New challenges for seismic risk mitigation in Urban Areas

Simone Barani, Aybige Akinci, Karin Sesetyan, Dario Albarello, Philippe

Gueguen, Stefano Parolai, Roberto Paolucci, Dimitris Pitilakis

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Simone Barani, Aybige Akinci, Karin Sesetyan, Dario Albarello, Philippe Gueguen, et al.. New challenges for seismic risk mitigation in Urban Areas. Frontiers in Earth Science, pp.178, 2022, 10.3389/978-2-83250-790-2 . hal-04323822

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NEW CHALLENGES FOR SEISMIC
RISK MITIGATION IN URBAN AREAS

EDITED BY: Simone Barani, Aybige Akinci, Karin Sesetyan,
Dario Albarello, Philippe Gueguen, Stefano Parolai,
Roberto Paolucci and Dimitris Pitilakis

PUBLISHED IN: Frontiers in Earth Science

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ISSN 1664-8714

ISBN 978-2-83250-790-2

DOI 10.3389/978-2-83250-790-2

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# NEW CHALLENGES FOR SEISMIC RISK MITIGATION IN URBAN AREAS

**Topic Editors:**

**Simone Barani**, University of Genoa, Italy

**Aybige Akinci**, Istituto Nazionale di Geofisica e Vulcanologia (INGV), Italy

**Karin Sesetyan**, Boğaziçi University, Turkey

**Dario Albarello**, University of Siena, Italy

**Philippe Gueguen**, UMR5275 Institut des Sciences de la Terre (ISTERRE), France

**Stefano Parolai**, Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (Italy), Italy

**Roberto Paolucci**, Politecnico di Milano, Italy

**Dimitris Pitilakis**, Aristotle University of Thessaloniki, Greece

**Citation:** Barani, S., Akinci, A., Sesetyan, K., Albarello, D., Gueguen, P., Parolai, S., Paolucci, R., Pitilakis, D., eds. (2022). New Challenges for Seismic Risk Mitigation in Urban Areas. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-83250-790-2
Table of Contents

**04 Amplification Behaviour of Compressional Waves in Unconsolidated Sediments**

Janneke van Ginkel, Elmer Ruigrok, Rick Wentinck and Rien Herber

**21 Evolution, Recent Progress and Perspectives of the Seismic Monitoring of Building Structures in Romania**

Alexandru Tiganescu, Iolanda-Gabriela Craifaleanu, Alexandru Aldea, Bogdan Grecu, Radu Vacareanu, Dragos Toma-Danila, Stefan-Florin Balan and Claudiu-Sorin Dragomir

**31 Combining Earthquake Ground Motion and Ambient Vibration Recordings to Evaluate a Local High-Resolution Amplification Model—Insight From the Lucerne Area, Switzerland**

Paulina Janusz, Vincent Perron, Christoph Knellwolf and Donat Fäh

**50 Testing Site Amplification Curves in Hybrid Broadband Ground Motion Simulations of M6.0, 24 August 2016 Amatrice Earthquake, Italy**

Marta Pischiutta, Aybige Akinci, Chiara Felicetta, Francesca Pacor and Paola Morasca

**68 Seismo-Stratigraphic Model for the Urban Area of Milan (Italy) by Ambient-Vibration Monitoring and Implications for Seismic Site Effects Assessment**

M. Massa, S. Lovati, R. Puglia, G. Brunelli, A. Lorenzetti, C. Mascandola, C. Felicetta, F. Pacor and L. Luzi

**88 Building Stock Classification Using Machine Learning: A Case Study for Oslo, Norway**

Federica Ghione, Steffen Mæland, Abdelghani Meslem and Volker Oye

**99 Evaluating the Minimum Number of Earthquakes in Empirical Site Response Assessment: Input for New Requirements for Microzonation in the Swiss Building Codes**

Vincent Perron, Paolo Bergamo and Donat Fäh

**113 Seismic Risk Assessment of Multiple Cities: Biases in the Vulnerability Derivation Methods for Urban Areas With Different Hazard Levels**

Maria Camila Hoyos and Andres Felipe Hernandez

**127 Detection of Hidden Faults Within the Fucino Basin From Single-Station Ambient Noise Measurements: The Case Study of the Trasacco Fault System**

Daniela Famiani, Fabrizio Cara, Giuseppe Di Giulio, Maurizio Vassallo and Giuliano Milana

**146 Variability of Physics-Based Simulated Ground Motions in Thessaloniki Urban Area and Its Implications for Seismic Risk Assessment**

J. Lin and C. Smerzini

**163 Applying the Damage Assessment for Rapid Response Approach to the August 24 M6 Event of the Seismic Sequence in Central Italy (2016)**

Bojana Petrovic, Chiara Scaini and Stefano Parolai
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Check for
updates

# Amplification Behaviour of Compressional Waves in Unconsolidated Sediments

Janneke van Ginkel¹,²*, Elmer Ruigrok²,³, Rick Wentinck⁴ and Rien Herber¹

¹Energy and Sustainability Research Institute Groningen, University of Groningen, Groningen, Netherlands, ²R&D Seismology and Acoustics, Royal Netherlands Meteorological Institute, De Bilt, Netherlands, ³Department of Earth Sciences, Utrecht University, Utrecht, Netherlands, ⁴Independent Researcher, Haarlem, Netherlands

Similar to horizontal earthquake motions, vertical motions are amplified dependent on the local site conditions which can be critical for the safety of certain structures. Production of natural gas in Groningen, the Netherlands, results in reservoir compaction causing low magnitude, shallow earthquakes which are recorded with a borehole seismic network. These recordings form an excellent data set to understand how shallow unconsolidated subsurface geology influences the amplification behaviour of compressional waves (P-waves). First, we present borehole and single-station techniques (amplification factors, empirical transfer functions (ETF) and V/H spectral ratio implementations) to quantify vertical amplification. We show that vertical-wave incidence is a reasonable assumption. All techniques are capable of emphasising the sites with strong amplification of vertical ground motion during an earthquake. Subsequently, we compare ETF with single-station methods with the aim to develop proxies for vertical site-response using spectral ratios. In a second step, we link vertical site-response with shallow subsurface conditions, like the P-wave velocity and peat content. To better understand the amplification mechanisms, we analytically simulate P-wave propagation. In the simulations, we compute synthetic transfer functions using realistic subsurface conditions and make a comparison with the ETF. The simulations support the hypothesis that thin layers of shallow gas, originating from the Holocene peat, result in wave amplification. We observe strong vertical site-response in particular in the eastern part of Groningen, with industrial facilities and pipeline infrastructure in the region. Here, if high vertical amplifications are persistent at large earthquake magnitudes, appreciable levels of vertical loading may be expected. This study demonstrates that vertical motions should be assessed separately from horizontal motions, given that the amplification behaviour of P-waves is affected by distinctive mechanisms.

**Keywords:** Earthquakes, vertical ground motion amplification, unconsolidated sediments, 2D wave propagation modeling, transfer functions, V/H spectral ratios

## OPEN ACCESS

**Edited by:**

Simone Barani,
University of Genoa, Italy

**Reviewed by:**

Mikhail Rodkin,
Institute of Earthquake Prediction
Theory and Mathematical Geophysics
(RAS), Russia

Hamid Zafarani,
International Institute of Earthquake
Engineering and Seismology, Iran

**\*Correspondence:**

Janneke van Ginkel
j.a.vanginkel@gmail.com

**Specialty section:**
This article was submitted to Solid Earth Geophysics, a section of the journal Frontiers in Earth Science

**Received:** 10 November 2021

**Accepted:** 09 March 2022

**Published:** 31 March 2022

**Citation:**
van Ginkel J, Ruigrok E, Wentinck R and Herber R (2022) Amplification Behaviour of Compressional Waves in Unconsolidated Sediments. *Front. Earth Sci.* 10:812658.
doi: 10.3389/feart.2022.812658
# 1 INTRODUCTION

Although the subsurface is subjected to earthquake shaking simultaneously in the horizontal and vertical directions, vertical motions have received less attention in ground motion studies than the horizontal counterpart. Primarily because it is generally assumed that the margins against gravity-induced static forces provide adequate resistance to dynamic forces induced by vertical ground motion. As a result, studies on the characteristics of vertical ground motion are limited in number, particularly at sites where the earthquake intensity is low. Many seismic design codes do not consider the vertical component of motion at all, or use a single scalar multiplication factor on the horizontal component of motion.

Newmark et al. (1973) suggest that the effect of the vertical response amplitude spectrum is typically represented as two-thirds of the horizontal spectrum. Subsequently, Eurocode 8 (CEN, 2004), propose a maximum vertical acceleration factor of 0.45, normalised over horizontal acceleration. In Eurocode 8 it is emphasised that vertical ground motion is not very much affected by the underlying ground conditions and hence no correction is made for possible amplification in shallow soils. However, analyses on multiple strong earthquakes ($M \ge 5$) have proven that vertical ground accelerations can exceed values of 2/3 of horizontal, especially at short periods and in near-source distance range. Hence, vertical motion should be treated separately from the horizontal component in ground-motion studies (Ambraseys and Simpson, 1996; Bommer et al., 2011; Bozorgnia and Campbell, 2016a,b; Elnashai and Papazoglou, 1997; Yang and Sato, 2000; Yang and Lee, 2007; Yang and Yan, 2009). In addition, Elgamal and He (2004) analysed borehole arrays across the world and observed amplification characteristics for compressional waves as for the shear-waves. Amplification mostly occurs in the top 20 m of the unconsolidated sediments but compressional waves have a different site-dependence. This means that vertical ground motion cannot be treated as a single-value fraction of horizontal ground motion but should be assessed independently.

Previously mentioned studies focus on high intensity earthquakes with a tectonic origin and relatively deep hypocentres. This paper investigates vertical ground motion for shallow (3 km), low magnitude ($M_{max} = 3.6$ thus far) earthquakes in Groningen, in the northeast of the Netherlands. Over the past decades, the extraction of natural gas from the Groningen gas field has triggered induced seismicity. Although the maximum magnitude of the local earthquakes is relatively low, the ground motions form a risk since the existing buildings and infrastructures are not built to withstand earthquake shaking. The Groningen shallow subsurface consists of low-velocity, unconsolidated sediments with strong site amplification which has been studied in detail for motions in the horizontal direction (Bommer J. J. et al., 2017; Rodriguez-Marek et al., 2017; van Ginkel et al., 2019, 2021). So far, it has been assumed that for these low-magnitude earthquakes, vertical ground motion is not of enough importance to incorporate into the ground motion model and in seismic design for buildings (Bommer J. et al., 2017). Neither have shake table

tests (Kallioras et al., 2020) and building response modeling (Malomo et al., 2019; Korswagen et al., 2019), applied on Dutch structures, included vertical motions into their tests. However, in the Groningen area, the impulsive component and the shallow nucleation depth of the induced seismicity results in relatively strong compressional waves (P-waves) (Dost et al., 2020). Moreover, specific near-surface geology could result in strong, and locally varying, P-wave amplification. Consequently, structures with large horizontal extent, such as bridges, pipelines or industrial facilities might suffer from a lateral difference in vertical motion from one end to the other. This can lead to stress within the structure and subsequently in failure (Saadeghvariri and Foutch, 1991).

The objective of this study is to qualify, and where possible quantify, vertical site-response based on local induced earthquake recordings in 69 borehole sites of the Groningen seismic network. For this network, we show that the amplification measured on the vertical component corresponds primarily to P-wave amplification. Site-effect related to the local geology is evaluated through an analysis of the spectral characteristics of both earthquake and ambient noise records. Subsequently, by comparing several seismic methods, we are able to develop single-station proxies for vertical site-response. Additionally, we perform 2D wave propagation simulations, in order to understand the effect of (sub-wavelength) subsurface conditions and angle of incidence of the earthquake waves.

# 2 GEOLOGICAL SETTING

The province of Groningen, in the north-east of the Netherlands (Figure 2), is experiencing induced earthquakes due to the exploitation of a large onshore gas field. The Rotliegend sandstone reservoir is located at 3 km depth and is faulted during the Jurassic to Cretaceous rifting period. Here, reservoir compaction due to pressure depletion by the extraction of has reactivated the existing normal fault system that traverses the reservoir layer throughout the whole field Buijze et al. (2017).

The Groningen region has a flat topography and the groundwater table reaches almost up to the surface. The sedimentary cover is formed by the Cenozoic soft sediments, named the North Sea Group (NSG). In this study, we focus on the shallow subsurface, which is composed of unconsolidated Pleistocene sands and clays, overlain by a very heterogeneous Holocene formation (Figure 1). The Holocene formation is subdivided into several members. In Groningen, the Wormer and Walcheren Members mainly consist of marine clays, silt and fine sand. Two peat layers subdivide these two members. In the northern part, the Naaldwijk Formation mainly consists of sandy channel systems (MeijlesWong et al., 2007, 2015).

# 3 DATA SET

In order to monitor seismicity in the Groningen gas field, the Royal Netherlands Meteorological Institute (KNMI) deploys the
<figure><img src="image_5.png" /></figure>

**FIGURE 1** | Schematic geological cross section of the upper 200 m of the soft sedimentary cover in the northeastern Groningen region (red line in inset). Note that the vertical scale is inflated for the top 50 m to highlight the stratigraphy of the Holocene formations on top of the Pleistocene (PL) formations. Borehole station G19 is featured because this site is used for wave-propagation modeling. The G-network vertical arrays consists 5 seismometers (accellerometer (blue square) at the surface and geophones (blue triangles) at depth) with a 50 m depth interval. This cross section is based on GeoTOP (www.dinoloket.nl).

<figure><img src="image_6.png" /></figure>

**FIGURE 2** | Map view of the Groningen borehole network in the northeast of the Netherlands. The triangles represent the surface location of each borehole site in the network. Each borehole contains an accelerometer at the surface and four 4.5 Hz geophones at depth with a 50 m depth spacing. The orange circles represent the local earthquakes with magnitude 2 or higher, recorded in the G-network between 05–2015 and 05–2019. Coordinates are shown within the Dutch National Triangulation Grid (Rijksdriehoekstelsel or RD) and lat/lon coordinates in the corners for international referencing. Background map: OpenStreetMap contributors, CC-BY-SA, www.openstreetmap.org.

Groningen shallow borehole network (Figure 2). This so called G-network spreads out over some 850 km² and consists of 69 stations (Dost et al., 2017), each station is equipped with three-component, 4.5 Hz geophones at 50 m depth intervals (50, 100,

150, 200 m) and an accelerometer at the surface. The stations are continuously recording since 2015 and the data is available via the data portal of Royal Netherlands Meteorological Institute (KNMI, 1993). In this paper we refer to “station” for the
<figure><img src="image_7.png" /><figcaption>**FIGURE 3** | 3-component seismogram recorded at the surface station of borehole G62 for the 08–01–2018 M3.4 Zeerijp earthquake. R = radial/east component, T = transverse/north component, Z = vertical component.</figcaption></figure>

entire string with an accelerometer at the surface and four geophones at depth, and refer to “seismometer” for a single sensor measurement at a certain depth. Stations G15, G43, G53, and G68 are discarded from analysis due to malfunctioning of seismometers during a number of high magnitude earthquakes, hence not sufficient data is available.

The induced earthquakes recorded with the G-network have a magnitude of completeness of 0.5 (Dost et al., 2017). A maximum magnitude of 3.6 occurred during the Huizinge earthquake in 2012. Most earthquakes in Groningen have a normal faulting mechanism (Buijze et al., 2017; Dost et al., 2020).

# 4 BACKGROUND

This chapter presents details on wave propagation to illustrate the assumption of near-vertical earthquake wave incidence in Groningen. Secondly, we present our definition of amplification based on reference conditions at depth.

## 4.1 Wave Propagation and Incidence

In this study we assess P-wave amplification behaviour by using local earthquake recordings of the vertical component of the Groningen borehole seismometers. For a straightforward analysis, we assume that the vertical component measures primarily P-waves. In this sections we show that this simplifying assumption largely holds.

