# Repeatable process for seismic microzonation using 1-D site-specific response spectra assessment approaches.
## Application to the city of Nice, France

Julie Régnier, Etienne Bertrand, Héloïse Cadet

► To cite this version:

Julie Régnier, Etienne Bertrand, Héloïse Cadet. Repeatable process for seismic microzonation using 1-D site-specific response spectra assessment approaches. Application to the city of Nice, France. Engineering Geology, 2020, 270, pp.105569. 10.1016/j.enggeo.2020.105569. hal-03578313

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Distributed under a Creative Commons Attribution 4.0 International License
Repeatable process for seismic microzonation using 1-D site-specific
response spectra assessment approaches. Application to the city of
Nice, France.

Julie Régnier¹, Etienne Bertrand¹ and Héloïse Cadet³.

1 Cerema Méditerranée, Équipe recherche Mouv-GS, Sophia-Antipolis, France

3 SAGE-ADRGT, Grenoble, France

keywords: seismic microzonation, response spectra amplification function, Nice.

We are testing three different approaches to assess the site effects based on 1D soil column
data. The first method follows the Eurocodes 8 (EC8) approach based on the determination of
Vs30 soil classes, the second one is based on an empirical relationship between Vs30 and the
fundamental resonance frequency of the site (f0) with a response spectra amplification
function proposed by Cadet et al (2013) and the last one is the application of a 1-D equivalent
linear simulation. Then, we propose a repeatable methodology to define the zonation and the
associated surface response spectrum from individual results. This methodology uses a shape
recognition algorithm to build homogeneous response spectra zones and the definition of a
specific EC8-like response spectrum adjusted to the distribution of the response spectra in
each zone. This has been applied to downtown Nice, a city located on the French Riviera that
faces the highest seismic hazard in the French mainland area. The city is built on two different
sedimentary valleys that are prone to lithological site effects. The ground acceleration around
1.8 Hz can be 20 times higher in the valleys than the one recorded on the surrounding Jurassic
rock outcrop. We defined a 3D geotechnical model, composed of 9 homogenous soil layers.
The surface elastic response spectra in pseudo-acceleration obtained with the three methods
are compared on the whole city. The results indicate that the amplification functions
methodology proposed by Cadet et al. (2013) provides results closer to the numerical
simulations compared to EC8. In addition, in five sites of the French Permanent
Accelerometric Network (RAP/RESIF) located in the valley of Nice, the site to reference
response spectra ratio from the numerical simulations and the spectra amplification function
proposed by Cadet et al (2013) were compared to earthquake recordings data. Although the
spectra amplification functions are closer to the observations, both evaluations underestimate

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the amplification as they could not capture complex site geometry. Using the repeatable
zonation process on the response spectra from the of the amplification function three different
zones are defined. The associated specific response spectra are significantly different from the
recommended EC8 response spectra.

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Introduction

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Seismic microzonation at a city scale provides a site-specific seismic hazard evaluation. It
considers the regional hazard, the local site effects (both lithological and topographical) and it
can also include induced effects such as liquefaction and landslides. Microzonations provide a
site-specific response spectrum that practitioner engineers can use to design the structures. In
addition to the actual EC8 seismic code, microzonation prevents the engineers from defining
the soil class which can be still a challenging task (Garofalo et al. 2016). Microzonation can
also go beyond the soil classification proposed in the building code to consider the site effects
with either alternative soil proxy parameters or even site-specific studies. Indeed, a large
number of studies have shown that alternative soil classifications with additional soil
parameters especially the fundamental resonance frequency could be proposed and provide
more accurate site effect evaluations (Luzi et al. 2011; H. Cadet et al. 2012; Derras et al.
2012; Régnier, Bonilla, et al. 2013; Pitilakis et al. 2018). The microzonation is therefore a
main tool for seismic risk mitigation at city scale level.

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In this paper we propose to test three methods to define the surface response spectrum that
considers the local site effects. The three methods followed the three levels of complexity
underlined in the TC4 manual of the International Society for Soil Mechanics and
Geotechnical Engineering provides a manual for seismic zonation (Technical Committee for
earthquake geotechnical engineering 1999). The first method considers the soil class as
defined in the Eurocode 8 (EC8), the second method is based on a spectral amplification
curve depending on both the fundamental resonance frequency and the Vs30 (average of the
shear wave velocity over the first 30 m of soil) (Cadet et al. 2012) and the last method
consists in a 1-D equivalent linear simulation of the seismic ground motion. We also propose
in this article an automatic and repeatable approach to group the response spectra in families
with similar shapes and to define an EC8-type response spectrum adapted to the distribution
of the response spectra of each family and consequently define a microzonation from
individual results.
We applied these methods to the city of Nice that faces the highest seismic hazard in the French metropolitan area. The city of Nice has been the subject of a large number of studies since the early '80s, showing that site effects strongly affect the alluvial valleys of the city with a first attempt of microzoning in Nice (Bard 1983). Furthermore, one of the first feasibility test conducted outside Japan about the use of ambient vibrations recordings to constrain lithological site effects was done in Nice (Duval, 1996). The author particularly recorded ambient vibrations on 64 points of a dense grid around the port area of the city and compared the deduced resonance frequency with the local geology as well as standard spectral ratio on earthquake data, showing that the method was very promising for seismic microzoning studies. 2D Sites effects were also studied in the central valley of Nice (Gélis et al. 2008; Semblat, Duval, and Dangla 2000). In 2007 we published a first attempt of a 3D geotechnical model of the quaternary infills (Bertrand et al. 2007) in the frame of the GEMGEP project, supported by the French Minister in charge of land planning and sustainable development. For this we carried out more than 700 ambient vibration measurements to obtain a zonation of the fundamental resonance frequencies in the sedimentary valley of the city (Duval et al. 2013).

The three site effects assessment methods are applied to 9000 soil columns located on the sedimentary valleys of Nice providing a surface response spectrum at each location. The response spectra are compared between the three site effects assessment methods and the amplification functions are compared to earthquake data at the five stations of the French permanent seismic network (RAP/RESIF) located in Nice. Finally, the microzonation of Nice is performed using the results of one method with the application of the automatic process to group the response spectra in families with similar shapes.

