# Identifying large vulnerable water reservoirs using passive seismic monitoring

R. Kramer, Y. Lu, Q. -Y. Wang, S. Serafin, A. Ceppi, G. Bokelmann

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

## To cite this version:

R. Kramer, Y. Lu, Q. -Y. Wang, S. Serafin, A. Ceppi, et al., Identifying large vulnerable water reservoirs using passive seismic monitoring. Earth and Planetary Science Letters, 2025, 653, 10.1016/j.epsl.2025.119223. insu-05082252

HAL Id: insu-05082252

https://insu.hal.science/insu-05082252v1

Submitted on 23 May 2025

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

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

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

Distributed under a Creative Commons Attribution 4.0 International License
# Identifying large vulnerable water reservoirs using passive seismic monitoring

R. Kramer<sup>a, ID,*</sup>, Y. Lu<sup>a, ID</sup>, Q.-Y. Wang<sup>b, ID</sup>, S. Serafin<sup>a, ID</sup>, A. Ceppi<sup>c, ID</sup>, G. Bokelmann<sup>a, ID</sup>

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

## ARTICLE INFO

Editor: H. Thybo

Dataset link: www.orfeus-eu.org

**Keywords:**

Seismic noise

Seismic monitoring

Near surface processes

Groundwater resources

## ABSTRACT

Seismic waves capture important insights into subsurface behavior. We introduce an adapted coda-wave interferometry approach to monitor the spatial variability of semi-daily periodic seismic velocity changes on a regional scale, applied to data collected across South and Central Europe. Our results reveal a broad spatial correlation between seismic velocity changes and air pressure fluctuations, suggesting that air pressure is the dominant driving force. Specifically, air pressure fluctuations modulate saturation levels within the capillary zone through the dynamic interplay between fluid and gaseous phases, producing significant seismic velocity changes. The mechanism explains the large variations observed in regions with high connectivity between the surface and saturated zone. We associate this enhanced interaction with the high vulnerability of shallow water resources. We propose inspecting semi-daily periodic seismic velocity changes as a new tool for gaining a regional view of water reservoir vulnerability.

## 1. Introduction

Climate-change induced droughts and increasing industrial activity, have significantly intensified water demand (Megdal, 2018). Addressing these global challenges requires understanding and managing water resources to ensure a sustainable and resilient society and ecosystem (Mukherjee et al., 2018). In this context, shallow groundwater resources are broadly available and easily accessible and therefore particularly important (Wang et al., 2014; Gao et al., 2018). However, the high permeability and connectivity associated with shallow aquifer systems raise concerns about potential pollution and contamination (Al-Hashimi et al., 2021). Thus, acquiring a better understanding of the vulnerability of this water resource is imperative.

Aquifer vulnerability refers to the risk of pollution or contamination by substances such as chemicals or wastewater (Jang et al., 2017; Heinz et al., 2006). Methods and tools that allow constraining vulnerability are important to enhance awareness among decision-makers and the public, promoting secure water usage practices. The assessment of aquifer vulnerability commonly relies on measuring barometric efficiency (Hussein et al., 2013; Odling et al., 2015). This metric represents the susceptibility of the groundwater table to air pressure fluctuations, and it offers information about aquifer vertical diffusivity as a crucial determinant

of vulnerability (Hussein et al., 2013). However, at present, measurements of barometric efficiency are confined to sparse point-wise borehole observations, subject to local geo-hydrological variabilities. This emphasizes the need for a novel efficient tool that can infer shallow aquifer properties at larger scales, ranging from individual reservoirs to regional hydrological units.

Recent studies (Clements and Denolle, 2018; Mao et al., 2022) have highlighted the potential of ambient noise seismology for imaging the shallow subsurface and for temporal monitoring beside registering seismic events. This approach can provide continuous tracking of (temporal variations) seismic velocities. Initially aimed at understanding the evolution of crustal stress associated with earthquakes (Brenguier et al., 2008; Obermann et al., 2014; Wang et al., 2019), many studies have also revealed that the observed seismic velocities are affected by environmental processes like seasonal temperature cycles, precipitation, snow, and air pressure fluctuations (Sens-Schönfelder and Wegler, 2006; Richter et al., 2014; Hillers et al., 2015; Wang et al., 2017; Gradon et al., 2021; Guillemot et al., 2021). Moreover, long-term investigations have drawn attention towards the notable impact of groundwater-level fluctuations on the seismic velocities (Sens-Schönfelder and Wegler, 2006; Gassenmeier et al., 2014; Lecocq et al., 2014; Voisin et al., 2017; Clements and Denolle, 2018; Rodríguez Tribaldos and Ajo-Franklin,

<footnote><sup>a</sup> Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria</footnote>

<footnote><sup>b</sup> Institut Terre et Environnement de Strasbourg (ITES) UMR 7063, Université de Strasbourg, EOST/CNRS, Strasbourg, France</footnote>

<footnote><sup>c</sup> Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy</footnote>

<footnote>* Corresponding author.
E-mail address: richard.kramer@univie.ac.at (R. Kramer).</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Maps of the study area. (a) Overview of topographical variations across Europe, including the main geological features (AF - Alpine Foreland, AM - Alpine Mountains, ApM - Apennines, DM - Dinarides, PB - Po Basin, and PM - Pyrenees). (b) Distribution of the broadband seismic stations. (c) Meteorological stations used within the study.</figcaption></figure>

2021). Recent research has further illuminated the impact of short-term periodic effects, which routinely alter seismic velocities (Mao et al., 2019; Sens-Schönfelder and Eulenfeld, 2019; Oakley et al., 2021; Kramer et al., 2023). Considered as repetitive phenomena overprinting transient events, observations on the periodic seismic velocity changes contain stable information about the subsurface processes (Oakley et al., 2021; Kramer et al., 2023).

This study aims at assessing the vulnerability of shallow aquifers across South-Central Europe using observations of air-pressure-induced seismic velocity changes. As demonstrated in e.g. Gradon et al. (2021) and Kramer et al. (2023), air pressure fluctuations and their interaction with aquifers lead to substantial changes in seismic velocities. We focus specifically on examining periodic air pressure fluctuations caused by atmospheric tides.

