# Cultural activity and impact of extreme weather events revealed by ambient seismic noise and perspective on quick clay failure monitoring in Oslo, Norway

Charlotte Bruland, Anna Maria Dichiarante, Andreas Köhler, Volker Oye, Ivan Van Bever, Eric Larose

<figure><img src="image_1.png" /></figure>

## To cite this version:

Charlotte Bruland, Anna Maria Dichiarante, Andreas Köhler, Volker Oye, Ivan Van Bever, et al. Cultural activity and impact of extreme weather events revealed by ambient seismic noise and perspective on quick clay failure monitoring in Oslo, Norway. Engineering Geology, 2025, 347, pp.107936. 10.1016/j.enggeo.2025.107936. hal-05384333

HAL Id: hal-05384333

https://hal.science/hal-05384333v1

Submitted on 26 Nov 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Graphical Abstract

Cultural activity and impact of extreme weather events revealed by ambient seismic noise and perspective on quick clay failure monitoring in Oslo, Norway

Charlotte Bruland, Anna Maria Dichiarante, Andreas Köhler, Volker Oye, Ivan Van Bever, Eric Larose

<figure><img src="image_3.png" /></figure>
Highlights

Cultural activity and impact of extreme weather events revealed by ambient seismic noise and perspective on quick clay failure monitoring in Oslo, Norway

Charlotte Bruland, Anna Maria Dichiarante, Andreas Köhler, Volker Oye, Ivan Van Bever, Eric Larose

* Cultural activity in an urban environment can be detected using low-cost sensors

* Seismic noise and low-cost sensors show potential for monitoring quick clay area

* A sharp drop in dv/v, anti-correlated with pore pressure, is observed during an extreme weather event

* Soil hysteresis may provide additional insights into quick clay failure
Cultural activity and impact of extreme weather events
revealed by ambient seismic noise and perspective on
quick clay failure monitoring in Oslo, Norway

Charlotte Bruland<sup>a</sup>, Anna Maria Dichiarante<sup>a</sup>, Andreas Köhler<sup>a,b</sup>, Volker Oye<sup>a,c</sup>, Ivan Van Bever<sup>a</sup>, Eric Larose<sup>d</sup>

<sup>a</sup>NORSAR, Gunnar Randers vei 25, 2007, Kjeller, Norway

<sup>b</sup>Department of Geosciences, UiT - The Arctic University of
Norway, N-9037, Tromsø, Norway

<sup>c</sup>Department of Geoscience, University of Oslo, 0371, Oslo, Norway

<sup>d</sup>ISTerre, CNRS, Université Grenoble Alpes, 38000, Grenoble, France

Abstract

The study of urban seismic noise offers a variety of approaches to monitor
cities, from source identification to structural investigations. We demon-
strate the potential for monitoring cultural activity and quick clay failure
in Oslo, Norway using urban noise and low-cost seismic sensors recording
between 2021 and 2024. We identify train passages, a rock concert, con-
struction blasts, local earthquakes and a meteor. To retrieve seismic velocity
variations, we apply seismic interferometry to three years of urban noise (1-
4 Hz) from four sensors, two in a mapped quick clay risk area near critical
infrastructures. Despite the urban variability of the noise field, we obtain
stable noise correlation functions using 1-day stacks. The extracted velocity
variations reveal changes in the ground due to freezing and thawing as well
due to pore pressure variations related to snowmelt and rainfall. Along with
anti-correlation of velocity variations with pore pressure, we observe hystere-
sis associated to the soil's ability to retain water under different moisture
conditions, which could provide insights into potential landslide hazard. A
sharp velocity drop accompanying the heavy rainfall associated with an ex-
treme weather event is observed. All these observations give us insights to
what subsurface changes can be resolved and expected over longer time pe-
riods, which could will allow us to identify unusual and permanent changes,
e.g., related to quick clay. By modeling dispersion curves and lowering the
velocity in a section of the sedimentary layer, we observe a decrease in sur-
face wave velocity within the frequency range of interest. Therefore, velocity variations at the site show potential for monitoring quick clay instabilities.

*Keywords:* urban noise, environmental seismology, quick clay, seismic interferometry

# 1. Introduction

The study of urban seismic noise allows for a variety of innovative approaches to monitor cities, including cultural activities and environmental changes. Ambient seismic noise measured in urban areas contain a multitude of complex, interfering signals, which makes traditional seismological data analysis a challenging task. Still, such records include retrievable information about the sources of the ambient seismic wavefield, the subsurface structure the waves are propagating through, and the time-varying properties of sources and propagation.

For example, with seismic noise we can track population movements from cultural activities, such as concerts, football matches, fireworks and political protests [1, 2], and identify periods of quieting, such as a result of the global COVID-19 pandemic lockdown measures [3] or locally, for example due to a truckers' strike in Brazil [2]. Seismic ambient vibrations can also be used to monitor road and subway traffic [1, 4].

Structural investigations of the urban sub-surface are essential for a variety of reasons, including risk assessment related to potential natural disasters such as earthquake site effects, sinkholes and landslides, the latter including identifying areas prone to landslides and pre-cursor signals (e.g., [5], [6], [7] [8]). In contrast to other geophysical exploration methods, such as active seismic measurements, using passive noise recording to image the sub-surface and changes therein is a cost-effective and logistically simpler approach, as it only requires deployment of a sensor network and no generation of artificial sources. Sensors can be deployed in different urban environments (e.g., in buildings or parks) and can operate under various conditions (e.g., in summer or winter) ([8]). One of the most popular methods is cross-correlating continuous noise records between seismic sensor pairs or auto-correlation of noise at a single sensor, which allows creating virtual sources, a method known as passive seismic interferometry [9, 10]. Passive noise interferometry (or coda wave interferometry if real events are used as sources) can be applied in a time-lapse manner to retrieve temporal seismic velocity changes [11]. This
technique has various applications, from structural health monitoring [12] to
monitoring environmental fluctuations, such as changes in groundwater level
[13, 14, 15], permafrost [16, 17, 18] and geothermal systems [19]. It has also
been applied to investigate natural hazards, with a wide range of applications
monitoring volcanic activity [20, 21, 22, 23, 24]. There are also some studies
on monitoring landslides with noise interferometry [6, 25, 26, 27].

In Norway, quick clay landslides are a substantial natural hazard, espe-
cially since many urban areas are located in potential quick clay hazard zones.
When disturbed, the structure of quick clay can suddenly collapse, flow and
behave as a liquid, potentially affecting large areas. Such quick clay slides
can be triggered by overloading, erosion, heavy rainfall, snow melt, earth-
quake or construction work, that disturb the quick clay sediments [28, 29].
Rørstadbotnen et al. [30] investigated changes in shear wave velocity during
construction work in an area with quick clay in the subsurface, in Trondheim,
Norway, using early arrival direct surface waves retrieved from ambient noise
cross-correlations and distributed acoustic sensing, and did not detect any
subsurface change, which is expected since the quick clay did not collapse and
also because direct waves are not very sensitive to tiny changes. However,
applications to detect small relative velocity changes in the later arriving
coda of the noise cross correlation in a quick clay area, are absent to the
authors' knowledge.

The later arriving coda is more sensitive to small subsurface changes be-
cause they travel longer through the medium, and are therefore suitable for
monitoring smaller changes in relative velocities [31, 32]. Monitoring quick
clay is becoming more important because of more extreme weather events,
new urban developments and increased pressure for building on land which
has not previously been used for this purpose. In this work, we investigate
the potential for monitoring subsurface changes and precursors of quick clay
landslides in an urban environment in Oslo, Norway, with coda wave inter-
ferometry. We use seismic noise and low cost seismic sensors in order to
demonstrate the feasibility of using ambient noise to identify rigidity loss
during, and hopefully prior to quick clay failure.

To get reliable measurements from passive seismic interferometry, we need
information about the ambient noise field and how it changes over time, as
well as seasonal and environmental velocity fluctuations. Therefore, we first
explore the passive seismic recordings of a temporary seismic network de-
ployed in the city of Oslo. We show the variety of seismic signals that can be
detected using an urban network of low-cost seismic sensors. Second, three
years of these urban seismic noise records are used to probe the subsurface in
a busy, urban area susceptible to quick clay landslides. Then, the extracted
velocity variations are related to environmental parameters, and the sensi-
tivity to a velocity change at depth in the frequency range used for analysis
is verified. The time period analysed experienced annual temperature varia-
tions ranging approximately from -20°C to ca. +25°C and included extreme
weather events, such as the low pressure system "Hans" in August 2023,
which produced heavy rainfall with estimated 80-100 mm of precipitation
within 24 hours. Lastly, the potential for quick clay failure monitoring is dis-
cussed. The three years of seismic measurements represent an initial effort to
establish a baseline of the seasonal and environmental velocity fluctuations
in the area, which is important to be able to detect velocity variations that
are permanent or out of the ordinary, and that could be related to changes
in quick clay stability.
## 2. The Oslo Seismic Network

Starting in May 2021, three-component Raspberry shake sensors were deployed at different locations in Oslo [33]. The sensors were installed in basements of private businesses, houses and public schools, covering areas with construction activities such as new water supply and metro tunnel project (Figure 1). The locations are also near critical infrastructure, such as the harbor and the cargo terminal. Additionally, the Norwegian Water Resources and Energy Directorate (NVE) has identified danger zones representing release areas for quick clay landslides based on topographical, geotechnical, and hydrological criteria. Three of these medium hazard zones are situated at or near the seismic sensors (Figure 1a-b, https://temakart.nve.no/tema/kvikkleire). The sensor network was gradually extended, with up to 9 sensors recording simultaneously from June 2022 to July 2023. Of these sensors five are located in the Alna area (ALNN1, ALNN2, ALNN3, ALNN4 and ALNN7), three in the Linderud Area (ALNN5, ALNN6 and ALNN8) and one at Ekeberg (EKBG1) (Figure 1a). Data are recorded continuously, with a sampling rate of 100 Hz, with the sensors having a corner frequency at about 4.5 Hz. Data are transferred in real-time. In this work, we focus on the Alna area by focusing on two of the stations (ALNN3 and ALNN7) located in a quick clay zone. Alna is a busy urban area in Oslo, with the largest cargo terminal in Norway, a shopping center, private residences and offices (Figure 1). For comparison, we also include two of the stations at Linderud (ALNN5 and ALNN6), approximately 1.3 km north of the Alna stations. We selected these stations based on the fact that they have the longest continuous records.

