# Imaging the Sediment Cover Offshore Central Chile With Surface-Wave Dispersion and P-Wave Conversion Using Distributed Acoustic Sensing

Clara Vernet, Alister Trabattoni, Marie Baillet, Martijn van den Ende, Diane Rivet

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## To cite this version:

Clara Vernet, Alister Trabattoni, Marie Baillet, Martijn van den Ende, Diane Rivet. Imaging the Sediment Cover Offshore Central Chile With Surface-Wave Dispersion and P-Wave Conversion Using Distributed Acoustic Sensing. Journal of Geophysical Research: Solid Earth, 2025, 130 (7), pp.e2024JB030507. 10.1029/2024jb030507. hal-05157073

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Distributed under a Creative Commons Attribution 4.0 International License
# JGR Solid Earth

## RESEARCH ARTICLE

10.1029/2024JB030507

**Special Collection:**

Solid Earth Geophysics as a
means to address issues of global
change

**Key Points:**

* Distributed acoustic sensing images the sediment cover in central Chile along a 145 km long submarine fiber optic cables

* We invert offshore DAS data using the phase velocity of Scholte-waves and the P-to-S conversion at the sediment-bedrock interface

* We observe spatial variability in S-wave velocity and sediment thickness along the Chilean margin associated with geological structures

**Supporting Information:**

Supporting Information may be found in
the online version of this article.

**Correspondence to:**

C. Vernet,
vernet@geoazur.unice.fr

**Citation:**

Vernet, C., Trabattoni, A., Baillet, M., van den Ende, M., & Rivet, D. (2025). Imaging the sediment cover offshore central Chile with surface-wave dispersion and P-wave conversion using distributed acoustic sensing. *Journal of Geophysical Research: Solid Earth*, 130, e2024JB030507. https://doi.org/10.1029/2024JB030507

Received 11 OCT 2024

Accepted 2 JUL 2025

**Author Contributions:**

**Conceptualization:** Clara Vernet,
Alister Trabattoni, Diane Rivet

**Data curation:** Marie Baillet

**Formal analysis:** Clara Vernet,
Alister Trabattoni

**Investigation:** Clara Vernet,
Alister Trabattoni, Diane Rivet

**Methodology:** Clara Vernet,
Alister Trabattoni, Diane Rivet

**Project administration:** Diane Rivet

© 2025. The Author(s).
This is an open access article under the
terms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.

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

# Imaging the Sediment Cover Offshore Central Chile With Surface-Wave Dispersion and P-Wave Conversion Using Distributed Acoustic Sensing

Clara Vernet¹ ID, Alister Trabattoni¹ ID, Marie Baillet¹ ID, Martijn van den Ende¹ ID, and Diane Rivet¹ ID

¹Université Côte d'Azur, Observatoire de la Côte d'Azur, CNRS, IRD, Géoazur, Valbonne, France

**Abstract** Distributed acoustic sensing (DAS) is an attractive solution for ocean-bottom seismological instrumentation, providing dense and long-range measurements of ground deformation along submarine fiber optic cables. By sensing a 145 km telecom cable in central Chile, we determined the sedimentary cover structure leveraging both surface wave dispersion and the time difference of arrival ($\Delta t$) between the transmitted P and converted S waves from the bedrock-sediment interface. We estimated the shear wave velocity (Vs) profile and sediment thickness by performing a joint inversion of Scholte-wave dispersion curves and $\Delta t$ along the fiber. The dispersion curves were extracted from the seismic coda of local and regional earthquakes using frequency-wavenumber analysis. The additional information provided by the observed $\Delta t$ improved depth inversion constraints, enhancing the stability of the retrieved sedimentary cover thickness. Our results show significant variations in thickness and Vs along the cable, providing new insights into the structure of sedimentary deposits along the margin. The influence of tectonic structures, such as faults and basins, is reflected in the observed velocity contrasts and sediment accumulation patterns. Southern structures, such as the Valparaiso Forearc Basin and canyons, are not characterized by significant sedimentary deposits ($<500 \text{ m.s}^{-1}$). Beyond the Punta Salinas Ridge, the sediment velocity increases slightly while crossing basin-like features. The proposed methodology, combining surface waves dispersion and P-to-S conversion, confirms the effectiveness of DAS for high-resolution subsurface imaging. These results highlight the importance of accounting for spatial variability in sedimentary velocity when modeling seismic hazards and tectonic dynamics in subduction zones.

**Plain Language Summary**

In this research, we used distributed acoustic sensing (DAS) technology to study the structure of seafloor sediments along a submarine telecommunications cable in central Chile. We analyzed data from regional earthquakes to measure the time difference between converted seismic waves at the boundary between bedrock and sediments. These time differences helped us estimate S-wave velocities and sediment thickness, giving a clearer picture of the structure of the seafloor sediments. Our results show variations in sediment thickness and velocity along the cable, reflecting different geological structures. This study demonstrates how DAS can help map the sediments beneath the seafloor.