Atmospheric tides are regular diurnal and semi-diurnal oscillations in the wind, temperature and pressure fields, which arise from the superposition of thermal effects and the gravitational force. The absorption of solar radiation in the atmosphere generates internal gravity waves that propagate downwards, contributing to both diurnal and semi-diurnal surface pressure oscillations (Chapman and Lindzen, 1970). In tropical areas, semidiurnal oscillations typically have greater amplitude than diurnal oscillations. Over land areas in the extratropics, the amplitude of diurnal oscillations is greatly enhanced by sensible heating from the ground (Dai and Wang, 1999). Semi-diurnal atmospheric pressure oscillations are supposed to be the main driving force for sub-daily periodic seismic velocity changes (Kramer et al., 2023). We propose that the strength of this seismic velocity cycle primarily reflects the air and water diffusivity above the groundwater table, which can serve as a proxy for barometric efficiency. The underlying mechanism will be elaborated upon in subsequent sections.

Studies of the barometric efficiency are conducted locally at boreholes (Hussein et al., 2013; Odling et al., 2015; Valois et al., 2022), and spatial mapping of this fundamental metric is hardly available. This is where our investigation becomes crucial, since it can provide such spatial information, allowing to capture the complexity of the geo-hydrogeological settings within Europe and the study area. Among the prominent geological features are the Po plain, the Alps, the Apennines, the Dinarides, the Pyrenees, and additional distinctive formations such as the Alpine Foreland (Fig. 1a). This diverse landscape gives rise to two primary types of aquifers, exhibiting spatial variations across the region (Cornu et al., 2013). Aquifers within consolidated rocks, such as limestones or sandstones, coexist alongside aquifers within unconsolidated rocks like sands (Cornu et al., 2013; Nistor, 2020). To capture the complex structure of the area, our study takes advantage of data obtained from meteorological services as well as seismic networks across southern and central Europe (Hetényi et al., 2018), to (i) demonstrate the feasibility of passive coda-wave monitoring utilizing typical broadband network setups with large inter-station spacing, and (ii) unveil the potential

of using seismic velocity observations to map the hydro-physical properties of underlying groundwater bodies.

## 2. Data and methods

### 2.1. Continuous seismic data

This study benefits from the growing number of broadband sensor installations across Europe in recent years. Notably, temporary deployments associated with the AlpArray project (Hetényi et al., 2018), have significantly contributed to filling the existing broadband station gaps in central and southern Europe. This has resulted in a dense broadband station coverage in the region during the considered period of data analysis, where the average inter-station distance is around 50 km. In total we gather four years (2016-2019) of continuous seismic data recorded at more than 1400 broadband seismic stations distributed over southern and central Europe, as shown in Fig. 1b. Following Lu et al. (2018), we pre-processed the continuous data reordered by each station in two main steps. Firstly, each hourly data was detrended, band-pass filtered, and instrument-response corrected. Secondly, we removed hourly data possibly containing earthquakes or other transient events. The remaining hourly segments were then whitened in the frequency domain and cross-correlated between station pairs with an inter-station distance between 25 km and 75 km.

### 2.2. Measurements of seismic velocity changes

Our objective is to extract short-term periodic seismic velocity changes from large-scale seismic arrays with relatively long interstation distances (around 50 km on average). Previous studies that focused on daily and sub-daily seismic velocity variations typically used relatively small arrays (Mao et al., 2019; Oakley et al., 2021; Kramer et al., 2023) or single stations (Sens-Schönfelder and Eulenfeld, 2019). Extending this approach for large-scale observations presents challenges: the interstation distances become larger, making it more difficult to obtain stable and reliable hourly observations over larger areas. To address this challenge, we propose a novel approach to generate pseudo high-temporal-resolution seismic velocity observations. We profit from the stable short-term periodic variations in the environmental driving forces such as air pressure and temperature. Resulting daily and sub-daily seismic velocities are expected to follow a similar repeating cycle each day. This is validated by inspecting spatio-temporal changes in the meteorological dataset (Fig. S1).

We calculate hourly cross-correlations within the frequency band of 0.5-1.5 Hz (Fig. 2b) to focus on near-subsurface behavior. As mentioned earlier, calculating hourly cross-correlations for large-scale investigations proves challenging due to the presence of long travel paths and strong attenuation, making it difficult to obtain reliable measurements
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Conceptualization of our long-distance high-temporal resolution monitoring approach. (a) Location of two stations of the Italian seismic network. (b) Normalized hourly cross-correlations between the two stations vertical component within the investigation time period. (c) Stacked cross-correlation for every hour. Each trace represents the stack of all corresponding hours within the time period. The arrow around 15 s indicates the theoretical arrival time of surface waves, followed by coda-wave windows.</figcaption></figure>

(Hadziioannou et al., 2011). Therefore, we stack the cross-correlations for corresponding 24-hour periods over the four-year time span (Fig. 2c). It is important to note that each cross-correlation is normalized by its maximum amplitude before stacking to mitigate the effects of transient events and highlight those of periodic phenomena that occur consistently over days. This approach yields a total of 24 unique cross-correlation functions (Fig. 2c) for each station pair, from which we measure changes in seismic velocity from coda waves.

In order to precisely define the coda-wave time window, we first measured the Rayleigh wave peak-envelope arrival time around the desired periods for each station pair. The obtained group velocities at 1-s period, the dominant period band used in this study, demonstrate a striking spatial correlation with the prominent geological features of the region. For instance, high velocities are evident in the Alps, contrasting with lower velocities observed in the Po plain and the Molasse basin (Fig. S3). This in a way validates our determination of Rayleigh-wave arrivals. Accordingly, we define the coda wave time window starting 2 s after the surface wave arrival and lasting for 30 s.

After identifying the coda window, we use the time stretching method (Sens-Schönfelder and Wegler, 2006) to determine the relative seismic velocity changes (dv/v). The underlying idea is that waveforms are compressed or stretched proportionally in response to dv/v variations in the medium. To find the corresponding dv/v value, we examine different stretching factors $\epsilon$, selecting the one that maximizes the similarity between the stretched correlation function and the reference. Employing this technique in a series of sub-time windows (with 4-s window length) within the coda, we calculate their median value as the final dv/v measurement of each station pair to stabilize our results and minimize procedural error.

