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# Automatic Earthquake Catalogs From a Permanent DAS Offshore Network

Marie Baillet, Diane Rivet, Alister Trabattoni, Jérôme Chèze, Fabrice Peix, David Ambrois, Martijn van den Ende, Clara Vernet, Claudio Strumia, Bertrand Potin, et al.

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## To cite this version:

Marie Baillet, Diane Rivet, Alister Trabattoni, Jérôme Chèze, Fabrice Peix, et al. Automatic Earthquake Catalogs From a Permanent DAS Offshore Network. Journal of Geophysical Research: Solid Earth, 2025, 130 (10), pp.e2025JB031565. 10.1029/2025JB031565. hal-05383475

HAL Id: hal-05383475

https://hal.science/hal-05383475v1

Submitted on 26 Nov 2025

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

RESEARCH ARTICLE

10.1029/2025JB031565

**Key Points:**

* We propose a workflow for seismic monitoring that combines Distributed Acoustic Sensing (DAS) with a classical seismological network

* We adopt state-of-the-art techniques developed for conventional seismological data to marine DAS data to extend sensitivity offshore

* Marine DAS data enhance the magnitude of completeness offshore near the trench and onshore, within a radius of 100 km around the cable

**Supporting Information:**

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

**Correspondence to:**

M. Baillet,  
marie.baillet@geoazur.unice.fr

**Citation:**

Baillet, M., Rivet, D., Trabattoni, A., Cheze, J., Peix, F., Ambrois, D., et al. (2025). Automatic earthquake catalogs from a permanent DAS offshore network. *Journal of Geophysical Research: Solid Earth, 130*, e2025JB031565. https://doi.org/10.1029/2025JB031565

Received 17 MAR 2025
Accepted 28 SEP 2025

**Author Contributions:**

**Conceptualization:** Marie Baillet,  
Diane Rivet

**Data curation:** Marie Baillet

**Investigation:** Marie Baillet, Diane Rivet, Alister Trabattoni, David Ambrois, Martijn van den Ende, Clara Vernet

**Methodology:** Marie Baillet, Diane Rivet,  
Claudio Strumia

**Project administration:** Diane Rivet

**Resources:** Bertrand Potin

**Software:** Marie Baillet, Jérôme Cheze,  
Fabrice Peix, David Ambrois,  
Claudio Strumia, Bertrand Potin

**Supervision:** Diane Rivet

**Validation:** Diane Rivet,  
Alister Trabattoni

**Visualization:** Jérôme Cheze

© 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.

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Automatic Earthquake Catalogs From a Permanent DAS
Offshore Network

Marie Baillet¹ ID, Diane Rivet¹ ID, Alister Trabattoni¹ ID, Jérôme Cheze¹, Fabrice Peix¹, David Ambrois¹, Martijn van den Ende¹ ID, Clara Vernet¹ ID, Claudio Strumia² ID, Bertrand Potin³ ID, Rodrigo Sanchez⁴, and Sergio Barrientos⁴

¹Université Côte d'Azur, Observatoire de la Côte d'Azur, CNRS, IRD, Géoazur, France, ²Physics Department, Università di Napoli Federico II, Napoli, Italy, ³Department of Geophysics, Universidad de Chile, Santiago, Chile, ⁴Centro Sismológico Nacional, Universidad de Chile, Santiago, Chile

**Abstract** Applying Distributed Acoustic Sensing (DAS) to sense offshore telecom cables offers a unique opportunity to expand permanent seismological networks far offshore with an unprecedented instrumentation density but presents challenges in managing and processing data. This study introduces a workflow for automating earthquake catalog generation by combining DAS arrays with regional on-land seismic networks. We apply it to the ABYSS offshore permanent observatory in central Chile, which began continuous recording in October 2023 on three 150-km segments of a regional dark-fiber telecommunication network. Our workflow includes state-of-the-art components originally developed for conventional seismic sensor data. We first show that PhaseNet, pretrained on conventional seismic stations, can perform automatic phase picking on DAS strain-rate data converted to velocity. In a second step, we demonstrate that the GaMMA associator, tuned with parameters adapted to our dense data set, can cluster picked arrivals of small and moderate-sized earthquakes. We then adapt NonLinLoc to compute hypocenter locations using numerous phase picks, combining DAS and onland seismic data for improved accuracy. Finally, we show that reliable local magnitudes can be estimated on DAS recordings using the Richter scale. The earthquake catalog is stored in an SQL database using QuakeML formalism and is accessible via a user-friendly webpage. By applying the workflow to onshore station recordings, we evaluate the benefits of adding permanent seafloor DAS instrumentation, demonstrating that offshore DAS significantly improves the detection of small-magnitude earthquakes and reduces the offshore magnitude of completeness to 1.3.