According to maps from the Geological Survey of Norway (NGU), both Alna and Linderud areas are underlain by sea and fjord deposits with continuous cover and bedrock consisting of Cambro-Silurian sequences which consist predominantly of shales and limestones. The sedimentary thickness map (https://geo.ngu.no/kart/losmasse_mobil/) marks both areas as covered by "sometimes very thick" sediments (see thickness map in Figure 1). However, this map is for a regional overview purpose only, and precise bedrock depths values are not reported. Borehole data from the National database for ground surveys (NADAG, https://geo.ngu.no/kart/nadag_mobil/ and from the National groundwater database (GRANADA, https://geo.ngu.no/kart/granada_mobil/, NGU) provide more detailed information about sediment thickness at the borehole location. The closest borehole to the ALNN3 sta-
tion (approximately 30 m away) indicates that the bedrock lies at 37 m depth, and that quick clay was encountered. However, bedrock depth can vary substantially over short lateral distances (Figure 1b). The closest borehole to the ALNN5 station at Linderud is located at larger distance from the station (approximately 300 m away), but generally shallower depth are observed in this area compared to the Alna site.

The cost of a three-component Raspberry Shake seismic sensor is less than 1000 Euros. For reference, broadband seismic sensors are 10 times as expensive, while geophones plus seismic digitizer are around 2-4 times the cost of a Raspberry Shake. Although mostly used for citizen science, Raspberry shakes have been used to quantify anthropogenic noise, for example road traffic [34], to show global reduction in noise levels as a result of the COVID lockdown [3], or to identify the signature of explosives used in a robbery [35]. Holmgren and Werner [36] used Raspberry shake sensors for initial examination of induced seismicity for seismic hazard purposes in Cornwall, UK. Bottelin and Baillet [37] used modal frequency to track rock column pre-failure with Raspberry shakes. Köhler et al. [33] used the seismic network of the current study to investigate a multitude of construction and quarry blasts in the city Oslo. Raspberry shakes have also been used for noise-based sub-surface investigations. Ghione et al. [38] utilized the Horizontal to Vertical Spectral Ratio (HVSR) from Raspberry Shake recordings in conjunction with a statistical geological mapping tool (COHIBA) to estimate both the depth to bedrock and the local average shear wave velocity in the uppermost 30 meters of the ground (Vs30). The Vs30 parameter is commonly employed in seismic hazard assessments to classify soil types and determine soil amplification. There are very few studies on the potential for ambient noise interferometry using Raspberry shakes, but the potential for passive shallow applications were demonstrated by Arosio et al. [39].
<figure><img src="image_4.png" /><figcaption>**Figure 1:** Overview maps of the study area: (a) Bedrock thickness map provided by the Geological Survey of Norway (NGU) draped on the 10 m resolution DEM (shaded relief) by the Norwegian Mapping Authority (https://hoydedata.no/LaserInnsyn2/). Seismic stations deployed during this study are shown in yellow, while existing broadband stations in red. Quick clay hazard areas mapped by the Norwegian Water Resources and Energy Directorate (NVE), quarries and relevant infrastructures are also shown. The location of the weather and hydrology stations are shown by a red and blue diamond, respectively. (b) Close up of the study area in a sliver also the location of the Alnabru cargo terminal, the metro, the Alna and Linderud stations, and the Bjerke Travbane. The Alna river and the piezometers in the Alna region are shown as light blue diamonds. Boreholes from the GRANADA and NADAG databases are shown color-coded by depth to bedrock (https://geo.ngu.no/kart/nadag_mobil/ and https://geo.ngu.no/kart/granada_mobil/, respectively).</figcaption></figure>
### 3. Ambient seismic noise levels

Seismic noise levels at different locations allow us to characterize the seismic signature of an urban environment. We begin with analysing changing noise levels over time. To evaluate the noise level at the different locations in Oslo, we use the probabilistic power spectral density [40] of the noise amplitude records, which is compared to global reference models for seismic noise, the New High- and New Low Noise model [41]. The models are constructed from the envelopes of a large number of vertical seismic records from globally distributed stations, so the low- and high noise models would correspond to the most quiet and noisy locations on Earth, respectively. Noise levels for eight different stations of the Oslo seismic network are shown in Figure 2.

All stations display high noise levels, where the majority of the power spectral densities are close to or even above the New High Noise model. This is characteristic for urban seismic records. Still, the noise levels vary across the network, especially for higher frequencies, with the highest noise levels observed at Alna (ALNN3, ALNN4 and ALNN7). Lower noise levels are observed at Linderud (ALNN5 and ALNN6) and Ekeberg (EKBG1). About 70 % of all cargo traffic by trains in Norway passes through the cargo terminal at Alna, and trucks deliver and collect cargo containers almost around the clock, which explains the higher noise levels. The heavy road traffic in the area due to the presence of office buildings, shopping malls, and private residences, contributes to the significant noise. These high noise levels provide the potential to utilize frequencies below the natural frequency of the instrument.
<figure><img src="image_5.png" /><figcaption>Figure 2: Noise levels at 8 different stations in the Oslo network: The probability power spectral density is calculated for hourly power spectral densities with an overlap of 50% for all available data from 2021 to 2023. The color scale shows the percentage of the spectra at a given period and amplitude, while the solid lines represent the New High and Low Noise models [41]. The dashed vertical lines point to the frequency range (1-4 Hz) that is used for the correlation analysis (see Section 4).</figcaption></figure>

Above 1 Hz, seismic noise in urban areas is dominated by anthropogenic sources [42], such as car and train traffic. Furthermore, we expect significant temporal variation between day and night as well as weekdays and weekend due to regular patterns in human rhythm of life. These two regimes (lower and higher noise levels) are visible as two curved areas with high counts for all stations in Figure 2. The temporal variation of the power spectral density for different frequencies is shown in Figure 3 for stations ALNN6, also clearly revealing the diurnal and weekly variations. ALNN6 is placed in the basement of a public school at Linderud. Hence, in addition, we clearly see the influence of school holidays, with lower noise levels for both the longer summer holidays in June, July and August, as well as shorter holidays, such as Easter, following the opening hours of the school. This is not the case for the stations located close to the cargo terminal, where activity is constantly high (Figure A.13). The urban rhythm may limit the applicability for seismic interferometry due to noise source variability affecting the velocity change measurements, but the consistently high noise levels at Alna can also be
beneficial for noise-based monitoring.

<figure><img src="image_6.png" /><figcaption>Figure 3: **Ambient noise variation over time at Linderud (ALNN6):** The top panel shows the power spectral density over time, while the middle panels show the temporal evolution of the power spectral density at 0.2 and 2 Hz in blue and orange, respectively. The lowermost panel shows two months selected from the data presented in the upper panels, highlighted with a red rectangle. The dashed horizontal lines in the top panel point to the frequency range (1-4 Hz) that is used for the correlation analysis (see Section 4).</figcaption></figure>

Below 1 Hz, seismic noise is mainly generated by natural sources, such as wind and ocean waves. For these frequencies, the amplitudes display seasonal variation, reaching their maxima during the northern hemisphere winter (Figure 3 and A.13). However, note that because of the corner frequency of the instrument and self noise below, sensitivity to these frequencies is limited [38].
# 4. Specific noise sources

After obtaining an overview about temporal and spatial variability of noise levels, we can search for specific anthropogenic events in this seismic seismic signature of the city of Oslo.

## 4.1. Concert

Music festivals and concerts are known to generate vibrations that can be recorded by seismic sensors [43, 44, 1, 45]. These signals typically exhibit narrow, harmonic frequency peaks between 1 and 10 Hz that last for minutes, resembling harmonic tremor observed at active volcanoes. On the 24th July 2022, 60 000 spectators witnessed a concert of the heavy-metal band Rammstein at about 1.5 km (see Bjerke Travbane in Figure 1b) from one of the sensors at Linderud (ALNN6 in Figure 1b). Signals originating from the concert were recorded at all seismic sensors in that area. Figure 4 shows the vibrations from the concert recorded at ALNN6.
<figure><img src="image_7.png" /><figcaption>Figure 4: **Seismic signature of Rammstein: Vibrations from the Rammstein concert on the 24th of July 2022 detected at Linderud:** a) shows the signal in time on the vertical component, bandpass filtered between 1 and 6 Hz, while b) displays the spectrogram of the signal calculated with a 30 s window length and 50 % overlap. The different songs are identified from the seismic records and the tempo (beats per second), fundamental frequency and first two harmonics, for the different songs are superposed on top of the same spectrogram in c) for the set list shown in d).</figcaption></figure>

Looking into the frequency content of the recorded signal over time, distinct spectral peaks can be observed between around 2 to 5 Hz. The different songs from the set list of the concert can be identified in the spectrogram,
with the frequency peaks corresponding to the tempo of the different songs. To obtain the tempo (beats per minute, BPM) of each song, we conducted an online search using publicly available data. The pianist's opening for Rammstein and the piano version of the Rammstein song "Engel" are not visible in our seismic noise records.

