRiv23 34.121 -117.947 650 323.7 398.9

93 SGRiv24 34.120 -117.945 721 321.8 398.9

94 SGRiv25 34.118 -117.943 669 322.1 398.9

95 SGRiv26 34.118 -117.940 607 324.3 398.9

96 SGRiv27 34.117 -117.937 669 326.6 398.9

97 SGRiv28 34.115 -117.939 607 327.2 398.9

98 SGRiv29 34.113 -117.941 729 330.4 398.9

99 SGRiv30 34.111 -117.943 600 343.3 398.9

100 SGRiv31 34.109 -117.945 588 353.7 398.9

101 SGRiv32 34.109 -117.948 652 346.7 398.9

102 SGRiv33 34.109 -117.951 577 339.7 398.9

21

103 SGRiv34 34.111 -117.954 586 326.8 398.9

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

104 SGRiv35 34.113 -117.956 525 318.6 398.9

105 SGRiv36 34.113 -117.959 547 318.1 420.0

106 SGRiv37 34.112 -117.962 436 332.1 420.0

107 SGRiv38 34.112 -117.966 476 352.4 420.0

108 SGRiv39 34.112 -117.970 525 359.8 393.5

109 SGRiv40 34.110 -117.973 503 354.2 393.5

110 SGRiv41 34.109 -117.975 580 355.4 313.1

111 SGRiv42 34.107 -117.977 514 361.9 313.1

112 SGRiv43 34.105 -117.979 538 365.7 313.1

113 SGRiv44 34.103 -117.982 514 363.9 313.1

114 SGRiv45 34.101 -117.984 429 361.4 313.1

115 SGRiv46 34.099 -117.986 434 340.8 317.4

116 SGRiv47 34.097 -117.989 460 320.3 317.4

117 SGRiv48 34.095 -117.991 469 308.3 317.4

118 SGRiv49 34.093 -117.994 461 304.3 312.2

119 SGRiv50 34.091 -117.995 561 307.6 315.1

120 SGRiv51 34.089 -117.997 402 307.8 307.4

121 SGRiv52 34.087 -117.999 382 294.4 311.5

122 SGRiv53 34.084 -118.000 430 283.8 311.5

123 SGRiv54 34.082 -118.000 411 273.8 315.1

124 SGRiv55 34.079 -118.001 390 270.4 315.1

125 SGRiv56 34.076 -118.002 381 268.0 315.1

126 SGRiv57 34.074 -118.002 401 264.8 315.1

127 SGRiv58 34.071 -118.003 401 262.9 315.1

128 SGRiv59 34.068 -118.004 347 261.7 315.1

22

129 SGRiv60 34.066 -118.005 352 260.8 315.1

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

130 SGRiv61 34.063 -118.006 341 260.9 315.1

131 SGRiv62 34.061 -118.007 378 263.3 312.2

132 SGRiv63 34.058 -118.008 368 265.2 312.2

133 SGRiv64 34.055 -118.008 383 266.8 312.2

134 SGRiv65 34.053 -118.009 333 271.8 312.2

135 SGRiv66 34.050 -118.011 397 275.7 312.2

136 SGRiv67 34.048 -118.012 355 277.3 310.8

137 SGRiv68 34.046 -118.014 349 287.7 306.8

138 SGRiv69 34.044 -118.017 337 302.7 306.6

139 SGRiv70 34.043 -118.019 398 307.4 306.5

140 SGRiv71 34.041 -118.022 396 306.0 322.1

141 SGRiv72 34.040 -118.025 371 297.3 322.1

142 SGRiv73 34.039 -118.028 338 286.8 322.1

143 SGRiv74 34.038 -118.031 373 294.4 322.1

144 SGRiv75 34.036 -118.034 339 290.3 302.2

145 SGRiv76 34.035 -118.037 367 287.8 302.2

146 SGRiv77 34.034 -118.040 360 282.0 302.2

147 SGRiv78 34.033 -118.043 341 268.1 292.1

148 SGRiv79 34.031 -118.045 401 260.7 299.0

149 SGRiv80 34.030 -118.048 347 259.3 307.7

150 SGRiv81 34.028 -118.051 363 258.8 319.4

151 SGRiv82 34.026 -118.052 371 264.2 319.4

152 SGRiv83 34.024 -118.054 424 268.6 319.4

153 SGRiv84 34.022 -118.055 368 269.7 320.6

154 SGRiv85 34.021 -118.057 430 280.4 320.6

23

155 SGRiv86 34.019 -118.059 370 285.7 316.1

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

156 SGRiv87 34.013 -118.062 361 299.6 318.0

157 SGRiv88 34.011 -118.064 373 307.9 313.8

158 SGRiv89 34.004 -118.069 414 308.9 332.6

159 SGRiv90 34.002 -118.070 389 299.2 400.9

160 SGRiv91 33.999 -118.070 388 291.2 299.2

161 SGRiv92 33.996 -118.071 369 282.1 299.2

162 SGRiv93 33.994 -118.072 375 271.4 299.2

163 SGRiv94 33.991 -118.073 379 260.1 299.2

