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\DOI{10.5802/crgeos.243}
\datereceived{2023-08-10}
\daterevised{2024-03-29}
\dateaccepted{2024-05-27}
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\def\TITREspecial{\relax}
\def\cdr@specialtitle@english{New Developments in Passive Seismic Imaging and Monitoring}
\def\cdr@specialtitle@french{Nouveaux d\'eveloppements en mati\`ere d'imagerie sismique passive et de surveillance}
\makeatother
\title{Potential and limitations of noise-based surface-wave tomography
for numerical site effect estimation: a case study in the French
Rh\^{o}ne valley}
\alttitle{Potentiel et limites de l'imagerie sismique passive en ondes
de surface pour l'estimation num\'erique des effets de site : un cas
d'\'etude dans la vall\'ee du Rh\^one (France)}
\author{\firstname{Fran\c{c}ois} \lastname{Lavou\'{e}}\CDRorcid{0000-0002-1723-4730}\IsCorresp}
\address{Institut de Radioprotection et S\^{u}ret\'{e} Nucl\'{e}aire (IRSN),
PSE-ENV, SCAN, BERSSIN/BEHRIG, Fontenay-aux-Roses, France}
\address{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, UGE, ISTerre, Grenoble, France}
\email[F. Lavou\'{e}]{francois.lavoue@univ-grenoble-alpes.fr}
\author{\firstname{B\'{e}r\'{e}nice} \lastname{Froment}\CDRorcid{0000-0001-5570-9365}}
\addressSameAs{1}{Institut de Radioprotection et S\^{u}ret\'{e} Nucl\'{e}aire (IRSN),
PSE-ENV, SCAN, BERSSIN/BEHRIG, Fontenay-aux-Roses, France}
\author{\firstname{C\'{e}line} \lastname{G\'{e}lis}\CDRorcid{0000-0002-0915-3535}}
\addressSameAs{1}{Institut de Radioprotection et
S\^{u}ret\'{e} Nucl\'{e}aire (IRSN), PSE-ENV, SCAN, BERSSIN/BEHRIG, Fontenay-aux-Roses, France}
\author{\firstname{Pierre} \lastname{Bou\'{e}}\CDRorcid{0000-0001-9153-4048}}
\addressSameAs{2}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc,
CNRS, IRD, UGE, ISTerre, Grenoble, France}
\author{\firstname{Emmanuel} \lastname{~Chaljub}\CDRorcid{0000-0001-5498-885X}}
\addressSameAs{2}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc,
CNRS, IRD, UGE, ISTerre, Grenoble, France}
\author{\firstname{Laurent} \lastname{Stehly}\CDRorcid{0000-0002-1854-7157}}
\addressSameAs{2}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc,
CNRS, IRD, UGE, ISTerre, Grenoble, France}
\author{\firstname{Sophie} \lastname{Beaupr\^{e}tre}}
\address{Sisprobe/EGIS, 3 rue du Dr Schweitzer, 38180 Seyssins, France}
\author{\firstname{Florent} \lastname{De Martin}\CDRorcid{0000-0003-3746-9067}}
\address{BRGM (French Geological Survey), Risks and Prevention Division, Orl\'{e}ans, France}
\author{\firstname{Lo\"{i}c} \lastname{~Gisselbrecht}\CDRorcid{0000-0002-1943-1076}}
\addressSameAs{1}{Institut de Radioprotection et S\^{u}ret\'{e} Nucl\'{e}aire (IRSN),
PSE-ENV, SCAN, BERSSIN/BEHRIG, Fontenay-aux-Roses, France}
\addressSameAs{2}{Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, UGE, ISTerre, Grenoble, France}
\author{\firstname{Marco} \lastname{Pilz}\CDRorcid{0000-0002-8575-579X}}
\address{GFZ, German Research Center for Geosciences, Potsdam, Germany}
\author{\firstname{Denis} \lastname{Moiriat}\CDRorcid{0000-0003-1264-122X}}
\addressSameAs{1}{Institut de Radioprotection et
S\^{u}ret\'{e} Nucl\'{e}aire (IRSN), PSE-ENV, SCAN, BERSSIN/BEHRIG, Fontenay-aux-Roses, France}
\author{\firstname{Edward Marc} \lastname{Cushing}\CDRorcid{0000-0002-1601-8323}}
\addressSameAs{1}{Institut de Radioprotection et
S\^{u}ret\'{e} Nucl\'{e}aire (IRSN), PSE-ENV, SCAN, BERSSIN/BEHRIG, Fontenay-aux-Roses, France}
\keywords{\kwd{Seismology}
\kwd{Seismic hazard}
\kwd{Site effects}
\kwd{Passive seismic imaging}
\kwd{Numerical simulations}}
\altkeywords{\kwd{Sismologie}
\kwd{Al\'ea sismique}
\kwd{Effets de site}
\kwd{Imagerie sismique passive}
\kwd{Simulations num\'eriques}}
\thanks{French National Research Agency (Agence Nationale pour la
Recherche, ANR, grant number ANR19-CE31-0029), German Research
Foundation (Deutsche Forschungsgemeinschaft, DFG, project number
431362334), HPC resources of TGCC under the allocation 2022-gen13461
made by GENCI, GRICAD infrastructure}
\begin{abstract}
In certain geological settings such as sedimentary basins, the ground
motion induced by an earthquake may be amplified by local site
conditions. Estimating these site effects is important for seismic
hazard assessment but can be difficult to do empirically due to the
scarcity of site-specific field data in time and space, especially in
low-to-moderate seismicity regions where the earthquakes needed for
measuring the site effects have long return periods. In this study, we
try to overcome these limitations and investigate an alternative
approach based on ambient seismic noise and numerical simulations. More
specifically, we use a 3D numerical model of seismic properties derived
from Ambient Noise Surface-Wave Tomography (ANSWT) for 3D numerical
simulations of seismic wave propagation, and consequently for a
numerical estimation of seismic amplification in the basin. We
illustrate the approach on a target site located in the French
Rh\^{o}ne valley, where the Messinian salinity crisis has dug a
paleo-canyon which is now filled by soft sediments in direct contact
with a harder substratum, thereby providing typical conditions for
significant site effects, as also observed by previous studies in the
area. This work makes use of two dedicated datasets. On one hand, we
use earthquake recordings acquired by a network of broadband stations
deployed over the target site over 8 months, in order to estimate
seismic amplification in the basin with respect to a rock-site
reference via Standard Spectral Ratios (SSR), which we consider as our
reference for evaluating our numerical results. On the other hand, we
exploit one-month-long ambient noise recordings acquired by a dense
array of 400 3C sensors. Prior to this work, this noise data was used
to build a 3D shear-wave velocity (\textit{V\textsubscript{S}}) model of the target site via
ANSWT, and also to estimate seismic amplification via noise-based
Standard Spectral Ratios (SSRn). The obtained ANSWT model well
reproduces the main geological structures of the basin, with lateral
variations of velocities at depth depicting the deeper parts of the
basin. However, our simulation results also show that some of its
limitations related to surface wave sensitivity and resolution
capability have an impact on the numerical amplification predicted in
the basin. In particular, this ANSWT model lacks clear basin edges
in order to efficiently trap seismic waves in the basin and to generate
significant 3D wave propagation effects (diffractions, reflections, and
generation of laterally propagating surface waves at the edges of the
basin). As a result, the numerical amplification predicted in
the ANSWT model remains dominated by a 1D response and does not reproduce
the broadband character of the observed amplification at locations affected
by significant 3D propagation effects.
On the other hand, the numerical amplification
predicted in the ANSWT model shows a good
agreement with the observations at locations that seem less affected by
3D propagation effects, including in complex regions of the model where
lateral variations must be taken into account. Our results therefore
contribute to identify and better understand the potential and
limitations of using ANSWT models for numerical site effect estimation.
This study allows us to propose perspectives for future work to improve
the approach, which remains promising for site effect assessment in
low- to moderate-seismicity contexts.
\end{abstract}
\begin{altabstract}
Dans les bassins s\'{e}dimentaires, le mouvement du sol suite \`{a} un
s\'{e}isme peut \^{e}tre amplifi\'{e} selon les conditions locales de
site. L'estimation de ces effets de site est importante pour
l'\'{e}valuation correcte et pr\'{e}cise de l'al\'{e}a sismique, mais
est souvent rendue difficile \`{a} faire exp\'{e}rimentalement du fait
du peu de donn\'{e}es disponibles sur un site sp\'{e}cifique, surtout
dans des r\'{e}gions de sismicit\'{e} faible \`{a} mod\'{e}r\'{e}e
o\`{u} les s\'{e}ismes n\'{e}cessaires \`{a} la mesure de ces effets de
site ont des longues p\'{e}riodes de retour. Dans cette \'{e}tude, nous
explorons une approche alternative aux m\'{e}thodes classiques
d'estimation des effets de site, en proposant de baser cette estimation
sur des donn\'{e}es de bruit sismique ambiant et sur des simulations
num\'{e}riques. Plus pr\'{e}cis\'{e}ment, nous proposons d'utiliser
des m\'{e}thodes d'imagerie passive, en particulier la tomographie
d'ondes de surface extraites du bruit sismique (\textit{Ambient Noise
Surface-Wave Tomography}, ANSWT) pour construire des mod\`{e}les
num\'{e}riques 3D des propri\'{e}t\'{e}s sismiques d'une zone
d'\'{e}tude, mod\`{e}les que nous utilisons ensuite au sein de
simulations num\'{e}riques de propagation d'ondes pour calculer une
estimation num\'{e}rique d'amplification sismique dans un bassin.