Different mechanisms have been proposed to generate concert tremors, such as the crowd moving to the beat or vibrations from the sound system, both through the Dirac comb effect (the Fourier transform of an evenly spaced series of spikes is a series of spikes, with frequency spacing between the spikes equal to the inverse of the period between the spikes in time). The vibrations observed at Linderud are likely attributed to a combination of the crowd moving to the beat of the music and the sound system.

## 4.2. Metro traffic

The seismic station recording the concert is located close to the metro station at Linderud (ALNN6, Figure 1b). The trains passing the station are observed in the spectrogram as vertical lines starting at around 5 Hz (Figure 4). Train passages typically generate tremor-like broadband emergent signals with harmonic spectra with frequencies between 2–40 Hz [46].

For the three stations located close to the Linderud metro station (ALNN5, ALNN6 and ALNN8), these train signals are dominating the passive records for frequencies above 5 Hz. In Figure 5, we show the detection of westbound and eastbound trains, appearing in time-frequency as equidistant spectral lines. As the trains approach the Linderud station and slow down to stop, the frequency of the signals decreases as the trains decelerate. Increasing frequencies are observed when the trains start to move again and accelerate when leaving the station. Several studies have aimed on characterizing train induced vibrations to understand the physical mechanisms behind the observed signals [4, 46, 47]. Lavoué et al. [47] showed that the spectra of the signals originating from trains are modulated by the distance between the wheels, with frequency content controlled by the distribution of axle loads over the rail, which largely depends on ground stiffness below the railway. Localized noise sources can cause problems for seismic interferometry, but also has potential as an opportune source for imaging and monitoring the subsurface.
<figure><img src="image_8.png" /><figcaption>Figure 5: **Seismic signature of trains at the Linderud train station:** a) Seismograms showing 10 minutes of vertical component passive recordings at % Linderud (ALNN8, ALNN5 and ALNN6). b) Spectrogram from the same stations calculated with 3 second windows and 90 % overlap.</figcaption></figure>
4.3. Blasts, earthquakes and meteors

While metro signals can only be recorded on close sensors, and concerts
are relative rare events, blasts generate signals which can be observed at
larger distances and are frequent in Oslo. Köhler et al. [33] analysed the data
of the same network used in this study to detect construction and quarry blast
in and around the city of Oslo. Between 2021 and 2023, over 1,200 blast sig-
nals from several distinct locations have been identified with up to 10 events
each day. The prominent signal observed throughout the entire network was
the surface wave (0.8-2 Hz). The blasts originate mainly from metro and
water tunnel constructions in Oslo. These infrastructure upgrades are nec-
essary due to the continuous population growth in Oslo. Monitoring blasts
in (growing) urban areas is beneficial for example to detect unusually large
events where the ground motion may lead to structural damages or simply
alerts citizens when they experience shaking. More details and examples of
blast signals can be found in Köhler et al. [33].

We also detect natural events, such as earthquakes. Figure 6 shows signals
from two earthquakes happening 5 minutes apart just across the Swedish
border at approximately 120 km southeast of Oslo with magnitudes 2.8 and
2.2, as well as a 5.1 magnitude earthquake in the North sea on the 21st March
2022[48]. Acoustics signals coupling with the ground from a meteor passing
over Oslo on the 24th of July 2021 were also detected on the seismic sensors.
The earthquakes are filtered between 4 and 8 Hz and a highpass 1 Hz is
applied for displaying the meteor.
<figure><img src="image_9.png" /><figcaption>Figure 6: Natural events recorded by the Oslo network: Signals from a magnitude 5.1 earthquake in the North Sea was recorded on the 21st March 022. On the 12th March 2022 two earthquakes that occurred close to the South Norwegian-Swedish border, happening five minutes apart with magnitudes 2.8 and 2.2, respectively. In addition, a meteor passing over Oslo was registered on the Raspberry shakes on the 24th July 2021.</figcaption></figure>

## 5. Measuring relative velocity changes from passive noise recordings

As shown in the previous section, ambient noise contains an abundance of hidden signals. Despite the high noise levels, we are able to identify train passages, a rock concert, construction blasts, local earthquakes and a meteor and, undoubtedly, many more signals could be retrieved from this
rich record. These examples highlighted the potential for urban monitoring, as already suggested by previous studies. Potential applications based on these findings include, but are not limited to monitoring traffic, trains and explosions. However, our focus up to this point has been on identifying sources and have not yet explored using the recorded noise for structural investigations, which may prove to be the most powerful application of urban seismic networks.

A decrease in seismic velocity can be a landslide precursor several days or hours before failure since it is a direct indication of softening of the un-derground layers [25, 8]. From seismic noise, we can measure the relative change in seismic velocity (dv/v) which allows us to potentially identify these precursors. However, a careful analysis has to be conducted to identify such precursors, since fluctuations in velocity are also caused by a number of other factors including noise field variations (source variability) or other environ-mental changes not being the monitoring target.

5.1. *Data processing*

We apply passive image interferometry [11] to three years of urban seismic noise records of four stations in the Oslo network, two located at Linderud (ALNN5 and ALNN6), close to a metro station, and two stations at Alna (ALNN3 and ALNN7), in a quick clay zone close to the cargo terminal (see Figure 1). In passive image interferometry, empirical Green's functions (EGFs) are first estimated using cross-correlation of seismic noise for short time windows. Second, the short-term noise correlations are compared to a long-term reference. The relative changes in elastic and structural properties of the medium are measured through subtle dilations and distortions in the coda of the EGF waveforms using the stretching method. The EGF coda is used instead of the direct (ballistic) arrivals since it is more sensitive to changes in the medium and less affected by noise source variability.

In our case, both autocorrelations and single station cross-correlations between vertical and horizontal components are computed. Before computing the correlations, the data is pre-processed in daily segments. The same processing is applied to all sensors and component combinations. First, data is downsampled to 25 Hz, then detrended, tapered and bandpass filtered in three bands: 1-2.5 Hz, 1.5-3 Hz and 2-4 Hz. We focus on high frequencies (between 1-4 Hz) as instrument noise dominates below 1 Hz [38] and the high sensitivity to the near surface, as quick clay is typically shallow. To exclude earthquakes and other transient events, amplitudes higher than 2.5 times the
standard deviation are clipped. We slice the data into 1-hour-long windows
and compute correlation functions in the Fourier domain.

Passive seismic interferometry is based on the assumption that the sensors
are evenly surrounded by uncorrelated noise sources, and/or stacking long
enough time series for the correlations to converge to the EGF. However,
Hadziioannou et al. [49] showed that these requirements can be relaxed for
monitoring purposes. Still, stable background noise sources, in time and
space, is important for stability of noise correlation functions. We stack the
hourly noise correlations to average the changes in noise sources. In general,
the more time we stack, the more stable noise correlation functions we get.
Since we know that noise is strongly affected by the urban rhythm, we use
both a 1-day stack, to average the day-nighttime noise variation, for higher
temporal resolution needed for monitoring, and a 7-day stack to average the
weekday-weekend noise variation for comparison. To get "daily" resolution
for the relative velocity change (dv/v) in the case if the 7-day-stack, we slide
one day forward and then stack the hourly correlations over the 7 previous
days to obtain the next data point in the dv/v time series.

The resulting daily correlation waveforms for the vertical component au-
tocorrelations over time from 2021-05-19 to 2024-07-26 for stations ALNN3
and ALNN7 at Alna filtered between 1-2.5 Hz are shown in Figure A.14.
Since the autocorrelation is symmetric, we only show one side. We have
stable coda wave arrivals for both stations up to 20 s. We see a clear shift
in the arrivals in the coda, with earlier arrivals, i.e., higher velocities during
winter months, dominating the temporal variation of the noise correlation
functions.

By measuring the relative time-shift between the noise correlation wave-
forms, we can retrieve the relative change in seismic velocity (dv/v) [31].
Here, we use the trace stretching technique, but there are also other methods
that can be used to estimate the seismic phase delays [50]. Before applying
the trace stretching to the 1-day- and 7-day-stacks, the correlations are again
filtered in the three target frequency bands: 1-2.5 Hz, 1.5-3 Hz and 2-4 Hz.
The average of the correlations for the whole time period is used as a ref-
erence for the stretching. For coda windows between 2 and 15 s (shown
in Figure A.14), we carry out a grid search over different stretching factors
between -0.1 to 0.1 with an increment of 0.0001, to allow for the large ve-
locity variations. We use the early coda and broader, overlapping frequency
bands to minimize cycle skipping due to large velocity fluctuations. The best
match is the stretching factor with the highest correlation coefficient, which
gives the estimate of relative velocity changes. We carry out the stretching separately for the causal and acausal side of the correlation waveform and average the obtained stretching factor. Since the coda is composed of scattered waves, it samples the medium multiple times/has a longer propagation path in the medium. Therefore, the method is sensitive to small changes in the medium and to a larger area.

For each component combination (ZZ, ZN, ZE, EE, EN and NN), we obtain a similarity matrix which contains the results from the trace stretching, i.e., the correlation coefficient between the stretched trace and the reference for the different stretching factors tested for all time steps (daily) of the time period analysed. We then compute the station averaged velocity change by averaging the similarity matrices of the different component combinations we want to use (can also be just a single one). The station averaged dv/v then corresponds to the stretching factor with the highest cumulative correlation coefficient of the averaged stretching matrices [51, 52].