**Plain Language Summary** Seismicity catalogs are important for studying the seismicity of a region. They make it possible to study the geodynamic processes and are used to monitor seismic activity. Their construction presents a number of challenges, especially in subduction zones where most earthquakes occur at sea. A recent technology called Distributed Acoustic Sensing (DAS) allows to extend terrestrial sensor networks, particularly at sea, by using the information transmitted by fiber-optic telecom cables, creating hundreds or even thousands of additional sensors. In this study, we present an automatic data processing procedure for creating seismicity catalogs combining DAS data from three 150 km-long segments off central Chile with data from onshore seismological sensors. This workflow demonstrates the extent to which the use of DAS can enhance seismicity catalogs, especially in the detection of small earthquakes. We also show that the methods commonly used to analyze seismometer data work on DAS data, in particular the tools used to create catalogs. To cope with the large volume of data and to enable optimum interoperability, we are using a database formatted according to current seismological standards and are making it possible to download and view the results via a public web interface.

# 1. Introduction

Earthquake catalogs serve as fundamental data sources for studying the earthquake statistics, distributions, and interactions, and help understanding the geodynamic processes. In particular, they are used to monitor seismicity as part of seismicity forecasts (Calais et al., 2022) and to assess seismic hazards and risks (Danciu et al., 2021). Constructing an accurate earthquake catalog in quasi-real time is challenging, particularly in subduction zones where a significant portion of seismic activity occurs offshore. Chile's coastal geography, characterized by sparse population centers and vast deserts, complicates the installation and maintenance of permanent instrumentation. In regions such as the Atacama Desert, where permanent seismic stations are sparse, catalogs are incomplete for
**Writing – original draft:** Marie Baillet, Diane Rivet, Jérôme Cheze, Fabrice Peix, David Ambrois

**Writing – review & editing:** Diane Rivet, Alister Trabattoni, Martijn van den Ende, Clara Vernet, Claudio Strumia, Rodrigo Sanchez, Sergio Barrientos

magnitudes lower than 3.5, which limits detailed seismotectonic analysis (Potin, Ruiz, et al., 2024). Only dense, temporary networks enable the assessment of the interplay between subduction interface coupling and the spatio-temporal distribution of seismicity (González-Vidal et al., 2023; Pastén-Araya et al., 2022). Such dense networks also significantly enhance the ability to detect transient phenomena, particularly those associated with aseismic slow-slip events (Münchmeyer et al., 2024). Even with denser networks deployed on land, accurately detecting and locating low-magnitude events close to the trench remains difficult with land-based stations alone. The observed paucity of shallow offshore (i.e., updip) seismicity may be due to the limited sensitivity of distant onshore stations. Nonetheless, measurements of offshore seismicity are important; they can help resolve the ambiguity caused by the trade-off between the width and the degree of coupling at the plate interface as land-based geodetic observations can be interpreted as indicating either a narrow, fully locked zone or a broader, partially locked zone along the megathrust (Wang and Tréhu, 2016). Offshore microseismicity can illuminate actively deforming sections bounded by strongly coupled ones. Moreover, foreshock activity preceding large interplate earthquakes can provide insights into the nucleation process (Ruiz et al., 2014). There is, therefore, a need to fill the observational gap on the seafloor. However, deploying traditional seismic sensors on the seafloor cannot be considered as a cost-effective option with current marine geophysical instruments.

Distributed Acoustic Sensing (DAS) on offshore telecom cables provides a valuable alternative, offering dense and potentially permanent observation capabilities that complement the onland network. This technology converts any fiber optic cable into a dense network of seismic sensors. DAS measures longitudinal strain along the fiber by detecting phase shifts in the back-scattered light of a laser pulse (Constantinou et al., 2017; Jousset et al., 2018; Lindsey et al., 2017). This approach provides sensor spacing on the order of 10 m, making it approximately 1,000 times denser than the densest traditional seismic networks. Moreover, DAS technology enables real-time monitoring of remote areas, reaching up to 150 km offshore, even in challenging environments like the seafloor (Lindsey et al., 2019; Sladen et al., 2019; Williams et al., 2023).