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## Dataset used

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### (1) Regional hazard in Nice area

The junction between the southern French-Italian Alps and the Ligurian Basin is one of the most seismically active areas among western European countries. It is defined by a daily microseismicity, moderate events (M> 4.5) occurring about every 5 years, and few large destructive historical earthquakes (M > 6) e.g. (Ferrari 1991; Larroque, Scotti, and Ioualalen 2012). One of the most seismically active zones in this region is situated in the northern part of the Ligurian basin. The magnitude of the earthquakes ranging from 3.0 to 6.0 reveals inverse or strike slip solutions compatible with a compressional state of stress. The largest earthquakes recently instrumentally
recorded offshore Nice occurred on December 26th, 1989 (MI 4.5; Béthoux et al. 1992) and the last one on February 25th, 2001 (MI 4.6; Courboulex et al. 2007). They were situated at 25 km of the coast, at the base of the continental slope, and had very similar reverse focal mechanisms. A recent bathymetric and seismic survey reveals morphotectonic features at the place of both epicenters, which could be related with an active fault (Scotti et al. 2008). Tens of kilometers to the East, in Italy, another structure has been detected in the area of the Ligurian earthquake (February 23rd, 1887). This strongest historical regional earthquake destroyed many villages on the Italian Ligurian coast and killed hundreds of persons. Its magnitude was larger than 6 (Bakun and Scotti 2006).

From the analysis of this seismological context, the RISK-UE project proposed a uniform spectrum response for three return periods (100, 475 and 1000 years) for the city of Nice (Bour et al. 2004). The project also selected 5 real time histories from the European Strong-Motion Database (Ambraseys et al. 2004). The characteristics of the earthquake and recorded stations are synthetized in the Table 1. This selection was made to fit both the 475 years return period uniform response spectra and the sismotectonic settings of the area. The authors made their selection among the data corresponding to a magnitude ranging between 5.7 and 6.3, an epicentral distance between 15 and 28 km and a focal depth between 8 and 10 km. No additional information since the Risk-ue study or the French national hazard map establishment indicate that a new regional hazard study needs to be performed. In this paper, we consider the EC8 regulation rock spectrum and we considered the set of the 5 acceleration time histories selected in the Risk-ue project for the 1-D equivalent linear simulations.

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

<figure><img src="image_3.png" /><figcaption>(b)</figcaption></figure>
122 **Figure 1:** (A) Uniform response spectra for a return period of 475 years for (in red on the left) and mean response spectra of the selected corresponding seismic motion for Nice (in green on the left). (B) Associated reference time series (right). Risk-ue, (Bour et al. 2004)

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<table><caption>**Table 1 : Characteristics of the strong ground motion used. Risk-ue, (Bour et al. 2004)**</caption><caption>Earthquakes</caption><thead><tr><th></th><th>Athens</th><th>Friuli1</th><th>Lazio<br>Abruzzo</th><th>Umbro<br>Marchigiano</th><th>Friuli2</th></tr></thead><tbody><tr><td>Country</td><td>Greece</td><td>Italy</td><td>Italy</td><td>Italy</td><td>Italy</td></tr><tr><td>Date</td><td>07/09/1999</td><td>15/09/1976</td><td>07/05/1984</td><td>26/09/1997</td><td>15/N9/1976</td></tr><tr><td>Latitude</td><td>38.132°N</td><td>46.300°N</td><td>41.733°N</td><td>43.023°N</td><td>46.2830°N</td></tr><tr><td>longitude</td><td>23.545°E</td><td>13.150°E</td><td>13.900°E</td><td>12.892°E</td><td>13.167°E</td></tr><tr><td>Depth (km)</td><td>9</td><td>7</td><td>8</td><td>7</td><td>15</td></tr><tr><td>Epicentral distance (km)</td><td>23</td><td>15</td><td>15</td><td>23</td><td>15</td></tr><tr><td>Mb</td><td>5.8</td><td>5.4</td><td>5.4</td><td>5.5</td><td>5.7</td></tr><tr><td>Ml</td><td>5.6</td><td>6</td><td>5.1</td><td>5.6</td><td>5.9</td></tr><tr><td>Ms</td><td>5.6</td><td>5.9</td><td>5.8</td><td>5.5</td><td>6</td></tr><tr><td>M0</td><td>7.8 10<sup>17</sup></td><td>1.3 10<sup>18</sup></td><td>7.8 10<sup>17</sup></td><td>3.82 10<sup>17</sup></td><td>6.3 10<sup>17</sup></td></tr><tr><td>Source mecanism</td><td>Normal</td><td>Normal</td><td>Normal</td><td>Normal</td><td>Normal</td></tr><tr><th colspan="6" style="text-align: center;">Stations</th></tr><tr><td>Name of the station</td><td>Athens 4</td><td>San Rocco</td><td>Atina</td><td>Borgo Cerreto Torre</td><td>San Rocco</td></tr><tr><td>Latitude</td><td>37.97°N</td><td>46.226°N</td><td>41.622°N</td><td>42.814°N</td><td>46.226°N</td></tr><tr><td>Longitude</td><td>23.703°E</td><td>12.993°E</td><td>13.802°E</td><td>12.915°E</td><td>12.993°E</td></tr><tr><td>Soil type</td><td>ROCK</td><td>ROCK</td><td>ROCK</td><td>ROCK</td><td>ROCK</td></tr><tr><td>PGA (m.s<sup>-2</sup>)</td><td>1.17</td><td>1.29</td><td>1.03</td><td>1.11</td><td>1.29</td></tr><tr><td>Component</td><td>NS</td><td>EO</td><td>NS</td><td>EO</td><td>NS</td></tr><tr><th colspan="6" style="text-align: center;">Signal processing</th></tr><tr><td>Coefficient</td><td>1.26</td><td>1.13</td><td>1.43</td><td>1.3</td><td>0.7</td></tr><tr><td>Sampling</td><td>0.02s</td><td>0.02s</td><td>0.02s</td><td>0.02s</td><td>0.02s</td></tr></tbody></table>

## (2) 3D geotechnical model of Nice

Nice spreads over 72 km² and 20% of the city is built upon recent alluvium deposits. Other parts of the city lie on Jurassic and Cretaceous rocks to the east and thick Pliocene conglomerates to the west. The Figure 2 illustrates a map of the available data. The colour circles indicate the fundamental resonance frequencies on the whole city and the black squares indicate the locations of the borehole stratigraphy. The plain squares indicate the boreholes that reach the bedrock.