## 5.2. Observed velocity variations due to environmental changes

We first consider the seismic velocity changes retrieved from vertical component autocorrelations. From previous landslide studies, we know that environmental influences can cause seasonal seismic velocity changes, which can vary from 1 to 6 % [8]. To distinguish velocity changes that could be related to potential instabilities in quick clay, we need to identify the seasonal, environmental fluctuations. Temperature, water content, snow depths and air pressure can affect seismic velocities in the sub-surface. Often, these effects overlap, making it difficult to differentiate between them. To attempt to understand the seismic velocity fluctuations in terms of subsurface changes, we relate the daily velocity changes to measurements of air temperature, precipitation, water level in a nearby river, and pore pressure in a well nearby. An overview of these environmental data compared to the dv/v at ALNN3 is shown in Figure A.15. The time period investigated includes a very dry summer in 2022, the extreme weather "Hans" in 2023, the very cold winter of 2023/2024 and a very wet summer in 2024. First, we try to entangle the effect of freezing and thawing on the near subsurface velocities.

### 5.2.1. Freezing and thawing

As the temperature decreases below zero and the soil gradually starts freezing, it becomes more rigid and the seismic waves travel faster. Therefore, we can use coda wave interferometry to track freezing and thawing cycles
[16, 18]. Steinmann et al. [53] showed that a frozen layer of less than 5 cm can cause apparent velocity increases above 2 %, depending on the components used for analysis, since Rayleigh and Love waves have different sensitivity to superficial freezing.

Figure 7 shows the extracted relative changes in seismic velocity and the highest correlation coefficient from the stretching in different frequency bands for station ALNN3 at Alna with air temperature, as well as a comparison for ALNN3, ALNN7, ALNN5 and ALNN6, in the frequency band 1.5-3 Hz. The latter frequency band provided stable results for all four stations. We use the 7-day-stack when comparing different frequency bands and stations to account for the different variations in noise sources. The strong increase in apparent velocities during the winter months, that we already observed in the correlation waveforms (Figure A.14), is very well anti-correlated with temperature. The freezing effect is visible in all frequency bands and stations used for analysis. For ALNN3, we observe a clear trend with stronger velocity changes for higher frequencies, with an increase of 2 % and 7 % for the highest frequencies used for analysis (Figure 7). The increase in seismic velocity is also accompanied by a decrease in correlation coefficient obtained from the stretching. The decorrelation increases with increasing frequencies.
<figure><img src="image_10.png" /><figcaption>Figure 7: **Freezing and thawing:** Relative changes in seismic velocities over three years calculated for three different frequency bands is displayed in the top panel, superposed on the air temperature measured. The middle panel shows the correlation coefficient from the associated stretching factors displayed in the top panel for the same frequency bands. In the bottom panel, the normalized dv/v for the four seismic stations is shown. Vertical lines are interpreted as freezing (blue) and thawing (red) from when the temperatures stay below or above zero degrees for 5 consecutive days.</figcaption></figure>

The winters over the time period analyzed become increasingly colder, both in terms of temperature and duration of freezing periods. The winter in 2022 and 2023 show a similar pattern with the coldest temperatures recorded in the beginning of the season (~15 January), while the winter 2024 has an
early onset with temperature below zero registered already in mid November,
but the lowest recorded temperature is registered in January.

Of the four stations we describe above, two are located in the mapped
quick clay hazard areas at Alna (ALNN3 and ALNN7) and two outside that
area at Linderud (ALNN5 and ALNN6). At Alna, thick sediments (ca. 37 m)
have been intercepted at the closest borehole and quick clay mapped within
the shallower 30 m. The presence of ca. 1m of fill material at the very top
of the borehole is also documented. No similar information is available for
Linderud but we can expect thinner sediment (ca. 10 m) over the bedrock
(ALNN5 and ALNN6). We observe the impact of frozen ground at both
location, with the freezing occurring simultaneously at all stations. How-
ever, thawing begins earlier at the Linderud stations. Given the recorded
air temperature, the maximum depth of the active thermal layer at both
locations has a maximum thickness of 3-4 m. Therefore, the difference in
thawing behavior may be linked to variations in shallow (less than 4 meters)
soil composition, which affects how water and heat are retained, as well as
differences in ground vegetation, which influence sunlight absorption. Addi-
tionally, snow cover, potentially impacted by human activities such as snow
removal, could play a role. However, it is not so far possible to conclusively
determine the underlying cause of the observed differences.

5.2.2. Soil moisture and groundwater levels

As the surface, when ice and snow melt and the ground thaws, we observe
a decrease in seismic velocities. Changes in the water content affect loading
and pore pressure in the subsurface. With more water in the ground, the
pore pressure increases and with an increase in pore pressure, the difference
between the confining pressure and pore pressure (i.e., effective pressure)
is decreasing, which reduces the seismic velocity. Therefore, seismic velocity
variations can be used to track changes in pore pressure or groundwater table
levels. Different studies have linked changes in seismic velocities to changes
in pore pressure or groundwater levels [11, 54, 55, 56].

We compare the precipitation and changes in groundwater level to the
measured changes in seismic velocity, alongside water level data from the
nearby Alna river in Figure 8, 1. The water level is derived from the pore
pressure measurements taken at a nearby piezometric well (PZ-9006 in Fig-
ure 1). Pore pressure measurements from the area are shown in Figure A.16
for the wells (PZ-9006, PZ-9009, HB-3720 and K-162) shown in Figure 1. The
water level in the river is included to complement and fill gaps in the other
datasets. A gradual decrease in seismic velocities, that takes longer than the
thawing of the frozen ground, associated with the influx of meltwater during
spring, is observed on all stations used for noise interferometry. At the Alna
stations (ALNN3 and ALNN7), we observe this effect for all analysed years
when higher water levels in the Alna river are measured, which further sug-
gests that the increase in meltwater is responsible for the velocity decrease.
The changes in seismic velocity for the stations at Linderud (ALNN5 and
ALNN6) are sharper, more rapid and larger in magnitude. This difference
could be due the difference in the depth of the bedrock at the two sites
(shallower at Linderud compared to Alna), which leads to different time that
will take to the sedimentary layer to get fully saturated, and to the different
porosity and permeability of both clay and bedrock.

Some of the drops in seismic velocity also coincide with precipitation and an increase in water level in the Alna river (Figure A.15). For example, there was one large rainfall event later in fall 2021, however, only one station (ALNN3) was recording at the time. Here, we can also see a clear decrease in dv/v (-0.5%, depending on the frequency band). The extreme weather event "Hans" in Southern Norway in August 2023 was associated with three heavy rainfall events in the Oslo region: on the 8th, 20th and 27th of August. The precipitation on the 8th of August lead to flooding that affected the power supply at ALNN3. This resulted in a gap of about 20 hours in the data at the start of the weather event, which causes the artifacts that we see at the at the start of the extreme weather (Figure A.15).

Still, we see a sharp drop in seismic velocity accompanying the heavy
rainfall associated with the beginning of "Hans" (Figure 8). The dv/v stays
low for the duration of the three rainfall associated with "Hans". The dv/v
for 2-4 Hz starts decreasing earlier than for the lower frequencies, which
means infiltration from the surface downwards, and returns to the same level
as before "Hans" faster than the other frequency bands. This is most likely
because the higher frequencies are more sensitive to the shallow changes in
soil moisture, rather than being sensitive to changes in (deeper) groundwater
level.

At ALNN3, for the lowest frequencies used for analysis, the dv/v is lower
for "Hans" than for the snowmelt in the spring, and also the previously
mentioned rainfall event during fall 2021. We see a similar pattern for the
other station in the quick clay area (ALNN7) with a sharp drop in velocity,
however, the dv/v is not lower for "Hans" than for the spring.

For the two stations at Linderud, we do not see a clear change in seismic
velocity with rainfall, even for the extreme weather event. This could be
attributed to direct runoff at the surface, due to difference in the shallow
subsurface at the two sites. It could also be because the noise correlation
functions are not stable enough to retrieve seismic velocity variations, and the
smaller changes are masked by the variation in noise sources. In section 5.3,
we attempt to increase the stability of the measurements.
<figure><img src="image_11.png" /><figcaption>Figure 8: Dv/v and water measurements: The top panel shows dv/v for station ALNN3 in three different frequency bands: 1-2.5 Hz (red), 1.5-3 Hz (green), 2-4 Hz (blue), using a 7 day stack, together with the water level in the Alna River, which flows through the quick clay risk area. The middle panel shows change in water level for PZ-9006 and dv/v with two different temporal resolutions, 1-day-stack in dark red and 7-day-stack in red, for 1-2.5 Hz. The bottom panel shows the dv/v for the four different stations for 1.5-3 Hz superposed on the daily precipitation. The grey shaded areas are gaps in the data. The station location and position of the river is marked in Figure 1.</figcaption></figure>

We see an anti-correlation between the change in seismic velocity and the groundwater level. The best correlation between the dv/v and groundwater level, we get for the lowest frequency band used in the study for ALNN3. Using a 7-day stack and only the time period when the ground is unfrozen, the dv/v and the groundwater level has a Pearson correlation coefficient,
$r$, of -0.69, pointing towards a linear relation between the two variables. Typically, $r$ larger than +/-.7 is considered a strong linear relationship. For the 1.5-3 Hz and 2-4 Hz band, $r$ equals -0.32 and -0.07, respectively. The lower the frequency, the deeper the sensitivity of the seismic waves. Hence, higher correlation at lower frequencies suggest that $dv/v$ seems to be most sensitive to changes in the (deeper) groundwater level, rather than water content near the surface which dissipates quickly into deeper layers. Higher pore pressure is associated with snow melt and many rainfall events. However, the highest pore pressure/groundwater levels observed, follows the extreme weather "Hans" in August 2023. This is also observed in the $dv/v$. For the summer of 2024, the velocities are overall lower than for the previous years, and stay low, especially for the lower frequency bands used for analysis. Although we do not have pore pressure data for this time period, it suggests that the groundwater level is higher throughout this summer compared to the previous years.