Since 2021, we have had the unique opportunity to collaborate with the Chilean National Seismological Center and the telecommunications company GTD to build an offshore seafloor seismic observatory in central Chile. This Chilean submarine telecom network runs along the entire 4,200 km of the Chilean coast with landing points every 200–400 km. Since October 2023, we have continuously monitored three 150-km long cable segments in Central Chile, using three Alcatel Submarine Networks OptoDAS interrogators (Waagaard et al., 2021). Performing DAS on offshore cables offers significant advantages for permanent earthquake observation. First, DAS enables continuous sensing in areas where maintaining conventional instrumentation is challenging, such as deserts and in the ocean. Second, while DAS is constrained to the cable's geometry, the GTD network in Chile is strategically located 20–30 km from the coast, ideally positioned close to the epicentral zone of megathrust earthquakes (Ruiz and Madariaga, 2018). Third, DAS can leverage existing telecommunication networks, which greatly reduces the logistics and cost of deployment.

While it is clear that DAS offers greater earthquake detectability performances than traditional sparse arrays (Nayak et al., 2021) and retrieves comparable ground motions used to estimate magnitudes (Lior et al., 2021; Strumia et al., 2024; Trabattoni et al., 2022), it also has limitations. These include challenges related to earthquake localization due to the often highly linear geometry, as is the case with most submarine cables, and related to the single-component nature of the measurements that limit phase identification. Finally, a practical challenge is presented by the vast quantities of data generated by DAS deployments, which pose difficulties for archival and data management (Quinteros et al., 2021).

Here, we propose to evaluate the added value of continuous DAS instrumentation on offshore cables in central Chile, compared to the existing permanent instrumentation on-land. We adopted and further developed a state-of-the-art processing chain used to build automatic catalogs from traditional seismic networks, and applied it to our amphibious network, which consists of 30,000 DAS sensing points, as well as the regional permanent land-based network. All steps of the workflow to generate an automatic earthquake catalog, ranging from phase picking, association, and localization to magnitude estimation, final interactive visualization, and data management, are described. We discuss the performance of this observatory for automatic catalog generation by comparing it to the catalog obtained using the same workflow on land-based data only. Finally, we describe the limitations of DAS data and explore potential approaches to improve the reliability of the automatic catalog while maintaining good detectability of small events.
<figure><img src="image_5.png" /><figcaption>**Figure 1.** Map of the amphibious seismic network in central Chile. The continuous black lines represent the sections of the offshore cables that are sensed by our three Distributed Acoustic Sensing (DAS) instruments, the black dashed line represents the cable section that is out of reach of our DAS instruments. The onland seismic stations used in this study are represented by inverted triangles and the colors indicate the different networks. The dotted line is the geographical border between Chile and Argentina.</figcaption></figure>

## 2. Offshore DAS and Onland Permanent Networks

### 2.1. DAS Data Recorded on Offshore Telecom Fiber Optic Cable

As part of the ABYSS project, starting 30 September 2023, we are currently recording continuous offshore DAS data with 3 OptoDAS instruments from ASN (Alcatel Submarine Network). As shown in Figure 1, a first instrument is placed in the city of Concón and senses a fiber going north toward the city of La Serena. This cable section is hence named CCN.N. At La Serena, the two other DAS instruments sense two other cable sections: SER. S, which is the fiber going south toward Concón; and SER. N, the fiber going north toward the city of Caldera. Access to the fibers is granted by the Chilean telecommunications company GTD which owns this telecom cable network.

All instruments acquire the data with a spatial sampling of 15 m and a gauge length of 30 m. The CCN.N and SER. S fibers are sampled at a frequency of 62.5 Hz over a length of 163 km while the SER. N fiber is sampled at 100 Hz over a length of 100 km. Owing to the progressive decrease of the signal-to-noise ratio due to the attenuation of the optical power we limit our use of the fiber length to a maximum of 150 km. To decrease the computational time taken by the catalog workflow operations, we choose to decimate the data spatially by a factor of 4, reducing the spatial resolution to 60 m.

To quantify the performance of our catalog workflow, we focus here on a data set composed of one month of strain-rate waveforms recorded on those 3 fibers from the 15th of September 2024 to the 16th of October 2024. The 3 fibers are registered with the network code F1 and a portion of the data is accessible via the French EPOS node with data subsampled every 5 km.