164 SGRiv95 33.989 -118.074 372 248.5 299.2

165 SGRiv96 33.986 -118.076 393 233.2 290.5

166 SGRiv97 33.984 -118.077 437 221.9 290.5

167 SGRiv98 33.981 -118.078 362 222.3 290.5

168 SGRiv99 33.979 -118.080 357 234.3 297.5

169 SGRiv100 33.977 -118.082 355 244.5 302.8

170 SGRiv101 33.974 -118.084 329 249.0 286.9

171 SGRiv102 33.972 -118.085 417 248.2 286.9

172 SGRiv103 33.970 -118.087 318 247.0 286.9

173 SGRiv104 33.968 -118.088 318 245.6 286.9

174 SGRiv105 33.965 -118.088 337 243.9 291.7

175 SGRiv106 33.962 -118.088 357 241.3 291.7

176 SGRiv107 33.960 -118.087 350 236.6 297.7

177 SGRiv108 33.957 -118.088 342 233.7 297.7

178 SGRiv109 33.955 -118.090 317 235.2 297.7

179 SGRiv110 33.953 -118.091 321 242.1 308.4

180 SGRiv111 33.951 -118.093 317 247.9 308.4

24

181 SGRiv112 33.948 -118.095 305 252.6 308.6

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

182 SGRiv113 33.946 -118.097 306 255.2 308.9

183 SGRiv114 33.944 -118.098 289 254.0 308.9

184 SGRiv115 33.941 -118.099 308 251.2 308.9

185 SGRiv116 33.939 -118.101 313 244.9 308.9

186 SGRiv117 33.936 -118.102 372 238.7 308.9

187 SGRiv118 33.934 -118.104 356 236.2 308.9

188 SGRiv119 33.932 -118.106 359 235.3 288.7

189 SGRiv120 33.929 -118.107 414 237.4 289.3

190 SGRiv121 33.922 -118.111 326 242.1 289.3

191 SGRiv122 33.919 -118.111 325 241.5 289.3

192 SGRiv123 33.911 -118.110 299 242.5 291.2

193 SGRiv124 33.909 -118.109 315 244.9 291.2

194 SGRiv125 33.906 -118.108 317 246.0 291.2

195 SGRiv126 33.903 -118.108 287 244.0 291.2

196 SGRiv127 33.901 -118.108 293 241.7 284.4

197 SGRiv128 33.898 -118.108 298 239.4 284.4

198 SGRiv129 33.895 -118.108 284 234.8 284.4

199 SGRiv130 33.892 -118.108 290 229.8 284.4

200 SGRiv131 33.890 -118.108 286 224.9 283.4

201 SGRiv132 33.887 -118.108 275 223.9 283.4

202 SGRiv133 33.884 -118.108 276 223.9 283.3

203 SGRiv134 33.882 -118.108 296 223.9 281.7

204 SGRiv135 33.874 -118.106 261 231.2 281.7

205 SGRiv136 33.871 -118.106 277 233.3 281.7

206 SGRiv137 33.868 -118.105 261 234.6 281.7

25

207 SGRiv138 33.866 -118.104 286 236.4 281.7

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

208 SGRiv139 33.863 -118.104 294 236.4 272.4

209 SGRiv140 33.860 -118.103 286 233.0 272.4

210 SGRiv141 33.858 -118.102 292 228.3 272.4

211 SGRiv142 33.855 -118.102 306 224.0 272.4

212 SGRiv143 33.853 -118.101 292 224.4 272.4

213 SGRiv144 33.850 -118.100 274 225.1 272.4

214 SGRiv145 33.847 -118.099 297 224.6 272.4

215 SGRiv146 33.845 -118.098 274 225.2 272.4

216 SGRiv147 33.843 -118.097 278 225.7 272.4

217 SGRiv148 33.840 -118.095 280 226.2 272.4

218 SGRiv149 33.838 -118.094 270 229.1 272.4

219 SGRiv150 33.835 -118.094 262 233.0 272.4

220 SGRiv151 33.832 -118.093 258 236.9 272.4

221 SGRiv152 33.830 -118.093 285 233.8 272.4

222 SGRiv153 33.827 -118.092 276 224.6 281.7

223 SGRiv154 33.824 -118.092 286 215.7 281.7

224 SGRiv155 33.821 -118.091 292 213.3 281.7

225 SGRiv156 33.819 -118.091 292 218.7 281.7

226 SGRiv157 33.816 -118.091 291 224.1 281.7

227 SGRiv158 33.813 -118.091 314 229.4 281.7

228 SGRiv159 33.811 -118.090 284 234.1 281.7

229 SGRiv160 33.808 -118.090 284 239.1 281.7

230 SGRiv161 33.806 -118.090 309 243.7 281.7

231 SGRiv162 33.803 -118.090 277 244.7 281.7

232 SGRiv163 33.800 -118.090 301 245.3 290.3

26

233 SGRiv164 33.798 -118.090 277 244.2 290.3

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

