# Modeling earthquake-induced seiche processes and subsequent homogenite deposition in lacustrine settings

Muhammad Naveed Zafar, Pierre Sabatier, Denys Dutykh, Hervé Jomard,

William Rapuc, Patrick Lajeunesse, Emmanuel Chapron

► To cite this version:

Muhammad Naveed Zafar, Pierre Sabatier, Denys Dutykh, Hervé Jomard, William Rapuc, et al. Modeling earthquake-induced seiche processes and subsequent homogenite deposition in lacustrine settings. Earth and Planetary Science Letters, 2025, 660, pp.119348. 10.1016/j.epsl.2025.119348. hal-05029307

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# Modeling earthquake-induced seiche processes and subsequent homogenite deposition in lacustrine settings

Muhammad Naveed Zafar a, b, *, c, Pierre Sabatier b, c, Denys Dutykh c, Hervé Jomard d, William Rapuc b, d, Patrick Lajeunesse e, Emmanuel Chapron f

## ARTICLE INFO

Editor: J.P. Avouac

Keywords:

Seiche
Homogenite deposit
Earthquake
Tsunami wave
Lake

## ABSTRACT

Earthquakes leave distinct signatures on lake systems, including event deposits that serve as valuable paleo-seismological archives. Among these deposits, homogenite layers are commonly associated with lake oscillations, i.e., seiches. Here, we investigate the seiche mechanism and the formation of a homogenite related sediment deposit within a lacustrine environment. This study focuses on the 1822 CE earthquake in the Western European Alps, which triggered subaqueous landslides in Lake Bourget (France). This event caused oscillations in the lake's water, which subsequently resulted in the formation of a homogenite layer in the deep basin. The underlying mechanism is resolved by presenting the first comprehensive numerical model via coupling of coseismic displacement, seismic wave propagation, and mass movement with the tsunami model. The numerical simulations show excellent agreement with the available geological and historical observations. The water disturbances caused by subaqueous landslides generated small tsunami waves with a maximum runup height of approximately 2.5 m. By analyzing the tsunami signals using Fourier spectral analysis and fast iterative filtering, we determined that seismic waves are the primary drivers of seiche, which excite the natural modes of Lake Bourget. Our numerical results confirm that the sediments found in the deep basin originated from one main subaqueous landslide and from tsunami erosion of littoral sands (backwash). However, the seismically induced seiche was solely responsible for keeping the fine-grained sediment cloud in suspension for several days and led to the formation of the homogenite layer (or seiche deposit) with typical grain orientation characteristics. The proposed numerical framework could also be effective in identifying whether landslides or delta collapses (linked to homogenite/megaturbidites) in closed lakes were triggered by seismic or nonseismic sources. This distinction is crucial for reconstructing the history of past earthquakes and associated hazards.

## 1. Introduction

Reconstructing past events such as earthquakes can be challenging, especially when faults are inaccessible, do not reach the Earth's surface, or occur beyond the reach of instrumental and historical data. In these situations, lacustrine deposits related to seismically induced surficial remobilization, subaqueous landslides or tsunamis are one of the main evidence from which we can collect paleoseismological information (Moernaut et al., 2017; Molenaar et al., 2021; Chapron et al., 1999, 2006, 2016; Van Rensbergen et al., 1999; Strasser & Anselmetti, 2008;

Beck, 2009; Wilhelm et al., 2016). These events often result in the formation of exceptional graded and relatively homogenous deposits known as megaturbidites, homogenites or seiche deposits (Rapuc et al., 2018; Schnellmann et al., 2002; Vermassen et al., 2023; Chapron et al., 2006; 2016; Lauterbach et al., 2012). Although both seismic and non-seismic activity can initiate turbidity currents (Liapidevskii & Dutykh, 2019; Liapidevskii et al., 2018), sedimentary characteristics need to be analyzed carefully to accurately determine (mega)turbidite origins (Sabatier et al., 2022). This depositional process is further complicated when other phenomena, such as seiches/tsunamis, influence these

<footnote>ª LAMA, Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS, Chambéry, France</footnote>

<footnote>ᵇ EDYTEM, Université Savoie Mont Blanc, CNRS, Chambéry, France</footnote>

<footnote>ᶜ Mathematics Department, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates</footnote>

<footnote>ᵈ Autorité de sûreté nucléaire et de radioprotection (ASNR), PSE-ENV/SCAN/BERSSIN, F-92260, Fontenay-aux-Roses, France</footnote>

<footnote>ᵉ Département de Géographie, Université Laval, Québec, Canada</footnote>

<footnote>f GEODE, Université Toulouse Jean Jaurès, CNRS Toulouse, France</footnote>

<footnote>* Corresponding author.
E-mail address: Muhammad-Naveed.Zafar@univ-smb.fr (M.N. Zafar).</footnote>
subaquatic event deposits.

Seiches are standing oscillatory waves with extended periods, typi-
cally occurring in partially or fully enclosed water bodies. These long-
period standing waves are formed when the triggering mechanism ex-
cites the natural resonant frequencies of the basin (Kvale, 1955; McGarr,
1965). When they excite the fundamental mode (the lowest eigenfrequ-
quency) of the basin, these waves exhibit the most prolonged periods of
oscillation (Kvale, 1955; McGarr 1965). Seiche currents significantly
impact muddy turbidites by keeping in suspension (or resuspending
remobilized) fine sediments within the water body (Beck, 2009; Chap-
ron et al., 1999; Mulder et al., 2009; Siegenthaler et al., 1987; Sie-
genthaler, 2021). The fine sediment cloud eventually settles during the
energy dissipation phase, resulting in the formation of a "homogenite" in
deep basins (Siegenthaler et al., 1987). In marine or lacustrine envi-
ronment, such homogenites are typically composed of a thick and ho-
mogeneous fine layer with sandy to silty laminae at their base (Chapron
et al., 1999; Cita et al., 1996; Kastens & Cita, 1981; Mulder et al., 2009;
Bertrand et al., 2008; Rapuc et al., 2018; Vermassen et al., 2023).

