ESurfEarth Surface DynamicsESurfEarth Surf. Dynam.2196-632XCopernicus PublicationsGöttingen, Germany10.5194/esurf-7-1-2019Potentials and pitfalls of permafrost active layer monitoring using the HVSR method: a case study in SvalbardHVSR active layer monitoringKöhlerAndreasandreas.kohler@geo.uio.noandreas.koehler.geo@gmail.comhttps://orcid.org/0000-0002-1060-7637WeidleChristianhttps://orcid.org/0000-0002-3864-2979Department of Geosciences, University of Oslo, Post Box 1047, 0316 Oslo, NorwayInstitute of Geosciences, Christian-Albrechts-Universität zu Kiel, Kiel, GermanyAndreas Köhler (andreas.kohler@geo.uio.no, andreas.koehler.geo@gmail.com)10January20197111622June201810July201827November201815December2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://esurf.copernicus.org/articles/7/1/2019/esurf-7-1-2019.htmlThe full text article is available as a PDF file from https://esurf.copernicus.org/articles/7/1/2019/esurf-7-1-2019.pdf
Time-lapse monitoring of the subsurface using ambient seismic noise is a
popular method in environmental seismology. We assess the reliability of the
horizontal-to-vertical spectral ratio (HVSR) method for monitoring seasonal
permafrost active layer variability in northwest Svalbard. We observe complex
HVSR variability between 1 and 50 Hz in the record of a temporary seismic
deployment covering frozen and thawed soil conditions between April and
August 2016. While strong variations are due to changing noise conditions,
mainly affected by wind speed and degrading coupling of instruments during
melt season, a seasonal trend is observed at some stations that has most
likely a subsurface structural cause. A HVSR peak emerges close to the
Nyquist frequency (50 Hz) in beginning of June which is then gradually
gliding down, reaching frequencies of about 15–25 Hz in the end of August.
This observation is consistent with HVSR forward modeling for a set of
structural models that simulate different stages of active layer thawing. Our
results reveal a number of potential pitfalls when interpreting HVSRs and
suggest a careful analysis of temporal variations since HVSR seasonality is
not necessarily related to changes in the subsurface. In addition, we
investigate if effects of changing noise sources on HVSRs can be avoided by
utilizing a directional, narrowband (4.5 Hz) repeating seismic tremor which
is observed at the permanent seismic broadband station in the study area.
A significant change of the radial component HVSR shape during summer months
is observed for all tremors. We show that a thawed active layer with very low
seismic velocities would affect Rayleigh wave ellipticities in the tremor
frequency band. We compile a list of recommendations for future experiments,
including comments on network layouts suitable for array beamforming and
waveform correlation methods that can provide essential information on noise
source variability.
Introduction
Environmental seismology is becoming an increasingly popular tool to study
earth surface processes and to monitor medium changes in the shallow
subsurface through ambient seismic noise analysis
. The latter approach is often based on noise
cross-correlation between two receivers which allows the estimation of the
medium's Green function under the condition of a random seismic noise
source distribution in time and space
. Continuous seismic noise
records therefore do not only allow the inversion of subsurface structures,
but also to measure temporal changes therein using seismic noise
interferometry . An
alternative and well-established single-station approach that makes use of
ambient seismic noise is the horizontal-to-vertical spectral ratio (H / V
spectral ratio or HVSR) technique e.g.,and references
therein. Peaks in
the HVSR curve are related to strong subsurface seismic velocity contrasts,
with shallower interfaces producing higher peak frequencies. The spectral
ratio can be inverted for the shallow subsurface structure based on the
diffuse wave field assumption
or by interpreting it as representing the frequency-dependent Rayleigh wave
ellipticity e.g.,. HVSRs have been shown to be
applicable in a wide range of settings, mostly for measuring site resonance
frequencies e.g., and mapping sediment
thickness, but also more recently in the cryosphere to measure ice properties
, glacier and ice sheet thickness
, or submarine permafrost depths
. Similar to noise interferometry, the HVSR
method does in theory allow time-lapse monitoring of the medium below the
station, given that the structural change is significant, a source effect can
be ruled out, and the Rayleigh wave ellipticity (or diffuse wave field model
parameters) can be extracted precisely enough from the spectral ratios.
It is well known that a seasonally frozen shallow surface layer can affect
the site response measured through HVSRs
. , for
example, reported a several-day-long HVSR amplitude decrease between 2 and
10 Hz during an air temperature drop below 0 ∘C in Grenoble,
France. Furthermore, more recently, a few studies interpreted seasonal
changes and emerging peaks in HVSRs at higher frequencies as being the result
of the thawing–freezing cycle of the permafrost active layer
. HVSRs therefore could bear
the potential to become a low-cost, passive, and non-invasive method for
long-term monitoring of permafrost with high temporal resolution. However,
due to the lack of calibration experiments in the field, to date no
standard procedure has been established for such an approach. More studies
are needed to explore its limitations and general applicability. For example,
a potential pitfall is interpreting HVSR variability as structural change
when it is actually due to changes in external site conditions such as noise
source distribution and/or meteorological parameters
. Violation of the assumption of stationary
noise sources might be avoided by using repeating and localized seismic
sources, similar to repeating earthquakes that are being used for coda wave
interferometry . Environmental seismological
research has identified a vast amount of such sources
, e.g., river noise ,
tremors in the cryosphere , and
anthropogenic structures .
In this study, we explore the potential of the HVSR method for permafrost
active layer monitoring using continuous seismic noise records of several
months from a temporary seismic deployment close to Ny-Ålesund on the
Arctic archipelago of Svalbard (Fig. ). We analyze and
compare observed seasonal HVSR variability with forward-modeled changes
expected from a thawed soil layer using the diffuse wave field theory.
Furthermore, we analyze HVSR changes of a periodically occurring, localized
seismic signal which has been present in the record of the permanent seismometer in
Ny-Ålesund in all available records since 2001. Finally, we discuss the
results and compile a list of recommendations for future field experiments
from the lessons learned in our study.
Study area, location of instrumentation, and seismic tremor source.
(a) Map of northwest Spitsbergen, part of the Arctic archipelago of Svalbard
(lower left corner), and location of permanent seismic station KBS. (b) Study
area around Ny-Ålesund and location of temporary BRA and KBSA arrays. The black
rectangle shows map section in panel (c). (c) More detailed location of seismic
stations and a coastal cliff with shallow cave shown in Fig. d being the source of a repeating seismic tremor (see
Sect. 5). Red stars are tremor locations between April and August 2016. Black lines
indicate azimuthal measurement uncertainty when using FK analysis
independently on both arrays. Center station of BRA array is BRA1. Numbers
indicate the other instrument locations. Background images: Copernicus
Sentinel data 2016.
Data
The permanent Global Seismic Network (GSN) and GeoForschungsNetz (GEOFON) station Kings Bay (KBS) (network
codes IU/GE) is located 1.2 km outside of the settlement of Ny-Ålesund
(Fig. a–b) within a subsurface 2 m × 2 m wide and
about 2.5 m deep concrete shelter. Only the channels recording with 40 Hz
sampling (broadband, high gain (BH) channels) are used. The 100 Hz data (high broadband, high gain (HH)
channels) are available in trigger mode only; i.e., solely transient seismic signals unsuitable for
noise analysis are being recorded. Between 12 April and 4 September 2016, a
temporary seismic network was deployed in the vicinity of Ny-Ålesund
(Fig. b–c). The deployment consisted of two small-aperture
seismic arrays built from 11 4.5 Hz three-component geophones connected to
Omnirecs DATA-CUBE data loggers, operating with a sampling frequency of
100 Hz. The Brandal (BRA) array array (eight stations) was deployed about 2.8 km northwest of
the settlement with an inter-station spacing of about 140 m (inner ring) and
500 m (outer ring), and three stations were distributed at about 120 m
distance around KBS (KBSA array). During installation, small holes were drilled
into the frozen ground to accommodate the geophone pins. Instruments were
covered first with sand and then buried under a rock pile (Fig. ). Ground coupling of the instruments degraded during
melt season and tilting occurred which increased noise levels in almost all
records. The stations were revisited on 25 August. While the three temporary
stations of the KBSA array were removed, the coupling and leveling of the BRA
array instruments were restored, and data were recorded for 10 more days. Note
that the temporary deployment was originally not designed as an active layer
monitoring experiment but for monitoring iceberg calving at nearby glaciers
. Similar to most seismic stations
, the seismic noise wave field measured on our
network is mainly composed of ocean microseisms at low frequencies
(<1 Hz) and a mixture of (here limited) cultural noise from the close
settlement of Ny-Ålesund and effects of local meteorological conditions
(wind, ocean swell at local coastline) at high frequencies (>1 Hz).
