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Transcript of Jin-Geocongress-10990.pdf
Comparison of Rayleigh wave dispersion relations from three surface wavemeasurements in a complex-layered system
X. Jin1, B. Luke1, J. Louie2
1Civil and Environmental Engineering, University of Nevada Las Vegas, NV 89154-4015; [email protected];
[email protected] Laboratory, University of Nevada Reno, NV 89557; [email protected]
Abstract:
Three widely-available surface wave measurement techniques, including two active-source methods, the Spectral
Analysis of Surface Waves (SASW) method and the Multi-channel Analysis of Surface Waves (MASW) method,
and one passive-source method, the Refraction Microtremor (ReMi) method, were applied at a complex-layered
site. Dispersion relations were compared to one another. Outcomes from the MASW measurement reveal a
fundamental and first-higher mode. All three methods agree closely in the frequency range over which only one
mode is present. The dispersion curve from the SASW method, which represents a superposition of modes, shifts
gradually from the fundamental mode to the first higher mode as frequency increases. The ReMi outcome tracks
only the fundamental mode over its range of analysis.
Introduction
With respect to geotechnical boring or drilling, seismic surface wave methods (SWM) provide a way to
obtain subsurface detail non-destructively and non-intrusively, hence saving time and money. Due to those
advantages, SWM have become popular and continue to develop rapidly. For example, a special edition of the
Journal of Environmental and Engineering Geophysics (O'Neill, 2005) contains work by sixteen research teams
from around the world, each using SWMs in different configurations and venues.
Surface wave methods capitalize upon the dispersive nature of Rayleigh waves in layered media to derive
shear wave velocity (VS) profiles of the subsurface. The SWM can be divided into two categories: active-source
and passive-source methods. Different as they are, SWM are all based on the following three steps: (1) field
testing, in which seismic waves are detected by mechanical sensors and recorded; (2) signal processing and
construction of dispersion curves or panels; and (3) inversion of the dispersion data to obtain a single VS profile
or a vertical slice, compiled from a series of VS profiles, of the subsurface. Construction of the dispersion relation
is a critical step. The accuracy of the inverted VS profile depends largely on the purity, specificity and accuracy of
the dispersion relation.
The Spectral Analysis of Surface Wave (SASW) method (Stokoe et al. 1994) has proven to be a valuable tool
for determining detailed VS profiles. This method uses receiver pairs and the phase spectral method to generate
an “effective” dispersion curve (Gucunski and Woods 1991), which comprises a superposition of surface wave
modes and other types of waves, when present.
The Multi-Channel Analysis of Surface Waves (MASW) method (Park et al. 1999) uses a multi-channel array,
which makes it possible to distinguish the fundamental-mode Rayleigh wave from higher modes and body waves.
The MASW and SASW methods usually involve active sources, which are optimized for the purpose of the
test. Passive-source methods capitalize on ambient noise instead. With respect to active methods, passive
methods cover broader and deeper volumes. However, used alone, the passive method can lack resolution in the
near-surface and is poorly suited for seismically quiet locations (Tokimatsu 1995). The Refraction Microtremor
(ReMi) method (Louie 2001) is a passive-source, multi-channel method that uses a linear array configuration.
The linear configuration has the advantage of simplicity – the test configuration is the same as might be used for
refraction testing – but it has the drawback that the predominant noise-arrival azimuth is unknown. Since energy
oblique to the array travels faster than energy along the array, the peaks in the frequency-slowness (f-p) image
would yield incorrect, higher velocities than the true Rayleigh-wave phase velocity, if enough noise is not
traveling in all directions. Therefore, the dispersion curve is picked along a “lowest-velocity envelope.” Upper-
and lower-bound values are also picked, to define a range. The upper boundary is along the energy peaks, and
the lower boundary is where the spectral ratios approach those of uncorrelated noise.
Each of the three SWM has been compared favorably against independent VS measurements, e.g., Stokoe et
al. (2003), Xia et al. (2002), and Stephenson et al. (in press) for SASW, MASW, and ReMi methods respectively,
though some discrepancies have been found, e.g., Brown et al. (2002). The three SWM have also been
compared against one another. In a test of a normally-dispersive hydraulic-fill site in Los Angeles, California
(GeoVision 2005), dispersion curves from these SWM matched one another very closely.
Here, dispersion relationships from the three SWM are developed for a complex-layered site. Complex
layering should give rise to the prominence of higher Rayleigh modes (Gucunski and Woods 1991; Roësset et al.
1991; Tokimatsu et al. 1992). Therefore, differences between multi-mode and superposed “effective” dispersion
curves should appear.