Reconstructing past earthquake-induced sedimentary events without
a complete understanding of depositional mechanisms and processes is
thus difficult. Determining the source mechanism responsible for seiches
is more challenging during compound events, such as coeval
earthquake-induced landslides, which are common events in lacustrine
settings (Siegenthaler et al., 1987; Chapron et al., 2006, 2016;

Lauterbach et al. 2012). However, landslides are not always triggered by earthquakes; their causes range from gravitational slope failure (Svennevig et al., 2024; Waldmann et al., 2021; Kremer et al., 2012) to floods (Vandekerkhove et al., 2020) or even spontaneous sediment overload (Girardclos et al., 2007; Hilbe & Anselmetti, 2014), which could be related to human activity (Gastineau et al., 2024). In this situation, it is crucial to clarify which phenomena, earthquakes or landslides (whether triggered by earthquakes or not) predominantly cause seiches leading to homogenite deposit. Addressing this question requires comprehensive high-resolution numerical modeling of multiple simultaneous phenomena, constrained by geological data to reduce uncertainties.

In this paper, we focus on the depositional mechanism of the seiche
wave and the formation of a well-known homogenite deposit in Lake
Bourget correlated with the 1822 CE earthquake (Chapron et al., 1999,
2005, 2016). For this purpose, we introduce a numerical model to
simulate tsunami waves generated simultaneously by the earthquake
shaking (including seismic waves) and coeval subaqueous landslides.
Additionally, computations of lake eigenmodes, tsunami signal analysis,
and bed shear stress calculations were performed. We utilize historical,
bathymetric and sediment core data to constrain our modeled results to
unravel the depositional mechanism behind the 1822 CE seiche wave
and the subsequent homogenite deposit in Lake Bourget. The modeling
process is summarized in the schematic diagram (Fig. S1).

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

**Fig. 1.** General location of Lake Bourget in the NW French Alps. (a) The multibeam bathymetric digital elevation model, integrating measurements from a Reson Seabat 8101 (240 kHz) multibeam echosounder (in water depths > 25 m), as detailed in Ledoux (2010), and a R2Sonic 2020 (400 kHz) multibeam echosounder, together with a LIDAR Optech TITAN (in water depths < 25 m), illustrates landslide 1 (fuchsia scar), landslide 2 (black scar), accumulated landslide deposit (brown elliptical shape), and the extent of homogeneous deposits (yellow elliptical shape) associated with the 1822 CE earthquake, according to Chapron (1999, 2005, 2016). Sediment core locations (circles) are indicated, and the colored circle diameters reflect the thickness of the 1822 CE homogeneous deposit, according to Chapron (1999, 2005), Jenny (2014), and Wilhelm (2022). The 1822 CE earthquake epicenter, located just north of Lake Bourget, is shown (star), along with the active faults of Culoz and Col du Chat, which border the lake. The Grésine Bay and the localities of Aix-les-Bains, Saint-Germain, and Brison, discussed in the text, are also indicated. (b) Enlarged view of the multibeam bathymetry illustrating the location of the two mass-wasting deposits (MWD) triggered by the 1822 CE earthquake along the western slopes of Lake Bourget, offshore from Hautecombe Abbey, where distal Rhône River flood deposits accumulate on outcropping Tertiary molasse benches (Chapron 2005). Volumes of slide scars and related deposits are estimated based on side-scan sonar, bathymetric, and seismic data, together with sediment core LDB01-1. (c) Thickness and location of the 1822 CE homogeneous deposit based on available sediment cores.
## 2. Study site and previous sedimentary data

Lake Bourget is one of the largest natural lakes in France, located in the northwestern Alps (45°45'N; 5°52'E). It is a narrow and over-deepened glacially formed lake with a north-south oriented basin (Fig. 1 a). The lake's maximum depth is 145 m; it is 18 km long and 2-3 km wide. Its sedimentary infill has been extensively analyzed for decades through various geophysical and sediment core studies (Arnaud et al., 2012; Chapron et al., 2005; Rapuc et al., 2024; Van Rensbergen et al., 1999). Toward the northern tip of the lake, the left-lateral Culoz strike-slip fault is considered the most likely seismogenic source at the origin of the 1822 CE earthquake (De La Taille et al., 2015), which is the most significant historical event ever recorded in the region with an epicentral intensity of VII-VIII (MSK64 intensity scale) reported in the Sisfrance database (Jomard et al., 2021). The 1822 earthquake caused intense shaking and damage to many buildings in the epicentral area located north of the Lake (Bernard et al., 1997; Vogt & Godefroy, 1979) and had a substantial impact on Lake Bourget. Riboud, (1822) observed that the lake water suddenly rose by two feet near Aix-les-Bains. Archbishop Billet (Billiet, 1851) reported that Hautecombe Abbey on the lake shore was severely damaged, and water had risen suddenly by several feet (about 0.5-1 m), exhibiting periodic rise and fall, and large bubbles observed on the water surface. An article dated 3 March 1822 in the Journal de Savoie reported that the lake water rose by 6 to 10 feet (2-3 meters) at Brison and Saint-Germain, located on the eastern shore opposite the landslide collapse (Fig. 1).

High-resolution seismic data, side-scan sonar, and multibeam echosounder bathymetry (Ledoux et al., 2010), together with short gravity cores, were analyzed to investigate lake-bottom sediments linked to the 1822 CE event (Chapron et al., 1999; 2005; 2016). These studies revealed that a seismic event triggered two subaqueous land-slides along the western shore of the lake, accumulating fine-grained distal Rhone River flood deposits (Chapron et al., 2005; Arnaud et al., 2012). The volume of the main landslide (Slide 1), estimated at $6.6 \times 10^5$ m³, was determined based on high-resolution bathymetry and seismic profiles (Chapron et al. 1999; Ledoux et al., 2010). In comparison, Slide 2 (documented based on high-resolution bathymetry, sub bottom chirp profiles and sediment core; Chapron et al., 2005) exhibited a considerably smaller volume of $2.0 \times 10^3$ m³. It was sampled in core LDB01-1 (Arnaud et al., 2005) and interpreted as a slump deposit.

The geological data suggest that Slide 1 initiated at a depth of 60 m on a steep slope of 60° and extended to a depth of 130 m, where the slope flattened (Fig. 1b). The transition from a steep 60° slope to a nearly flat slope caused most of the landslide material to accumulate at the foot of the collapse zone at 130 m. The debris flow can continue to move across slopes as shallow as 1° to 2° once water saturation sufficiently reduces its yield strength to initiate movement (Curry, 1966). During this event, very fine sediments (4–14 µm) with increasing water saturation were transported further and preferentially settled in the deep center of the lake's basin (Fig. 1). These well-dated sediment sequences in the deep central basin are characterized by homogeneous deposits related to this historical earthquake (Chapron, 1999; 2005, 2016; Jenny et al., 2014; Wilhelm et al., 2022). However, the primary mechanism driving the seiche wave and the formation of this homogenite remains unknown. Therefore, our investigation focused on understanding this depositional mechanism and further assessing the impact of the 1822 CE event.