Nous illustrons cette approche avec un cas d'\'{e}tude situ\'{e} dans
le bassin de Tricastin, en vall\'{e}e du Rh\^{o}ne (France), o\`{u} la
crise de salinit\'{e} messinienne a creus\'{e} un pal\'{e}o-canyon
maintenant rempli de s\'{e}diments meubles en contact avec un
substratum indur\'{e}, cr\'{e}ant ainsi des conditions propices \`{a}
l'\'{e}mergence d'effets de site significatifs document\'{e}s par
des \'{e}tudes pr\'{e}c\'{e}dentes. Ce travail utilise deux jeux de
donn\'{e}es compl\'{e}mentaires. Nous utilisons d'une part des
donn\'{e}es de sismicit\'{e} enregistr\'{e}es par un r\'{e}seau de
stations large bande d\'{e}ploy\'{e} pendant 8 mois sur la zone
d'\'{e}tude. Ces donn\'{e}es de sismicit\'{e} nous permettent de
mesurer l'amplification sismique dans le bassin par rapport \`{a} une
r\'{e}f\'{e}rence au rocher via le calcul de rapports spectraux
standards (\textit{Standard Spectral Ratios}, SSR) qui nous servent de
points d'observation de r\'{e}f\'{e}rence pour \'{e}valuer nos
r\'{e}sultats num\'{e}riques. D'autre part, nous exploitons des
enregistrements continus de bruit sismique acquis par un r\'{e}seau
dense de 400 capteurs autonomes 3-composantes. En amont de ce travail,
ces donn\'{e}es de bruit ont \'{e}t\'{e} utilis\'{e}es pour construire
un mod\`{e}le 3D de vitesse des ondes de cisaillement (\textit{V\textsubscript{S}}) par
ANSWT, ainsi que pour estimer l'amplification sismique dans le bassin
via des SSR bas\'{e}s sur le bruit sismique (\textit{noise-based} SSR,
SSRn). Le mod\`{e}le ANSWT obtenu reproduit bien les structures
g\'{e}ologiques attendues \`{a} grande \'{e}chelle, avec des variations
lat\'{e}rales de vitesses en profondeur correspondant aux parties
profondes du bassin. Cependant, nos r\'{e}sultats de simulation
montrent aussi que certaines limites de ce mod\`{e}le li\'{e}es \`{a}
la sensibilit\'{e} et \`{a} la capacit\'{e} de r\'{e}solution des ondes
de surface utilis\'{e}es pour l'imagerie ont un effet sur
l'amplification num\'{e}rique pr\'{e}dite dans le bassin. En
particulier, ce mod\`{e}le ANSWT ne comporte pas de limites franches
entre le bassin et les sites au rocher affleurant en surface. Par
cons\'{e}quent, dans les simulations num\'{e}riques effectu\'{e}es dans
ce mod\`{e}le, les ondes sismiques ne sont pas pi\'{e}g\'{e}es de
mani\`{e}re efficace dans le bassin, dont les bords --- ou plus
pr\'{e}cis\'{e}ment, le manque de bords --- ne g\'{e}n\`{e}rent pas de
diffractions significatives d'ondes de surface susceptibles de se
propager lat\'{e}ralement dans le bassin. L'amplification sismique
pr\'{e}dite dans le bassin reste donc domin\'{e}e par une r\'{e}ponse
1D, et ne reproduit pas le caract\`{e}re large bande de l'amplification
observ\'{e}e en certains points du bassin, visiblement affect\'{e}s par
des effets de propagation 3D. En revanche, l'amplification
num\'{e}rique pr\'{e}dite dans le mod\`{e}le montre un bon accord avec
les observations en d'autres points du bassin, moins affect\'{e}s par
ces effets de propagation 3D, notamment dans des zones relativement
complexes o\`{u} les variations lat\'{e}rales de la structure du
sous-sol doivent \^{e}tre prises en compte. Dans ces conditions,
l'imagerie passive semble fournir une caract\'{e}risation satisfaisante
du sous-sol \`{a} m\^{e}me d'expliquer l'amplification observ\'{e}e.
Nos r\'{e}sultats permettent donc d'identifier et de mieux comprendre
le potentiel et les limites de l'utilisation de mod\`{e}les sismiques
3D construits par imagerie sismique passive pour l'estimation
num\'{e}rique des effets de site. Ces r\'{e}sultats nous am\`{e}nent
\`{a} proposer plusieurs perspectives d'am\'{e}lioration de cette
approche, qui reste prometteuse pour l'estimation des effets de site en
contexte de sismicit\'{e} faible \`{a} mod\'{e}r\'{e}e.
\end{altabstract}
\maketitle
\vspace*{1pc}
\twocolumngrid
\end{noXML}
\section{Introduction}\label{sec1}
In sedimentary basins, the impedance contrast at the interface between
soft sedimentary layers and the underlying bedrock leads to the
trapping of seismic waves within the sedimentary in-filling. This gives
rise to complex wave phenomena (body wave resonance, generation and
diffraction of surface waves at the edges of the basin, vertical and
lateral reverberations, focusing effects) which highly depend on the
three-dimensional (3D) geometry of the basin and often result in an
increased amplitude (or ``amplification'') and duration of the ground
motion (or ``duration lengthening''). This modification of ground
motion due to local geology is referred to as \textit{site effects}.
Site effects have been the subject of many studies, especially since
the devastating 1985 Mexico earthquake that brought to light the
influence of local soil conditions on the strong amplification of
ground motion observed in Mexico City, despite the long distance to the
seismic source \citep[e.g.,][]{Campilloetal1989}. This example, along
with many other observations around the world
\citep[e.g.,][]{Kawase1996,Gravesetal1998,Lebrunetal2001,
Rotenetal2008,Bindietal2011,Ktenidouetal2016},
makes it clear that the quantification of these site effects is
essential for seismic hazard assessment (SHA). Because they are related
to local soil conditions, site effects can be highly variable from one
site to another, and therefore require site-specific studies for a
robust estimation that accounts for the whole\unskip\break
complexity of wave
phenomena, in particular in 3D geological structures.
The French-German DARE project (\textit{Dense ARray for site effect
Estimation}) has been conceived and designed in this line. The idea is
to implement various and complementary approaches to perform a detailed
study of site effects at a target site located in the French Rh\^{o}ne
valley. The area hosts critical facilities including nuclear
installations, thereby motivating the need for robust SHA studies
locally. This site is located on the deep and elongated Messinian
Rh\^{o}ne Canyon, whose geometry and lithological characteristics make
it a good candidate for generating multidimensional site effects. DARE
is centred on the exploitation of dense and complementary datasets
acquired in the area \citep{Fromentetal2022a}. The project proposes to
investigate the interest of using such datasets for a robust estimation
of site effects, especially in low-to-moderate seismicity areas such as
metropolitan France.
One first, standard approach considered in DARE relies on earthquake
recordings through the calculation of so-called site/reference Standard
Spectral Ratios \citep[SSR,][]{Borcherdt1970}. SSR estimate the local
seismic amplification by direct comparison between earthquake
seismograms simultaneously recorded at a given \textit{site} station
laying on a sedimentary basin (subject to \textit{site effects)} with
respect to a nearby \textit{reference} station (typically on a bedrock
outcrop, considered free of site effects). This empirical method has
proven to be efficient for a robust quantification of site effects in
various configurations. The implementation of this method may however
show some difficulties in low-to-moderate seismicity areas, such as
mainland France \citep{Traversaetal2020},
where moderate to large earthquakes $(M_w>5.0)$ have long return
periods, therefore requiring long deployments. Alternative approaches
may thus be considered to complement such seismicity-based analysis of
site effects. To this end, the DARE project investigates two main
research tracks in order to explore the potential of using weak but
ubiquitous vibrations---known as ambient seismic noise---as an
alternative source of data for estimating site effects due to complex
wave propagation in sedimentary basins \citep[e.g.,][]{Boueetal2016}.
On one hand, seismic noise can be used directly for empirical
estimations of site effects, via H/V analysis
\citep[e.g.,][]{Bonnefoy-Claudetetal2006,Spicaetal2017,vanGinkeletal2019}
or noise-based SSR estimations
\citep[e.g.,][]{Perronetal2018,Gisselbrechtetal2023}. On the other
hand, ambient noise could also be used in a more indirect way as the
initial ingredient to build seismic models of the target site that
could then be used for numerical prediction of the ground motion and
thus of seismic amplification. The present paper focuses on this
numerical\unskip\break
aspect.
By giving access to the complete wavefield, simulations help to
evaluate the spatial variability of site effects and more importantly
to understand the underlying physical parameters to which they are
sensitive \citep[e.g.,][]{DeMartinetal2021}. 1D modelling of body-wave
resonance phenomena is a first way to calculate the response of a
sedimentary layer overlaying a rigid bedrock
\citep[e.g.,][]{Thomson1950, Haskell1953}. However, in
the presence of complex geological structures, several studies have
shown the limitation of 1D numerical simulations, and the necessity of
2D or 3D simulations of seismic wave propagation to reproduce the
observed amplifications
\citep[e.g.,][]{Kawase1996,Smerzinietal2011,Matsushimaetal2014,Ktenidouetal2016}.
\citet{Gelisetal2022} reach the same conclusions for our target site,
as they observe that 1D simulations do not reproduce the amplification
measured in the Tricastin basin, in particular regarding its maximum
amplitude (up to a factor 8) and its broadband spectral character,
typical of 3D wave propagation effects
\citep{Chavez-Garciaetal2000,CornouBard2003,Bindietal2009,Micheletal2014}. The conclusions of this previous study by
\citet{Gelisetal2022} form the motivation for the use of 3D numerical
simulations in our work. 3D simulations have become more affordable
lately thanks to the rapid increase of computational resources and to
the development of dedicated software, using in particular
spectral-element methods \citep[SEM,
e.g.,][]{KomatitschVilotte1998,DeMartin2011,Trinhetal2019}. These
developments led to many applications, notably in sedimentary basins
with complex geometries that require detailed simulations
\citep[e.g.,][]{Komatitschetal2004, Maufroyetal2015,
Maufroyetal2016,Chaljubetal2010,Chaljubetal2015,Paoluccietal2015,Thompsonetal2020,DeMartinetal2021,
Panzeraetal2022}.
These simulations however require an accurate knowledge of the
subsurface, both in terms of geometry and of seismic properties
\mbox{($S$-wave}
velocity $V_S$, $P$-wave velocity $V_P$, density ${\rho}$, and $S$- and
$P$-wave attenuation factors $Q_S$ and $Q_P$). As a consequence, these
numerical approaches also rely on field data and geophysical surveys in
order to constrain numerical models and define reliable input
parameters for the simulations. On the other hand, seismic data
(earthquake and noise recordings) are also essential to provide
observations which the outputs of the simulations can be compared and
calibrated with.