## 1. Introduction

Distributed acoustic sensing (DAS) offers a promising solution for high-resolution seismic monitoring, particularly in remote and challenging environments such as the deep ocean (Lior et al., 2021; Spica et al., 2020), glaciers (Walter et al., 2020; Zhou et al., 2022) or volcanic regions (Jousset et al., 2022). By leveraging pre-existing or dedicated fiber optic cables, DAS enables the recording of broadband seismic signals with a continuous sampling of the spatial dimension. This technique has proven invaluable for imaging crustal sediments and providing insights into shallow structures, such as sediment deposition (Fukushima et al., 2022, 2025; Spica et al., 2020, 2022) and fault zones imaging (Atterholt et al., 2022; Cheng et al., 2021; Lindsey et al., 2019). In underwater settings, DAS has facilitated the extraction of seismic surface wave dispersion curves from ambient noise, allowing for high-resolution S-wave velocity profile estimation (Guerin et al., 2020; Lior et al., 2022; Spica et al., 2020; Viens et al., 2022; Zeng et al., 2021). While ambient noise tomography provides good control of the velocity profiles, it is typically not as accurate as active source experiments in precisely locating strong impedance contrast features such as the bedrock-sediment interface.

In the presence of a sedimentary cover, due to its particularly low velocity and high spatial variability (especially in terms of thickness), incoming seismic wavefronts are delayed and distorted. This raises a challenge for accurate
**Supervision:** Alister Trabattoni,
Martijn van den Ende, Diane Rivet

**Validation:** Diane Rivet

**Writing – original draft:** Clara Vernet

**Writing – review & editing:**

Alister Trabattoni, Martijn van den Ende,
Diane Rivet

travel time modeling and, hence, for precisely localizing seismic events from measurements made at the ocean-bottom with DAS (Baillet et al., 2025; Trabattoni et al., 2025). It has been shown that in marine areas covered by sediments, DAS recordings clearly display a P-to-S conversion (hereafter referred to as the Ps phase) that occurs at the bedrock-sediment interface (Trabattoni et al., 2023). In this context, the travel time difference between the transmitted P-wave and the Ps-wave can be directly related to the average sediment P- and S-wave velocities, and thickness (e.g., Doran & Laske, 2019). Trabattoni et al. (2024) proposed an inversion scheme to jointly invert for the hypocenters of recorded events along with a very first order propagation model of the sediments. While it resulted in an improved fit of the data, the first order information obtained about the sedimentary structure is limited, presents strong uncertainties, and is not immune to overfitting. To improve the accuracy of earthquake hypocenter localization and to identify active structures in the upper crust, it is therefore beneficial to enhance the precision of near-surface velocity structures in sedimentary areas (Baillet et al., 2025).

In central Chile, several tomographic studies and shallow crustal imaging have been conducted through seismic-reflection and wide-angle seismic surveys (von Huene et al., 1997; Laursen & Normark, 2003; Contreras-Reyes et al., 2015). These studies provide valuable insights into the subduction dynamics and tectonic processes of the region. In addition to studying the sedimentary structure of the continental basement, the presence of an un-consolidated sediment cover has been characterized as a heterogeneous shallow layer with very slow S-wave velocity ($<$500 m.s⁻¹) and reduced P-wave velocity ($<$2,000 m.s⁻¹). This layer contains the history of recent fault activity and reflects the dynamics of sediment recharge, a process through which sediments are supplied, transported, and accumulated in a given area by natural mechanisms such as landslides, erosion, and depositional flows (Brayshaw & Hassan, 2009; Goldfinger et al., 2017).

High-resolution studies using active sources are expensive and labor-intensive, while passive source studies often lack the resolution and coverage needed to image the shallowest sedimentary cover ($<$1 km). This limitation prevents a full understanding of the recent kinematics and current tectonic activity of offshore sedimentary basins, which are essential for seismic hazard modeling (e.g., Perea et al., 2012) and regional geological evolution. Previous studies have used Ps phases to estimate sediment thickness and velocity, relying on ocean bottom seismometers (OBS) to analyze converted phases (e.g., Agius et al., 2018; Doran & Laske, 2019; Harmon et al., 2007; Kim et al., 2021). These methods have provided valuable constraints on the sedimentary structure based on broadband, low-frequency recordings. However, OBS deployments typically provide sparse spatial coverage due to limited number of instruments, and are expensive methods that involve oceanographic campaigns. In contrast, DAS offers significantly higher spatial resolution through dense, continuous sampling along existing submarine fiber-optic cables (Fukushima et al., 2025). This enables low-cost and large-scale deployments. In terms of frequency content, DAS provides broadband signals with low-frequency sensitivity capabilities, as reported in various studies (Jin & Roy, 2017; Norbeck et al., 2023). However, because DAS is also sensitive to temperature changes, temperature-dependent noise affects the low-frequency content and overcomes the expected Earth and tides signals below 0.02 Hz (Ide et al., 2021). While the deeper structures have been well characterized offshore central Chile (Contreras-Reyes et al., 2015; Dominguez et al., 1998; Laursen & Normark, 2003), the shallow sediments remain less understood. A detailed image of those sediments is nonetheless crucial for accurately identifying subsurface structures, such as the sediment-bedrock interface, lateral variations or reflecting scatters, which is essential for precise earthquake localization with DAS (Trabattoni et al., 2024), and for assessing sedimentary processes.