## 2.3. Measurements of atmospheric pressure

We compile an atmospheric pressure dataset covering a similar spatial extent as the seismic dataset (Fig. 1c). Measurements of surface

atmospheric pressure are routinely collected by meteorological services at hourly or sub-hourly temporal resolution and ingested in operational data assimilation and weather prediction systems worldwide (Bauer et al., 2015). This study combines data transmitted to the WMO Global Telecommunication System and archived in NOAA's freely accessible Integrated Surface Database (Smith et al., 2011) with additional measurements obtained by national weather services upon request or through open data repositories. Measurements covering Italy were extracted by the Meteonetwork database (Giazzi et al., 2022), a citizen science open crowdsourced meteorological network that shares data from thousands of weather stations online on a sub-hourly basis. Because of heterogeneous data conventions, our merged hourly dataset (approx. 1500 stations from 01/2016 to 12/2019) contains a combination of station-level pressure and pressure reduced to sea level. The former is needed in this study. Missing records of surface pressure were reconstructed from sea-level pressure by inverting the pressure reduction formula (World Meteorological Organization (WMO), 1954). This was accomplished using information on surface temperature and elevation, but neglecting the influence of atmospheric humidity. Considering data subsets in which both surface pressure and reduced pressure are available, we verified that this approximation in the inversion of pressure reduction has a negligible impact on the reconstructed diurnal variability of station pressure.

## 2.4. Mapping spatial daily and semi-daily behavior of seismic velocities and meteorological parameters

To analyze the dominant periodic behavior, we decompose the measured seismic velocity changes (dv/v) into multiple sinusoidal functions with the periods of 24 h ($S_1$) and 12 h ($S_2$), representing the daily and semi-daily components respectively.

In order to capture and visualize spatial variations in the sub-daily behavior of the seismic velocity changes, we employed a regionalization procedure, spatially averaging measurements from individual station
<figure><img src="image_6.png" /><figcaption>Fig. 3. Comparison of the periodic patterns of seismic velocity changes and air pressure (averaged over the entire study area). The orange and blue dots represent the averaged measurements of (a) seismic dv/v and (b) air pressure. The dashed black line indicates the synthetic data from fitting sinusoidal functions using 24-hour, 12-hour, and 8-hour periods. The red and blue lines represent the fitted daily and semi-daily terms, respectively. The $S_3$ curve has been omitted for illustration reasons and can be found in the supplementary section (see Fig. S2).</figcaption></figure>

pairs. This involved defining a grid that covers the area of interest. Subsequently, we computed the average dv/v time series from station pairs within a 200 km radius around each centered grid point. To mitigate the influence of station pairs farther from the center, we applied a spatial Gaussian weighting function, effectively scaling down their impact. As basis for this we used the mid-point between the two stations and its distance from the grid point.

We adopted the aforementioned approach similarly for analyzing meteorological data. However, we reduced the radius to 50 km. This adjustment accounts for the strong spatial variability of meteorological variables across short spatial scales, influenced by factors like topography and other local effects.

# 3. Results and discussion

## 3.1. Periodic behavior of seismic velocity changes and air pressure

As described in the section before we decomposed the seismic velocities and air pressure into their main components. We further introduce the period of 8 hours ($S_3$) for the seismic velocities, as recognized by Mao et al. (2019) and Kramer et al. (2023).

This will reduce the fitting residual error for the whole investigation area (see Fig. 3A; Fig. S2 and Fig. S4). Other minor components with higher frequencies only lead to a slightly improved fit. We focus on the daily and semi-daily components, which dominate the periodic changes. The spatial analysis of $S_3$ shows that it is roughly half of the $S_2$ amplitude. With that, we confirm earlier observations made from the GERES array in southern Germany using a conventional seismic noise coda-wave monitoring workflow (Kramer et al., 2023). Fig. 3a shows that the averaged amplitudes of $S_1$ and $S_2$ over the study area are in a similar range of magnitude, both on the order of $10^{-5}$. The $S_1$ component shows a peak at approximately 14 hours, while the minimum occurs in the early morning hours around 2 hours. The $S_2$ component peaks around 5 hours and 17 hours, with minima occurring at approximately 11 hours and 23 hours.

A similar picture emerges when we decompose the signal of air pressure into $S_1$ and $S_2$ patterns (Fig. 3b). The two sinusoidal signals show amplitudes of 35 Pa and 43 Pa. The $S_1$ component exhibits a peak at approximately 6 hours, while the minimum occurs in the late afternoon hours around 17 hours. Conversely, the $S_2$ component demonstrates an inverse slightly time shifted trend to seismic velocity changes, with peaks around 11 hours and 23 hours, and minima occurring at approximately 4 hours and 16 hours. These observations highlight the distinct periodic behaviors of air pressure, shedding light on its potential correlations with seismic phenomena.

Several studies (Hillers et al., 2015; Mao et al., 2019; Sens-Schönfelder and Eulenfeld, 2019; Oakley et al., 2021; Kramer et al., 2023) have identified the various driving forces that contribute to a significant

$S_1$ component in the dv/v observation. These driving forces encompass temperature variations within the subsurface, leading to changing thermoelastic strain (Berger, 1975; Ben-Zion and Leary, 1986). Additionally, thermal-induced self-noise from the instrumentation has been recognized as another contributing factor (Wielandt, 2012). The quantification of the latter effect is challenging due to variations in the installation standards for different station networks. Furthermore, we have identified that fluctuations in air pressure can also play a significant role in the diurnal variation of seismic velocities (Kramer et al., 2023). In contrast to that, the $S_2$ is predominantly caused by changes in the air pressure (Kramer et al., 2023). Other factors such as solid Earth tides or soil moisture variations cannot explain the observations. This would help separate various driving forces, with its behavior more directly attributed to subsurface properties, particularly the hydrophysical properties of underlying groundwater bodies. Thus, we focus on the spatial pattern of the $S_2$ component in the following and elaborate on its meaning and potential applications.

## 3.2. Spatial variations of semi-daily velocity changes

Mapping the amplitude of the $S_2$ component (Fig. 4a) reveals significant spatial variability in this semi-daily behavior of seismic velocity changes across South-Central Europe.

Areas north of the Alps exhibit relatively low amplitudes, measuring below $0.25 \cdot 10^{-4}$, whereas areas to the south tend to display higher amplitudes, occasionally exceeding $0.5 \cdot 10^{-4}$. Particularly high amplitudes exist along the southern Alps in northern Italy and Slovenia, and along the Apennines in central Italy. The $S_2$ component of air pressure (Fig. 4b) exhibits a simpler spatial pattern characterized by a North-South difference, with smooth variations mostly in the range of 40 to 55 Pa. The highest amplitudes are observed in the border region between Slovenia and Croatia. Additionally, other notable features above the average of roughly 47 Pa are observed in southeastern France, within the Po plain in northern Italy, and along the Italian peninsula, following the mountain ridges of the Apennines.