Most studies involving 3D numerical simulations for site effect
estimation rely on layered models
\citep[e.g.,][]{TabordaBielak2013,Maufroyetal2016,DeMartinetal2021,
Panzeraetal2022}.
In these models, layers are separated by interfaces associated to sharp
impedance contrasts, and seismic properties are usually assumed either
homogeneous or varying only vertically within each layer. The geometry
of the interfaces is often derived from geological knowledge (borehole
data, field campaigns, surface mapping or interpreted cross-sections),
from the interpretation of active seismic migrated images, or from H/V
spectral ratio analysis, while seismic properties are often estimated
from a limited number of local measurements such as Ambient Vibration
Analysis (AVA) and extrapolated to the entire layers, assuming vertical
but no lateral variations within the layers
\citep[e.g.,][]{Manakouetal2010, Molinarietal2015,
Cushingetal2020, Panzeraetal2022}.
In the present paper, we propose an alternative approach that uses
ambient-noise surface-wave tomography (ANSWT) for building 3D seismic
models of the target area. ANSWT is a tomographic method that has
proven very successful for imaging shear-wave velocities $(V_S)$ at the
crustal and lithospheric scales \citep[e.g.,][]{Shapiroetal2005} and
more recently at smaller, basin scales
\citep[e.g.,][]{Boueetal2016,Chmieletal2019}. Taking full advantage of
the deployment of dense seismic networks, this passive seismic imaging
technique is particularly attractive. ANSWT has the advantage to
provide a quantitative model of shear-wave velocity structure in 3D,
including both vertical and lateral variations, at relatively low cost.
However, ANSWT provides smoother models compared to active seismics and
to the geology-based layered models usually considered in numerical
simulations. The aim of this paper is to investigate the use of
standard ANSWT models for the numerical estimation of seismic
amplification and for site effect assessment. More precisely, the
question we address here is the following: what are the potential and
limitations of 3D numerical simulations based on standard ANSWT models
to assess seismic amplification in complex sedimentary basins?
The paper is organized as follows. Section~\ref{sec2} presents the
target zone, its geological context, and the seismic data acquired in
the frame of the DARE project. Section~\ref{sec3} reminds the steps of
the standard ANSWT workflow used to build a 3D $V_S$ model of the
target site, and explains how we derive other seismic properties
($V_P$, ${\rho}$, $Q_S$, $Q_P$) in order to build a full 3D seismic
model. In Section~\ref{sec4}, we present our numerical results and the
seismic amplification predicted in the 3D model, which we compare with
observed, earthquake-based SSR, as well as with noise-based SSRn and
with 1D approximations. Finally, we discuss the results in
Section~\ref{sec6}, highlighting the potential of using ANSWT models
for the seismic characterization of sedimentary basins, but also
underlining some limitations in their use for the numerical estimation
of site effects, which leads us to propose perspectives for future
work.
\section{Target site and data}\label{sec2}
The DARE project targets the area of the Tricastin Nuclear Site (TNS)
in the French Rh\^{o}ne Valley (Figure~\ref{fig1}). The TNS is located
on the Messinian Rh\^{o}ne canyon that was dug about 6~million years
ago during the Messinian Salinity Crisis (MSC) by the paleo-Rh\^{o}ne
river in older geological formations. In this area,
the geological basement is mainly\unskip\break
constituted by hard and thick (several hundreds of
meters) lower Cretaceous limestones (Barremian), so-called ``Urgonian''
limestones, which we consider to be the reference \textit{bedrock}
unit in the region. These Urgonian limestones are overlain by more
detrital lower to upper Cretaceous (Aptian to Turonian) formations
(sands, sandstones, and marls), and then by Tertiary marine and
continental detrital formations. These units are called hereafter
``post-Urgonian Cretaceous (and/or Tertiary) formations'' in order to
distinguish them both from the Urgonian bedrock and from post-Messinian
sediments. Indeed, after the MSC, the canyon has been filled with
Pliocene sediments of marine (sands and clays) and continental
(fluviatile conglomerates) origins, nowadays covered by the Rh\^{o}ne
lower-to-recent Quaternary terraces. In 2019, when the DARE project was
initiated, the local geology of the Messinian canyon remained poorly
documented in the region of Tricastin. \citet{Gelisetal2022} provide
some first insights about the canyon rims and the local subsurface
characteristics based on borehole data, geological study, and local 1D
geophysical characterization campaigns (H/V and AVA measurements). This
first study provided local knowledge about the characteristics
(thickness and $V_S$ velocities) of the sedimentary canyon in-filling
and of the underlying bedrock at two sites in the area. A first site is
located 2--3~km south of the TNS (seismic station E1/BOLL in
Figure~\ref{fig1}), on top of the sedimentary basin. At this site,
\citet{Gelisetal2022} show that the base of the canyon reaches a depth
of at least 500~m, incising---or, at least, lying directly on top
of---high-velocity Urgonian limestones. The 2nd site is station G6/ADHE on
a nearby outcrop of Urgonian limestones, which \citet{Gelisetal2022}
characterize as a hard rock site with an estimated $V_S30$ (the average
shear-wave velocity in the first 30~m) of about 2000~m/s. At larger
depth, the 1D $V_S$ profiles show velocity rapidly increasing with
depth and reaching about 3000~m/s beyond 50~m depth.
\citet{Gelisetal2022} present the various criteria for considering
G6/ADHE as a good reference for SSR calculations. It is worth noting
that $V_S$ profiles obtained at ADHE and BOLL are consistent (i) with
available geological data
\citep[e.g.,][]{Bagayoko2021,DoCoutoetal2024}
and (ii) with each
other in terms of mean velocities at depth, therefore giving a
reference velocity of $V_S$ ${\cong}$ 3000~m/s for the deep
Urgonian\unskip\break
substratum.
\begin{figure*}
\includegraphics{fig01}
\vspace*{-4pt}
\caption{\label{fig1}Simplified geological map of the target zone and
localisation of the two deployments (red circles: nodal array, blue
triangles: broadband network). Background colors correspond to main
geological units (see legend). The extent of the map corresponds to the
domain of interest for numerical simulations (excluding 15-km-wide
margins).}
\end{figure*}
From this first knowledge of the canyon, a 10~km $\times$ 10~km area
surrounding the imprint of Pliocene and Quaternary sediment deposits
around the TNS\unskip\break
(Figure~\ref{fig1}) was targeted. This extension allows
us to embed the edges of nearby outcrops of Cretaceous series incised
by the canyon, that constitute the basement of the canyon sedimentary
in-filling. Most of this target zone is located in a heavily
industrialized area, including the widespread TNS, a hydroelectric dam,
towns, several railroads and a highway. The associated anthropogenic
activity controls the distribution of high-frequency noise sources
locally \citep{Gisselbrechtetal2023}.
Two complementary seismic campaigns were carried out in the framework
of the DARE project \citep{Fromentetal2022a}. The first campaign
consisted of deploying 400 3-component seismic nodes over a 10~km
$\times$ 10~km area for one month (red dots in\unskip\break
Figure~\ref{fig1}).
This campaign targeted the recording of seismic ambient noise. A second
campaign consisted of deploying about 50 broadband stations over the
same target area for more than eight months and targeted the recording
of seismicity (including teleseismic events, regional, and local
seismicity). These two datasets \citep{Pilzetal2021,Fromentetal2023b}
are presented in detail in a data paper \citep{Fromentetal2022a} and
are publicly available (see section \textit{Data and software
availability}).
The present study exploits the ambient noise data recorded by the
400-node network. In the methodological approach adopted in this work,
seismicity data recorded by the 50-station network will only be used
here to compare our numerical estimations of seismic amplification with
observations. Three-component Geospace GSX nodes have been used for the
dense nodal experiment whose design is shown in Figure~\ref{fig1}. The
average inter-node distance is about 800~m over the area. About half of
the stations spaced 200--250~m apart are used to form a denser grid
located south of the TNS. Similarly, two dense east--west lines
following two roads are located north of the TNS. Information about
data completeness, noise levels, and overall data quality is available
in \citet{Fromentetal2022a}. A detailed characterization of seismic
noise sources recorded by the nodal network is given in
\citet{Gisselbrechtetal2023}.
\begin{figure*}
\vspace*{-4pt}
\includegraphics{fig02}
\caption{\label{fig2}Regionalization of the tomographic domain in
sub-areas based on the clustering of local dispersion curves.}
\end{figure*}
\section{Soil model from noise-based medium
characterization}\label{sec3}
\subsection{3D $V_S$ model from ANSWT}\label{sec3.1}
As mentioned in the introduction, the dataset of continuous seismic
noise recorded by the nodal array was processed using a standard ANSWT
workflow to obtain a 3D model of shear-wave velocity, as presented in
\citet{Fromentetal2022b}. In order to bring elements for the discussion
of the results obtained by using this model, we detail the workflow
used in this previous work hereafter:
\begin{enumerate}[(1)]
\leftskip=-1.2pc
\item
Cross-correlations of seismic signals between all pairs of stations of
the nodal array (except 6 nodes located outside the dense $10\times
10$~km grid, and 24 nodes with unusable data, resulting in 69,192 valid
cross-correlations). Continuous signals were cross-correlated over
30-min-long time windows after spectral whitening, and stacked over the
one-month duration of the acquisition. The cross-correlations of all
three components (N,E,Z) provided the full cross-correlation tensor.
Inter-station cross-correlations were then rotated in terms of radial
(RR) and transverse (TT) components assuming straight inter-station
paths. The vertical components (ZZ) of the cross-correlations were used
for exploiting Rayleigh waves and the transverse (TT) for exploiting
Love waves.