Frequent calving activity at nearby tidewater glaciers during summer and
autumn mainly affects intermediate
frequencies between 1 and 10 Hz.
HVSRs from ambient seismic noise
Vertical and horizontal component spectral amplitudes and
HVSRs at three stations of the
temporary deployment. Dotted lines indicate trend of gliding peak
frequencies, question marks ambiguous or unclear peaks, and vertical dashed
line date of instrument maintenance (BRA array) or removal (KBSA array). Air
temperature (red) and daily averaged wind speed (black) measured in
Ny-Ålesund are shown on top. The dashed dark red line is soil temperature at
0.39 m depth at the Bayelva permafrost observation site
at 1.6 km distance from BRA and 2.4 km from KBS.
We compute daily averaged amplitude spectra for the vertical and horizontal
components for all stations. Each continuous daily seismic record is divided
into 15 min long time windows, and the median of the absolute values of
the corresponding Fourier spectra is computed. Spectra are smoothed by
convolution with a boxcar function (width: 1000 frequency samples with
df=0.0038 Hz). The horizontal spectra are computed from the north and east
components as
north×north+east×east before
computing the spectral ratios. Figures and show results for a selection of stations together with
daily air temperature, soil temperature at 0.39 m depth at a nearby borehole
, and wind speed measured in Ny-Ålesund (see Figs. and for the rest of the stations).
Spectra and HVSRs between April and the beginning of September show complex
variability. Spectral amplitudes and HVSRs increase strongly in the course of
a few days between the middle and end of May when air temperatures begin to stay
above 0 ∘C. This does not happen simultaneously at all stations
(e.g., earlier for KBSA2 and BRA2). Furthermore, high wind speed correlates
well with high spectral amplitudes during melt season and with short-term
HVSR changes (mostly higher-amplitude ratios). Stations KBSA2, KBSA4, BRA2,
BRA4, and BRA5 show long-term HVSR trends, i.e., a weak, sometimes diffuse,
spectral peak apparently gliding from high frequencies (50 Hz) in the
beginning of June towards low frequencies in the end of August (15–25 Hz).
However, wind-related short-term HVSR variability is often stronger than, and
therefore masking, this long-term trend. At stations KBSA2 and BRA2, the
gliding peak trend can be better followed on days of low wind speed. Even if
no clear (gliding) peak frequency can be observed over the whole measurement
period, stations BRA7 and BRA8 exhibit a strong maximum at 30 Hz for several
days during a calm period in mid-July (Figs. and ). Most stations of the BRA array show a clear change in
the HVSRs after maintenance on 25 August. For example, for BRA2, the gliding
frequency peak becomes more pronounced. At BRA1 and BRA4, HVSR amplitudes
decrease at all frequencies, while at BRA3 (Fig. ) a new
peak emerges. In addition to the gliding peak at higher frequencies, stations
BRA5 and KBSA4 show another weak HVSR peak between 10 and 20 Hz which also
seems to have a slight temporal variability in June (decreasing and
increasing peak frequency). In contrast to the temporary stations, a HVSR
peak is observed at KBS close to 20 Hz with amplitudes correlating well with
wind speed but without clear seasonal variations (Fig. ).
Same as Fig. for three more stations. The gray
dashed vertical line indicates change of color scale on 10 July. Color
scale is clipped at high HVSRs (black) for BRA5 and KBSA4 to enhance
visibility of the weak gliding peak on days of low wind speed. The scale used
before 10 July is provided to the left.
These observations clearly suggest that HVSR variability in our records is
complex and cannot merely be explained by a single process such as a structural
change in the shallow subsurface. The general increase of seismic noise at the
onset of and during the melt season is probably mostly due to flowing water
and wind. The variability reflects local noise conditions at each individual
station affected by topography, vicinity to streams (BRA1, BRA5, and BRA7),
exposure to wind, and extent and timing of degrading instrument coupling
related to the progress of snow and soil thawing. Stronger correlation with
wind speed is probably due to vibration of the instrument losing coupling,
which also affects HVSR amplitudes. Hence, HVSRs do not represent the site
response during these time periods. The short-term HVSR variability is
therefore not related to a structural change and frequency peaks not
necessarily to subsurface interfaces. However, the long-term trend (gliding
peak frequency) cannot be easily explained by changing noise conditions and
is most likely related to a structural change such as the increasing thaw
depth below the station (see discussion below). In fact, the onset of the
gliding coincides well with the soil temperature at 0.39 m depth reaching
0 ∘C. When instrument vibrations dominate and/or ground coupling is
too degraded, this structural effect seems to be too weak to be visible
during particular time periods or during the entire record for some stations
(e.g., BRA1, BRA4). When coupling is restored, strong, non-structural HVSR
amplitude peaks disappear (BRA1, BRA4) and/or HVSR peaks, presumably due to
subsurface structure, are more clearly revealed (BRA2).
Modeled HVSRs
In order to evaluate the effect of the permafrost active layer, we model
HVSRs for a series of subsurface seismic velocity models using the diffuse
wave field theory, which takes into account surface and body waves
HVInv,. The thaw depth in
the Ny-Ålesund area can reach up to 2 m in summer
. The total permafrost depth is between 100
and 150 m . The
seismic S-wave velocity change in the active layer is significant, ranging
from 0.1 to 0.5 km s-1 in unfrozen wet soil, depending on liquid water
saturation, to 0.9–2.5 km s-1 in frozen conditions e.g.,. We
use a 1-D subsurface velocity reference model (Table )
inspired by the geological information available e.g., Fig. 4
in. We modify the model by introducing an active
layer of different thickness (0–2.5 m) and seismic S-wave velocity
(Vs =0.1–1.0 km s-1) to simulate different stages during the thawing
process (Fig. a–c). The active layer thickness is either fixed
and seismic velocity is being decreased stepwise, or the seismic velocity is
fixed and the thaw depth is increased successively. The latter model is
presumably closer to the real situation; however, there might also be a
gradual warming/thawing of the soil from top to bottom leading to a
decreasing effective seismic velocity in the active layer over time. In
addition, we correct the modeled HVSRs with the instrument response of the
geophone to simulate the effect of the anti-aliasing filter at the Nyquist
frequency (50 Hz).
(a–c) HVSRs
modeled using the diffuse wave field method and subsurface models of
increasing thaw depth d or decreasing S-wave velocity Vs in the active
layer. The reference model in Table is modified accordingly.
Black models include Rayleigh, Love and body waves. Gray models in
panel (c) include no Love waves. The gray area indicates tremor frequency band (see
Sect. 5). Dashed curves are modeled HVSRs above the Nyquist frequency without using
the anti-aliasing filter of field instruments. (d) Measured HVSRs at station BRA2
on four different days showing a peak gliding to lower frequencies.