A linear relation between water level and $dv/v$ has previously been observed [54, 57]. However, a close analysis of our observations reveals two distinct paths when plotting $dv/v$ against pore pressure change: (i) the wetting curve, which shows how the soil retains water as it gets progressively wetter during infiltration, and (ii) the drying curve, which shows how the soil releases water as it dries. This hysteresis effect, previously been observed by Garambois et al. [58], Gaubert-Bastide et al. [59] at larger scale, shows that under typical conditions, the drying curve lies above the wetting curve. For a given suction (capillary pressure), drying soil retains more water than wetting soil. This behavior is attributed to differences in pore structure and surface tension effects during wetting and drying cycles. We can therefore use the observed hysteresis as an additional observation to better understand the soil characteristics and its behavior under changes in moisture condition. The negative pore pressure values also point towards the soil being unsaturated.
<figure><img src="image_12.png" /><figcaption>Figure 9: Dv/v vs. pore pressure: the extracted relative velocity changes are plotted against changes in pore pressure, with (top) colors representing different time-periods. The first (blue) identified in the right top panel shows the increase in water content during snowmelt, followed by a drying period. A close-up is shown in the bottom left panel. The second period (green) is shown as close-up in the bottom right panel, and represents the subsurface drying process followed by wetting period due to rainfall.</figcaption></figure>

5.3. Including horizontal components to increase stability and improve tem-
poral resolution of noise correlations

Up to this point, we have only looked at results from vertical compo-
nent auto-correlations. Often, the horizontal components are discarded due
to lower signal-to-noise ratio of the EGFs. Here we investigate whether
considering all components and combinations, as well as averaging different
component combinations [51], can increase the stability of our measurements
and in turn improve the temporal resolution of our dv/v measurements.

Typically, 1-day-stacks, meaning one day resolution, is used for monitor-
ing natural hazards using seismic noise interferometry. In our investigation,
we aim for the same. The changes in seismic velocities retrieved from different component combinations are shown in Figure A.17 and A.18, for Alna and Linderud respectively. In general, the averaging makes the dv/v curves smoother and can remove artifacts in the velocity changes for example due to noise source variations.

Apparent changes in seismic velocity can be caused by changes in the noise sources. Because our stations are placed in an urban environment and we are using high frequencies, our noise sources change with pattern in the anthropogenic activity (section 3). Due to the week-weekend variations in the noise field, weekly fluctuations are visible in the dv/v, especially for the 2-4 Hz band (Figure 10 and A.19 in the supplementary material). In Figure 10 we see that averaging the results from the stretching helps smooth out these weekly variations. For example, looking at spring to winter 2023 for ALNN7 at Alna, we now obtain similar patterns of decreasing velocities with meltwater increase and with the occurrence of "Hans" for the 2 to 4 Hz frequency band as for the other frequency bands and other stations, using the station average (Fig. 10). Using vertical components only, this pattern in dv/v was not as clear.

The early part of the coda of the noise correlation functions is dominated by surface waves [60]. In the case of the vertical component autocorrelation, we only observe Rayleigh waves, since Love wave ground motion has only horizontal components. For all other component combinations, we have to consider a mix of Rayleigh and Love waves.

Since effects of Rayleigh and Love waves are averaged, the component averaging can also cause issues by removing or dampening sudden, real changes. For example, the vertical component autocorrelation for frequencies 1-2.5 Hz at ALNN3, we observe lower relative velocities for the extreme weather event "Hans" than during snow melt, which is consistent with pore pressure measurements. That is not the case for results obtained for results from component averaging. Also at Linderud, not only the urban noise source variations are smoothed when averaging over all components, but also the sharp variations that we observed for the 1.5-3 Hz frequency band in spring are removed. We observe different, non-consistent dv/v variations during the freezing periods for different component combinations. This could be related to Love waves and Rayleigh waves responding differently to the frozen layer [53], cycle skipping when comparing EGFs during stretching due to large velocity changes (dv/v is underestimated), less stable correlations for some component combinations or the location of the sources. Therefore, averaging
the similarity matrix does not remove the cycle skipping effects during the winter months. For example, for ALNN3 (Figure A.17), averaging produces more unstable results for the lowest frequency band used for analysis.

Nevertheless, although systematically interpreting and averaging all component combinations does not improve results, we can still use individual combinations including horizontal components in some frequency bands which achieve high correlation coefficient and less cycle skipping. Depending on what feature you want to focus on, frozen or unfrozen conditions, and which station you want to use, individual combinations including horizontal components in some frequency bands which achieve high correlation coefficient and less cycle skipping. For example, at Linderud for station ALNN5 in Figure A.20 and A.18, we get more stable results the lowest frequency band from the station average (averaging over all component combinations), while for the highest frequency band we get the most stable results when using either the east component autocorrelation or the east-vertical cross component correlation.

In addition to the weekly variations, we can also spot the effect of specific events on the noise correlation functions, for example, there is a spike on May 1st, which is a national holiday, as well as for the day of the Rammstein concert that we investigated in Section 3. Most likely, seismic noise originated from a dominant direction on these days and, thus, the interferometry results were biased. These artifacts contributed to changes in the noise field are reduced by including horizontal components, either by averaging all component combinations, using individual horizontal component autocorrelations or single station cross-correlation.

In general, there are more changes in the noise field at Linderud, which means less favorable conditions for seismic interferometry, as it leads to stronger weekly variations in $dv/v$. On the other hand, at Alna, where anthropogenic activity is more constant, we have more favorable conditions for seismic interferometry. In all cases we still see the $dv/v$ variation due to the urban noise variation, which should be taken into consideration when interpreting the results. However, the changes that we can trace back to environmental velocity fluctuations in the subsurface are larger than the changes that we see for changes in noise sources.
<figure><img src="image_13.png" /><figcaption>Figure 10: **Effect of averaging over different component combinations at ALNN7:** The dvv for vertical component autocorrelation and for averaging over different component combinations for a 1-day-stack is shown for three frequency bands, 1-2.5 Hz, 1.5-3 Hz and 2-4 Hz, starting with the lowest frequency band in the top panel. The shaded area represents the rms error for the stretching results [61] of the vertical component autocorrelation. The averaging reveals a similar trend for the 2-4 Hz band as for the lower frequency bands with lower velocities in the spring and for the extreme weather event, that was previously not visible only using the vertical component.</figcaption></figure>

**6. Depth sensitivity and dispersion curve modeling**

For the stretching method to compute dv/v, we used the early coda, which is typically dominated by surface waves. The sensitivity to surface waves decreases rapidly with depth. For depths larger than 2/3 of the wavelength, the surface waves are no longer sensitive to the structure [60]. For a velocity range between 50-300 m/s, that corresponds to 11-66 m, 15-88 m and 19-114 m, for the 2-4 Hz, 1.5-3 Hz and 1-2.5 Hz bands respectively. These depth
ranges are well aligned with the occurrence of quick clay in the depth in our study area, making this approach relevant for the potential detection of environmental and subsurface changes that could precede a quick clay-related landslide. Furthermore, the different depths of the bedrock at the two analyzed sites at Alna are probably captured to some extent by all frequency ranges.

To assess whether the frequencies used in our analysis are sensitive to change at the depth where the quick clay could destabilize and initiate a landslide, we model the Rayleigh wave dispersion curves using the Geopsy software [62] and HVInv software [63]. We inverted the measured horizontal to vertical spectral ratio (H/V) data from ALNN3 to derive a representative (best-fitting) 1D-velocity for this site (Figure 11). To test the sensitivity, we introduced two cases, a and b, by reducing the Vs velocity of the best-fitting model by 10% and 40%, respectively, for a 5 m thick layer at 5 m depth. The dispersion curve modeling shows a decrease in surface wave velocity within the frequency range of interest, with the largest changes in the highest frequency band. For the center frequency of the different frequency bands (1-2.5 Hz, 1.5-3 Hz and 2-4 Hz), we observe changes of -0.0890%/-0.5935%, -0.6903%/-4.7754% and -1.1751%/-7.0358% for a 10% and 40% velocity decrease, respectively. Expected associated changes in density and Vp have not been taken into account here.

# 7. Discussion and potential for quick clay failure monitoring

In this study, our aim is to obtain information on the feasibility of using low-cost seismic sensors for quick clay failure monitoring systems in urban quick clay areas, by analyzing the seismic velocity variation (dv/v) derived from urban seismic noise. First, we have investigated the variability of the noise field (see Section 3). The temporal resolution of velocity variations is limited by changes in the noise field, i.e., strong day-night and week-weekend variation. Despite the significant variability in the noise field, mostly due to variations associated with the urban rhythm of life, we are able to obtain stable noise correlation functions for 1-day stacks. The week-weekend fluctuations due to human activity are visible in our dv/v results; however, these changes are smaller than the changes associated with subsurface changes and lie within the dv/v measurement error. However, these urban noise variations could mask the smaller dv/v variations, and therefore we recommend a
<figure><img src="image_14.png" /><figcaption>Figure 11: **Dispersion curve modeling:** (left) Vs profile with inverted model with misfit less than 100. (top right) Horizontal to vertical spectral ratio. Measured H/V for the ALNN3 station are shown in yellow as mean and standard deviation. (c) Dispersion curve showing the fundamental and first mode. The best-fit inverted model is shown as black line and the two cases, a and b corresponding to a reduction of 10% and 40% Vs velocity as red and blue lines, respectively.</figcaption></figure>

careful analysis of all dv/v patterns including different components and their
average in different frequency bands.