\item
Semi-automatic picking of fundamental-mode group-velocity dispersion
curves using frequency-time analysis
\citep[FTAN,][]{Dziewonskietal1969,Levshinetal1989}. A statistical quality
control of the picked dispersion curves was used to reject outliers
falling outside two standard
deviations of the distribution of picked
values. After this quality\unskip\break
control, a total of 17,031 Love dispersion
curves between 0.4 and 3~Hz and 29,719 Rayleigh dispersion curves
between 0.35 and 6.5~Hz were kept for tomography (i.e., 25\% and 43\%
of the full dataset, respectively). The tomography is therefore
considered to be well constrained up to 3~Hz (by both Love and Rayleigh
data) and partially constrained up to 5--6~Hz (only by Rayleigh data).
\item
Frequency-dependent 2D traveltime tomography \citep{Barminetal2001} in
order to convert inter-station dispersion curves into local dispersion
curves, i.e.\ build group velocity maps. This step was performed via a
linearized inversion involving regularization in the form of norm
damping and lateral smoothing. The choice of these regularization
parameters plays a role in the resolution of the final model.
\item
Inversion of local group-velocity dispersion curves into local 1D $V_S$
profiles using a Neighbourhood Algorithm
\citep{Sambridge1999,Mordretetal2014}. Here the use of a global
optimization scheme allowed for a statistical exploration of the model
space and provided an average of best-fitting models for each 1D $V_S$
profiles, which were then linearly interpolated into a 3D $V_S$ model.
\end{enumerate}
The tomographic process was guided by assumptions derived from the
geological knowledge at the time of this first imaging. In particular,
for the 1D depth inversion (step 4), the expectation of a strong
velocity contrast between sediments and bedrock led to a
parameterization of the 1D profiles consisting in two smooth layers
(represented by splines functions) potentially separated by a velocity
discontinuity (if required by the data). This parameterization was
adapted locally within sub-areas defined by clustering the local
dispersion curves after the 2D tomography stage (step 3), using a
data-driven \textit{K-means} algorithm \citep{MacQueen1967}. The
resulting four sub-areas are shown in Figure~\ref{fig2} and turn out
to well coincide with previous geological knowledge. Area \#1 (in blue
in Figure~\ref{fig2}) corresponds to the deepest parts of the basin.
Area \#2 (in green) corresponds to the shallower northwestern edge of
the basin, including the northwesternmost corner where Urgonian
limestones are outcropping. Area \#3 (in yellow) corresponds to the
eastern edge and its outcrops of post-Urgonian/pre-Messinian
formations (limestones, sands, sandstones, and marls of Aptian to
Miocene ages). Finally, Area \#4 corresponds to a complex zone,
including the so-called Lapalud island, formed by post-Urgonian
Cretaceous units (limestones, sandstones and marls of Aptian to
Turonian ages) expected to have lower seismic velocities than the hard
Urgonian limestones
\citep[e.g.,][]{Bagayoko2021,DoCoutoetal2024}. In addition to
adapting the parameterization of the 1D inversion in these sub-areas,
the sub-arrays were also used to perform FK analysis and estimate an
average phase velocity curve that was used to constrain the 1D depth
inversion of local group velocity curves within each sub-area.
Besides the assumption of a two-layer model with smooth velocity
variations within each layer, we did not impose any strong constraint
to the inversion. This preliminary 3D $V_S$ model is therefore mainly
data driven, its purpose being precisely to investigate how such a
model, based on seismic noise only via a \textit{blind} ANSWT
workflow, can be used to predict seismic amplification in the basin.
This remains a fairly open question, considering that the ANSWT
procedure uses phase information only (amplitudes are discarded by
spectral whitening in the cross-correlations).
In order to use the ANSWT model for numerical simulations, the 3D $V_S$
volume is extrapolated outside the tomographic domain, both laterally
and vertically, to define seismic properties in the full simulation
domain, which extends laterally by 15~km from the limits of the domain
of interest (\textit{SEM domain} in Figure~\ref{fig2}), and
vertically down to 30~km depth.\unskip\break
Because, in a first time, we want to
rely on our data-driven ANSWT model and avoid making strong \mbox{assumptions}
about the subsurface, lateral extrapolation is performed invariantly at
a given depth. Downward vertical extrapolation consists in a smooth
transition from the bottom of the tomographic model (1000~m below
ground surface) to a constant velocity of 3000~m/s at 1500~m depth.
This value of $V_S = 3000$~m/s is chosen for consistency with prior
measurements in the area \citep{Gelisetal2022}. It also roughly
corresponds to the maximum velocity of the tomographic model at 1~km
depth, and therefore ensures a positive velocity gradient with respect
to depth. Outside the basin, we also perform a vertically invariant
extrapolation upwards in order to assign seismic properties to
topographical heights above basin level.
Figure~\ref{fig3} gives several views of the obtained 3D $V_S$ model.
Figure~\ref{fig3}a gives a 3D view that includes N--S and E--W vertical
cross-sections through the $V_S$ model (left and back panels, first
colorbar), as well as the 1200-m/s iso-velocity surface that can be
associated to the interface between sediments and bedrock (second
colorbar). Figure~\ref{fig3}b gives a 3D view of the numerical model,
after extrapolation of the ANSWT model outside the tomographic domain
over the full domain of interest (but excluding 15-km-wide margins,
which just consist of further extrapolation).\unskip\break
Figure~\ref{fig3}c shows
a vertical N--S cross-section along the expected axis of the Rh\^{o}ne
paleo-canyon \citep{Gelisetal2022,Fromentetal2022b}, passing through
the TNS and through station E1/BOLL [on which we will focus later on
to illustrate our results, and compare them with those of
\citealp{Gelisetal2022}]. Figure~\ref{fig3}d shows a vertical E--W
cross-section in the south of the tomographic domain, through the
Lapalud island and station D0 (on which we will also focus later on to
illustrate our results).
\begin{figure*}
\includegraphics{fig03}
\bcaption{\label{fig3}}{2}{3D Vs model. (a)~3D view showing the interface
between sediments and bedrock
\citep[adapted from][]{Fromentetal2022b}.
(b)~3D view of the numerical model after
extrapolation outside the tomography domain (excluding 15-km-wide
margins). Green dots represent nodes and broadband stations.
(c)~South--north vertical cross-section along the axis of the
paleo-Rh\^{o}ne canyon. (d)~West--east vertical cross-section through
the northern part of the array and station A4. (e)~West--east vertical
cross-section through the southern part of the array and the Lapalud
island. In (c--e), the dashed box corresponds to the
tomography domain. Red dots and blue triangles represent nodes and
broadband stations located within 500~m of the sections (see
Figure~\ref{fig2} for the location of the sections).}
\end{figure*}
As already described by \citet{Fromentetal2022b}, the ANSWT $V_S$
model agrees well with the main geological structures expected in the
area. In particular, the range of estimated shear-wave velocity values
are consistent with previous studies \citep[e.g.,][]{Gelisetal2022},
with values ranging from 500 to about 1200--1400~m/s in the sediments,
and from about 1700--2000 to 3000~m/s in the underlying bedrock.
Moreover, the velocity discontinuity between the two layers (which
roughly corresponds to the 1200-m/s iso-velocity surface shown in
Figure~\ref{fig3}a) coincides well with the expected depth of the
paleo-canyon, at least for its deeper parts, with a north--south axis
corresponding to the paleo-Rh\^{o}ne and a southwestern branch
corresponding to the paleo-Ard\`{e}che
(Figure~\ref{fig3}a). Between
these two branches, the model also depicts the so-called Lapalud island
with higher velocities reaching shallower depths, representing
post-Urgonian Cretaceous units\unskip\break (Figures~\ref{fig3}a,c).
However, when looking in more detail, we notice that some features of
the model are questionable. In particular, the model does not display
high velocities reaching the surface, even in regions where we expect
surface outcrops of Cretaceous formations (especially in the
northwestern corner and on the eastern edge of the basin,
Figures~\ref{fig3}a, b). Instead, the model exhibits a lower-velocity
layer ($500 < V_s < 1000$~m/s) with a thickness of at least 200~m
over the entire domain (Figures~\ref{fig3}b--d). This low-velocity layer
is not restricted to the basin, which is therefore not well delimited
laterally. We will see in the following that this lack of basin edges
will have consequences in terms of seismic amplification.
In spite of these limitations, the fact that the 3D $V_S$ model derived
from ANSWT well depicts the expected large-scale geometry of the
Messinian paleo-valley at depth, including some complex structures such
as the Lapalud island, motivates us to use this model for numerical
simulations, in order to look at the seismic amplification that it may
generate. This, however, first requires the definition of other seismic
properties in the considered 3D volume. We will now present how we
define these properties, and we will distinguish between properties
that are constrained by the same seismic noise data as the $V_S$ model
(namely $Q_S$) and other properties that are estimated by other means
($V_P$, density, $Q_P$).
\subsection{Estimation of shear-wave quality factors
$Q_S$}\label{sec3.2}
Besides ANSWT, the seismic noise recorded by the dense array is
processed via Q-SPAC analysis \citep{Prietoetal2009} to estimate
seismic attenuation parameters $(Q_S)$, following the methodology of
\citet{Boxbergeretal2017}. To this end, the study area is subdivided
into 15 sub-arrays, each of them containing 15 to 25 seismic nodes.
\begin{figure*}
\includegraphics{fig04}
\caption{\label{fig4}Calibration of a site-specific $V_S/Q_S$
relationship using Q-SPAC inversion results.}
\end{figure*}
In a first step, the Extended Spatial AutoCorrelation (ESAC) method is
adapted to first obtain mean 1D $V_S$ profiles below each sub-array by
joint inversion of Rayleigh wave dispersion and H/V \mbox{spectral} ratios,
and then estimate frequency-dependent Rayleigh-wave attenuation factors
from the mean 1D $V_S$ profiles
\citep{Ohorietal2002,Boxbergeretal2011}. In a second step, individual
1D $Q_S$ profiles are obtained from the inversion of the Rayleigh-wave
attenuation coefficients by constraining the $V_{S}$ profiles to the
values obtained in the first step, following \citet{Xia2014}. It is
worth noting that the seismic attenuation discussed here does not
distinguish between intrinsic and scattering attenuation. Furthermore,
while the Rayleigh-wave attenuation factors of the input data depend on
frequency, the $Q_S$ parameter of the obtained layered model is assumed
to be frequency-independent. Finally, following \citet{Xiaetal2002},
we disregard the contributions of P waves on the Rayleigh-wave
attenuation\break factors.