### 2.2. Permanent Onland Seismic Networks

We complement our DAS offshore network with 39 permanent broad-band seismic stations belonging to three permanent networks publicly available through EarthScope Data Services; C1(Red Sismologica Nacional, Universidad de Chile, 2012), OC (Observatorio Sismológico CIGEOBIO CONICET) and WA (Universidad Nacional de San Juan, Argentina). The onland station coverage is poor on the coastline next to the sensed fiber sections and denser around Santiago (Figure 1). Two stations taken from Argentinian networks complete our coverage on the deeper part of the slab. In this study we will refer to all the onland stations as the Onland network.

## 3. Workflow for an Automatic Earthquake Catalog Using DAS Data

In order to evaluate the performance of our workflow on DAS data, we chose to create three catalogs: (a) a first catalog using only DAS data (named the DAS catalog) to better understand the limitations of offshore-only instrumentation, (b) a second catalog obtained from land-based data only (named the Onland catalog), and finally (c) a third catalog created using both DAS and land-based data (named the Combined catalog). The same methods were used to build the three catalogs to be able to compare them properly and at each step of the workflow. The catalogs were saved in a database compatible with the QuakeML standard format, described later in Section 4.1.

### 3.1. Automatic Phase Picking

To automatically detect earthquakes and build our data set of P and S picks we use the state-of-the art arrival-time picking method PhaseNet (Zhu and Beroza, 2018). This algorithm, based on a deep-neural network, picks arrivals by phase and assigns probabilities of belonging to each phase or noise. The PhaseNet model was originally trained on waveforms recorded by several types of 3-components velocimeters sampled at 100 Hz. To provide a compatible data format, strain-rate recordings are converted to velocity following the method described by Trabattoni et al. (2023). The traces recorded at 62.5 Hz are resampled to 100 Hz and the absent components are filled with zeros as recommended. We used SeisBench (Woollam et al., 2021) to evaluate the performance of
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Comparison between manual, PhaseNet (model DiTing) and PhaseNet-DAS picks for 9 earthquakes after the association algorithm step (see Section 3.2). The earthquakes were acquired in 2021 during the POST project (Rivet et al., 2022) on the Concón fiber with the same instrument as for the ABYSS project. The data is in strain-rate, bandpass filtered between 1 and 20 Hz, manual picks are in orange (only the Ps and Ss phases are displayed), PhaseNet picks (probability ≥0.35) are in magenta and PhaseNet-DAS (probability ≥0.7) picks are in blue. PhaseNet-DAS is better than PhaseNet on the picking precision but tends to produce spurious picks aligned across many channels due to coherent noise. The earthquake assoicator we use then struggle to remove those pick alignments.</figcaption></figure>

various pre-trained PhaseNet models when applied to our data. The pre-trained model that worked best with our data set turned out to be DiTing (Zhao et al., 2023) that was specifically trained on land-based seismometer data from China. Models trained on other global data sets were not as performant as seen in Figure S1 in Supporting Information S1. We did not use PhaseNet-DAS (Zhu et al., 2023) as the reliability of the results on our data set are mitigated. For the picking analysis, we took nine earthquakes as reference and picked them manually with the Xpick software (Trabattoni et al., 2024). As Seen in Figure 2, the arrival time precision of PhaseNet-DAS is better than the one of PhaseNet. However, PhaseNet-DAS occasionally picks up instrumental background noise, resulting in spurious pick alignments across multiple channels. These vertically aligned false picks can be difficult for the earthquake associator to filter out, leading to challenges in event association. Moreover, for distant earthquakes (hypocenter ≥200 km), PhaseNet-DAS tends to mix P and S phases in the S wavefront while PhaseNet also has difficulties but pick more S phases on the entire front as seen in Figure S2 in Supporting Information S1. The difficulties we are facing with PhaseNet-DAS may be due to the fact that the model was trained on data sampled at 100 Hz with a channel spacing of 8–10 m, parameters that are different than ours, and
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Picking performance of PhaseNet on Distributed Acoustic Sensing records. (a) Strain-rate to velocity converted waveforms of a small local earthquake recorded on the CCN.N fiber with a zoom on the Ps and Ss wavefronts. The earthquake is the event (d) of Figure 2, bandpass filtered between 2 and 20 Hz. (b) Same as (a) with automatic picks of PhaseNet (light blue for the P phase and dark blue for the S phase) compared to manual picks (pink for the P phase and red for the S phase). In these earthquake records, as for most of them, the Pp phase cannot be seen. (c) Distribution of the time differences between the PhaseNet detected P arrivals and the manually picked Ps arrival with the mean (μ) and standard deviation (σ) for the nine earthquakes of Figure 2. Because PhaseNet is unable to distinguish the Pp and Ps arrival and because the Pp arrival is very faint, PhaseNet tends to pick the Ps arrival. (d) Same as (c) but for the difference between the PhaseNet detected S arrivals and the manually picked Ss arrival. The error distribution has a low variance and indicates a good consistency of picking.</figcaption></figure>