Comparing velocity and air pressure reveals a general spatial correlation (Fig. 4), particularly regarding the north-south division relative to the Alps. Such a correlation is expected given the influence of air pressure on the amplitude of seismic velocity changes. Yet, this can not explain important detailed spatial discrepancies. For instance, the highest anomaly of air pressure does not coincide with the highest anomaly of seismic velocity changes. In general, we conclude that a stronger sub-daily air pressure cycle does not necessarily result in a stronger $S_2$ component in seismic velocity changes, suggesting that the local geo-hydrological variability plays a crucial role in semi-daily velocity behavior.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Spatial distribution of the amplitude of S₂. (a) S₂ amplitude for seismic velocity change (dv/v). The green contours outline aquifers characterized by continuous carbonates, moderate to high permeability, and large pore size (Cornu et al., 2013; Chen et al., 2017; Xanke et al., 2022). (b) S₂ amplitude of air pressure oscillations. The white contour outlines the Alpine Mountains. (Note: the amplitude of the air pressure refers to a de-meaned time series. The amplitude of air pressure oscillations (about 50 Pa) is much smaller than the absolute value of sea-level pressure (about 10⁵ Pa).</figcaption></figure>

### 3.3. Mechanisms explaining the velocity changes

The spatial correlation between semi-daily changes in seismic velocity and air pressure, as well as in previous studies (Kramer et al., 2023) suggest that air pressure is the main cause of semi-daily velocity variations. In the following we present the mechanisms underlying sub-daily seismic velocity changes and their implications for characterizing geo-hydrological features of shallow water reservoirs. Seismic velocity changes measured within the 0.5-1.5 Hz frequency range exhibit depth sensitivities highly restricted to the shallow subsurface (Qin et al., 2022; Fokker et al., 2023). As discussed earlier the only reasonable explanation for the sub-daily velocity changes are fluctuations in the air pressure above the observation point. As a direct effect, air pressure influences the unsaturated zone, causing compression during high air pressure and extension during low air pressure phases (Silver et al., 2007). Soils or weathered rocks, prevalent in the unsaturated zone, would experience significant water redistribution, thereby altering shallow seismic velocities.

Shallow seismic velocity changes, as demonstrated by (Solazzi et al., 2021), are strongly influenced by capillary effects in partially saturated soils. The capillary fringe, which is a thin layer above the water table between the unsaturated and saturated zones, responds to changes in pressure by rising or falling. This dynamic behavior of the capillary fringe is crucial as it mediates the relationship between air pressure fluctuations and alterations in seismic velocities. Understanding this relationship requires a conceptual grasp of the conditions in the subsurface. The subsurface medium comprises solid, fluid, and gaseous phases (Peck, 1960; Norum and Luthin, 1968). Above the water table, the pores of the solid phase are filled with water, entrapped air, and air that can move freely and is connected to the atmosphere (Peck, 1960). When air is disconnected from the atmosphere, it becomes entrapped air, unable to move freely due to blockages by the fluid phase (Norum and Luthin, 1968). If air is entrapped in the zone around the water table and within the capillary fringe, changes in air pressure will induce alterations in this zone, as it is compressible (Norum and Luthin, 1968; Turk, 1975). Under high air pressure, entrapped air compresses, occupying less pore space, and water from the saturated zone is drawn upward by capillary forces to fill this space. Consequently, this results in a decrease in the water table level and an expansion of the capillary fringe. Conversely, if air pressure decreases, entrapped air expands, pushing water downwards the water table, leading to a rise in the water table level and a

reduction in the thickness of the capillary fringe (Turk, 1975). Fig. 5 illustrate this dynamic process. The impact of air pressure on changes in the water table varies considerably depending on the type of aquifer and can reach up to 6 cm (Turk, 1975). High air pressure results in a thicker capillary fringe, increasing saturation of the probed medium and causing a decrease in shear-wave seismic velocity (Toksöz et al., 1976; Mavko et al., 2009; Dong and Lu, 2016; Solazzi et al., 2021). Conversely, under low air pressure conditions, the opposite effect occurs. Laboratory experiments confirm that this effect occurs almost instantly (Norum and Luthin, 1968; Turk, 1975). Based on our experimental setting we found an average time delay between air pressure and dv/v of approx. 0.6 h (see Fig. S5). The interplay among air pressure, water table, and capillary layer clarifies the observed negative correlation between the S2 component of seismic observations and atmospheric conditions (Fig. 3). It is important to note that other effects could also contribute to variations in the seismic velocity, e.g. changes in the pore pressure due to air pressure variations (Clements and Denolle, 2018) altering the seismic velocities. While this effect could further contribute to the velocity changes it is not able to explain the observed phase delays (Fig. S5), since the reaction would be quasi-instantaneous.

### 3.4. Sub-daily seismic velocity changes as an indicator of vulnerable water reservoirs

Building upon the links established earlier a high amplitude in the S₂ of seismic velocity changes would indicate a strong interaction of air pressure with the subsurface. This reflects the connectivity that allows the air pressure to diffuse through the unsaturated zone. Delving into various (hydro)geological settings across Europe, we can discern specific conditions that favor this pronounced behavior.

The highest anomalies in Fig. 4a overlap with consolidated aquifers composed of continuous carbonate rocks exhibiting moderate to high permeability and large pore size, known as karst formations (Cornu et al., 2013; Chen et al., 2017; Nistor, 2020). This aquifer type is characterized by features such as caves, sinkholes, fractures, underground rivers and interconnected conduits (Ford and Williams, 2007). These features formed by corroding effects lead to a stronger secondary porosity, dominating the water regime (Ford and Williams, 2007). These properties enable karsts to effectively redistribute and infiltrate water in short periods, evident in a strong fluctuation of the water table (Serpelloni et al., 2018). This provides a unique setting to pronounce the semi-daily
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Schematic evolution of the capillary fringe under different air pressure conditions. The left panel illustrates the decrease in capillary thickness under low air pressure, resulting in an increase in seismic wave velocity. The right panel depicts the process under high air pressure, where the capillary fringe becomes thicker with a lowered water table. The zoom-in in the middle shows the alteration in compressible air space with and without water infiltration (modified after Fabbri et al. (2019)). The orange shaded areas schematically represent the parts sampled by the coda waves.</figcaption></figure>

behavior of the seismic velocities. Particularly noteworthy are sinkholes and other openings to the atmosphere, such as fractures, which provide a direct path for air pressure to act on the capillary zone. This configuration within the geological formations presented here results in a stronger impact of air pressure changes. While we believe that the sub-daily variations are mainly driven by the atmospheric tides it is noteworthy that karst systems undergo further significant non-periodic changes due to meteorological and tectonic factors like presented in Barajas et al. (2021). Our spatial mapping reveals three significant groundwater reservoirs in Europe following this pattern.