This study proposes a joint inversion using surface wave analysis and the travel time difference between the transmitted P-wave and the converted S-wave (Ps time delay) at the bedrock-sediment interface to constrain sediment thickness and velocity with DAS data. For this purpose, we leverage a fiber optic cable in central Chile that runs along the coast, approximately 30 km offshore, to image the lateral variations of the sedimentary layer over a distance of more than 100 km. After introducing the data set and the context, we first measure the Ps delay using manually picked phases from 20 earthquakes. Subsequently, we focus on surface (Scholte) waves velocity extracted from the seismic coda. We then introduce a joint inversion method to independently extract the depth of the bedrock-sediment interface from Ps delay and to invert for the shear wave velocities from the surface waves. Finally, we construct a 145-km long 2D shear wave velocity profile with a lateral resolution of 2 km, which we discuss in the context of known geological structures in the area.
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Map of central Chile and DAS deployment. (a) The fiber-optic cable between Concón and La Serena is shown in black and the 150 km section sensed during the POST experiment is indicated in red. Colored solid circles correspond to the 20 earthquakes that were used in this study. The three main geomorphological features are the Juan Fernandez Ridge (JFR), the Punta Salinas Ridge (PSR) and the Valparaíso Forearc Basin (VFB). (b) Bathymetry profile along the fiber-optic cable that crosses the edges of the VFB between 30 and 80 km and the PSR around 90 km.</figcaption></figure>

## 2. Data and Methods

### 2.1. DAS Acquisition on Concón-La Serena Fiber-Optic Cable

This study utilizes the data set acquired during the POST experiment (Submarine Earthquake Observation Project) that was carried out from 27 October 2021 to 3 December 2021, on a dark fiber-optic cable located offshore Central Chile (31°30 S–33°00 S) (Figure 1). An OptoDAS interrogator (Alcatel Submarine Networks) was connected to this cable, which extends from the city of Concón (100 km northwest of Santiago) to La Serena, located 300 km north of Concón (Figure 1a). The OptoDAS uses a long chirp laser pulse, which significantly enhances the signal-to-noise ratio (SNR) and extends the usable sensed fiber length up to 150 km (Waagaard et al., 2021). To handle the large volume of DAS data, we use Xdas, a Python library designed for efficient data management, compilation, and processing (Trabattoni et al., 2025). The cable depth is assumed to follow the bathymetry of the seafloor (Figure 1b), starting from the Concón landing station, reaching the upper continental slope (at kilometer 30 along the fiber), and then going northwards along the Chilean subduction trench and 30 km off the coast. During the experiment, the first 150 km of the fiber optic cable were sensed. DAS measurements were acquired recording strain rate with a time sampling of 125 Hz, a gauge length set to 8.16 m and a corresponding channel spacing of 4.08 m resulting in 37,500 virtual seismic sensors.

The regional geological setting exhibits a prominent geomorphological feature with the Valparaíso Forearc Basin (VFB) located between 32°20 S and 33°00 S (Figure 1a). It is bordered to the north by La Ligua Canyon and the Punta Salinas Ridge (PSR; 80–110 km on Figure 1b) and to the south by Aconcagua Canyon (20–28 km). The southern section of the cable (up to 80 km) is positioned on the upper slope of the basin, and its northern part is uplifted crossing the PSR. The northern part of the basin exhibits intense deformation, where the PSR extends obliquely from the trench to the coastline (Dominguez et al., 1998). The uplift is attributed to the subduction of the Juan Fernández Ridge (JFR) extension (Figure 1a; Laursen & Normark, 2003). This region has been extensively
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Strain-rate of an earthquake (M<sub>L</sub>2.8) that occurred on 11/30/2021 at 08:38:31 (UTC). Data are decimated in time and space by a factor 5 and are bandpass filtered between 0.5 and 25 Hz. (a) The arrival times of P, Ps, and S are manually picked and displayed in blue, orange and green respectively. The coda is defined to begin 15 s after the arrival of body waves and to last 2 min. (b) Zoom on the body-waves arrivals. The P arrival has a more regular and smoother wavefront than the S arrival. Its amplitude is smaller making it undetectable for most events. The S arrival has a higher amplitude and a travel time fluctuation along the fiber that is similar to the Ps phase. Figure S1 in Supporting Information S1 shows the entire record of the earthquake.</figcaption></figure>

studied through seismic surveys (Flueh et al., 1998; von Huene et al., 1997), which provided valuable insights into its morphology and crustal structure. The VFB is filled with Late Paleogene to Quaternary clastic sediments (Becerra et al., 2017; Laursen & Normark, 2003). The cable crosses many canyons and topographic features on the upper slope of VFB, such as gullies, extensional faults that dip landward and potential strike-slip faults, while several normal faults affect the northern slope of the PSR (Dominguez et al., 1998; von Huene et al., 1997). The zone is known for destructive historical earthquakes with the 1985 Valparaiso earthquake (M<sub>w</sub>8.0), the 2015 Illapel earthquake (M<sub>w</sub>8.3), and the 2010 Maule earthquake (M<sub>w</sub>8.8), 300 km to the south.

## 2.2. P-to-S Waves Conversion Across the Sediment Cover

During the 1-month period experiment, thousands of earthquakes were detected by an automatic detection workflow based on PhaseNet and GaMMA (Baillet et al., 2025; Zhu & Beroza, 2019; Zhu et al., 2022). For most earthquakes, both the direct P-wave and S-wave are visible. But among these events (about one tenth), we observed that some P-waves are transmitted and converted into two arrivals separated by less than 2 s. The first phase, with an amplitude usually close to the noise floor, has a smooth wavefront compared to the second phase, which has a higher amplitude and corrugated wavefront. As proposed by Trabattoni et al. (2024), the first faint arrival is interpreted as the direct P-wave, the second arrival as a conversion of a P-wave into an S-wave at a bedrock-sediment interface and the third arrival as the direct S-wave. In this study, we refer to these as P, Ps, and S, respectively (Figure 2a). The Sp wave exists but is rarely, if ever, noticeable because of the Ps scattering train hiding this smaller amplitude signal.

