Characterization of selected station sites of the Hellenic Accelerometer Network (HAN)

Characterization of selected station sites of the Hellenic Accelerometer Network (HAN) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Characterization of selected station sites of the Hellenic Accelerometer Network (HAN) Nikolaos Theodoulidis, Fabrice Hollender, Pauline Rischette, Ioannis Grendas, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9370620/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Abstact In this study site characterization of twelve accelerometer station sites of the Hellenic Accelerometers Network (HAN) is presented. An established European protocol is applied that consists of non-invasive active (Multichannel Analysis of Surface Waves: MASW) and passive (Ambient Vibration Analysis: AVA) methods. Data processing is carried out systematically, using new techniques of dispersion curves estimation and their inversion to obtain 1D shear wave velocity profiles at the examined sites. Surface geology and other site information previously indicated six sites as reference “rock” and six as stiff to soft soil. After data processing in this study, it was determined that only five of them fulfil the engineering bedrock criterion ( \(\:{V}_{S30}\) > 800 m/s), with one of them - station ART2 (Arta) - identified as very hard rock ( \(\:{V}_{S30}\) ~2000 m/s). The remaining seven investigated sites fall within stiff to soft soil categories (380 ≤ \(\:{V}_{S30}\) ≤ 618 m/s). The results show that, for stiff soil and rock sites, geology and topographic slope- inferred \(\:{V}_{S30}\) values, in some cases significantly, especially for \(\:{V}_{S30}\) > 500 m/s. These results highlight the importance of direct geophysical investigation for reliable site classification and for reducing bias in ground motion modeling, e.g. Ground-Motion Models (GMMs) and Generalized Inversion Techniques (GITs). The updated station sites characterization metadata set provides a reference framework for future updates of the Hellenic Accelerometers Network recordings and contributes to the harmonization of European strong-motion stations metadata within EPOS as well as in the European Strong Motion database. Site Characterization Accelerometer Stations Engineering Reference Site Ambient Noise Array & MASW geophysical methods Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction Site characterization is an important component in assessing seismic hazard, usually incorporating in situ shear wave velocity as a function of depth ( \(\:{V}_{S}\left(z\right)\) ), as well as the estimation of site amplification and the fundamental frequency of the site ( \(\:{f}_{0}\) ). All site characteristics are captured by the ground motion recordings and depend on several geophysical parameters that characterize surficial geological layers with thickness of several up to few hundred meters. Site characterization of accelerometer stations is of high importance since its information can contribute to the full and efficient use of accelerogram recordings. Accelerogram databases usually provide such data and metadata for robustly defining Ground Motion Models (GMMs), as well as for Generalized Inversion Techniques (GITs) applications, which are used in seismic hazard assessment. However, it is known that accelerometer recordings on reference rock sites, that is, recording sites free of site effects, are limited worldwide compared to other soil types (e.g. soft rock, stiff or soft soils). Consequently, proposed GMMs are generally not well constrained for reference rock ground-motion prediction, and they better represent soft and stiff soils. In addition, application of GIT may lead to improved results when recordings on reference rock sites and associated data is included in the dataset to constrain inversions (e.g. Nakano et al. 2015 ; Shible et al. 2022 ; Grendas et al. 2022 ; Shible et al. 2023a ). Site characterization can be performed by invasive or non-invasive techniques. Non-invasive geophysical methods offer an affordable alternative for acquiring a \(\:{V}_{S}\) profile using body or surface wave techniques at the surface of the investigated site contrary to invasive methods that require drilling of a borehole (Garofalo et al. 2016 ; Foti et al. 2018 ). Non-invasive methods can be differentiated into active-source array methods (e.g. multichannel analysis of surface waves: MASW) and passive-source array methods (e.g. Ambient Vibration Array: AVA). The definition of a reference rock site is a debatable issue (e.g. Steidl et al. 1996 ). To this direction quantitative measures have been proposed based mainly on shear wave velocity of the uppermost geologic layers. One of them refers to the time-averaged shear wave velocity in the uppermost 30 m of the examined site, the well-known \(\:{V}_{S30}\) value introduced (Borcherdt 1994 ). This measure has been adopted by many seismic codes worldwide among which is the Eurocode 8: EC8 (CEN 2004 ), by considering a site as a reference rock when \(\:{V}_{S30}\) is equal or greater than 800 m/s. Due to limitations/constrains of this parameter in estimating site amplification in a broad range of frequencies, another parameter has recently been introduced that is defined by the bedrock depth at which \(\:{V}_{S}\) is equal or greater than 800 m/s, known as \(\:{H}_{800}\) , the so-called engineering bedrock (e.g. Cultrera et al. 2021 ; Di Giulio et al. 2012 ). This parameter along with the site fundamental frequency ( \(\:{f}_{0}\) ) proved to be more informative than \(\:{V}_{S30}\) alone. Since the amplification of ground motion can start at the seismic bedrock level where the shear wave velocity reaches approximately 3 km/s, generic amplification factors have been proposed (e.g., Boore and Joyner 1997 ; Margaris and Boore 1998 ). Consequently, the shear wave velocity profile up to that depth though hard to measure, could be very useful for rational estimation of site amplification. Characterizing sites of seismic stations has now become essential, especially for strong motion databases, since recordings are shaped by their substructure dynamic properties ( \(\:{V}_{S}\left(z\right)\) , \(\:{V}_{p}\left(z\right)\) , ρ etc.) the well-known site effects. During the past two decades many efforts have been made towards station site characterization worldwide (in Japan Kik-NET: https://www.kyoshin.bosai.go.jp/kyoshin/docs/overview_kyoshin_index_en.html ; in USA: Yong et al. 2013 ; in New Zealand: Van Houtte et al. 2012 ; in S. Korea: Lee et al, 2025 ; among others) as well as in Europe (Foti et al. 2011 ; Hollender et al. 2018 ; Lanzano et al. 2021 ; Pilz et al. 2020 ; among others). In Greece the first effort for station site characterization (Theodoulidis and Papazachos 1992 ) was based on geology and mechanical characteristics of surface soil layers, ranking them in three rough categories, namely, “rock”, “stiff” and “soft” soil formations. Οver time, especially in the last 15 years, sporadic measurements (e.g. Cross hole, Down hole, Ambient Vibration Arrays) have been performed at some station sites. Results of these efforts have been included in Stewart et al. ( 2014 ) along with inferred \(\:{V}_{S30}\) values based on a combination of proxies including the surface geology, the topographic slope and the terrain category. Later, Margaris et al. ( 2021 ) provided a flat file of ground motion intensity measures in Greece together with their metadata, including site characterization parameters in terms of surface geology and \(\:{V}_{S30}\) values, for more than 330 accelerometer stations in Greece. However, only about 20% of them have been characterized by in-situ geotechnical or/and geophysical methods (invasive or/and non-invasive) performed at a distance \(\:\le\:\) 100 m from the station while most of the stations were characterized by proxy inferred \(\:{V}_{S30}\) values and no analytical \(\:{V}_{S}\left(z\right)\) profile was given. In addition, regarding “rock” reference stations with \(\:{V}_{S30}\) values equal or greater than 800 m/s, very few of them (< 5) have been characterized till now, showing \(\:{V}_{S30}\) values ranging between 839 \(\:\:\le\:\:{V}_{S30}\) \(\:\le\:\) 1183 m/s (Margaris et al. 2021 ). Papadopoulos et al. ( 2023 ) conducted site characterization at six accelerometer station sites in the metropolitan area of Thessaloniki using surface wave methods along with the electrical resistivity technique. Recently, Ktenidou et al. ( 2026 ), questing reference stations in Greece, presented a methodology for site characterization of sixty seismic stations owned by the Geodynamic Institute of the National Observatory of Athens. Their approach was based on several criteria, including mainly Horizontal-to-Vertical Spectral ratios (HVSRs) of earthquake recordings along with several other proxies (e.g. geology, slope, installation conditions), derived from existing datasets and metadata, without additional field measurements. By compiling all descriptors and derived amplification characteristics from strong motion data, they co-evaluated the overall potential of the examined sites as reference stations. No numerical values were attributed to each presented parameter, but they opted to assess all data together to show an overall qualitative assessment, resulting in five preferred rock reference stations. From the above it follows that the number of accelerometer station sites in Greece assigned quantitative characterization (e.g. \(\:{V}_{S30}\) , \(\:{V}_{S}\left(z\right)\) , Vsp-profiles, depth to bedrock) based on in situ geophysical measurements remains limited, especially regarding the “rock” reference sites. In this study combined noninvasive passive and active geophysical techniques are applied at twelve accelerometer station sites in Greece, following a standardized procedure (Hollender et al. 2018 ) to provide a quantitative, measurement-based and uncertainty-included improvement for earthquake site characterization within the Hellenic Accelerometer Network (HAN). The analysis focuses on six stations installed on geologic “rock” and six on stiff to soft soil conditions. Determination of \(\:{V}_{S}\left(z\right)\) 1D-profiles down to the seismological bedrock depth ( \(\:{V}_{S}\ge\:\:\) 3 km/s), as well as of \(\:{V}_{S30}\) and \(\:{H}_{800}\) values and site class according to EC8, is pursued. To form a holistic picture of site characterization, surface geology is also provided for the examined stations. The results of this study aim at contributing to the improvement of GIT determined parameters (of source, path, site) and the accuracy of GMMs for “rock” site conditions. 2 SELECTION OF STATION SITES 2.1 reference “Rock” station sites To establish GMMs specific to very hard rock conditions and adapted to the Euro-Mediterranean context a fundamental study has been conducted by Shible et al. ( 2026 ). This approach was based on deconvolving accelerograms using GIT results. This method, initially developed using Japanese data (Shible et al. 2023a ; Shible et al. 2023b ), requires the identification of reference stations with the lowest possible site effects for which S-wave velocity profiles are available. While such information already existed for station sites in several European countries (France, Italy, Switzerland, etc.), this was not the case for Greece, even though this country provides a significant portion of the European accelerogram records. We were, therefore, motivated to identify accelerometer station sites in Greece that could serve as reference, and then to characterize them during a field campaign which was conducted in November 2023. We describe hereafter the process that led to the pre-selection of the six potential reference stations. The accelerometric network in Greece (Hellenic Accelerometer Network, HAN) has been developed over the past 50 years in various deployment periods with different types of accelerometers, spanning from the analog era to the digital and the broadband digital. The primary network operators in Greece are the Institute of Engineering Seismology & Earthquake Engineering (ITSAK) and the Geodynamic Institute of the National Observatory of Athens (GEIN-NOA). The main milestones toward the development of HAN have been, (i) the establishment of ITSAK in early 1980s when the first planning and deployment of the HAN was implemented with installation of 50 analog type accelerometers throughout Greece (Theodoulidis et al. 1986 ), and (ii) the later update and extension of the network by ITSAK and GEIN-NOA with about 200 broadband digital accelerometers. Today HAN counts more than 300 active accelerometer stations. Details about the evolution of the HAN can be found in Margaris et al. ( 2014 ). Best practices in site characterization include the determination of the full \(\:{V}_{S}\left(z\right)\) profiles down to rock, mainly based on geophysical investigation. Regarding site characterization of the HAN stations in terms of \(\:{V}_{S30}\) values, for the vast majority (~ 80%) an inferred \(\:{V}_{S30}\) value has been attributed, rather than a measured one. Most such inferred values have been based on the surface geology and the gradient slope-proxy as has been proposed by Stewart et al., ( 2014 ). Such an approach is recommended compared to the gradient-only approach of Wald and Allen, ( 2007 ). For the rest ~ 20% of the stations a measured \(\:{V}_{S30}\) is allocated within a ‘geophysical measurement-to-station’ distance less than 100 m. However, even in this 20% there are only very few “rock” reference stations ( \(\:{V}_{S30}\ge\:\:\) 800m/s) of HAN. Consequently, “rock” reference stations have been mainly characterized based either on inferred values or on a qualitative surface geology description (EAGME geological maps, scale 1:500 000 https://www.eagme.gr/ ). Only recently, Horizontal Spectral Amplification Factors (HSAFs) as well as HVSRs have been proposed for stations of the HAN based on the Generalized Inversion Technique (GIT), as well as on HVSR of S-waves (Grendas et al. 2021 ; Maragkakis 2022 ; Theodoulidis et al. 2022 ). We used these previously proposed HSAFs and HVSRs in combination with certain criteria with respect to their averaged values and standard deviation over selected frequency ranges. These criteria, along with the reasoning for the examined frequency ranges, are listed in Table 1 . The criteria refer to: ­ The lowest standard deviation in \(\:{\text{l}\text{o}\text{g}}_{10}\) , i.e., mostly flat amplification over the selected frequency range. ­ The lowest average response (over the selected frequency range), i.e., the lowest level or amplification values. Following the application of the criteria in Table 1 , a list of 17 stations was obtained, which are located throughout Greece (Fig. 1 ). Table 1 Criteria applied to previously proposed HSAF and HVSR for HAN stations (see