234 SGRiv165 33.795 -118.089 284 227.2 290.3

235 SGRiv166 33.792 -118.090 266 219.3 290.3

236 SGRiv167 33.790 -118.092 251 217.6 290.3

237 SGRiv168 33.787 -118.093 266 223.7 290.3

238 SGRiv169 33.785 -118.094 258 222.7 290.3

239 SGRiv170 33.782 -118.095 258 236.8 235.4

240 SGRiv171 33.780 -118.096 250 255.0 235.4

241 SGRiv172 33.777 -118.097 250 253.7 235.4

242 SGRiv173 33.775 -118.097 245 252.0 235.4

243 SGRiv174 33.772 -118.097 253 242.1 235.4

244 SGRiv175 33.769 -118.097 241 225.2 235.4

245 SGRiv176 33.767 -118.097 271 208.5 235.4

246 SGRiv177 33.764 -118.097 269 200.4 235.4

247 SGRiv178 33.761 -118.098 268 216.0 235.4

248 SGRiv179 33.758 -118.098 266 234.6 235.4

249 SGRiv180 33.756 -118.100 256 247.1 235.4

250 SGRiv181 33.754 -118.102 264 246.0 235.4

251 SGRiv182 33.753 -118.105 248 242.0 235.4

252 SGRiv183 33.751 -118.107 229 244.0 235.4

253 SGRiv184 33.749 -118.110 234 251.4 235.4

254 SGRiv185 33.747 -118.112 251 261.2 235.4

255 SGRiv186 33.745 -118.114 306 253.7 235.4

256 SGRiv187 33.742 -118.115 307 241.0 235.4

257 SGRiv188 33.740 -118.116 307 223.3 235.4

258 SMS* 34.015 -118.456 376 367.9 372.6

27

259 SPF* 34.060 -118.646 483 752.2 3218.6

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

260 SRN* 33.828 -117.790 305 489.0 2150.6

261 STC* 34.303 -119.185 325 495.9 443.8

262 STG* 33.664 -117.770 279 315.8 2217.1

263 STS* 33.791 -118.193 244 277.7 235.4

264 SVD* 34.110 -117.099 505 682.6 733.5

265 TOV* 34.157 -118.821 882 367.7 322.0

266 USC* 34.019 -118.286 348 297.6 315.9

267 VTV* 34.560 -117.330 377 361.3 2930.4

268 WLT* 34.009 -117.951 265 350.7 311.0

269 WSS* 34.171 -118.649 322 405.9 400.7

270 WTT* 33.949 -118.256 264 247.8 281.7

271 LB - City Hall 33.768 -118.196 381.2 282.9 235.4

272 Carson - Water St 33.836 -118.240 160.6 207.1 256.3

273 Northridge - 17645 Saticoy St 34.209 -118.517 280.9 291.6 271.4

274 Downey - Birchdale 33.920 -118.137 245.1 243.5 272.6

275 San Bernardino - 2nd & Arrowhead 34.103 -117.288 325.8 321.0 273.2

276 Rancho Cucamonga - FF 34.104 -117.574 390.2 356.6 273.2

277 Downey - Co Maint Bldg 33.924 -118.167 271.9 246.9 273.4

278 Hacienda Heights - Colima 33.990 -117.943 337 554.1 280.1

279 Beverly Hills - 14145 Mulhol 34.132 -118.439 355.8 427.3 283.6

280 Newhall - Fire Sta 34.387 -118.533 269.1 336.1 285.2

281 Canoga Park - Topanga Can 34.212 -118.606 267.5 294.1 287.3

282 LA - Hollywood Stor FF 34.090 -118.339 316.5 354.5 303.4

283 Tarzana - Cedar Hill 34.160 -118.534 257.2 398.8 310.2

284 Tarzana - Cedar Hill A 34.160 -118.534 257.2 398.8 310.2

28

285 2516 Via Tejon PV 33.801 -118.387 280.6 666.2 313.8

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

286 Arleta - Nordhoff Fire Sta 34.236 -118.439 297.7 319.7 319.3

287 Whittier Narrows Dam upstream 34.020 -118.053 298.7 306.9 320.6

288 LA - Baldwin Hills 34.009 -118.361 297.1 406.5 338.3

289 Santa Monica City Hall 34.011 -118.490 336.2 373.6 338.5

290 LA - Saturn St 34.046 -118.355 308.7 321.5 338.6

291 LA - Sepulveda VA Hospital 34.249 -118.478 380.1 309.5 348.3

292 LA - 116th St School 33.929 -118.260 301 304.8 348.7

293 Colton - So Cal Edison 34.059 -117.312 302 298.9 352.7

294 Colton Interchange - Vault 34.060 -117.300 275 320.9 352.7

295 Inglewood - Union Oil 33.905 -118.279 316 331.1 356.5

296 Carbon Canyon Dam 33.912 -117.838 235 405.4 356.7