In the end, 15 layered 1D profiles representative of average $V_S$ and
$Q_S$ values as a function of depth are derived, one for each of the 15
sub-arrays of the nodal network. $V_S$ profiles are found to be in
general agreement with the ANSWT model (with\unskip\break
deviations smaller than
15\%). These profiles are used to calibrate a relationship between
$V_S$ and $Q_S$ values in the form of a 6-order polynom [after
\citealp{TabordaBielak2013}, see Figure~\ref{fig4}] which is then used to
derive a 3D $Q_S$ model from the 3D ANSWT $V_S$ model. This is expected
to provide more realistic and site-specific $Q_S$ values than assuming
generic relationships (e.g., $V_S/Q_S=10$), or other relationships
from the literature calibrated for other sites [e.g.,
\citealp{TabordaBielak2013}, see Figure~\ref{fig4}]. The $Q_S$ values obtained
from our site-specific relationship range from 21 for small $V_S$
values in shallow sediments to 360 for high $V_S$ values in the deep
substratum\break (Table~\ref{tab1}).
\begin{table*}
\caption{\label{tab1}Ranges of seismic properties in the two layers of
the 3D $V_S$ model}
\begin{tabular}{ccccccccccc}
\thead
& \multicolumn{2}{c}{$V_S$~(m/s)} &
\multicolumn{2}{c}{$V_P$~(m/s)} &
\multicolumn{2}{c}{Density~(kg/m$^3$)} &
\multicolumn{2}{c}{$Q_S$} &
\multicolumn{2}{c}{$Q_P$}\\
\cline{2-3}
\cline{4-5}
\cline{6-7}
\cline{8-9}
\cline{10-11}
& min & max & min & max & min & max & min & max & min & max \\
\endthead
1st layer (sediments) & \0500 & 1400 & 2000 & 2675 & 2075 & 2225 & \021 & 130 &
260 & 350 \\
2nd layer (bedrock) & 1700 & 3070 & 3075 & 5300 & 2300 & 2640 & 170 & 340 &
420 & 760
\botline
\end{tabular}
\end{table*}
\subsection{Other seismic parameters ($V_P$, $Q_P$,
density)}\label{sec3.3}
Unlike $V_S$ and $Q_S$ properties, $P$-wave velocity $V_P$ and quality
factor $Q_P$, and density parameters are not (or poorly) constrained by
our ambient noise data, which are typically dominated by surface waves.
\textit{In situ} geotechnical measurements performed in the vicinity of
the TNS down to approximately 30~m depth provide us with local
estimations of $V_P$, $V_S$, and density \citep{Moiriat2019}. These
measurements show $V_P/V_S$ ratios of up to 8 in very shallow
sediments (Quaternary limons and alluvions forming thin (${<}$20~m)
layers and lenses, which are not included in the ANSWT due to its lack
of sensitivity to these shallow layers), and of about 4 for values of
$V_S\approx500$~m/s in the blue marls encountered below 15 to 20~m
depth. It is worth noting that these values of $V_S\approx500$~m/s are
in very good agreement with the $V_S$ values of the shallow part of the
basin in the ANSWT model which, according to the smallest wavelength
considered in the tomography (${\approx}$100~m), we can regard as
effective $V_S$ values for the basin sediments in the first 30 to 50~m.
Based on these geotechnical measurements, we calibrate a site-specific
$V_P/V_S$ relationship of the form
{\begin{equation}\label{eq1}
\frac{V_{P}}{V_{S}}=
1.73+a\,\mathrm{e}^{-bV_{S}},
\end{equation}}\unskip
with $a = 9.46$ and $b = -2.82\times 10^{-3}$. This \textit{ad hoc}
relationship is designed such as to yield a $V_P/V_S$ ratio of 1.73
for large $V_S$ values (deep bedrock), corresponding to the usual
assumption of a Poisson's solid, while the calibration yields
$V_P/V_S$ ratios of about 4 for $V_S\approx500$~m/s, as indicated by
\textit{in situ} geotechnical data.
In lack of sufficient density measurements, we use Gardner's empirical
law [\citealp{Gardneretal1974}, equation~(7);
\citealp{Brocher2005}, equation~(2)] in order to relate density to
$P$-wave velocity $V_P$. This law is supposed to be valid for sedimentary
rocks such as limestones. We verify that the density values obtained
for the range of seismic velocities encountered in our ANSWT model
roughly coincide with expected values for the known lithologies in the
area (limestones, sandstones, and marls), as well as with the
abovementioned geotechnical measurements.
Finally, we must define values for the $P$-wave quality factor $Q_P$ in
our 3D model, although this parameter is expected to have very little
impact on the amplification of the SH excitation that we will simulate.
In lack of any constraint on these $Q_P$ values, we derive them from
$Q_S$, $V_S$, and $V_P$ values, under the assumption that the
compressibility quality factor is much larger than the shear quality
factor [$Q_\kappa\gg Q_\mu$, \citealp{DahlenTromp1998},
equation~(9.59), p.~350].
Table~\ref{tab1} summarizes the ranges of values of the different
seismic properties in the final 3D seismic model.
\section{Numerical estimation of seismic\newline amplification}\label{sec4}
\subsection{Simulation of seismic wave propagation}\label{sec4.1}
Simulations of seismic wave propagation in the constructed 3D model are
performed using the EFISPEC software \citep{DeMartin2011}, which makes
use of a time-domain spectral-element method (SEM) solving the 3D
equation of motion in visco-elastic media. A hexahedral mesh is
designed for simulations valid up to 5~Hz, which is about the maximum
frequency used to constrain the ANSWT model (at least with Rayleigh
data, see Section~\ref{sec3.1}). The simulation domain extends
laterally 15~km further than our domain of interest (Figures~\ref{fig1}
and~\ref{fig2}) and vertically down to 30~km, in order to mitigate
parasite reflections on domain boundaries, where the absorbing
condition is based on a paraxial approximation \citep{Stacey1988}. This
leads to a total of 6.5~M elements, with an element size that varies
between 100~m and 300~m from the shallow to the deeper parts of the
model. In order to reproduce the assumptions underlying SSR
calculations, the simulated source is a vertically-incident plane wave
with SH polarization in the $X$- (east--west) or $Y$- (north--south)
direction, injected at 5~km depth. We perform two simulations, one for
each polarization of the plane wave, for a duration of 60~s. Each
simulation costs 3500 CPUh and is parallelized on 480 cores. The source
time function is a low-pass-filtered
Dirac delta function, filtered\unskip\break
below 5~Hz, such as to remain in the
domain of validity of the
simulation (and of the ANSWT model) and to be able to visualize and
exploit simulated outputs directly, without filtering them at the
post-processing stage.
Figure~\ref{fig5} shows snapshots of the simulated wavefield on the
free surface at different times. The vertically-incident plane wave
first arrives after approximately 2.2~s of simulation in the
northwestern corner, which has slightly higher velocities than the rest
of the domain (Figure~\ref{fig5}a). At 2.5~s, amplitudes are saturated
over the entire domain while the plane wave is reflecting on the free
surface (Figure~\ref{fig5}b). At 3.5 and 4~s (Figures~\ref{fig5}c,
d), high amplitudes are visible in the deepest parts of
the basin, reflecting 1D basin amplification due to vertical body-wave
resonance, but also outside the tomographic domain, due to topographic
effects, in particular on the margins that are not constrained by the
tomography. After 4.5~s (Figures~\ref{fig5}e, f), late
reverberations are slightly visible in the basin (likely due to
reflections on the edges of the basin), but also in the margins of the
model outside the tomographic domain (due to topographic effects).
These snapshots show that waves are not efficiently trapped in the
basin, as we would expect in a well-delimited basin
\citep[e.g.,][]{Chaljubetal2015,DeMartinetal2021}. Instead, waves
escape in the low-velocity shallow layer, resulting in an amplification
over the entire domain, in particular in the margins of the simulation
domain that are not constrained by the tomography.
\begin{figure*}
\includegraphics{fig05}
\vspace*{-4pt}
\caption{\label{fig5}Snapshots of the simulated wavefield ($v_y$
component excited by a vertically-incident SH plane wave polarized in
the $Y$ direction) on the free surface at different times. The black
contour corresponds to the iso-depth 375~m of the velocity
discontinuity between sediments and bedrock, delineating the deepest
part of the basin. The dashed line corresponds to the tomographic
domain. The full movie of wave propagation is provided in Supplementary
Material.}
\vspace*{-4pt}
\end{figure*}
Based on these first observations and despite some limitations, we will
now continue to analyse our simulation results, especially in terms
of predicted amplification, with the objective to better understand the
limitations of our ANSWT model, which we just started to point out, and
thus to draw perspectives about how such ANSWT models could be improved
to better reproduce basin amplification. We propose to start by looking
into the seismograms simulated at the locations of known stations,
including E1/BOLL that has been investigated by \citet{Gelisetal2022},
as well as A4 that serves as a reference station for the calculation of
our empirical, earthquake-based SSR (see Supplementary Material for
more details). We shall specify here that station A4 is located on the
same kind of Urgonian outcrop and presents similar H/V characteristics
as station G6/ADHE that was used as a reference station by
\citet{Gelisetal2022}.