Description logs of nearly 450 boreholes were used to build a geotechnical model of the recent alluvium deposits (Infoterre website, see. Data and Resources section). They help in particular to define the lithostratigraphic soil column of the soil profile in the valleys. Because those boreholes are essentially linked to previous planned constructions (such as road network
or important building), their distribution is heterogeneous over the studied area. Furthermore, in the valleys, less than 40% of the boreholes are reaching the rock basement. Nevertheless, all of these boreholes have been analysed to define a representative soil column made of 9 sedimentary layers (Figure 3 and Table 2). Shear wave velocity of these layers were obtained from available Standard Penetration Test (SPT) values using several empirical correlation laws described in the literature and synthetized in Hasancebi and Ulusay (2007) and one array of ambient vibration measurements at the center of the Central valley (at NALS station). Because of its cost, an extended boring survey was not feasible to complete our data set. Traditional seismic profiling was also not intended, as it is not possible to use intensive explosive sources in town.

Recent years have seen many studies using ambient vibration measurements for site effect estimation. Especially, the very simple H/V technique was proven to be suitable for microzoning studies (Bour et al. 1998; Duval et al. 1996, 2001; Lachet et al. 1996; LeBrun et al. 2004; Tuladhar et al. 2004) although some limitation were pointed out when dealing with 2D or 3D structures (Bard 2005). Nevertheless, this technique alone provides at least the fundamental eigenfrequency of the site under investigation. But assuming the shear wave velocity in the sediment it helps to constrain the depth of the bedrock thanks to the well-known $f=Vs/4H$ relationship.

We measured ambient vibration at almost 700 points homogeneously distributed in the city of Nice (Duval et al. 2013). The recordings were processed according to the Nakamura technique (Nakamura, 1989) obtaining thus the fundamental resonance frequency of the sedimentary infill at each recording point and the corresponding bedrock depth, assuming an average Vs velocity derived from the SPT data. The lowest frequency recorded is below 1 Hz at the center of the central valley. As already mentioned, most of the boreholes reaching the bedrock are located at the edges of the valley. The fundamental resonance frequencies are needed to constrain the sedimentary to bedrock depth interface especially at the center of the valley, where it is expected to the deepest part of the valley. Adding to these data set some earthquake recordings from the French permanent accelerometric network (RAP) in Nice and a microtremor array recording (Bonnefoy-Claudet, 2004), we are able to better constrain the subsurface soil model of Nice as well as to extend it where no boreholes were existing.

The velocity of the bedrock was chosen equal to 1000 m/s according to the generic rock profiles provided by Boore et Joyner (1997). Using the formula valid between 30 to 190 m at depth ($Vs = 3.542z^{0.407}$), the shear wave velocity lies between 850 m/s at 30 m and 1387.5
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m/s at 100 m at depth. The value of the velocity is also in agreement with velocity provided in carbonates by Anselmetti et Eberli (1993).

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<figure><img src="image_4.png" /></figure>

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<caption>**Figure 2 :** Available data: Fundamental resonance frequency from H/V ambient vibration recordings at 700 points on the alluvial valleys of Nice and borehole stratigraphy locations.</caption>

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<figure><img src="image_5.png" /><figcaption>Figure 3: Bedrock depth in the geotechnical model of the alluvial valleys of Nice</figcaption></figure>

<table><caption>Table 2: Soil column description of the model</caption><thead><tr><th></th><th>Lithology</th><th>Vs* (m/s)</th><th>ρ<sup>†</sup> (Kg/m<sup>3</sup>)</th><th>PI<sup>‡</sup></th><th>σ'<sup>§</sup> (kN/m<sup>2</sup>)</th></tr></thead><tbody><tr><td>L1</td><td>Backfill</td><td>180 ± 60</td><td>1900</td><td>0</td><td>10</td></tr><tr><td>L2</td><td>Sand and gravel</td><td>290 ± 80</td><td>1900</td><td>0</td><td>30</td></tr><tr><td>L3</td><td>Silt and clay</td><td>200 ± 100</td><td>1700</td><td>20</td><td>50</td></tr><tr><td>L4</td><td>Sand and Gravel</td><td>330 ± 80</td><td>2100</td><td>0</td><td>90</td></tr><tr><td>L5</td><td>Fine sand</td><td>250 ± 60</td><td>1800</td><td>0</td><td>100</td></tr><tr><td>L6</td><td>Sand and gravel</td><td>300 ± 70</td><td>2100</td><td>0</td><td>120</td></tr><tr><td>L7</td><td>Silt and clay</td><td>220 ± 100</td><td>1800</td><td>10</td><td>140</td></tr><tr><td>L8</td><td>Sand and gravel</td><td>290 ± 90</td><td>2000</td><td>0</td><td>170</td></tr><tr><td>L9</td><td>Coarse gravel</td><td>300</td><td>2000</td><td>0</td><td>280</td></tr><tr><td>B</td><td>Bedrock</td><td>1000</td><td>2100</td><td></td><td></td></tr></tbody></table>

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<footnote>* Is the shear wave velocity</footnote>

<footnote>† Is the density</footnote>

<footnote>‡ PI: Plasticity Index</footnote>

<footnote>§ Is the effective confining pressure</footnote>
# Site effect assessment

## (1) Presentation of the three methods used

Three methods with increasing complexity were used to assess the surface response spectra that includes the site effects. The first method is based on the soil classification defined in the Eurocode 8. This classification, divided into 5 soil classes, is based on the Vs30 value (or the NSPT) with a geological description considering the depth of the substratum. The Table 3 synthesizes this classification.