Reference seismic velocity models for the study site based on
geological site information available and
adjusted to explain observed Rayleigh wave ellipticities and phase
velocities. Winter model: frozen active permafrost layer. Summer model:
unfrozen active layer. HS: half space. Geological units in
: U1: sandstone, U2: shale, U3: chert, glauconitic
sandstone, U4: dolomite, limestone, U5: basement. ACL: thawed active layer.
Vp: seismic P-wave velocity. Vs: seismic S-wave velocity.
Winter model Summer model UnitThicknessVpVsDensityThicknessVpVsDensity(m)(km s-1)(km s-1)(g cm-3)(m)(km s-1)(km s-1)(g cm-3)21.00.11.5ACL902.51.02.0882.51.02.2U1/U2373.01.352.2373.01.352.2U31235.03.02.41235.03.02.4U33506.03.52.73506.03.52.7U4HS6.43.83.0HS6.43.83.0U5
As expected, results show the emergence of a HVSR peak related to the
increasing or deepening velocity contrast in the shallow subsurface. The
peak frequency decreases to values between 12 and 20 Hz for maximum thaw
depths, depending on how low the S-wave velocity is assumed to drop. Spectral
ratio amplitudes are affected down to 5 Hz. Due to the upper frequency limit
at 50 Hz, HVSR peaks begin to emerge below the Nyquist frequency at about
35 Hz, increase in amplitude (Fig. c), and then glide towards
lower frequencies if the S-wave velocity decreases below 0.3 km s-1 (Fig. b).
The contribution of Love waves in the ambient noise depends on site
conditions and affects the amplitude of the HVSR peak but in most cases
does not change the peak frequency itself .
Furthermore, noise source characteristics can lead to variations in the
fraction of Love waves . In case Love waves are
excluded from our forward computation, the HVSR amplitudes are significantly
lower compared to the full diffuse wave field; however, the peak frequency is
unaffected (Fig. c). The amplitude differences between models
including and excluding Love waves are of the same order as amplitude
variations for apparent peaks resulting from velocity reduction or thaw depth
increase close to the Nyquist frequency.
HVSRs from a repeating seismic tremor
For better discriminating the causes of HVSR variability, analysis could be
restricted to seismic records of a particular localized, repeating, and
directional noise source. Furthermore, observations within longer time
periods are essential to validate the HVSR seasonality observed above.
However, since the permanent station KBS has a lower sampling rate, we cannot
resolve the relevant frequency range above 20 Hz. Furthermore, since the
about 2.5 m deep KBS shelter sits on permanently frozen soil, the effect of
active layer variability on HVSRs is expected to become smaller at higher
frequencies since decreasing wavelengths sense less of the surrounding medium
and more of the concrete shelter. This could explain the lack of a clear HVSR
seasonality close to 20 Hz (Fig. ). However, this might
be different if a dominant contribution of seismic signals with longer
wavelengths exists. In fact, we observe such a signal at KBS and explore its
potential to resolve active layer changes.
The tremor
A characteristic feature at KBS is a pronounced change in the character of
ambient seismic noise during certain time periods all year round and in all
available records from 2001 to 2016 (except for data gaps between 2001 and
2004). A tremor-like signal occurs, typically lasting for about several hours
(Figs. a and ) in a narrow frequency
band between 3 and 6 Hz, with a temporally stable spectral peak on the
vertical component at 4.5 Hz (Fig. c). A remarkably clear
semi-diurnal occurrence pattern is observed in the temporal distribution of
spectral amplitudes which correlates well with the sea level measured in
Ny-Ålesund (Fig. a). We will refer to this signal as a
“repeating tremor” or simply “tremor”.
Repeating seismic tremor measured at KBS. (a) Temporal distribution
of spectral amplitude between 3.4 and 5.7 Hz and water level (chart datum)
at the end of January in 2008 and 2016. The high spectral power lasting several hours
are tremor time periods which correlate with ocean tides. Gray areas indicate
automatic tremor detections. Horizontal dashed lines show the relative change in
daily wind speed. (b) Temporal distribution of seismic tremor detections
(counts in 2 weeks). (c) Monthly averaged amplitude spectra of seismic
tremor detections (vertical component) and of a selection of monthly time
periods without tremors (2016 only). (d) Suggested tremor source: coastal
cliff with shallow marine cave (Fig. c).
We detect repeating tremors automatically in the entire available KBS record
using a short-time over long-time average (STA/LTA) trigger algorithm applied
to a time series of vertical component spectral amplitudes (see Appendix B
for details). All tremor detections between 2001 and 2016 occur around
semi-diurnal tidal maxima in Ny-Ålesund. However, during neap tides and
low wind speeds, almost no tremors are detected (see average daily wind speed
in Fig. a). The Fourier transform of the time series of
log-spectral powers used for the detector fits remarkably well with the ocean
tide spectrum and therefore confirms tidal modulation (Fig. ). Furthermore, the number of tremors varies
seasonally, with more detections from late summer to late spring (Fig. b).
We use the temporary KBS and BRA arrays to locate tremors which occurred
during the deployment period in 2016 by means of frequency–wavenumber
analysis FK; and
the spatial mapping by multi-array beamforming method SMAB, in
the Supplement of. Figure c
shows that the tremor source is spatially stationary and very localized at
the shoreline in the area of the harbor of Ny-Ålesund. Location accuracy
is limited because of the resolution limit of array beamforming given the
tremor wavelength (about 400 m). A possible source location is a 270 m long
and 3–4 m high cliff with a shallow cave-like opening at 200 m distance to
the east of the harbor (Fig. d). Another potential source is
the harbor dock, a grounded artificial structure with an extent of about
100 m. However, ocean wave activity should cause vibration of the dock at
high as well as low tides, unless an unknown mechanism causes vibrations
only if the water level reaches the upper part of the structure. We therefore
have more evidence for the cliff at the marine cave being the source of the
tremor. A reasonable source mechanism for the tremor signal is therefore
slamming of breaking sea waves at the cliff during high tides and significant
ocean wave activity ,
often accompanied by high wind speeds. At low tides and/or high tides
during the neap tide cycle, a narrow beach is exposed and the ocean waves do
not reach the cliff, which explains the temporal distribution of tremor
occurrences. Furthermore, ocean wave activity usually being stronger during
autumn and winter and spring tides being strongest around the equinox in
March and September, is a good explanation for the seasonality (Fig. b). Our observations are consistent with previous studies on
ocean wave cliff interaction causing microseismic cliff-top ground motion
within a frequency band of 1 to 50 Hz
, with peaks around 10 Hz
and tidal modulation
. The slamming forces of breaking ocean waves might
be stronger in the cave because of the confined space, which could be an
explanation for the signal strength even at 2 km distance (BRA array). No
similar signals are observed from a few other shoreline cliffs in the area
which are located between mostly flat beaches.
Beamforming analysis of the vertical components of the KBSA array suggests
that the tremor signal consists predominantly of surface waves. Apparent
seismic phase velocities show typical dispersion, with values between 1.5 and
2.0 km s-1 (Fig. b). In contrast to frequencies below
2 Hz and above 6 Hz, where ambient seismic noise dominates the wave field,
the back-azimuth in the tremor frequency range fluctuates only slightly and
points clearly to north on average (Fig. a).
Tremor spectrum and polarization
We analyze all three spatial wave field components to gain more insight into
the propagation properties of the seismic tremor. Figure b
shows the spectral amplitudes of the radial component for a single tremor
testing different back-azimuth angles. The spectrum is computed as the median
of individual amplitude spectra obtained for 15 min long time windows.
The first and last 35 min, where the tremor gradually emerges or
disappears, are not analyzed to prevent ambient seismic noise affecting the
results. The following results are representative for all other tremors
between 2001 and 2016. It is striking that high spectral amplitudes on the
horizontal components alternate between the frequency ranges of 3–4 and
4–5 Hz for different back-azimuth angles, whereas on the vertical component
the entire frequency range of 3–5 Hz dominates (Fig. a).