text for references) for the selection of candidate rock-reference stations identified as those with the lowest on average amplification and standard deviation. Stations that fulfil the set criteria are listed in the last column. No. Criteria Function Frequency Range [Hz] Consequence List of stations 1 Lowest average & standard deviation HSAF (1) 0.3–15 Allows selection of low amplification stations (almost flat response) and standard deviation in a broad frequency range SEIS, ART2, NAX1, AIG2, KYP2, VSK1 2 Lowest average & standard deviation HSAF 0.3-8.0 Allows to avoid high-frequency effects and GIT trade-offs SEIS, ART2, VSK1, PAT5, AST1, ATH5, AIG2 3 Lowest average HSAF 0.3-1.0 Allows to avoid stations with deep basin sediments, where 1D simulations can be insufficient. Possible Amplification will be due to shallow layers. SEIS, NAX1, STL1, ART2, RLN1, LMS2, SIA1 4 Lowest std HVSR (2) 0.3-8.0 Can contribute to selecting stations with flat response. SIA1, SEIS, FRS1, TRP1, ART2, LEO1, PIR1 (1) HSAF: Horizontal Site Amplification Function. (2) HVSR: Horizontal-to-Vertical Spectral Ratio. In Table 2 the selected 17 stations are sorted according to the number of criteria they satisfy and the number of available strong motion records in the employed dataset. The latter spans the period 1973–2018. All candidate “rock” stations have recorded > 30 strong motion records, i.e., a sufficient number in the statistical sense. Among the 17 stations, it was finally decided to keep as candidate “rock” reference stations for characterizations, only those satisfying at least 2 out of 4 initial selection criteria, that is 6 accelerometer stations. An exception was made for station NAX1 located on the island of Naxos, that was excluded from the list due to logistic reasons (e.g., costly access and implementation of afield campaign). However, it was replaced by station ATH5, which although satisfies only one criterion, it is of high importance due to its location in the capital of Greece, Athens, where more than 10 stations are installed in a variety of soil conditions, and it is housed in the building of National Parliament. Station AST1 satisfying also one criterion but it is excluded because its location is close enough to the VSK1 station. Table 2 Sorted list - according to the number of satisfied selection criteria and available strong motion records - of the17 stations examined as candidate “rock” sites for site characterization. No. Station Code Latitude Longitude Number of satisfied criteria Number of records in database (1973–2018) 1 ART2 39.1474 20.99368 4 100 2 SEIS 40.6318 22.9628 4 69 3 AIG2 38.2417 22.0726 2 99 4 NAX1 37.1014 25.3741 2 98 5 SIA1 40.2573 21.5534 2 90 6 VSK1 38.4091 20.5640 2 85 7 AST1 38.5416 21.0895 1 124 8 ATH5 37.9754 23.7371 1 39 9 FRS1 39.2934 22.3844 1 101 10 KYP2 37.2497 21.6670 1 84 11 LMS2 40.8349 21.1418 1 68 12 PAT5 38.2959 21.7951 1 84 13 PIR1 37.9372 23.6426 1 84 14 RLN1 36.0891 28.0869 1 56 15 STL1 40.6624 22.9353 1 42 16 TRP1 37.5112 22.3634 1 99 17 LEO1 37.1689 22.8637 1 93 Finally, the selected potential “rock” reference stations for site characterization are the ART2 (Arta), SEIS (Thessaloniki), AIG2 (Aigion), SIA1 (Siatista), VSK1 (Vasilikiades), and ATH5 (Athens). Their HSAF, VSAF and earthquake HVSR are shown in Fig. 2 (Maragkakis, 2022 ). 2.2 Stiff and soft station sites The selection of the rest six stiff and soft soil station sites was based on the framework of a parallel project regarding the effect of buildings on strong motion recordings, which involved measurements carried out inside and close to the buildings (Rischette et al., 2026 ). This project required accurate characterization of the under-study sites and motivated us to include them as target sites of the present study, as well. These station sites are the ARG2 (Argostoli), LEF2 (Lefkas), PLA1 (Thessaloniki), ITS1 (Thessaloniki), JAN2 (Ioannina), and LXR2 (Lixouri) (Fig. 1 ). Their HSAF, VSAF and earthquake HVSR are shown in Fig. 2 (after Maragakis, 2022). Surface geology, based on geological maps around the selected six stiff and/or soft soil stations are presented in Fig. 3 . 3 METHODS AND DATA used During the past two decades, site characterization using non-invasive methods based on the measurement of surface wave dispersion feature to obtain a \(\:{V}_{S}\left(z\right)\) profile by inversion, is constantly gaining ground worldwide (Foti et al. 2018 ; Yong et al. 2022 ). The methodology used to characterize the twelve selected Greek stations is the same one that was developed and implemented for the characterization of French stations within the Epos-France framework and detailed in Hollender et al. ( 2018 ). We use the active MASW method to characterize the uppermost superficial layers (up to ~ 10 m) and the passive AVA method for investigating larger depth (up to several hundred meters in the geometries we implement), following the recommendations set out in the InterPACIFIC guidelines (Foti et al. 2018 ). 3.1 Multichannel Analysis of Surface Waves (MASW) and Acquisition Layout The MASW method (Park et al. 1999 ) investigates the dispersion of surface waves (Rayleigh or/and Love waves) to generate a 1D S-wave velocity ( \(\:{V}_{S}\left(z\right)\) ) profile. It deserves mentioning that during the generation of surface waves with a compressional source, more than \(\:2/3\) of the total seismic energy is carried by Rayleigh waves (Richart et al., 1970 ). In heterogenous media, such as the shallow layers of the Earth, Rayleigh wave velocity varies with depth, and different frequency bands of Rayleigh waves propagate at different phase velocities. Consequently, different wavelengths exist for each frequency, and this phenomenon is called dispersion. Dispersion of surface waves is utilized to study the elastic properties of surface geological formations (Nazarian et al., 1983 ; Park et al., 1998 ; Stokoe et al., 1994 ). Dispersion mainly depends on \(\:{V}_{S}\) in geological formations. More details about the applicability and limitations of the active MASW method can be found, among others, in Foti et al., ( 2018 ). The acquisition layout includes a linear array of receivers with the shot position in-line with the receivers. The geometry is defined by the array length \(\:L\) , the receiver spacing \(\:\varDelta\:x\) , and the source distance from the receivers. The nominal measurement protocol that we adopted for standard acquisitions is as follows: a measuring tape is stretched out on the ground over a length of 86 m. Twenty-four 5 Hz-geophones are arranged with a spacing of \(\:\varDelta\:x=\) 2 m from coordinate \(\:x=\) 20 m to coordinate \(\:x=\) 66 m (i.e., \(\:L=\) 46 m). In general, shots were hit in five positions: 20 m and 4 m from the first geophone (i.e., at coordinates \(\:x=\) 0 and 16 m), at the centre of the array (i.e., at \(\:x=\) 43 m), and at 4 and 20 m beyond the last geophone (i.e., at \(\:x=\) 70 and 86 m). These measurements are taken twice, once with 24 vertical geophones for Rayleigh wave recordings, and a second time with 24 horizontal geophones for Love wave recordings. In the first case, the ground is struck vertically with a 4 kg sledgehammer. In the second case, the ground is struck with this sledgehammer as horizontally as possible in holes previously dug in a V-shape. MASW was implemented as close to the accelerometer station as possible. An example of the MASW experimental line is shown in Fig. 4 for the ITS1 station. We then applied a ‘FK-linear’ type processing to the data using Geopsy software (Wathelet et al. 2020 ), supplemented by tools developed in Matlab. FK processing is applied to each ‘sledgehammer shot’. The best shots are then selected, and a ‘stack’ is performed in the frequency domain (this approach allows for better preservation of high frequencies and objective selection of the best shots by visualizing the result of the FK processing for each of them). Figure 5 (upper raw) shows the results obtained after stacking in the frequency domain for the MASW acquisition performed at station PLA1 (vertical polarity). The resolution limits of MASW are debated in the scientific community. The most “optimistic” allow the picking of the dispersion curves up to a wavelength equal to the length of the MASW measurement line (Foti et al., 2018 ). However, O’Neil, ( 2003 ) has shown that it is more prudent to limit the picking of the dispersion curves to 0.4 times the total length of the profile. Numerical simulations carried out by Riaño et al. ( 2024 ) tend to confirm O’Neil's, (2003) conclusions. In the present study, we adopted the latter, stricter criterion. Figure 5 (bottom raw) shows the portions of the dispersion curves that have been selected for the example site for further analysis. 3.2 Ambient Vibration Array (AVA) method The AVA method complements the MASW method. It is a passive method that uses energy generated by anthropogenic or natural noise sources and generally allows for greater investigation depths. It involves positioning several sensors in various geometries (triangles, circles, etc.) to record ambient vibrations. More details about the ΑVΑ deployment and data analyses methods can be found in the literature (among others: Bard et al., 2010 ; Cornou et al., 2006 ; Wathelet et al., 2008 ; Di Giulio et al., 2012 ; Foti et al., 2018 ; Garofalo et al., 2016 ; Yong et al., 2013 ). To carry out AVA measurements, we used 36 SmartSolo IGU-16HR 5 Hz three-component seismic nodes. Aswith MASW, the adopted deployment geometry is the one that has been proposed and successfully used to characterize stations in the French accelerometer network (Hollender et al. 2018 ). This approach includes the deployment of successive circular arrays; each composed of seven sensors distributed evenly around a central station. The radii of the circles increase progressively, with each new circle having a radius three times greater than that of the previous circle. The nominal acquisition plan includes five circles with respective radii of 5 m (R1), 15 m (R2), 45 m (R3), 135 m (R4), and 405 m (R5). The objective is to enable simultaneous acquisition on at least two successive circles, such as R1 + R2, R2 + R3, etc. This requires the availability of at least 15 sensors, i.e., two circles of seven sensors each, plus the central sensor. For the field measurements of this study, we had enough sensors to deploy all 5 circles simultaneously. The minimum acquisition times are as follows: ­ R1 + R2: 45 minutes ­ R2 + R3: 1 hour ­ R3 + R4: 2 hours ­ R4 + R5: 3 hours This standard acquisition plan typically allows prospecting depths of 300 to 500 m to be reached. Obviously, the nominal geometry is subject to local accessibility constraints. However, the AVA method is “tolerant” to deviations from the perfect circular design geometry. Circles are often off-center to take access conditions into account, but also sometimes to keep all acquisition points on relatively homogeneous geology. For station SIA1, due to undesired restrictions in space, an L-shaped layout for AVA has been deployed in place of the two smaller circular arrays (R1 and R2). An example of the geometry of the AVA deployment is shown in Fig. 4 for the ITS1 station. The geometries of the deployments for all herein studied sites are given in the Online Resource (Figures ESM 1 to ESM 12). The following systematic processing was applied to the AVA data: ­ “Classic” FK (McMechan and Yedlin 1981 ), applied to the vertical component only for the analysis of Rayleigh wave dispersion, ­ HRFK (or “high-resolution” FK) (Capon 1969 ), also applied to the vertical component only for the analysis of Rayleigh wave dispersion, ­ Transverse HRFK, applied to the horizontal components for the analysis of Love wave dispersion, ­ RTBF (Wathelet et al. 2018 ), which is a 3-component processing method that analyzes the entire ellipticity of Rayleigh waves, resulting in Rayleigh wave dispersion curves accompanied by an analysis of the polarity of the ellipticity, allowing for better mode identification. These methods were applied to the following array sets: R1, R1R2, R2, R2R3, R3, R3R4, R4, R4R5, and R5 using the Geopsy software package. For efficiency, these processing were performed using command lines rather than the Geopsy graphical interface. Post-processing (visualization of results, semi-automatic scoring of scatter plots, etc.) was performed using in house Matlab scripts. It should be noted that we favored the FK-type processing and did not perform any SPAC-type processing. Figure 6 shows example results obtained after processing ambient noise recordings by the FK, HRFK and RTBF methods for the PLA1 station site. The results of the RTBF processing are presented by differentiating between: (1) the dispersion curves obtained for negative ellipticities (RTBF-: retrograde Rayleigh wave particle motion), and (2) the dispersion curves obtained for positive ellipticities (RTBF+: prograde particle motion). As in MASW, before plotting the dispersion curves, it is important to calculate the array resolution limits, which depend on the acquisition geometry. The low-frequency resolution limit of an array is called \(\:{\lambda\:}_{max\:}\) (or \(\:{k}_{min\:}\) ). For FK processing, it is advisable not to pick the dispersion curve below this value. For HRFK or RTBF processing, it is possible to pick up to a maximum wavelength of \(\:2{\lambda\:}_{max\:}\) (or \(\:{k}_{min\:}/2\) ) (Foti et al. 2018 ). Figure 7 shows the results presented in Fig. 6 with a different color scale and adding the portions of dispersion curves that were picked. We prioritize the results obtained with RTBF processing: experience shows that these are generally the most reliable data. Regarding the requirements for site characterization performed in this study - using MASW and/or AVA techniques - are required:2 vehicles, 48 geophones with a datalogger, 36 seismic nodes, and a team of 5 people to acquire sufficient data within a single day per site. 3.3 HVSR We also applied the HVSR method to the sites studied. The HVSR method (Nakamura 1989, Bard 2008) is based on recording ambient vibrations measured on a single 3-component sensor. It consists of calculating the ratio of the Fourier spectra of the horizontal and vertical components. It allows the estimation of the fundamental resonance frequency of a site to be determined (f_0). The phenomenology explaining the existence of this peak is dominated by the frequency variation in the ellipticity of the ground motion associated with Rayleigh waves. The f_0 value is also often used in the final inversion process, where it complements the dispersion curves (joint inversion). Figure 8 summarizes the HVSR results for the twelve characterized sites. In the frequency range 0.2–15 Hz, stations AIG2, ART2, ATH5, SEIS, SIA1, and VSK1 exhibit nearly flat HVSRs with amplitudes below 2. As expected from ambient