<table><caption>Table 3: description of the type of soil in the Eurocode 8</caption><thead><tr><td>Type of soil</td><td>Description</td><td>Vs30</td><td>NSPT</td></tr></thead><tbody><tr><td>A</td><td>Rock or other rock-like geological formations, including at most 5 m of weaker material at the surface.</td><td>&gt;800 (m/s)</td><td>-</td></tr><tr><td>B</td><td>Deposits of very dense sand, gravel, or very stiff clay, at least several tens of metres in thickness, characterised by a gradual increase of mechanical properties with depth.</td><td>360-800 (m/s)</td><td>&gt;50</td></tr><tr><td>C</td><td>Deep deposits of dense or medium-dense sand, gravel or stiff clay with thickness from several tens to many hundreds of metres.</td><td>180-360 (m/s)</td><td>15-50</td></tr><tr><td>D</td><td>Deposits of loose-to-medium cohesion-less soil (with or without some soft cohesive layers), or of predominantly soft-to-firm cohesive soil.</td><td>&lt;180 (m/s)</td><td>&lt;15</td></tr><tr><td>E</td><td>A soil profile consisting of a surface alluvium layer with vs values of type C or D and thickness varying between about 5 m and 20 m, underlain by stiffer material with vs &gt; 800 m/s.</td><td></td><td></td></tr></tbody></table>

The site classification based on Vs30 values and with large Vs30 bins has been largely debated. The correlation between Vs30 and the amplification at high frequency is not constrained (Castellaro and Mulargia 2009; Idriss 2009). Indeed, Vs30 is a superficial and average parameter that cannot reflect the complexity of the Vs profile at depth (e.g. Cadet 2007; Castellaro, Mulargia, et Rossi 2008; Frankel, Carver, et Williams 2002; Park et Hashash 2005; Régnier et al. 2014). Finally, Vs30 is not easily obtained and will be assorted with strong uncertainties with a coefficient of variation of up to 3% for invasive method, up to 6% for surface waves methods and greater than 35% for methods based only on geological information (Moss 2008).

Alternative soil classifications have been proposed and an overview of these can be found in Luzi et al. (2011). Among them, Cadet et al. (2012) proposed a site amplification prediction equation (hereafter called SAPE) based on the value of the fundamental resonance frequency of the site ($f_0$) and on the mean shear wave velocity over the first Z meters of depth (Z can be
5, 10, 20 and 30 m). This amplification equation (eq.1) is defined to obtain the site-specific elastic response spectrum from the elastic response spectrum at a standard rock defined by Vs30 of 800 m/s.

$$A_x(f_i) = \left( \frac{X_{refi}}{X} \right)^{\alpha_i} \qquad \text{eq. 1}$$

Where $A_x$ represents the amplification, equation obtained using different set of input parameters ($f_0$ only, $f_0$ and Vs5, $f_0$ and Vs10, $f_0$ and Vs20, $f_0$ and Vs30). $\alpha_i$ and $X_{refi}$ are variables which depend on the frequency $f_i$ and on the set of parameters used. Besides, the bandwidth frequency is modulated by the value of the fundamental frequency of the site.

$$f_i = \frac{f_0 f(i)}{2}, \text{with } f \in [0.05 - 40] \text{Hz} \qquad \text{eq. 2}$$

The eq. 1 is then multiplied to the rock spectrum to obtain the surface spectrum that include the site effects. This method is the second method used to consider site effect in this article.

The first two methods do not consider the non-linear behavior of the soil which can have a large impact on site response even for moderate seismic motions (e.g. Régnier et al. 2013). Therefore, the last method is a 1-D equivalent linear approach. The equivalent linear approach consists in a linear wave propagation solution that is iterated with an adjustment of the shear modulus and the attenuation values according to the maximal shear strain reached in the middle of each soil layers at each iteration. Because of its ease of application, this method has been widely used for ground motion simulations. At high level of strain, this method can over attenuate the high frequency component of the seismic signal and overestimate the resonant frequency amplitudes (Yoshida and Iai 1998). But for moderate seismic loading (below 0,2g), which is the case in Nice, the results are shown to be satisfactory with respect to non-linear computations (Hartzell et al 2004; Régnier et al. 2016). The site response formulation considered in the code TREMORN29 (Hartzell et al. 2004) is based on the original code by Schnabel et al (1972). The soil parameters needed for the numerical method are the shear wave velocity, the thickness, the density, the damping ratio and the shear modulus decay and damping curves in each layer of the soil column. The shear modulus and damping curves provide the degree of non-linearity in each soil layer.

## (2) Application to Nice
The main geotechnical parameter for applying the three site effect assessment methods is the Vs profile. From the Vs profile, we calculate Vs30 and the fundamental resonance frequency (considering the soil 1-D and linear). These two parameters are sufficient to apply the first two site effects assessment methods.

For the equivalent linear approach, we extract also the density value and the damping ratio for the nine lithological soil layers. We define a shear modulus decay and damping curves based on the equations developed by Ishibashi and Zhang (1993) for the nine geological formations of the 3D geotechnical model. This formula is valid for both sand and clay materials and the effect of confining pressure and plasticity index on modulus reduction behavior are considered as indicated in the equations 7 and 8:

$$ \frac{G}{G_{max}}(\gamma) = K(\gamma, IP)(\sigma'_{m})^{m(\gamma,IP)-m_{0}} $$

eq. 3

$$ D(\gamma) = \frac{0.333(1 + e^{-0.0145PI^{1.3}})}{2} \left[ 0.586 \left( \frac{G}{G_{max}} \right)^2 - 1.547 \left( \frac{G}{G_{max}} \right) + 1 \right] $$

eq. 4

Where D is the damping ratio, G the shear modulus, γ the shear strain. The function K and m are described in (Ishibashi and Zhang 1993). A mean confining pressure and plasticity index were attributed to the 9 geological formations of the geotechnical model.

The nine shear modulus decay and damping curves are illustrated in the Figure 4 considering an average confining pressure as indicated in the Table 2.
<figure><img src="image_6.png" /><figcaption>**Figure 4:** Shear modulus decay and damping curves selected from the formula of Ishibashi et al. (1993). These curves were adapted to the 9 geological formations considered in the city of Nice.</figcaption></figure>

The input data needed to apply the three site effects assessment methods are extracted from the 3D geotechnical model at 9000 locations. For the equivalent linear approach, the 5 acceleration time histories consistent with the regional seismic hazard are used to compute the non-linear site response and the response spectrum at the surface. The arithmetic average of the surface response spectra is then calculated.