Fig. 4a I. shows the karst in the southern calcareous Alps including the karst plateau and adjacent karst in the Dinarides (Chen et al., 2017; Xanke et al., 2022). It is the largest and most pronounced anomaly within our study. This might be due to the continuity of the formation over a large area. Fig. 4a II. overlays with the Apennine carbonate platform in the central Apennines, recognized as a substantial water-bearing formation (Mantovani et al., 2019; Xanke et al., 2022). Our method images the central part of this formation. The third reservoir in southeastern France (Fig. 4a III.), located in the western Alps and the southern France basin (Xanke et al., 2022), is also identified with our method. However, this reservoir exhibits a less pronounced anomaly. We found two reasons for that. Firstly, the reservoir sustained by continuous karst is smaller compared to the others (Chen et al., 2017) and is closely located to discontinuous carbonates (Chen et al., 2017) and non-aquiferous rocks (Cornu et al., 2013). Secondly, the station coverage in this area is sparse, implying stronger smoothing.

Further notable geo-hydrological units across the study area are semi- to high-altitude mountain ridges such as the Alps or the Apennines. They are permeated with fissures and cracks (Somers and McKenzie, 2020) which are not necessarily intra-connected or open to the surface leading to trapping of the fluids and a lower level of permeability compared to other aquifer types. As a consequence of these geological attributes, these aquifers show a slower infiltration rate and interaction between air pressure variations and the subsurface in these regions is inherently limited. Another large-scale geo-hydrological unit is the Po Basin. It is characterized by marine deposits (Wendland et al., 2008), and accordingly, large pore sizes and high permeability (Cornu et al., 2013). While we observed a pronounced anomaly in the central-eastern Po Basin, drawing conclusions about its seismic response to semi-daily air pressure changes for its western part is difficult due to limited station coverage (Fig. 1b).

Our method provides a new assessment of barometric efficiency. This could be used to correct for air pressure influence in routine hy-

drological borehole measurements. To do so, it would be necessary to remove the actual air pressure force signal from the dv/v. By doing that it would be ensured that the results purely reflect the reaction of the system to those changes. Knowing which reservoirs are likely to have a stronger interaction with air pressure potentially help to get more precise groundwater level measurements (Rasmussen and Crawford, 1997). More importantly, it serves as a measure of vertical diffusivity (Hussein et al., 2013), which is closely linked to the vulnerability of the aquifer. Our technique allows large-scale mapping of this metric and therefore overcomes the point-wise character of the well-based method. Our study points out karst as a dominant feature in air pressure-subsurface interaction. This highlights the importance of this formation, since karst aquifers are renowned for their easy accessibility, making them an essential sources of potable water for approximately 25% of the world's population (Ford and Williams, 2007; Stevanovic, 2019). This accessibility makes them vital resources for buffering the increasing demand for water needed for both society and industry. Naming their essential role in water management, karst formations contribute significantly to sustainable water resources. However, the fast infiltration, permeability and flow patterns of karst formations also expose them to significant vulnerabilities, particularly in terms of contamination (Heinz et al., 2006; Ford and Williams, 2007; Stevanovic, 2015). Various contaminants can easily infiltrate these aquifers posing risks to both the environment and humans. In this regard, the method presented in this study offers the potential to effectively assess vulnerable aquifers which is essential for their responsible management and protection. This entails large-scale mapping, exemplified in the context of southern-central Europe, and also more focused small-scale applications aimed at better understanding individual aquifers or hydrological units.

### 3.5. *Challenges and limitations*

Our study demonstrates the feasibility of extracting periodic patterns of dv/v from large regional arrays, with these patterns showing clear correlations to large-scale water reservoirs influenced by interactions between air pressure changes and the capillary zone. While assessing this important parameter yields valuable insights, it comes at certain costs.

One of the main challenges is stabilizing the dv/v measurements across long inter-station distances without losing the temporal component. As described earlier, we stacked the data over both time (4 years) and space (200 km), resulting in a smoothed representation across these dimensions. While this approach aligns with our primary objective, it imposes limitations and introduces potential ambiguities in data in-
terpretation. The spatial smoothing imposes certain thresholds on the detectability of water reservoirs in our analysis. Larger and spatially continuous reservoirs are detectable through higher amplitude signals; however, when an aquifer is limited in size or discontinuous, averaging can reduce or obscure these signals, resulting in minimal or no detectable anomaly. By stacking and averaging our analysis over a 4-year period, we aim to exclude potential effects from non-periodic environmental drivers (e.g., precipitation) as well as natural variations, such as annual cycles of groundwater levels. Although this approach supports the interpretation of periodic behaviors on a large scale, it limits our sensitivity to these parameters. While our method effectively detects aquifers that exhibit strong interactions with air pressure changes - serving as an indicator of vulnerability - it is not capable of distinguishing between different aquifer types or aquifer depth. For instance, a strong interaction may occur in both karst systems with open channels and shallow sedimentary aquifers.

## 4. Conclusion

We have demonstrated the feasibility of investigating daily and sub-daily seismic velocity changes within large array settings. With an adapted coda-wave interferometry approach we are able to study the spatial variability of semi-daily seismic velocity changes. We associate fluctuations of air pressure with changes of the saturation in the capillary zone through an interplay between the fluid and the gaseous phase to explain the observed dv/v variations. This offers valuable information on the vulnerability of shallow water resources in southern-central Europe, particularly those with high connectivity, which exhibit a pronounced interaction with air pressure.

However, covering a vast area with different geological, hydrological and meteorological settings imposes certain limitations and challenges. One such challenge is ensuring reliable dv/v measurements. While averaging over a distance helps stabilize measurements, it comes at the cost of spatial resolution, potentially omitting small hydrological formations.