vibration analyses, these stations show HVSR signatures typical of rock sites. For station SEIS, the mean HVSR curve exceeds 2 only at high frequencies (f_0> 15 Hz), and for SIA1 a similar increase is observed at even higher frequencies (f_0> 30 Hz). In contrast, stations ARG2, ITS1, JAN2, LEF2, LXR2, and PLA1 show distinct single or double peaks at both low (f_0 2.0 Hz) frequencies, with corresponding amplitudes between 2.5 and 5, indicative of stiff to soft soil conditions. It should be noted that the JAN2 site posed significant challenges for the noise analysis, with strong lateral variability observed among the AVA array stations. The site is highly urbanized, affected by multiple local noise sources (heavy traffic, pedestrian activity), as well as by the presence of underground structures, such as a nearby underground parking garage. The HVSR response at this site should therefore be interpreted with caution, particularly the large-amplitude peak between 0.8 and 1 Hz. 4 INVERSION AND RESULTS 4.1 Dispersion Curves and Inversion Inversion consists of using the information contained in the dispersion curves (Rayleigh and/or Love waves), possibly supplemented by the site resonance frequency or the complete Rayleigh wave ellipticity curve, to calculate one or more S-wave velocity profiles compatible with this information. All other information available on the site that could help to better constrain the inversion is considered: geotechnical data providing orders of magnitude for soil stiffness, geological information providing, for example, the depth of the bedrock from a borehole or allowing its geological nature to be determined, etc. The depth of the bedrock, if it is known with certainty (e.g., from drilling), can be incorporated into the inversion. The inversion process is iterative: the algorithm starts from an initial velocity model, directly calculates the resulting dispersion curves (and other possible parameters), and quantifies the difference (or “misfit”) between the obtained dispersion curves and the experimental ones. The algorithm then modifies the initial velocity profile, performs a new direct calculation, a new estimation of the misfit, etc. The performance of the algorithm lies in its ability to modify the velocity profile to obtain a minimal misfit with a minimal number of iterations. We performed the dispersion curves inversions using the Dinver tool (Wathelet 2008 ) from the Geopsy suite, which uses a neighborhood algorithm. The classic approach consists of searching for the profile for which the misfit is minimal: this is referred to as the best-estimate search. We applied this approach considering certain limitations. When inversion algorithms are set to search for a best-estimate solution, they often focus on a local minimum and ultimately produce a non-realistic profile. Furthermore, the production of a single velocity profile does not reflect the non-uniqueness of the inversion. Non-uniqueness implies that several velocity profiles can satisfy the same dispersion curve and its standard deviation, often with equally small misfits. We therefore applied a slightly different approach that instead of inverting for a single, best-estimate profile, it inverts for a large number of profiles, often referred to as “acceptable misfit profiles” (Lomax and Snieder, 1995 ). This set of velocity profiles are linked to dispersion curves that remain “acceptable”, i.e., within some user-defined level of the error bars of the measured dispersion curves. During the inversion process, several thousand “acceptable” profiles are retained. Prior to the inversion step, all the picked dispersion curves (Rayleigh, Love, fundamental and possibly higher modes) are gathered. These different dispersion curves are then assembled (by site, wave type, and mode), merged, and possibly smoothed to obtain the widest possible dispersion curve per site. These dispersion curves, known as “targets” (or “target”), are established with their uncertainties and constitute the main input data for the inversion step. The results of the inversion are illustrated in Fig. 9 , using the ITS1 station as an example. The upper part of this figure shows the different “target” dispersion curves corresponding to the experimental measurements, including error bars. In the case of station ITS1, three Rayleigh modes were identified (the fundamental mode R0, and the first and second higher modes R1 and R2), as well as the fundamental Love mode (L0). The lighter shaded lines represent the forward-computed dispersion curves using the “acceptable velocity profiles” (several thousand) obtained by the inversion process. The center row of the figure shows these acceptable velocity profiles at different scales. This representation includes all the thousands of compatible profiles (in gray), as well as a subset of 33 randomly selected profiles whose distribution in \(\:{V}_{S30}\) is representative of that obtained for all the profiles resulting from the acceptable misfit approach (several thousand). Details of this random sampling of the numerous profiles to account for the variability are presented in Hollender et al. ( 2018 ). Finally, the lower row of this figure illustrates the distributions of \(\:{V}_{S30}\) , \(\:{H}_{800}\) , and the probabilities of the examined site to belong to any of the soil classes defined by EC8. The same representation is provided for all twelve characterized station sites in the Online Resource (Figures ESM 13 to ESM 24). The 33 randomly chosen 1D \(\:{V}_{S}\left(z\right)\) profiles for all the 12 examined station sites are summarized in Fig. 10 along with their average \(\:{V}_{S30}\) values. In all cases in this work, the \(\:{V}_{S30}\) value was computed from the ground surface down to a depth of 30 meters. This value characterizes an intrinsic property of the site ( \(\:{V}_{S30}^{Site}\) ). For stations installed below the surface, an additional \(\:{V}_{S30}\) value was computed from the installation depth ( \(\:{z}_{station}\) ) down to \(\:{z}_{station}\) + 30 m. This parameter is referred to here as \(\:{V}_{S30}^{Station}\) . In Table 3 , a summary of the parameters extracted from the \(\:{V}_{S}\left(z\right)\) profiles ( \(\:{V}_{S30}^{Site}\) , \(\:{V}_{S30}^{Station}\) , \(\:{H}_{800}\) and EC8 soil category) is given. The proposed standard deviations were calculated assuming that the distributions are broadly normal, which is generally an acceptable assumption for the \(\:{V}_{S30}\) parameter. However, this assumption does not always hold true for the \(\:{H}_{800}\) parameter, whose determination is more sensitive to the choices made in the processing protocol, particularly the inversion settings. This behavior is particularly evident in the case of station ITS1 (see Fig. 9 ). We have therefore added a column in Table 3 providing a qualitative comment on the shape of the H₈₀₀ distribution. Table 3 Summary of selected parameters of site characterization for the twelve investigated HAN stations. Station code Station depth [m] \(\:{\varvec{V}}_{\varvec{S}30}^{\varvec{S}\varvec{i}\varvec{t}\varvec{e}}\) [m/s] \(\:{\varvec{V}}_{\varvec{S}30}^{\varvec{S}\varvec{t}\varvec{a}\varvec{t}\varvec{i}\varvec{o}\varvec{n}}\) [m/s] \(\:{\varvec{H}}_{800}^{\varvec{S}\varvec{i}\varvec{t}\varvec{e}}\) [m] \(\:{\varvec{H}}_{800}^{\varvec{S}\varvec{i}\varvec{t}\varvec{e}}\) distribution remark EC8 soil class (site) EC8 soil class (station) AIG2 0 618 ± 27 618 ± 27 25 ± 9 roughly normal B B ARG2 0 540 ± 36 540 ± 36 16 ± 19 left-truncated normal B [70%] E [30%] B [70%] E [30%] ART2 0 1979 ± 74 1979 ± 74 1 ± 1 left-truncated normal A A ATH5 3 855 ± 18 1149 ± 38 2 ± 0.3 left-truncated normal A A ITS1 3 467 ± 11 548 ± 11 131 ± 49 bimodal B B JAN2 2 369 ± 13 402 ± 15 34 ± 17 bimodal B [85%] C [15%] B LEF2 3 366 ± 10 380 ± 10 70 ± 152 bimodal B [70%] C [30%] B [95%] C [5%] LXR2 2 444 ± 9 489 ± 9 121 ± 11 roughly normal B B PLA1 3 421 ± 6 485 ± 6 205 ± 123 bimodal B B SEIS 1.5 1049 ± 41 1395 ± 41 5 ± 1 roughly normal A [60%] E [40%] A SIA1 0 900 ± 22 900 ± 22 12 ± 4 bimodal A A VSK1 0 1359 ± 73 1359 ± 73 4 ± 2 roughly normal A A In both Fig. 10 and Table 3 , the site of station ART2 clearly stands out from the other station sites, with a \(\:{V}_{S30}^{Station}\) reaching a value of almost 2000 m/s. Stations SEIS and VSK1 also show relatively high values of \(\:{V}_{S30}^{Station},\:i.e,\:\) between 1350 and 1400 m/s. The station with the lowest \(\:{V}_{S30}^{Station}\) value is LEF2 (= 380 m/s). In Fig. 11 we compare the measured \(\:{V}_{S30}^{Site}\) values for the twelve stations of the HAN in this study with the corresponding inferred values reported in Stewart et al. ( 2014 ) based on the surface geology and the topographic slope. Although the number of examined sites is limited, there is a significant deviation of the plotted points from the bisector. This suggests that the previously inferred \(\:{V}_{S30}^{Site}\) significantly underestimate the reality, especially for \(\:{V}_{S30}^{Site}>\) 500 m/s. 5 CONCLUSIONS This study provides a homogeneous and detailed geophysical characterization of twelve accelerometric stations of the Hellenic Accelerometer Network (HAN), combining the active MASW and the passive AVA surface-wave methods. Apart from providing measured \(\:{V}_{S30}\) values and \(\:{V}_{S}\) profiles to complement the small tank of such information on permanent Greek accelerometric stations, the consistent application of the “acceptable misfit” inversion approach allows for the quantification of the epistemic uncertainty in the proposed profiles and related site parameters. The results show that, for stiff soil and rock sites, geology- and slope-inferred \(\:{V}_{S30}\) values often underestimate the actual \(\:{V}_{S30}\) , in some cases markedly, especially for \(\:{V}_{S30}\) > 500 m/s. Only a few sites (ART2, SEIS, VSK1) exhibit genuinely hard-rock characteristics ( \(\:{V}_{S30}\) > 1,000 m/s and \(\:{H}_{800}\) < 5 m). Station ART2, with a \(\:{V}_{S30}\) value close to 2000 m/s, clearly stands out as the most suitable candidate for a reference “rock” site in Greece, at least among the studied station sites. The observed discrepancies between proxy-based indicators and in situ measurements highlight that indirect approaches alone may not always provide a sufficiently robust basis for site classification, particularly for critical applications such as reference rock-site selection. For stations intended to serve as references in GMMs or GITs frameworks, field measurements appear essential to ensure quantitative reliability and to avoid systematic bias. These results emphasize the importance of direct geophysical investigations for reliable site classification and for reducing bias in Ground-Motion Models (GMMs) and Generalized Inversion Techniques (GITs). This new set of station site characterization metadata provides a reference framework for future updates of the HAN database and contributes to the harmonization of the European strong-motion station metadata within EPOS and ESM databases ( https://esm-db.eu ). The results of this study have already been used in the derivation of a new Ground-Motion Model for very hard-rock conditions (Shible et al., 2026 ) as well as for investigating the impact of buildings on ground motion recordings, the so-called Soil-Structure Interaction-SSI (Rischette et al., 2026 ). Declarations Competitive Interests The authors declare that they have no known competing financial or non-financial interests that could have appeared to influence the work reported in this paper. Funding This work was supported by the French Atomic and Alternative Energies Commission [CEA] and Electricité de France [EDF], within the framework of the CASHIMA3 and SIGMA3 research programs on seismic hazard assessment. SIGMA3 (2024–2028, www.sigma-programs.com ) is funded by an industrial consortium composed of Electricité de France [EDF], French Atomic and Alternative Energies Commission [CEA], České energetické závody [Czech Energy Company, CEZ] Group, Pacific Gas and Electric Company [PG&E], VTT research center, SwissNuclear, Central Research Institute of the Electric Power Industry [CRIEPI], Stük, Uniper and Vattenfall. In addition, it was partially supported by the Earthquake Planning & Protection Organization’s project "Updated Seismic Hazard Maps in the Area of Greece (2025, No. 12788)". Authors Contributions All authors contributed to data acquisition and data analysis used in this study. The overall structure and the main writing of the manuscript were carried out by N. Theodoulidis and F. Hollender. The latter significantly contributed and supervised the data analyses as well. All authors reviewed the manuscript, contributed to the revisions, and approved the final version. Acknowledgements The authors would like to thank Isabelle Douste-Bacqué, Margaux Buscetti, Harris Moustakas, Vasiliki Tsoukala and Maria-Georgia Dasoula for their help in acquiring the geophysical measurements, and for performing first stages of their treatment. Data Availability The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request. References Bard P-Y (2008) Foreword: The H/V technique: capabilities and limitations based on the results of the SESAME project. Bull Earthq Eng 6:1–2. https://doi.org/10.1007/s10518-008-9059-4 Bard PY, Cadet H, Endrun B, Hobiger M, Renalier F, Theodulidis N, Ohrnberger M, Fäh D, Sabetta F, Teves-Costa P, Duval AM (2010) From non-invasive site characterization to site amplification: recent advances in the use of ambient vibration measurements. Earthq Eng Eur 105–123 Boore DM, Joyner WB (1997) Site Amplifications for Generic Rock Sites. 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J Seismol 26:557–566. https://doi.org/10.1007/s10950-022-10104-w Supplementary Files ELECTRONICSUPPLEMENT.