Maximum amplitudes in both frequency bands correspond to perpendicular
directions which do not coincide with the propagation direction from north to
south, as inferred from vertical component FK analysis. In fact, the maximum
between 4 and 5 Hz is about 40∘ off the propagation direction.
(a, b) Example amplitude frequency spectra at KBS for a tremor
occurring between 12 May 2016 03:02:00 UTC and 12 May 2016 06:17:00 UTC for vertical
and radial components assuming different back-azimuth (baz) values. (c) Correlation
coefficient between vertical and Hilbert-transformed radial component
assuming different back-azimuth values in different frequency bands. High
values are expected in the case of Rayleigh waves on the radial component. Tremor
back-azimuth from vertical FK analysis (FK back-azimuth) and apparent back-azimuth
corresponding to maximum correlation between 4 and 5 Hz (apparent back-azimuth) are
indicated. Discrepancy is probably due to azimuthal anisotropy.
We evaluate the tremor polarization by cross-correlating the vertical and the
Hilbert-transformed radial component. In the case of dominant surface waves, the
radial component for a back-azimuth of 0∘ (location of tremor
source) should yield a pure Rayleigh wave with elliptic polarization.
However, according to Fig. c, the polarization maximum is
clearly shifted towards positive back-azimuth angles between 4 and 5 Hz
coinciding well with the radial component amplitude maximum. On the other
hand, correlation of vertical and Hilbert-transformed radial components
between 3 and 4 Hz, and thus ellipticity, is very low for all back-azimuth
angles. This suggests that Rayleigh waves on the horizontal components only
dominate between 4 and 5 Hz for an (apparent) back-azimuth of about 40∘.
Furthermore, it seems that Love waves from the same direction
dominate between 3 and 4 Hz since maximum amplitudes are observed for a
rotation angle of 130∘, the corresponding transverse component. The
lack of Rayleigh wave energy on the radial component in this frequency band
and the presence on the vertical component can be explained by a trough in
the frequency-dependent ellipticity. It remains, however, unclear why Love
waves disappear between 4 and 5 Hz.
The back-azimuth discrepancy between vertical FK and polarization analysis
may be due to azimuthal anisotropy or a misorientation of the KBS
instrument. The latter possibility can be excluded since systematic bias
towards positive back-azimuth angles is also observed on the temporary
stations of the KBSA array. Furthermore, an analysis of P-wave polarization
from regional earthquakes at KBS revealed a similar behavior. There is a
systematic back-azimuth-dependent bias at KBS between polarization angle and
expected back-azimuth (Fig. ). This bias is positive at 0∘ back-azimuth. Subsurface geology in the Ny-Ålesund area exhibits
southwest-dipping sediment layers Figs. 3 and 4
in which could give rise to azimuthal anisotropy,
i.e., a rotation of the polarization ellipsoid (clockwise from north) with
respect to propagation direction (north to south). A quantification and
further analysis of this finding are beyond the scope of this paper and should
be subject of future studies.
Variability of Rayleigh wave ellipticity
(a) Rayleigh wave ellipticities for fundamental and two higher modes
in the tremor frequency band computed from the reference model and modified
model by introducing a 2 m thick top low-velocity layer supposed to
represent a thawed active permafrost layer. (b) Temporal variation of tremor
radial-to-vertical spectral ratios (RVSRs) averaged over individual months and the years 2010–2016. Average RVSRs for
March and August and standard deviations show that the seasonal change in
amplitude is significant and consistent (p<0.01 between 4.0 and 5.8 Hz for
Welch's T test). (c) Air temperature measurements in Ny-Ålesund (10-day
running average), soil temperature at 0.59 m depth dark red
dashed;, and monthly averaged root mean square (rms) difference for frequency
range (4.0–5.5 Hz) between averaged RVSRs in February 2016 and each tremor
RVSR. Standard deviations are shown as gray areas. The years 2001–2004 are not
shown because of long data gaps.
We compute HVSRs of all tremor records at KBS to analyze the Rayleigh wave
ellipticity using the same processing as for the ambient noise. Since we
found clear evidence that the angle separating Rayleigh and Love waves on the
radial and tangential components does not coincide with the propagation
direction inferred from the vertical component (Fig. ) and
as suggested by the tremor source location, we compute the radial-to-vertical
spectral ratios (RVSRs) using a back-azimuth of 40∘. Figure b shows that the RVSRs are very stable and their
standard deviations low within the tremor frequency band. A complex
peak–trough shape of the RVSR curve is revealed. After testing different (1-D)
subsurface velocity models based on our reference model (Table ), it turned out that this behavior can only be explained
by a mixture of fundamental and higher-mode Rayleigh wave ellipticities
(compare Fig. a and b). The first trough at 4 Hz can be
related to the ellipticity minimum of the fundamental and first higher modes.
The fundamental mode peak below 3 Hz lays outside the tremor band and is
probably therefore not revealed. The first RVSR peak between 4 and 5 Hz
seems to coincide with the first higher-mode ellipticity maximum. The next
trough would then be related to an ellipticity minimum which results from the
superposition of first and second higher modes. At the upper limit of the
tremor band at 6 Hz, another peak could be related to the second higher-mode
peak.
The radial component HVSRs of all tremor occurrences between 2001 and 2016
exhibit very similar shapes (monthly averaged RVSRs are provided in the
Supplement S03). However, there is a slight but significant (p<0.01 for
equal mean hypothesis in Welch's T test) seasonal variation in the amplitudes
between 4.0 and 5.8 Hz (Fig. b). The amplitudes are
higher during the summer months between June and September. We quantify the
RVSR variability by computing the root mean square (rms) difference between 4.5 and 5.5 Hz with
respect to the average RVSR of tremor records in February 2016 (Fig. c), which reveals a clear seasonality in all years. As
soon as air and ground temperatures increase above 0 ∘C, rms values
increase rapidly, before dropping again in autumn when temperatures approach
negative degrees.
Discussion of the reliability of HVSRs for permafrost monitoring
The results of our field measurement and theoretical modeling reveal a number
of challenges and pitfalls when attempting to use HVSRs to monitor the active
permafrost layer. In the case of ambient seismic noise, the general broadband
HVSR amplitude increase and the emergence of amplitude peaks in the beginning
and during the melt season could be mistaken for a direct structural effect
of the active layer. Furthermore, strong HVSR peaks resulting from short-term
changes in the noise sources, e.g., wind affecting the instrument directly or
generating noise at or at close proximity to the measurement site, could be
misinterpreted as HVSR peaks related to subsurface interfaces if the
recording period is too short or wind speed measurements are not available.
At the same time, a weaker structural peak might be masked by such noise
sources. Moreover, an emerging HVSR peak close to the Nyquist frequency could
be an artifact of the instrument anti-aliasing filter (Fig. c);
i.e., it could be related to an emerging peak at higher frequencies and would
lead to an overestimation of the thaw depth if the apparent peak is
misinterpreted. Furthermore, a frequency-dependent seasonal change of the
relative contribution of Rayleigh and Love waves will affect HVSR amplitudes
and could give rise to misinterpretation of the caused HVSR variability that
is not related to a structural change. Finally, for measuring HVSR changes
caused by the active layer, seismic instruments have to be deployed on top of
or inside the soil, which naturally leads to degrading coupling, tilt, and/or
instrument vibrations during thawing. The processes above include issues
known from previous studies to affect HVSRs. For example,
mentioned among other effects strong tilt,
strong wind when recording next to a feature connected to the ground, and
heavy rain. The main focus of was the
frequency range below 20 Hz; however, one would expect these issues to
become even more relevant at higher frequencies, which is a reason why it was
recommended to restrict HVSR analysis to frequencies below 10 Hz.