Second, we investigated the extracted velocity changes and interpreted
spatio-temporal variations with respect to environmental measurements. The
dv/v results obtained in our study are mainly due to shallow changes in the
sub-surface, being dominated by environmental effects. Environmental fac-
tors, such as freezing and thawing, were found to produce large velocity
variations, as similarly observed by [64, 53]. During winter months, freezing and thawing dominate velocity variations and could therefore mask potential changes related to other subsurface processes[64]. However, it may give us other insight into the effect of freezing and thawing on the ground properties. The process of freezing and thawing cycles significantly affects the mechanical and hydraulic properties of clay soils. Repeated freeze-thaw cycles alter soil structure, affecting its strength, cohesion, and hydraulic conductivity. For example, in terms of hydraulic properties, [65] have explored the behavior of compacted clay specimens in both laboratory and field under freeze-thaw conditions. They observed that the overall hydraulic conductivity remained largely unchanged. However, redistribution and desiccation of water, mostly above but also just below the freezing plane caused a localized increases in hydraulic conductivity. In terms of mechanical property, [66] conducted a laboratory experiment and revealed that with an increasing number of freeze-thaw cycles, the static strength and cohesion of the clay decreased exponentially, indicating a deterioration in the soil structure. However, they also observed an exponential increase of the friction coefficient. This suggests that while the soil becomes weaker overall, it might resist more to sliding. Observations of freezing and thawing cycles via dv/v as we show here therefore has the potential to better understand these processes.

Our results also reveal changes in pore pressure/water level in the subsurface. In contrast to borehole point measurements, seismic methods have the advantage that they are sensitive to a larger area and we can use different frequencies to probe different depths and give a more complete picture of the subsurface. It is also non-invasive. These measurements can therefore complement the pore pressure measurements in the area used for monitoring during the construction of the new tunnels for water supply in Oslo.

We also observe a hysteresis between the dv/v and the pore pressure which can be used to visualize the soil behavior under changes in moisture condition. An increase in the distance between the wetting and drying curves is indicative of a change in soil behavior, linked to its ability to retain and drain water derived from precipitation or snowmelt. This change can lead to higher pore pressures, reduced soil strength, and ultimately, slope failure (e.g. [67, 68]). Thus, the hysteresis effect can also provide insight into potential landslide hazard. By understanding how the soil's ability to retain and release water changes over time, hysteresis analysis can help to predict critical shifts in pore pressure and mechanical stability, improving monitoring systems and mitigation strategies for landslides.
In order to monitor and alert on quick clay instability, we need: i) more or less constant dv/v when the quick clay is stable, meaning stable noise sources as well as limited environmental influences, and ii) that the frequencies that we are using are sensitive at the depth where the quick clay could destabilize, so that a reduction in rigidity would be visible in the dv/v.

We summarize the different possible outcomes for failure precursor monitoring in Figure 12. Can we expect the dv/v to not vary when there is no change in quick clay stability? In the case of stability of the noise sources and limited environmental changes, which could potentially be modeled and removed, we can indeed expect the dv/v to remain stable in the case that the soil remains stable.

Do we observe any unusual changes in dv/v when the soil remains stable, i.e. can we avoid false alarms? During the time that we investigated, there are no drops in dv/v that seem unusual, except for the extreme weather event Hans. However, the fact that the drops in dv/v associated with Hans correlates well with pore pressure measurements and water levels and it is limited in time, well explains that it is caused by environmental forcing and would allow modeling and removing the change from the dv/v time series.

Would we detect a quick clay failure precursor if there is one? For quick clay, we do not know what to expect: the change might be larger than the -7% drop observed by Mainsant et al. [6] several days before a clay landslide, but also the time lag between this change and the slide might be very short. Since we did not observe/record during a quick clay failure in this study, we cannot define a precursor. However, through dispersion curve modeling (Figure 11), we verify that the frequencies used in our study are sensitive to a velocity change at the depths where we expect the quick clay to destabilize, indicating the potential for detecting such changes.

Lastly, could there be a failure that we are not able to detect in the dv/v, i.e., a false negative? For answering this we need further and broader experiments. However, there could of course occur dv/v variations due to environmental forcing, such as freezing and thawing effects, that could mask a small medium change related to quick clay failure.

## 8. Conclusion

In summary, our study gave us valuable insights into what kind of subsurface changes can be resolved and are expected over longer time periods and under stable conditions in the quick clay area under investigation in Oslo.
<figure><img src="image_15.png" /><figcaption>Figure 12: Summary of possible outcomes from failure quick clay precursor monitoring</figcaption></figure>

This will potentially allow us to identify unusual or permanent changes that cannot be explained by the environmental parameters investigated in this study. This builds a baseline for a quick clay failure monitoring system, and demonstrates the importance and opportunity to conduct further tests and experiments in the field. Through dispersion curve modeling, we verified the sensitivity of surface waves in the frequency band of interest to velocity change at depth. Therefore, observed velocity variations at the site could have potential for monitoring quick clay instabilities.

## Acknowledgements

We acknowledge the Norwegian Research Council for their support through the GEObyIT project (grant no. 311596) and Centre for Geophysical Forecasting (project no. 309960). We also acknowledge the Oslo municipality and Ingjerd Mørck for providing pore pressure measurements and for the valuable discussions.

## Data and code availability

Seismic data from the Oslo network until September 2023 are openly available at the Norwegian EIDA node [69] with the network code 4X [70].
Weather data is obtained from The Norwegian Meteorological Institute (https://frost.met.no/index.html). Information about the water level in the Alna river is available through The Norwegian Water Resources and Energy Directorate (NVE) (https://sildre.nve.no/).

The analysis was done using the Obspy [71], Numpy [72] and SciPy [73] libraries. Seismic [74] was used for computing auto-correlations and single station cross-correlations and measuring relative velocity changes. Geopsy [62] and HVInv [63] were used in the H/V inversion and dispersion curves. Figures were created using Matplotlib [75].
Appendix A. Appendix

<figure><img src="image_16.png" /><figcaption>Figure A.13: Ambient noise variation over time at Alna (ALNN7). Top panel shows the power spectral density over time, while the middle panels show the temporal evolution of the power spectral density at 0.2 and 2 Hz in blue and orange, respectively. The lowermost panel shows 2 months selected from the data presented in the upper panels, highlighted with a red rectangle.</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>Figure A.14: **Correlations over time at Alna:** One day-stacked vertical component autocorrelations for ALNN3 and ALNN7 from 2021-05-19 to 2024-07-26, which is the time period used for analysis. The x-axis shows the time lag ($\tau$) in seconds.</figcaption></figure>

The red vertical lines show the part of the coda used for stretching.

## Pore pressure measurements

At Alna, the local pore pressure is measured by piezometric wells (PZ-9006, PZ-9009, HB-3720 and K-1620 in Figure 1). They measure pressure head, which corresponds to how tall the water column is above the sensors. From the measurements, we remove atmospheric pressure and convert to water level. The water table is the depth where the ground is fully saturated with water. Figure A.16 shows the pore pressure for the different wells after subtracting the atmospheric pressure. Wells are point measurements, and
<figure><img src="image_18.png" /><figcaption>Figure A.15: Overview of relative changes in seismic velocities and environmental parameters: dv/v for ALNN3 vertical component autocorrelation for a 1-day-stack superposed on temperature, daily precipitation, water level in a nearby river and groundwater level from a nearby well. The two arrows correspond to the day with the most precipitation and the coldest day from the time period investigated. Pink shaded areas show gaps in the environmental data.</figcaption></figure>

are therefore very sensitive to local lateral variations. Two of the wells are
drilled in hard rock (HB-370 og K-1620). The wells in hard rock typically
react faster and stronger to changes in groundwater content, because they measure the groundwater moving quickly in cracks in bedrock, compared to for example a measurement in clay, where the water moves more slowly. HB-370 is at 16,2 meters depth, 80,5 meters above sea level and is placed in casing in sediments. K-1620 is at 7 meters depth, 77,3 meters above sea level, also in sediments with casing. In addition, K-1620 is artesic. PZ-9009 is placed at 19,8 meters depth, at 63,9 meters above sea level, above the bedrock in sand and gravel. It is located close to the Alna river and therefore has induced pore pressure from the river, especially visible for the extreme weather "Hans" which causes the spikes in the data August 2023. PZ-9006 is at 13,3 meter depth, 79,8 meters above sea level and is placed above bedrock in clay. Except for the large spikes, PZ-9006 and PZ-9009 display very similar trends, with higher pore pressure during snow melt and accompanying the extreme weather, suggesting that the well PZ-9006 is not dry. Negative pore pressure values could be due to a calibration error or that the atmospheric pressure subtracted is from a different location and elevation, or that the ground is unsaturated. Since PZ-9006 displays seasonal variation, it is further away from the river, and is the closest to our stations at Alna (49 meters from ALNN7 and 90 meters from ALNN3), we use this well for analysis. After converting the changes in pore pressure to water level, this corresponds to water level changes of less than half a meter for the time period we have measurements from.

## References

[1] J. Díaz, M. Ruiz, P. S. Sánchez-Pastor, P. Romero, Urban seismology: On the origin of earth vibrations within a city, Scientific reports 7 (2017) 15296.