the spatial coherence of the picks, taken into account, must play a role in the results. A way to benefit from 2D models that are more accurate in wave front picking, ensuring better consistency of P and S picks along the waveform and reduced sensitivity to impulsive noise, is to train the picker on our data.

A specific feature of earthquake recordings on seabed cables is the partial conversion of the P-wave arrival caused by the high velocity contrast at the interface between the sedimentary layer and the bedrock (Trabattoni et al., 2024; Vernet et al., 2025). It results in three distinct wavefronts that correspond to the direct P-wave, the converted P-to-S wave front at the sediment interface, and the direct S-waves. We named these as follows; Pp, Ps, and Ss. The latter two have an irregular wavefront because the S-waves slow down more in the sediment compared to the P-waves, and are hence more sensitive to the local velocity structure. One limitation of PhaseNet is that it cannot distinguish the Pp from the Ps arrival (Figure 3). To estimate the behavior of the picker in presence of sedimentary phase splitting, we compared the automatic picks obtained with PhaseNet for the nine events of Figure 2 with manual ones. The P-phase automatically picked by PhaseNet usually closely follows the Ps-phase, but is often biased toward the Pp arrival that arrives ~1s before (Figure 3c). For most earthquakes (more than ~80%), the Pp arrival cannot be visually distinguished, and PhaseNet picks only the Ps phase because of its higher amplitude compared to the direct Pp. On average the PhaseNet P-phase is picked 0.14s before the manual Ps phase. The automatic S phase, on the other hand, corresponds almost perfectly with the manual picks (Figure 3d) with a slight tendency for the automatic picker to pick earlier than the manual picks with an average of 0.09 s (probably due to a different picking strategy). For consistency, we associate the Ps phase to the P label and the Ss
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Automatic picking, association and cleaning over 3 minutes of data acquired on the CCN.N fiber. (a) Strain-rate data, bandpass filtered between 1 and 20 Hz. (b) Automatic PhaseNet P (blue dots) and S (orange dots) picking results. (c) GaMMA phase association results. Three clusters (in green, red and purple) are separated from false positive detections (noise picks in black). (d) Results after the cleaning steps. In this step, the false-positive event 1 that is due to instrumental noise has been reclassified as noise.</figcaption></figure>

phase to the S label. However, in some cases, less than ~10% of the earthquakes, PhaseNet tends to pick the Pp phase as illustrated in Figure S3 in Supporting Information S1.

As described in Trabattoni et al. (2024), the time of arrival of the Ps and Ss phases is influenced by lateral variations of the sediments properties. To compensate for the delays imposed by sediments that are not accounted for in our 1D regional velocity model (see Section 3.3), we apply sediment corrections to each virtual sensor as described by Trabattoni et al. (2024), taking updated values for the three fibers from Vernet et al. (2025). Figure S4 in Supporting Information S1 shows the density of automatically picked P phases, before and after sediment corrections for 666 events. Sediment corrections, calibrated on manual picks, effectively reduce the per channel residuals of the PhaseNet picks in the localization process (see Section 3.3) by improving the modeling of the time of arrival from the 3D velocity model.

## 3.2. Phase Association and Cleaning

To associate the phases, we use the GaMMA (Gaussian Mixture Model Algorithm) algorithm (McBrearty et al., 2022). This algorithm is based on a bayesian gaussian mixture model, which is an unsupervised machine learning method for clustering phase picks. For this association method, we use a 1-D velocity model composed of 17 layers and a Vp/Vs of 1.76 that was inverted for central Chile with the VELEST algorithm (Kissling et al., 1995) by Marot et al. (2014). While GaMMA successfully finds earthquakes on DAS data, it also suffers from false positives either due to bursts of instrumental noise or acoustic events (T-waves or submarine explosions). Some real seismic events are split into multiple clusters of picks; each associated to a supposedly different seismic event. Some other events have their P and S phases mixed with P and S false detections. To clean our data set, we apply three post-processing steps (Figure 4, see Figure S5 in Supporting Information S1 for more example cases). In this cleaning process, we define a conservative strategy to discard as many false positives as possible and, as a result, the procedure likely discards some true positive detections as well. This process consists of the three following steps:

1. After the association, some instrumental noise may still be incorrectly identified as earthquakes. Events with more than 7% of identical arrival time picks are discarded.