Figure~\ref{fig6} shows seismograms (Figure~\ref{fig6}a) simulated at
stations E1/BOLL (in blue) and A4 (in orange) and their amplitude
spectra (Figure~\ref{fig6}b). In Figure~\ref{fig6}b, thin lines
represent raw spectra and thick lines represent spectra smoothed using
Konno-Omachi smoothing with a bandwidth $b= 40$
\citep{KonnoOhmachi1998} which are then used to compute spectral
ratios\unskip\break
(Figure~\ref{fig6}c). As expected, the signal simulated at station
E1/BOLL has a long duration because of wave reverberations in the basin
(only the first 11~s of the signals are shown in Figure~\ref{fig6},
but reverberations generate non-negligible amplitudes---of the order of
about 10\% of the maximum peak amplitude---over the entire 60-s duration
of the simulation). The signal simulated at station A4, on the other
hand, does not correspond to our expectations for a reference rock site
precisely because it also contains late reverberations of similar,
non-negligible amplitudes as station E1. Apart from a high-amplitude
arrival at about 4~s, probably due to a vertical body-wave reflection
in the basin, the ground motion in E1 does not seem much amplified
compared to the one in A4. In the Fourier domain (Figure~\ref{fig6}b),
the amplitude spectra at stations E1 and A4 differ mostly by the
frequencies of their respective peaks at low frequencies: around 0.5~Hz
for E1 (blue line in Figure~\ref{fig6}b), which corresponds well to
the 1D resonance of the basin \citep{Gelisetal2022}, and around 1~Hz
for A4 (orange line in Figure~\ref{fig6}b), which rather corresponds
to the 1D resonance of the shallow low-velocity layer extrapolated
outside the actual extent of the basin (if we consider a relation
between resonance frequency $f_{\mathrm{res}}$ and layer thickness $h$
of the form $f_{\mathrm{res}} = V_S^{\mathrm{avg}}/(4h)$, with
$V_S^{\mathrm{avg}}$ the average $V_S$ velocity in the layer).
\begin{figure*}
\includegraphics{fig06}
\vspace*{4pt}
\caption{\label{fig6}(a)~Seismograms simulated at stations E1/BOLL (in
blue) and A4 (in orange) and their amplitude spectra (b), for a
vertically-incident plane wave source polarized in the $Y$ direction
($v_y$ components are shown). Green curves show the reference signal
considered for computing amplification, which corresponds to the
recording of the incident plane wave at 10~km depth. (c)~Spectral
ratios.}
\vspace*{2pt}
\end{figure*}
As a consequence, we cannot consider the signal simulated at A4 as a
reference for estimating amplification via spectral ratios: the
resulting amplification would be dramatically underestimated,
especially at frequencies corresponding to the 1D resonance of the
shallow low-velocity layer (e.g., 1~Hz, see red curve in
Figure~\ref{fig6}c). Instead, we consider a deep reference point,
located at 10.050~km depth, i.e., at the same distance from the depth
at which the plane wave source is injected (5~km) as the free surface
in the basin (which has an average elevation of 50~m asl). The
seismogram extracted from this deep reference point is time-windowed,
such as to retain only the incident plane wave, which has travelled in
a homogeneous medium between its injection\unskip\break
point at 5~km depth and this
reference point at 10.050~km depth, therefore yielding a clean signal
(green curves in Figure~\ref{fig6}). The amplitude spectrum of this
windowed signal is then multiplied by 2, which corresponds to the
amplitude spectrum of a point that would be located on the free
surface. In doing so, we therefore define a reference amplitude
spectrum that is equivalent to the theoretical response of a point on
the free surface above a homogeneous halfspace that would have the same
seismic properties as the deep part of our 3D numerical model
(including visco-elastic attenuation, which makes this deep reference
spectrum different from the spectrum of the injected source time
function) and that is consistent with the wavefield simulated at the
free surface (since it is extracted from the simulated wavefields, and
not computed separately). We refer to the spectral ratio computed with
this deep reference as the amplification function (AF), as opposed to
the standard spectral ratio (SSR) computed with a\unskip\break
reference point
located on the free surface. We have verified that AF and SSR are
identical in the case of a surface reference point located above a
homogeneous subsurface, and far away from basin edges (clean numerical
reference). As a consequence, our numerical amplification functions
remain comparable to empirical SSRs computed with respect to a clean
empirical reference on the field, which is the case of station A4.
Figure~\ref{fig6}c shows the resulting amplification functions for
station E1 (in blue), with a clear peak around 0.55~Hz corresponding to
basin resonance, and for station A4 (in orange), with a peak at 1~Hz,
related to the resonance of the artificially extrapolated low-velocity
layer. In the following, we will now analyse in more detail these
numerical amplification functions, with a particular focus on points
located in the basin (since we know that rock sites outside the basin
are misrepresented in our model, and therefore may provide less
relevant amplification\unskip\break
results).
\subsection{Numerical amplification}\label{sec4.2.}
In this section,
we will analyse in three different ways the numerical
amplification simulated in our ANSWT model. First, we will compare our
numerical amplification functions and their spectral characteristics to
empirical SSR measurements based on earthquake data recorded during the
deployment of the DARE broadband network. We will exemplify this
comparison at two specific locations in the basin, which we have
identified as representative of two different signatures that we find
of particular interest for our scope, especially when compared to a 1D
SH response approximation, which we will also provide as another point
of comparison. Second, we will comment on the spatial variability of
the numerical amplification predicted in our model, for a few example
frequencies. Third, we will have a closer look at this spatial
variability at low frequency, and more precisely at the spatial pattern
of the (mis)match between our
numerical amplification and two empirical
amplification estimations based on earthquake and noise data.
\subsubsection{Local comparison of numerical amplification with
earthquake-based SSR and 1D\newline amplification}
Figure~\ref{fig7} shows the amplification at stations E1 (a) and D0 (b)
as a function of frequency (see Figure~\ref{fig1}\unskip\break
for the location of
these two stations). Purple lines represent the empirical,
earthquake-based SSR computed with respect to reference station A4 and
their uncertainties (purple dashed lines). A catalog of these empirical
SSR for all stations of the DARE broadband network is provided in
Supplementary Material, together with details on the computation of
these SSR. The green line corresponds to the SH amplification computed
in a 1D model extracted from the 3D model below the station of interest
(right panels), using the \textit{gpsh} function of the Geopsy
software \citep{Watheletetal2020}. The numerical amplification of the
horizontal component $(\mathrm{AF}_H)$ computed in the 3D tomographic model is
shown as dark blue and red dashed lines for vertically-incident plane
wave sources polarized in the E--W $(\mathrm{PW}_X)$ and N--S $(\mathrm{PW}_Y)$
directions, respectively. The set of grey curves corresponds to the
amplification of the horizontal component for plane wave sources
polarized in different directions ($\mathrm{PW}_\theta$, with $\theta$ the
polarization azimuth), showing the variability of the amplification
with respect to source polarization. These curves are obtained by a
linear combination of the two wavefields simulated for plane wave
sources polarized in the $X$ and $Y$ directions, in order to reproduce, by
linearity of the wave equation with respect to the source, the
wavefield generated by a plane wave source polarized in any given
direction.
\begin{figure*}
\includegraphics{fig07}
\caption{\label{fig7}Amplification spectra at stations E1 (a) and D0
(b). Dark blue and red dashed lines: numerical amplification of the
horizontal component $(\mathrm{AF}_H)$ in the 3D tomographic model, for
vertically-incident plane wave sources polarized in the E--W $(\mathrm{PW}_X)$
and N--S $(\mathrm{PW}_Y)$ directions, respectively. Grey curves: amplification
of the horizontal component for plane wave sources polarized in
different directions $(\mathrm{PW}_{\theta})$. Green line: 1D SH amplification
$(\mathrm{AF}_{\mathrm{SH}}^{1\mathrm{D}})$ computed in a 1D model extracted from the 3D model
below the station of interest (right panels, where the dashed line
below 1~km depth corresponds to the part of the model that is not
constrained by ANSWT and is extrapolated to a constant $V_S=3000$~m/s).
Purple lines: empirical, earthquake-based SSR$_EQ$ computed with
respect to reference station A4, and their uncertainties (purple dashed
lines). Note that uncertainties do not appear for high frequencies
where the SSR is often constrained by a single event, which does not
enable to compute a standard deviation (see Supplementary Material for
details).}
\end{figure*}
The two stations considered in Figure~\ref{fig7}, E1 and D0, exhibit
two different amplification patterns that can be linked to their
respective locations in the basin, and eventually to the corresponding
subsurface structures. Below station E1, we know from previous studies
that the paleo-canyon is quite deep [about 560~m according to
\citealp{Gelisetal2022}], and that a thick pile of Pliocene sediments
probably lies directly on top of the hard substratum of Urgonian
limestones. This results in a significant
observed\unskip\break
amplification (up to
$7\pm2$) above the main resonance frequency of the canyon (about 0.5~Hz
for this location/depth, according to \citet{Gelisetal2022}. Here it
is worth noting that the numerical results do not retrieve this
observed level of amplification (with a maximum of about $4\pm 1$), nor
its broadband character: while the observed amplification exhibits a
plateau above 0.55~Hz, which is commonly interpreted as the signature
of 3D wave propagation effects [due, in particular, to laterally
propagating surface waves generated at the edges of the basin,
see e.g.\ \citealp{CornouBard2003,Bindietal2009}], the numerical
amplification computed from 3D numerical simulations (envelope of gray
curves) remains quite close from the 1D resonance peaks predicted in a
local 1D model extracted below the considered station (green curve).