**Figure 3** shows a typical 3-component recording at the Earth's surface in Groningen. P-waves are primarily recorded on the vertical component (Z), S-waves map primarily to the radial (R)

and transverse (T) components. The well separation in wave types is largely due to the low near-surface velocities and hence small angles of incidence. In the top 200°m, P-wave velocities range, for most sites, between 1 and 2 km/s (Hofman et al., 2017). At close range to the epicenter, angles of incidence are close to zero. At larger range, P-waves have apparent horizontal propagation velocities of approximately 5.1 km/s (Jagt et al., 2017) resulting in angles of incidence varying between 11 and 23° in the top 200 m. Similar values are empirically found by Hofman et al. (2017). These angles of incidence results in 86–96% of the P-wave mapping to the vertical component. For S-waves, angles of incidence are even smaller in the near-surface due to very low velocities, down to about 35 m/s (Zwanenburg et al., 2020). And hence, not much S-wave energy can be recorded on the vertical component, as can be seen in **Figure 3**.

Further details of the near-surface propagation can be seen at **Figure 4**. In panel 1) the amplification can be noted when comparing the signal recorded at 200 m depth and at the surface. It can be seen that most of this amplification occurs in the top 50°m. The particle motion on the radial-vertical plane is presented in **Figures 4B,C**. The time window (0–4 s) around the first P-arrival shows that this arrival primarily oscillates in the vertical direction. The time window (5–9 s) around the first S-wave arrival, on the other hand, shows it has a near-horizontal polarization. P-S and S-P conversions do occur in the near-surface, but are small in size, again due to the small angles of incidence. In 4) and 5) the difference in Fourier Amplitude Spectra (FAS) are shown for the 200 m and surface levels. The FAS illustrate that the largest amplitudes reside between 2 and 10 Hz.

Therefore, also from an engineering point of view, processing of the raw earthquake records was performed in the frequency band of 1–10 Hz. This study uses the three-component data set of 19 earthquake recordings from local events with magnitude two or higher. Earthquakes in this magnitude range have sufficient energy to be recorded in the entire network and are therefore usable for assessing site-response. The FAS includes source, path and site-effect. Site-effects are extracted by taking spectral ratio's and or averaging over earthquakes with different hypocentres (and thus different source and path effects).

Further details on propagation from source to surface can be deduced from finite-difference simulations. The supplementary material contains the details of the model setup and input data for this simulation. **Figure 5** shows the seismic wave field in the subsurface originating moderate-size earthquakes at reservoir depth (3 km), mimicing a limited shear rupture in a fault plane. The figure shows three snapshots of the vertical (Z-component) and horizontal (R-component) particle velocities after the start of the event. The shape of seismic wave fronts deforms to almost horizontal when they reach the shallow subsurface. Hence, a borehole geophone near the epicentre measures mainly P-waves on the sensor for the vertical particle velocity. The waveform simulations support the observations from the earthquake recordings as presented in **Figure 4** and the assumption of almost vertical P-waves.

The following analyses of earthquake wave propagation includes time windows of 20's after earthquake rupture, and comprises not just the first P-wave arrival time window as
<figure><img src="image_8.png" /></figure>

**FIGURE 4** | Earthquake record in particle velocity for station G62 for the Zeerijp earthquake on January 2018 with magnitude 3.4 for a 20 s window after earthquake origin time. With in panel (A) 3-component borehole seismogram. (B) Particle motion in the R-Z plane for the direct P-wave arrival for a time window of 0–4 s for G62 surface seismometer. (C) Particle motion in the R-Z plane for the direct S-wave arrival for a time window of 5–9 s for G62 surface seismometer. (D) Fourier Amplitude Spectra of the 200 m seismometer. (E) Fourier Amplitude Spectra of the surface seismometer.

presented in **Figure 4B**. We deliberately choose this extended time window because in the frequency domain, the signal becomes more stable by including complex body-wave reverberations of the earthquake coda wave window. These complex arrivals experience similar amplification between 200'm depth and the surface.

## 4.2 Ground Motion Amplification in Groningen

In the Groningen region, S-wave ground motion amplification is mainly governed by two factors; firstly the reduction in

seismic velocities with decreasing depth and secondly, the presence of a velocity contrast at a certain depth causing resonance in the near-surface layer (van Ginkel et al., 2019). If this resonance of this near-surface layer has a similar resonance frequency as the structures at the surface, hazardous shaking can develop. In addition, shaking will not only occur at a single fundamental frequency but also at overtones. Amplification of P-waves follows the same physics as for S-waves, but has a different susceptibility to the near-surface lithology. Firstly, as P-waves have longer wavelengths, a deeper portion of the near-surface zone is relevant for resonance. Secondly, different impedance
<figure><img src="image_9.png" /></figure>

contrasts might be important, e.g., the interface between a water-saturated clay and sand has a high S-wave, but a low P-wave impedance contrast.

The example borehole seismograms and the FAS (Figure 4) show a major increase in amplitudes between the vertical component of the 200 m depth and the surface seismometer. Here, local site amplification occurs, on top of the free-surface effect. This amplification in the top 200 m is observed in multiple seismograms across the Groningen network.

Generally, amplification is quantified with respect to a hard rock outcrop over a full spectrum, however, these measurements are lacking in Groningen due to the 800–1,000 m thick sediment cover over the entire area. Alternatively, we define a reference site as a hypothetical outcrop with P-wave velocity of 1,900 m/s and a density of 2040 kg/m³. These are the values that are found, on average, in Groningen at 200 m depth (Romijn, 2017; Hofman et al., 2017).

# 5 METHODOLOGY

Amplification defined in the frequency-domain uses the Fourier Amplitude Spectra (FAS) of the earthquake records. The FAS ($U_{ij}(f)$), for the ith event and jth station, can be written as a convolution (i.e., multiplication in the frequency domain) of a source, path, site-effect and instrument term:

$$U_{ij}(f) = S_i(f)G_{ij}(f)L_j(f)I_j(f), \quad (1)$$

where $S_i$ is the source term, $G_{ij}$ is the path term (between the ith event and the jth station), $L_j$ is the site term, and $I_j$ is the instrument-response term. $I_j$ is known and removed before further processing the data. This leaves the source, path and site terms. Different approaches are used to extract from this the site term.

## 5.1 Empirical Transfer Functions

Quantification of site-effects across a frequency range of 1–10 Hz is performed by calculating borehole empirical transfer functions from local earthquake recordings. As shown in the previous section, we assume that vertical component is comprised of mainly P-waves in the 20 s time window used for the earthquake data processing. Therefore, the empirical transfer functions ($T_{m,n}$) represent P-wave amplification and are defined as a division of the Fourier amplitude spectra at two different depth levels

$$T_{m,n}(f) = \frac{U_m}{U_n}, \quad (2)$$

where m is the depth level of interest and n the reference horizon (Liu and Tsai, 2018; Rong et al., 2019). In $U_m$ and $U_n$ the source and path terms (Eq. 1) are (nearly) identical and hence the transfer function is only a description of the local propagation effects. When $U_m$ is chosen at the Earth’s surface, $T_{m,n}$ describes
site-effects and $|T_{m,n}|$ describes the frequency-dependent amplification with respect to the reference horizon.

In order to further improve estimation of the site-effect, we average the deconvolution (Equation 2) over 19 events with magnitudes > 2.0. This can be seen as an implementation of seismic interferometry by deconvolution (Wapenaar et al., 2010). We use 20 s long time windows for particle velocity recordings on the vertical component of the borehole stations. In this implementation, for each event the deconvolution is applied as in Equation 2. Subsequently, the deconvolution results are stacked to enhance stationary contributions. With a reference horizon at 200 m depth and the level of interest at the Earth's surface, the transfer function has both a causal and acausal part. The causal part maps upward-propagating waves, from the reference level to the surface. The acausal part maps downward-propagating waves back to the free surface (Nakata et al., 2013). To describe amplification, we are only interested in the causal part. We select this causal part of the estimated transfer function and compute its Fourier amplitude spectrum to obtain a measure of frequency-dependent amplification. The resulting amplitude spectrum we call the empirical transfer function (ETF). In order to get an estimate of uncertainty in the results, the above processing sequence is applied per earthquake and the standard deviation is computed from the resulting distribution.

## 5.2 V/H Spectral Ratios

Instead of the frequently used Horizontal-to-Vertical Spectral Ratio (HVSR) to estimate site-effects, we explore the option of using the inverse of the HVSR, the vertical-to-horizontal spectral ratio (VHSR). The HVSR-method (Nakamura, 1989, 2019) is based on the assumption that the vertical component spectrum is quite flat, hence a peak in the spectral ratios is generated by a peak in the horizontal component spectrum, which would be related to S-wave resonance. However, the spectrum of the vertical component is not flat, as demonstrated in the previous sections and supported by the findings of Sarmadi et al. (2021). By taking the inverse of HVSR, we can also not assume that the horizontal component spectrum is flat. However, peaks and troughs related to P-wave resonance and S-wave resonance are generally well separated in frequency. Comparing the spatial distribution between amplification established by the ETF and the one obtained with the VHSR, makes it possible to assess whether the VHSR can be used as tool to act as a proxy for P-wave amplification. Also, Lermo and Chavez-Garcia (1993) uses the VHSR to estimate resonance frequencies and peak amplitudes for the vertical component of ground motion from local earthquake recordings.

From earthquake recordings, generally time windows are picked containing direct arrivals of seismic waves to calculate the HVSR for site-response estimations (Chin and Aki, 1991; Mayeda et al., 1991; Kato et al., 1995; Su et al., 1996; Bonilla et al., 1997; Sánchez-Sesma et al., 2011). However, given that the local earthquakes are shallow and at short range, the window of the direct arrivals is very short, hence this implementation of VHSR is unstable. As alternative we use the coda window, which includes reverberations of the P-and S arrivals and reveals information on the local structure. Perron

et al. (2018) evaluate the standard spectral ratio curves of the direct S and P-arrivals and the coda wave window and show that both parts of the waveform provide similar results. The signal must be of long enough duration to include sufficient reverberations to produce any resonance peak. The longer the picked window, the more back-scattered waves coming from many azimuths (illustrated in Figure 5) are included in the signal, resulting in a directionally averaged site-effect. By taking the ratio of the vertical and horizontal components, the propagation effects included in the signal largely vanish. Data processing for obtaining the VHSR from local event recordings is carried out in the following steps:

* Application of bandpass filter on earthquake recordings of 1–10 Hz

* Selection of a 15 s coda window, starting at (hypocentral distance/mean Vs.) + 5 s after earthquake origin time.

* Check whether the local earthquakes have a sufficient signal-to-noise ratio for the selected coda wave time window

* From the coda wave window, calculation of VHSR from power spectral densities (PSDs). Following (the reciprocal of) the procedure described in van Ginkel et al. (2020), the VHSR is computed from the vertical component (Z) and horizontal components (E and N) as:

$$VHSR = \frac{\sqrt{PSD_Z}}{\sqrt{PSD_E + PSD_N}}, \quad (3)$$

where the horizontal components are averaged by vector summation.

* Per site, averaging of the VHSR curves (by stacking in the frequency domain) over all local events. Figures 9A–F shows examples.

* Picking the peak amplitude for each averaged VHSR curve

# 6 RESULTS AND INTERPRETATION

## 6.1 Amplification Factors

We calculate an overall amplification factor in the range of 1–10 Hz over the borehole vertical array, following the procedure described in van Ginkel et al. (2019, 2021). The AF is computed for each borehole site by taking the ratio of the maximum amplitudes recorded within 20 s after rupture time at the vertical component of the surface and the 200 m deep seismometer. The amplitude at the surface was divided by a factor of 2 in order to remove the effect of free surface amplification. Next, the AF per borehole is obtained by repeating the above procedure for all available M > 2.0 events and subsequently averaging the values. A signal-to-noise threshold is applied on the events.

Throughout the borehole network, a maximum AF of 2.7 is reached at the eastern edge of the network, while other locations do not experience P-wave amplification at all (Figure 6). Hence, this AF-plot presents a first indication of the spatial variability across the G-network of vertical ground-motion amplification.
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**FIGURE 6** | (A) Spatial distribution of the amplification factors computed based on earthquake records on the vertical component of the borehole seismometer at 200 m and at the surface. (B) Spatial distribution of the amplification factors computed based on earthquake records on the horizontal components of the borehole seismometer at 200 m and at the surface, modified from van Ginkel et al. (2019).

A

G62

B

G60

G

C

G37

D

G31

E

G19

F

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**FIGURE 7** | (A) Each panel depicts the empirical transfer functions (ETFs) between the seismometer at 200 m depth and the surface (red) and standard deviation (pale red area) for sites G19, G31, G37, G60, and G62, displaying the frequency-dependent amplification. The blue lines illustrate the ETFs between the seismometers at 200 m and 50 m. Borehole G62 additionally depicts the ETF between the surface and 50 m seismometer (gray). Borehole G58 is added as example of a borehole site with no amplification measured. (B) Spatial distribution of the peak amplitudes from the ETF for 60 borehole station sites.

Amplification factors from records on the horizontal components are calculated by van Ginkel et al. (Figure 4; 2019) and display a different spatial pattern than the vertical AFs, which is further investigated in the following sections.

## 6.2 Empirical Transfer Functions

In order to get an estimate of uncertainty, the above processing sequence is applied per event and from the resulting distribution, the standard deviation is computed and examples are plotted in
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**FIGURE 8** | (A) Example of acceleration response spectrum for the surface seismometer at station G60 from the Westerwijtwerd earthquake of M = 3.4, where the blue line represents the PSA at the vertical component, the gray line the eastern horizontal component and in black the northern horizontal component. (B) Averaged V/H from response spectra for each surface seismometer, processing 20 earthquake recordings, at each defined period plotted together with the mean (red) and median (blue) and extreme values (dashed gray) per period. The shaded area depicts the mean plus-minus one standard deviation, the black dashed line illustrates the 0.45 value as proposed by Eurocode 8 and the dot-dash the 2/3 value proposed by the ASCE.

**Figure 7A–F.** For these examples, also the ETF between 200 and 50 m depth is shown. It can be seen that most of the amplification occurs in the top 50 m.

Some ETFs show multiple side peaks, but the peak with largest amplitude value is identified as resonance peak, following the recommendation by Zhu et al. (2020). At some sites, this resonance peak has a large contribution to the overall amplification. Applying smoothing on the FAS and ETF can lead to suppression and shifting of peaks. We apply no smoothing and pick the largest amplitude and corresponding frequency. G58 has been added to **Figure 7F** to illustrate an ETF for a site with no amplification of P-waves. For 60 borehole stations, the ETF is computed. Subsequently, for each site, the corresponding peak amplitude for the 200 m -interval ETF is identified. **Figure 7G** depicts the spatial distribution of these peak amplitudes. Here, the distribution of amplitudes shows highest values in the eastern section of the region.

## 6.3 V/H From Spectral Acceleration

The previous section describes maximum amplification across a frequency range using the Fourier amplitude spectra of earthquake recordings of the borehole seismometers. However, Kramer. (1996) and Eurocode 8 (CEN, 2004) suggest using spectral ordinates at a varying range of periods in the acceleration response spectrum as ground-motion parameter.

As input, earthquake recordings (20 s after earthquake origin time) at the surface are taken to calculate the pseudo spectral acceleration (PSA). Subsequently response spectra (Figure 8A) for all three components, for each surface seismometer site, are calculated for pre-defined spectral periods (0.01–5.0 s), and taking the standard critical damping factor of 5% (Kramer, 1996; CEN et al., 2004). Per a defined period and per event, a V/H ratio is calculated from the PSA by dividing the vertical PSA with the geometric mean of the

horizontal components. Subsequently, for each pre-defined period, an average V/H is calculated over all events and plotted in **Figure 8B**. As shown in 8b, in Groningen the V/H PSA value for certain locations at short periods is exceeding the standard values of 0.45 and 2/3 by Eurocode 8 and ASCE. Moreover, it is shown that for each period, there is a vast range of site-specific V/H PSA values. At long periods (T > 0.3 s) the average value becomes closer to the single values proposed in literature. At short periods (T < 0.3 s) the average is considerably higher (0.81 for T = 0.1 s).

## 6.4 V/H Spectral Ratios

In this section we assess an additional single-station method for characterizing the spatial distribution of P-wave amplification. In Groningen, the induced events do not include surface waves at the short ranges within the G-network. The coda-based VSHR is therefore primarily a spectral ratio of P- and S-wave reverberations in the unconsolidated sediments. At many sites in Groningen, VSHR curves reach levels above 1 for distinct frequencies (Figure 9A). Location G58 is added as reference illustrating a VHSR below 1; here the horizontal component is dominating over the vertical for all presented frequencies. Figure 9A is illustrating the spatial distribution of VHSR peak amplitudes across the Groningen area.