To directly use them as a measure of barometric efficiency further steps are required. This includes the deconvolution of the air pressure force signal from the dv/v measurements. Further, seismic velocity behavior is a complex superposition of local characteristics. To account for that, some calibrations between the air pressure, groundwater depth, near-surface structure, and dv/v need to be done. Since these factors could be quantified locally, we anticipate that our method may be even more effective at local scales.

Consequently, studying semi-daily velocity variation, as a special case of dv/v monitoring becomes a tool for hydrology and even for environmental research. The seismic outputs, upon calibration and standardizing, may in principle serve as one of the core metrics or fundamental indices for the assessment of barometric efficiency, and evaluation of vulnerability. Further research could elevate this approach to a means of distinguishing various aquifer characteristics, beyond in-situ point-wise measurements. This may advance our comprehension of subsurface conduits, connectivity, permeability, and potential fault systems within the formation.

Establishing this kind of workflow as a time-lapse approach would help examine how these properties change over time that is essential for tracking environmental changes in the context of climate changes. Doing this would transform the regional seismic networks also into valuable hydrological monitoring systems. This could contribute to a comprehensive understanding of trends in the subsurface permeability and the karst formations, offering deeper insights into our water resources using passive seismology.

### CRediT authorship contribution statement

**R. Kramer:** Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Y. Lu:** Writing – review & editing, Supervision, Methodology, Data curation, Conceptualization. **Q.-Y. Wang:**

Writing – review & editing, Supervision, Conceptualization. **S. Serafin:** Writing – review & editing, Data curation, Conceptualization. **A. Ceppi:** Writing – review & editing, Data curation, Conceptualization. **G. Bokelmann:** Writing – review & editing, Supervision, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

The authors thank the editor, Hans Thybo, as well as Piero Poli and an anonymous reviewer for their questions and constructive feedback, which helped us improve our manuscript. We thank the operators of the permanent and temporary seismic networks across Europe who make the data available through EIDA (http://www.orfeus-eu.org/data/eida/). The authors thank the meteorological services of Germany (Deutscher Wetterdienst), Austria (Geosphere Austria), Switzerland (Bundesamt für Meteorologie und Klimatologie), France (MétéoFrance), and Slovenia (Slovenian Environmental Agency) for making their measurements accessible for this research. Further we thank Meteonetwork for compiling the data across Italy and making it available for us.

## Appendix A. Supplementary material

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.epsl.2025.119223.

### Data availability

The seismic data is available through EIDA (European Integrated Data Archive). This service is provided by ORFEUS (www.orfeus-eu.org). The meteorological dataset can be shared upon request.

### References

Al-Hashimi, O.A., Hashim, K., Loffill, E., Marolt Cebasek, T., Nakouti, I., Faisal, A., Ansari, N., 2021. A comprehensive review for groundwater contamination and remediation: occurrence, migration and adsorption modelling. Molecules 26. https://doi.org/10.3390/molecules26195913.

Barajas, A., Poli, P., D'Agostino, N., Margerin, L., Campillo, M., 2021. Separation of poroelastic and elastic processes of an aquifer from tectonic phenomena using geodetic, seismic, and meteorological data in the Pollino region, Italy. Geochem. Geophys. Geosyst. 22, e2021GC009742. https://doi.org/10.1029/2021GC009742. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021GC009742.

Bauer, P., Thorpe, A., Brunet, G., 2015. The quiet revolution of numerical weather prediction. Nature 525, 47–55. https://doi.org/10.1038/nature14956.

Ben-Zion, Y., Leary, P., 1986. Thermoelastic strain in a half-space covered by unconsolidated material. Bull. Seismol. Soc. Am. 76, 1447–1460.

Berger, J., 1975. A note on thermoelastic strains and tilts. J. Geophys. Res. 80, 274–277. https://doi.org/10.1029/JB080i002p00274.

Brenguier, F., Campillo, M., Hadzioannou, C., Shapiro, N.M., Nadeau, R.M., Larose, E., 2008. Postseismic relaxation along the San Andreas fault at Parkfield from continuous seismological observations. Science 321, 1478–1481. https://doi.org/10.1126/science.1160943.

Chapman, S., Lindzen, R.S., 1970. Atmospheric Tides. Springer, Dordrecht.

Chen, Z., Auler, A.S., Bakalowicz, M., Drew, D., Griger, F., Hartmann, J., Jiang, G., Moosdorf, N., Richts, A., Stevanovic, Z., Veni, G., Goldscheider, N., 2017. The World Karst Aquifer Mapping project: concept, mapping procedure and map of Europe. Hydrogeol. J. https://doi.org/10.1007/s10040-016-1519-3.

Clements, T., Denolle, M.A., 2018. Tracking groundwater levels using the ambient seismic field. Geophys. Res. Lett. 45, 6459–6465. https://doi.org/10.1029/2018GL077706.

Cornu, J.F., Eme, D., Malard, F., 2013. The distribution of groundwater habitats in Europe. Hydrogeol. J. https://doi.org/10.1007/s10040-013-0984-1.

Dai, A., Wang, J., 1999. Diurnal and semidiurnal tides in global surface pressure fields. J. Atmos. Sci. 56. https://doi.org/10.1175/1520-0469(1999)056<3874:DASTIG>2.0.CO;2.

Dong, Y., Lu, N., 2016. Dependencies of shear wave velocity and shear modulus of soil on saturation. J. Eng. Mech. 142. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001147.

Fabbri, A., Wong, H., Lai, B., Bui, T., Branque, D., 2019. Constitutive elasto-plastic model for fine soils in the unsaturated to saturated saturation zone. Comput. Geotech. 111, 10–21. https://doi.org/10.1016/j.compgeo.2019.03.001.
Fokker, E., Ruigrok, E., Hawkins, R., Trampert, J., 2023. 4d physics-based pore pressure monitoring using passive image interferometry. Geophys. Res. Lett. 50. https://doi.org/10.1029/2022GL011254.

Ford, D., Williams, P., 2007. Introduction to Karst. John Wiley & Sons, Ltd.

Gao, X., Huo, Z., Xu, X., Qu, Z., Huang, G., Tang, P., Bai, Y., 2018. Shallow groundwater plays an important role in enhancing irrigation water productivity in an arid area: the perspective from a regional agricultural hydrology simulation. Agric. Water Manag. 208, 43–58.