297 Arcadia - Campus Dr 34.130 -118.036 367.5 325.8 364.0

298 LA - Century City CC South 34.062 -118.416 278 364.5 366.4

299 LA - N Faring Rd 34.089 -118.435 255 520.0 373.6

300 Simi Valley - Katherine Rd 34.264 -118.666 557.4 557.0 383.5

301 Newhall - W Pico Canyon Rd. 34.391 -118.622 285.9 521.7 389.1

302 LA - UCLA Grounds 34.068 -118.439 398.4 378.8 391.2

303 San Marino - SW Academy 34.115 -118.130 379.4 393.3 391.6

304 LA - N Westmoreland 34.082 -118.298 315.1 327.7 394.1

305 Sylmar - Olive View Med FF 34.326 -118.444 440.5 524.8 396.9

306 LA - Wadsworth VA Hospital South 34.052 -118.451 413.8 380.8 397.3

307 LA - Brentwood VA Hospital 34.063 -118.463 416.6 409.2 397.3

308 Sylmar - Converter Sta East 34.312 -118.481 370.5 395.9 408.0

309 San Gabriel - E Grand Ave 34.091 -118.093 401.4 339.1 409.5

310 LA - City Terrace 34.053 -118.171 365.2 381.4 410.3

29

311 Pasadena - CIT Athenaeum 34.139 -118.121 415.1 354.8 412.5

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

312 Jensen Filter Plant 34.312 -118.496 373.1 437.7 418.9

313 Pasadena - Fair Oaks & Walnut 34.149 -118.150 430.7 333.6 422.5

314 Sylmar - Converter Sta 34.311 -118.490 251.2 397.1 426.9

315 Santa Felita Dam (Outlet) 34.460 -118.753 389 491.8 443.6

316 Garvey Res. - Control Bldg 34.049 -118.112 468.2 370.9 446.5

317 LA - Obregon Park 34.037 -118.178 349.4 365.8 450.0

318 Rinaldi Receiving Sta 34.281 -118.478 282.3 383.7 468.2

319 Pacoima Kagel Canyon 34.296 -118.375 508.1 513.9 478.9

320 Reche Canyon - Olive Dell Ranch 34.004 -117.223 488 520.1 483.4

321 Castaic - Old Ridge Route 34.564 -118.642 450.3 758.2 508.4

322 Jensen Filter Plant Generator 34.313 -118.498 525.8 465.2 531.1

323 LA - Chalon Rd 34.086 -118.481 740.1 567.4 537.8

324 LA 00 34.106 -118.454 706.2 497.1 540.2

325 LA Dam 34.294 -118.483 629 391.3 543.5

326 LA - Griffith Park Observatory 34.118 -118.299 1015.9 584.9 593.7

327 Lake Hughes #12 34.571 -118.560 602.1 539.9 612.0

328 Lake Hughes #12A 34.571 -118.560 602.1 539.9 612.0

329 Pacoima Dam (upper left) 34.334 -118.396 2016.1 697.3 733.5

330 Santa Susana Ground 34.232 -118.710 715.1 544.8 752.9

331 LA - Wonderland Ave 34.114 -118.380 1222.5 494.7 758.5

332 Lake Hughes #9 34.608 -118.558 670.8 561.4 810.4

333 El Centro Array #13 32.709 -115.683 249.9 219.8 822.5

334 Superstition Mtn Camera 32.955 -115.825 362.4 434.8 822.5

335 Rancho De Anza 33.348 -116.400 329 691.9 822.5

336 El Centro Array #3 32.894 -115.380 162.9 242.6 825.0

30

337 El Centro Array #4 32.864 -115.432 208.9 239.5 825.0

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

338 El Centro Array #5 32.855 -115.466 205.6 225.8 825.0

339 El Centro Array #6 32.839 -115.487 203.2 271.9 825.2

340 El Centro Array #2 32.916 -115.366 188.8 251.1 825.3

341 El Centro Array #7 32.829 -115.504 210.5 253.3 825.4

342 Holtville Post Office 32.812 -115.377 202.9 249.9 825.4

343 Westmorland Fire Sta 33.037 -115.623 193.7 244.5 825.4

344 Brawley Airport 32.991 -115.513 208.7 246.8 825.5

345 Calipatria Fire Station 33.127 -115.516 205.8 231.6 825.7

346 El Centro Array #8 32.811 -115.532 206.1 202.7 825.7

347 Parachute Test Site 32.929 -115.701 348.7 296.3 825.8

348 El Centro Differential Array 32.796 -115.535 202.3 215.8 825.9

349 El Centro Array #11 32.752 -115.594 196.3 205.0 825.9

350 El Centro Array #9 32.794 -115.549 213.4 219.7 825.9