**(3) Comparison of the surface response spectra**

We compare the results of the three approaches by calculating the discrepancy between two methods at a time for each period of the response spectra. The discrepancy is calculated according to the equations eq. 5, eq. 6 and eq. 7. It is calculated in percentage using the root mean square deviation over the 9000 locations where the surface response spectra has been computed, which is then normalized by the average response spectrum coming from the numerical simulations when the comparison involves the numerical results or with the SAPE for the comparison between the EC8 and the SAPE spectra.
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$$ \varepsilon_{Num-SAPE}(T) = \frac{\sqrt{\frac{1}{Npt} \sum_{i=1}^{Npt} (RS_{num}(T) - RS_{SAPE}(T))^2}}{\sqrt{\frac{1}{Npt} \sum_{i=1}^{Npt} RS_{Num}(T)}} $$

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eq. 5

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$$ \varepsilon_{Num-EC8}(T) = \frac{\sqrt{\frac{1}{Npt} \sum_{i=1}^{Npt} (RS_{num}(T) - RS_{EC8}(T))^2}}{\sqrt{\frac{1}{Npt} \sum_{i=1}^{Npt} RS_{num}(T)}} $$

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eq. 6

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$$ \varepsilon_{SAPE-EC8}(T) = \frac{\sqrt{\frac{1}{Npt} \sum_{i=1}^{Npt} (RS_{SAPE}(T) - RS_{EC8}(T))^2}}{\sqrt{\frac{1}{Npt} \sum_{i=1}^{Npt} RS_{SAPE}(T)}} $$

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eq. 7

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With, Npt the 9000 locations where the surface response spectra have been calculated.

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The results are illustrated in Figure 5. Except at the period of 0.2 s, the discrepancy is lower between the SAPE method and the numerical simulations compared to the EC8 method with the numerical simulations. The peak of discrepancy at 0.2 s corresponds to the edge of the rock site EC8 response spectrum plateau. For most of the sites, it corresponds to the peak amplitude of the surface response spectrum calculated with the SAPE method. Indeed, above this period the rock site response spectrum EC8 decreases drastically and the amplification function from the SAPE approach, with a maximum at the resonant period of the site, cannot compensate this decrease.

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<figure><img src="image_7.png" /><figcaption>**Figure 5:** Comparison of the average discrepancy (root mean square deviation) over the 9000 locations, between the surface response spectra computed using the EC8 method (EC8), the SAPE method and the average surface response spectrum from the equivalent linear analysis (num)</figcaption></figure>

We choose nine locations across the Paillon valley of Nice (passing by one accelerometric station called NROC) to perform a comparison of the surface sediment to rock response spectra ratio between the SAPE method and the numerical simulations illustrated in the Figure 6. This profile indicates a 400 m width valley having a maximal depth of sediments of 70 m at the center. Both methods provide a maximal amplification at the same frequency which is the fundamental resonance frequency of the site. However, the amplifications provided by the SAPE are higher than the ones predicted by the 1-D numerical simulations along the profile with a discrepancy greater at the center of the valley where the site resonance frequency is low. At the edges of the valley the results are closer. The comparisons made is valid for a moderate seismicity zone and the discrepancy between numerical simulation and SAPE approach could be larger for a higher seismicity zone. If we consider similar soil (with similar non-linear properties) the amplification function obtained with the equivalent linear method will be lower at high frequency for larger input motions because of the non-linear behaviour of the soil.
<figure><img src="image_8.png" /><figcaption>**Figure 6:** Comparison of the ratio between the surface response spectra and the rock reference response spectrum coming from the application of the SAPE approach and the numerical method in 9 locations along the Paillon valley. In the subplots, the red curve is the response spectrum from the SAPE method, and the blue curve is the average response spectrum coming from the application of the equivalent linear method using 5 different time histories. The plot below represents the geometry of the Paillon Valley with the depth of the nine geological formations defined in the 3D geotechnical model. The yellow dotted curve refers to the sediment to bedrock interface.</figcaption></figure>

#### (4) Comparison with empirical data

We compare the sediment to rock response spectra ratio coming from the application of the last two methods with SSR (Standard Spectral Ratio) obtained with earthquake recordings at five sites of the permanent accelerometric network of France (RAP/RESIF see data availability section) located in the central and Paillon valleys of Nice. The locations of the stations are indicated in the Figure 7.

Among the RAP stations, two are located at rock (NBOR which was replaced by NCER in December 2008) and are used as reference stations to perform SSR. We select earthquakes with an epicentral distances to the center of the city lower than 250 km. The characteristics of the selected earthquakes and the PGA (Peak Ground Acceleration in m/s²) are also reported in the Table 4. The recordings are corrected from the response of the instrument. A base line correction (removing of the mean of the signal) and a tapering of the signal are performed.
Then, the response spectra are calculated. For each sedimentary stations NSJA, NPOR, NALS, NROC and NLIB, the average site to reference response spectra ratio over the recordings is computed using 4, 3, 8, 10 and 10 earthquake recordings for each station respectively.