Gassenmeier, M., Sens-Schönfelder, C., Delatre, M., Korn, M., 2014. Monitoring of environmental influences on seismic velocity at the geological storage site for CO2 in Ketzin (Germany) with ambient seismic noise. Geophys. J. Int. 200, 524–533. https://doi.org/10.1093/gji/ggu413.

Giazzi, M., Peressutti, G., Cerri, L., Fumi, M., Riva, I., Chini, A., Ferrari, G., Cioni, G., Franch, G., Tartari, G., Galbiati, F., Condemi, V., Ceppi, A., 2022. Meteonomwork: an open crowdsourced weather data system. Atmosphere 13, 928. https://doi.org/10.3390/atmos13060928.

Gradon, C., Brenguier, F., Stammeijer, J., Mordret, A., Hindriks, K., Campman, X., Lynch, R., Boué, P., Chmiel, M., 2021. Seismic velocity response to atmospheric pressure using time-lapse passive seismic interferometry. Bull. Seismol. Soc. Am. 111, 3451–3458. https://doi.org/10.1785/0120210069.

Guillemot, A., van Herwijnen, A., Larose, E., Mayer, S., Baillet, L., 2021. Effect of snow-fall on changes in relative seismic velocity measured by ambient noise correlation. Cryosphere 15, 5805–5817. https://doi.org/10.5194/tc-15-5805-2021.

Hadzioinnou, C., Larose, E., Baig, A., Roux, P., Campillo, M., 2011. Improving temporal resolution in ambient noise monitoring of seismic wave speed. J. Geophys. Res., Solid Earth 116. https://doi.org/10.1029/2011JB008200.

Heinz, B., Birk, S., Liedl, R., Geyer, T., Straub, K., Bester, K., Kappler, A., 2006. Vulnerability of a karst spring to wastewater infiltration (Gallusquelle, Southwest Germany). Austrian J. Earth Sci. 99, 11–17.

Hetényi, G., Molinari, I., Clinton, J., et al., 2018. The AlpArray seismic network: a large-scale European experiment to image the Alpine orogen. Surv. Geophys. 39, 1009–1033. https://doi.org/10.1007/s10712-018-9472-4.

Hillers, G., Ben-Zion, J., Campillo, M., Zigone, D., 2015. Seasonal variations of seismic velocities in the San Jacinto fault area observed with ambient seismic noise. Geophys. J. Int. 202, 920–932. https://doi.org/10.1093/gji/ggv151.

Hussein, M.E.A., Odling, N.E., Clark, R.A., 2013. Borehole water level response to barometric pressure as an indicator of aquifer vulnerability. Water Resour. Res. 49, 7102–7119. https://doi.org/10.1002/2013WR014134.

Jang, W.S., Engel, B., Harbor, J., Theller, L., 2017. Aquifer vulnerability assessment for sustainable groundwater management using drastic. Water 9. https://doi.org/10.3390/w9100792.

Kramer, R., Lu, Y., Bokelmann, G., 2023. Interaction of air pressure and groundwater as main cause of sub-daily relative seismic velocity changes. Geophys. Res. Lett. 50. https://doi.org/10.1029/2022GL011298.

Lecocq, T., Longuevergne, L., Pedersen, H., Brenguier, F., Stammler, K., 2014. Monitoring ground water storage at mesoscale using seismic noise: 30 years of continuous observation and thermo-elastic and hydrological modeling. Sci. Rep. 7. https://doi.org/10.1038/s41598-017-14468-9.

Lu, Y., Stehly, L., Paul, A., the AlpArray Working Group, 2018. High-resolution surface wave tomography of the European crust and uppermost mantle from ambient seismic noise. Geophys. J. Int. 214, 1136–1150. https://doi.org/10.1093/gji/ggy188.

Mantovani, E., Viti, M., Babbucci, D., Tamburelli, C., Cenni, N., 2019. How and why the present tectonic setting in the Apennine belt has developed. J. Geol. Soc. 176. https://doi.org/10.1144/jgs2018-175.

Mao, S., Campillo, M., van der Hilst, R., Brenguier, F., Stehly, L., Hillers, G., 2019. High temporal resolution monitoring of small variations in crustal strain by dense seismic arrays. Geophys. Res. Lett. 46, 128–137. https://doi.org/10.1029/2018GL079944.

Mao, S., Lecointre, A., van der Hilst, R.D., Campillo, M., 2022. Space-time monitoring of groundwater fluctuations with passive seismic interferometry. Nat. Commun. 13. https://doi.org/10.1038/s41467-022-32194-3.

Mavko, G., Mukerji, T., Dvorkin, J., 2009. The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media. Cambridge University Press.

Megdal, S.B., 2018. Invisible water: the importance of good groundwater governance and management. npj Clean Water. https://doi.org/10.1038/s41545-018-0015-9.

Mukherjee, S., Mishra, A., Trenberth, K., 2018. Climate change and drought: a perspective on drought indices. Curr. Clim. Change Rep. 4. https://doi.org/10.1007/s40641-018-0098-x.

Nistor, M.M., 2020. Groundwater vulnerability in Europe under climate change. Quat. Int. 547, 185–196. https://doi.org/10.1016/j.quaint.2019.04.012.

Norum, D.I., Luthin, J.N., 1968. The effects of entrapped air and barometric fluctuations on the drainage of porous mediums. Water Resour. Res. 4, 417–424. https://doi.org/10.1029/WR004i002p00417.

Oakley, D., Forsythe, B., Gu, X., Ny blade, A., Brantley, S., 2021. Seismic ambient noise analyses reveal changing temperature and water signals to 10s of meters depth in the critical zone. J. Geophys. Res., Earth Surf. 126. https://doi.org/10.1029/2020JF005823.

Obermann, A., Froment, B., Campillo, M., Larose, E., Planès, T., Valette, B., Chen, J.H., Liu, Q.Y., 2014. Seismic noise correlations to image structural and mechanical changes

associated with the mw 7.9 2008 Wenchuan earthquake. J. Geophys. Res., Solid Earth 119, 3155–3168. https://doi.org/10.1002/2013JB010932.

Odling, N.E., Serrano, R.P., Hussein, M.E.A., Riva, M., Guadagnini, A., 2015. Detecting the vulnerability of groundwater in semi-confined aquifers using barometric response functions. J. Hydrol. 520, 143–156. https://doi.org/10.1016/j.jhydrol.2014.11.016.