351 Bonds Corner 32.693 -115.338 223 219.9 825.9

352 EC County Center FF 32.793 -115.562 192.1 231.5 825.9

353 El Centro Imp. Co. Cent 32.793 -115.562 192.1 231.5 825.9

354 El Centro Array #10 32.781 -115.536 202.9 224.0 825.9

355 EC Meloland Overpass FF 32.773 -115.447 186.2 219.7 826.3

356 El Centro Array #1 32.960 -115.319 237.3 252.5 826.6

357 Calexico Fire Station 32.670 -115.493 231.2 251.5 826.6

358 El Centro Array #12 32.718 -115.637 196.9 204.8 827.1

359 Seeley School 32.795 -115.691 200 240.4 827.9

360 Bombay Beach Fire Station 33.353 -115.733 257 297.7 829.6

361 Pasadena - Old Seismo Lab 34.149 -118.171 969.1 458.4 829.8

362 Pasadena - CIT Kresge Lab 34.149 -118.171 969.1 462.5 829.8

31

363 Salton Sea Wildlife Refuge 33.177 -115.616 191.1 226.5 833.7

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

364 North Shore - Salton Sea Pk HQ 33.504 -115.913 265.2 355.1 838.1

365 Salton City 33.280 -115.984 324.5 362.5 841.5

366 Anza - Pinyon Flat 33.607 -116.453 724.9 487.5 1549.3

367 San Onofre - So Cal Edison 33.370 -117.560 442.9 343.7 2266.7

368 Taft Lincoln School 35.150 -119.460 385.4 392.6 2409.6

369 Vasquez Rocks Park 34.492 -118.327 996.4 422.8 2513.8

370 Joshua Tree 34.130 -116.314 379.3 457.0 2555.6

371 Santa Barbara Courthouse 34.423 -119.700 515 371.1 2714.4

372 Rancho Palos Verdes - Luconia 33.740 -118.335 508.8 743.6 2884.7

374 Gormon - Oso Pump Plant 34.808 -118.720 308.4 389.8 2917.0

375 Yermo Fire Station 34.904 -116.823 353.6 234.7 2930.6

376 Wheeler Ridge - Ground 35.027 -118.791 347.7 399.6 2932.9

377 Pearblossom Pump 34.508 -117.922 529.1 450.1 2979.0

378 Palmdale Fire Station 34.580 -118.110 452.9 317.6 2989.4

379 Palmdale - Hwy 14 & Palmdale 34.581 -118.135 551.6 419.5 3000.2

380 Little Rock Post Office 34.521 -117.991 442 387.8 3044.6

381 Malibu - Point Dume Sch 34.013 -118.800 349.5 398.7 3058.3

382 Tehachapi Pump 34.942 -118.827 669.5 644.0 3064.5

383 Cedar Springs, Allen Ranch 34.278 -117.335 813.5 675.5 3093.9

384 Fort Tejon 34.870 -118.900 394.2 751.7 3153.1

385 Lake Hughes #1 34.674 -118.430 425.3 421.2 3162.0

386 Cedar Springs Pumphouse 34.310 -117.300 477.2 481.8 3176.7

387 Wrightwood - 6074 Park Dr 34.360 -117.630 486 548.5 3187.3

388 Leona Valley #6 34.604 -118.244 327.4 505.8 3270.3

389 Idyllwild - Keenwild Fire Sta. 33.708 -116.717 845.4 459.1 3355.4

32

390 Menifee Valley - Murrieta&Scott 33.649 -117.205 503 457.8 3390.4

Site Numbe

r Site NameLatitud

e LongitudeMeasure

d Topo CVM

391 Perris - San Jacinto & C Street 33.787 -117.230 518 377.5 3578.8

392 Stirrup_Road 33.597 -116.982 702.2 319.2 3285.7

393 Benton_Road 33.593 -116.924 694.9 432.4 3121.3

394 Round_Top 33.521 -116.906 832.1 488.8 3139.4

395 Anza_Hoodoo 33.482 -116.849 809.6 323.6 2982.7

396 Tooth 33.217 -116.465 544.2 715.9 2943.2

397 Beaumont_South 33.904 -116.987 656.6 440.7 2631.0

398 Menifee 33.780 -117.153 703.6 471.3 3531.2

399 Perris 33.789 -117.240 744.6 430.7 3579.0

400 Mockingbird_Tank 33.844 -117.363 902.1 381.5 3443.7

401 Gavilan 33.800 -117.395 704.9 384.3 3463.8

*Newly published measurement this study. Measured denotes site characterizations not predicted from any

proxy. Topo denotes site characterizations predicted using topographic slope proxy. CVM denotes site

characterizations predicted from SCEC Community Velocity Model Version 4.0.