We assume that the rays travel nearly vertically through the sediment cover, justified by the large velocity contrast at the interface, which is assumed to be locally horizontal. In this case, for a uniform layer, we introduce the travel time difference Δt between P and Ps, or Ps time delay. For a given velocity, the thicker the layer, the greater the Δt (Figure 3). Measuring the Ps time delay provides constraints on both the sediment shear velocity and thickness along the fiber-optic cable (Agius et al., 2018; Doran & Laske, 2019; Harmon et al., 2007).
<figure><img src="image_6.png" /><figcaption>**Figure 3.** P- and Ps-wave ray paths in sediments of thickness *h*. Due to the velocity contrast between the bedrock and the sediments, the incoming P-wave is transmitted and converted into a P and S wave, respectively, which tend to bend toward the vertical as they propagate through the sediments. The angle $\theta$ between P and Ps is therefore neglected. The arrival times $t_P$ and $t_{Ps}$ of the two waves at the receiver (red inverted triangle) represent the transmitted and converted P-waves at the bedrock-sediment interface. The same principle applies to transmitted/converted S-waves. P- and S-wave velocities in sediments are indicated by $v_P$ and $v_S$, and in the bedrock by $v_{Pn}$ and $v_{Sn}$.</figcaption></figure>

## 2.3. Measurements of the Sediment Ps Time Delay

Twenty earthquakes with clear P onset were selected (listed in Table S1 in Supporting Information S1). Using the Xdas picker (i.e., Xpick, Trabattoni et al., 2024), we manually picked the P-, Ps- and S-waves (Figure 2). Automatic phase picking algorithms (such as PhaseNet) could not be used as they are unable to distinguish P from Ps and often misplace the arrival onset when both phases are observed (Baillet et al., 2025). From the arrival time difference between P and Ps, a travel time delay ($\Delta t$), measured every 100 m, is obtained for each earthquake until 145 km on the cable (Figure 4). No $\Delta t$ could be picked for the first 1.5 km located onland. Note that the selected earthquakes have different local magnitudes ($M_L$ in Table S1 in Supporting Information S1) and are located in a wide area around the cable (Figure 1a). The consistency of the $\Delta t$ measurements across different events indicates that they are not very sensitive to the earthquake back-azimuth, supporting the hypothesis of nearly vertical ray paths in the sediments. However, for certain events, the $\Delta t$ values show a much wider spread. This is likely due to challenges in consistently identifying the Ps arrival, which can occur when the strong P-wave train masks the Ps phase, or when multiple scattering interfaces interfere with its identification.

## 2.4. Measurement of the Surface Wave Velocity Using Surface Wave Dispersion

In the context of passive imaging of seabed structures, the ambient surface waves are essentially those produced by micro-seismic noise sources, in particular those generated locally by reflections at the coast (e.g., Guerin et al., 2022), and thus depend on the (limited) frequency content of oceanic gravity waves. To constrain the shallowest layers, an alternative is to use the seismic coda of local earthquakes. The seismic coda, which constitutes the late part of the seismic signal, contains scattered energy, including surface waves that are sensitive to shallow depths and influenced by small velocity perturbations in the near-surface layers (Campillo & Paul, 2003; Obermann et al., 2013; Snieder, 2006). In underwater settings, by selecting surface waves traveling along the cable direction, Scholte-wave dispersion curves can be extracted and then inverted into 1D velocity models using sliding sections along the fiber. These 1D velocity models are then assembled to build a 2D shear velocity model. In a layered model, the phase velocity of the fundamental Scholte-wave is a function of the profile of three quantities: P-wave velocity, S-wave velocity and density. In the present scenarios, these profiles are characterized by discontinuities at the boundary of the three main geological units considered: the ocean, the sediments and the bedrock.

In this study, we used the seismic coda of the 20 selected earthquakes to measure the phase velocity of the fundamental Scholte-wave mode on f-k diagrams (Figure 5). We considered as the coda the signals arriving 15 s after the manually picked S-wave onset (Figure 2a). Depending on the local magnitude of each earthquake, 60 s (below $M_L$2.5), 120 s (between $M_L$2.5 and $M_L$4) or 180 s (above $M_L$4) of the coda signal were selected. An example of a full earthquake record with its coda selection is presented in Figure S1 in Supporting Information S1.