Peck, A.J., 1960. The water table as affected by atmospheric pressure. J. Geophys. Res. 1896–1977 (65), 2383–2388. https://doi.org/10.1029/JZ065i008p02383.

Qin, L., Qiu, H., Nakata, N., Deng, S., Levander, A., Ben-Zion, Y., 2022. Variable daily autocorrelation functions of high-frequency seismic data on Mars. Seismol. Res. Lett. 94, 746–758. https://doi.org/10.1785/0220220196.

Rasmussen, T., Crawford, L., 1997. Identifying and removing barometric pressure effects in confined and unconfined aquifers. Ground Water 35, 502–511. https://doi.org/10.1111/j.1745-6584.1997.tb00111.x.

Richter, T., Sens-Schönfelder, C., Kind, R., Asch, G., 2014. Comprehensive observation and modeling of earthquake and temperature-related seismic velocity changes in northern Chile with passive image interferometry. J. Geophys. Res., Solid Earth 119, 4747–4765.

Rodríguez Tribaldos, V., Ajo-Franklin, J.B., 2021. Aquifer monitoring using ambient seismic noise recorded with distributed acoustic sensing (das) deployed on dark fiber. J. Geophys. Res., Solid Earth 126. https://doi.org/10.1029/2020JB021004.

Sens-Schönfelder, C., Eulenfeld, T., 2019. Probing the in situ elastic nonlinearity of rocks with Earth tides and seismic noise. Biophys. Rev. Lett. 122. https://doi.org/10.1103/PhysRevLett.122.138501.

Sens-Schönfelder, C., Wegler, U., 2006. Passive image interferometry and seasonal variations of seismic velocities at Merapi volcano, Indonesia. Geophys. Res. Lett. 33, 109–119.

Serpelloni, E., Pintori, F., Gualandi, A., Scoccimarro, E., Cavaliere, A., Anderlini, L., Belardinelli, M.E., Todesco, M., 2018. Hydrologically induced karst deformation: insights from gps measurements in the Adria-Eurasia plate boundary zone. J. Geophys. Res., Solid Earth 123, 4413–4430. https://doi.org/10.1002/2017JB015252.

Silver, P.G., Daley, T.M., Niu, F., Majer, E.L., 2007. Active source monitoring of cross-well seismic travel time for stress-induced changes. Bull. Seismol. Soc. Am. 97, 281–293. https://doi.org/10.1785/0120060120.

Smith, A., Lott, N., Vose, R., 2011. The integrated surface database: recent developments and partnerships. Bull. Am. Meteorol. Soc. 92. https://doi.org/10.1175/2011BAMS3015.1.

Solazzi, S.G., Bodet, L., Holliger, K., Jougnot, D., 2021. Surface-wave dispersion in partially saturated soils: the role of capillary forces. J. Geophys. Res., Solid Earth 126. https://doi.org/10.1029/2021JB022074.

Somers, L.D., McKenzie, J.M., 2020. A review of groundwater in high mountain environments. WIREs Water 7. https://doi.org/10.1002/wat2.1475.

Stevanovic, Z., 2015. Karst Aquifers: Characterization and Engineering. Springer, Cham [U.A.].

Stevanovic, Z., 2019. Karst waters in potable water: a global scale overview. Environment. Earth Sci. https://doi.org/10.1007/s12665-019-8670-9.

Toksöz, M.N., Cheng, C.H., Timur, A., 1976. Velocities of seismic waves in porous rocks. Geophysics 41, 621–645. https://doi.org/10.1190/1.1440639.

Turk, L., 1975. Diurnal fluctuations of water tables induced by atmospheric pressure changes. J. Hydrol. 26, 1–16. https://doi.org/10.1016/0022-1694(75)90121-3.

Valois, R., Rau, G.C., Vouillamoz, J.M., Derode, B., 2022. Estimating hydraulic properties of the shallow subsurface using the groundwater response to Earth and atmospheric tides: a comparison with pumping tests. Water Resour. Res. 58, e2021WR031666. https://doi.org/10.1029/2021WR031666.

Voisin, C., Romero Guzmán, M., Réflox, A., Taruselli, M., Garambois, S., 2017. Groundwater monitoring with passive seismic interferometry. J. Water Resour. Prot. 9, 1414–1427. https://doi.org/10.4236/jwarp.2017.912091.

Wang, P., Yu, J., Pozdnjakov, S.P., Grinevsky, S.O., Liu, C., 2014. Shallow groundwater dynamics and its driving forces in extremely arid areas: a case study of the lower Heihe river in northwestern China. Hydrol. Process. 28, 1539–1553. https://doi.org/10.1002/hyp.9682.

Wang, Q.Y., Brenguier, F., Campillo, M., Lecointre, A., Takeda, T., Aoki, Y., 2017. Seasonal crustal seismic velocity changes throughout Japan. J. Geophys. Res., Solid Earth 122, 7978–8002. https://doi.org/10.1002/2017JB014307.

Wang, Q.Y., Campillo, M., Brenguier, F., Lecointre, A., Takeda, T., Hashima, A., 2019. Evidence of changes of seismic properties in the entire crust beneath Japan after the mw 9.0, 2011 Tohoku-oki earthquake. J. Geophys. Res., Solid Earth 124, 8924–8941. https://doi.org/10.1029/2019JB017803.

Wendland, F., Blum, A., Coetsiers, M., Gorova, R., Griffioen, J., Grima-Olmedo, J., Hinsby, K., Kunkel, R., Marandi, A., de Melo, M.T., Panagopoulos, A., Pauwels, H., Ruisi, M., Traversa, P., Vermooten, S., Walraevens, K., 2008. European aquifer typology: a practical framework for an overview of major groundwater composition at European scale. Environ. Geol. 55, 77–85. https://doi.org/10.1007/s00254-007-0966-5.

Wielandt, E., 2012. Seismic sensors and their calibration. In: New Manual of Seismological Observatory Practice, vol. 2, pp. 1–51.

World Meteorological Organization (WMO), 1954. Technical Note No. 7. Reduction of Atmospheric Pressure: Preliminary Report on Problems Involved. WMO-No. 36, TP. 12, Technical Report. WMO.

Xanke, J., Goldscheider, N., M., B., 2022. Mediterranean karst aquifer map (medkam). 1:5,000,000. Berlin, Karlsruhe, Paris.