2. We identify and discard any fortuitous alignments of false positives along the time-of-arrival curves that are inverted by GaMMA. We assume that the continuous nature of DAS recording implies continuous detection over significant segments of a cable, that is, for a detection on a given channel, there is a high probability that the same phase is detected on the neighboring channels. If for one cluster a cable detects fewer than 20 phase picks we consider all detections of that particular cable as spurious and we discard them. After this process, if the entire number of detections on the three fibers does not exceed 100, we consider the entire cluster as spurious and we discard the event.
3. We merge clusters of picks that should correspond to the same event, but that are identified as multiple separate events. This case often occurs when an event is detected on several cables at the same time. Pairs of clusters for which the last pick of the first event is less than 5 s before the first pick of the following one, are merged. The GaMMA earliest origin time of both events is kept with its associated initial localization. This crude initial estimation of the origin time and hypocenter location will be refined later (see Section 3.3).

### 3.3. Localization

We locate the earthquakes with NonLinLoc (Lomax, 2000), a classic hypocenter localization algorithm commonly used with conventional seismic data. NonLinLoc utilizes a velocity model to calculate seismic wave travel times and employs an efficient search algorithm that partitions the parameter space into hierarchical cells. This structure allows for a rapid and effective exploration of potential solutions in three-dimensional space. NonLinLoc aims to maximize the likelihood location of the probability density function, thereby estimating the most probable hypocenter of the seismic event based on the observed travel times. Although the NonLinLoc algorithm does not assume a limit on the number of phases, it was not designed to ingest the thousands of measurement points that DAS provides. Its computation time increases quadratically with the number of phases and linearly with the number of iterations required to localize an event. We limit the number of phases provided to the algorithm by keeping phases from a given station if it is at a given distance or more from the next triggered station, and set the maximum number of iterations to converge. This gives us a good trade-off between accuracy and computational time. The GaMMA localization is used as an initial guess to improve the rate of convergence. As explained in Section 3.1, sediment corrections were applied to reduce the localization residuals.

We locate each event using two different velocity models and different parameters as follows:

1. We use the same 1-D velocity model as the one we use for the GaMMA association (see Section 3.2) and choose a minimum of 300 m distance between two nearby stations, a maximum of 4,000 iterations and a minimum node size of 0.01° (see Figure S6 in Supporting Information S1).

2. We use two 3D tomography models with 4 km grid size that covers our region published by Potin, Ruiz, et al. (2024), and that were built for the routine processing of the Chilean Centro Sismológico Nacional (CSN, Universidad de Chile). We had to adapt the parameters to the grid by using a maximum of 4,000 stations equally distributed along the fibers and increasing the minimum distance to 1,000 m between two nearby stations so that we could increase the maximum number of iterations to 50,000 and decrease the minimum node size to 0.005°.

The localization results were better with the 3D velocity model, displaying less outliers and a more concentrated seismicity around the slab interface. We decided to use those results in this study. However, we found interesting to show that relatively good localizations could be performed with a 1D velocity model as not all the regions have a 3D model available and because the computational time is smaller and requires less computer resources (see the 1D results for localization on Figure S7 in Supporting Information S1).

### 3.4. Magnitude Estimation

#### 3.4.1. Local Magnitude

Local magnitude ($M_L$) defined by Richter (1935) uses the peak horizontal displacement for fast and reliable magnitude determinations. It requires accounting for geometrical spreading and attenuation, which can be calibrated for each region to account for the local Earth's structure. In regions with complex tectonics, such as a subduction thrust, 3D effects might play an important role. Additionally, accounting for site effects generally reduces the variability in magnitude estimates and improves accuracy.