<figure><img src="image_7.png" /><figcaption>**Figure 4.** The travel time delays ($\Delta t$) between the transmitted P and converted S phases at the bedrock-sediment interface were estimated from manual picks. The $\Delta t$ values for 20 detected earthquakes are represented in gray, with the average $\Delta t$ displayed in dark blue ($\mu$) and the zone between the first and third quartiles colored in light blue (Q1, Q3). The mean value over the entire fiber is 0.762 s. No values could be picked for the first 1.5 km (on land cable).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Frequency wavenumber spectra (f-k) of the strain-rate signal for 2 km-long segments along the fiber-optic cable. The energy boundary of lowest apparent phase velocity (i.e., of lowest slope) was identified as the fundamental Scholte-wave mode and was manually picked (white dashed lines) between 0.2 and 6 Hz. Some f-k (e.g., 44 km segment) show a clear dispersive feature with multiple curves that are associated either with phases converted at the base of strong-contrast layers of the upper crust, or with higher Scholte-wave modes.</figcaption></figure>

We then divided each coda section into 1-min segments to perform analyses on a fixed window length to maintain the same resolution in frequency for all events. For all codas, the computed f-k diagrams are very similar and do not depend on the azimuth of the earthquake (Figure S2 in Supporting Information S1). This suggests that the coda surface wave dispersion is primarily sensitive to subsurface structure rather than the earthquake source. We subsequently generated f-k diagrams for 2 km sliding sections along the entire fiber, shifting the section every 500 m, resulting in a total of 295 f-k diagrams for each earthquake. These were then averaged by section across all earthquakes to increase the SNR. The SNR varies along the DAS record segment, which is likely attributed to complex interactions with shallow structures and site effects that locally attenuate or amplify the signal (Lior et al., 2021; Wang et al., 2018). For the low SNR sections, from 50 to 58 km and from 110 to 116 km, we reduce the spatial resolution to increase the SNR by generating f-k diagrams for 4 km sliding sections, every 1 km. Moreover, in five f-k plots, for the segments 117, 117.5, 118, 118.5, and 137 km, no dispersion curves were picked. Examples of the resulting 275 diagrams are shown in Figure 5. Some of these display several branches representing either higher modes, surface waves arriving with different azimuth, or different wave types (e.g., Love). In this study, we focus exclusively on the fundamental mode of Scholte wave phase velocity, as our primary objective is to better constrain the shallow subsurface, without including high-order modes. This mode was identified as the observed branch of lowest apparent velocity (Guerin et al., 2020; Lior et al., 2021) and was manually picked, for frequencies between 0.2 and 6 Hz. These branches typically exhibit a symmetric pattern, with dispersive energy propagation (e.g., 44 km segment), although they sometimes have unusual shapes (e.g., 18.5 km segment in Figure S3 in Supporting Information S1), possibly due to a sudden change in the medium at this location (vertical discontinuity).

## 2.5. Parametrization of the Forward Model

To image the sedimentary cover along the fiber, we perform a joint inversion of both Ps time delays and surface wave velocity measurements. The apparent velocity extracted from the f-k analysis (white dashed line in Figure 5), once the slope is converted from frequency-wavenumber to phase velocity-period, can be used as a dispersion curve, which corresponds to the observed model $v_s^{obs}$. We first present the forward model used to compute surface wave dispersion curves and the $\Delta t$ delay. Then, we define the misfit function and outline the methodology used for the inversion process.

To model the measured shear-wave velocity $v_s$ of an underwater sediment cover, we modified the equation given by Mordret et al. (2014), which represents the velocity within the sediment as an increasing power law:

$$v_s(z) = v_0 - \left( \frac{z - z_0}{z_{\max} - z_0} \right)^{\alpha} (v_{\max} - v_0). \quad (1)$$
<figure><img src="image_9.png" /><figcaption>**Figure 6.** 1D velocity profiles in colors with different $\alpha$ values for a sedimentary layer. The parameters are set to $z_0 = 1200$ m, $z_{max} = 1500$ m, $v_0 = 150$ m.s$^{-1}$ and $v_{max} = 1000$ m.s$^{-1}$. The average $\alpha$ for the 1D models is 0.45. The depth is given relative to sea level, $z - z_0 = 0$ indicating the seafloor.</figcaption></figure>

Where $z$ is the depth, $v_0$ the S-wave velocity at the top of the sediments, $\alpha$ a power-law parameter, and $z_0$ the bathymetric depth. This formulation includes two additional parameters to Mordret et al. (2014) being $z_{max}$ the depth of the sedimentary layer, and $v_{max}$ the maximum velocity at the bottom of the sediment layer. This approach ensures a smooth velocity gradient, making it well-suited for modeling shallow sediments where velocity transitions are typically gradual rather than abrupt. Moreover, this expression automatically constrains $v_s$ to not exceed the bedrock velocity and allows for flexibility in maintaining a high impedance contrast between the shear velocities in sediment and the bedrock. The parameter $\alpha$ shapes the power law curve to allow for a smooth increase in velocity (Figure 6). Scholte waves have very slow shear velocities in shallow sediments (tens to few hundreds of m.s$^{-1}$), while compressional velocities are slightly higher than seawater velocities (~1.5 km.s$^{-1}$; Nolet & Dorman, 1996). Hence, $v_p$ is calculated with the following empirical equation, that holds to first order for all clastic silicate rocks: $v_p = 1.16 v_s + 1.36$ (Castagna et al., 1985). However, since Scholte waves are primarily sensitive to $v_s$ and only weakly influenced by $v_p$, this calculation is of secondary importance in this analysis. The density $\rho$ (g.cm$^{-3}$) of each layer is derived from $v_p$ as follows: $\rho = 1.74v_p^{0.25}$ (Brocher, 2005).