<table><caption>Table 4: Information on the earthquakes used for computing the SSR.</caption><thead><tr><th rowspan="2">Date</th><th rowspan="2">Mag</th><th rowspan="2">Depth</th><th colspan="10">PGA(m/s²)</th></tr><tr><th>DEpi<br>(Nice)</th><th>lat</th><th>long</th><th>NSJA</th><th>NPOR</th><th>NALS</th><th>NROC</th><th>NLIB</th><th>NBOR</th><th>NCER</th></tr></thead><tbody><tr><td>01/22/2000</td><td>2,6</td><td>2</td><td>71</td><td>44,34</td><td>7,35</td><td>-</td><td>-</td><td>-</td><td>0,0010</td><td>-</td><td>0,0001</td><td>-</td></tr><tr><td>08/21/2000</td><td>5,0</td><td>10</td><td>158</td><td>44,86</td><td>8,44</td><td>-</td><td>-</td><td>-</td><td>0,0240</td><td>0,0073</td><td>0,0060</td><td>-</td></tr><tr><td>12/20/2000</td><td>3,0</td><td>5</td><td>11</td><td>43,78</td><td>7,37</td><td>-</td><td>-</td><td>0,0088</td><td>0,0194</td><td>0,0039</td><td>0,0044</td><td>-</td></tr><tr><td>02/06/2001</td><td>4,1</td><td>8</td><td>119</td><td>44,12</td><td>8,66</td><td>-</td><td>-</td><td>-</td><td>0,0125</td><td>-</td><td>0,0014</td><td>-</td></tr><tr><td>02/25/2001</td><td>4,5</td><td>5</td><td>28</td><td>43,49</td><td>7,47</td><td>-</td><td>-</td><td>0,1815</td><td>0,0847</td><td>0,0912</td><td>0,0396</td><td>-</td></tr><tr><td>03/14/2001</td><td>3,8</td><td>7</td><td>57</td><td>43,50</td><td>7,94</td><td>-</td><td>-</td><td>-</td><td>0,0179</td><td>-</td><td>0,0022</td><td>-</td></tr><tr><td>03/27/2001</td><td>2,1</td><td>5</td><td>19</td><td>43,53</td><td>7,32</td><td>-</td><td>-</td><td>0,0014</td><td>-</td><td>-</td><td>0,0001</td><td>-</td></tr><tr><td>07/01/2001</td><td>3,4</td><td>5</td><td>100</td><td>44,58</td><td>7,03</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0,0016</td><td>0,0003</td><td>-</td></tr><tr><td>01/26/2002</td><td>3,0</td><td>5</td><td>73</td><td>44,36</td><td>7,27</td><td>-</td><td>-</td><td>-</td><td>0,0034</td><td>-</td><td>0,0006</td><td>-</td></tr><tr><td>04/11/2003</td><td>4,9</td><td>5</td><td>179</td><td>44,87</td><td>8,83</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0,0064</td><td>0,0038</td><td>-</td></tr><tr><td>12/20/2003</td><td>3,3</td><td>5</td><td>88</td><td>44,49</td><td>7,21</td><td>-</td><td>-</td><td>-</td><td>0,0026</td><td>-</td><td>0,0004</td><td>-</td></tr><tr><td>09/02/2006</td><td>4,2</td><td>5</td><td>32</td><td>43,92</td><td>7,55</td><td>-</td><td>-</td><td>0,0294</td><td>0,0351</td><td>0,0119</td><td>0,0052</td><td>-</td></tr><tr><td>10/24/2006</td><td>3,7</td><td>10</td><td>19</td><td>43,55</td><td>7,38</td><td>-</td><td>-</td><td>0,0211</td><td>0,0347</td><td>0,0073</td><td>0,0026</td><td>-</td></tr><tr><td>04/07/2014</td><td>4,9</td><td>5</td><td>60</td><td>44,23</td><td>7,43</td><td>0,0284</td><td>-</td><td>0,1322</td><td>-</td><td>-</td><td>-</td><td>0,0074</td></tr><tr><td>11/06/2015</td><td>4,2</td><td>5</td><td>97</td><td>44,47</td><td>6,69</td><td>0,0034</td><td>0,0029</td><td>-</td><td>-</td><td>0,0073</td><td>-</td><td>0,0012</td></tr><tr><td>07/30/2016</td><td>4,1</td><td>11</td><td>137</td><td>44,94</td><td>7,21</td><td>0,0022</td><td>0,0032</td><td>0,0094</td><td>-</td><td>0,0056</td><td>-</td><td>0,0006</td></tr><tr><td>11/10/2016</td><td>4,2</td><td>2</td><td>105</td><td>44,29</td><td>6,24</td><td>0,0023</td><td>0,0038</td><td>0,0144</td><td>-</td><td>0,0078</td><td>-</td><td>0,0011</td></tr></tbody></table>

Figure 8 illustrates the comparison of SSR coming from earthquake recordings (black curves) with the evaluation performed in this study using the SAPE approach and the 1-D numerical simulation. The first main observation is similar to the one made on Figure 6; the SAPE approach provides higher amplifications than the ones predicted by the 1-D numerical simulations at each site but with a same frequency peak. The second main observation is that the empirical amplifications are even higher except for the site NLIB. The empirical amplifications can be up to three times at the stations NSJA and NALS at the site resonance frequency. The discrepancy between the 1-D numerical evaluation, the SAPE approach and the empirical evaluation is likely to be due to 2D or 3D geometrical effects. The geometry of the valley can have a large impact on the site amplification with the diffraction of the seismic waves at the edge of the valley and the creation of surface waves that cannot be captured by 1-D model. From a simplified geometry (Bard and Bouchon 1985) indicates a bi-dimensional
353

resonance for deep and shape valley with a systematic increase of the amplification compared
to a 1-D evaluation.

354

355

The SAPE curves, although based on 1-D site parameters, were built using empirical
correlations between site parameters and recorded ground motions over a wild range of sites
in the Japanese accelerometric network KiK-net. Many of the KiK-net sites in Japan are not
necessarily 1-D sites (Régnier et al. 2014; Thompson et al. 2012). Therefore, the site
amplification function involves as well 2D or 3D effects which can explain the higher
amplification observed compared to 1-D numerical simulation.

356

357

358

359

360

361

We can mention also that the SAPE curves are based on a rock defined for Vs30 of 800 m/s.
The rock under the stations NBOR and NCER was characterized using ambient variation
arrays and an averaged Vs30 of 1090 m/s (± 216m/s) was found. The reference stations have
an average Vs30 slightly larger than 800 m/s. According to (Cadet et al 2012), the
amplification function observed could be slightly lower (by a factor of around 0.8 above 1Hz
for 1100m/s) if we wanted to correct from the difference of the rock reference velocity. This
correction would reduce the discrepancy between the observations and the predictions but the
SAPE amplification would be still much lower than the observations.

362

363

364

365

366

367

368

369

(Gélis et al. 2008) compare the 1-D and 2-D simulations on one profile of the city of Nice in
the central valley. The study indicates that 2-D site effect assessment method provides a
larger amplification in the valley of the city.

370

371

372

Looking at the East-West 2D profiles under each of the five RAP stations, illustrated in the
Figure 7, we can see that under the station NLIB, the sedimentary basin is quite flat which can
explain the proximity between the 1-D evaluation and the empirical evaluation. The deepest
valleys are under the stations NROC, NSJA and NALS with 75, 65 and 70 m of sediments.

373

374

375

376

The stations NSJA and NALS, where the empirical evaluation indicates the maximal
amplification, are both located under the steep part of the valley, whereas NROC is below a
part of the valley that is flat. The location of the RAP sites and the geometry of the valley can
explain the large amplifications observed at NALS and NSJA that cannot be captured by 1-D
evaluations.

377

378

379

380
<figure><img src="image_9.png" /><figcaption>Figure 7: 2D Sedimentary valley profiles under the stations RAP</figcaption></figure>

381

382

383
<figure><img src="image_10.png" /><figcaption>**Figure 8:** Comparison of the empirical response spectra site to reference ratio (SSR) with the one evaluated with the SAPE and the numerical approaches.</figcaption></figure>

## Microzonation

### (1) Definition of families of response spectra

The three previous methods are based on 1-D soil parameters. Once applied to 3D geotechnical model, we obtain the surface response spectra distributed on a regular grid of a geographic area. While the zonation is directly obtained for EC8 response spectrum, the response spectra coming from the application of the second or third methods are site-specific and are not distributed in site classes.