[2] S. T. R. Maciel, M. P. Rocha, M. Schimmel, Urban seismic monitoring in Brasília, Brazil, Plos one 16 (2021) e0253610.

[3] T. Lecocq, S. P. Hicks, K. Van Noten, K. Van Wijk, P. Koelemeijer, R. S. De Plaen, F. Massin, G. Hillers, R. E. Anthony, M.-T. Apoloner, et al., Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures, Science 369 (2020) 1338–1343.

[4] N. Riahi, P. Gerstoft, The seismic traffic footprint: Tracking trains,
<figure><img src="image_19.png" /><figcaption>Figure A.16: **Pore pressure and groundwater level at Alna:** The top panel shows the measured pore pressure at three different locations at Alna. The lower panel shows the groundwater level calculated from the pore pressure measurements above. The locations of the different wells are shown in Figure 1</figcaption></figure>

aircraft, and cars seismically, Geophysical Research Letters 42 (2015) 2674-2681.

[5] R. Tomás, A. Abellán, M. Cano, A. Riquelme, A. Tenza-Abril, F. Baeza-Brotons, J. Saval, M. Jaboyedoff, A multidisciplinary approach for the investigation of a rock spreading on an urban slope, Landslides 15 (2018) 199-217.

[6] G. Mainsant, E. Larose, C. Brönnimann, D. Jongmans, C. Michoud, M. Jaboyedoff, Ambient seismic noise monitoring of a clay landslide: Toward failure prediction, Journal of Geophysical Research: Earth Surface 117 (2012).

[7] G. Milana, G. Cultrera, P. Bordoni, A. Bucci, F. Cara, R. Cogliano, G. Di Giulio, D. Di Naccio, D. Famiani, A. Fodarella, et al., Local site effects estimation at Amatrice (Central Italy) through seismological methods, Bulletin of Earthquake Engineering 18 (2020) 5713-5739.

[8] M. Le Breton, N. Bontemps, A. Guillemot, L. Baillet, É. Larose, Landslide monitoring using seismic ambient noise correlation: challenges and applications, Earth-Science Reviews 216 (2021) 103518.
[9] O. I. Lobkis, R. L. Weaver, On the emergence of the Green's function in the correlations of a diffuse field, The Journal of the Acoustical Society of America 110 (2001) 3011-3017.

[10] M. Campillo, A. Paul, Long-range correlations in the diffuse seismic coda, Science 299 (2003) 547-549.

[11] C. Sens-Schönfelder, U. Wegler, Passive image interferometry and seasonal variations of seismic velocities at Merapi Volcano, Indonesia, Geophysical research letters 33 (2006).

[12] J. Salvermoser, C. Hadziioannou, S. C. Stähler, Structural monitoring of a highway bridge using passive noise recordings from street traffic, The Journal of the Acoustical Society of America 138 (2015) 3864-3872.

[13] T. Lecocq, L. Longuevergne, H. A. Pedersen, F. Brenguier, K. Stammler, Monitoring ground water storage at mesoscale using seismic noise: 30 years of continuous observation and thermo-elastic and hydrological modeling, Scientific reports 7 (2017) 14241.

[14] E. Fokker, E. Ruigrok, R. Hawkins, J. Trampert, Physics-based relationship for pore pressure and vertical stress monitoring using seismic velocity variations, Remote Sensing 13 (2021) 2684.

[15] V. Rodríguez Tribaldos, J. B. Ajo-Franklin, Aquifer monitoring using ambient seismic noise recorded with distributed acoustic sensing (DAS) deployed on dark fiber, Journal of Geophysical Research: Solid Earth 126 (2021) e2020JB021004.

[16] S. James, H. Knox, R. Abbott, M. Panning, E. Screaton, Insights into permafrost and seasonal active-layer dynamics from ambient seismic noise monitoring, Journal of Geophysical Research: Earth Surface 124 (2019) 1798-1816.

[17] F. Lindner, J. Wassermann, H. Igel, Seasonal freeze-thaw cycles and permafrost degradation on Mt. Zugspitze (German/Austrian Alps) revealed by single-station seismic monitoring, Geophysical Research Letters 48 (2021) e2021GL094659.
[18] J. Albaric, D. Kühn, M. Ohrnberger, N. Langet, D. Harris, U. Polom, I. Lecomte, G. Hillers, Seismic monitoring of permafrost in Svalbard, Arctic Norway, Seismological Society of America 92 (2021) 2891–2904.

[19] P. Sánchez-Pastor, A. Obermann, M. Schimmel, C. Weemstra, A. Verdel, P. Jousset, Short-and long-term variations in the reykjanes geothermal reservoir from seismic noise interferometry, Geophysical Research Letters 46 (2019) 5788–5798.

[20] F. Brenguier, N. M. Shapiro, M. Campillo, V. Ferrazzini, Z. Duputel, O. Coutant, A. Nercessian, Towards forecasting volcanic eruptions using seismic noise, Nature Geoscience 1 (2008) 126–130.

[21] B. A. Chouet, R. S. Matoza, A multi-decadal view of seismic methods for detecting precursors of magma movement and eruption, Journal of Volcanology and Geothermal Research 252 (2013) 108–175.

[22] M. Schimmel, E. Stutzmann, J. Gallart, Using instantaneous phase coherence for signal extraction from ambient noise data at a local to a global scale, Geophysical Journal International 184 (2011) 494–506.

[23] T. Masterlark, M. Haney, H. Dickinson, T. Fournier, C. Searcy, Rheologic and structural controls on the deformation of okmok volcano, Alaska: FEMs, InSAR, and ambient noise tomography, Journal of Geophysical Research: Solid Earth 115 (2010).

[24] Z. Duputel, V. Ferrazzini, F. Brenguier, N. Shapiro, M. Campillo, A. Nercessian, Real time monitoring of relative velocity changes using ambient seismic noise at the Piton de la Fournaise volcano (La Réunion) from January 2006 to June 2007, Journal of Volcanology and Geothermal Research 184 (2009) 164–173.

[25] E. Larose, S. Carrière, C. Voisin, P. Bottelin, L. Baillet, P. Guéguen, F. Walter, D. Jongmans, B. Guillier, S. Garambois, et al., Environmental seismology: What can we learn on earth surface processes with ambient noise?, Journal of Applied Geophysics 116 (2015) 62–74.

[26] C. Voisin, S. Garambois, C. Massey, R. Brossier, Seismic noise monitoring of the water table in a deep-seated, slow-moving landslide, Interpretation 4 (2016) SJ67–SJ76.
[27] N. Bontemps, P. Lacroix, E. Larose, J. Jara, E. Taipe, Rain and small earthquakes maintain a slow-moving landslide in a persistent critical state, Nature Communications 11 (2020) 780.

[28] A. Khaldoun, P. Moller, A. Fall, G. Wegdam, B. de Leeuw, Y. Méheust, et al., Quick Clay and Landslides of Clayey Soils, Physical Review Letters 103 (2004).

[29] T. Okamoto, J. O. Larsen, S. Matsuura, S. Asano, Y. Takeuchi, L. Grande, Displacement properties of landslide masses at the initiation of failure in quick clay deposits and the effects of meteorological and hydrological factors, Engineering geology 72 (2004) 233-251.

[30] R. A. Rørstadbotnen, H. Dong, M. Landrø, K. Duffaut, K. Growe, U. Kakhkhorov, S. Wienecke, J. Jacobsen, Quick clay monitoring using distributed acoustic sensing: A case study from Rissa, Norway, Geophysics 88 (2023) B267-B283.

[31] R. Snieder, A. Grêt, H. Douma, J. Scales, Coda wave interferometry for estimating nonlinear behavior in seismic velocity, Science 295 (2002) 2253-2255.

[32] A. Obermann, T. Planès, C. Hadziioannou, M. Campillo, Lapse-time-dependent coda-wave depth sensitivity to local velocity perturbations in 3-D heterogeneous elastic media, Geophysical Journal International 207 (2016) 59-66.

[33] A. Köhler, E. Myklebust, A. M. Dichiarante, V. Oye, Monitoring urban construction and quarry blasts with low-cost seismic sensors and deep learning tools in the city of Oslo, Norway, Seismica 3 (2024).

[34] D. Healy, Listening to Manchester: Using citizen science Raspberry Shake seismometers to quantify road traffic, 2023.

[35] K.-G. Hinzen, H. Krummel, B. Weber, C. Fleischer, Forensic view on two Raspberry Shake burglagrams, Journal of Seismology 26 (2022) 863-873.

[36] J. M. Holmgren, M. J. Werner, Raspberry shake instruments provide initial ground-motion assessment of the induced seismicity at the united
downs deep geothermal power project in Cornwall, United Kingdom,
The Seismic Record 1 (2021) 27–34.

[37] P. Bottelin, L. Baillet, Original insights into rock slope damage processes until collapse from passive seismic monitoring, Geophysical Research Letters 51 (2024) e2024GL109139.

[38] F. Ghione, A. Köhler, A. M. Dichiarante, I. Aarnes, V. Oye, Vs30 and depth to bedrock estimates from integrating hvr measurements and geology-slope approach in the Oslo area, Norway, Frontiers in Earth Science 11 (2023) 1242679.

[39] D. Arosio, A. Aguzzoli, L. Zanzi, L. Panzeri, D. Scaccabarozzi, Lab and Field Tests of a Low-Cost 3-Component Seismometer for Shallow Passive Seismic Applications, Earth and Space Science 10 (2023) e2023EA002934.

[40] D. E. McNamara, R. P. Buland, Ambient noise levels in the continental United States, Bulletin of the seismological society of America 94 (2004) 1517–1527.