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 23 Apr, 2026 Editor invited by journal 13 Apr, 2026 Editor assigned by journal 13 Apr, 2026 First submitted to journal 09 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9370620","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628357204,"identity":"b5b07cf3-fddb-4513-a707-b5260071de0c","order_by":0,"name":"Nikolaos Theodoulidis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYFACHiCukAASbMTqYANpOSMhQaIWxjYGErSYy/cefPBxnkWdfPuxBMave4jQYtnGl2w4c5uEhMGZtAPMMs+I0GJwjMdMmhekhSG9gVniAHFazH/zzpGQkO9/TrwWM2beBmCI3Ug7wPiBOC05xpIzjklIbrjxLOEwA1FaDp8x/PChpo5fvj/N8OEPYrSggMM8pOpgYPxBspZRMApGwSgYCQAAeX8ypTAkKwMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0169-9197","institution":"Inst. Engineering Seismology and Earthquake Engineering","correspondingAuthor":true,"prefix":"","firstName":"Nikolaos","middleName":"","lastName":"Theodoulidis","suffix":""},{"id":628357205,"identity":"9332c066-b7f9-41f2-a703-8b3bf1376097","order_by":1,"name":"Fabrice Hollender","email":"","orcid":"","institution":"CEA Cadarache Centre: Commissariat a l'energie atomique et aux energies alternatives Centre de Cadarache","correspondingAuthor":false,"prefix":"","firstName":"Fabrice","middleName":"","lastName":"Hollender","suffix":""},{"id":628357206,"identity":"c44e7ee4-922c-4f2a-b1fb-b102bb97716d","order_by":2,"name":"Pauline Rischette","email":"","orcid":"","institution":"CEA Cadarache Centre: Commissariat a l'energie atomique et aux energies alternatives Centre de Cadarache","correspondingAuthor":false,"prefix":"","firstName":"Pauline","middleName":"","lastName":"Rischette","suffix":""},{"id":628357207,"identity":"d7d99180-530c-4ac1-9657-9a16acd55b88","order_by":3,"name":"Ioannis Grendas","email":"","orcid":"","institution":"Inst. Engineering Seismology and Earthquake Engineering","correspondingAuthor":false,"prefix":"","firstName":"Ioannis","middleName":"","lastName":"Grendas","suffix":""},{"id":628357208,"identity":"2650cdd7-6090-47e5-80dd-d2b6e44a577b","order_by":4,"name":"Zafeiria Roumelioti","email":"","orcid":"","institution":"University of Patras, Department of Geoiogy","correspondingAuthor":false,"prefix":"","firstName":"Zafeiria","middleName":"","lastName":"Roumelioti","suffix":""},{"id":628357209,"identity":"75b47601-dacc-416f-808a-fe23c236c4d2","order_by":5,"name":"Hussein Shible","email":"","orcid":"","institution":"BRGM: Bureau de Recherches Geologiques et Minieres","correspondingAuthor":false,"prefix":"","firstName":"Hussein","middleName":"","lastName":"Shible","suffix":""}],"badges":[],"createdAt":"2026-04-09 15:42:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9370620/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9370620/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108734864,"identity":"a77d27c5-2ccf-487c-a033-f3cebac52206","added_by":"auto","created_at":"2026-05-07 19:56:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1170714,"visible":true,"origin":"","legend":"\u003cp\u003eAccelerometer stations of the Hellenic Accelerometer Network (blue triangles) and the 17geologic “rock” stations selected after applying the criteria of Table 1 (red \u0026amp; yellow triangles). Sites characterized in the present study correspond to yellow triangles and 6 additional stiff-to-soft soil stations (black circles).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9370620/v1/c2b0314c4b4b3d007dba2fa7.png"},{"id":108807044,"identity":"616695fc-da36-48de-9dd9-b8ef80f257d2","added_by":"auto","created_at":"2026-05-08 15:29:59","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":700174,"visible":true,"origin":"","legend":"\u003cp\u003eHorizonal Spectral Amplification Factors (black line) and Horizontal-to-Vertical Spectral Ratios (red line) for the 12 studied stations (after Maragakis, 2022).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9370620/v1/638820eccfc4dbf57ce7c63b.jpeg"},{"id":108734867,"identity":"9436f232-6f6b-40a4-bab3-7f1fa277760e","added_by":"auto","created_at":"2026-05-07 19:56:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1935765,"visible":true,"origin":"","legend":"\u003cp\u003eSurface geology around the 12 studied sites. Geologic formations are grouped in five (5) categories according to their geologic age, based on maps in scale 1:50.000 (https://www.eagme.gr/). H: Holocene, PL: Pleistocene, Q: Quaternary undivided, T: Tertiary, MP: Mesozoic \u0026amp; Paleozoic (Stewart et al. 2014).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9370620/v1/f469d34b85f541317ef789f9.png"},{"id":108806401,"identity":"5eec8f12-2821-4383-b1bc-51ad9268ea0e","added_by":"auto","created_at":"2026-05-08 15:28:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1521574,"visible":true,"origin":"","legend":"\u003cp\u003eConfiguration of MASW (red line) and AVA deployments (colored circles) for the ITS1 station site (left: overview; the area in the square is shown in close view to the right).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9370620/v1/c2524b3f780f80bcc5499cb8.png"},{"id":108734868,"identity":"f510918e-f687-4fe1-85f6-efd85f883ad0","added_by":"auto","created_at":"2026-05-07 19:56:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":532509,"visible":true,"origin":"","legend":"\u003cp\u003eDispersion curves for shots at various distances in color (top panels) and in grayscale (bottom panels). The latter include the picking (pink symbols) of the average phase velocities ± 1s.d. per frequency. Dashed and solid black lines mark the theoretical array resolution, i.e., the largest wavenumber resolved (kmin) and kmin/2, respectively. Our study adopts the stricter boundary, i.e., we usually pick phase velocities up to kmin/2. Results correspond to the PLA1 station site.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9370620/v1/317d503618302817ab871d87.png"},{"id":108734870,"identity":"db0ba8ff-4b5e-473b-b990-906f00d947b1","added_by":"auto","created_at":"2026-05-07 19:56:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1533426,"visible":true,"origin":"","legend":"\u003cp\u003eDispersion Curves for various apertures of the AVA, using different processing techniques (from left to right column: FK, HRFK, RTBF\u003csup\u003e-\u003c/sup\u003e, RTBF\u003csup\u003e+\u003c/sup\u003e; see text for details). Example results correspond to the PLA1 station site.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9370620/v1/2a0650e8874ce2384ed311d9.png"},{"id":109067856,"identity":"98d816d5-58f0-4e87-abfb-59493b907cb9","added_by":"auto","created_at":"2026-05-12 10:01:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1447858,"visible":true,"origin":"","legend":"\u003cp\u003eAs in Figure 7 but in grayscale and with the addition of symbols to demonstrate the manually picked parts of the dispersion curves at PLA1.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9370620/v1/3a7fdba0a87cf544d32097e9.png"},{"id":108734872,"identity":"3150d689-1fcc-47d3-86a3-e74022731858","added_by":"auto","created_at":"2026-05-07 19:56:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":560872,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental HVSRs for all investigated stations (solid line) ±1sd.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9370620/v1/9f92fac84c79dff78c32247b.png"},{"id":108807047,"identity":"041570df-c5da-45b1-87ae-b39f0d56e70c","added_by":"auto","created_at":"2026-05-08 15:30:00","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":566289,"visible":true,"origin":"","legend":"\u003cp\u003eInversion results for station ITS1 using the “Acceptable Misfit” approach, which consists of estimating many profiles compatible with the experimental data to estimate the uncertainty of the results. Top row: measured empirical dispersion curves and associated error bars; in light shades: ‘direct’ dispersion curves recalculated from the inverted profiles. Middle: in gray, inverted velocity profiles (several thousand); in blue, random selection of 33 profiles sampling the uncertainty. Bottom, from left to right: distribution of V\u003csub\u003eS30\u003c/sub\u003e \u0026nbsp;values (calculated on all velocity profiles of “acceptable misfit”); distribution of H\u003csub\u003e800\u003c/sub\u003e \u0026nbsp;values; probability of compliance with soil classes A to E of Eurocode 8 (calculated on all velocity profiles).\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9370620/v1/7c4f5968acfa326827bfdc2c.png"},{"id":108734875,"identity":"285aa794-143e-4933-b969-323e201729e5","added_by":"auto","created_at":"2026-05-07 19:56:15","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":187889,"visible":true,"origin":"","legend":"\u003cp\u003e33 selected 1D V\u003csub\u003eS\u003c/sub\u003e (z) profiles per station for all 12 investigated sites and their estimated V\u003csub\u003eS30\u003c/sub\u003e values.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-9370620/v1/041c92c5691e79c3339e6d90.png"},{"id":108807196,"identity":"81e59395-65e3-4df3-b847-62f293cbc1aa","added_by":"auto","created_at":"2026-05-08 15:30:18","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":45362,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the experimentally estimated V\u003csub\u003eS30\u003c/sub\u003e values in this study with those previously inferred \u0026nbsp;\u0026nbsp;based on geology and topographic slope (Stewart et al. 2014).\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-9370620/v1/30c1698ec85f83f4ee19495c.png"},{"id":109069253,"identity":"fb76a8a4-8442-4e4d-8e26-02b1d57f3197","added_by":"auto","created_at":"2026-05-12 10:22:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10621403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9370620/v1/01310eae-8eee-4f0d-ac88-4217b9a29b6a.pdf"},{"id":108805863,"identity":"259ed112-4883-4a07-b217-89cf780a3add","added_by":"auto","created_at":"2026-05-08 15:27:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6844449,"visible":true,"origin":"","legend":"","description":"","filename":"ELECTRONICSUPPLEMENT.docx","url":"https://assets-eu.researchsquare.com/files/rs-9370620/v1/6b9edc6c03cc31545d42afcd.docx"}],"financialInterests":"","formattedTitle":"Characterization of selected station sites of the Hellenic Accelerometer Network (HAN)","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eSite characterization is an important component in assessing seismic hazard, usually incorporating \u003cem\u003ein situ\u003c/em\u003e shear wave velocity as a function of depth (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\left(z\\right)\\)\u003c/span\u003e\u003c/span\u003e), as well as the estimation of site amplification and the fundamental frequency of the site (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{0}\\)\u003c/span\u003e\u003c/span\u003e). All site characteristics are captured by the ground motion recordings and depend on several geophysical parameters that characterize surficial geological layers with thickness of several up to few hundred meters. Site characterization of accelerometer stations is of high importance since its information can contribute to the full and efficient use of accelerogram recordings. Accelerogram databases usually provide such data and metadata for robustly defining Ground Motion Models (GMMs), as well as for Generalized Inversion Techniques (GITs) applications, which are used in seismic hazard assessment. However, it is known that accelerometer recordings on reference rock sites, that is, recording sites free of site effects, are limited worldwide compared to other soil types (e.g. soft rock, stiff or soft soils). Consequently, proposed GMMs are generally not well constrained for reference rock ground-motion prediction, and they better represent soft and stiff soils. In addition, application of GIT may lead to improved results when recordings on reference rock sites and associated data is included in the dataset to constrain inversions (e.g. Nakano et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shible et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Grendas et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shible et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSite characterization can be performed by invasive or non-invasive techniques. Non-invasive geophysical methods offer an affordable alternative for acquiring a \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\)\u003c/span\u003e\u003c/span\u003e profile using body or surface wave techniques at the surface of the investigated site contrary to invasive methods that require drilling of a borehole (Garofalo et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Foti et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Non-invasive methods can be differentiated into active-source array methods (e.g. multichannel analysis of surface waves: MASW) and passive-source array methods (e.g. Ambient Vibration Array: AVA).\u003c/p\u003e \u003cp\u003eThe definition of a reference rock site is a debatable issue (e.g. Steidl et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). To this direction quantitative measures have been proposed based mainly on shear wave velocity of the uppermost geologic layers. One of them refers to the time-averaged shear wave velocity in the uppermost 30 m of the examined site, the well-known \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e value introduced (Borcherdt \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). This measure has been adopted by many seismic codes worldwide among which is the Eurocode 8: EC8 (CEN \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), by considering a site as a reference rock when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e is equal or greater than 800 m/s. Due to limitations/constrains of this parameter in estimating site amplification in a broad range of frequencies, another parameter has recently been introduced that is defined by the bedrock depth at which \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\)\u003c/span\u003e\u003c/span\u003e is equal or greater than 800 m/s, known as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{800}\\)\u003c/span\u003e\u003c/span\u003e, the so-called engineering bedrock (e.g. Cultrera et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Di Giulio et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This parameter along with the site fundamental frequency (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{0}\\)\u003c/span\u003e\u003c/span\u003e) proved to be more informative than \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e alone. Since the amplification of ground motion can start at the seismic bedrock level where the shear wave velocity reaches approximately 3 km/s, generic amplification factors have been proposed (e.g., Boore and Joyner \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Margaris and Boore \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Consequently, the shear wave velocity profile up to that depth though hard to measure, could be very useful for rational estimation of site amplification.\u003c/p\u003e \u003cp\u003eCharacterizing sites of seismic stations has now become essential, especially for strong motion databases, since recordings are shaped by their substructure dynamic properties (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\left(z\\right)\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{p}\\left(z\\right)\\)\u003c/span\u003e\u003c/span\u003e, ρ etc.) the well-known site effects. During the past two decades many efforts have been made towards station site characterization worldwide (in Japan Kik-NET: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kyoshin.bosai.go.jp/kyoshin/docs/overview_kyoshin_index_en.html\u003c/span\u003e\u003cspan address=\"https://www.kyoshin.bosai.go.jp/kyoshin/docs/overview_kyoshin_index_en.