### 6.4.1 VHSR From the Ambient Seismic Field

Site-effects are commonly assessed by using the ambient seismic noise field, (e.g., Bonnefoy-Claudet et al. (2006)). In Groningen, the noise level is sufficiently high in order to exceed the sensitivity of the surface accelerometers (Koymans et al., 2021). In addition to coda VHSR, the VHSR is also estimated from 1 month of ambient noise field measurements (VHSR<sub>ASF</sub>), using the approach presented in van Ginkel et al. (2020, 2019).
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**FIGURE 9** | (A–F) Each panel depicts the mean VHSR (black line) and standard deviation (dashed line) for the surface seismometer for the stations G19, G31, G37, G60, G62, where high VHSR amplitudes are calculated. Station G58 is added as reference to show a VHSR for a location with no amplification of P-waves. The blue line indicates the VHSR from the ambient noise field. (G) Spatial distribution of coda-based VHSR peak amplitudes for each surface seismometer in the Groningen network. (H) Spatial distribution of noise-based VHSR peak amplitudes for each surface seismometer in the Groningen network.

In **Figures 9A–F**, the mean $VHSR_{ASF}$ curves are added in blue for comparison with the $VHSR_{coda}$ curve characteristics in order to investigate whether noise can be used as a proxy for site amplification. From the VHSR curves, the corresponding peak amplitudes are determined for each surface seismometer in the Groningen network. In general, the noise-based VHSR peak amplitudes are larger than the coda-based VHSR peak amplitudes. This is a common observation, and is most likely caused by the strong presence of surface waves in the noise. The surface-wave ellipticity has notches which inflate the amplitude levels of VHSR (Bonnefoy-Claudet et al., 2006; Konno and Ohmachi, 1998). **Figures 9G,H** presents the peak amplitudes from coda-based and noise-based VHSR, for each surface seismometers in the Groningen network.

The spatial distribution of amplitudes exhibits a comparable pattern for both types of VHSR.

## 6.5 Relationship With Subsurface Conditions

The AF, ETF and V/H ratio peak amplitude distribution demonstrate a consistent pattern of increased peak amplitudes measured in the eastern part of the Groningen region (Figures 6, 7, 9). In order to understand this large variation in amplification, this section elaborates on the effect of shallow subsurface conditions on amplification behaviour of P-waves.
<figure><img src="image_28.png" /><figcaption>FIGURE 10 | (A) Correlation between P-wave velocities for the top 50 m and ETF peak amplitudes, fitted with an exponential decaying function (Eq. 4). (B) Spatial distribution of the P-wave velocity values (Hofman et al., 2017) for the top 50 m for each borehole location (circles) plotted on top of the cumulative thickness map of Holocene peat.</figcaption></figure>

### 6.5.1 Seismic Velocities

Wave amplification is largely determined by local variations in seismic velocities. For each borehole location, P-wave interval velocities are known for the upper 200 m, with a 50 m interval resolution (Hofman et al., 2017). These velocities were computed by applying seismic interferometry to events recorded within the borehole stations. Since the majority of the peak amplitude increase arises in the top 50 m, the ETF peak amplitudes (Section 6.2) are plotted against the average P-wave velocities $V_p$ for the top 50 m (Figure 10A). All strong amplifications (high ETF peak amplitudes) occur at velocities below 1,400 m/s. The relation between the near-surface P-wave velocity and the maximum amplification from the ETF is fitted ($R_{sq} = 0.52$) by an exponentially decaying function (Eq. 4). This empirical relation can be used to estimate the maximum amplification $A$ in the vertical direction at other sites with unconsolidated sediments, when the average velocity $V_p$ over the top 50 m is known:

$$A = 2.4 + 114e^{-0.004V_p} \quad (4)$$

Next, the interval P-wave velocity distribution is compared to the shallow lithostratigraphy throughout Groningen. For this purpose, cumulative thickness maps for different lithologies are extracted from the digital geological model GeoTOP (Stafleu et al. (2011; 2021), www.dinoloket.nl). The P-wave velocity distribution satisfactorily corresponds to areas with accumulations of peat in the shallow subsurface (10b). Thus, peat accumulations correlate with relatively low interval P-wave velocities. As a consequence, high ETF peak amplification develops during an earthquake where peat accumulates.

### 6.5.2 Effect of Gas on P-Wave Velocity

Since peat originates from organic material, methane can be generated, stored or migrated upwards and subsequently being trapped in the overlying sediments during burial. Although the peat layers are relatively thin in the Groningen field (less than 1 and up to 6 m throughout the top 50 m), their presence and resulting gas content can significantly reduce the P-wave velocity $V_p$ [m/s]. The effect of gas on $V_p$ can be understood from the Biot-Gassmann equations for wave propagation in poro-elastic media, such as soil Biot (1962) or Fjaer et al. (2008). Take that for seismic frequencies the so-called undrained condition holds and that the relative fluid displacement with respect to the rock can be neglected. In this case, $V_p = \sqrt{K''/\rho}$ where $\rho$ [kg/m³] is the bulk density and $K'' = K + \alpha^2 M + 4G/3$ [Pa] where K and G [Pa] are the bulk and shear moduli of the drained rock and $\alpha = 1 - K/K_s$ [-] is the so-called Biot constant. $K_s$ [Pa] is the bulk modulus of the grains in the rock. M [Pa] is a poro-elastic constant which can be expressed as:

$$M = \frac{K}{(1 - \alpha)\alpha - \phi(1 - \alpha - K/K_f)} \quad (5)$$

Where $\phi$ [m³/m³] is the pore volume fraction and $K_f$ [Pa] is the effective bulk modulus of the fluid. For a fluid containing both gas and water at equal pressure, $1/K_f = S_w/K_{fw} + (1 - S_w)/K_{fn}$. $S_w$ [m³/m³] is the volume fraction of pore water or the water saturation. $K_{fw}$ and $K_{fn}$ [Pa] are the bulk moduli of water and gas. In the shallow subsurface, $K_{fn} \ll K_{fw}$. Even for very low gas saturations, $K_f \approx S_n/K_{fn}$. Further, $K \ll K_s$ at least for clay and sand. So this implies, $\alpha \approx 1$ and $M \approx K_f/\phi \approx S_n/(\phi K_{fn})$. Since unconsolidated soil has a reasonable porosity, $K'' \approx K + 4G/3$. So, the fluid stiffness hardly contributes to the stiffness of the rock and $K''$ approximates the so-called drained bulk modulus of soil. A direct consequence is that $V_p$ values for peat at shallow depth
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**FIGURE 11** | (A) Theoretical transfer functions (TTF) for P-wave velocity Profile 1 for various angles of incidence. (B) TTF for P-wave velocity Profile 2 for various angles of incidence. (C) Theoretical (blue) and empirical (red) transfer function for borehole G19. (D) Excitation (in velocity) for the P-waves on the vertical plane (blue) and S-waves on the horizontal plane (red) at 200 m (dashed) and at the surface (solid) with θ of 20°. The inset depicts the related particle motion at surface for the P-wave arrival time window.

can become quite low. Values in the range 0.2–0.5 km/s are possible as has been measured by Mad Said et al. (2015); Zimmer et al. (2007). In the following we show the effect of low $V_p$-layers in the shallow subsurface on P-wave amplification by computing theoretical transfer functions (TTFs) from 2D wave propagation simulations. As has been shown in **Section 4.1**, at the near surface, the incoming waves have a maximum angle of incidence of 23°. Hence, simulations have been done for 0, 10 and 20° angles of incidence.

## 6.6 Shallow 2D Wave Propagation Simulations

From 2D wave propagation simulations in the shallow subsurface, we calculate the impact of subsurface lithology on P-wave amplification. Details on the simulations like the velocity and damping profiles, and the model setup are presented in the **Supplementary Material**. The effect of gas saturated layers in the shallow subsurface is implicitly included in the choice of the $V_p$ profiles. In particular, we have selected the lithology around borehole location G19 in the Groningen field where the ETF

displays high peak amplitudes for frequencies around 6, 7 Hz. Instead of 1D simulations, we deliberately performed 2D simulations to understand also the effect of the angle of incidence on wave amplification and on P-to-S wave conversion. Moreover, we compared the empirically-derived ETFs with the synthetic or theoretical ones (TTFs) from the simulations. The supplementary material presents the simulation details, the input data and model set up (Supplementary Section 1.2).

### 6.6.1 Theoretical Transfer Functions

The theoretical transfer functions (TTF) are calculated from P-wave displacement velocities recorded at the vertical component of probe 1 at the surface and probe 2 at 200 m depth (Supplementary Figure S3). Since the shallow P-wave velocity profile is hypothetical (Supplementary Section 1.2) first a sensitivity analysis of the TTF on the P-wave velocity profile in performed (Figures 11A,B). For both profiles, the TTFs for various angles of incidence ($\theta$ = 0, 10 and 20°) exhibit similar characteristics in terms of peak frequencies and amplitudes. Furthermore, **Figure 11A** shows that the effect of the wave
front incidence angle is minor on the TTF curve characteristics. Higher angles of incidence shift the TTF peak frequency from 6.6 to 6.9 Hz as the travel time of the wave between the probe locations reduces.

**Figure 11C** compares the ETF with the TTF for velocity Profile 1 for zero angle of incidence ($\theta = 0°$). Both transfer functions have a peak between 6 and 8 Hz, which peak is related to near-surface amplification. Between 2 and 3 Hz, the character of the transfer functions is quite different. The TTF peak around 2.5 Hz develops due to a notch at the 200 m recording. This notch is caused by the interference of up- and down-going waves. In the simulations, the subsurface is assumed laterally uniform. In reality, the subsurface is laterally quite heterogeneous, suppressing or disrupting such a notch. As a result, the ETF has no strong peak around 2.5 Hz. Moreover, the TTF is based on the propagation of a single wavelet while the ETF is an average from multiple earthquakes with various angles of incidence and azimuths. With this simulated transfer functions, we show that a varying angle of incidence is not of major influence on the amplification behaviour of P-waves.

### 6.6.2 P-To-S Wave Conversion

For non-zero angles of incidence on a layer interface, P-to-S wave conversion occurs. This conversion is of practical importance since prior to the direct S-wave arrival, P-waves might convert to S-waves and extend the period of exposure of buildings to horizontal ground motions during an earthquake. For the simulated small angles of incidence a limited P-to-S wave conversion can be seen in **Figure 11D**. It shows the simulated vertical P-wave and horizontal S-wave displacement velocities at 200 m and at the surface for a wave front with an angle of incidence of 20°. At around 0.23 s, when the high amplitude P-wave arrives at the surface, only minor S-wave excitation develops. Additionally, the particle motion plot (inset) for this time window comprises mainly a motion in the vertical (Z) plane. Since the generated S-wave amplitudes remain only a fraction of the P-wave amplitudes (less than 20%), this P-to-S wave conversion is deemed unimportant for the amplification and duration of the ground motion.

## 7 DISCUSSION

This paper presents various empirical methods for the qualification of site-effects on vertical ground motion amplification of signals originating from induced earthquakes for the Groningen gas field. In characterising amplification in the vertical direction, we used 1) amplification factors, 2) borehole transfer functions, 3) V/H from spectral accelerations, and 4) V/H spectral ratio's from earthquake coda-waves and the ambient seismic field. All four approaches exhibit a similar distribution of the degree of amplification in the vertical direction for each borehole site. In a second step, we explain this site-dependent amplification distribution depending upon the shallow subsurface composition and perform simulations in order to model the effect of low-velocity peat layers on P-wave propagation. In the following paragraphs we discuss the

validity, uncertainties and the approaches presented, as well as the limitations.

Throughout this study we assume P-wave dominated vertical motions due to nearly vertical incidence of the seismic waves, hence the absence of direct wave type conversions. The following arguments show the validity of the previous hypothesis: 1) the range of angles of incidence (0–20°) is small and 2) empirical as well as theoretical particle motion plots mainly show motion in the vertical plane within the P-wave arrival time window. These criteria lead to an expectation we measure predominantly P-waves.

To test the influence of non-zero incidence, we varied the angle of incidence in the synthetic 2D wave propagation simulations. As shown in **Figure 11D**, varying the angle of incidence of the wave front does not lead to major P-to-S-wave conversions. Therefore, we consider the assumption that the vertical component represents the amplification of the P-waves as valid.

Previous studies (Kruiver et al., 2017; van Ginkel et al., 2019) have shown that horizontal ground motion amplification mostly occurs in the near-surface (top 50 m). Also the seismograms for the vertical component display most of the amplitude increase in this top 50 m, see **Figure 4**. Due to the free surface effect, constructive interference of the down going wave might influence the recordings on the seismometer at 50 m. At 200 m this interference can be excluded, hence we decided to compute transfer functions over the 200 m interval following the approach by van Ginkel et al. (2021) (**Figure 7**).

Because of the data richness in Groningen, we are able to test, compare and evaluate borehole and single-seismometer techniques. The purpose of this comparison is to evaluate which single-station method (V/H PSA and VHSR) yields the best proxy to identify sites with the potential of vertical ground-motion amplification. **Figure 12** compares the peak amplitudes of the various techniques. We are aware that the techniques include amplification computed from response spectra, FAS, as well as particle velocity in the time-domain. Hence the peak amplitudes are treated qualitatively in order to investigate whether all techniques identify sites with high amplitudes. Panel 12a is added to display the correlation between the AF and ETF peak amplitudes. It shows that the relatively simple approach of computing AFs enables us to identify locations with high P-wave amplification.

**Figures 12B–E** illustrate the correlation between maximum amplification (ETF peak amplitudes) and three possible single-station proxies for amplification: peak amplitudes from V/H pseudo-spectral accelerations for a period of 0.01 s, coda-based VHSR and noise-based VHSR. These data points are fitted, using $A_0 = a^*e^{b_*P}$ as a functional form, where $A_0$ represents the peak amplitude and $P$ the input proxy. *a* and *b* are the two unknown parameters that are found through the fitting exercise. The correlation coefficient ($R_{sq}$) is calculated to qualify the fit. The spatial distributions of peak amplitudes for respectively the ETF (**Figure 7**) and VHSR (**Figures 9G,H**) display a similar pattern, but discrepancy still remains between the absolute amplitudes. This is expressed in the relatively low $R_{sq}$-values in **Figures 12B–D**. Still each of the three presented methods makes it
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**FIGURE 12** | Each panel depicts the correlation between the ETF and (A) Amplification factor from maximum amplitudes of earthquake records, (B) the V/H from pseudo-spectral accelerations at 0.01 s, (C) the coda-based peak amplitudes of the VHSR and (D) the noise-based peak amplitudes of the VHSR, and (E) the correlation between the VHSR coda and noise. Each blue line illustrates the fitting function between the two parameters.

possible to identify the borehole sites with potentially high vertical ground motion amplification during an earthquake.

For this study we use mainly local earthquake signals to estimate seismic site-effect in the vertical direction, following for example, Perron et al. (2018); Bommer et al. (2011). These approaches have the disadvantage that they require the occurrence of earthquakes in the first place. For that reason, a comparison between the VHSR of the earthquake signal and from the ambient noise field is made. The noise VHSR reasonably resembles the earthquake VHSR in terms of curve characteristics and peak amplitudes (Figure 12D), hence the noise VHSR can act as first proxy for increased P-wave amplification at certain locations, in case of absence of earthquakes. In general, measurements of noise microtremors have proven to be very informative for site-response estimation and remain a valuable input for seismic site-response zonation (Bonnefoy-Claudet et al., 2006; Molnar et al., 2018). However, the VHSR coda and noise absolute amplitudes have some discrepancies, mainly because the noise is composed of a mixture of surface and body waves. Further discussion about this discrepancy is beyond the scope of this paper.

The 2D full waveform simulations (Figure 5) show that the angle of incidence of the wavefront at the surface near the earthquake epicentre is relatively small (<20°). For the expected range of angles of incidence, the TTFs appear to be quite similar and amplification not influenced by inclining earthquake waves. This also holds for the TTFs of two

comparable but different synthetic $V_p$ profiles, showing that the TTFs are not very sensitive to uncertainties in $V_p$ in gas containing shallow subsurface layers, such as peat. Also, the simulations indicate that P-to-S conversion in the shallow subsurface is minor.