## 3. Numerical models

### 3.1. Mass movement model

We used the two-layer depth-averaged 2HD (two horizontal dimensions) viscoplastic debris flow model “BingClaw”, based on Herschel-Bulkley rheology, to simulate mass movement. The model is designed for cohesive, clay-rich landslides (Kim et al., 2019) and operates within an Eulerian framework. BingClaw is a 2D extension of the

work of Huang & García, (1997) and Imran et al. (2001). The model accurately simulates subaqueous landslides, handling complex layered structures, and has been validated against geological observations for the 8150 BP Storegga landslide (Løvholt et al., 2017) and the ca. 11 700 cal BP Lake Aiguebelette landslide (Zafar et al., 2024). BingClaw considers two layers: an upper "plug layer", where sediment moves without deformation, and a bottom "shear layer", which undergoes deformation when the shear stress exceeds the material's yield strength. It uses the finite volume scheme to solve the mass balance equations for the entire depth (including both the plug and shear layers) and separately solves the momentum equations for each layer.

The model also incorporates the remolding or softening effect due to water content by decreasing the yield strength $\tau_y$ as a function of accumulated shear, proposed by (De Blasio et al., 2005), given as:

$$ \tau_{y(y)} = \tau_{y,\infty} + (\tau_{y,0} - \tau_{y,\infty})e^{-\Gamma\gamma}, \quad (1) $$

where $\tau_{y,0}$ and $\tau_{y,\infty}$ represent the initial and residual yield strengths, respectively. The variable $\gamma$ denotes the accumulated shear deformation at the bed, and $\Gamma$ is a dimensionless coefficient that determines the rate of remolding over time. For further details, refer to Kim et al. (2019).

### 3.2. Coseismic displacement and seismic wave propagation models

For earthquake-induced tsunami simulations, the initial step is to convert the coseismic slip and seismic wave propagation into surface displacements, which serve as bathymetric changes during tsunami simulations (Dutykh et al., 2006; Dutykh & Dias, 2007, 2009). Coseismic displacement is typically achieved via the well-known Okada model (Dutykh & Dias, 2007; Okada, 1985), where the Earth is modeled as a perfectly elastic half-space. The model takes the uniform slip on rectangular fault patches as input and calculates the resulting 3D displacement of the Earth's surface by integrating a Green's function solution to the half-space problem. However, the Okada solution only approximates the final static deformation of the Earth's surface and does not account for the dynamic seismic wave propagation that occurs during an earthquake. For modeling seismic waves, we therefore use the well-known Axitra code (Cotton & Coutant, 1997), which simulates seismic wave propagation in 3D via flat-layer models. This approach is based on discrete wavenumber theory (Bouchon, 1981) in conjunction with the reflectivity method (Kennett & Kerry, 1979). We incorporate uniform computational grids to resolve the seismic wavefield in both space and time. The three displacement components are computed at each grid point using Earth layer properties and seismic source parameters as inputs. The high-resolution displacement data within the specified extents are stored for subsequent tsunami modeling and peak ground displacement (PGD) calculation. PGD is calculated by extracting the maximum displacement magnitude over time at each grid point.

### 3.3. Tsunami propagation model

A two-dimensional depth-averaged nonlinear shallow water equation (NSWE) model called GeoClaw (ClawPack Development Team, 2020) is used to simulate tsunami wave propagation and inundation. GeoClaw is a widely used modeling tool for geophysical flows. It is a version of Clawpack and has been validated and verified against the National Tsunami Hazard Mitigation Program (NTHMP) Benchmark Problems (González et al., 2011) and applied to a variety of past tsunamis, including the 2011 Japan tsunami (Melgar & Bock, 2015) and 2015 Chilean tsunami (Melgar et al., 2016). Furthermore, GeoClaw tsunami simulations for other historical tsunamis, along with comparisons to tide gauge results, are detailed in the report by Adams and LeVeque (2018). The model solves NSWE by implementing the shock-capturing Godunov-type finite volume scheme (Dutykh et al., 2011; LeVeque, 2002) on rectangular grids with adaptive mesh refinement. It handles wet-dry interfaces effectively for inundation modeling.
For a detailed description of GeoClaw, see LeVeque et al. (2011).

### 3.4. Coupled earthquake-landslide-induced tsunami model

This paper proposes a coupled earthquake-landslide-induced tsunami model that incorporates mass movement, coseismic displacement, and seismic wave propagation. We converted the output spatiotemporal displacement data generated by the earthquake and landslide models into time-dependent bathymetry changes to couple with the tsunami model GeoClaw. In addition to the vertical displacement, we also consider the east-west and north-south horizontal displacements, by incorporating the contribution of horizontal displacement of a sloping bottom to the vertical displacement (Dutykh et al., 2012; Tanioka & Satake, 1996). This coupled approach accounts for lakebed bathymetry variations resulting from mass movement and earthquake models. The resulting perturbations in the lakebed are directly transferred to the overlying water column in the tsunami model, leading to the formation and propagation of tsunami waves.

### 3.5. Natural modes and frequencies of a basin

The Helmholtz equation is commonly used to determine the natural modes of oscillation and the corresponding frequencies of a basin. These modes are essential for understanding how waves propagate and resonate in enclosed bodies of water. To derive the Helmholtz equation for a basin with nonuniform depth, we start by considering the governing equation for longwave propagation in shallow water:

$$ \frac{\partial^2 \eta}{\partial t^2} = g \left( \frac{\partial}{\partial x} \left( D \frac{\partial \eta}{\partial x} \right) + \frac{\partial}{\partial y} \left( D \frac{\partial \eta}{\partial y} \right) \right), \quad (2) $$

where $\eta(x, y, t)$ represents the water surface elevation at any point $(x, y)$ and time $t$, $D(x, y)$ represents the water depth at location $(x, y)$, and $g$ represents the acceleration due to gravity. A harmonic solution in time of the form $\eta(x, y, t) = \exp(i\omega t)\eta_0(x, y)$ is introduced, which leads to the derivation of the Helmholtz equation:

$$ \nabla \cdot (D(x, y) \nabla \eta_0(x, y)) + \frac{\omega^2}{g} \eta_0(x, y) = 0, \quad (3) $$

where $\eta_0(x, y)$ is the spatial part of the water surface elevation and where $\omega$ is the angular frequency of the wave. To solve this equation, we discretized it over a rectangular grid using the finite difference method and applied Neumann boundary conditions. This approach allows us to compute both the natural modes and the corresponding frequencies of oscillation for the basin.