We use the open-source Python library `disba` (Luu, 2024) to compute the forward model of the Scholte-wave dispersion curves. For each segment along the cable, we define a 1D shear velocity model composed of a water layer, a sedimentary cover discretized into $n = 30$ layers located at depth $z_i$

(increasing this number did not change the result), and a bottom half-space. In total, we perform an inversion for four parameters: $v_0$, $v_{max}$, $z_{max}$, and $\alpha$. To control the potential trade-offs between model parameters and ensure that we do not overfit our data, we incorporate a measured $\Delta t$ as a regularization term in the inversion, providing additional constraints on sediment velocities and thickness (Figure 4). The time delay $\Delta t$ is a function of the sediment thickness, shear velocity and compressional velocity, expressed in a 1D layered model with the following equation (e.g., Doran & Laske, 2019). The dependence on distance is neglected in this expression for clarity, as the formulation applies to a localized segment of the cable:

$$ \Delta t = t_{Ps} - t_p = \frac{h}{n} \sum_{i=1}^{n} \left( \frac{1}{v_S(z_i)} - \frac{1}{v_P(z_i)} \right). \qquad (2) $$

Where $t_{Ps}$ is the arrival time of the Ps-wave defined by the summation over all layers in the model of the thickness of the layer $h = z_{max} - z_0$, divided by the S-wave velocity $v_S$ in that layer at depth $z_i$, with $z_i = z_0 + \frac{h}{n}$ (the same principle applies to $t_p$). We then define the objective function to minimize the sum of the squared differences between the observed $v_s^{obs}$ and computed $v_s^{cal}$ Scholte-wave phase velocities (Equation 1) across all frequencies, regularized by the squared difference between the observed and calculated Ps time delays $\Delta t$ (Equation 2). The misfit is then expressed as:

$$ L = \sum_{i=1}^{N} (v_s^{obs}(z_i) - v_s^{cal}(z_i))^2 + W(\Delta t^{obs} - \Delta t^{cal})^2. $$

Where $N$ is the total number of discrete frequencies. The weighing factor $W$ is introduced to balance the data misfit with the regularization term, and is set to 0.8.

To run the inversion, we use the Basin-Hopping algorithm introduced by Wales and Doye (1997). This method combines a global stepping algorithm with a local minimization at every step. In each iteration, it randomly perturbs the parameters, finds the local minimum and decides whether to accept or reject the parameters based on the minimized objective function. When the algorithm has converged, the 1D model with the best misfit is retrieved. We perform the inversion sequentially for each cable segment, using the best-fit parameters of the
<table><caption>**Table 1**
Parameter Ranges for the Sedimentary Cover Model Inversion</caption><thead><tr><th>Parameters</th><th>Minimum</th><th>Maximum</th><th>Unit</th></tr></thead><tbody><tr><td>v<sub>0</sub></td><td>0</td><td>500</td><td>m.s<sup>-1</sup></td></tr><tr><td>v<sub>max</sub></td><td>200</td><td>3,500</td><td>m.s<sup>-1</sup></td></tr><tr><td>z<sub>max</sub> - z<sub>0</sub></td><td>30</td><td>4,000</td><td>m</td></tr><tr><td>α</td><td>0.1</td><td>0.9</td><td>Ø</td></tr></tbody></table>

previous section as the starting parameters for the subsequent section. The final 1D models are then concatenated along the cable into a 2D shear velocity model.

## 2.6. Inversion Settings

The model space is bounded by a priori minimum and maximum physical constraints. The structure and tectonics of the central Chilean margin are well characterized by seismic reflection and tomographic studies (Contreras-Reyes et al., 2015; Laursen & Normark, 2003; von Huene et al., 1997). In the area of interest, the P-wave velocity of the sedimentary layer near the coast is ~2 km.s⁻¹ and between ~4.5 and ~6 km.s⁻¹ in the bedrock (continental crust) (Becerra et al., 2017; Contreras-Reyes et al., 2015; Von Huene et al., 1997). Based on this knowledge, the bounds on the inverted parameters are set as shown in Table 1 to sufficiently cover the conceivable parameter space. We then fixed the parameters in the water layer: v<sub>S</sub> = 0, v<sub>P</sub> = 1500 m.s<sup>-1</sup>, ρ = 1.025 g.cm<sup>-3</sup>, and the height of the water column following the bathymetry of the fiber-optic cable shown in Figure 1b. The S-wave velocity v<sub>Sn</sub> of the bottom half-space (bedrock) is set to 3500 m.s<sup>-1</sup> and is calculated using the ratio √3v<sub>Pn</sub>, where v<sub>Pn</sub> is the P-wave velocity described by von Huene et al. (1997) and Contreras-Reyes et al. (2015).

Figure 6 compares several velocity models with different values of α, which controls the power law curvature. For α = 1 the velocity increases linearly, for α = 0, the velocity is constant and equal to v<sub>max</sub>. These two extreme cases are not representative of the velocity gradient in sediments (Bard & Gariel, 1986). The value range of α has to be in between to have a smoothly increasing velocity with depth.