The wide range of V/H and ETF peak amplitude distributions found in the Groningen borehole network are consistent with the observations from Ambraseys and Simpson (1996); Bozorgnia et al. (2000); Liu and Tsai (2018); Elgamal and He (2004); Yang and Sato (2000). Empirically we show that vertical ground motion amplification is very site dependent and comprises a different spatial pattern than it is horizontal counterpart. We show that peat content plays an important role in P-wave ground motion amplification, while S-wave amplification is largely controlled by the stiffness of the Holocene sediments (van Ginkel et al., 2019, 2021). These insights are relevant for the Groningen region and as a consequence, it is not recommended to use the standard practise for assigning a single-value fraction of horizontal ground motion in order to assess motion in the vertical direction as suggested by CEN. (2004); Newmark et al. (1973); Loads (2017) is not recommended.

Furthermore, we show that in absence of a borehole network, the methods using a single surface seismometers are a reliable first proxy to highlight locations with likely elevated amplification in the vertical direction. The borehole earthquake transfer functions indicate that mainly the eastern part of Groningen (borehole locations G19, G62, G60, G31, and G37) is experiencing vertical
ground motion amplification (Figure 10). These borehole sites are in the vicinity of large industrial facilities and pipeline infrastructure nearby the city of Delfzijl. If high vertical amplifications are persistent at large magnitudes, appreciable levels of vertical loading may be expected.

## 8 CONCLUSIONS

An interdisciplinary approach based on detailed geological and geophysical analyses is performed to assess the potential for local P-wave amplification in an unconsolidated shallow sediment setting. Near-surface P-wave amplification is in the Groningen setting primarily recorded on the vertical component of the borehole seismometers. This study demonstrated empirically and analytically that vertical ground motion amplification occurs especially at sites with low P-wave velocities. Regarding the influence of shallow local geology, it could be shown that peat-generated gas impacts the P-wave velocities. The data richness in Groningen allowed the analysis of borehole earthquake amplification factors and transfer functions as well as a comparison with the analysis of single-station techniques, using local earthquake records and noise data. Qualitatively, there is a good agreement between the earthquake ground motion amplification, as determined with the various approaches. To this extent we showed that surface seismometer recordings can be used as first proxy to indicate the site-effect of ground motion amplification by P-waves to an earthquake. Furthermore, the theoretical transfer functions appear to be quite similar for various angles of incidence, hence the level of amplification is not influenced by inclining earthquake waves.

The P-wave amplifications (up to a factor of 2.7) observed especially at the eastern part of the Groningen study area, illustrate the significance of a detailed study of amplification in the vertical direction. Given that this amplitude distribution shows a different pattern than for the amplification of the horizontal ground motion, we conclude that vertical ground motion amplification by low magnitude earthquakes at shallow depth cannot be treated as an average percentage of horizontal ground motion. In Groningen, unconsolidated sediments with low $V_p$ lead to significant P-wave amplification, and should be considered to be included in predictive ground motion equations. P-to-S wave conversion in the shallow subsurface is found to be unimportant.

## REFERENCES

* Ambraseys, N. N., and Simpson, K. A. (1996). Prediction of Vertical Response Spectra in Europe. *Earthquake Engng. Struct.* Dyn. 25, 401–412. doi:10.1002/ (sici)1096-9845(199604)25:4<401::aid-eqe551>3.0.co;2-b

* Biot, M. A. (1962). Mechanics of Deformation and Acoustic Propagation in Porous media. *J. Appl. Phys.* 33, 1482–1498. doi:10.1063/1.1728759

* Bommer, J., Dost, B., Edwards, B., Kruiver, P., Meijers, P., Rodriguez-Marek, A., et al. (2017a). V4 Ground-Motion Model (GMM) for Response Spectral Accelerations, Peak Ground Velocity, and Significant Durations in the Groningen Field. A report prepared for NAM. Available from: https://www.nam.nl/feiten-en-cijfers/onderzoeksrapporten.html.

Although the gas production in Groningen will be ceased in the coming years, knowledge on vertical ground motion amplification by shallow and low magnitude earthquakes in a soft sedimentary setting is also key in other areas with seismic hazard, either in the Netherlands or at any site across the globe with similar conditions.

## DATA AVAILABILITY STATEMENT

Publicly available datasets were analyzed in this study. This data can be found here: http://rdsa.knmi.nl/dataportal/.

## AUTHOR CONTRIBUTIONS

JvG: method development, data analysis, provided input for the 2D simulations, writing of manuscript with input from all co-authors.

ER: daily advisor, input on data analysis method and results,

performed text input. RW: performed the 2D wave propagation

simulations and performed text input. RH: promotor, initiator of this

research project, advisor, final text editor.

## FUNDING

This work is funded by EPI Kenniscentrum.

## ACKNOWLEDGMENTS

Figures are produced in Matlab, except **Figure 1** is produced in QGIS. The authors like to thank Jan Stafleu from TNO for compiling the cumulative thickness maps based on GeoTOP.

The authors like to thank the reviewers Mikhail Rodkin and Hamid Zafarani for their valuable input.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feart.2022.812658/full#supplementary-material

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# Evolution, Recent Progress and Perspectives of the Seismic Monitoring of Building Structures in Romania

Alexandru Tiganescu¹,²*, Iolanda-Gabriela Craifaleanu²,³, Alexandru Aldea², Bogdan Grecu¹, Radu Vacareanu², Dragos Toma-Danila¹, Stefan-Florin Balan¹ and Claudiu-Sorin Dragomir³,⁴

¹National Institute for Earth Physics (INFP), Magurele, Romania, ²Department of Reinforced Concrete Structures, Technical University of Civil Engineering Bucharest (UTCB), Bucharest, Romania, ³National Institute for Research and Development in Construction, Urban Planning and Sustainable Spatial Development, URBAN-INCERC, Bucharest, Romania, ⁴Department of Environment and Land Reclamation, University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania

**OPEN ACCESS**

**Edited by:**
Dimitris Pitilakis,
Aristotle University of Thessaloniki,
Greece

**Reviewed by:**
Sotirios Argyroudis,
Brunel University London,
United Kingdom
Kosmas Dragos,
Hamburg University of Technology,
Germany

**\*Correspondence:**
Alexandru Tiganescu
alexandru.tiganescu@infp.ro

**Specialty section:**
This article was submitted to Geohazards and Georisks, a section of the journal Frontiers in Earth Science

**Received:** 20 November 2021

**Accepted:** 04 March 2022

**Published:** 07 April 2022

**Citation:**
Tiganescu A, Craifaleanu I-G, Aldea A, Grecu B, Vacareanu R, Toma-Danila D, Balan S-F and Dragomir C-S (2022) Evolution, Recent Progress and Perspectives of the Seismic Monitoring of Building Structures in Romania. Front. Earth Sci. 10:819153. doi: 10.3389/feart.2022.819153

The seismic instrumentation of structures in order to assess their condition and to track it over long periods or after representative events has proven to be a topic of large interest, under continuous development at international level. The seismic hazard of Romania poses one of the most dangerous threats for the country, in terms of potential physical and socio-economic losses. In recent years, taking advantage of the new scientific and technological advances, among which the exponential growth in computational resources, significant improvements have been made in extending the seismic networks for structural monitoring and using the data as input for products and services addressed not only to the research community but also to stakeholders. The paper covers focused aspects of the topic for Romania, referring to past developments of the most important institutions and seismic networks in the country and the current status, including the research and regulatory gaps. Currently, three main research and academic institutions perform structural health monitoring of twenty-two buildings in Romania. As the number of monitored buildings grows and new actors in the private sector start to get involved in the process, the need for data standardization and a regulatory framework increases. Ongoing national and international projects (PREVENT, SETTING, TURNkey) address these issues and outline the roadmap for future actions of the main institutions responsible for seismic risk reduction, including authorities, research and academia.

**Keywords:** seismic monitoring, structural health monitoring, building structures, seismic instrumentation, Vrancea earthquakes

## INTRODUCTION

An essential activity for seismic countries is the monitoring and tracking of the condition of the building stock, aiming to ensure the safety of the population and quick recovery after extreme events. This endeavor has proved important not only for preparedness, mitigation and decision-making in emergency situations, but also for opening and supporting a wide range of multi-disciplinary research approaches. The condition assessment of aging structures and infrastructures is becoming a
more and more critical issue, especially when developing life extension and replacement strategies. A cost-effective maintenance strategy should aim for minimizing the total life-cycle cost of a structure, considering the costs for preventive maintenance, inspection, monitoring, repair, and failure losses (Bergmeister et al., 2003). The importance and the benefits were analyzed, by assessing the Value of Information (VoI) for structural health monitoring (SHM) systems, by Pozzi and Der Kiureghian (2011) and Kamariotis et al. (2022).

The main objectives of SHM are to assess the structural condition and to rapidly detect the changes that could reveal damage occurrence, based on vibration recordings. The research in the field of SHM was initiated with a special focus on the aerospace, nuclear power and gas exploration industries (Doebling et al., 1996; Sohn et al., 2004). The following decades witnessed a large and diversified development of SHM approaches and methods, supported by the progress of sensing technology, computer hardware and software and leveraged by the need of integration of SHM in earthquake early-warning (EEW) systems (Cosenza et al., 2010; Wu and Beck, 2012; Su et al., 2020; Iaccarino et al., 2021; Sivasuriyan et al., 2021).

In Romania, a country affected by recurring earthquakes originating from various shallow and intermediate-depth sources (Radulian et al., 2000), a large percentage of the building stock dates from before 1963 (Lungu et al., 2008; Pavel et al., 2016), the year of the enforcement of the first mandatory seismic design code, with many of them being highly vulnerable. According to data from the latest National Census (2011), more than 40% of the residential building stock in the whole country and more than 44% in the capital city Bucharest were erected before 1963. The significant losses generated by the 1977 Vrancea earthquake (when almost 33,000 buildings were partially or completely damaged), highlighted the need for an improved seismic design of buildings and for extending seismic instrumentation.

The extensive implementation of SHM systems and rapid damage assessment tools is nowadays essential for assisting decision-makers to set up strategies for the retrofit of the vulnerable building stock. Several countries have already elaborated specific guidelines and standards for the seismic instrumentation of buildings (Çelebi, 2000) and SHM (ISIS Canada, 2001; Mufti, 2002—Canada; Teshigawara et al., 2004—Japan; Moreu et al., 2018; Yang et al., 2017—China; Porter et al., 2004; Rücker et al., 2006). At present, no detailed regulations for SHM exist in Romania, even though several buildings and infrastructures are monitored and several research projects in the field have been completed or are in progress.

The article presents an overview of the evolution and current status of the seismic instrumentation of building structures in Romania, with reference to the international research and regulatory framework and to the national implementation. It covers a broad perspective, from long-term SHM under operational conditions to seismic monitoring of structures under weak-to-moderate Vrancea earthquakes. The current research gaps regarding the seismic instrumentation of structures in Romania are

discussed, as well as potential future actions to overcome these issues, including the improvement of the national legislation in the field.

## EVOLUTION OF THE SEISMIC INSTRUMENTATION OF BUILDING STRUCTURES IN ROMANIA

In Romania, seismic monitoring of buildings started in the 1960's, when buildings in several cities were instrumented, mainly for scientific purposes, by the National Institute for Building Research, INCERC<fnref n="1" /> (Georgescu et al., 2010). By the time the M<sub>W</sub> 7.4, 4 March 1977, Vrancea earthquake occurred, four accelerographs were installed at the top and in the basement of two reinforced concrete (RC) buildings, located in the cities of Bucharest (RC shear walls, 11 stories) and Galati (RC frames, 12-story) (Berg et al., 1980; Balan et al., 1982). The first reference also mentions partially instrumented multistory buildings, with accelerographs installed, at that time, either in the basement or near the top, located in Bucharest (RC frames, 13 stories), Bacau (RC shear walls) and Focsani (masonry, 3 stories). In addition, in the years before the 1977 earthquake, an extensive campaign was conducted to determine the dynamic characteristics of various buildings, by ambient vibrations measurements. The database compiled from these measurements was used, after the earthquake, as a reference to assess modifications of natural periods for 47 residential buildings in Bucharest, with various structural systems and numbers of stories ranging from 8 to 18 (Balan et al., 1982). The availability of the reference values was crucial for later seismic vulnerability assessments, given that a large part of the mentioned buildings was based on standardized designs. It was shown that an increase of the natural period of vibration of the buildings with less than 20–25% was associated with low damage, percentages of 25–50% corresponded to light damage, while multiple, systematic or local and significant damage was observed for percentages higher than 50%.

The seismic network of INCERC evolved significantly after the 1977 earthquake, when new strong motion accelerographs were used for the instrumentation of multistory residential buildings, hotels, public and administrative buildings (Craifaleanu et al., 2011). The height of the monitored buildings ranged between 4 and 11 stories, with the recording equipment typically placed in the basement and at the top floor. In 2010, the seismic network of URBAN-INCERC consisted of over 100 stations, with 11 instrumented buildings (Georgescu et al., 2010). A database of seismic records obtained on buildings instrumented by INCERC during strong earthquakes (M<sub>W</sub> 7.1, 30 August 1986; M<sub>W</sub> 6.9, 30 May 1990, and M<sub>W</sub> 6.4, 31 May 1990), was compiled (Boria et al., 2013, 2014, 2015; Craifaleanu and Boria, 2015). The seismic data recorded in buildings were analyzed by Popescu

<footnote n="1">¹Today a branch of the National Institute for Research and Development in Constructions, Urban Planning and Sustainable Spatial Development, URBAN-INCERC</footnote>
and Demetriu (1994, 1994b, 1996) and by Demetriu and Borcia (Demetriu and Borcia, 2001; Demetriu, 2002). For the data recorded on a RC building during the 1986 earthquake, Popescu and Demetriu (1994) identified five vibration modes on each direction by running a system identification algorithm based on fitting of multivariate autoregressive model (MAR), assuming a multi-input single-output system. Popescu and Demetriu (1994) reported the initial (35–40 s) nonlinear behavior of a 12-story RC building during the 1986 earthquake, based on recorded acceleration components.

Two buildings in the Bucharest area and an experimental building at INCERC were instrumented in 1996–1998 in the framework of the Collaborative Research Center “Strong Earthquakes: A Challenge for Geosciences and Civil Engineering” project SFB 461 (Wenzel, 1997), with the National Institute for Earth Physics (INFP), the Technical University of Civil Engineering Bucharest (UTCB) and INCERC as partners (Aldea et al., 2004b). In 2003, for 3 months, one pair of strong motion instruments was deployed in a 11-story RC building, headquarters of the Institute of Atomic Physics (TURN), to study the influence of the building structure on the seismic waveforms. The monitoring was conducted within the framework of the Urban Seismology (URS) project (Ritter et al., 2005), having as partners the University of Karlsruhe and INFP.

Another structure of interest, instrumented by National Centre for Seismic Risk Reduction (NCSRR<fnref n="2" />), was the Faculty of Civil, Industrial and Agricultural Buildings (FCCIA) of UTCB, a RC frame, low-code building. The experimental data recorded during ambient vibration monitoring campaigns were used to validate its numerical model. In addition, the soil-structure interaction (SSI) analysis revealed slight interaction effects, however with no significant numerical impact (Demetriu et al., 2012).

The progress in the seismic instrumentation of buildings occurred in the broader context of the general development of the seismic networks in Romania. In addition, it should be mentioned that distinct monitoring is performed, by other organizations, for dams, bridges or for the subway lines in Bucharest. These construction categories are, however, beyond the scope of this paper.

## CURRENT STATUS OF STRUCTURAL HEALTH MONITORING FOR BUILDINGS IN ROMANIA. RECENT PROJECTS

With the enforcement of the 2006 and 2013 editions of the Romanian seismic design codes, P100-1/2006 (UTCB, 2006) and P100-1/2013 (UTCB, 2013), both drafted by UTCB, the seismic monitoring of structures has gained additional momentum. The in force code state mandatory instrumentation for importance-exposure class I buildings, as

well as for buildings higher than 45 m above ground level, located in areas with peak ground design acceleration values equal or greater than 0.25 g. In addition, since 2005, a Ministerial Order (OMTCT/OMAI No. 1995/1160 from 2005/2006) requires all the public and private buildings to be instrumented, if they have more than 16 stories (or are more than 50 m-high) or have a developed area larger than 7,500 m<fnref n="2" />. At present, INFP, URBAN-INCERC and UTCB monitor twenty-two buildings in Romania (Figure 1 and Table 1). Information on instrument types and representative photos are provided in the Supplementary Material.