Nevertheless, in order to resolve a HVSR peak caused by the active layer, we
need to take these frequencies into account.
Another finding of is strong effects related
to the nature of the shallow uppermost layer. Thick (>10–15 cm) mud and
ploughed and/or water-saturated soil were shown to lead to higher HVSR
amplitudes and appearance of artificial peaks at higher frequencies. Similarly,
we have clear indications for a shallow structural variation causing a
temporal change in the HVSRs at 5 out of 11 seismic stations and short-term
HVSR peaks at two more stations during days of low wind speed that can be
attributed to the permafrost active layer (Fig. ). The gliding
frequency peaks are consistent with a realistic active layer thawing process
starting in the beginning of June and reaching, consistently with the modeling
results, a thaw depth of about 2 m and S-wave velocities between
0.15–0.25 km s-1 at the end of the summer. The best example is
station BRA2, where a peak emerges in May at 46 Hz (probably underestimated
because of the anti-aliasing filter) from a flat HVSR curve measured in April
(Fig. d). Subsequently, the peak frequency decreases to 38 Hz in
June, 33 Hz in July, and 22 Hz in August. Furthermore, HVSR peak amplitude
ratios lay in the range of the modeled values. BRA2 was located at the
eastern foot of a small hill, probably shielding the instrument more
efficiently from wind coming dominantly from west. Hence, our results suggest
that HVSRs can indeed be used to monitor the thawing–freezing cycle in
permafrost, given that a careful analysis of the temporal variability has
been carried out as pointed out above. However, more calibration experiments
are necessary to relate peak frequency directly to thaw depth and soil
properties, as well as to identify preferable sites for such measurements.
Effect of permafrost active layer on HVSR measurements. Comparison
of observed ambient noise HVSR peak frequencies for stations BRA2, BRA4,
BRA5, KBSA2, and KBSA4 (solid lines) and modeled peak frequencies (red symbols) taking into account anti-aliasing filter of seismic
instrument. For station BRA2, additional black symbols and error bars show
peak frequencies and uncertainties corresponding to days in Fig. d.
For the x axis on top representing modeled thaw depth, we
assume a square root dependency with time from the beginning of June. Tremor RVSR
rms values from Fig. are shown. The rms and peak
frequencies follow a similar trend.
As a special case of the known seasonal effect on HVSRs related to the
thawing–freezing cycle e.g.,, variability
caused by the permafrost active layer has been reported previously
. Instead of geophones,
same experiment as
used Posthole sensors buried in the active layer, since these instruments are
less sensitive to tilt. Such an instrumentation would therefore eliminate
some of the noise issues we face with our deployment. Furthermore, in that
study, emerging HVSR peaks between 10 and 30 Hz were observed during summer,
which, however, could not be explained by the relatively shallow active layer
thickness of 68 cm at the study site. described
seasonal HVSR variability at a seismic station in southern Svalbard. Since a
permanent station was used with 100 Hz sampling, higher frequencies were
resolved than possible at KBS, and instrument coupling was not an issue.
However, similar to our results, the authors acknowledged that low-frequency
HVSR peaks (e.g., at 12 Hz) and overall seasonal HVSR amplitude increase are
due to wind noise and/or human activity at the research station in summer.
They also described a peak, but not gliding as in our case, emerging in June
at 40 Hz close to the Nyquist frequency, accompanied by a minimum at
30–35 Hz, which they attribute to active layer thawing. The observations of
both previous studies support our conclusion that HVSR interpretation must be
done carefully, as strong HVSR peaks or amplitude increases in general are not
necessarily related to shallow structural changes, although they appear
seasonally.
A station network allows us to pursue different approaches than simply
applying the single-station HVSR method. Beside two-station noise
interferometry to measure seismic velocity changes ,
array analysis makes it also possible to measure the frequency-dependent
ratio of Rayleigh and Love waves on the horizontal components
3c-MSPAC; and to analyze noise directionality
through array beamforming . However, the
minimum inter-station spacing must be carefully adapted to the frequency
range to be resolved. Since our array geometries were designed to detect and
locate calving events between 1 and 10 Hz, we cannot use these array methods
due to spatial aliasing and lacking wave field correlation at frequencies
higher than 10 Hz. A more adequate station setup would potentially allow
differentiating between effects of changes in Love wave contribution, noise
sources, and propagation medium on HVSR variability. We tried ambient noise
interferometry between our array stations as well. However, we encountered
lack of waveform correlation due to too-large inter-station distances and
locally uncorrelated noise at frequencies higher than 10 Hz. Hence, no
seasonal velocity changes related to the active layer could be measured
as successfully done by .
Utilizing a localized and repeating seismic signal for permafrost monitoring
might be an alternative to ambient noise HVSRs. The seasonal variations
observed in our tremor RVSRs could be either due to changes in the
propagation medium or the tremor source itself. In general, the HVSR method
is supposed to remove source effects. In our case, for example, the tremor
source magnitude variability should affect the vertical and radial components
of the Rayleigh wave measured at KBS in the same way. However, we cannot
fully exclude the possibility that noise not related to the tremor increases more on the horizontal components during summer
than on the vertical component. If the RVSR variability is due to medium changes, the active
permafrost layer is a good candidate to explain our observations, though the
strongest amplitude increase is expected at much higher frequencies (Fig. ). Nevertheless, modeling Rayleigh wave ellipticities shows that
the tremor frequency band is slightly affected. We obtain a clear increase in
ellipticity for the first and second higher modes above 4.5 Hz for a model
assuming very low S-wave velocities in the active layer (Table , Fig. a). This is consistent with
, who observed a significant HVSR amplitude
change within the same frequency band (2–10 Hz) caused by a 0.75 m deep
frozen layer. However, we cannot exactly reproduce our measured RVSR change
due to lacking knowledge about the relative contribution of Rayleigh waves
modes and possibly body waves, as well as probably deviations from a 1-D
subsurface structure that exist due to dipping layer in the study area
. Modeling ellipticities using 2-D or 3-D structures
might help to better explain our observations. The presence of a repeating,
localized tremor signal at higher frequencies around the HVSR peak directly
related to the unfrozen layer in summer would allow us to asses the
seasonality with higher certainty through directly measuring the peak's
frequency change. This potential has to be followed up with more related
studies in the future.
Ambient noise and tremor HVSRs complement each other in our case study. The
gliding HVSR peak frequency can only be measured from a short record
(temporary network), while a long-term record is available for KBS to analyze
interannual variability. However, since a permanent station within a shelter
structure such as KBS might not be sensitive enough to active layer
variability at high frequencies or have a too-low sampling frequency, signals
with longer wavelengths are needed. Analyzing the tremor signal allows
measuring HVSR variability at lower frequencies that would otherwise (i.e.,
with ambient noise) not be sensitive enough to resolve active layer thawing.
Although the measured quantities are different, ambient noise HVSR peak
frequencies and tremor HVSR rms values exhibit consistent variability during
the measurement period (see Fig. ), presumably related to the
same cause, i.e., the active permafrost layer.
Summary and recommendations
We apply the HVSR method to a temporary seismic deployment and the permanent
station KBS in northwest Svalbard to investigate its applicability for
permafrost active layer monitoring. As expected, ambient noise HVSR
variability is strongly affected by changing external site conditions but
also reveals a seasonal trend. A gliding peak frequency between 50 and 15 Hz
is observed, which most likely indicates a deepening thaw depth from June until
September, as confirmed by modeled HVSRs using the diffuse wave field
assumption. Furthermore, we describe a repeating ocean-swell- and
tide-related seismic tremor in the record of KBS. We are able to extract the
frequency-dependent ellipticity from the tremor radial-to-vertical spectral
ratios. We find a significant seasonal variation between 4.5 and 5.5 Hz.