# 3. Results

## 3.1. Dispersion Curves Inversion

The inversion was applied to the 275 segments along the fiber to obtain 1D local velocity models (Figure 7). The dispersion curve in Figure 7a (white dashed line), converted from the slope of the f-k analysis and corresponding to the observed model v<sub>s</sub><sup>obs</sup>, is inverted through thousands of synthetic trials v<sub>s</sub><sup>cal</sup>, with the best result shown as a black line in Figure 7a. When the Ps delay regularization is not included in the inversion (i.e., when the misfit computation does not minimize the error between the calculated and observed Δt), the resulting 1D velocity models do not converge at depth (Figure 7b), leading to a wide range of possible solutions. An example of Δt calculated after an inversion without the Ps delay constraint is presented in Figure S4 in Supporting Information S1. Adding the regularization term ensures proper convergence, as demonstrated by the synthetic data validation in Figure S5 in Supporting Information S1 and the final model results in Figure 7c. A comparison between the observed and synthetic dispersion curves along the fiber is shown in Figure 8a, with the inverted time

<figure><img src="image_10.png" /><figcaption>**Figure 7.** Inversion for the 82.5 km section (Figure 5). Each black line and white dashed line represent the best synthetic and observed models, respectively. The best 10,000 models are displayed in color (misfit increases from red to blue). (a) Synthetic dispersion curves inverted with Equation 1. (b) 1D velocity models associated with the synthetic dispersion curves for an inversion without the Δt regularization leading to no convergence at depth, and a wide range of possible solutions. (c) 1D velocity models associated with the synthetic dispersion curves for an inversion constrained by Δt. Additional examples are provided in Figure S7 in Supporting Information S1.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Figure 8.** (a) Observed and calculated dispersion curves. The colors for each plot follow the distance along the cable. (b) Calculated travel time delays $\Delta t$ every 500 m that constrain the data.</figcaption></figure>

delays $\Delta t^{cal}$ displayed every 500 m in Figure 8b. We observe a significant variation in the velocity of the dispersion curves all along the fiber. Between 1.5 and 8 km, and after 100 km, dispersion curves have higher S-wave velocities than along the rest of the cable, suggesting faster sediments. The lowest velocities are found between 30 and 80 km, coinciding with the VFB. Additional comparisons between observed and calculated dispersion curves are provided in Figure S6 in Supporting Information S1.

## 3.2. Retrieving a 2D Shear Wave Velocity Model

Overall, in the final 2D shear velocity model, the depth of the sedimentary layer varies from a few tens of meters to ~1,300 m, and the S-wave velocities in the sediments vary from tens of m.s⁻¹ to a few hundred of m.s⁻¹ over most of the profile. On the continental shelf, from 1.5 to 8 km, the model captures deeper structures with higher velocities, close to the bedrock velocity (Figure 9a). Apart from this section, a strong velocity contrast between the sediment cover and the bedrock is always recovered by the inversion at locations where the travel time delay $\Delta t$ could be picked. In a first approximation, the sediment thickness (h) models the observed $\Delta t$ well (Figure 9b). Additionally, we observe that the mid-depth S-wave velocity per section ($Vs_{mean}$) increases slightly as we move northward.

## 4. Discussion

Our final model ranges in sediment thickness from 70 to 1,200 m (Figure 9). We observe lateral variability in S-wave velocity within the sediments, with the average velocity increasing toward the northern part of the study area (Figure 9b). The continental shelf is characterized by a steep velocity gradient, corresponding to the part where a relatively low Ps time delay (<0.4 s) is detected. This higher velocity section is in agreement with a thin (<100 m) quaternary deposit on top of metamorphic igneous rocks (Contreras-Reyes et al., 2014). On the continental slope, between 8 and 20 km, two sedimentary basins are observed with sediments accumulating up to 500 m until the Aconcgagua Canyon. This area coincides with sections where anomalous f-k shapes are observed (e.g., the 18.5 km section in Figure S3 in Supporting Information S1), which we could potentially associate with steeply dipping discontinuities such as faults or basin edges. These features can cause seismic waves to scatter at reflectors, which
<figure><img src="image_12.png" /><figcaption>**Figure 9.** (a) The final 2D velocity model is constructed by concatenating the results of the 1D inversions every 500 m. The gray area represents the water layer. The velocity in the bedrock layer (black area) is set to 3,500 m.s⁻¹. The bathymetry profile crosses the Aconcagua Canyon (AC) between 20 and 28 km, the edges of the Valparaíso Forearc Basin (VFB) between 30 and 80 km, La Ligua Canyon (LLC) at 80 km, and the Punta Salinas Ridge (PSR) around 90 km. (b) Comparison between the thickness (h) of the sedimentary cover, the travel time delays (Δt) between transmitted P and converted S waves, and the average S-wave velocity per section (Vsmean).</figcaption></figure>

may explain the observed unusual shapes in the f-k analysis. As a result, the apparent velocity at this location is likely inaccurate, leading to an over- or underestimation of the effective penetration depth and introducing uncertainty in the 2D Vs model.