At URBAN-INCERC, the National Network for the Seismic Monitoring and Protection of Building Stock is the department in charge of the operation of the seismic network, including the instrumented buildings (Dragomir et al., 2015a; Dragomir et al., 2015b; Dragomir et al., 2016; Dragomir et al., 2021). Currently, URBAN-INCERC monitors, mainly for research purposes, eight buildings with various functions and occupancies, located in Bucharest (7) and Iasi (1); six of these are connected online to the Data Center of the Institute. The instrumentation of these buildings consists of at least two sensors (ground floor/basement and top); two of them also have sensors close to the building, in free-field conditions. Other buildings have at present only ground-level sensors installed, complete instrumentation being envisaged in the future. In addition, short-term building vibration monitoring is being conducted, generally focused on actions induced by industrial or transportation activities. In a study conducted by Dragomir et al. (2017b), the fundamental period (0.18 s) of the Biotechnology Faculty building (BTH) was experimentally determined based on the Fourier Spectra (FS) of several recordings and validated with the values from the design code (0.15 s) and by using the Operational Modal Analysis tool of the ARTEMIS Modal Pro software<fnref n="3" /> (0.19 s). Dragomir et al. (2017a) estimated the fundamental frequency for two other buildings, a 10-story RC shear walls apartment block (BLA) and a 15-story RC shear walls office building (APL), using noise and earthquake data. Applying the FS, they found fundamental frequencies of $f_x$ = 1.73 Hz and $f_y$ = 2.05 Hz for the first building, and $f_x$ = 1.5 Hz and $f_y$ = 1.3 Hz for the second building, respectively. Moreover, there is an ongoing experimental project for real-time damage detection in buildings (Dragomir et al., 2019; Dragomir et al., 2020) using ARTEMIS and an extensive campaign, in the framework of the ECOSMARTCONS project, for the seismic instrumentation of the premises of national research institutes all over the country. Starting with 2022, the Data Center of URBAN-INCERC has implemented SeisComP<fnref n="4" />.

Significant progress in seismic instrumentation was made within the Japan International Cooperation Agency (JICA) Technical Cooperation Project “Reduction of Seismic Risk for Buildings and Structures”, in which the NCSRR instrumented four representative buildings in Bucharest (Aldea et al., 2004a; Aldea et al., 2007a; Aldea et al., 2007b): the Romanian National

<footnote n="2">²NCRRS functioned between 2003 and 2010. The seismic instrumentation installed by NCRRS continued to be operated by URBAN-INCERC and at present by UTCB</footnote>

<footnote n="3">³https://svibs.com</footnote>

<footnote n="4">⁴https://www.seiscomp.de</footnote>
<figure><img src="image_39.png" /></figure>

Television (TVR), the BRD-SG Tower (BRD), and two residential multistory buildings (BLD1 and BLD2). Several detailed analyses of the modal frequencies, based on ambient vibration and earthquake data, were performed on the BRD-SG Tower, a newly constructed RC office building (Demetriu and Aldea, 2006). The SSI effect was investigated based on free-field and borehole data by Aldea et al. (2007c). For the same building, Perrault et al. (2013) proposed a methodology to reduce the uncertainty of the single-building fragility curve using experimental data. First, a linear MDOF model was adjusted for experimental modal analysis using a Timoshenko beam model (Boutin et al., 2005) and based on Anderson's criteria (Anderson, 2004). Then, the structure's response to a large set of accelerograms simulated by the SIMQKE software (Gasparini and Vanmarcke, 1976) was computed and, for the final step, the fragility curves were constructed by comparing numerical inter-story drift with the threshold criteria provided by the Hazus methodology (FEMA, 2003) for the slight damage state. Recent research on SHM, performed by UTCB, widened the scope of previous studies, approaching heritage buildings, such as the minaret of the Royal Mosque in Constanța (Aldea et al., 2018), and traditional Romanian timber framed masonry houses (Aldea et al., 2020).

In 2011, a heritage building of the University of Economic Studies (ASE), located in Bucharest, was retrofitted using seismic isolators and viscous dampers, the first action of this kind in Romania. INFP was in charge of SHM and of the efficiency assessment of this innovative solution, by placing accelerometers under and above the seismic isolators. Data recorded during two seismic events ($M_W$ 5.5, 28 October 2018 and $M_W$ 4.8, 31 January 2020) revealed a reduction of the acceleration amplitude by a factor ranging from 2.0 to 3.8, for the two horizontal components. The same promising results were reported for the same earthquakes on another heritage structure equipped with earthquake-protection system in Bucharest, the Arch of Triumph (ARC), with reductions of acceleration amplitude by a factor up to 4.5 (Balan et al., 2020).

INFP is currently monitoring 10 buildings, with 36 sensors (Table 1). The instrumentation setup consists of strong motion sensors located mainly at the ground (or basement) level, at an intermediate floor and at the roof level. New low-cost sensors (Raspberry Shake<fnref n="5" /> RS3D and RS4D) are tested to extend the building monitoring network in the framework of the TURNkey<fnref n="6" />

<footnote n="5">⁵https://raspberryshake.org</footnote>

<footnote n="6">⁶https://earthquake-turnkey.eu</footnote>
<table><caption>**TABLE 1** | Characteristics of the instrumented structures.</caption><thead><tr><th>Institution</th><th>Station code</th><th>Construction year/period</th><th>Structure type</th><th>Number of stories<sup>a</sup></th><th>No. of sensors</th><th>Location of sensors</th><th>Instrument code<sup>b</sup></th></tr></thead><tbody><tr><td rowspan="10">URBAN-INCERC</td><td>APL</td><td>2008</td><td>RC Shear walls</td><td>2B + GF + 14S</td><td>3</td><td>B, 4th S, 14th S</td><td>GRN + EPI</td></tr><tr><td>SMU</td><td>1978/retrofitted in 1996</td><td>RC Shear walls</td><td>B + GF + 13S</td><td>3</td><td>GF, 6th S, 14th S</td><td>K2 + EPI</td></tr><tr><td>IGS1&2</td><td>1968</td><td>RC Frames</td><td>B + GF + 7S + partial story</td><td>2</td><td>GF, partial story</td><td>ETNA2</td></tr><tr><td>MEC1&2</td><td>1969</td><td>RC Frames</td><td>B + GF + 6S + mechanical floor</td><td>2</td><td>B, mechanical floor</td><td>ETNA2</td></tr><tr><td>BLA2&3</td><td>1971</td><td>RC Shear walls</td><td>B + GF + 10S</td><td>2</td><td>B, 10th S</td><td>GRN; ETNA</td></tr><tr><td>BTH</td><td>2016</td><td>RC Frames and shear walls</td><td>B + GF + 2S</td><td>3</td><td>B, partial story + free-field</td><td>GRN + EPI</td></tr><tr><td>VNS</td><td>2000s</td><td>RC Shear walls</td><td>3B + GF + 14S + mechanical floor</td><td>2</td><td>3rd B, roof</td><td>ETNA</td></tr><tr><td>IAS7</td><td>1985</td><td>RC Frames</td><td>GF + 3S</td><td>3</td><td>GF, 3rd S + free-field</td><td>GRN + EPI</td></tr><tr><td rowspan="4">UTCB</td><td>BLD1</td><td>1980s</td><td>RC frames</td><td>B + GF + 10S</td><td>4</td><td>1st S, 5th S, 11th S, 12th S</td><td>K2 + EPI</td></tr><tr><td>BLD2</td><td>1960s</td><td>RC frames</td><td>B + GF + 6S</td><td>4</td><td>B, 4th S, 7th S + free-field</td><td>K2 + EPI</td></tr><tr><td>TVR</td><td>1960s</td><td>RC frames</td><td>B + GF + 13S</td><td>3</td><td>B, 14th S, 15th S</td><td>K2 + EPI</td></tr><tr><td>BRD</td><td>2003</td><td>RC dual</td><td>3B + GF + 18S</td><td>2</td><td>3rd B, 19th S</td><td>K2 + EPI</td></tr><tr><td rowspan="15">INFP</td><td>ARC</td><td>1922/retrofitted in 2016</td><td>RC</td><td>27 m</td><td>3</td><td>GF, top + free-field</td><td>TSA-SMA; K2 + EPI</td></tr><tr><td>ASE</td><td>1905/retrofitted in 2011</td><td>Masonry</td><td>B + GF + 2S + attic</td><td>2</td><td>GF</td><td>K2 + EPI</td></tr><tr><td>TURN</td><td>1973/retrofitted in the 1990s</td><td>RC shear walls</td><td>B + GF + 9S</td><td>10</td><td>B, 1st S, 3rd S, 6th S, 7th S, 10th S</td><td>IDAS + TSA-100S; RS4D; RS3D</td></tr><tr><td>FOCR</td><td>1971</td><td>RC frame</td><td>GF + 8S</td><td>3</td><td>B, 4th S, 8th S</td><td>TSA-SMA</td></tr><tr><td>EFR</td><td>2008</td><td>RC frame</td><td>B + GF + 2S</td><td>3</td><td>B, GF, 3rd S</td><td>RS4D</td></tr><tr><td>DRG</td><td>1982</td><td>Large panel structure (precast shear walls structure)</td><td>B + GF + 8S</td><td>3</td><td>B, 5th S, 8th S</td><td>RS4D</td></tr><tr><td>BAL</td><td>before 1963 (&lt;1940)</td><td>Unreinforced Masonry</td><td>B + GF + Attic</td><td>2</td><td>B, attic</td><td>RS4D</td></tr><tr><td>DRT</td><td>before 1963</td><td>Large panel structure (precast shear walls structure)</td><td>GF + 8S</td><td>3</td><td>GF, 5th S, 9th S</td><td>RS4D</td></tr><tr><td>TIT</td><td>1963–1977</td><td>Large panel structure (precast shear walls structure)</td><td>B + GF + 10S</td><td>3</td><td>GF, 5th S, 10th S</td><td>RS4D</td></tr><tr><td>LAS</td><td>2008</td><td>RC frame</td><td>3B + GF + 11S</td><td>4</td><td>3rd B, GF, 5th S, 11th S</td><td>RS4D</td></tr></tbody></table>

and PREVENT<fnref n="7" /> projects. Low-cost sensors (Micro-electromechanical systems - MEMS accelerometers) have proven useful and provided promising results when used for early-warning systems (Nof et al., 2019), small local earthquake detection (Cascone et al., 2021) or even initial ground-motion assessment (Holmgren and Werner, 2021). However, their usability and reliability for SHM has not yet been extensively studied. The very high level of digital noise is masking any type of low-amplitude ambient vibrations. This type of sensors should be of paramount importance in case of earthquakes with M<sub>w</sub> > 6.0, given the amount of data they can provide from a larger number

of instrumented structures, when compared to professional equipment, within the same monitoring expenses.

The data from all stations are transmitted in real-time to the Romanian National Data Center (RONDC) of INFP. For data acquisition, quality control and recording, real-time data processing and exchange, network status monitoring, automatic and interactive event detection and location, waveform archiving and distribution, INFP has run, since 2008, SeisComP, in parallel with Kinemetrics Antelope<fnref n="8" /> (Marmureanu et al., 2021).

<footnote><sup>a</sup>B—basement story; GF—ground floor; S—story/stories.</footnote>

<footnote><sup>b</sup>The instrument type and representative photos are presented in **Supplementary Table S1**—Supplementary Material.</footnote>

<footnote n="7">⁷https://prevent.infp.ro</footnote>

<footnote n="8">⁸The Boulder Real-Time Technologies, Inc. (BRTT) https://brtt.com</footnote>
Recently, Tiganescu et al. (2020) analyzed the dynamic characteristics (fundamental period and damping ratio) of the three representative high-rise buildings from Bucharest, based on ambient vibration data recorded during a two-day measurement campaign. The fundamental periods obtained using FS analysis, Random Decrement Technique (Cole, 1973) and Transfer Function were validated against results computed using empirical formulas from the design code corresponding to each building. The values were consistent for both the fundamental period and the damping ratio of the buildings, regardless of the method and of the measurement day. However, small diurnal and weekly variations were reported for the two parameters, due to small differences in atmospheric conditions and building occupancy at different moments of data acquisition.

Preliminary analysis of earthquake data recorded on structures during the latest moderate magnitude Vrancea seismic event (M<sub>W</sub> 5.5, 28 October 2018) highlighted different behaviors and trends, depending on the structural characteristics and of the existence of earthquake-protection system. Amplification and reduction of motion on different frequency ranges were revealed, with clear peaks corresponding to the dynamic characteristics of the buildings (Tiganescu et al., 2019).

The Bighorn module, an extension of the Antelope package, is also used at INFP to perform seismic monitoring of structures. The system computes near real-time response spectra and issues alarms, depending of the level of exceedance of a preset limit spectra. This procedure was tested for Bucharest using the 28 October 2018 earthquake data (Balan et al., 2019). The reporting service is currently performed in an offline environment, on request. The permanent seismic stations installed in buildings were used in a recent study conducted by Grecu et al. (2021) to assess the effect of the COVID-19 related restrictions on the level of high-frequency content of the ambient vibrations generated by human activity. Significant noise reductions (40–80%) on the 15–40 Hz frequency range for stations in and near buildings were associated to the mobility restrictions of people working inside the office buildings and with the shift to online classes for educational units.

In the context of other studies highlighting the influence of atmospheric conditions on the dynamic parameters of structures (Clinton et al., 2006; Herak and Herak, 2009; Mikael et al., 2013; Guéguen and Tiganescu, 2018), a case-study building (TURN) was instrumented with both seismic sensors and a meteorological station (Tiganescu et al., 2021a), in the framework of the PREVENT project. A fundamental frequency variation analysis was conducted on a 72-h dataset of ambient vibration and earthquake data, using the Frequency Domain Decomposition method (Brincker et al., 2001). Small variations of the fundamental frequency were observed in the ambient vibration regime, while for the forced vibrations (earthquake) the variation was larger (drop of 10%) and followed by a recovery. Moreover, correlation of the atmospheric and environmental conditions (mainly air temperature, relative humidity and wind speed) with the building's natural frequency was tested, but with no sharp conclusions due to limited timespan.

DISCUSSION

A large number of buildings, representing different typologies (construction period, structural system, material, height, exposure to earthquakes, vulnerability) were previously and are currently instrumented in Romania, as a need for acquiring pre-, during and post-event vibration data. From the point of view of the coverage of areas of interest considering seismic hazard levels and building exposure, there is still a need to instrument and monitor structures that could be affected by crustal earthquakes (Figure 1A), in seismic zones such as Banat, Fagaras-Campulung, Crisana-Maramures or nearby Shabla, Bulgaria.

The effort is ongoing by means of national and international projects involving seismic instrumentation of structures and the development of web platforms for data and metadata inventory (SETTING<sup>9</sup> or TURNkey) and waveform acquisition, processing and visualization (PREVENT). Data standardization for easy integration in international infrastructures such as European Plate Observing System - EPOS (Luzi et al., 2016; Astorga et al., 2020) and for use in international research projects is another objective that the Romanian research and engineering community working on SHM is envisaging.

In addition, the low and narrow-band frequency content of the ground motion, observed in Bucharest for large-magnitude Vrancea earthquakes (Lungu and Cornea, 1988; Lungu et al., 1992; Craifaleanu, 2011), and its effect on different building typologies (Ambraseys 1977), needs further investigations. Outcomes of the seismic monitoring of structures can also significantly help as input for refined rapid seismic loss estimates, using already available systems such as SeisDaRo (Toma-Danila and Armas, 2017).