Although these frequencies are less sensitive to shallow medium changes, we
show that Rayleigh wave ellipticities are still affected by the thawed
permafrost active layer.
Our results demonstrate that active layer monitoring would benefit from more
purpose-built seismic networks and that interpretation of spectral ratio
variability must be done carefully to exclude non-structural effects. We
confirm previous general recommendations and known issues of the HVSR method
, which have become even more important at the high
frequencies needed to resolve the active layer HVSR peak. In summary, we
suggest the following recommendations, including and emphasizing those given
previously and being of special relevance for future passive seismic
experiments that have the goal to measure permafrost active layer variability:
The seismic sampling rate should be at least 200 Hz to capture HVSR peaks of shallow, emerging interfaces and
to avoid misinterpretation of apparent peaks close to the Nyquist frequency.
If logistically feasible, repeated maintenance at temporarily deployed instruments during the melt season is strongly
recommended to keep ground coupling stable. Digging instruments deeper into the soil (if deployment is done during thawed conditions)
and/or using Posthole sensors, if affordable, is an alternative . Cementing the sensor a few decimeters
below the surface on a small plate might be another option .
A careful evaluation of HVSR variability caused by non-structural effects e.g., must be
performed, for example, using co-located wind speed measurements. As noted in previous studies, time periods with strong wind noise
should be excluded from analysis and/or an efficient wind shielding should be used.
The deployment of small-aperture seismic arrays with minimum four elements and with minimum inter-station distances not larger than
5 to 10 m is recommended to allow
measuring the frequency- and time-dependent contribution of Rayleigh and Love waves at high frequencies (3c-SPAC method) since
a change would affect HVSR amplitudes ;
measuring changing noise source directionality and resulting effects on HVSRs (back-azimuth measurements with beamforming/FK
analysis);
combining HVSR measurements with seismic noise interferometry
; and
comparison and evaluation of HVSRs of close stations affected by more similar local noise and ground conditions.
Making use of repeating directional noise sources if applicable has the potential to avoid source variability
affecting the HVSRs. If the frequency content of such a source is too low, temporal HVSR increase might still be connected
to a peak at higher frequencies. In addition, a purpose-built linear seismic array aligned with propagation direction would
allow the application of noise interferometry.
HVSR analysis cannot yet be considered to be a stand-alone tool to measure
permafrost active layer variability without including seismic expert
knowledge and taking into account site-dependent factors. However, our study
clearly shows the potential of the HVSR method. We are confident that more
case studies, long-term experiments, and improved instrumental setups will
help to establish this approach as a useful supplementary tool in permafrost
research.
Data of station KBS are freely available through IRIS . The
seismic record of the temporary network stations will become freely accessible through the
Geophysical Instrument Pool Potsdam (GIPP) after 1 October 2020 (http://gipp.gfz-potsdam.de/webapp/projects/view/536, ).
Measured sea level data from Ny-Ålesund were obtained from https://www.kartverket.no/en/sehavniva/Lokasjonsside/?cityid=9000015&city=Ny- C385lesund
(). Meteorological data are available
from and soil temperatures at station Bayelva from . Copernicus Sentinel data
from 2016 were used in Fig. .
Supporting figures for HVSRs from ambient seismic noise
Figure shows examples of deployed seismic sensors. Figures and show the HVSRs for the
stations not shown in the main text.
Photos of a station of the KBSA array after deployment in April and a
station of the BRA array during data retrieval in August (geophone
uncovered).
Same as Fig. for three more stations.
Same as Fig. for three more stations.
Automatic detection and temporal distribution of the repeating tremor
Repeating tremors in the KBS record are detected using a STA/LTA trigger
applied to a time series of vertical component spectral amplitudes. We
compute the logarithm of spectral power between 3.4 and 5.7 Hz in
non-overlapping 150 s long time windows. A STA length of 25 min, a LTA
length of 25 h, and a STA/LTA threshold of 1.15 are used. If the threshold
is exceeded for a sample (time window), the occurrence of a tremor is
declared. Samples are assigned to the same tremor if gaps between exceeded
thresholds are shorter than 1 h. If the gap is longer, the onset of a new
tremor is declared. Detections with duration less than 25 min are sorted
out. All detection parameters are found by evaluating if clear, visually
identified tremors are correctly detected, while minimizing the number of
false detections. Visual post-processing is done to reject a few false
positives so that only real tremors are used for further processing. The list
of all detected tremors is provided in the Supplement S02. Tremors were
detected around semi-diurnal tidal maxima in Ny-Ålesund (Fig. ), except during neap tides and at low wind speed. Sometimes
two tremors are declared if the amplitudes exhibit a two-sided distribution,
i.e., peaks at the start and the end of a tremor (see, for example, 26 January 2008 and 28 January 2016 in Fig. a). The amplitude spectrum
of the time series of log-spectral powers used for the detector shows
prominent semi-diurnal tidal peaks (Fig. , Darwin
symbols of tides: M2, S2, N2). Furthermore, diurnal (K1, O1),
terci-diurnal (M3), and quarti-diurnal (M4) peaks are clearly revealed.
The neap–spring tide cycle (14.75 days, Msf) appears as a weak
peak in the spectrum. In some years (2003, 2004, 2009–2011), the number of
tremor detections drops in the beginning of the year, which could be an effect
of sea ice preventing ocean wave activity. Note that in recent years (from
about 2013), no land-fastened sea ice has been observed at the coast of Ny-Ålesund (personal communication, Christopher Nuth, 2018).
Example of repeating seismic tremor waveforms recorded at KBS.
Detected tremor onsets are indicated by yellow stars. Waveform data of the
tremor on 22 January 2008 are provided in the Supplement S01.
Amplitude spectrum of time series of log-spectral powers of KBS
vertical component seismic data (see Fig. a) and of measured
sea level in Ny-Ålesund between 2005 and 2016. Gray triangles indicate
theoretical ocean tide periods at (from left to right, Darwin symbol of tide
in brackets) 3.105 h (M8), 4.14 h (M6), 6.21 h (M4), 8.28 h
(M3), 12.0 h (S2), 12.42 h (M2), 12.658 h (N2), 23.93 h
(K1), 25.82 h (O1), and 14.75 days (Msf).
Supporting figures for tremor spectrum and polarization
Figure shows results of FK analysis for a tremor record.
Measured back-azimuth at KBSA array and P-wave polarization angle for regional
earthquakes are shown in Fig. .
Example of FK analysis of vertical components
of KBSA array for a tremor occurring between 12 May 2016 03:02:00 UTC and 12 May 2016 06:17:00 UTC. (a) All back-azimuth (baz) measurements at maximum beam power
and with coherency (normalized beam power) >0.7 for 600 s long time
windows during tremor occurrence (gray symbols) and median with median
deviation (black). (b) Color-coded histogram (counts) of phase velocity
measurements for same time windows as in panel (a) and median with median
deviation.
Back-azimuth measured with FK analysis at KBSA array vs. station's
P-wave polarization angle measured from regional earthquakes.
The supplement related to this article is available online at: https://doi.org/10.5194/esurf-7-1-2019-supplement.
AK and CW initiated the study. AK processed and analyzed the seismic data and prepared the manuscript.
CW was responsible for field instrumentation and assisted in the field experiment and manuscript editing.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “From process to signal – advancing environmental seismology”.
It is a result of the EGU Galileo conference, Ohlstadt, Germany, 6–9 June
2017.