In addition to the Helmholtz equation, we also use the theoretical formula of Merian's (LeBlond & Mysak, 1981) to compute the frequencies and periods of the seiche. The formula for determining the period of the Nth mode in a rectangular basin, characterized by a length L and a uniform average depth h, is as follows:

$$ T_k = \frac{2L}{N} \sqrt{gh}, \quad (4) $$

where $T_k$ is the oscillation period, N represents the mode number and g refers to the gravitational acceleration.

### 3.6. Bed shear stress

The bed shear stress is an important parameter for determining the initiation and transport of sediment particles along the bed of a water body (Rashidi et al., 2023; Van Rijn, 2007; Siegenthaler, 2021). We use the Chezy equation to calculate the bed shear stress $\tau$.

The friction coefficient $C_f$ for fully turbulent flow is calculated via the Chezy formula (Bobrowsky & Marker, 2018):

$$ C_f = 5.75 \log \left( \frac{12h}{k_b} \right), \quad (4a) $$

where h is the local water depth and where $k_b$ is the bed roughness parameter. The local flow velocity magnitude, or speed (u), is derived from the momentum components $hu$ and $hv$ via the following relation:

$$ u = \sqrt{\frac{(hu)^2 + (hv)^2}{h^2}}. \quad (5) $$

Given the speed and friction coefficient, the bed shear stress is then calculated as:

$$ \tau = \frac{u^2 \rho_f}{c_f^2}. \quad (6) $$

Here, $\rho_f$ represents the density of water.

### 3.7. Numerical methods for tsunami signal analysis

Fourier spectral analysis (Cooley & Tukey, 1965) and fast iterative filtering (FIF) (Cicone et al., 2016) are used to analyze the paleotsunami waveforms generated by our tsunami model. Standard Fourier spectral analysis is commonly used to identify the energy distribution across different frequencies. However, when dealing with nonlinear and nonstationary tsunami time series, neither the standard Fourier transform nor the wavelet transform is the most effective approach (Stallone et al., 2020). These methods produce linear decompositions (Stallone et al., 2020), which are not ideal for tsunami signals.

To address this, we apply the advanced FIF method to decompose tsunami signals. FIF breaks the waveform into simpler components called intrinsic mode functions (IMFs) to analyze the time-varying characteristics of the signal. After that, we applied the Hilbert transform (Cohen, 1995) to each IMF to calculate the average instantaneous frequency, enabling a detailed time-frequency analysis. This analysis aims to decompose the waveforms to determine which frequencies and modes dominate within the lake over time.

## 4. Numerical results

### 4.1. Landslide simulations

We conducted mass movement simulations of the coeval 1822 earthquake-induced landslides using the BingClaw rheological model. To accurately model the 1822 event, we reconstructed the pre-event bathymetry on the basis of geological records via GIS. We removed the landslide deposit from the current bathymetry that remained near the collapse site. We then implemented the remolding process to simulate the progressive reduction in sediment yield strength due to water intake as it underwent shear deformation. This process models the scenario where the initially high yield strength and gentle slope caused most of the landslide material to remain near the collapse area while easily remolding finer sediments carried further into the deep basin. The initial yield strength $\tau_{y,0}$ was set at 300 Pa, which gradually decreased as a function of the remolding coefficient $\Gamma = 0.005$, as described in Eq. (1). The yield strength is highly sensitive to the runout distance (Kim et al., 2019). We conducted multiple tests for yield strength and constrained the value by using observational runout distances from sediment cores data. In our simulations, we set the non-Newtonian fluidity index n for shear-thinning Herschel-Bulkley flow to 0.5, following Løvholt et al. (2017) and Zafar et al. (2024). We also included the added mass to more accurately capture the interaction between the subaqueous landslide and the surrounding water. The value of the added mass coefficient is influenced by the ratio of landslide thickness to length (Enet & Grilli, 2007). Given our landslide's relatively low thickness-to-length ratio (Fig. 1) and consistent with previous research (Kim et al., 2019; Zafar
et al., 2024), we selected a value of 0.1. By using all these parameters, we performed mass movement simulations on rectangular grids with a cell size of 10 m. Snapshots of the thickness for both landslides at different times are presented in Fig. 2. The initial direction of the landslides is primarily eastward, reaching its final deposit location within 5 minutes. Fine sediments separated from the main landslide (slide 1) deposits extend toward the deep basin, arriving there in 36 minutes.

## 4.2. Earthquake simulations

The 1822 CE earthquake, characterized as left-lateral strike-slip, had an estimated magnitude ranging from Mw 5.5 to 6.0 (Thouvenot et al., 1990). Its epicenter was located at 45.82°N, 5.82°E (Jomard et al., 2021), and occurred at a depth of approximately 10 km (Manchuel et al., 2018). For the Mw 6.0, we determined the fault length of 14.5 km and width of 6 km via the empirical relationships proposed by Wells & Coppersmith, (1994). The fault pitch or striation angle relative to the horizontal direction was determined to be 10° ± 5° (De La Taille et al., 2015). This indicates a minor vertical component in fault movement, with the southern block moving upward relative to the northern block. We considered the lower bound of the estimated fault pitch, applying a vertical slip of 4 cm, which corresponds to 10% of the 40 cm horizontal slip leading to a rake angle of 5.7°. The dip and strike angles were set to 89° and 126°, respectively. Using these parameters, we used the Okada model to compute the resulting 3D displacement, capturing both the horizontal and vertical movements of the Earth's surface (Fig. 3a-c).

Synthetic seismograms (<10 Hz) of ground motion at Lake Bourget for the 1822 CE Mw 6.0 earthquake were computed via discrete wave-number code. The P- and S-wave velocities were obtained from Virieux et al. (2024), derived from local earthquake tomography in the Western Alps using a 33-year seismicity database. The corresponding quality factors $Q_p$ and $Q_s$ and density values were estimated via the empirical formula provided by Brocher, (2007). The rise time is calculated as 0.8 using the empirical formula of Gusev & Chebrov, (2019). We computed synthetic seismograms in the time domain at spatial grids with a resolution of approximately 20 m. In Fig. 3d, we show the spatial distribution of peak ground displacement (PGD) across Lake Bourget ranging from 0 to 0.37 m, along with the synthetic seismogram recorded at the

lake's deep basin (Fig. 3e). The peak ground displacement is more dominant on the northern side of the basin but primarily occurs in the east-west direction. These findings are consistent with historical records discussed in Section 2 (Vogt & Godefroy, 1979; Billet, 1851).