There is also a crucial need to continuously develop and upgrade the national guidelines regarding SHM, including clear requirements for modern digital sensors, standard installation procedures, data acquisition and processing. Currently, there are no specific procedures for the elaboration, checking and approval of the seismic instrumentation plan. A better definition of the technical specifications of the digital accelerometers is needed, regarding their minimum sensitivity, the maximum amplitude that can be recorded, the frequency sampling, the storage, and the time precision. Moreover, online access should be mandatory ensured for easy maintenance and periodical checks on the system operational status. The data processing and results interpretation should be performed by specialists, using well-established routines and algorithms, to obtain reliable results and to avoid any artefact errors or uncertainties that can arise and propagate during the signal processing stage.

In the recent years, the collaboration between Romanian institutions involved in the health monitoring of structures (URBAN-INCERC, UTCB and INFP) has been enforced by joint research projects and publications (Tiganescu et al., 2021a; Tiganescu et al., 2021b; Tiganescu et al., 2021c; Marmureanu et al., 2021). A system integrating URBAN-INCERC's SHM system and INFP's

<footnote n="9">⁹https://setting.epos-ro.eu</footnote>
EEW was proposed by Dragomir et al. (2016). The SETTING project, as well as the Romanian consortium<fnref n="10" /> contributing in the EPOS research infrastructure<fnref n="11" /> aim to provide a national research platform consisting of a standardized inventory of organizations which could provide data, products, and services relevant for the field of Earth Sciences—including SHM relevant categories. The platform will be designed to meet the needs of various user communities (research, academia, industry and general public). The effort to strengthen the collaboration with local and central authorities has gained momentum, as well, as several researchers from the three institutions are participating in the elaboration of a national strategy for the seismic risk reduction of the building stock, and in the development of a national emergency procedure in case of a strong earthquake. A special SHM section will be held at the third European Conference on Earthquake Engineering and Seismology (Bucharest, 2022), as a step towards bringing together the significant actors in the field and bridging the gap between research, academia and industry.

## AUTHOR CONTRIBUTIONS

AT and I-GC designed the paper, and all the authors contributed to the manuscript. AA and RV contributed to the part referring to UTCB network, I-GC and C-SD contributed to the part referring to the URBAN-INCERC network, AT, BG, DT-D, and S-FB contributed to the part referring to the INFP network. DT-D designed Figure 1. All the authors contributed to the Discussion section.

## FUNDING

Part of the developments received support through the following INFP Projects PREVENT (Open system for integrated civil structures monitoring; no. PN-III-P2-2.1-PED-2019-0832) and Pre-Quake (Preparing for the next major earthquake: exploring new scientific opportunities; No. PN-III-P1-1.1-PD-2019-0969) supported grants from the Romanian Ministry of Research, Innovation and Digitalization, CNCS-UEFISCDI within PNCDI III, as well as the SETTING Project (Integrated thematic services in the

field of Earth observation: a national platform for innovation; No. 108206) cofinanced from the Regional Development European Fund (FEDR) through the Operational Competitivity Programme 2014–2020; TURNkey (Towards more earthquake-resilient urban societies through a multisensor-based information systemenabling earthquake forecasting, early warning and rapid response actions, available at https://earthquake-turnkey.eu/, GA 821046), funded by the European Commission (EC); The paper refers also some of the results obtained at INCD URBAN-INCERC within the projects PN 18-35 01 01, “Research for the development of an instrumental detection system for structural damage caused by seismic or non-seismic sources”, and PN 19-33 01 01, “Research on the implementation of an integrated system for ensuring the security of the constructed space, with semi-automatic generation of PGA maps provided by seismic actions or other vibratory sources and quick evaluation of vulnerability of instrumented buildings”, funded in the framework of the “Nucleu” Programmes CONCRET and ECOSMARTCONS by the Romanian Ministry of Research, Innovation and Digitalization.

## ACKNOWLEDGMENTS

Japan International Cooperation Agency (JICA) is gratefully acknowledged for the donation of equipment for seismic instrumentation in the frame of the Technical Cooperation Project “Reduction of Seismic Risk for Buildings and Structures” Ref.#7241011E0, equipment operated at present by UTCB. The authors also thank to Editor DP and two independent reviewers for their helpful and constructive reviews, which lead to an improvement of the manuscript.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feart.2022.819153/full#supplementary-material

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# Combining Earthquake Ground Motion and Ambient Vibration Recordings to Evaluate a Local High-Resolution Amplification Model — Insight From the Lucerne Area, Switzerland

Paulina Janusz¹*, Vincent Perron¹, Christoph Knellwolf² and Donat Fäh¹

¹Swiss Seismological Service, ETH Zürich, Zürich, Switzerland, ²Verkehr und Infrastruktur, Abteilung Naturgefahren, Kanton Luzern, Kriens, Switzerland

## OPEN ACCESS

**Edited by:**
Dimitris Pitlakis,
Aristotle University of Thessaloniki,
Greece

**Reviewed by:**
Iolanda-Gabriela Craifaleanu,
Technical University of Civil
Engineering of Bucharest, Romania
Maria Manakou,
Aristotle University of Thessaloniki,
Greece

*Correspondence:
Paulina Janusz
paulina.janusz@sed.ethz.ch

**Specialty section:**
This article was submitted to Geohazards and Georisks, a section of the journal Frontiers in Earth Science

**Received:** 28 February 2022
**Accepted:** 22 March 2022
**Published:** 09 May 2022

**Citation:**
Janusz P, Perron V, Knellwolf C and Fäh D (2022) Combining Earthquake Ground Motion and Ambient Vibration Recordings to Evaluate a Local High-Resolution Amplification Model—Insight From the Lucerne Area, Switzerland.
Front. Earth Sci. 10:885724
doi: 10.3389/feart.2022.885724

Amplification factors are often estimated using empirical methods based on earthquake ground motion; however, especially in low-seismicity urban areas, recording a statistically representative number of high-quality signals may take years. Hence, the attempts to use ambient vibration instead have progressed. This includes the development of the hybrid site-to-reference spectral ratio (SSRh) method that combines earthquake and ambient vibration recordings. We applied the method in the Lucerne area in central Switzerland that is characterized by low-to-moderate seismicity but was struck by several strong earthquakes in historical times (i.e., Mw 5.9 in 1,601) and is located in a glacial basin filled with unconsolidated deposits prone to significant amplification. To develop the high-resolution local site amplification model for the city of Lucerne using the SSRh method, we took advantage of a small seismic monitoring network installed in the Lucerne area in total for about a year and the stations of the Swiss Strong Motion Network (SSMNet). In addition, we performed two extensive surveys to record ambient vibrations and used dozens of measurements performed in the area since 2001. The resulting amplification model referring to the Swiss reference bedrock conditions indicates high-amplification factors (up to 10-fold) for a broad range of frequencies. The model is consistent with geological data and site response proxies such as $f_0$ values. The direct comparison of our results with the SSR amplification functions for several sites shows good agreement. However, the model is characterized by high uncertainty and influenced by daily variation of the noise wavefield, as well as the spatial distribution of the stations of the seismic network. We also discussed the extent of the applicability of the method, concluding that the main factor influencing its performance is not the distance but the similarity of the site condition between the stations.

**Keywords:** seismic site effects, seismic hazard, urban areas, microzonation, ambient vibration, earthquake ground motion
# INTRODUCTION

Estimation of site effects plays a crucial role in local seismic hazard and risk assessment. Indeed, the local soil condition can modify seismic ground motions' amplitude and duration and impose significant spatial variability. In particular, the thick and soft sedimentary basins can considerably amplify the seismic waves. Events in 1985 in Mexico City and 1906 in the San Francisco area are examples, where earthquake-induced damage was significantly increased due to local site effects (e.g., Bard, 1997). Urban areas are especially vulnerable to earthquakes because of high-population density and the accumulation of exposed infrastructure. Due to recent rapid urban development, seismic risks in the cities cannot be neglected, even in countries characterized by moderate seismic hazards such as Switzerland.

The empirical site response can be evaluated from ground motion observations using the standard spectral ratio technique (SSR—Borcherdt, 1970) referenced to the local outcropping rock or methods based on the generalized inversion scheme (e.g., Andrews et al., 1986; Bindi et al., 2009) such as empirical spectral modeling (ESM) by Edwards et al. (2013). This method is used to obtain the empirical amplification functions with respect to the Swiss reference rock profile (Poggi et al., 2011). Nevertheless, the empirical approaches require a good statistical number of recordings with a high signal-to-noise ratio. In low-to-moderate seismicity urban areas that are characterized by the high background noise level, instruments often need to operate for years before recording a significant number of earthquakes. The associated cost of such deployment together with the lack of free-field space impedes using earthquake observations to assess the site response at high spatial resolution in urban environments. On the contrary, ambient vibration measurements can be performed easily and quickly even in densely populated cities. The ambient noise horizontal-to-vertical spectral ratio (HVSR) method first introduced by Nogoshi and Igarashi, (1971) and then revised and promoted by Nakamura, (1989) is commonly applied to determine the fundamental resonance frequency f0 of the site (e.g., Bonnefoy-Claudet et al., 2006; Fäh et al., 2001). However, the HVSR amplitude cannot be interpreted as a measure of amplification factors (e.g., Bonilla et al., 1997; Perron et al., 2018a; Poggi and Fäh, 2016). Therefore, the attempts to use ambient vibration to estimate directly the amplification factors were made by calculating spectral ratios from ambient noise recordings (SSRn—Kagami et al., 1982). As in the SSR method, the source and path components are assumed similar for both stations, and the spectral ratio expresses only the site effect term. It is, however, a strong statement regarding ambient noise, and many authors demonstrated that the SSRn approach overestimates the rock-relative amplification factors (e.g., Field et al., 1990; Perron et al., 2018a). Some other authors showed that it enables only to estimate the shape of the amplification curve (e.g., Lermo and Chavez-Garcia, 1994; Yamanaka et al., 1993), while several authors observed merely the similarity of the

frequency of the SSRn main peak and fundamental frequency of resonance f0 (e.g., Field et al., 1990). No correlation was observed in other studies (e.g., Field, 1996). Perron et al. (2018a) provided detailed literature reports on this topic. The strong influence of close transient noise sources and effects of the impedance contrast between sediments and rock on the ambient noise wavefield may be the main reasons for the failure of the SSRn approach (Perron et al., 2018a). Hence, Perron et al. (2018a) introduced the hybrid approach (SSRh) combining the SSR and SSRn methods (Figure 1). The main idea of the SSRh method is to perform the SSRn approach only between sites located inside the sedimentary basin to map the spatial variation of the site response. Then, the SSRn curves are corrected using the rock-relative SSR at a few stations (at least one) inside the basin where earthquake recordings are available. The SSRh approach has shown comparable results to the direct SSR based on earthquake recordings (Perron et al., 2018a; Perron et al., 2022). In addition, the SSRh method allows for much higher spatial resolution because ambient noise can be fast recorded across wide areas, and it requires only a limited number of permanent seismic stations to be present in the region.

In this study, we will focus on Lucerne that is middle-sized but a densely populated town in central Switzerland (Figure 2A). The city is located on a soft sedimentary basin that is prone to site effects. During the last 50 years, the seismicity in central Switzerland has been low (Gisler et al., 2004); however, several strong historical earthquakes are evidenced, including an event in 1,601 with a moment magnitude Mw of 5.9 (Fäh et al., 2011). It was the strongest historical event in central Switzerland in the past millennium and one of the strongest events in the whole of Switzerland (Schwarz-Zanetti et al., 2003) and was followed by a 4–5 m-high tsunami (Schnellmann et al., 2004).

<figure><img src="image_41.png" /><figcaption>**FIGURE 1** | Schematic visualization of the SSRh method. Capital U indicates earthquake ground motion while u is the ambient vibration recording.</figcaption></figure>
<figure><img src="image_42.png" /><figcaption>**FIGURE 2** | (A) Red rectangle on the map of Switzerland shows the location of the investigated area. (B) Simplified geological map based on Geological Vector Datasets GeoCover (s.geo.admin.ch/95a803e945) (C) Ground classes in the Lucerne area according to SIA 261 (SIA, 2020) (s.geo.admin.ch/96572c02d9). Stations of local seismic monitoring networks are shown.</figcaption></figure>

Our study focuses on the application and optimization of the SSRh approach for the Lucerne area. We developed an amplification model at high spatial resolution and in a broad frequency range using the SSRh method. We verified the results by comparing them to the earthquake-based amplification functions. In addition, we tested several parameters that may influence the results. Moreover, we compared our results to geological data and to the fundamental resonance frequency that we mapped across the area. The aim of this article is not only to show an example of the SSRh method application but also to offer practical guidelines and advice to future users.

## GEOLOGICAL SETTING AND INSTRUMENTATION

The study area is a relatively small basin filled with unconsolidated Quaternary fluvio-lacustrine deposits (Figure 2B), mainly consisting of interspersed layers of sand, gravel, clay, and silt (Keller + Lorenz, 2010; Poggi et al., 2012). Such predominantly soft sediments are classified as soil classes D, C, and E (Figure 2C), according to the Swiss building code classification (SIA, 2020), which is defined in terms of $V_{s30}$ ranges and is similar to EC8 classification (EC8, 2004). The basin was
<figure><img src="image_43.png" /><figcaption>**FIGURE 3** | Stations of seismic monitoring networks located in the Lucerne area and single-station ambient noise measurements. Short-period seismometers are part of the temporary local network, while accelerometers belong to SSMNet (Michel et al., 2014; Hobiger et al., 2021).</figcaption></figure>

formed by the inter- and intraglacial processes in clastic sedimentary rocks, such as hard sandstones, siltstones, and mudstones deposited in the Subalpine Molasse basin. Hence, the bedrock interface geometry is complex; the southern part of the study area is a long (about 4 km), very narrow basin (about 700 m in the narrowest part) with sediment thickness reaching 150 m. In the northern part of the basin, where the historical Lucerne's old town and train station are located, the basin shape is more asymmetrical with the thick sedimentary layers by the lakefront (50–100 m thick) extending southwest into a shallower terrace with gradually decreasing sediment thickness.

In November 2019, nine temporary seismic stations and then again in December 2020, ten temporary seismic stations were deployed in the Lucerne area for 5–6 months to record local and teleseismic earthquakes (Figure 3, Figure 4A). It consisted of 3-component short-period seismometers Lennartz 5 s (LE-3D 5-s) associated with Centaur digitizers. In total, most of the stations were recording for about one year. All sensors were buried 0.4 m below the ground, except for one (LUZ03) which was situated in

the underground parking; the instrument was laid directly on the concrete floor. In addition, our dataset was supplemented by three permanent accelerometers of the Swiss Strong Motion Network (SSMNet—Hobiger et al., 2021; Michel et al., 2014). Some details concerning the deployment and geological and geotechnical characteristics of the sites can be found in **Supplementary Table 1A** (in Appendix).

Two ambient vibration measurement campaigns were performed in June 2020 and in April 2021, (Figure 3, Figure 4B) respectively, while the temporary seismic stations were still operating. In total, we recorded at least 1–2 h of ambient noise at 100 sites using the same sensor-digitizer configuration as for the earthquake monitoring (LE-3D 5-s—Centaur). During short-time measurements, sensors were not buried but installed on tripods directly on the ground. In addition to the temporary seismic stations, longer ambient noise recordings were performed at four sites with a buried sensor to capture the 24 h variations of the ambient noise seismic wavefield inside the basin. During both campaigns, the ambient vibration measuring points were
<figure><img src="image_44.png" /><figcaption>FIGURE 4 | (A) LUZ02—example of the station of the temporary network. (B) Example of measurement of ambient noise during the survey in June 2020. The setup consists of a Lennartz 5-s sensor and Centaur digitizer.</figcaption></figure>

recording simultaneously with earthquake monitoring stations, and the instruments deployed for 24 h. In addition, there were short overlaps (from a few minutes to one hour) between some short-term measuring points, but only a maximum of six of them were recording at the same time. Moreover, we supplemented our dataset with a few hundred short ambient vibration recordings (Figure 3) performed in the Lucerne area during the last 20 years (e.g., Poggi et al., 2012). These old recordings were used to map the fundamental resonance frequency in detail across the area but not to develop the amplification model since the SSRh cannot be applied without the simultaneous presence of the earthquake monitoring stations.

## METHODOLOGY AND PROCESSING

All earthquake and ambient vibration recordings that we used are first pre-processed using the ObsPy library (Beyreuther et al., 2010); in other words, instrumental correction and bandpass filter (a cosine taper with corner frequencies: 0.01, 0.05, 95, and 100 Hz) are applied.