Acknowledgements
This study was financed by the Norwegian Research Council funded CalvingSEIS
(244196/E10) and SEISMOGLAC (213359/F20) projects. Seismic instrumentation
for temporary network was provided by the Geophysical Instrument Pool of GFZ
Potsdam, Germany. Special thanks go to Christopher Nuth (PI of CalvingSEIS)
for organizing logistics in Ny-Ålesund and for helping, together with
Cesar Deschamps-Berger, during instrument deployment. We used ObsPy
for seismic data analysis. Figures were produced
using GMT . Rayleigh wave ellipticities were computed
using Geopsy (http://www.Geopsy.org, last access: 7 January 2019). We thank Antonio Garcia Jerez for
providing us with the HV-Inv software to model HVSRs using the diffuse
wave field theory. We thank Lukas Preiswerk, Philippe Guéguen, and one
anonymous reviewer for reviewing the manuscript.
Edited by: Fabian Walter
Reviewed by: Lukas Preiswerk and one anonymous referee
ReferencesAbbott, R., Knox, H. A., James, S., Lee, R., and Cole, C.: Permafrost Active
Layer Seismic Interferometry Experiment (PALSIE), Tech. rep., Sandia
National Laboratories (SNL-NM), Albuquerque, NM (United States),
available at: https://prod.sandia.gov/techlib/access-control.cgi/2016/160167.pdf (last access: 7 January 2019), 2016.Adams, P. N., Anderson, R. S., and Revenaugh, J.: Microseismic measurement of
wave-energy delivery to a rocky coast, Geology, 30, 895–898,
10.1130/0091-7613(2002)030<0895:MMOWED>2.0.CO;2, 2002.Albuquerque Seismological Laboratory (ASL)/USGS: Global Seismograph Network
(GSN – IRIS/USGS), 10.7914/sn/iu, 1988.Bartholomaus, T. C., Amundson, J. M., Walter, J. I., O'Neel, S., West, M. E.,
and Larsen, C. F.: Subglacial discharge at tidewater glaciers revealed by
seismic tremor, Geophys. Res. Lett., 42, 6391–6398,
10.1002/2015GL064590, 2015.Beyreuther, M., Barsch, R., Krischer, L., Megies, T., Behr, Y., and Wassermann,
J.: ObsPy: A Python toolbox for seismology, Seismol. Res. Lett.,
81, 530–533, 10.1785/gssrl.81.3.530, 2010.Boike, J., Juszak, I., Lange, S., Chadburn, S., Burke, E., Overduin, P. P.,
Roth, K., Ippisch, O., Bornemann, N., Stern, L., Gouttevin, I., Hauber, E.,
and Westermann, S.: Soil data at station Bayelva (1998–2017, level 2,
version 1), 10.1594/PANGAEA.882061, 2017.Boike, J., Juszak, I., Lange, S., Chadburn, S., Burke, E., Overduin, P. P., Roth, K., Ippisch, O., Bornemann, N., Stern, L.,
Gouttevin, I., Hauber, E., and Westermann, S.: A 20-year record (1998–2017) of permafrost, active layer and meteorological
conditions at a high Arctic permafrost research site (Bayelva, Spitsbergen), Earth Syst. Sci. Data, 10, 355–390, 10.5194/essd-10-355-2018, 2018.Bonnefoy-Claudet, S., Cotton, F., and Bard, P.-Y.: The nature of noise
wavefield and its applications for site effects studies: A literature
review, Earth-Sci. Rev., 79, 205–227,
10.1016/j.earscirev.2006.07.004, 2006.Bonnefoy-Claudet, S., Köhler, A., Cornou, C., Wathelet, M., and Bard,
P.-Y.: Effects of Love waves on microtremor H/V ratio, B. Seismol. Soc. Am., 98, 288–300, 10.1785/0120070063,
2008.Burtin, A., Cattin, R., Bollinger, L., Vergne, J., Steer, P., Robert, A.,
Findling, N., and Tiberi, C.: Towards the hydrologic and bed load monitoring
from high-frequency seismic noise in a braided river: The “torrent de St
Pierre”, French Alps, J. Hydrol., 408, 43–53,
10.1016/j.jhydrol.2011.07.014, 2011.Chatelain, J.-L., Guillier, B., Cara, F., Duval, A.-M., Atakan, K., and Bard,
P.-Y.: Evaluation of the influence of experimental conditions on H/V
results from ambient noise recordings, B. Earthq. Eng.,
6, 33–74, 10.1007/s10518-007-9040-7, 2008.Cox, B. R., Wood, C. M., and Hazirbaba, K.: Frozen and unfrozen shear wave
velocity seismic site classification of Fairbanks, Alaska, J. Cold Reg. Eng., 26, 118–145, 10.1061/(ASCE)CR.1943-5495.0000041,
2012.Dickson, M. E. and Pentney, R.: Micro-seismic measurements of cliff motion
under wave impact and implications for the development of near-horizontal
shore platforms, Geomorphology, 151, 27–38,
10.1016/j.geomorph.2012.01.006, 2012.Earlie, C. S., Young, A. P., Masselink, G., and Russell, P. E.: Coastal cliff
ground motions and response to extreme storm waves, Geophys. Res. Lett., 42, 847–854, 10.1002/2014GL062534, 2015.García-Jerez, A., Piña-Flores, J., Sánchez-Sesma, F. J.,
Luzón, F., and Perton, M.: A computer code for forward calculation and
inversion of the H/V spectral ratio under the diffuse field assumption,
Comput. Geosci., 97, 67–78, 10.1016/j.cageo.2016.06.016,
2016.GFZ: GIPP Experiment Database, available at: http://gipp.gfz-potsdam.de/webapp/projects/view/536,
last access: 7 January 2019.Guéguen, P., Langlais, M., Garambois, S., Voisin, C., and
Douste-Bacqué, I.: How sensitive are site effects and building response
to extreme cold temperature? The case of the Grenoble's (France) City Hall
building, B. Earthq. Eng., 15, 889–906,
10.1007/s10518-016-9995-3, 2017.
Haldorsen, S. and Heim, M.: An Arctic groundwater system and its dependence
upon climatic change: an example from Svalbard, Permafrost Periglac., 10, 137–149,
1999.Haldorsen, S., Heim, M., and Lauritzen, S.-E.: Subpermafrost groundwater,
western Svalbard: Paper presented at the 10th Northern Res. Basin Symposium
(Svalbard, Norway – 28 Aug./3 Sept. 1994), Hydrol. Res., 27, 57–68,
10.2166/nh.1996.0019, 1996.James, S., Knox, H., Abbott, R., and Screaton, E.: Improved moving window
cross-spectral analysis for resolving large temporal seismic velocity changes
in permafrost, Geophys. Res. Lett., 44, 4018–4026,
10.1002/2016GL072468, 2017.Jones, E. V., Rosser, N., Brain, M., and Petley, D.: Quantifying the
environmental controls on erosion of a hard rock cliff, Mar. Geol., 363,
230–242, 10.1016/j.margeo.2014.12.008, 2015.King, M., Zimmerman, R., and Corwin, R.: Seismic and electrical properties of
unconsolidated permafrost, Geophys. Prospect., 36, 349–364,
10.1111/j.1365-2478.1988.tb02168.x, 1988.