This suggests that the ANSWT model does not generate significant
amounts of laterally propagating surface waves within the basin, which
can be easily understood from our observations of the model
(Figure~\ref{fig3}) and of the wavefield (Figure~\ref{fig4}), where we
already noted the lack of clear lateral basin edges, resulting in a
lack of wave trapping within the basin. Nevertheless, the numerical
amplification computed in the 3D ANSWT model is not strictly identical
to a 1D amplification. In particular, it exhibits non-negligible
variations with respect to source polarization for some frequencies,
including in the vicinity of the main resonance frequency
(0.45--0.55~Hz). While this source-related variability would probably
deserve a more detailed investigation
\citep[e.g.,][]{Maufroyetal2017}, it is a clear indication that the
ANSWT model well includes some 3D characteristics of the basin that
cannot be captured by purely 1D approximations. Moreover, it should
also be noted that the local 1D profile extracted from the 3D ANSWT
model below station E1 and the associated 1D amplification (green
curves in Figure~\ref{fig7}a) are very similar to the results obtained
by \citet[][their figure~8d]{Gelisetal2022}
for this location. This suggests that the ANSWT well played
its role in terms of local $V_S$ estimation, just as well as the local
Ambient Vibration Analysis performed by \citet{Gelisetal2022}, but
with the added value of imaging the lateral variations of the 1D $V_S$
structure in the basin.
\begin{figure*}
\includegraphics{fig08}
\vspace*{-4pt}
\caption{\label{fig8}Maps of the amplification of the horizontal
component at 0.5, 1, 2, and 4~Hz, for a vertically-incident SH plane
wave polarized in the $Y$ direction (N--S). The black line represents the
iso-375-m depth contour of the interface between the two layers
considered in the 1D depth inversion. Margins outside the tomographic
domain have been masked.}
\vspace*{-4pt}
\end{figure*}
Regarding station D0, it is located at the border of the Lapalud
island, where the subsurface has a complex structure due to the
presence of the late lower Cretaceous units in between the
Pliocene\unskip\break
sediments and the Urgonian bedrock
\citep{Bagayoko2021,DoCoutoetal2024}. These units are made
of sandstones and marls, and have intermediary seismic velocities
compared to the Pliocene and Urgonian lithologies, which in the $V_S$
profiles is represented by a smooth gradient of velocity progressively
increasing with depth. This results in a lower level of observed
amplification (up to $3 \pm 0.5$). This amplification also displays a
broadband character as a function of frequency, but for a different
reason as previously: in this case, the amplification plateau above
0.5~Hz is due to the smooth gradient of
increasing\unskip\break
velocity with depth,
and not to 3D wave propagation effects (since 1D amplification also
shows this broadband character). Interestingly, both 3D and 1D
numerical results show a good agreement with the observations in this
case, which again suggests that the amplification is not dominated by
3D propagation effects at this location, but by 1D vertical resonance,
so that the 1D approximation is sufficient to explain the observations.
It should be noted, however, that this agreement is only possible
because the tomography well played its role in terms of (3D)
characterization of the local (1D) velocity structure.
\subsubsection{Spatial variability of the numerical\newline amplification}
One of the interests of numerical simulations is to provide access to
the spatial variability of the amplification in the basin.
Figure~\ref{fig8} shows amplification maps at different frequencies. At
0.5~Hz, most of the amplification corresponds to the deepest part of
the basin (delineated by the iso-375-m depth contour of the interface
between sediments and bedrock), as expected. At higher frequencies,
however, amplification does not behave as expected. At 1~Hz,
amplification is larger outside the basin than inside, which is likely
due to the resonance of the shallow 200-m-thick low-velocity layer at
this frequency. At 2~Hz, the amplification pattern is less clear but
seems to concern mainly the slopes of the basin. At 4~Hz, results
simply do not seem to be relevant, and are probably dominated by the
topographic effects identified in the wavefield snapshots
(Figure~\ref{fig5}). In the following, we focus our analysis to the
low-frequency part of the amplification, in the frequency range
0.3--0.7~Hz, related to the deepest parts of the basin (note however
that this frequency range is wide enough to cover a relatively wide
range of canyon depths, from about 300~m to 700~m). More specifically,
we propose to look at the ratio between our numerical amplification and
the observed amplification in this frequency range, for all broadband
stations (earthquake-based SSR$_{\mathrm{EQ}}$, Figure~\ref{fig9}) and all
nodes (noise-based SSRn, Figure~\ref{fig10}).
\begin{figure*}
\includegraphics{fig09}
\vspace*{-5pt}
\caption{\label{fig9}Mean ratio between numerical amplification and
earthquake-based SSR$_{\mathrm{EQ}}$ between 0.3 and 0.7~Hz (note the
logarithmic color scale).}
\vspace*{-7pt}
\end{figure*}
\begin{figure*}
\vspace*{3pt}
\includegraphics{fig10}
\vspace*{5pt}
\caption{\label{fig10}Mean ratio between numerical amplification and
noise-based SSRn \citep{Gisselbrechtetal2023} between 0.3 and 0.7~Hz
(note the logarithmic color scale).}
\vspace*{4pt}
\end{figure*}
\subsubsection{Spatial variability of the misfit between\newline numerical
amplification and observed SSR}
\paragraph{Comparison to earthquake-based SSR$_{\mathrm{EQ}}$}
Figure~\ref{fig9} shows
the spatial distribution, for all broadband
stations, of the mean ratio between our numerical amplification
$\mathrm{AF}_{\mathrm{num}}$ (log-average of the gray curves in
Figure~\ref{fig7}) and the observed earthquake-based amplification
SSR$_{\mathrm{EQ}}$ with respect to reference station A4 in the frequency range
0.3--0.7~Hz. According to the logarithmic color scale, dark red and
dark blue colors represent stations where the numerical amplification
underestimate and overestimate the observations by a factor of 4,
respectively. Empty triangles correspond to stations for which we could
not estimate empirical SSR$_{\mathrm{EQ}}$, due to an insufficient
signal-to-noise ratio of the earthquake recordings or to a lack of data
(see Supplementary Material for details). The dashed-dotted line
represents the surface imprint of the canyon rim, as interpreted from
geological mapping (Figure~\ref{fig1}), which provides a visual guide
to locate the results with respect to the basin. The dotted line
delimits the domain constrained by the tomography, outside which
laterally-invariant extrapolation is performed (Figure~\ref{fig3}) and
makes the results hardly interpretable, in particular at rock sites
(e.g., A4, G6/ADHE). Within the tomography domain, however, the ratio
between numerical and observed amplification is always comprised
between 0.5 and 2, and is close to 1 for some stations. This suggests
that the ANSWT model, although not perfect and still improvable, is
able to explain, in some extent, the amplification observed in this
low-frequency range, and in various parts of the basin (where,
incidentally, the frequency range 0.3--0.7~Hz may include different
amplification phenomena depending on the location in the basin and on
the underlying/surrounding geological structures). In order to look in
more detail at the spatial pattern of the (mis)match
between\unskip\break
numerical
and observed amplification, we now propose to look at the ratio between
numerical and noise-based amplification.
\paragraph{Comparison to noise-based SSRn}
Similarly to\unskip\break
Figure~\ref{fig9}, Figure~\ref{fig10} shows the spatial
distribution, for all nodes, of the mean ratio between our numerical
amplification $\mathrm{AF}_{\mathrm{num}}$ and the observed noised-based
amplification SSRn in the frequency range 0.3--0.7~Hz, in which we
consider SSRn estimations to be reliable \citep{Gisselbrechtetal2023}.
Indeed, several studies have reported a good agreement between
noise-based SSRn and earthquake-based SSR in this frequency band
\citep[e.g.,][]{LermoChavez-Garcia1994, Bard1999},
mostly because distant sources dominate the ambient noise wavefield at
these frequencies \citep[e.g.,][]{Bonnefoy-Claudetetal2006}. In order
to further mitigate undesired effects of local and transient
low-frequency sources (such as wind), \citet{Gisselbrechtetal2023}
computed these SSRn using short time windows (1~min) restricted to
quieter nighttime recordings. Note also that these empirical SSRn have
been estimated with respect to a node co-located with reference station
G6/ADHE. The similarities between Figures~\ref{fig9} and~\ref{fig10}
in the vicinity of broadband stations suggests that the two empirical
estimations, based on earthquake and ambient noise, are overall
consistent, and that the choice of the reference station, A4 or
G6/ADHE, has very little effect on the results in this low-frequency
band, as also suggested by the SSR ${\cong}$ 1 between the two stations
(see Supplementary Material). Thanks to the density of the nodal array,
Figure~\ref{fig10} gives a nice spatial view of the (mis)match
between\unskip\break
empirical and numerical amplification, and enables to look at its
spatial pattern in more detail.
In particular, we see again that our numerical amplification
over-estimates the observations in the northwesternmost corner of the
domain, including at rock sites where we know that Urgonian limestones
are outcropping, but also in the north-west of the basin (south-west of
Pierrelatte), suggesting that the ANSWT model might over-estimate
sediment thickness in this area. In contrast, amplification seems
under-estimated in the deepest part of the basin, along the north--south
axis of the paleo-Rh\^{o}ne canyon, as well as along the
paleo-Ard\`{e}che in the south-west of the domain. Knowing that ANSWT
provides a similar estimate of the canyon depth at station E1 as AVA
\citep{Gelisetal2022}, this\unskip\break
underestimation could suggest a lack of 3D
wave propagation effects in these parts of the model, all the more that
they also correspond to regions susceptible to be affected by laterally
propagating surface waves generated at basin edges.
Interestingly, one of the areas where the match between numerical and
empirical amplification seems the most satisfying corresponds to the
Lapalud island in the South of the domain. Again, this suggests that
the ANSWT model is successful in estimating the subsurface velocity
structure in this region, despite its relative complexity due to the
presence of heterogeneous post-Urgonian Cretaceous units. It also
suggests that local 1D effects may be dominant in the amplification at
this location, which may be intuitively understood as the fact that the
top of the island itself is not affected by waves that are diffracted
by (/away from) the island. The ratio between numerical and noise-based
amplification is also quite satisfying north-west from Lapalud, where
the basin is shallower \citep{DoCoutoetal2024},
as well as on its eastern margin, which suggests that the
post-Urgonian Cretaceous and Tertiary units that form this margin do
not have the same properties as the rock sites located on Urgonian
outcrops (A4, G6/ADHE), and experience some amplification compared to
these hard rock sites. These lateral variations of bedrock properties,
rarely taken into account in numerical studies, are important to
consider, as they affect not only the local response, and thus the
choice of a reference for SSR calculation \citep{Steidletal1996}, but
also the impedance contrast at the sediment/bedrock interface.
\section{Discussion}\label{sec5}
The aim of this paper was to investigate if and how a standard ANSWT
model could be used for the numerical estimation of seismic
amplification in sedimentary basins. In light of our results, we can
state that ANSWT is surely a method of choice for depicting the
large-scale velocity structure of the subsurface, but that a standard,
purely data-driven, ANSWT workflow alone is visibly not sufficient to
generate seismic models that well reproduce the observed amplification
in complex sedimentary basins where 3D wave propagation effects are
significant.
In particular, we clearly saw the consequences of the lack of
well-marked lateral basin edges, and thus of laterally propagating
surface waves in the basin. There are several reasons for this lack of
shallow lateral contrasts in the model, in relation to the sensitivity
and resolution capacity of the surface wave data used for tomography.