## 4.3. Natural modes of Lake Bourget

Using the Helmholtz equation, we calculated the eigenfrequencies and their spatial distributions of oscillation patterns for Lake Bourget. We plotted the modes corresponding to the four smallest eigenfrequencies, which represent the standing wave patterns in the lake (Fig. 4). The smallest eigenfrequency corresponds to the simplest mode, called the fundamental mode, which consists of a node in the center of the lake and antinodes at either end. The wavelength in this mode is twice the length of the basin. The second, third, and fourth modes are overtones of the fundamental mode.

In addition to the numerically determined natural frequencies, we also calculated the theoretical frequencies via Marine's formula for a rectangular basin. Using the 18 km length and 90 m average depth of Lake Bourget, we computed the fundamental longitudinal mode of the lake, along with the next three modes (Table S1).

## 4.4. Tsunami simulations

### 4.4.1. 1822 CE landslide-induced tsunami waves

For the 1822 CE landslide-induced tsunami, first we converted the outputs from the landslides model into time-dependent bathymetry changes and then integrated them into the tsunami model to calculate wave propagation and inundation. Rectangular computational grids with a cell size of approximately 10 m were used to simulate the tsunami wave. A snapshot of the wave amplitude at 30 s and the time series of synthetic gauges are presented in Fig. 5a. The black dots numbered 1-5 indicate the locations of the synthetic gauges used to estimate the tsunami's impact at various lake depths. Gauge 1 (45.76° N, 5.85° E) is located at a depth of 136 m on the front side of the collapse. Gauge 2 (45.74° N, 5.86° E), placed at 145 m in the deepest part of the basin, corresponds to the area where the 1822 CE homogenite deposit is recorded in the geologic record. Gauge 3, placed on the frontal shore (0.5 m depth, 45.77° N, 5.86° E), captures the tsunami wave impact.

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Snapshots showing the simulated thickness of the landslide 1 and 2 at various time intervals ranging from 0 to 36 minutes.</figcaption></figure>
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** (a) East-west, (b) north-south, and (c) vertical components of surface displacement resulting from fault movement during the 1822 CE earthquake, computed using the Okada model, along with the Lake Bourget boundary line in fuchsia color. (d) Peak ground displacement is visualized for the Lake Bourget region. (e) Synthetic seismograph placed at the central basin.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** The oscillation patterns (eigenmodes) corresponding to the four lowest eigenfrequencies of Lake Bourget are presented, with locations indicated for the central basin (CB) and Grésine Bay (GB).</figcaption></figure>

Gauges 4 (45.80° N, 5.82° E) and 5 (45.65° N, 5.87° E) are positioned at a depth of 0.5 m on the northern and southern shores, respectively, to monitor seiche wave effects along the longitudinal direction. Tsunamis generated by subaquatic landslides produce short and highly directional waves that dissipate rapidly. The highest wave amplitude reached 4 m near the slide 1 collapse area, whereas gauge 1 captured a peak of approximately 2.35 m (Fig. 5b). In the deeper basin, the maximum amplitude was 20 cm, recorded by gauge 2. The first leading wave reached the frontal shore in approximately 2 min. Gauge 3, which is located on the frontal shore, recorded a maximum amplitude of ca. 1.8 m. Gauges 4 and 5 exhibit relatively longer wave periods than the other gauges, but they do not possess sufficient energy to sustain the wave in the lake for an extended period.

### 4.4.2. 1822 CE earthquake-induced tsunami waves

For the earthquake-induced tsunami models, we coupled both horizontal and vertical displacement generated from coseismic fault movement and seismic wave propagation with the tsunami model. These displacements are interpreted as varying-bathymetry over time to simulate a resulting tsunami wave. The numerical simulations (Fig. 5c, d) are performed with the same resolution used for landslide-induced tsunamis. Tsunami waves generated by earthquakes have, however, significantly longer wavelengths than shorter and more localized waves generated by landslides. This difference in wavelength between these two triggering mechanisms will lead to a distinct wave effect on Lake Bourget. Long waves from earthquakes retain more energy, allowing them to persist longer in the basin. This time, more intense wave activity was observed on the northern (Gauge 4) and southern (Gauge 5) sides of the lake (Fig. 5d). The most significant vertical displacement is recorded by Gauge 4. However, the overall amplitude is lower than that of landslide-triggered tsunami (Fig. 5a,b).

### 4.4.3. Coupled earthquake-landslide-induced tsunami waves

Through our simulations, we demonstrate the combined effects of subaquatic landslides, coseismic displacement, and seismic waves on the

water surface. We present the results for two distinct time windows: one where landslide-induced waves dominate (Fig. 6 a,b,c) and another where earthquake-induced waves prevail (Fig. 6d,e,f). The landslide-generated waves dissipate quickly, with low effects after 20 minutes. After this time, long-period waves begin to oscillate in Lake Bourget. By extending the simulation to several days, we observe that earthquake-induced standing waves, known as seiche waves, persist and oscillate in the lake for more than four days with a maximum period of 1200 sec. We also performed inundation modeling and provide the runup heights for both tsunami (Fig. 6c) and seiche (Fig. 6f) waves in Lake Bourget. The maximum tsunami runup height was 2.5 m on the frontal shore and approximately up to 1 m on the remaining shores of the basin (Fig. 6c). The runup height for seiche waves is limited to 25–30 cm (Fig. 6f). The maximum tsunami runup height over time throughout the simulation and inundation distances is presented in Figure S2, where we can observe that the maximum amplitude reaches 4 m at the collapse side. The inundation distance is minimal because of the steep slope of the basin.

### 4.5. 1822 CE tsunami signal analysis

To analyze the 1822 CE tsunami signals, we used the Fourier spectral analysis of waveforms generated by both the landslide and earthquake at gauge 4 is presented in Fig. 7a and 7b, respectively. The vertical lines indicate the energy distribution at the natural eigenfrequencies of Lake Bourget. These eigenfrequencies were calculated by solving the Helmholtz equation, as illustrated in Fig. 4. The frequency analysis reveals that the tsunami generated by an earthquake produces long-period waves with significantly greater energy than those generated from the subaquatic landslides. In contrast, a tsunami from the landslides is more effective at generating shorter-period waves. Specifically, an earthquake excites the fundamental mode of a lake approximately 350 times more efficiently than a landslide does. This approach evaluates the distribution of energy across different frequency bands.