[41] J. Peterson, et al., Observations and modeling of seismic background noise, volume 93, US Geological Survey Reston, VA, USA, 1993.

[42] J. Groos, J. Ritter, Time domain classification and quantification of seismic noise in an urban environment, Geophysical Journal International 179 (2009) 1213–1231.

[43] S. Erlingsson, A. Bodare, Live load induced vibrations in Ullevi Stadium—dynamic dynamic soil analysis, Soil Dynamics and Earthquake Engineering 15 (1996) 171–188.

[44] P. Denton, One step beyond, Astronomy and Geophysics 55 (2014) 5–30.

[45] G. Tepp, I. Stubailo, M. Kohler, R. Guy, Y. Bozorgnia, Shake to the Beat: Exploring the Seismic Signals and Stadium Response of Concerts and Music Fans, Authorea Preprints (2024).

[46] F. Fuchs, G. Bokelmann, A. W. Group, Equidistant spectral lines in train vibrations, Seismological Research Letters 89 (2018) 56–66.
[47] F. Lavoué, O. Coutant, P. Boué, L. Pinzon-Rincon, F. Brenguier, R. Brossier, P. Dales, M. Rezaeifar, C. J. Bean, Understanding seismic waves generated by train traffic via modeling: Implications for seismic imaging and monitoring, Seismological Society of America 92 (2021) 287–300.

[48] A. E. Jerkins, V. Oye, C. Alvizuri, F. Halpaap, T. Kværna, The 21 March 2022 m w 5.1 Tampen Spur Earthquake, North Sea: Location, Moment Tensor, and Context, Bulletin of the Seismological Society of America 114 (2024) 741–757.

[49] C. Hadziioannou, E. Larose, O. Coutant, P. Roux, M. Campillo, Stability of monitoring weak changes in multiply scattering media with ambient noise correlation: Laboratory experiments, The Journal of the Acoustical Society of America 125 (2009) 3688–3695.

[50] T. D. Mikesell, A. E. Malcolm, D. Yang, M. M. Haney, A comparison of methods to estimate seismic phase delays: Numerical examples for coda wave interferometry, Geophysical Journal International 202 (2015) 347–360.

[51] L. Illien, C. Sens-Schönfelder, K.-Y. Ke, Resolving minute temporal seismic velocity changes induced by earthquake damage: the more stations, the merrier?, Geophysical Journal International 234 (2023) 124–135.

[52] P. Makus, C. Sens-Schönfelder, L. Illien, T. R. Walter, A. Yates, F. Tilmann, Deciphering the whisper of volcanoes: Monitoring velocity changes at Kamchatka’s Klyuchevskoy group with fluctuating noise fields, Journal of Geophysical Research: Solid Earth 128 (2023) e2022JB025738.

[53] R. Steinmann, C. Hadziioannou, E. Larose, Effect of centimetric freezing of the near subsurface on Rayleigh and Love wave velocity in ambient seismic noise correlations, Geophysical Journal International 224 (2021) 626–636.

[54] T. Clements, M. A. Denolle, Tracking groundwater levels using the ambient seismic field, Geophysical Research Letters 45 (2018) 6459–6465.
[55] C. Voisin, M. A. R. Guzmán, A. Réfloch, M. Taruselli, S. Garambois, Groundwater monitoring with passive seismic interferometry, Journal of Water Resource and Protection 9 (2017) 1414–1427.

[56] E. Fokker, E. Ruigrok, R. Hawkins, J. Trampert, 4D physics-based pore pressure monitoring using passive image interferometry, Geophysical Research Letters 50 (2023) e2022GL101254.

[57] Q.-Y. Wang, F. Brenguier, M. Campillo, A. Lecointre, T. Takeda, Y. Aoki, Seasonal crustal seismic velocity changes throughout Japan, Journal of Geophysical Research: Solid Earth 122 (2017) 7987–8002.

[58] S. Garambois, C. Voisin, M. Romero Guzman, D. Brito, B. Guillier, A. Réfloch, Analysis of ballistic waves in seismic noise monitoring of water table variations in a water field site: added value from numerical modelling to data understanding, Geophysical Journal International 219 (2019) 1636–1647.

[59] T. Gaubert-Bastide, S. Garambois, C. Bordes, C. Voisin, L. Oxarango, D. Brito, P. Roux, High-resolution monitoring of controlled water table variations from dense seismic-noise acquisitions, Water Resources Research 58 (2022) e2021WR030680.

[60] A. Obermann, T. Planès, E. Larose, C. Sens-Schönfelder, M. Campillo, Depth sensitivity of seismic coda waves to velocity perturbations in an elastic heterogeneous medium, Geophysical Journal International 194 (2013) 372–382.

[61] R. L. Weaver, C. Hadziioannou, E. Larose, M. Campillo, On the precision of noise correlation interferometry, Geophysical Journal International 185 (2011) 1384–1392.

[62] M. Wathelet, J.-L. Chatelain, C. Cornou, G. D. Giulio, B. Guillier, M. Ohrnberger, A. Savvaidis, Geopsy: A user-friendly open-source tool set for ambient vibration processing, Seismological Research Letters 91 (2020) 1878–1889.

[63] A. García-Jerez, J. Piña-Flores, F. J. Sánchez-Sesma, F. Luzón, M. Perton, A computer code for forward calculation and inversion of the h/v spectral ratio under the diffuse field assumption, Computers & geosciences 97 (2016) 67–78.
[64] M. Gassenmeier, C. Sens-Schönfelder, M. Delatre, M. Korn, Monitoring of environmental influences on seismic velocity at the geological storage site for CO2 in Ketzin (Germany) with ambient seismic noise, Geophysical Journal International 200 (2014) 524–533.

[65] C. H. Benson, M. A. Othman, Hydraulic conductivity of compacted clay frozen and thawed in situ, Journal of Geotechnical Engineering 119 (1993) 276–294.

[66] M. Wang, S. Meng, Y. Sun, H. Fu, Shear strength of frozen clay under freezing-thawing cycles using triaxial tests, Earthquake Engineering and Engineering Vibration 17 (2018) 761–769.

[67] Z. Yang, H. Cai, W. Shao, D. Huang, T. Uchimura, X. Lei, H. Tian, J. Qiao, Clarifying the hydrological mechanisms and thresholds for rainfall-induced landslide: in situ monitoring of big data to unsaturated slope stability analysis, Bulletin of Engineering Geology and the Environment 78 (2019) 2139–2150.

[68] H. Han, B. Shi, L. Zhang, Prediction of landslide sharp increase displacement by svm with considering hysteresis of groundwater change, Engineering Geology 280 (2021) 105876.

[69] L. Ottemöller, J. Michalek, J.-M. Christensen, U. Baadshaug, F. Halpaap, Ø. Natvik, T. Kværna, V. Oye,UiB-NORSAR EIDA node: Integration of seismological data in Norway, Seismological Society of America 92 (2021) 1491–1500.

[70] Andreas Köhler, Geobyit seismic network in oslo, norway, 2021. URL: https://www.fdsn.org/networks/detail/4X_2021/. doi:10.7914/3DMS-SJ84.

[71] L. Krischer, T. Megies, R. Barsch, M. Beyreuther, T. Lecocq, C. Caudron, J. Wassermann, ObsPy: A bridge for seismology into the scientific Python ecosystem, Computational Science & Discovery 8 (2015) 014003.

[72] C. R. Harris, K. J. Millman, S. J. Van Der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, et al., Array programming with NumPy, Nature 585 (2020) 357–362.
[73] P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, et al., SciPy 1.0: fundamental algorithms for scientific computing in Python, Nature methods 17 (2020) 261–272.

[74] P. Makus, C. Sens-Schönfelder, SeisMIC-an Open Source Python Toolset to Compute velocity changes from ambient seismic noise, 2023.

[75] J. D. Hunter, Matplotlib: A 2D graphics environment, Computing in science & engineering 9 (2007) 90–95.
<figure><img src="image_20.png" /><figcaption>Figure A.17: Relative velocity changes extracted from autocorrelations and cross-component-correlations Alna: dvv for all component combinations is displayed for frequency bands 1-2.5 Hz in red, 1.5-3 Hz in green and 2-4 Hz in blue for the two stations at ALNN3 and ALNN7. The average of the stretching results from the different combinations is shown in the lower panels for each frequency band.</figcaption></figure>
<figure><img src="image_21.png" /><figcaption>Figure A.18: Relative velocity changes extracted from autocorrelations and cross-component-correlations Linderud: dvv for all component combinations is displayed for frequency bands 1-2.5 Hz in red, 1.5-3 Hz in green and 2-4 Hz in blue for the two stations at ALNN5 and ALNN6. The average of the stretching results from the different combinations is shown in the lower panels for each frequency band.</figcaption></figure>
<figure><img src="image_22.png" /></figure>

Figure A.19: Effect of averaging over different component combinations for snowmelt and Hans at ALNN3:
<figure><img src="image_23.png" /></figure>

Figure A.20: **Effect of cultural events on dvv at Linderud:** The changes in relative seismic velocity is shown for ALNN5 for different frequency bands (1-2.5 Hz, 1.5-3 Hz, 2-4 Hz). The shaded area represents the rms error for the stretching results [61] of the vertical component autocorrelation. The weekly variations in noise level show up as spikes in the dvv, spaced around 7 days apart. For 2-4 Hz, we see an impact on the national holiday May 1st and the Rammstein concert (Figure 4). Note that both of these events are on Sundays, when we would expect the weekly variations to show up. These artifacts contributed to changes in the noise field are reduced by including horizontal components, either by averaging all component combinations, using the horizontal component autocorrelations or single station cross correlation, show in the bold darker colored curves.