As for the SSR method, a number of local and teleseismic earthquakes are extracted and processed for each site of the local temporary network and permanent SSMNet stations. In total, 44 events (Supplementary Table 2A in Appendix) are analyzed considering a part of the signal from P-wave arrival until coda (Perron et al., 2018b), requiring that the signal-to-noise ratio (SNR) is at least 3 for frequency bands longer than half an octave. To ensure that the earthquake source and path components are approximately the same for both considered stations, the SSR is computed for a given event if the distance between two stations was much shorter than the approximated epicentral distance; the factor of 5 was chosen. The SSR curve is then smoothed using the Konno and Ohmachi, (1998) algorithm with a b-value of 40.

The final amplification function for each pair is a geometric mean of several realizations of the SSR. The horizontal component

is defined here as the geometric mean of the eastern and northern components. ESM amplification functions (Edwards et al., 2013) are automatically computed for all stations of the Swiss network including temporary deployment but only for local earthquakes and if SNR is more than three in a broad frequency range (at least an order of magnitude).

As for the SSRn technique, we randomly selected one week (10-16.03.2021) of continuous noise recording for temporary stations; for short-term ambient vibration measurements, the whole recordings (often 1–2 h long) are used. All recordings are divided into shorter windows before applying a short-time Fourier transform, where the window length and overlap value are dependent on the signal length to optimize the computing time; typically, the length of the window is 40 s with 50% overlapping. Noise-based spectral ratios for each short window are averaged using a geometric mean after excluding outliers and smoothed with the Konno and Ohmachi (1998) algorithm with a b-value of 40.

As for the SSRh technique, the SSR and SSRn computed in previous steps are used. For SSR functions, only frequency bands where at least two earthquakes contribute are considered because several stations of our local monitoring network might serve as a potential intermediate station; hence, to avoid subjectivity, we calculated a weighted geometrical mean of several SSRh realizations computed using different intermediate stations with the squared inverse of the difference of $f_0$ values between the site ($f_0^s$) and the intermediate station ($f_0^i$) as a weight ($w$). However, usage of other weights is also tested (e.g., inverse of the squared distance between stations) and is discussed later in the present article.

$$w = \frac{1}{(f_0^i - f_0^s)^2}$$

While mapping the amplification variability for the Lucerne area using ambient vibration data, we have a limited choice of
<figure><img src="image_45.png" /><figcaption>**FIGURE 5** | (A) Amplification functions (horizontal component) for stations located in the Lucerne area using the SSR method referenced to the rock station LUZ01. The center of each plot corresponds to the station location. All plots have the same scale as the plot in (B). In the background, the thickness of the unconsolidated deposits map is shown. (B) ESM amplification function (Edwards et al., 2013) for rock station LUZ01. (C) Number of contributing events for each frequency for all sites for the SSR method.</figcaption></figure>
<figure><img src="image_46.png" /><figcaption>**FIGURE 6** | Comparison between SSRn and SSR for all combinations of short-period seismometers. Each column corresponds to one site and each row to one reference station. The distances between sites and f0 values are indicated. In the case of SLUK and SLUW, the short-period seismometers deployed close to the permanent accelerometer were not operating simultaneously with all stations. The map shows the interpolated map of f0; for the rock sites where a peak was indiscernible, the value of 20 Hz was allocated for visualization purposes.</figcaption></figure>
Reference: LUZ01

<figure><img src="image_47.png" /><figcaption>**FIGURE 7** | Comparison of the relative amplification functions for sedimentary sites using the SSR, SSRn, and SSRh methods with standard deviation (reference—rock station LUZ01).</figcaption></figure>

intermediate stations because we needed to remove part of our
temporary network earlier. For the dataset collected in June 2020,
only stations HOR02, HOR03, KRI01, and KRI02 were available;

during the campaign in April 2021, we had our disposal stations
LUZ02, LUZ03, and LUZ05, as well as SLUK and SLUW. Because
the last two are accelerometers, we decided not to directly
compare ambient vibration recorded by accelerometers to short-period seismometers; instead, we deployed short-period seismometers very close for about 24 h to record ambient noise.

Finally, we developed a detailed interpolated amplification map for the Lucerne basin at several frequencies. The model is referenced to either a local rock outcrop reference station or a Swiss reference rock profile (Poggi et al., 2011) by multiplying the values by the ESM amplification function (Edwards et al., 2013) for a local rock station. For measurement points where only ambient vibration data are available, the SSRh functions are used to estimate amplification, while for stations of the temporary and permanent monitoring networks, the SSR used is supplemented by SSRh at higher frequencies if there are not enough earthquake recordings.

The uncertainty of the final amplification model is a combined geometric standard deviation of SSRn and SSR, and ESM amplification functions are as follows:

$$ \text{std}(\text{SSRh}) = \exp\left(\sqrt{\log(\text{std}(\text{SSR}))^2 + \log(\text{std}(\text{SSRn}))^2 + \log(\text{std}(\text{ESM}))^2}\right). $$

In the case of using several intermediate stations, the weighted geometric standard deviation of such several realizations is also included.

In addition, for mapping the fundamental frequency of resonance $f_0$ across the area, we used the HVSR calculated with the RayDec method (Hobiger et al., 2009), which emphasizes the influence of Rayleigh and suppresses the body and Love wave impact, allowing to retrieve the Rayleigh wave ellipticity curve. We utilized all available ambient vibration recordings since 2001, including data from the temporary and permanent stations, for which we chose 4 h randomly from continuous noise records considering only night to decrease the influence of cultural noise. The $f_0$ values are picked manually at each ambient noise recording point separately using the HVSR curve, but the consistency of the $f_0$ for neighboring points is verified. Finally, we interpolated the detailed $f_0$ model for the Lucerne area.

# RESULTS AND DISCUSSION

## The Rock-Relative Amplification Functions Using Earthquake Data

The SSR and ESM amplification functions are used in this study to adjust the SSRn to the rock condition and to verify that these noise-based amplification functions give similar results as earthquake-based ones. In Figure 5A, the SSR amplification functions with respect to the rock station LUZ01 for each station of the temporary and permanent seismic monitoring networks are shown. In the background, the map of the thickness of unconsolidated deposits derived from the bedrock elevation model and provided by the Federal Office of Topography (Swisstopo) is displayed. The comparison indicates a good agreement between the thickness of sediments and the SSR amplification functions, at least regarding the first peak at low frequency. In addition, for LUZ01, the ESM amplification function is plotted (Figure 5B). For stations located on thick sedimentary layers (above 50 m thick), the peak amplification is observed at about 0.8–1.2 Hz, and amplification

exceeds a factor of 10 (i.e., HOR02, HOR03, LUZ02, LUZ03, LUZ04, SLUK, and SLUW). The high amplification values present a plateau over a broad range of frequencies. Even for stations located closer to the basin edges such as KRI01, KRI02, and LUZ05, site effects are not negligible; above about 2 Hz, the amplification factors reach even 6-fold in the case of KRI01. In addition, we observed two peaks on the amplification functions for stations in the city center (i.e., LUZ03, SLUK, SLUW, and LUZ02) and a broad response with no clear peak for the stations closer to the basin edge (i.e., KRI02, etc.), and this may be due to the complexity of the basin and possible 2D site effects. For the stations located on the rock (SLUB and HOR01), the relative amplification function is close to unity; similarly, the ESM amplification function for LUZ01 shows negligible amplification in the frequency range of 0.5–3 Hz compared to the Swiss reference rock profile.

The standard deviation for the SSR represented by the gray band is relatively small; however, the number of events exceeding the required signal-to-noise ratio (SNR) is low mainly due to the high background noise level and relatively short recording time of the stations (~1 year). The frequency band where many high-quality earthquakes are recorded is very narrow; on average, about 15 earthquakes contribute between 0.6 and 2 Hz. For higher frequencies, the number of events with sufficient SNR decreases rapidly (Figure 5C), and so far for some stations (e.g., LUZ05), we have recorded no earthquakes with high enough SNR at higher frequencies. Nevertheless, the frequency of the first and highest peak amplification often coincides with the frequency band with the highest number of contributing events; hence, the highest amplification values can be treated with relatively high confidence. However, to resolve the amplification function better at higher frequencies and to create a more reliable amplification model, more high-quality events are needed; therefore, a longer recording time is required.

The ESM adopts higher standards accepting only very good recordings (SNR>3 on a 10-Hz frequency band minimum) from local earthquakes solely; hence, in low-seismicity areas such as Lucerne, the station should be deployed even longer to reach a good statistical significance. Therefore, we used the ESM amplification functions only for local rock sites in order to refer the amplification model for the Lucerne area to the Swiss reference rock profile. It allows comparing the results between different methods and between different areas in Switzerland. In addition, it enables the calculation of site-specific hazard spectral acceleration maps. However, a high standard deviation of the ESM amplification functions compared to the SSR increases the uncertainty of the final model.

## Validity and Performance of the Method

Figure 6 shows a comparison between SSR (in red) and SSRn (in black) for all combinations of short-period seismometers in the Lucerne area. An average SSRn over one week of recording is plotted and in the case of the SLUK and SLUW stations, it is plotted for about 24 h. In the last two columns, at least one of the station pairs is located under rock conditions. The rock-relative SSRn overestimates the amplification as was also observed by other authors (e.g., Field et al., 1990). However, when considering spectral ratios between pairs of stations located on the soil, the
<table><caption>**TABLE 1** | RMS between SSR and SSRh calculated using different strategies of weighting intermediate stations. More explanations in the text.</caption><thead><tr><th>Weight</th><th>LUZ04</th><th>LUZ03</th><th>LUZ02</th><th>HOR03</th><th>HOR02</th><th>KRI01</th><th>KRI02</th><th>LUZ05</th></tr></thead><tbody><tr><td>f<sub>0</sub></td><td>0.1896</td><td>0.1891</td><td>0.1869</td><td>0.2196</td><td>0.2668</td><td>0.2132</td><td>0.2528</td><td>0.3407</td></tr><tr><td>Distance</td><td>0.2290</td><td>0.4065</td><td>0.2318</td><td>0.3473</td><td>0.2531</td><td>0.2175</td><td>0.2930</td><td>0.3487</td></tr><tr><td>Distance + selection</td><td>0.1844</td><td>0.2180</td><td>0.2146</td><td>0.2204</td><td>0.2565</td><td>0.2406</td><td>0.2128</td><td>0.3321</td></tr></tbody></table>

<figure><img src="image_48.png" /></figure>

SSRn and SSR fit quite well, and most of the discrepancies are within the SSRn mean ± standard deviation. These observations are also confirmed in **Figure 7**, where the SSR, SSRn, and SSRh are compared for all stations located in the sedimentary basin with respect to the rock station LUZ01; the SSRh curves are calculated as a weighted mean of all intermediate stations with $f_0$ difference as a weight. As expected, the rock-relative SSRn overestimates the amplification factors for frequencies higher than the frequency

of the peak amplitude, while the mean SSRh curves are consistent with the SSR; the discrepancies are observed at higher frequencies and are often within one SSRh standard deviation. Nevertheless, the uncertainty of the SSRh and SSRn methods is much higher than in the case of the earthquake-based approach because of the high variability of the ambient vibration wavefield in time.

The direct comparison between SSR and SSRn for stations inside the sedimentary basin is a straightforward way to assess the
<figure><img src="image_49.png" /></figure>

**FIGURE 9** | Fundamental resonance frequency f₀ for more than 300 points in the Lucerne area compared to (A) the map of the thickness of the unconsolidated deposits, (B–D) amplification map derived using the SSRh method for 1, 1.5, and 2 Hz, respectively. The reference for amplification maps is the Swiss reference rock profile.

applicability of the SSRh method in a given area. The similarity between curves verifies experimentally that the basin site response can be estimated using a soil-to-soil spectral ratio of ambient vibration. While the noise-based spectral ratios between the sedimentary site and the rock cannot be employed to obtain reliable amplification factors, the SSRn between stations inside the sedimentary basin gives a good estimation of the basin response and can be corrected using SSR to obtain rock-relative amplification. A detailed discussion of the potential reasons for these observations can be found in Perron et al. (2018a). In case the SSR-SSRn comparisons show weak or no correlation for most of the stations, the SSRh method cannot be

used in the basin, at least using that specific configuration of intermediate stations. The reasons may be among others, namely, very great distance, very different site conditions, or very much variable ambient vibration wavefields. Nevertheless, experimental evidence of the SSRh reliability was observed in other sites (Perron et al., 2018a; Perron et al., 2022).

In **Figure 6**, we can observe that the agreement between SSRn and SSR varies from one pair of stations to another. Based on the map of the thickness of unconsolidated deposits (Figure 5A) and fundamental resonance frequency map (Figure 6), the Lucerne basin can be divided into three parts: the northern and southern deep basins characterized by low f₀ and the middle basin with
<figure><img src="image_50.png" /><figcaption>**FIGURE 10** | (A) Direct comparison of SSR and SSRh for the station LUZ03 calculated using different strategies considering the intermediate stations. (B) Relative difference between SSR and SSRh calculated using different strategies. More explanations in the text.</figcaption></figure>

shallower sediments and higher $f_0$. The best fit is obtained for nearby stations located in the same part of the basin, for example, the spectral ratio between HOR02 and HOR03 located in the deep southern part or between KRI01 and KRI02 in the shallower basin gives similar SSRn and SSR amplification functions. We observed the flat spectral ratios without significant peaks between these stations indicate that the site response is similar for both of them. All other combinations (e.g., KRI02 and HOR03) where we see an apparent peak show some discrepancy between SSRn and SRR, especially misestimating the amplitude of peak amplification. It may indicate that the sources controlling the noise wavefield are much different in those parts of the basin; hence, the assumptions required to retrieve site effects using SSRn are not valid. Similarly, the SSRn calculated for any pair of the close by group of LUZ02, LUZ03, SLUK, SLUW, and LUZ04 provides a curve that is almost identical to the corresponding SSR function. All are located in the northern part of the Lucerne basin characterized by high sediment thickness and low $f_0$. However, the stations located in northern and southern deep basins also give good results when combined, even though the distances between them are relatively high. We also observed a flat SSR and SSRn for those pairs. These examples indicate the higher importance of closeness of the site condition and lower significance of the spatial proximity between stations to the maximal extent of our experimental area. A simple indicator of similarity of the site condition is the $f_0$ value. Nevertheless, in Sion (Perron et al., 2022), no significant difference in the goodness of the fit between different station pairs was noticed; however, the difference of $f_0$ between most of the stations was also insignificant. Therefore, this effect needs to be investigated for several other case studies to study what is the decisive factor affecting the goodness of the fit between noise- and earthquake-based spectral ratios.

We suspected that with increasing distance, the fit would become worse, even if the site condition remains the same. However, because of the lack of short-period seismometers

nearby that are located inside the sedimentary basin, we were not able to investigate the applicability of the method with the increasing distance for the Lucerne area. At this moment, the furthest pair in Lucerne for which good compatibility between SSRn and SSR is observed is 4.9 km apart. In Argostoli (Perron et al., 2018a), the maximum distance was about 1.2 km, while in Sion (Perron et al., 2022) it was more than 13 km.

In addition, when SSRh functions are considered, the best fit is observed (Figure 7; Table 1) for stations located in the deep northern part of the basin (LUZ02, LUZ03, and LUZ04), while worse for the stations situated in the shallower part of the basin, especially LUZ05 which is characterized by the highest $f_0$ value. More investigation needs to be carried out; however, these results may indicate that the method performance is the best for sites located on the thick sedimentary layers, decreasing toward the basin margins. In Perron et al. (2022), it was noticed that for stations located at the edges of the Rhône valley, the SSRh method gives poor results, especially at low frequencies. We have recently deployed two new temporary stations in the Lucerne area close to the basin margin in order to test that behavior; however, not enough earthquakes have been recorded so far to derive the empirical amplification functions.