For the last two approaches, we propose a repeatable procedure to go from discrete results to a mircrozonation. The first step is to define families of response spectra and the second step is to define an EC8-type response spectrum adapted to the specific distribution of response spectra in a considered family.

The families of response spectra are defined using a K-means algorithm (MacQueen, 1967).

This algorithm is a clustering method based on the sum-of-square criterium. The variation of the parameters inside a group is minimized. This algorithm is sensitive to the initial partition
and therefore it is repeated at least four times. The adequate number of groups correspond to the one providing the higher proximity intra-group with respect to the other groups. Each response spectra are represented by four parameters: the maximal amplitude ($A_{max}$), the associated period ($T_{max}$), the amplitude at 0.6s ($T_{06}$) and the slenderness of the response spectrum (Sl) defined as the ratio between the maximal amplitude and the period range [$T_{max}^{-} - T_{max}^{+}$] in which the amplitude spectrum is larger than $A_{max}$, as defined in equation 3.

$$Sl = \frac{A_{max}}{T_{max}^{-} - T_{max}^{+}} \qquad \text{eq. 8}$$

The four selected parameters are a mix of periods, amplitudes and slenderness. Therefore, they were normalized to avoid any effects of weight due to a difference in the order of magnitude between parameters.

## (2) Definition of an EC8-type response spectrum ($RS_{EC8}$) adapted to a distribution of response spectra

Once the groups of response spectra are defined, we can calculate for each group the average (geometric mean, called $RS_m$) and standard deviation. Considering a lognormal distribution, the $1\sigma$ lower band and $1\sigma$ upper band are computed. An automatic process to derive a $RS_{EC8}$ from a lognormal distribution of response spectra is proposed. Developing an automatic process guarantee objective and repeatable results. This automatic definition process (ADP) is based on the definition of an EC8-type response spectrum composed of four segments and defined by five parameters; three characteristics periods: TB, TC, TD, a site coefficient S and a coefficient to define the amplitude of the plateau, R. The equations defining a $RS_{EC8}$ are recall in the equation 4.

$$RS_{EC8} = \begin{cases} a_g S \left[ 1 + \frac{T(\eta R - 1)}{T_B} \right] & 0 < T < T_B \\ a_g S \eta R & T_B < T < T_C \\ a_g S \eta R \left[ \frac{T_C}{T} \right] & T_C < T < T_D \\ a_g S \eta R \left[ \frac{T_C T_D}{T^2} \right] & T_D < T < 4s \end{cases} \qquad \text{eq. 9}$$

In the Eurocode 8, R is fixed to be equal to 2.5, $\eta$ is the damping correction factor; this parameter is fixed with a reference value of 1 for 5% of viscous damping, $a_g$ is the rock ground acceleration. To define a $RS_{EC8}$, 4 parameters have to be determined (S and the three characteristics periods). The method ADP is based on three postulates:
(1) The RS<sub>EC8</sub> at period 0s is equal to a<sub>g</sub>S and the Plateau is a percentage (P) of the maximal amplitude of the response spectrum A<sub>max</sub> which lead to the equation 5:

$$S = P \frac{A_{max}}{2.5a_{g}}$$

eq. 10

(2) The EC8-type response spectra have the value of the mean response spectrum at the first period step which is in our case 0.02s: RS<sub>EC8</sub>(0.02) = RS<sub>m</sub>(0.02). Using equation 4, the value of T<sub>B</sub> is given by the equation 6:

$$T_B = 0.03 \frac{RS_m(0.02) - a_g S}{a_g S}$$

eq. 11

(3) The EC8-type response spectrum envelops the distribution (RS<sub>m</sub> + 1 σ: upper-bound) above the plateau. To determine T<sub>C</sub> and T<sub>D</sub>, we set up an iterative process to find the value of the characteristics periods which make the regulation spectrum as close as possible from the upper-bound of the distribution. At the first iteration T<sub>C</sub> is equal to the period where the upper bound of the response spectra RS<sup>+</sup> is equal to a percentage P of A<sub>max</sub> (above T<sub>max</sub>) and T<sub>D</sub> is equal to T<sub>C</sub>; The value of T<sub>D</sub> is increased (with a 0.01s step) until the RS<sub>EC8</sub> envelops the upper bound RS. If T<sub>D</sub> reaches 4s and no values of T<sub>D</sub> suits, then T<sub>C</sub> is increased (with a 0.01s step), T<sub>D</sub> is initialized to T<sub>C</sub> and the previous test is performed again.

A similar process was followed by (Pousse et al. 2005) to associate an “EC8-type” spectra to a distribution.
<figure><img src="image_11.png" /><figcaption>**Figure 9:** Explanation of the Automatic Definition Process (ADP) to find the values of the EC8-type response spectrum adjusted to a distribution of response spectra. The black lines indicate the distribution of the response spectra and the colour lines are the EC8-type response spectra evolution within the iterations to find the best set of parameters. The thick red line is the final EC8-type response spectrum.</figcaption></figure>

### (3) Microzonation of Nice

The final microzonation of Nice is performed with the surface response spectra coming from the SAPE approach. This method was chosen as it provides the closest results to the empirical observations of SSR.

First, the whole set of surface response spectra is divided into two groups: the first group contains sites with a $V_{S30}$ above 1000 m/s and a $f_0$ above 20 Hz. It gathers the sites on the edge of the valleys with very little sediments that can be blended with the rock outcrop, the second group represents the sedimentary soils. Among the surface response spectra on sediments, we applied the k-means clustering approach. The response spectra are characterized by 4 parameters: the maximal amplitude ($A_{max}$), the associated period ($T_{max}$), the amplitude at 0.6s ($T_{06}$) and the slenderness of the response spectrum (Sl). We tested different number of groups from 3 to 5, and end-up with 3 groups which maximize the intra-group correlation. The Figure 10 illustrates the distribution of the response spectra (average and +/- 1 $\sigma$ considering a lognormal distribution) for the four groups of sites. We can observe that all the distributions are statistically different at least at the 68 percentiles. The group 2 and 3
provide similar maximal amplitude at 2s but the group 3 indicates a much higher high period content. As shown in Figure 11, the group 3 of sites (magenta curves) concerns the deeper part of the valleys characterized by lower resonance frequencies and are associated to response spectra with a large amplitude at high period. As expected, the group 1 represents the sites close to the edge of the valleys (green curves) and the group 2 of sites (blue curves) are intermediate sites.