The Aconcagua (20–28 km) and La Ligua (80 km) canyons are not marked by significant sedimentary fills and have an average velocity of about 300 m.s⁻¹. Sedimentary deposits are present along the edges of the VFB (between 30 and 80 km), but they do not clearly highlight the basin feature, since the location of the cable is on the edge of the basin, on a steep slope, where thick sediments (>1 km) are not expected. The PSR is divided between a thin sedimentary layer and a basin-like sedimentary accumulation with velocities increasing with depth. This structure bounds the northern part of the profile, which has numerous basin-like features (>1 km) filled with higher-velocity sediments (up to 1,300 m.s⁻¹), interspersed with a thinner layer (<100 m) associated with small reliefs (around 120 and 140 km on the profile). The retrieved sediment thickness at 94 and 110 km along the profile (with an uncertainty of about 2 km) is estimated to be 700–900 m. This result can be compared with wide-angle and seismic refraction data previously acquired in the area, which indicate sediment thicknesses of approximately 600–1,330 m based on velocities and two-way travel times (Becerra et al., 2017; Von Huene et al., 1997). Reflection seismic profiles reveal several normal faults affecting the northern slope of the PSR (Becerra et al., 2017; Dominguez et al., 1998), structures that facilitates sediment accumulation and could explain the different thicknesses in this area, including a prominent normal fault at 90 km on the profile (Laursen et al., 2002), while Maldonado et al. (2021) mapped normal faults on the coast in this area. Von Huene et al. (1997) emphasized that the crest of the PSR has a fragmented morphology indicative of an erosive process, characterized by numerous submarine canyons and rills. Along with potential strike-slip faults along the basin margins to the south (Dominguez et al., 1998), the topography of the upper continental slope transitions from a gentle escarpment to an abrupt slope scarp, as noted by Contreras-Reyes et al. (2014, 2015). This fluctuation can potentially influence the distribution of sedimentary deposits along the profile. Around 31°00 S (Figure 1), north of the cable, the upper slope is cut by canyons and landward-dipping faults with offsets of several meters. These fault scarps can extend through the top of the basement and the seafloor. South of this area, just north of the PSR (32°00 S), the upper slope also exhibits landward-dipping faults and a generally steeper dip compared to the northern region which is consistent with the segmented sediment morphology that our results seem to display (Becerra et al., 2017; Dominguez et al., 1998; Ranero et al., 2006). Contreras-Reyes et al. (2015) observe a decreasing velocity contrast between the sedimentary layer and the bedrock in this area, which correlates with our results of higher velocity gradients.

We additionally tested varying the half-space velocity vsn between 2,000 and 5,000 m.s⁻¹, which yielded similar models as in Figure 9 for the sediment cover. This indicates a lack of sensitivity to lower frequencies, which are not sufficiently informative to constrain the velocity beyond 1 km depth. Integrating ambient noise cross-correlation or high-order modes of the Scholte wave phase velocity could improve sensitivity to lower
frequencies and enhance depth resolution. Despite the weak lateral constraints, some regions in the model still show consistent depth, without any discontinuities (e.g., 5–15 km, 38–48 km, and 125–140 km sections). The integration of Ps time delays in the DAS data inversion places essential constraints on sediment thickness and greatly improves subsurface imaging with DAS. Without this constraint, the inversion remains undetermined, resulting in a wide range of 1D velocity models that satisfy the objective function (Figure S4a in Supporting Information S1). Moreover, when attempting to calculate the Ps delay with the unconstrained 2D Vs model, we cannot recover the initially observed Ps delay (Figure S4b in Supporting Information S1), further highlighting the non-uniqueness of the solution and the crucial role of including this constraint in the inversion process.

We decided to use seismic codas over ambient noise due to their superior homogenization of the sources and SNR in our data. An unfiltered signal remains detectable in the coda for several kilometers beyond 100 km, as seen on the 101 km section in Figure 5, and even beyond this point, noise primarily obscures the signal energy with white noise without altering the f-k shape. Beyond 106 km, however, the rapidly increasing noise amplitude reduces the resolution, leading to greater uncertainty in velocity estimates. Past 130 km, the dispersive energy observed in the f-k analysis is significantly weaker, making velocity picks less accurate. Hence, our results after 130 km become more biased, and it would be wiser to only consider the Ps time delay as a reliable quantity for constraining the sediment structure.

With continuous monitoring of central Chile (ERC Abyss project; Baillet et al., 2025; Rivet et al., 2025), this analysis could be repeated over different time periods and seasons to investigate potential variations in sediment thickness, particularly following large floods, and to assess the impact of seasonal changes.

## 5. Conclusion

This study demonstrates the effective application of Ps phase to the inversion of DAS data, providing independent constraints on seafloor sediment thickness and velocity structure. This approach enables better-resolved imaging of offshore subsurface structures in central Chile compared to previous studies. We constructed a 2D shear wave velocity model of 145 km along the continental margin in central Chile, with a spatial resolution of 2 km. A significant degree of spatial variability in shear velocity is observed all along the cable, with a notable increase in velocity in the northern part of the model, and on the continental shelf. The thickness of the sediment layer fluctuates significantly along the margin, correlating to the first order with the travel time delay $\Delta t$ observed from the Ps phase (Trabattoni et al., 2024). Our results provide new insights into the structure of the sedimentary deposits along the continental margin. The influence of tectonic structures, such as faults and basin edges, is reflected in the observed velocity contrasts and sediment accumulation patterns. This improved knowledge of the subsurface can serve to increase the accuracy of local (re)located hypocenters, and to better model interactions with the subsurface (e.g., seismic-acoustic transmission and reflection, relevant for regional oceanographic studies).

## Data Availability Statement

The data are available in Rivet et al. (2025), and the scripts used to produce the figures in Vernet et al. (2025). Analyses were conducted using Xdas python library (Trabattoni et al., 2025). Figures were made using matplotlib version 3.9.3 (Hunter, 2007). Map in Figure 1 was created using PyGMT version 0.10.0 (Tian et al., 2025).

## Acknowledgments

This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 101041092—ABYSS). We thank the anonymous reviewers and editor for their constructive feedback and thoughtful suggestions, which helped improve the clarity and quality of the manuscript.

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