To further explore the temporal evolution of the modes and identify
<figure><img src="image_9.png" /></figure>

T = 0.5 min

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

T = 0.5 min

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /><figcaption>**Fig. 5.** (a) Illustrates a snapshot of wave amplitudes generated by landslides at 0.5 min, while (b) presents the time series data from the synthetic gauges indicated in (a). (c) Depicts the wave amplitudes caused by an earthquake (including coseismic displacement and seismic waves) at 0.5 min, and (d) shows the time series data from the gauges indicated in (c). Black dots in (a) and (c) denote the positions of synthetic gauges.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>**Fig. 6.** (a) Snapshot of surface elevation at 0.5 minutes for the coupled earthquake-landslide-induced model; (b) time series of wave amplitude; and (c) runup height map for the first 20 minutes, during which the landslide-induced wave is dominant. (d,e) Time series of wave amplitude from 20 minutes to 96 hours; and (f) runup height map, illustrating the period during which seiche waves dominate.</figcaption></figure>

which phenomena excite specific modes over time, we applied fast iterative filtering (FIF). This technique effectively differentiated the modes generated by the landslide from those induced by the earthquake (Fig. 7c, d). Upon decomposing the signal, it became evident that the earthquake excited the first three natural frequencies of Lake Bourget, whereas the landslide-induced tsunami failed to excite these modes. Although the landslide generated longer-period waves close to the fundamental frequency, these waves had significantly lower amplitudes and thus carried less energy, which is consistent with the results from the Fourier analysis.

Finally, we applied FIF to the coupled earthquake-landslide tsunami model to evaluate the signals of the complete scenario. We used FIF on signals from 0 to 4 days with varying time windows to observe which modes were dominant during the initial stages and which persisted over time. The analysis (Fig. 7e–h) showed that the third mode (423 sec period / 0.0002361 Hz) dissipated in 6 hours, while the second mode (600 sec period / 0.001667) dissipated within 48 hours. The longest fundamental mode, which corresponds to the longitudinal seiche (1200 sec period / 0.000833 Hz), remained active throughout the entire simulation period for four days, exhibiting sustained amplitude with only minimal energy loss. In comparison, the shorter waves that belong to the landslide dissipate rapidly. This approach allowed us to better understand the mechanisms responsible for generating specific modes in

Lake Bourget.

### 4.6. Bed shear stress

To estimate the potential for sediment erosion and mobilization during the propagation of tsunami and seiche waves in Lake Bourget, we applied the Chezy equation (Eq. 3) to calculate the bed shear stress. For the bed roughness coefficient, Nigg et al., (2021) suggested values in ranges from 0.02 to 0.06 for Alpine Lake Lucerne, which experienced a tsunami and subsequently seiche following the 1601 CE Weggis slide (Vermassen et al, 2023). Based on this recommendation and the comparable bed characteristics of Lake Bourget, we chose $k_b$ values 0.02, 0.04, and 0.06. By applying the Chezy equation, we calculated the maximum bed shear stress ($\tau$) during the tsunami wave-dominated phase (0–1 hour) (Fig. S3), and the seiche wave-dominated phase (2–96 hours) (Fig. S4). Tsunami-induced bed shear stress exceeded 2 N/m² along the frontal shore of the landslide collapse. The bed shear stress induced by the seiche wave was, however, negligible across the lake basin, with localized peak values along the shores reaching approximately 0.02 N/m².
<figure><img src="image_15.png" /><figcaption>**Fig. 7.** Analysis of tsunami signals at Gauge 4 (shown in Figure 5), obtained from both earthquake-induced and landslide-induced tsunami models. (a) Fourier spectral analysis for earthquake and landslide, (b) the ratio of energies. Vertical lines indicate energy at natural eigenfrequencies. (c, d) Fast Iterative Filtering (FIF) of the same signals, with green boxes marking Lake Bourget's natural frequencies, showing that the earthquake excites the lake's first three eigenfrequencies, while the landslide does not. (e–h) FIF analysis of the combined earthquake-landslide model at Gauge 4 (provided in Figure 6) over successive intervals: (e) 0–2 hours, (f) 2–6 hours, (g) 6–24 hours, and (h) 24–96 hours, illustrating the persistence of seiche waves induced by seismic activity for over four days.</figcaption></figure>
# 5. Discussion

## 5.1. Validation of the landslide model

An essential first step in modeling landslide-induced palaeotunamis is to validate the landslide run-out distance and thickness of the final deposits. High-resolution multibeam bathymetry data, seismic profiles, and core analyses provide the ground to validate and refine our landslide simulations. Based on geological observations, the average thickness of the 1822 CE Slide 1 deposits is approximately 6 m at a depth of 130 m (Chapron et al., 1999), while Slide 2 has a thickness of 0.5 m, as sampled in core LDB01-1 (Arnaud et al., 2005). Sediment core data (Fig. 8) indicate that the thickness of the 1822 CE homogenite deposits ranges from 0 to 17 cm in the deep central basin. Using the minimal mass estimate, our numerical simulation closely reproduces the landslide deposit thickness for both slides (Fig. 8). The simulated distribution of fine deposits in the deeper basin also closely matched the observations (Fig. 8). In cores B15, LDB17-P7, and B-11A, the sedimentary deposits are thicker than those in the other cores, and our simulations successfully reproduce this thickness distribution. This good agreement between the simulated and observed data allows us to confidently use these results to simulate the resulted tsunami/seiche in Lake Bourget.

## 5.2. Depositional mechanism beyond seiches

The witnesses of the 1822 CE earthquake reported a periodic rise and fall of the lake's water (Bernard et al., 1997; Billiet, 1851). Our high-resolution modeling results show a significant contrast in the behaviour of waves associated with both landslide and earthquake processes, especially in their capacity to generate standing waves within a closed basin. The landslides generated highly directional, short waves that dissipated quickly (Fig. 5a,b), while the seismic waves produced long-period standing waves (Fig. 5c,d). These standing waves (seiches) form when the triggering mechanism excite the basin's natural resonant frequencies (Kvale, 1955; McGarr, 1965). To explore this behavior, we analyzed the tsunami waveforms obtained from landslides or those from earthquake, recorded at Gauge 4 (Fig. 7). The results from Fourier frequency spectral (Fig. 7a,b) show that earthquake generated long-period waves more effectively than landslides, resulting in seiche waves. The FIF analysis further confirmed that the earthquake excited the three smallest natural eigenfrequencies of Lake Bourget (Fig. 7c), while

landslide-induced waves are failing to excite them (Fig. 7d). These results highlight that the seismic waves were the primary driver of seiche activity in Lake Bourget during the 1822 CE event. Following this confirmation, we ran the coupled earthquake-landslide-induced tsunami model and extended the simulation for four days (Fig. 6). It revealed that the seiche wave persisted in the lake for more than four days.