<figure><img src="image_12.png" /><figcaption>**Figure 10** : Distribution of the surface response spectra per group of shape according to the K-means clustering process.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Figure 11** : Microzonation of the city of Nice with the response spectra computed with the SAPE approach.</figcaption></figure>

For each distribution, a regulation-type response spectrum is defined and is illustrated in the **Figure 12**. The characteristics of the response spectra are available in the Table 5 along with the characteristics of the EC8 response spectra for comparison. As expected, the specific EC8-type response spectra are above the upper-bound of the distribution at periods above TC and the value of the plateau represents a percentage of the peak amplitude. In the same figure, we compared the specific EC8-type response spectra to the ones from the EC8 seismic code. The specific response spectra are very different from the EC8 ones. The main differences are the value of TC and TD that are lower in the specific response spectra and the value of site coefficient S that is much higher which indicates that the periods of the plateau of the response spectra are narrower in the specific response spectra but higher in amplitude. In addition, the amplitude of the specific response spectra at higher periods can be higher than the EC8 spectra except for the EC8-D response spectrum.
<figure><img src="image_14.png" /><figcaption>**Figure 12 :** Regulation-type response spectra of each group of sites (subplots from A to D). The comparison with EC8 response spectra is illustrated in the subplot (e).</figcaption></figure>

<table><caption>**Table 5 :** Comparison of the Regulation-type specific and EC8 response spectra parameters</caption><thead><tr><th></th><th>TB</th><th>TC</th><th>TD</th><th>S</th><th>R</th></tr></thead><tbody><tr><td>EC8-A</td><td><strong>0.03</strong></td><td><strong>0.2</strong></td><td><strong>2.5</strong></td><td><strong>1</strong></td><td><strong>2.5</strong></td></tr><tr><td>EC8-B</td><td>0.05</td><td>0.25</td><td>2.5</td><td>1.35</td><td>2.5</td></tr><tr><td>EC8-C</td><td>0.06</td><td>0.4</td><td>2</td><td>1.5</td><td>2.5</td></tr><tr><td>EC8-D</td><td>0.1</td><td>0.6</td><td>1.5</td><td>1.5</td><td>2.5</td></tr><tr><td>EC8-E</td><td>0.08</td><td>0.45</td><td>1.25</td><td>1.25</td><td>2.5</td></tr><tr><td><strong>Group 1</strong></td><td><strong>0.05</strong></td><td><strong>0.23</strong></td><td><strong>1.78</strong></td><td><strong>1.72</strong></td><td><strong>2.5</strong></td></tr><tr><td><strong>Group 2</strong></td><td><strong>0.08</strong></td><td><strong>0.33</strong></td><td><strong>1.23</strong></td><td><strong>2.26</strong></td><td><strong>2.5</strong></td></tr><tr><td><strong>Group 3</strong></td><td><strong>0.08</strong></td><td><strong>0.39</strong></td><td><strong>1.44</strong></td><td><strong>2.06</strong></td><td><strong>2.5</strong></td></tr></tbody></table>
507 **Conclusion**

508

509 Three methods to compute surface response spectra that include site effects were tested. The
510 first method is based on the definition of a soil class defined from Vs30 and is the one from the
511 Eurocode 8 (EC8) regulation. The second method is the SAPE approach that defined Spectra
512 Amplification Prediction Equation to be applied to a reference rock spectrum that was
513 developed from a statistical analysis of earthquake recordings and site proxy parameters of
514 the Japanese KiK-Net network (Fujiwara et al. 2004). This method uses simple site
515 parameters: the Vs30 and the fundamental resonance frequency (f0). The last method involves
516 a 1-D equivalent linear numerical simulation with the requirement of the definition of the
517 whole soil column including, Vs values and non-linear parameters. We applied these methods
518 to 9000 soil columns extracted from the 3D geotechnical model of Nice.

519 Among the two simplified approaches, the SAPE methods provides closer results to the
520 numerical simulations. The comparison of the amplification functions (in terms of response
521 spectra) coming from the SAPE and the numerical simulation indicates similar amplified
522 frequencies but a larger amplification in the SAPE method at the center of the valleys and not
523 at the edges. This method, based on empirical correlations, intrinsically take into account
524 complex site geometry that cannot be captured in 1-D simulations, which may explain this
525 difference. The comparison of the amplification functions with earthquake data illustrate the
526 limitations of 1-D approach which cannot capture the site-specific 2-D and 3-D geometry.
527 The evaluations underestimate the amplification at some location in the valley of Nice where
528 1-D soil profile hypothesis is no longer valid.

529 The final microzonation was performed with the response spectra computed with the SAPE
530 approach. We adopted a repeatable approach that is composed of two steps. In the first step,
531 the response spectra are grouped into different families according to four parameters that
532 defined the response spectrum shape: Amax, the maximum spectral amplitude, Tmax the
533 corresponding period, the amplitude of the response spectrum at 0.6 s (T06) and the
534 slenderness of the response spectrum. In the second step, an EC8-type response spectrum is
535 defined and adjusted to the distribution of the response spectra for each family.

536 We divided the response spectra in four groups, one composed of rock sites and the three
537 others composed of sediment sites. For the sediment sites, the specific EC8-type response
538 spectra are different from the EC8 regulation ones, with a high amplitude at the plateau and at
539 higher periods. The characteristics of the specific response spectra indicate a higher soil
540 coefficient and lower values of TC and TD. The discrepancy indicates that the use the specific
541 regulation-type response spectra is conservative compared to the EC8 ones except for the soil
type D above 0.5s.

543 The approach SAPE provides a more accurate estimation of the surface response spectra with
544 a simplified application compared to the EC8 regulation estimation and to the 1-D numerical
545 simulations. This approach consider only 1-D parameters and cannot captured strong
546 geometrical effects such as observed at some of the French Permanent Accelerometric
547 network (RAP/RESIF) stations in Nice.

548

549 **Data Availability**

550 Datasets from earthquake recordings used in this article can be found
551 at http://seismology.resif.fr/#ByEventDataAccessPlace:, an online seismic data database. Last
552 access, may 2019.

553 The VS30 at the RAP/RESIF stations can be found in https://rap.resif.fr/spip.php?article46

554 Dataset of log descriptions was used to define the geotechnical model of Nice from the
555 website http://infoterre.brgm.fr/page/banque-sol-bss. Last access may 2011.

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