Site description
The Engineering Geophysics Test Site (EGTS), dimensions 80 m × 70 m, is located on the campus of the
University of Nevada Las Vegas (UNLV; see map at http://www.ce.unlv.edu/egl/test-site/). The site has been
characterized using different surface-based and intrusive geophysical methods. The shallow stratigraphy
comprises stiff, lightly- to heavily-carbonate-cemented fines, sands and/or gravels interspersed among much
softer uncemented clays and sands (Tecle et al. 2003). Such lithology is not uncommon in arid settings. Here, a
significant heavily-cemented layer, approaching two meters thick, occurs at approximately 2.5 meters depth.
The test line is located along the south edge of the EGTS. The three measurements have different array lengths
but share the same center point.
Data acquisition
The SASW data were collected by UNLV researchers in partnership with colleagues from Utah State
University (USU). One-Hz vertical geophones were placed in a linear array to test nine spacings ranging from 1
to 80 m (Liu et al. 2005). Data were collected using a four-channel signal analyzer. One of the channels is used
to record the source function so that the receiver functions can be convolved with it, thus reducing effects of
uncorrelated noise. An instrumented sledgehammer was used to excite the wave energy for short receiver
spacings. For longer receiver spacings, USU’s 2040-kg trailer-mounted drop-weight source was used.
The MASW data were collected by UNLV researchers using sixty, 4.5 Hz, vertical geophones at 0.5 m
spacing in a linear array. A sledgehammer was used to generate the source energy and a seismograph was used to
record the dataset. The sampling rate and the recording length were 0.5 ms and 1 s respectively. Separations
between source and nearest sensor varying from 7 to 15 m were tested. Records were stacked to improve signal-
to-noise ratio.
The ReMi data were acquired by UNLV researchers using twelve, 4.5-Hz vertical geophones at 10 m spacing,
in a linear array, and a seismograph. Ten, 30-second records were collected with 2 ms sampling rate using a
refraction seismograph. This is a minimal number of channels for a ReMi survey, providing a stringent test of
the technique.
Data processing
For the SASW data, individual dispersion curves generated for each receiver spacing are superimposed (Fig.
1). The composite is then condensed to approximately one hundred points.
The software package SurfSeis (Kansas Geological Survey) was used to process the MASW dataset. The
outcome with the 15-m source offset yielded the best outcome. Fundamental and first-higher modes can be
clearly distinguished in the amplitude image (Fig. 2).
The ReMi dataset was processed using the software SeisOpt ReMi (Optim LLC). Figure 3 shows the f-p
image with best picks and ranges. The dispersion curve is computed by inverting each slowness pick to obtain a
velocity value.
Figure 1. SASW method: Dispersion curves for each receiver spacing and condensed composite
Figure 2. MASW method: Dispersion panel with picks, 15-m source offset
Figure 3. ReMi measurement: Dispersion panel with best-estimate and bounding picks
While all three techniques described here are used commercially, each requires a measure of subjective input;
in other words, the data interpreter should be knowledgeable, skillful, and experienced.
Comparisons
The dispersion relations from SASW, MASW and ReMi measurements are superimposed in Fig. 4.
Frequency spans for the three methods differ, due to the sources used: the SASW method covers the broadest
range, the ReMi method resolves the low-frequency end of the spectrum, and the MASW method covers a mid-
range.
The three methods agree closely in the frequency range over which only the fundamental mode is present, up
to 30 Hz. The ReMi method’s error bars illustrate a commonality among surface-based methods: uncertainty
Figure 4. Comparisons of dispersion relation from SASW, MASW and ReMi measurements
increases with decreasing frequency, which correlates to increasing depth. The SASW dispersion curve matches
well with that from ReMi except at the lowest frequency, where uncertainties are highest. The fundamental-mode
MASW data match the others for frequencies up to that where the first-higher mode appears. Here, the SASW
dispersion curve starts a gradual transition from the fundamental to the first-higher mode. At frequencies above
56 Hz, it falls back below the first-higher mode response. The ReMi data from this experiment were not
resolved at the higher frequencies where multiple modes were observed.
This comparison confirms expectations that a profile with a high stiffness contrast will elicit considerable
higher-mode response. In this case a dispersion relation generated by superposition of energy spectra will
contain significant higher-mode energy. In theory, as long as the wave propagation phenomena are modeled
correctly in the inversion, both the multi-modal approach (e.g., MASW) and the energy-superposition approach
(SASW) should yield correct VS profiles.
Acknowledgments
We thank UNLV students and associates J. Jernsletten, Y. Liu, J. O’Donnell, S. Saldaña, V. Skidmore, and J.
Wixon, for the their assistance with data collection. We thank USU’s J. Bay and J. Gilbert for assistance and use
of equipment. We thank UNLV’s C. Snelson and the IRIS consortium for use of equipment. Parts of this work
were supported by Lawrence Livermore National Laboratory (LLNL) under contract no. W-7405-ENG-48.
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