33

Table 3 - VSZ Values

Site Name VS10 VS30 VS50 VS100

AGO 300 409 474 545

BRE 201 238 282 385

BTC 248 273 318 419

CFS 240 320 343 441

CLT 211 304 377 509

Cottonwood/Eagles 497 823 1002 1197

CPP 210 279 332 399

CRN 311 340 382 421

CSMIP 596 863 1044 1239

DEC 391 450 548 734

DEV 310 499 612 738

DGR 405 554 609 659

DJJ 298 336 380 428

DLA 174 233 273 388

FMP 281 350 411 492

FON 417 509 574 635

FUL 217 306 397 512

GR2 305 414 538 715

GSA 279 337 386 499

HLL 229 302 347 404

HLN 294 347 398 599

JTNP_Line1 373 517 560 711

JTNP_Line2 342 482 525 706

JTNP_Line3 335 433 489 615

JTNP_Line4 300 387 438 575


34

JTNP_Line5 264 363 426 533

KIK 328 383 469 613

KNW1 461 700 824 951

KNW2 480 726 819 906

LAF 209 281 311 339

LBW2 196 278 311 368

LCG 237 330 366 406

LFP 363 466 507 545

LGB 287 350 428 553

LGU 424 563 642 831

LKL 245 330 391 478

LLS 148 218 276 392

LTP 194 258 291 353

MGE 234 277 348 475

MIS 289 363 502 705

MLS 276 336 366 515

MOP 206 246 284 352

MUR 340 630 893 1298

MWC 300 557 697 860

NOT 203 312 455 540

OGC 352 372 389 469

OLI 218 312 375 484

Orocopias 604 793 884 967

OSI 254 420 572 860

PAS 375 633 777 1098

PASC 423 807 987 1185

PDE 187 257 342 459

35


PDR 274 321 388 478

PDU 470 503 595 697

PER 320 556 778 1108

PFO1 598 932 1133 1350

PFO2 420 676 803 935

PSR 557 765 884 954

QUG 387 442 480 525

RDM1 413 662 824 1009

RDM2 397 610 686 757

RIN 239 331 434 601

RIO 363 473 510 554

RPV 477 567 647 736

RSS 311 387 430 488

RUS 215 300 348 438

RVR 311 634 847 1132

SDD 375 412 438 529

SES 408 447 485 583

SMS 311 376 429 530

SPF 388 483 569 783

SRN 229 305 362 425

STC 257 325 397 501

STG 268 279 307 403

STS 175 244 305 420

SVD 373 505 670 911

THX 212 300 327 413

TOV 781 882 917 950

USC 282 348 390 472

36


VTV 279 377 416 619

WLT 257 265 305 388

WSS 203 322 392 593

WTT 178 264 330 448

37

Table 4 - ZV Calculations

Site Name Z500 VS30(Z500) Z1000 VS30(Z1000) Z1500 VS30(Z1500)