After confirming that seismic waves induced the seiche in the lake, we validated its modes against results from the Helmholtz equation and the theoretical Merian formula. Remarkably, the three seiche modes characterized by frequencies of 0.00083 Hz, 0.00166 Hz, and 0.002361 observed in the tsunami simulations (Fig. 7) closely matched with those predicted by Helmholtz equations (Fig. 4) and the Merian formula (Table S1). This close agreement provides strong validation of the model and demonstrates that seismic activity effectively generated standing seiche waves in Lake Bourget.

## 5.3. Homogenite genesis

Our subaquatic landslide simulations demonstrate that the majority of landslide deposits settled rapidly at 130 m during the initial phase of the event, while finer sediments were transported to the deeper basin. Once sediment is eroded and mobilized, its transport path is determined by the settling velocity of the particle, as well as the strength of the current and the level of turbulence (Boggs, 2006). Typically, for sediments of similar density, larger particles with higher settling velocities settle more rapidly than smaller particles (Jaffe & Gelfenbuam, 2007). The sand-silt base, characterized by relatively high settling velocities, was therefore deposited during the high-energy phase of landslide motion. This interpretation is based on sediment core data, which show that fine homogeneous sediments follow the lower sand-silt base (Fig. 9c) (Chapron et al., 1999). The impact of the tsunami wave begins to decrease after 20 minutes, allowing a seiche wave to dominate within the lake.

To investigate how seiche waves influence the deposition of fine sediments in the deep basin and contribute to the formation of the homogenite layer, we examined the oscillation patterns of the wave. We presented amplitude as a function of distance by adding spatial cross-sections along the lake in the north-south direction to capture the fundamental mode (Fig. 9a). The oscillation pattern observed in the simulation (Fig. 9a, b) aligns precisely with the pattern predicted by the Helmholtz equation (Fig. 4). During the initial phase of the seiche wave,

<figure><img src="image_16.png" /><figcaption>**Fig. 8.** (a) Average thickness of the Slide 1 deposit, and thickness (denoted as d) of the associated homogenite deposit, along with core LDB01-1, which samples Slide 2, as interpreted from geological data (Chapron 1999); (b) Modeled landslide deposit thickness derived from numerical simulations.</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>**Fig. 9.** (a) Displays spatial cross-sections along the lake's N–S direction, with the red line representing wave amplitude at 96.24 hours and the purple line at 96.40 hours. (b) Shows the amplitude variation along the distance obtained from the spatial cut in (a). (c) Presents the sediment core B11a from the deep basin, highlighting thickness, mean grain size, skewness, and sorting.</figcaption></figure>

the fundamental, second, and third modes oscillate in the lake (Fig. 7e). The horizontal displacement is at its maximum at the nodes, with significant back-and-forth motion but no vertical displacement (Katopodes, 2018). In contrast, vertical displacement peaks at antinodes with no corresponding horizontal movement (Katopodes, 2018). In the fundamental mode, the position of fine deposits is situated near the node (Figs. 4 and 9b), whereas in the second and third modes, it coincides with the antinodes (Fig. 4), where vertical motion is maximal. In the initial phase of seiche wave, the second and third seiche modes display higher amplitudes and energies (Fig. 7e). This strong vertical movement is crucial in maintaining fine sediments in suspension within the water column. However, the third seiche mode dissipates after approximately two hours (Fig. 7f), leaving the second and fundamental modes active in the lake. In this scenario, sediments are affected by both horizontal and vertical motions, allowing fine particles to remain suspended for longer periods. The second mode almost dissipates about 24 hours (Fig. 7g), leaving only the fundamental mode (Fig. 7h). In this phase, horizontal motion dominates over vertical movement. This back-and-forth horizontal displacement plays an essential role in influencing sediment grain orientation, allowing fine particles to settle gradually as the seiche wave energy dissipates. This prolonged seiche activity, at least for 4 days as predicted by our model, corresponds with the observed homogenite deposits (Fig. 1 and 9c), where particle orientation is evident through particle foliation observed via the anisotropy of magnetic susceptibility (Beck, 2009; Chapron, 1999).

Furthermore, we calculated the bed shear stress to investigate the origin of the formation of the homogenite deposits. Chapron et al. (1999) and Beck (2009) suggested that these homogenite deposits may not exclusively be released by the landslide but could have been eroded by a subsequent seiche wave. Sediment erosion occurs when the shear stress exceeds the critical threshold, i.e., $\tau > \tau_{cr}$ (Van Rijn, 2007), but the critical bed shear stress depends on the grain size distribution and cohesion of the sediment bed (Houwing & Van Rijn, 1998). Field and laboratory studies on the initiation of motion in fine sediment beds have

been carried by Houwing (2000) and Van Rijn et al. (1993). Van Rijn et al. (1993) reported critical shear stress values ranging between 0.1 and 0.3 N/m² for weakly consolidated mud beds, while Houwing (2000) reported a range of 0.1 to 0.5 N/m². For non-consolidated fine sediments, the critical shear stress typically falls within the range of 0.1 to 0.2 N/m² (Mengual et al., 2017). Thus, whether for consolidated or non-consolidated sediments, the threshold for initiation of fine sediments appears at or above 0.1 N/m².

The maximum bed shear stress induced by seiche waves along the northern and southern lake shores averaged approximately 0.005 N/m², with localized peak values reaching up to 0.02 N/m² (Fig. S4). These values remain significantly below the critical threshold of 0.1 N/m² necessary to erode previously deposited fine sediments. This stress level is also below the critical threshold for non-cohesive sediments, which decreases from 0.183 to 0.025 N/m² as particle sizes decrease from 250 to 4 µm (Van Rijn, 2007). Therefore, based on our simulations and the bed shear stress results, we suggest that the homogenite layer observed in the sediment cores is directly associated with the 1822 CE landslide deposits and that the seiche effect cannot remobilize previously deposited sediment in Lake Bourget.