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; in USA: Yong et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; in New Zealand: Van Houtte et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; in S. Korea: Lee et al, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; among others) as well as in Europe (Foti et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hollender et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lanzano et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pilz et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; among others).\u003c/p\u003e \u003cp\u003eIn Greece the first effort for station site characterization (Theodoulidis and Papazachos \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) was based on geology and mechanical characteristics of surface soil layers, ranking them in three rough categories, namely, \u0026ldquo;rock\u0026rdquo;, \u0026ldquo;stiff\u0026rdquo; and \u0026ldquo;soft\u0026rdquo; soil formations. Οver time, especially in the last 15 years, sporadic measurements (e.g. Cross hole, Down hole, Ambient Vibration Arrays) have been performed at some station sites. Results of these efforts have been included in Stewart et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) along with inferred \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e values based on a combination of proxies including the surface geology, the topographic slope and the terrain category. Later, Margaris et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) provided a flat file of ground motion intensity measures in Greece together with their metadata, including site characterization parameters in terms of surface geology and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e values, for more than 330 accelerometer stations in Greece. However, only about 20% of them have been characterized by in-situ geotechnical or/and geophysical methods (invasive or/and non-invasive) performed at a distance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e100 m from the station while most of the stations were characterized by proxy inferred \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e values and no analytical \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\left(z\\right)\\)\u003c/span\u003e\u003c/span\u003e profile was given. In addition, regarding \u0026ldquo;rock\u0026rdquo; reference stations with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e values equal or greater than 800 m/s, very few of them (\u0026lt;\u0026thinsp;5) have been characterized till now, showing \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e values ranging between 839\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\le\\:\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e 1183 m/s (Margaris et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Papadopoulos et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) conducted site characterization at six accelerometer station sites in the metropolitan area of Thessaloniki using surface wave methods along with the electrical resistivity technique.\u003c/p\u003e \u003cp\u003eRecently, Ktenidou et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), questing reference stations in Greece, presented a methodology for site characterization of sixty seismic stations owned by the Geodynamic Institute of the National Observatory of Athens. Their approach was based on several criteria, including mainly Horizontal-to-Vertical Spectral ratios (HVSRs) of earthquake recordings along with several other proxies (e.g. geology, slope, installation conditions), derived from existing datasets and metadata, without additional field measurements. By compiling all descriptors and derived amplification characteristics from strong motion data, they co-evaluated the overall potential of the examined sites as reference stations. No numerical values were attributed to each presented parameter, but they opted to assess all data together to show an overall qualitative assessment, resulting in five preferred rock reference stations.\u003c/p\u003e \u003cp\u003eFrom the above it follows that the number of accelerometer station sites in Greece assigned quantitative characterization (e.g. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\left(z\\right)\\)\u003c/span\u003e\u003c/span\u003e, Vsp-profiles, depth to bedrock) based on in situ geophysical measurements remains limited, especially regarding the \u0026ldquo;rock\u0026rdquo; reference sites. In this study combined noninvasive passive and active geophysical techniques are applied at twelve accelerometer station sites in Greece, following a standardized procedure (Hollender et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) to provide a quantitative, measurement-based and uncertainty-included improvement for earthquake site characterization within the Hellenic Accelerometer Network (HAN). The analysis focuses on six stations installed on geologic \u0026ldquo;rock\u0026rdquo; and six on stiff to soft soil conditions. Determination of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\left(z\\right)\\)\u003c/span\u003e\u003c/span\u003e 1D-profiles down to the seismological bedrock depth (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\ge\\:\\:\\)\u003c/span\u003e\u003c/span\u003e3 km/s), as well as of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{800}\\)\u003c/span\u003e\u003c/span\u003e values and site class according to EC8, is pursued. To form a holistic picture of site characterization, surface geology is also provided for the examined stations. The results of this study aim at contributing to the improvement of GIT determined parameters (of source, path, site) and the accuracy of GMMs for \u0026ldquo;rock\u0026rdquo; site conditions.\u003c/p\u003e"},{"header":"2 SELECTION OF STATION SITES","content":"\u003cp\u003e2.1 \u003cspan type=\"BoldSmallCaps\" class=\"BoldSmallCaps\" name=\"Emphasis\"\u003ereference \u0026ldquo;Rock\u0026rdquo; station sites\u003c/span\u003e\u003c/p\u003e \u003cp\u003eTo establish GMMs specific to very hard rock conditions and adapted to the Euro-Mediterranean context a fundamental study has been conducted by Shible et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). This approach was based on deconvolving accelerograms using GIT results. This method, initially developed using Japanese data (Shible et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Shible et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e), requires the identification of reference stations with the lowest possible site effects for which S-wave velocity profiles are available.\u003c/p\u003e \u003cp\u003eWhile such information already existed for station sites in several European countries (France, Italy, Switzerland, etc.), this was not the case for Greece, even though this country provides a significant portion of the European accelerogram records. We were, therefore, motivated to identify accelerometer station sites in Greece that could serve as reference, and then to characterize them during a field campaign which was conducted in November 2023. We describe hereafter the process that led to the pre-selection of the six potential reference stations.\u003c/p\u003e \u003cp\u003eThe accelerometric network in Greece (Hellenic Accelerometer Network, HAN) has been developed over the past 50 years in various deployment periods with different types of accelerometers, spanning from the analog era to the digital and the broadband digital. The primary network operators in Greece are the Institute of Engineering Seismology \u0026amp; Earthquake Engineering (ITSAK) and the Geodynamic Institute of the National Observatory of Athens (GEIN-NOA). The main milestones toward the development of HAN have been, (i) the establishment of ITSAK in early 1980s when the first planning and deployment of the HAN was implemented with installation of 50 analog type accelerometers throughout Greece (Theodoulidis et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), and (ii) the later update and extension of the network by ITSAK and GEIN-NOA with about 200 broadband digital accelerometers. Today HAN counts more than 300 active accelerometer stations. Details about the evolution of the HAN can be found in Margaris et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBest practices in site characterization include the determination of the full \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\left(z\\right)\\)\u003c/span\u003e\u003c/span\u003e profiles down to rock, mainly based on geophysical investigation. Regarding site characterization of the HAN stations in terms of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e values, for the vast majority (~\u0026thinsp;80%) an inferred \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e value has been attributed, rather than a measured one. Most such inferred values have been based on the surface geology and the gradient slope-proxy as has been proposed by Stewart et al., (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Such an approach is recommended compared to the gradient-only approach of Wald and Allen, (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). For the rest\u0026thinsp;~\u0026thinsp;20% of the stations a measured \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e is allocated within a \u0026lsquo;geophysical measurement-to-station\u0026rsquo; distance less than 100 m. However, even in this 20% there are only very few \u0026ldquo;rock\u0026rdquo; reference stations (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\ge\\:\\:\\)\u003c/span\u003e\u003c/span\u003e800m/s) of HAN. Consequently, \u0026ldquo;rock\u0026rdquo; reference stations have been mainly characterized based either on inferred values or on a qualitative surface geology description (EAGME geological maps, scale 1:500 000 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eagme.gr/\u003c/span\u003e\u003cspan address=\"https://www.eagme.gr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ).\u003c/p\u003e \u003cp\u003eOnly recently, Horizontal Spectral Amplification Factors (HSAFs) as well as HVSRs have been proposed for stations of the HAN based on the Generalized Inversion Technique (GIT), as well as on HVSR of S-waves (Grendas et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Maragkakis \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Theodoulidis et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We used these previously proposed HSAFs and HVSRs in combination with certain criteria with respect to their averaged values and standard deviation over selected frequency ranges. These criteria, along with the reasoning for the examined frequency ranges, are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The criteria refer to:\u003c/p\u003e \u003cp\u003e\u0026shy; The lowest standard deviation in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{l}\\text{o}\\text{g}}_{10}\\)\u003c/span\u003e\u003c/span\u003e, i.e., mostly flat amplification over the selected frequency range.\u003c/p\u003e \u003cp\u003e\u0026shy; The lowest average response (over the selected frequency range), i.e., the lowest level or amplification values.\u003c/p\u003e \u003cp\u003eFollowing the application of the criteria in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a list of 17 stations was obtained, which are located throughout Greece (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCriteria applied to previously proposed HSAF and HVSR for HAN stations (see text for references) for the selection of candidate rock-reference stations identified as those with the lowest on average amplification and standard deviation. Stations that fulfil the set criteria are listed in the last column.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency Range [Hz]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConsequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eList of stations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest average \u0026amp; standard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHSAF \u003csup\u003e(1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAllows selection of low amplification stations (almost flat response) and standard deviation in a broad frequency range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSEIS, ART2, NAX1, AIG2, KYP2, VSK1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest average \u0026amp; standard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHSAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3-8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAllows to avoid high-frequency effects and GIT trade-offs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSEIS, ART2, VSK1, PAT5, AST1, ATH5, AIG2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHSAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3-1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAllows to avoid stations with deep basin sediments, where 1D simulations can be insufficient. Possible Amplification will be due to shallow layers.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSEIS, NAX1, STL1, ART2, RLN1, LMS2, SIA1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest std\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHVSR \u003csup\u003e(2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3-8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCan contribute to selecting stations with flat response.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSIA1, SEIS, FRS1, TRP1, ART2, LEO1, PIR1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(1) HSAF: Horizontal Site Amplification Function.\u003c/p\u003e \u003cp\u003e(2) HVSR: Horizontal-to-Vertical Spectral Ratio.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e the selected 17 stations are sorted according to the number of criteria they satisfy and the number of available strong motion records in the employed dataset. The latter spans the period 1973\u0026ndash;2018. All candidate \u0026ldquo;rock\u0026rdquo; stations have recorded\u0026thinsp;\u0026gt;\u0026thinsp;30 strong motion records, i.e., a sufficient number in the statistical sense. Among the 17 stations, it was finally decided to keep as candidate \u0026ldquo;rock\u0026rdquo; reference stations for characterizations, only those satisfying at least 2 out of 4 initial selection criteria, that is 6 accelerometer stations. An exception was made for station NAX1 located on the island of Naxos, that was excluded from the list due to logistic reasons (e.g., costly access and implementation of afield campaign). However, it was replaced by station ATH5, which although satisfies only one criterion, it is of high importance due to its location in the capital of Greece, Athens, where more than 10 stations are installed in a variety of soil conditions, and it is housed in the building of National Parliament. Station AST1 satisfying also one criterion but it is excluded because its location is close enough to the VSK1 station.