AGO 11.655 459.756 35.198 640.244

BRE 40.125 609

BTC 38 614

CFS 9.5 384

CLT 23.776 543.225 80.186 873.848

Cottonwood/Eagles 0 357 5.5 951 17.625 1488

CPP 33.1 498.78

CRN 24.777 468.667

CSMIP 0 596 14.75 1523 14.75 1523

DEC 17.949 460.569 41.492 1110.976 41.492 1110.976

DEV 12.2 690 0 928

DGR 0 405

DJJ 32.401 489.024

DLA 42 673

FMP 17.016 401.22 33.8 613.415

FON 5 472

FUL 7 323

GR2 10.256 342.683 35.664 1064.634 35.664 1064.634

GSA 28.205 494.444 64.569 857.588

HLL 53.613 486.585

HLN 47 464

JTNP_Line1 9 641 64.5 1230 64.5 1230

JTNP_Line2 9 607 52 1110 52 1110

JTNP_Line3 9 507 41.5 829

JTNP_Line4 19.875 545 50.625 844


38

JTNP_Line5 7.5 448 38 711

KIK 5 418.7 30 892

KNW1 0 460.959 12.5 1069.178 16.898 1123.973

KNW2 0 455.479 13.194 1014.384 13.194 1014.384

LAF 26.786 372.222

LBW2 60.84 472.764

LCG 65.968 461.924

LFP 10.256 523.171 35.664 589.024

LGB 14.452 377.913 63.637 849.458

LGU 3.963 466.26 44.522 1178.455 44.522 1178.455

LKL 18.881 410.976 35.664 615.854

LLS 24.476 461.924 60.84 778.455

LTP 57.343 467.886

MGE 10 305 34.5 748

MIS 30 416 30 1183 30 1183

MLS 21.096 396.341 47.786 869.512

MOP 50.117 464.634

MUR 3 406 12.25 1263

MWC 4 483 12.25 1121

NOT 15.6 576 15.6 576

OGC 40.326 432.927 57.11 628.049

OLI 23.31 445.122 40.093 684.146

Orocopias 0 604 13.5 1067 134.25 1547

OSI 12.587 659.756 27.273 1255.962 27.273 1255.962

PAS 1.981 483.604 11.305 876.558 20.979 1181.436

PASC 3.25 374 7 1482 7 1482

PDE 21.212 476.016 31.002 697.154

39


PDR 20.746 412.06 69.931 628.862

PDU 3.73 471.951 47.552 842.683

PER 7.83 582 7.83 582

PFO1 0 597.945 11.422 1260.046 21.445 1671.005

PFO2 0 419.863 11.422 899.315 13.878 1118.493

PSR 5.25 564 12.25 1102 12.25 1102

QUG 18.415 542.683 52.448 579.268

RDM1 0 413.014 11.122 899.315 22.357 1301.142

RDM2 0 392.466 12.821 844.521

RIN 23.077 497.154 34.266 978.184

RIO 3.963 489.024 41.492 606.098

RPV 8.858 517.48 45.921 854.878

RSS 25.991 516.125 62.355 581.165

RUS 30.536 442.683 47.319 593.902

RVR 2.564 419.919 6.993 863.821 15.618 1671.951

SDD 37.063 544.309 73.427 836.992

SES 10.256 502.575 70.163 857.588

SMS 28.672 543.225 65.036 784.417

SPF 8.625 498.78 33.1 815.854 69.464 1909.079

SRN 36.364 515.854 36.364 515.854

STC 17.249 398.78 34.033 679.268

STG 42.424 584.146 42.424 584.146

STS 24.242 490.108 60.606 745.935

SVD 11.305 525.61 27.506 1310.163 73.87 1472.764

THX 8 400 29.5 765

TOV 0 619.106 36.83 984.959

USC 25.875 475.474 62.239 654.336

40


VTV 5.25 456 47.625 1208 47.625 1208

WLT 31.469 406.64 67.833 647.561

WSS 23.077 558.13 48.485 1215.312 48.485 1215.312

WTT 29.837 532.385 66.201 814.228

41

Figure 1. Location of all site characterizations used in this study. T

he complete data set of site characterizations is subdivided into three

subsets based on estimated N

EH

RP classification from

Wills et al. 2000: basin (green) - D

-DE

NE

HR

P rating, soil (yellow) - C

-CD

rating, and rock (rock) - B

-BC

rating. Ventura basin (V

), San Fernando Valley (SFV

), Chino B

asin (ChB

), Los A

ngeles Basin (L

AB

), San Bernadino

Valley (SB

V), C

oachella Valley (C

V) and Im

perial Valley (IV

) are labeled also.

42

VSF

V

LA

B

ChB

SBV

IV

C

43

Basin

Soil

Rock

All

0 100 200 300 400 500 600 700 800 900

Histogram of Mean

Basin

Soil

Rock

All

0 200 400 600 800 1000 1200

Histogram of Standard Deviation

Figure 2. Mean (upper) and Standard Deviation (lower) of the complete data set and subsets.

Units in m/s

Units in m/s

44

Figu

re 3

. Plo

t of T

opog

raph

ic S

lope

est

imat

ions

ver

sus D

irec

t Mea

sure

men

ts

45

Figu

re 4

. V

S30

map

s cr

eate

d u

sing

nat

ural

lo

g tr

ansf

orm

ed d

ata.

AL

L S

ITE

SB

ASI

N

SOIL

RO

CK

46

AL

L S

ITE

SB

ASI

N

SOIL

RO

CK

Figu

re 5

. V

S30

map

s cr

eate

d

usin

g

non-

tran

sfor

med

dat

a.

47

AL

L S

ITE

SB

ASI

N

SOIL

Figu

re 6

. Haz

ard

map

s (V

S30

< 36

0 m

/s).

All

map

s cr

eate

d fr

om n

atur

al lo

g tr

ansf

orm

ed d

ata

of c

ompl

ete

data

set

and

B

asin

and

Soi

l su

bset

s.

War

mer

col

ors

deno

te a

rea

with

hi

gh p

roba

bilit

y of

VS3

0 <

360

m/s

. R

ock

sub

set

has

no

valu

es b

elow

thre

shol

d va

lue.

48

ALL SITES

BASIN

Figure 7. Hazard maps (VS30 < 180 m/s). Both maps created from natural log transformed data of complete data set and Basin subset. Warmer colors denote area with high probability of VS30 < 180 m/s. Rock and Soil subsets have no values below threshold value.

% Probability (Vs30 < 180 m/s)

% Probability (Vs30 < 180 m/s)

49

Ln u

nits%

Differen

ce0.14-13+150.22-20+250.30-26+350.38-32+46

BA

SIN

SOIL

RO

CK

Figu

re 8

. Con

tour

of s

patia

l sta

ndar

d de

viat

ion

from

app

licat

ion

of k

rigi

ng a

lgor

ithm

on

com

plet

e da

ta se

t and

subs

ets.