Köhler, A., Ohrnberger, M., and Scherbaum, F.: The relative fraction of
Rayleigh and Love waves in ambient vibration wavefields at different European
sites, in: Proceedings of the third International Symposium on the Effects
of Surface Geology on Seismic Motion, Grenoble, France, Paper Number/Abstract ID: 83, 2006.Köhler, A., Ohrnberger, M., Scherbaum, F., Wathelet, M., and Cornou, C.:
Assessing the reliability of the modified three-component spatial
autocorrelation technique, Geophys. J. Int., 168, 779–796,
10.1111/j.1365-246X.2006.03253.x, 2007.Kartverket: Water level and tidal information Ny-Ålesund, available at:
https://www.kartverket.no/en/sehavniva/Lokasjonsside/?cityid=9000015&city=Ny-%C3%85lesund
, last access: 7 January 2019.Köhler, A., Nuth, C., Schweitzer, J., Weidle, C., and Gibbons, S. J.:
Regional passive seismic monitoring reveals dynamic glacier activity on
Spitsbergen, Svalbard, Polar Res., 34, 26178,
10.3402/polar.v34.26178, 2015.Köhler, A., Nuth, C., Kohler, J., Berthier, E., Weidle, C., and Schweitzer,
J.: A 15 year record of frontal glacier ablation rates estimated from seismic
data, Geophys. Res. Lett., 43, 12155–12164,
10.1002/2016GL070589, 2016.Kula, D., Olszewska, D., Dobiński, W., and Glazer, M.:
Horizontal-to-vertical spectral ratio variability in the presence of
permafrost, Geophys. J. Int., 214, 219–231,
10.1093/gji/ggy118, 2018.
Kvaerna, T. and Ringdal, F.: Stability of various fk estimation techniques,
in: NORSAR Semiannual technical summary, 29–40, NORSAR Scientific Report
1-86/87, 1986.Lachet, C. and Bard, P.-Y.: Numerical and theoretical investigations on the
possibilities and limitations of Nakamura's technique, J. Phys. Earth, 42, 377–397, 10.4294/jpe1952.42.377, 1994.Larose, E., Carrière, S., Voisin, C., Bottelin, P., Baillet, L.,
Guéguen, P., Walter, F., Jongmans, D., Guillier, B., Garambois, S.,
Florent, G., and Chris, M.: Environmental seismology: What can we learn on
earth surface processes with ambient noise?, J. Appl. Geophys.,
116, 62–74, 10.1016/j.jappgeo.2015.02.001, 2015.LeBlanc, A.-M., Fortier, R., Allard, M., Cosma, C., and Buteau, S.: Seismic
cone penetration test and seismic tomography in permafrost, Can. Geotech. J., 41, 796–813, 10.1139/t04-026, 2004.Lévêque, J.-J., Maggi, A., and Souriau, A.: Seismological constraints
on ice properties at Dome C, Antarctica, from horizontal to vertical spectral
ratios, Antarct. Sci., 22, 572–579, 10.1017/S0954102010000325,
2010.Lunedei, E. and Malischewsky, P.: A review and some new issues on the theory
of the H/V technique for ambient vibrations, in: Perspectives on European
Earthquake Engineering and Seismology, 371–394, Springer,
10.1007/978-3-319-16964-4_15, 2015.
Nakamura, Y.: A method for dynamic characteristics estimation of subsurface
using microtremor on the ground surface, QR Railway Tech. Res. Inst., 30,
25–33, 1989.Neuffer, T. and Kremers, S.: How wind turbines affect the performance of
seismic monitoring stations and networks, Geophys. J. Int.,
211, 1319–1327, 10.1093/gji/ggx370, 2017.Norman, E. C., Rosser, N. J., Brain, M. J., Petley, D. N., and Lim, M.: Coastal
cliff-top ground motions as proxies for environmental processes, J.
Geophys. Res.-Oceans, 118, 6807–6823, 10.1002/2013JC008963,
2013.Ohrnberger, M., Schissele, E., Cornou, C., Bonnefoy-Claudet, S., Wathelet, M.,
Savvaidis, A., Scherbaum, F., and Jongmans, D.: Frequency wavenumber and
spatial autocorrelation methods for dispersion curve determination from
ambient vibration recordings, in: Proceedings of the 13th World Conference on
Earthquake Engineering, 946,
available at: http://www.iitk.ac.in/nicee/wcee/article/13_946.pdf (last access: 7 January 2019), 2004.Overduin, P. P., Haberland, C., Ryberg, T., Kneier, F., Jacobi, T., Grigoriev,
M., and Ohrnberger, M.: Submarine permafrost depth from ambient seismic
noise, Geophys. Res. Lett., 42, 7581–7588,
10.1002/2015GL065409, 2015.Parolai, S., Picozzi, M., Richwalski, S., and Milkereit, C.: Joint inversion
of phase velocity dispersion and H/V ratio curves from seismic noise
recordings using a genetic algorithm, considering higher modes, Geophys. Res. Lett., 32, L01303, 10.1029/2004GL021115, 2005.Picotti, S., Francese, R., Giorgi, M., Pettenati, F., and Carcione, J. M.:
Estimation of glacier thicknesses and basal properties using the
horizontal-to-vertical component spectral ratio (HVSR) technique from passive
seismic data, J. Glaciol., 63, 229–248,
10.1017/jog.2016.135, 2017.re3data.org: eKlima, 10.17616/R3Q63H, 2018.Sabra, K., Gerstoft, P., Roux, P., Kuperman, W., and Fehler, M.: Extracting
time-domain Green's function estimates from ambient seismic noise,
Geophys. Res. Lett., 32, L03310, 10.1029/2004GL021862, 2005.Saccorotti, G., Piccinini, D., Cauchie, L., and Fiori, I.: Seismic noise by
wind farms: a case study from the Virgo Gravitational Wave Observatory,
Italy, B. Seismol. Soc. Am., 101, 568–578,
10.1785/0120100203, 2011.Sánchez-Sesma, F. J.: Modeling and inversion of the microtremor H/V
spectral ratio: physical basis behind the diffuse field approach, Earth Planets Space, 69, 92,
10.1186/s40623-017-0667-6, 2017.Sens-Schönfelder, C. and Wegler, U.: Passive image interferometry and
seasonal variations of seismic velocities at Merapi Volcano, Indonesia,
Geophys. Res. Lett., 33, L21302, 10.1029/2006GL027797, 2006.Sens-Schönfelder, C. and Wegler, U.: Passive image interferometry for
monitoring crustal changes with ambient seismic noise, C. R.
Geosci., 343, 639–651, 10.1016/j.crte.2011.02.005, 2011.Shapiro, N. and Campillo, M.: Emergence of broadband Rayleigh waves from
correlations of the ambient seismic noise, Geophys. Res. Lett., 31,
1615–1619, 10.1029/2004GL019491, 2004.Snieder, R.: The theory of coda wave interferometry, Pure Appl.
Geophys., 163, 455–473, 10.1007/s00024-005-0026-6, 2006.van der Ploeg, M. J., Haldorsen, S., Leijnse, A., and Heim, M.: Subpermafrost
groundwater systems: Dealing with virtual reality while having virtually no
data, J. Hydrol., 475, 42–52,
10.1016/j.jhydrol.2012.08.046, 2012.Wessel, P. and Smith, W. H. F.: New, improved version of GMT released, EOS
T. Am. Geophys. Un., 79, 579–579,
10.1029/98EO00426, 1998.
Westermann, S., Wollschläger, U., and Boike, J.: Monitoring of active layer dynamics at a permafrost site on Svalbard using multi-channel
ground-penetrating radar, The Cryosphere, 4, 475–487, 10.5194/tc-4-475-2010, 2010.Xu, G., Yang, Z., Dutta, U., Tang, L., and Marx, E.: Seasonally frozen soil
effects on the seismic site response, J. Cold Reg. Eng.,
25, 53–70, 10.1061/(ASCE)CR.1943-5495.0000022, 2010.Yan, P., Li, Z., Li, F., Yang, Y., Hao, W., and Bao, F.: Antarctic ice sheet thickness estimation using the horizontal-to-vertical
spectral ratio method with single-station seismic ambient noise, The Cryosphere, 12, 795–810, 10.5194/tc-12-795-2018, 2018.Young, A. P., Guza, R. T., O'Reilly, W. C., Burvingt, O., and Flick, R. E.:
Observations of coastal cliff base waves, sand levels, and cliff top shaking,
Earth Surf. Proc. Land., 41, 1564–1573,
10.1002/esp.3928, 2016.