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSorted list - according to the number of satisfied selection criteria and available strong motion records - of the17 stations examined as candidate \u0026ldquo;rock\u0026rdquo; sites for site characterization.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of satisfied criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of records in database (1973\u0026ndash;2018)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eART2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.1474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.99368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.6318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.9628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.2417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.0726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNAX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.1014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.3741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.2573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.5534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.4091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.5640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAST1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.5416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.0895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.9754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.7371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFRS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.2934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.3844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKYP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.2497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.6670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLMS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.8349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.1418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAT5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.2959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.7951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePIR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.9372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.6426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRLN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.0891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.0869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.6624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.9353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.5112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.3634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLEO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.1689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.8637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFinally, the selected potential \u0026ldquo;rock\u0026rdquo; reference stations for site characterization are the ART2 (Arta), SEIS (Thessaloniki), AIG2 (Aigion), SIA1 (Siatista), VSK1 (Vasilikiades), and ATH5 (Athens). Their HSAF, VSAF and earthquake HVSR are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Maragkakis, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eStiff and soft station sites\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eThe selection of the rest six stiff and soft soil station sites was based on the framework of a parallel project regarding the effect of buildings on strong motion recordings, which involved measurements carried out inside and close to the buildings (Rischette et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). This project required accurate characterization of the under-study sites and motivated us to include them as target sites of the present study, as well. These station sites are the ARG2 (Argostoli), LEF2 (Lefkas), PLA1 (Thessaloniki), ITS1 (Thessaloniki), JAN2 (Ioannina), and LXR2 (Lixouri) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Their HSAF, VSAF and earthquake HVSR are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (after Maragakis, 2022). Surface geology, based on geological maps around the selected six stiff and/or soft soil stations are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 METHODS AND DATA used","content":"\u003cp\u003eDuring the past two decades, site characterization using non-invasive methods based on the measurement of surface wave dispersion feature to obtain a \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\left(z\\right)\\)\u003c/span\u003e\u003c/span\u003e profile by inversion, is constantly gaining ground worldwide (Foti et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yong et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The methodology used to characterize the twelve selected Greek stations is the same one that was developed and implemented for the characterization of French stations within the Epos-France framework and detailed in Hollender et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We use the active MASW method to characterize the uppermost superficial layers (up to ~\u0026thinsp;10 m) and the passive AVA method for investigating larger depth (up to several hundred meters in the geometries we implement), following the recommendations set out in the InterPACIFIC guidelines (Foti et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMultichannel Analysis of Surface Waves (MASW) and Acquisition Layout\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eThe MASW method (Park et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) investigates the dispersion of surface waves (Rayleigh or/and Love waves) to generate a 1D S-wave velocity (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\left(z\\right)\\)\u003c/span\u003e\u003c/span\u003e) profile. It deserves mentioning that during the generation of surface waves with a compressional source, more than \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:2/3\\)\u003c/span\u003e\u003c/span\u003e of the total seismic energy is carried by Rayleigh waves (Richart et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). In heterogenous media, such as the shallow layers of the Earth, Rayleigh wave velocity varies with depth, and different frequency bands of Rayleigh waves propagate at different phase velocities. Consequently, different wavelengths exist for each frequency, and this phenomenon is called dispersion. Dispersion of surface waves is utilized to study the elastic properties of surface geological formations (Nazarian et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Stokoe et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Dispersion mainly depends on \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\)\u003c/span\u003e\u003c/span\u003e in geological formations. More details about the applicability and limitations of the active MASW method can be found, among others, in Foti et al., (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe acquisition layout includes a linear array of receivers with the shot position in-line with the receivers. The geometry is defined by the array length \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:L\\)\u003c/span\u003e\u003c/span\u003e, the receiver spacing \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:x\\)\u003c/span\u003e\u003c/span\u003e, and the source distance from the receivers. The nominal measurement protocol that we adopted for standard acquisitions is as follows: a measuring tape is stretched out on the ground over a length of 86 m. Twenty-four 5 Hz-geophones are arranged with a spacing of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:x=\\)\u003c/span\u003e\u003c/span\u003e 2 m from coordinate \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x=\\)\u003c/span\u003e\u003c/span\u003e 20 m to coordinate \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x=\\)\u003c/span\u003e\u003c/span\u003e 66 m (i.e., \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:L=\\)\u003c/span\u003e\u003c/span\u003e 46 m). In general, shots were hit in five positions: 20 m and 4 m from the first geophone (i.e., at coordinates \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x=\\)\u003c/span\u003e\u003c/span\u003e 0 and 16 m), at the centre of the array (i.e., at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x=\\)\u003c/span\u003e\u003c/span\u003e 43 m), and at 4 and 20 m beyond the last geophone (i.e., at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x=\\)\u003c/span\u003e\u003c/span\u003e 70 and 86 m). These measurements are taken twice, once with 24 vertical geophones for Rayleigh wave recordings, and a second time with 24 horizontal geophones for Love wave recordings. In the first case, the ground is struck vertically with a 4 kg sledgehammer. In the second case, the ground is struck with this sledgehammer as horizontally as possible in holes previously dug in a V-shape. MASW was implemented as close to the accelerometer station as possible. An example of the MASW experimental line is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for the ITS1 station.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then applied a \u0026lsquo;FK-linear\u0026rsquo; type processing to the data using Geopsy software (Wathelet et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), supplemented by tools developed in Matlab. FK processing is applied to each \u0026lsquo;sledgehammer shot\u0026rsquo;. The best shots are then selected, and a \u0026lsquo;stack\u0026rsquo; is performed in the frequency domain (this approach allows for better preservation of high frequencies and objective selection of the best shots by visualizing the result of the FK processing for each of them).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (upper raw) shows the results obtained after stacking in the frequency domain for the MASW acquisition performed at station PLA1 (vertical polarity). The resolution limits of MASW are debated in the scientific community. The most \u0026ldquo;optimistic\u0026rdquo; allow the picking of the dispersion curves up to a wavelength equal to the length of the MASW measurement line (Foti et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, O\u0026rsquo;Neil, (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) has shown that it is more prudent to limit the picking of the dispersion curves to 0.4 times the total length of the profile. Numerical simulations carried out by Ria\u0026ntilde;o et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) tend to confirm O\u0026rsquo;Neil's, (2003) conclusions. In the present study, we adopted the latter, stricter criterion. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (bottom raw) shows the portions of the dispersion curves that have been selected for the example site for further analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAmbient Vibration Array (AVA) method\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eThe AVA method complements the MASW method. It is a passive method that uses energy generated by anthropogenic or natural noise sources and generally allows for greater investigation depths. It involves positioning several sensors in various geometries (triangles, circles, etc.) to record ambient vibrations. More details about the ΑVΑ deployment and data analyses methods can be found in the literature (among others: Bard et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Cornou et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wathelet et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Di Giulio et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Foti et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Garofalo et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yong et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo carry out AVA measurements, we used 36 SmartSolo IGU-16HR 5 Hz three-component seismic nodes. Aswith MASW, the adopted deployment geometry is the one that has been proposed and successfully used to characterize stations in the French accelerometer network (Hollender et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This approach includes the deployment of successive circular arrays; each composed of seven sensors distributed evenly around a central station. The radii of the circles increase progressively, with each new circle having a radius three times greater than that of the previous circle.\u003c/p\u003e \u003cp\u003eThe nominal acquisition plan includes five circles with respective radii of 5 m (R1), 15 m (R2), 45 m (R3), 135 m (R4), and 405 m (R5). The objective is to enable simultaneous acquisition on at least two successive circles, such as R1\u0026thinsp;+\u0026thinsp;R2, R2\u0026thinsp;+\u0026thinsp;R3, etc. This requires the availability of at least 15 sensors, i.e., two circles of seven sensors each, plus the central sensor. For the field measurements of this study, we had enough sensors to deploy all 5 circles simultaneously.\u003c/p\u003e \u003cp\u003eThe minimum acquisition times are as follows:\u003c/p\u003e \u003cp\u003e\u0026shy; R1\u0026thinsp;+\u0026thinsp;R2: 45 minutes\u003c/p\u003e \u003cp\u003e\u0026shy; R2\u0026thinsp;+\u0026thinsp;R3: 1 hour\u003c/p\u003e \u003cp\u003e\u0026shy; R3\u0026thinsp;+\u0026thinsp;R4: 2 hours\u003c/p\u003e \u003cp\u003e\u0026shy; R4\u0026thinsp;+\u0026thinsp;R5: 3 hours\u003c/p\u003e \u003cp\u003eThis standard acquisition plan typically allows prospecting depths of 300 to 500 m to be reached. Obviously, the nominal geometry is subject to local accessibility constraints. However, the AVA method is \u0026ldquo;tolerant\u0026rdquo; to deviations from the perfect circular design geometry. Circles are often off-center to take access conditions into account, but also sometimes to keep all acquisition points on relatively homogeneous geology. For station SIA1, due to undesired restrictions in space, an L-shaped layout for AVA has been deployed in place of the two smaller circular arrays (R1 and R2). An example of the geometry of the AVA deployment is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for the ITS1 station. The geometries of the deployments for all herein studied sites are given in the Online Resource (Figures ESM 1 to ESM 12).\u003c/p\u003e \u003cp\u003eThe following systematic processing was applied to the AVA data:\u003c/p\u003e \u003cp\u003e\u0026shy; \u0026ldquo;Classic\u0026rdquo; FK (McMechan and Yedlin \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1981\u003c/span\u003e), applied to the vertical component only for the analysis of Rayleigh wave dispersion,\u003c/p\u003e \u003cp\u003e\u0026shy; HRFK (or \u0026ldquo;high-resolution\u0026rdquo; FK) (Capon \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1969\u003c/span\u003e), also applied to the vertical component only for the analysis of Rayleigh wave dispersion,\u003c/p\u003e \u003cp\u003e\u0026shy; Transverse HRFK, applied to the horizontal components for the analysis of Love wave dispersion,\u003c/p\u003e \u003cp\u003e\u0026shy; RTBF (Wathelet et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which is a 3-component processing method that analyzes the entire ellipticity of Rayleigh waves, resulting in Rayleigh wave dispersion curves accompanied by an analysis of the polarity of the ellipticity, allowing for better mode identification.