50

APPENDIX - VARIOGRAMS

The following contains the parameters used to create the variograms utilized in the

kriging algorithm as well as the plotted variograms. Experimental variograms are created

using the method from Pannatier (1996). The lowest measurement at a location is used in

the creation of the experimental variogram. All theoretical variograms shown are

spherical in nature unless otherwise denoted. The formula used to create the theoretical

spherical variogram is:

g(h )=¿¿h<1⇒C [1 . 5h−0 .5 h3 ]+Nuggeth ³1⇒C+ Nugget

where C is the Scale and h is the lag distance/Length (Pannatier, 1996). The Scale is the

distance between two points where the data is considered to have no correlation. The

Nugget quantifies the short scale variability (i.e. epistemic or measurement uncertainty).

The sill is the sum of Scale and Nugget (figure 9).

In figure 12, two types of theoretical variograms are shown, spherical and power. We

chose the spherical variogram to use in kriging algorithm for the Basin Data Subset

because, unlike the power variogram, the scale is evident.

51

Nugget

Sill

Length

Sca

Figure 9. Variogram generated using transformed Complete Data Set. The Varogram Grid Parameters are input into Surfer 8 to generate the variogram. The Variogram Plot Parameters are used to manipulate the experimental variogram and adjust the theoretical variogram. These key parameters are further indentified on the variogram itself. The variogram has a directional tolerance of 90 degrees or is omni-directional.

Variogram Grid Parameters

Max Lag distance 480000Angular Divisions 180Radial Divisions 100

Variogram Plot Parameters

Nugget 6.673E-08Length 55130Scale 42570

Lag Width 7500Max Lag Distance 150000

Variance 32693.16933587

52




Nugget 6.673E-08Length 55130Scale 42570


Variance 32693.16933587

Figure 10. Variogram generated using non-transformed Complete Data Set. The Varogram Grid Parameters are input into Surfer 8 to generate the variogram. The Variogram Plot Parameters are used to manipulate the experimental variogram and adjust the theoretical variogram. These key parameters are further indentified on the variogram itself. The variogram has a directional tolerance of 90 degrees or is omni-directional.

53

Figure 11. Variogram generated using transformed Basin Subset. The Varogram Grid Parameters are input into Surfer 8 to generate the variogram. The Variogram Plot Parameters are used to manipulate the experimental variogram and adjust the theoretical variogram. These key parameters are further indentified on the variogram itself. The variogram has a directional tolerance of 90 degrees or is omni-directional.




Nugget 0.00Length 60000Scale 0.09


Variance 0.062905969497256

54

Figure 12. Variogram generated using non-transformed Basin Subset. The Varogram Grid Parameters are input into Surfer 8 to generate the variogram. The Variogram Plot Parameters are used to manipulate the experimental variogram and adjust the theoretical variogram. These key parameters are further indentified on the variogram itself. The variogram has a directional tolerance of 90 degrees or is omni-directional.




Nugget 0.00Length 59000Scale 10000


Variance 7487.5257344799

55

Figure 13. Variogram generated using transformed Rock Data Subset. The Varogram Grid Parameters are input into Surfer 8 to generate the variogram. The Variogram Plot Parameters are used to manipulate the experimental variogram and adjust the theoretical variogram. These key parameters are further indentified on the variogram itself. The variogram has a directional tolerance of 90 degrees or is omni-directional.






Variance 0.11708202408273

56

Figure 14. Variogram generated using non-transformed Rock Data Subset. The Varogram Grid Parameters are input into Surfer 8 to generate the variogram. The Variogram Plot Parameters are used to manipulate the experimental variogram and adjust the theoretical variogram. These key parameters are further indentified on the variogram itself. The variogram has a directional tolerance of 90 degrees or is omni-directional.




Nugget 943.30Length 47400Scale 103600


Variance 92071.510176899

57

Figure 15. Variograms generated using transformed Soil Data Subset. The Varogram Grid Parameters are input into Surfer 8 to generate the variogram. The Variogram Plot Parameters are used to manipulate the experimental variogram and adjust the theoretical variogram. These key parameters are further indentified on the variogram itself. The variogram has a directional tolerance of 90 degrees or is omni-directional.






Variance 0.10202777504412

58






Variance 0.10202777504412

Figure 16. Variograms generated using non-transformed Soil Data Subset. The Varogram Grid Parameters are input into Surfer 8 to generate the variogram. The Variogram Plot Parameters are used to manipulate the experimental variogram and adjust the theoretical variogram. These key parameters are further indentified on the variogram itself. The variogram has a directional tolerance of 90 degrees or is omni-directional.

59

Appendix-Cumulative Distribution plots

The cumulative distribution plots of the complete data set, before and after

transformation, as well as the plots of the gridded values produced from each of these.

Figure 17. Cumulative Distribution Plots. The upper plots are the non-transformed and transformed measurements. The lower plots are the gridded values created using the respective measurements. Points closer to the diagonal line are closer to Gaussian or Normal distribution. Notice that the transformed measurements and gridded values are more similar.

60

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