The eastern shore experienced during the tsunami wave significant shear stress exceeding 2 N/m² (Fig. S3), which is far above the 0.1 N/m² threshold required for fine sediment erosion. This result confirms that erosion of littoral sands (backwash) occurred during the tsunami wave along the frontal shore of the landslide collapse, as validated by samples from cores B17, B7, and B13A (Chapron et al., 1999), indicated in Fig. 1. We thus conclude that the sediments found in the deep basin originated from one main subaqueous landslide and from tsunami-induced backwash. The initial sand-silty layer settled during the high-energy phase driven by a turbidity current (Fig. 10a). Subsequently, fine sediment clouds remained suspended for longer times because of seiche related to seismic waves (Fig. 10b). Finally, the sediments settled under the back-and-forth motion of the seiche, forming homogenite deposits with a particle orientation (Fig. 10c).
<figure><img src="image_18.png" /><figcaption>**Fig. 10.** Process of homogenite formation in a lacustrine environment under seiche waves: (a) initial phase, within 1 hour; (b) intermediate phase, over 2–24 hours; (c) final phase, extending to 24–96 hours or more.</figcaption></figure>

## 5.4. Constrained uncertainties in landslide-induced paleotsunamis

Landslide-induced paleotsunamis involve significant uncertainties due to the complex nature of landslide processes and their sensitivity to bathymetry and landslide volume (Løvholt et al., 2015; Siegenthaler, 2021). High-resolution seismic and bathymetric surveys and multiple sediment core data enable us to accurately estimate landslide volume, deposit thickness, and runout distance, which increases model reliability and minimizes uncertainties. However, a key challenge in landslide-induced tsunami modeling is the uncertainty related to the collapse behavior of the landslide—whether it exhibits retrogressive behavior or occurs in a single event. This uncertainty is often difficult to resolve solely from sediment deposits. However, historical tsunami data can help constrain this major uncertainty. Historical records in the study area indicate a runup height of approximately 2–3 m at Brison and Saint Germain, located on the frontal shore of the landslide collapse, and around 0.5 m near Aix-les-Bains. Our numerical model accurately reproduces this runup height (Fig. 6c and S2), which indirectly validates

the landslide modeling. Therefore, the use of numerical models with geological and historical data offers a robust interdisciplinary approach for understanding and addressing the complexities of paleotsunamis.

## 5.5. Limitations of the current modeling approach

The fluid dynamics models used in this study are based on shallow water assumptions. Thus, they inherit the limitations of the shallow water approximation. In particular, they do not accurately represent the shorter components of the wave spectrum. Furthermore, certain simplifications in the current version of the utilized models should be noted:

(1) The landslide model “BingClaw” used in this study has some inherent simplifications. This approach neglects shear stresses acting in planes perpendicular to the local tangent plane, which may influence flow dynamics in complex terrains. Second, instead of deriving the normal stresses from a tensor formulation of the Herschel-Bulkley rheology, BingClaw uses a hydrostatic pressure gradient, effectively treating the material like water and neglecting the yield strength in this
regard (Kim et al., 2019). The likely effect of this approximation is to increase the lateral as well as longitudinal spreading of the flow. This would lead to an underestimation of the flow depth and an overestimation of the bed shear stress and thus to shorter run-out distances.

(2) The seismic wave model 'Axitra' assumes a flat-layered medium, ignoring horizontal heterogeneity and site-specific effects; this simplification may overlook interactions between seismic waves and lateral geological variations.

(3) The GeoClaw tsunami model does not account for horizontal displacements. This phenomenon is particularly significant for strike-slip earthquakes, where horizontal motion plays a crucial role. To address this, we incorporate the horizontal displacement of a sloping bottom into the vertical displacement (Dutykh et al., 2012; Tanioka & Satake, 1996) to partially mitigate this limitation.

# 6. Conclusion

This study provides a comprehensive numerical reconstruction of complex depositional processes at Lake Bourget during the 1822 CE earthquake. By coupling the coseismic displacement, seismic wave propagation and subaqueous landslides with a tsunami model, we investigated the mechanisms behind the formation of seiche waves and the development of a homogenite layer in the lake's deep basin. Our simulations were validated via high-resolution bathymetric, and sediment cores data, as well as historical data, which show excellent agreement between the numerical simulations and observations. Extensive modeling of tsunami waves, waveform analysis, and computation of the lake's natural modes reveal that seismic waves excite the lake's eigenmodes 350 to 700 times more efficiently than landslides, playing a dominant role in seiche formation. This seismically induced seiche persisted more than four days in Lake Bourget. Our results indicate that sediments in the deep basin originated from a main subaqueous landslide (Slide 1) and tsunami-induced backwash. The initial sand-silty layer was deposited during the high-energy phase of the landslide motion, whereas the homogenite deposits are directly linked to seiche activity. These seiche waves play a crucial role in suspending and settling fine sediments under lake oscillation, which produces fine-grain orientation in the homogenite deposit, following Siegenthaler et al., 1987 and Siegenthaler (2021). Therefore the 1822 CE homogenite should rather be define as a seiche deposit. Our interdisciplinary approach also provides an effective framework for determining whether landslides or delta collapses, associated with homogenite or megaturbidites, in closed lakes were triggered by seismic or non-seismic sources.

# CRediT authorship contribution statement

**Muhammad Naveed Zafar:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Pierre Sabatier:** Writing – review & editing, Visualization, Validation, Supervision, Resources, Investigation, Data curation, Conceptualization. **Denys Dutykh:** Writing – review & editing, Validation, Supervision, Methodology, Investigation, Conceptualization. **Hervé Jomard:** Writing – review & editing, Validation, Resources, Investigation. **William Rapuc:** Writing – review & editing, Validation, Resources. **Patrick Lajeunesse:** Writing – review & editing, Resources. **Emmanuel Chapron:** Writing – review & editing, Visualization, Validation, Resources, Investigation, Data curation.

# 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.

# Data availability statement

The simulation data are available under the Creative Commons Attribution Share Alike 4.0 International license in the Zenodo repository: https://doi.org/10.5281/zenodo.14264666. This repository includes all necessary output files and scripts to run and reproduce our simulation results.

# Acknowledgments

We thank CEN Savoie for providing the Lake Bourget high resolution multibeam bathymetric data along Lake Bourget shoreline and down to 25 m water depth. MNZ gratefully acknowledges the financial support received from the MESRI and ED MSTII in the form of a PhD scholarship to conduct research for his doctoral thesis.

# Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.epsl.2025.119348.

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