\u003c/p\u003e \u003cp\u003eThese methods were applied to the following array sets: R1, R1R2, R2, R2R3, R3, R3R4, R4, R4R5, and R5 using the Geopsy software package. For efficiency, these processing were performed using command lines rather than the Geopsy graphical interface. Post-processing (visualization of results, semi-automatic scoring of scatter plots, etc.) was performed using in house Matlab scripts. It should be noted that we favored the FK-type processing and did not perform any SPAC-type processing.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows example results obtained after processing ambient noise recordings by the FK, HRFK and RTBF methods for the PLA1 station site. The results of the RTBF processing are presented by differentiating between: (1) the dispersion curves obtained for negative ellipticities (RTBF-: retrograde Rayleigh wave particle motion), and (2) the dispersion curves obtained for positive ellipticities (RTBF+: prograde particle motion). As in MASW, before plotting the dispersion curves, it is important to calculate the array resolution limits, which depend on the acquisition geometry. The low-frequency resolution limit of an array is called \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}_{max\\:}\\)\u003c/span\u003e\u003c/span\u003e (or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{k}_{min\\:}\\)\u003c/span\u003e\u003c/span\u003e). For FK processing, it is advisable not to pick the dispersion curve below this value. For HRFK or RTBF processing, it is possible to pick up to a maximum wavelength of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:2{\\lambda\\:}_{max\\:}\\)\u003c/span\u003e\u003c/span\u003e (or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{k}_{min\\:}/2\\)\u003c/span\u003e\u003c/span\u003e) (Foti et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e with a different color scale and adding the portions of dispersion curves that were picked. We prioritize the results obtained with RTBF processing: experience shows that these are generally the most reliable data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding the requirements for site characterization performed in this study - using MASW and/or AVA techniques - are required:2 vehicles, 48 geophones with a datalogger, 36 seismic nodes, and a team of 5 people to acquire sufficient data within a single day per site.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003e3.3 HVSR\u003c/h2\u003e\n\u003cp\u003eWe also applied the HVSR method to the sites studied. The HVSR method (Nakamura 1989, Bard 2008) is based on recording ambient vibrations measured on a single 3-component sensor. It consists of calculating the ratio of the Fourier spectra of the horizontal and vertical components. It allows the estimation of the fundamental resonance frequency of a site to be determined (f_0). The phenomenology explaining the existence of this peak is dominated by the frequency variation in the ellipticity of the ground motion associated with Rayleigh waves. The f_0 value is also often used in the final inversion process, where it complements the dispersion curves (joint inversion).\u003c/p\u003e\n\u003cp\u003eFigure 8 summarizes the HVSR results for the twelve characterized sites. In the frequency range 0.2\u0026ndash;15 Hz, stations AIG2, ART2, ATH5, SEIS, SIA1, and VSK1 exhibit nearly flat HVSRs with amplitudes below 2. As expected from ambient vibration analyses, these stations show HVSR signatures typical of rock sites. For station SEIS, the mean HVSR curve exceeds 2 only at high frequencies (f_0\u0026gt; 15 Hz), and for SIA1 a similar increase is observed at even higher frequencies (f_0\u0026gt; 30 Hz). In contrast, stations ARG2, ITS1, JAN2, LEF2, LXR2, and PLA1 show distinct single or double peaks at both low (f_0\u0026lt; 1.0 Hz) and high (f_0\u0026gt; 2.0 Hz) frequencies, with corresponding amplitudes between 2.5 and 5, indicative of stiff to soft soil conditions.\u003c/p\u003e\n\u003cp\u003eIt should be noted that the JAN2 site posed significant challenges for the noise analysis, with strong lateral variability observed among the AVA array stations. The site is highly urbanized, affected by multiple local noise sources (heavy traffic, pedestrian activity), as well as by the presence of underground structures, such as a nearby underground parking garage. The HVSR response at this site should therefore be interpreted with caution, particularly the large-amplitude peak between 0.8 and 1 Hz.\u003c/p\u003e"},{"header":"4\tINVERSION AND RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eDispersion Curves and Inversion\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eInversion consists of using the information contained in the dispersion curves (Rayleigh and/or Love waves), possibly supplemented by the site resonance frequency or the complete Rayleigh wave ellipticity curve, to calculate one or more S-wave velocity profiles compatible with this information. All other information available on the site that could help to better constrain the inversion is considered: geotechnical data providing orders of magnitude for soil stiffness, geological information providing, for example, the depth of the bedrock from a borehole or allowing its geological nature to be determined, etc. The depth of the bedrock, if it is known with certainty (e.g., from drilling), can be incorporated into the inversion.\u003c/p\u003e \u003cp\u003eThe inversion process is iterative: the algorithm starts from an initial velocity model, directly calculates the resulting dispersion curves (and other possible parameters), and quantifies the difference (or \u0026ldquo;misfit\u0026rdquo;) between the obtained dispersion curves and the experimental ones. The algorithm then modifies the initial velocity profile, performs a new direct calculation, a new estimation of the misfit, etc. The performance of the algorithm lies in its ability to modify the velocity profile to obtain a minimal misfit with a minimal number of iterations.\u003c/p\u003e \u003cp\u003eWe performed the dispersion curves inversions using the Dinver tool (Wathelet \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) from the Geopsy suite, which uses a neighborhood algorithm. The classic approach consists of searching for the profile for which the misfit is minimal: this is referred to as the best-estimate search. We applied this approach considering certain limitations. When inversion algorithms are set to search for a best-estimate solution, they often focus on a local minimum and ultimately produce a non-realistic profile. Furthermore, the production of a single velocity profile does not reflect the non-uniqueness of the inversion. Non-uniqueness implies that several velocity profiles can satisfy the same dispersion curve and its standard deviation, often with equally small misfits.\u003c/p\u003e \u003cp\u003eWe therefore applied a slightly different approach that instead of inverting for a single, best-estimate profile, it inverts for a large number of profiles, often referred to as \u0026ldquo;acceptable misfit profiles\u0026rdquo; (Lomax and Snieder, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). This set of velocity profiles are linked to dispersion curves that remain \u0026ldquo;acceptable\u0026rdquo;, i.e., within some user-defined level of the error bars of the measured dispersion curves. During the inversion process, several thousand \u0026ldquo;acceptable\u0026rdquo; profiles are retained.\u003c/p\u003e \u003cp\u003ePrior to the inversion step, all the picked dispersion curves (Rayleigh, Love, fundamental and possibly higher modes) are gathered. These different dispersion curves are then assembled (by site, wave type, and mode), merged, and possibly smoothed to obtain the widest possible dispersion curve per site. These dispersion curves, known as \u0026ldquo;targets\u0026rdquo; (or \u0026ldquo;target\u0026rdquo;), are established with their uncertainties and constitute the main input data for the inversion step.\u003c/p\u003e \u003cp\u003eThe results of the inversion are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, using the ITS1 station as an example. The upper part of this figure shows the different \u0026ldquo;target\u0026rdquo; dispersion curves corresponding to the experimental measurements, including error bars. In the case of station ITS1, three Rayleigh modes were identified (the fundamental mode R0, and the first and second higher modes R1 and R2), as well as the fundamental Love mode (L0). The lighter shaded lines represent the forward-computed dispersion curves using the \u0026ldquo;acceptable velocity profiles\u0026rdquo; (several thousand) obtained by the inversion process. The center row of the figure shows these acceptable velocity profiles at different scales. This representation includes all the thousands of compatible profiles (in gray), as well as a subset of 33 randomly selected profiles whose distribution in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e is representative of that obtained for all the profiles resulting from the acceptable misfit approach (several thousand). Details of this random sampling of the numerous profiles to account for the variability are presented in Hollender et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Finally, the lower row of this figure illustrates the distributions of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{800}\\)\u003c/span\u003e\u003c/span\u003e, and the probabilities of the examined site to belong to any of the soil classes defined by EC8. The same representation is provided for all twelve characterized station sites in the Online Resource (Figures ESM 13 to ESM 24).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 33 randomly chosen 1D \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\left(z\\right)\\)\u003c/span\u003e\u003c/span\u003e profiles for all the 12 examined station sites are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e along with their average \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e values. In all cases in this work, the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e value was computed from the ground surface down to a depth of 30 meters. This value characterizes an intrinsic property of the site (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}^{Site}\\)\u003c/span\u003e\u003c/span\u003e). For stations installed below the surface, an additional \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e value was computed from the installation depth (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{z}_{station}\\)\u003c/span\u003e\u003c/span\u003e) down to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{z}_{station}\\)\u003c/span\u003e\u003c/span\u003e+ 30 m. This parameter is referred to here as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}^{Station}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a summary of the parameters extracted from the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\left(z\\right)\\)\u003c/span\u003e\u003c/span\u003e profiles (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}^{Site}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}^{Station}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{800}\\)\u003c/span\u003e\u003c/span\u003e and EC8 soil category) is given. The proposed standard deviations were calculated assuming that the distributions are broadly normal, which is generally an acceptable assumption for the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e parameter. However, this assumption does not always hold true for the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{800}\\)\u003c/span\u003e\u003c/span\u003e parameter, whose determination is more sensitive to the choices made in the processing protocol, particularly the inversion settings. This behavior is particularly evident in the case of station ITS1 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). We have therefore added a column in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e providing a qualitative comment on the shape of the H₈₀₀ distribution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of selected parameters of site characterization for the twelve investigated HAN stations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStation code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation depth [m]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{V}}_{\\varvec{S}30}^{\\varvec{S}\\varvec{i}\\varvec{t}\\varvec{e}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e[m/s]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{V}}_{\\varvec{S}30}^{\\varvec{S}\\varvec{t}\\varvec{a}\\varvec{t}\\varvec{i}\\varvec{o}\\varvec{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e[m/s]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{H}}_{800}^{\\varvec{S}\\varvec{i}\\varvec{t}\\varvec{e}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e[m]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{H}}_{800}^{\\varvec{S}\\varvec{i}\\varvec{t}\\varvec{e}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003edistribution remark\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEC8\u003c/p\u003e \u003cp\u003esoil class (site)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEC8\u003c/p\u003e \u003cp\u003esoil class (station)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e618\u0026nbsp;\u0026plusmn;\u0026nbsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e618\u0026nbsp;\u0026plusmn;\u0026nbsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e25\u0026nbsp;\u0026plusmn;\u0026nbsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eroughly normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e540\u0026nbsp;\u0026plusmn;\u0026nbsp;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e540\u0026nbsp;\u0026plusmn;\u0026nbsp;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e16\u0026nbsp;\u0026plusmn;\u0026nbsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eleft-truncated normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB [70%]\u003c/p\u003e \u003cp\u003eE [30%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB [70%]\u003c/p\u003e \u003cp\u003eE [30%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eART2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1979\u0026nbsp;\u0026plusmn;\u0026nbsp;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1979\u0026nbsp;\u0026plusmn;\u0026nbsp;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1\u0026nbsp;\u0026plusmn;\u0026nbsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eleft-truncated normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e855\u0026nbsp;\u0026plusmn;\u0026nbsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1149\u0026nbsp;\u0026plusmn;\u0026nbsp;38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2\u0026nbsp;\u0026plusmn;\u0026nbsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eleft-truncated normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e467\u0026nbsp;\u0026plusmn;\u0026nbsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e548\u0026nbsp;\u0026plusmn;\u0026nbsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e131\u0026nbsp;\u0026plusmn;\u0026nbsp;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebimodal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJAN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e369\u0026nbsp;\u0026plusmn;\u0026nbsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e402\u0026nbsp;\u0026plusmn;\u0026nbsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e34\u0026nbsp;\u0026plusmn;\u0026nbsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebimodal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB [85%]\u003c/p\u003e \u003cp\u003eC [15%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e366 \u0026plusmn;\u0026nbsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e380 \u0026plusmn;\u0026nbsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e70 \u0026plusmn;\u0026nbsp;152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebimodal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB [70%]\u003c/p\u003e \u003cp\u003eC [30%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB [95%]\u003c/p\u003e \u003cp\u003eC [5%]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLXR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e444\u0026nbsp;\u0026plusmn;\u0026nbsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e489\u0026nbsp;\u0026plusmn;\u0026nbsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e121\u0026nbsp;\u0026plusmn;\u0026nbsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eroughly normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e421\u0026nbsp;\u0026plusmn;\u0026nbsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e485\u0026nbsp;\u0026plusmn;\u0026nbsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e205\u0026nbsp;\u0026plusmn;\u0026nbsp;123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebimodal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1049\u0026nbsp;\u0026plusmn;\u0026nbsp;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1395\u0026nbsp;\u0026plusmn;\u0026nbsp;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e5\u0026nbsp;\u0026plusmn;\u0026nbsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eroughly normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA [60%]\u003c/p\u003e \u003cp\u003eE [40%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e900\u0026nbsp;\u0026plusmn;\u0026nbsp;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e900\u0026nbsp;\u0026plusmn;\u0026nbsp;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e12\u0026nbsp;\u0026plusmn;\u0026nbsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebimodal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVSK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1359\u0026nbsp;\u0026plusmn;\u0026nbsp;73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1359\u0026nbsp;\u0026plusmn;\u0026nbsp;73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e4\u0026nbsp;\u0026plusmn;\u0026nbsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eroughly normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn both Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the site of station ART2 clearly stands out from the other station sites, with a \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}^{Station}\\)\u003c/span\u003e\u003c/span\u003e reaching a value of almost 2000 m/s. Stations SEIS and VSK1 also show relatively high values of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}^{Station},\\:i.e,\\:\\)\u003c/span\u003e\u003c/span\u003e between 1350 and 1400 m/s. The station with the lowest \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}^{Station}\\)\u003c/span\u003e\u003c/span\u003e value is LEF2 (=\u0026thinsp;380 m/s).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e we compare the measured\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}^{Site}\\)\u003c/span\u003e\u003c/span\u003e values for the twelve stations of the HAN in this study with the corresponding inferred values reported in Stewart et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) based on the surface geology and the topographic slope. Although the number of examined sites is limited, there is a significant deviation of the plotted points from the bisector. This suggests that the previously inferred \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}^{Site}\\)\u003c/span\u003e\u003c/span\u003e significantly underestimate the reality, especially for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}^{Site}\u0026gt;\\)\u003c/span\u003e\u003c/span\u003e 500 m/s.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5 CONCLUSIONS","content":"\u003cp\u003eThis study provides a homogeneous and detailed geophysical characterization of twelve accelerometric stations of the Hellenic Accelerometer Network (HAN), combining the active MASW and the passive AVA surface-wave methods. Apart from providing measured \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e values and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S}\\)\u003c/span\u003e\u003c/span\u003e profiles to complement the small tank of such information on permanent Greek accelerometric stations, the consistent application of the \u0026ldquo;acceptable misfit\u0026rdquo; inversion approach allows for the quantification of the epistemic uncertainty in the proposed profiles and related site parameters.\u003c/p\u003e \u003cp\u003eThe results show that, for stiff soil and rock sites, geology- and slope-inferred \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e values often underestimate the actual \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e, in some cases markedly, especially for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e \u0026gt; 500 m/s. Only a few sites (ART2, SEIS, VSK1) exhibit genuinely hard-rock characteristics (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e \u0026gt; 1,000 m/s and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{800}\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 5 m). Station ART2, with a \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e value close to 2000 m/s, clearly stands out as the most suitable candidate for a reference \u0026ldquo;rock\u0026rdquo; site in Greece, at least among the studied station sites.\u003c/p\u003e \u003cp\u003eThe observed discrepancies between proxy-based indicators and in situ measurements highlight that indirect approaches alone may not always provide a sufficiently robust basis for site classification, particularly for critical applications such as reference rock-site selection. For stations intended to serve as references in GMMs or GITs frameworks, field measurements appear essential to ensure quantitative reliability and to avoid systematic bias.\u003c/p\u003e \u003cp\u003eThese results emphasize the importance of direct geophysical investigations for reliable site classification and for reducing bias in Ground-Motion Models (GMMs) and Generalized Inversion Techniques (GITs). This new set of station site characterization metadata provides a reference framework for future updates of the HAN database and contributes to the harmonization of the European strong-motion station metadata within EPOS and ESM databases (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://esm-db.eu\u003c/span\u003e\u003cspan address=\"https://esm-db.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The results of this study have already been used in the derivation of a new Ground-Motion Model for very hard-rock conditions (Shible et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) as well as for investigating the impact of buildings on ground motion recordings, the so-called Soil-Structure Interaction-SSI (Rischette et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompetitive Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial or non-financial interests that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the French Atomic and Alternative Energies Commission [CEA] and Electricit\u0026eacute; de France [EDF], within the framework of the CASHIMA3 and SIGMA3 research programs on seismic hazard assessment. SIGMA3 (2024\u0026ndash;2028, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.sigma-programs.com\u003c/span\u003e\u003cspan address=\"http://www.sigma-programs.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is funded by an industrial consortium composed of Electricit\u0026eacute; de France [EDF], French Atomic and Alternative Energies Commission [CEA], Česk\u0026eacute; energetick\u0026eacute; z\u0026aacute;vody [Czech Energy Company, CEZ] Group, Pacific Gas and Electric Company [PG\u0026amp;E], VTT research center, SwissNuclear, Central Research Institute of the Electric Power Industry [CRIEPI], St\u0026uuml;k, Uniper and Vattenfall. In addition, it was partially supported by the Earthquake Planning \u0026amp; Protection Organization\u0026rsquo;s project \"Updated Seismic Hazard Maps in the Area of Greece (2025, No. 12788)\".\u003c/p\u003e\u003ch2\u003eAuthors Contributions\u003c/h2\u003e \u003cp\u003eAll authors contributed to data acquisition and data analysis used in this study. The overall structure and the main writing of the manuscript were carried out by N. Theodoulidis and F. Hollender. The latter significantly contributed and supervised the data analyses as well. All authors reviewed the manuscript, contributed to the revisions, and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors would like to thank Isabelle Douste-Bacqu\u0026eacute;, Margaux Buscetti, Harris Moustakas, Vasiliki Tsoukala and Maria-Georgia Dasoula for their help in acquiring the geophysical measurements, and for performing first stages of their treatment.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBard P-Y (2008) Foreword: The H/V technique: capabilities and limitations based on the results of the SESAME project. 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J Seismol 26:557\u0026ndash;566. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10950-022-10104-w\u003c/span\u003e\u003cspan address=\"10.1007/s10950-022-10104-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bulletin-of-earthquake-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"beee","sideBox":"Learn more about [Bulletin of Earthquake Engineering](https://www.springer.com/journal/10518)","snPcode":"10518","submissionUrl":"https://submission.nature.com/new-submission/10518/3","title":"Bulletin of Earthquake Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Site Characterization, Accelerometer Stations, Engineering Reference Site, Ambient Noise Array \u0026 MASW geophysical methods","lastPublishedDoi":"10.21203/rs.3.rs-9370620/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9370620/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eAbstact\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this study site characterization of twelve accelerometer station sites of the Hellenic Accelerometers Network (HAN) is presented. An established European protocol is applied that consists of non-invasive active (Multichannel Analysis of Surface Waves: MASW) and passive (Ambient Vibration Analysis: AVA) methods. Data processing is carried out systematically, using new techniques of dispersion curves estimation and their inversion to obtain 1D shear wave velocity profiles at the examined sites. Surface geology and other site information previously indicated six sites as reference \u0026ldquo;rock\u0026rdquo; and six as stiff to soft soil. After data processing in this study, it was determined that only five of them fulfil the engineering bedrock criterion (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e \u0026gt; 800 m/s), with one of them - station ART2 (Arta) - identified as very hard rock (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e ~2000 m/s). The remaining seven investigated sites fall within stiff to soft soil categories (380 \u0026le; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e \u0026le; 618 m/s). The results show that, for stiff soil and rock sites, geology and topographic slope- inferred \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e values, in some cases significantly, especially for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{S30}\\)\u003c/span\u003e\u003c/span\u003e \u0026gt; 500 m/s. These results highlight the importance of direct geophysical investigation for reliable site classification and for reducing bias in ground motion modeling, e.g. Ground-Motion Models (GMMs) and Generalized Inversion Techniques (GITs). The updated station sites characterization metadata set provides a reference framework for future updates of the Hellenic Accelerometers Network recordings and contributes to the harmonization of European strong-motion stations metadata within EPOS as well as in the European Strong Motion database.\u003c/p\u003e","manuscriptTitle":"Characterization of selected station sites of the Hellenic Accelerometer Network (HAN)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 19:56:10","doi":"10.21203/rs.3.rs-9370620/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-23T11:56:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Bulletin of Earthquake Engineering","date":"2026-04-13T21:25:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T05:41:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bulletin of Earthquake Engineering","date":"2026-04-09T11:36:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bulletin-of-earthquake-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"beee","sideBox":"Learn more about [Bulletin of Earthquake Engineering](https://www.springer.com/journal/10518)","snPcode":"10518","submissionUrl":"https://submission.nature.com/new-submission/10518/3","title":"Bulletin of Earthquake Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"db0e48a1-291a-4eba-825d-1357c76ed0a8","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T19:56:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 19:56:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9370620","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9370620","identity":"rs-9370620","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}