## Amplification Map for the Lucerne Area

Because of the promising results using the stations of the seismic monitoring network, we applied the SSRh method for 100 single-station ambient noise measurements that we performed in 2020 and 2021. Figures 8A–C show the maps of the amplification factors for three frequencies with respect to the Swiss reference rock profile. Based on the model, we can expect amplification factors of more than 10 at the fundamental frequency (between 0.8 and 1.5 Hz) in the deep parts of the basin (>50 m of sediments). Significant amplifications are also expected for these sites at higher frequencies (up to 5 Hz). Due to the lower number of recorded earthquakes at frequencies higher than 5 Hz, the reliability of the
<figure><img src="image_51.png" /><figcaption>**FIGURE 11** | Example of the influence of ambient noise daily variations on SSRn. Each of the plots (A–G) corresponds to one day and each line to one hour of recording. The colors indicate the time of the day when noise was recorded. The black line is a mean over 24 h, and the red line is SSR. Plot (H) shows the mean SSRn over 7 days with standard deviation compared to the SSR.</figcaption></figure>

model above that frequency is limited. Some amplification is also evident closer to the basin edges where the sediment thickness is lower. The uncertainty of the model that varies depending on the frequency (Figure 8D) is relatively high, especially due to the variability of the ambient vibration wavefield and high standard deviation for the ESM amplification functions (Figure 5B). The animations showing the amplification and the uncertainty for frequencies between 0.2 and 20 Hz for local reference (LUZ01) and the Swiss reference rock profile can be found in the **Supplementary Materials**. Generally, in the Lucerne area, the lowest uncertainty values are characteristic for frequencies between 1 and 2 Hz that coincide with the range where the highest amplification values are often observed. However, for most of the points, the standard deviation often exceeds 2. For frequencies lower than 1 Hz and higher than 2 Hz, the values are even higher. In a noisy city such as Lucerne, high uncertainty values cannot be significantly reduced because of the variable nature of the recorded noise.

To verify if the obtained model predicts the reliable amplification factors and their spatial variability, a very dense network of stations deployed for several months would be needed. A cost-effective but a less solid approach is to use a few test sites for validation and to compare the model with site response proxies such as $f_0$ values and other geological information. Although the amplitude of the HVSR curve cannot be used to predict amplification factors directly (e.g.,

Bonilla et al., 1997; Perron et al., 2018a), the high amplification values are expected at the frequency of the HVSR peak and above (Poggi and Fäh, 2016). We mapped the frequency of the peak of the Rayleigh wave ellipticity function for the Lucerne basin using more than 300 points (Figure 9A). In Figures 9B–D, the amplification maps from the SSRh method for 1, 1.5, and 2 Hz are shown together with the points, where the $f_0$ value is similar or lower than the respective frequency. To account for the uncertainty, the broader ranges are adopted, for example, for an amplification map for 1 Hz; the points where $f_0$ is between 0.75 and 1.25 are highlighted. We can observe a good correlation for all shown frequencies, especially in the southern part of the basin, indicating that our model is consistent with $f_0$, which is one of the important site response proxies. The $f_0$ values as shown in Figure 9A are in good agreement with the thickness of unconsolidated deposits derived from gravity measurements. This indicates that $f_0$ is mainly controlled by that parameter, confirming that our amplification model is also consistent with the thickness of sediments. In the northern part of the basin, the observed agreement between the SSRh amplification, $f_0$, and the sediment thickness is still clearly visible but less distinctly. The reason is probably the dominance of the artificial fillings (Figure 2B) that increases the variability of the site response and due to higher density of the infrastructure in the city center affecting the ambient noise wavefield. In the future, we would
<figure><img src="image_52.png" /><figcaption>**FIGURE 12** | Comparison between SSR and SSRn for all combinations of stations located inside the sedimentary basin. The SSRn is a mean over 7 days, respectively, considering the whole day (black), the night between 00:00–3:00 UTC (blue), and the day between 08:00–11:00 UTC (green).</figcaption></figure>

also apply other methods such as canonical correlations (Panzera et al., 2021) or simulations of wave propagation using numerical modeling that may help us to validate the model.

## On the Choice of the Intermediate Stations and the Weighting Process

In the SSRh method, the intermediate stations located in the basin are used in order to estimate the rock-relative amplification function for each site. In case more than one station can serve as an intermediate station, adequate decisions should be made. Generally, two main scenarios exist, either one intermediate station can be chosen for each site or an average of several realizations using different intermediate stations can be calculated. In both approaches, a subjective decision needs to be made concerning the spatial extent where usage of a specific intermediate station is justifiable. In our study, we decided to calculate the weighted mean of many realizations in order to avoid too many subjective decisions. Analyzing the comparison between SSR and SSRn (Figure 6) can provide some hints if the area has to be divided into zones according to the usability of the intermediate stations.

First, we tested the inverse of square distance weight that emphasizes the influence of nearby stations. On the one hand, the basis of the SSRn is an assumption that is similar to the earthquake data, and we can retrieve the site effect component by assuming that source and path terms for both stations are the same. Hence, the emphasis on proximity and yet similarity of the wavefield seems to be a valid choice. However, as we showed, good results are obtained by also using stations located further away but similar in terms of the site condition. Therefore, another tested option was the inverse of squared difference of $f_0$ values to indicate the similarity of the site condition. The RMS between SSR and SSRh for different approaches is shown for each station (Table 1). For many stations, the approach based on $f_0$ seems to give better results (Figure 10); for some stations, no significant difference between both methods was noticed (Table 1). However, often, the highest differences are concentrated in the frequency band with the highest amplification values (Figure 10), indicating that this effect should not be neglected. However, if we divide intermediate stations into two regions (i.e., the deep basin and the shallow basin) and use the distance as a weight, the results for many stations improve significantly, giving similar results as
LUC survey (8 - 9.04.2021)

Reference: LUZ01

<figure><img src="image_53.png" /><figcaption>FIGURE 13 | Variability of the SSRh results between all stations that recorded for at least 24 h in the period 8-9.04.2021.</figcaption></figure>

using $f_0$ weighting (Table 1). We also compared several other
possible weights (e.g., logarithmic difference of $f_0$, inverse vs.
inversed square of distance); however, no significant
improvements were noticed.

In the case of Lucerne, the weighting using $f_0$ difference and
careful selection of stations and then usage of distance as a weight
give similar results. However, we assumed that the former is a
more objective scheme allowing to use all possible stations as
intermediate stations as long as the HVSRs are similar, indicating the similarity of profiles. However, an initial test including several stations with earthquake-derived amplification function is strongly recommended to verify the validity of the results. The validity of the SSRh approach should therefore be tested in areas of interest before being applied. For instance, in the area of Sion (Perron et al., 2022), calculating the median value of different intermediate stations appeared to be the optimal choice. Nevertheless, based on our findings, we recommend planning the deployment of a temporary network having in mind the influence of both the distance and site conditions and trying to cover different sites to sample the basin's site response variability.

## Influence of Ambient Vibration Daily Variation

The amplitude of ambient vibration recordings shows systematic cyclic variations due to the changing intensity of human activities (Bonnefoy-Claudet et al., 2006). We investigated the influence of that variability on the performance of the SSRh method. In **Figures 11A-G**, the SSRn function for seven consecutive days is compared to SSR, where each line corresponds to one hour of the noise recording with colors indicating the time. The mean SSRn over 7 days (**Figure 11H**) is comparable to the SSR, while if we consider short 1-h recordings, large variability is observed, especially during weekdays where the clear separation between daily and nightly recordings is visible. In addition, **Figure 12** shows the comparison between SSRn calculated using only nightly and daily recordings for all station combinations. The main reason is a changing intensity of the noise wavefield, which is not proportional for all the stations. The observed variability differs between different station pairs and usually increases with distance but not necessarily, the presented example is a pair situated close to each other; however, the variability is still relatively high. One of the conclusions is that 1-h recordings are too short to sample the variability of the ambient noise wavefield; the mean over a longer period is needed to obtain reliable results. For the majority of the pairs, the 24 h average is quite stable (as in **Figures 11A-G**) with slightly better results obtained during weekends or during nights (**Figure 12**) because of the lower influence of close human-generated transient noise sources. However, if we consider station pairs located under similar geological conditions, the difference between night and day recordings is slight (**Figure 12**), and in many cases, it is the mean over the whole day that has the best fit, while for other combinations (e.g., HOR/KRI) using night recordings improve significantly the correlation with the SSR curve.

While for permanent or semi-permanent stations that are recording continuously, daily variations are not a significant issue because a mean over a long time can always be computed or only night hours can be considered, the ambient vibration measurements are often performed during busy weekday hours. In addition, when a limited number of stations are available, a compromise between the recording time and a number of points needs to be made. Of course, the best strategy is to use hundreds of stations and to measure for at least 24 h at each site, which allows calculating the relatively stable mean. Such dense measurement campaigns become more and more feasible with the development

of low-cost and portable instruments such as the seismic nodes. An alternative approach is to use a temporary network first to assess the significance of the variability due to the daily changes of noise intensity and to plan the measurement campaign accordingly, for instance, by avoiding recording during specific days or hours at given sites. Another option is to limit the usage of some intermediate stations to small, restricted areas assuming the noise wavefield intensity changes similarly in the proximity.

In the case of the data that we collected during measurement campaigns in June 2020 and April 2021, we could not verify how strong the influence of the ambient vibration's daily variation is; however, most of the data were collected during weekdays and day hours. The recordings are often only 1–2 h long because of the time constraints and a small number of available instruments. **Figure 13** shows the variability of the SSRh functions during the measurements in 2021 for sites where we have at least 24 h of recordings. We observed some dispersion of the results, mostly at low frequencies; however, the variability is not very significant compared to the ambient vibration observations which were made using the temporary network on different days (e.g., **Figure 11**). It allows us to assume that single 1–2 h of recordings that we collected are enough to create a relatively reliable amplification model. However, we identified some points for which the amplification using the SSRh method seems specifically suspicious (e.g., too high compared to the neighboring points or characterized by a strange shape at low frequencies), which may be due to the disturbances by strong artificial noise sources.

## CONCLUSIONS AND OUTLOOK

Empirical site-to-reference methods using earthquake observations are commonly used to estimate the site response. However, in low-seismicity urban areas, the effective application of such methods is limited due to the scarcity of earthquake recordings. Seismic stations have to be deployed for a considerable time in order to record a significant number of high-quality earthquake ground motions. On the other hand, ambient noise measurements are easy to perform and are cheaper than the deployment of the monitoring network. They allow achieving higher spatial resolution; however, it was shown by many authors that the amplification factors derived using ambient noise are overestimated when referred to rock sites. In this study, we tested the SSRh method that combines earthquakes and ambient noise recordings in order to estimate the variability of amplification factors with high spatial resolution. The SSRh technique allows avoiding the limitations of the sole earthquake or noise-based approaches. First of all, the amplification factors estimated using the SSRh approach show good agreement with the classical SSR at the tested sites. In addition, the detailed amplification maps produced for the Lucerne area show consistency with tested site response proxies (i.e., $f_0$ and thickness of the unconsolidated deposits). Second, a dense long-term monitoring network is not necessary to map the amplification with high spatial resolution and for a broad range of frequencies. The minimum strategy is to deploy only two stations—one on the rock and one in the basin; however, installation of more instruments is strongly recommended,
especially for the initial testing phase in particular when soil
characteristics in the area show strong variability. On the other
hand, one of the disadvantages has relatively high uncertainty
compared to the SSR due to the daily variability of the ambient
vibration wavefield.

The validity of the SSRh approach is probably very site-
specific. Therefore, we recommend before applying the
technique to test its performance by comparing it with the
SSR and verifying the influence of different factors on a small
seismic monitoring network, which is deployed in a way to
sample different site conditions and regions characterized by
different ambient noise intensities. Nevertheless, based on our
observation in the Lucerne area, we conclude that concerning the
performance of the SSRh method, the distance between stations is
not as important as the similarity of the site condition, at least to
the maximum extent of the seismic stations in the Lucerne area.
In addition, in case the SSRh function for a given site can be
derived using different intermediate stations, the most optimal
approach for the Lucerne area was to calculate a weighted mean of
many realizations with weights indicating the similarity of the site
condition (e.g., using f₀ value). Last but not least, while the
amplification functions estimated using 24 h of ambient noise
recording seem to be robust, the results using shorter recordings
may show some variability. Generally, more reliable outcomes are
obtained during times of lower human activity; however, the
improvement is significant only in some cases. Our
recommendation is to use longer ambient noise recordings,
preferably 24 h, and/or to plan the measurement campaign
accordingly to minimize the influence of changing the
intensity of the ambient noise wavefield.

We showed experimentally that the SSRh method provides
comparable results as the empirical approaches based on
analyzing earthquakes' ground motion at several sites.
Nevertheless, a better theoretical understanding needs to be
provided, preferably confirmed by numerical simulations. Even
though the derived amplification maps are consistent with
geological data and some site response proxies (i.e., f₀), it has
to be verified if the method allows to correctly estimate the
variability of the basin response either experimentally or by
using other indirect methods. We installed two new seismic
monitoring stations in the Lucerne area to check if the SSRh
techniques correctly predict the amplification close to the basin
margins. Moreover, in November 2021, we repeated the
installation of the seismic monitoring network in order to
assess more reliable earthquake-base amplification factors in
the area; hence, the presented model will be consequently
updated.

The results for the Lucerne area indicate high amplification
factors reaching or exceeding 10 for the peak frequency
(1–1.5 Hz) in some parts of the basin; significant amplification
is also predicted for higher frequencies and in shallower parts.
Such results indicate the seismic hazard is considerably increased
in the Lucerne area. Hence, we will further investigate the city of
Lucerne and its surroundings using other empirical and
numerical methods and perform a detailed site response
analysis considering the non-linear soil behavior in order to
assess the seismic hazard and risk in the area more specifically.

DATA AVAILABILITY STATEMENT

The datasets presented in this article are not readily available
because the data of the stations of the temporary network (doi.
org/10.12686/sed/networks/xy) will be made publicly available at
the end of the project. The data of short-term ambient vibration
measurements are available on request after the end of the project
(doi.org/10.12686/sed-stationcharacterizationdb). Requests to
access the datasets should be directed to paulina.janusz@sed.
ethz.ch.

AUTHOR CONTRIBUTIONS

PJ—first authorship, the main contributor. VP—co-supervisor of the project and contributor in the field and for data processing. CK—local government representative and contributor in the field. DF—main supervisor of the project, funding provider and project manager.

FUNDING

This PhD project is performed in the framework of the URBASIS-EU project-New challenges for Urban Engineering Seismology (grant agreement no 813137) funded by the European Union's Horizon 2020 ITN program, which focuses on the seismic hazard and risk in urban areas.

ACKNOWLEDGMENTS

The study is also a part of the “Earthquake Risk Model for
Switzerland” (ERM-CH) project conducted at the Swiss
Seismological Service (SED). We would not be able to install
our temporary monitoring network and perform measurements
if not for the permission and support of local authorities (Kanton
Luzern, Stadt Luzern, Gemeinde Horw) and several institutions
in Lucerne (Verkehrshaus der Schweiz, Pfarrei St. Paul,
Kantonsschule Alpenquai, and Armee-Ausbildungszentrum
Luzern), Horw (Schulhaus Biregg, Friedhof Horw, and Seefeld
Horw), and Kriens (Pfarrei Bruder Klaus). We would also like to
acknowledge and Zimmermann, and Faesi, Franziska Vercueil,
Rafał Lupa, Erich Wigger, Paul Studhalter, Philippe Ehrler, Toni
Amstutz, and Hans Kauffmann for their cooperation and help
with planning, installations, and maintenance of the network. We
also acknowledge Pascal Graf, Dario Chieppa, Miroslav Hallo,
Agostiny Lontsi, Carla Schöb, Nora Schweizer, and Manuel Studer
for their help during the fieldwork. We would like also to thank the
reviewers and editors for their help in improving this article.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at:
https://www.frontiersin.org/articles/10.3389/feart.2022.885724/
full#supplementary-material
**Supplementary Video S1** | Amplification factors with respect to the rock station LUZ01 derived using the SSRh method for the Lucerne area for the frequency range between 0.2 and 20 Hz. The amplification factors for stations of the temporary network are shown outside the map.

**Supplementary Video S2** | Amplification factors with respect to the Swiss reference rock profile derived using the SSRh method for the Lucerne area for the frequency range between 0.5 and 20 Hz. The amplification

functions for stations of the temporary network are shown outside the map.

**Supplementary Video S3** | Standard deviation for the amplification factors with respect to the rock station LUZ01.

**Supplementary Video S4** | Standard deviation for the amplification factors with respect to the Swiss reference rock profile.

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