First, the upper layers of the subsurface (first 50~m in the basin,
where the minimum wavelength is about 100~m; first 200~m outside the
basin, where the minimum wavelength is about 400~m) are poorly
constrained by our measurements of the dispersion of fundamental Love
and Rayleigh modes in the considered frequency range (0.3--5~Hz).
Second, due to this weak data sensitivity and to the relative weight of
regularization (smoothing), there is a smearing of low-velocity
anomalies from the center to the edges of the basin at the 2D
tomography stage, all the more that the edges of the model are less
well constrained by data coverage, given the imprint of the array. This
understanding is important because it gives precise clues about how to
improve future ANSWT applications for site effect assessment in
sedimentary basins. In terms of acquisition design, it suggests that
the seismic array should probably extend more outside the basin, in
order to better constrain basin edges and surrounding bedrock
properties. More generally, it suggests that we should seek more
information about basin edges, either in the seismic data themselves or
elsewhere.
In this preliminary study, indeed, we deliberately adopted a purely
data-driven ANSWT approach, in order to assess its potential and
limitations. But if surface wave data alone are not sufficient to
generate sharp lateral variations, then we should turn to other sources
of information which we first need to collect and then to integrate in
the tomographic process in order to constrain our models. In our case,
the surface imprint of the Messinian paleo-canyon is known from
geological mapping (Figure~\ref{fig1}), field campaigns, and borehole
data. Besides, active seismic profiles are also available in the area.
The interpretation of these profiles (time-domain migrated sections),
together with geological field campaigns and borehole data, allows for
a high-resolution identification of geological horizons
\citep{Bagayoko2021,DoCoutoetal2024}.
These horizons, however, cannot be considered as a strict ground truth,
because the inference of their depth depends on $P$-wave velocity values
assumed for time-to-depth conversion. Moreover, these geological
horizons do not necessarily correspond to seismic impedance contrasts,
as discussed in \citet{Fromentetal2022b}, and need to be interpolated
over the entire domain of interest. Nevertheless, these horizons could
be used as prior information in order to constrain the geometry of
subsurface layers in the tomographic process. This will be the subject
of further work following directly from this study, which enables us to
pinpoint more precisely where this prior information should be
introduced: namely both at the 2D tomography stage, where lateral
coherency could be enforced using these geological constraints rather
than blind smoothing, and at the 1D inversion stage, where interface
depths could be constrained directly.
Finally, the computation of H/V ratios from the ambient noise data
recorded by the dense nodal array also results in the estimation of an
interface related to an impedance contrast that controls seismic
amplification, and this information could also be worth taken into
account in the tomography and in our simulations. In practice, however,
how to take this interface into account is not completely obvious,
given that (i)~the interpretation of this interface in terms of depth
also implies some assumption about $V_S$ values in the
sediments, (ii)~this interface may not correspond to geological
stratigraphic horizons, or not always to the same horizons, and may
even not be continuous \citep{Fromentetal2022b}.
In all these cases, the consideration of such prior information in the
tomographic process would be best handled in the frame of probabilistic
methods, such as trans-dimensional tomography
\citep[e.g.,][]{Bodinetal2012,Galettietal2017} or Bayesian approaches
\citep[e.g.,][]{Luetal2020,Nouibatetal2022}. This would at least
require the use of state-of-the-art imaging techniques, and probably
some dedicated methodological developments as well, in particular to
properly evaluate and propagate uncertainties throughout the whole
ANSWT workflow, starting from reliable prior data uncertainty related
to dispersion curve picking, and ending with robust posterior
uncertainties on model parameters, which could then be used in our
numerical simulations to explore the variability of the predicted
amplification within the range of model uncertainties. As a direct
perspective of this work, we are currently investigating such
probabilistic methods in order to improve our tomographic model of the
Tricastin basin, and explore the sensitivity of our numerical
amplification to input data and model features, within realistic
uncertainty ranges. Another important---and yet open---question related
to data and model uncertainties concerns the frequency range in which
we can reliably predict seismic amplification, based on a certain set
of informations to design our numerical models.
Finally, the lack of 3D characteristics in the ANSWT model likely comes
in part from the 1D approximation which the standard 2-step ANSWT
procedure relies on for interpreting surface-wave dispersion in terms
of velocity structure as a function of depth. This assumption could be
overcome by using other imaging techniques, such as 1-step 3D ANSWT
\citep[e.g.,][]{Zhangetal2018} or full waveform inversion
\citep[FWI, e.g.,][]{VirieuxOperto2009,Luetal2020}. FWI, in particular, would
enable one to fully consider 3D wave propagation, as well as
higher-mode surface waves, and eventually body waves (if present in the
cross-correlations), that would bring additional information on the
impedance contrast between sediments and bedrock, and potentially
provide higher-resolution models with sharper lateral variations
associated to basin edges. We would recommend the investigation of such
waveform tomography techniques for building seismic models for site
effect assessment as a long-term research track, keeping in mind that
FWI applications in complex subsurface settings are always challenging,
and that noise-based FWI is still the subject of current developments
and raises challenges of its own, in particular regarding the effect of
noise sources on parameter estimation
\citep[e.g.,][]{Trompetal2010,Sageretal2018}. Standard ANSWT models
such as the one considered in this study could be used as initial
models for further waveform inversion approaches.
\section{Conclusion}\label{sec6}
In this paper, we have used a 3D $V_S$ model of a complex sedimentary
basin built via a standard ANSWT workflow to perform numerical
simulations of seismic wave propagation, and compute ground motion
amplification within the basin. The tomographic model well depicts the
main geological structures of the basin via its lateral variations of
seismic velocities at depth, but it also suffers from limitations
related to surface wave sensitivity and resolution capabilities,
especially in its shallow part. In particular, this ANSWT model lacks
of clear basin edges in order to efficiently trap seismic waves and
generate significant amounts of surface waves susceptible to propagate
laterally in the basin. As a result, the numerical amplification
predicted in this ANSWT model presents some 3D characteristics, such as
non-negligible variations with respect to the polarization of the
incident seismic wave, but also lacks of other expected 3D features,
such as the broadband signature of observed amplification at locations
affected by laterally propagating surface waves coming from basin
edges. On the other hand, in basin sites where a 1D response is
sufficient to explain the local amplification, the numerical
amplification predicted in the ANSWT model show a good agreement with
the observations, including in complex regions of the basin where
lateral variations of the deeper subsurface velocity structure must be
taken into account in order to reproduce the local 1D response. The
tomography also enables one to image lateral variations of bedrock
properties which are rarely taken into account in numerical site effect
estimations. These observations still concur in considering noise-based
tomography as a promising method for building seismic models for site
effect assessment in sedimentary basins, provided we can compensate the
lack of data sensitivity to shallow and sharp lateral variations, and
improve the reconstruction of basin edges.
Our results therefore show the potential of the approach, while
identifying its limitations and giving perspectives for future work,
which should aim at considering other imaging paradigms (e.g.,
noise-based waveform inversion) and at including prior information from
other sources of geological and geophysical data into the tomographic
process, in order to better constrain the geometry of the basin, in
particular its surface boundaries in the shallow subsurface which is
poorly constrained by surface-wave data alone.
\section*{Declaration of interests}
The authors do not work for, advise, own shares in, or receive funds
from any organization that could benefit from this article, and have
declared no affiliations other than their research organizations.
\section*{Data and software availability}
The datasets acquired in the frame of the DARE project are freely
available via their doi:
\href{https://doi.org/10.14470/L27575187372}{10.14470/L27575187372}
\citep{Pilzetal2021},
\href{https://doi.org/10.15778/RESIF.3T2019}{10.15778/RESIF.3T2019}
\citep{Fromentetal2023a}, and
\href{https://doi.org/10.15778/RESIF.XG2020}{10.15778/RESIF.XG2020}
[\citealp{Fromentetal2023b}, see also
\citealp{Fromentetal2022a}, for details]. The EFISPEC software
\citep{DeMartin2011} is freely available at
\url{https://gitlab.brgm.fr/brgm/efispec3d} (last accessed 25 January
2024).
\section*{Funding}
This work is part of the DARE project (\textit{Dense ARray for site
effect Estimation}, \url{https://anr.fr/Projet-ANR-19-CE31-0029}, last
accessed 25 January 2024), funded by the French National Research Agency
(\textit{Agence Nationale pour la Recherche}, ANR, grant number
ANR19-CE31-0029) and by the German Research Foundation
(\textit{Deutsche Forschungsgemeinschaft}, DFG, project number
431362334). ISTerre is part of Labex OSUG@2020 (ANR10 LABX56).
Numerical simulations have been performed with the EFISPEC software
developed at BRGM [\citealp{DeMartin2011},
\url{https://gitlab.brgm.fr/brgm/efispec3d}, last accessed 25 January
2024]. This work was granted access to the HPC resources of TGCC under
the allocation 2022-gen13461 made by GENCI, and of the GRICAD
infrastructure (\url{https://gricad.univ-grenoble-alpes.fr}, last
accessed 25 January 2024), which is supported by Grenoble research
communities.
\CDRGrant[ANR]{ANR19-CE31-0029}
\CDRGrant[DFG]{431362334}
\CDRGrant[ANR]{ANR10 LABX56}
\CDRGrant[TGCC]{2022-gen13461}
\section*{Acknowledgements}
The geological context of the studied area was discussed and completed
with the help of Damien Do Couto, Nanaba Bagayoko, Ludovic Mocochain
and Jean-Loup Rubino. We warmly acknowledge them.
\back{}
\section*{Supplementary data}
Supporting information for this article is available on the journals
website under \printDOI\ or from the author.
\CDRsupplementaryTwotypes{supplementary-material}{\cdrattach{crgeos-243-suppl.pdf}}
\CDRsupplementaryTwotypes{supplementary-material}{\cdrattach{File-S1.mp4}}
\CDRsupplementaryTwotypes{supplementary-material}{\cdrattach{File-S2.pdf}}
\bibliographystyle{crgeos}
\bibliography{crgeos20230720}
\refinput{crgeos20230720-reference.tex}
\end{document}