In practice, various approaches have been used to compute $M_L$ for regional catalogs in Chile. For instance, Sippl et al. (2018) applied the Hutton and Boore (1987) relationship, which was originally developed for intraplate seismicity in California. Alternatively, more generalized models that incorporate physics bases correction factors together with other signal features have also been utilized (Münchmeyer et al., 2020). For DAS data an analysis of the site and coupling effects at each channel, along with calibration with regional $M_L$ catalog as proposed by Yin et al. (2023), could be implemented for a better consistency between catalogs.
In our workflow, we use the original general formulation of Richter (1935) which provides a satisfactory agreement with the catalog computed by the CSN. Here, we estimate the local magnitude $M_L$ independently of the Onland $M_L$ estimation. Our objective is to determine if converting DAS data to velocity and using a standard $M_L$ equation is robust enough. The comparison with the Onland $M_L$ is indicative of the reliability of our estimates. For DAS data, we first converted the strain-rate measurements to displacement using spatial integration (Trabattoni et al., 2024) and applied the Wood-Anderson transfer function (sensitivity = 2800, poles = [-6.2832 - 4.7124j, -6.2832 + 4.7124j] and zeros = [0, 0] as commonly specified). The local magnitude is then computed as:

$$M_L = \log A + \log\left(\frac{D}{100}\right) + 3 \quad (1)$$

in which A is the simulated Wood-Anderson instrument maximum amplitude in mm and D is the hypocentral distance in km. This equation describes that an earthquake with a $M_L$ of 3 generates a ground displacement of 1 mm amplitude recorded by a seismic station placed at 100 km epicentral distance.

### 3.4.2. Moment Magnitude

While $M_L$ is a good estimator for relatively small earthquakes, the moment magnitude ($M_w$) is more suitable for large events as it does not saturate for body waves. However, $M_w$ can be estimated for relatively small earthquakes (with $M_L$ equal or above 2.5) taking into account the high spatial density of our DAS sensors. Indeed, as for $M_L$, by estimating $M_w$ on each individual channel before averaging it for all the channels, we can get a robust $M_w$ given that the spectral analysis is less influenced than $M_L$ by the local coupling, which is highly variable from one channel to another. For the events of $M_L$ equal to or above 2.5, we estimate the $M_w$ based on the method developed by Strumia et al. (2024) on raw strain-rate DAS waveforms. This approach first selects channels where the S-phase arrival has been detected and where the signal-to-noise ratio is sufficient (SNR > 4). The strain-rate spectrum, after being temporarily integrated twice to approximate the displacement spectrum, is then compared to appropriately scaled Brune spectral models. The moment inversion requires an estimation of the superficial S-wave velocity at the receiver. We use an average value of 1.0 km/s which is higher than the value used by Strumia et al. (2024). This can be justified by the inferred higher S-wave velocities of the sediments around the SER cables (Vernet et al., 2025) that were not considered in the analysis of Strumia et al. (2024). We use a density of 2,700 kg/m³ at the source and at the receivers. The S-wave speed at the source of 4.5 km/s and the regional attenuation factor of 800 are taken from the velocity model of Marot et al. (2014) to model the spectral amplitude. $M_w$ estimations on each selected channel are averaged per fiber to get the final estimate. Fibers with fewer than 10 $M_w$ measurements are discarded, events without remaining measurements on all fibers combined are left without $M_w$ estimation.

### 3.5. Combining DAS Offshore Data With Onland Seismic Data

To detect as many events as possible with the best accuracy, we jointly use DAS and land-based seismic stations. While the previous sections can be applied as-is to each of the DAS and Onland networks to get the DAS and Onland catalogs separately, performing the data fusion between very dense (DAS) and very sparse measurement points (seismic stations) to get the Combined catalog requires a few additional steps.

First, we associate pairs of events from the DAS and Onland catalogs as a single event if they have an origin time difference smaller than 5 s. Given the uncertainties associated with localizations computed using only DAS data, we are not associating events by comparing the hypocenter locations. Then, for each combination found, the picks are merged and the location and magnitude re-estimated to produce the Combined catalog. To avoid overwhelming the contribution of the onland data when combined with hundreds of DAS picks, the imbalance in the number of phases is automatically handled by NonLinLoc based on the combined network's azimuthal gap. For $M_L$, the final estimation is the median between the $M_L$ s estimated from DAS and from onland data separately. Figure 5 summarizes the different steps we took to build the three catalogs. Merging the two networks greatly improves the localization precision of the events detected by the DAS network, especially by decreasing the errors at depth and in longitude.
<figure><img src="image_9.png" /><figcaption>**Figure 5.** Schematic representation of the workflow stages to build the three catalogs using exclusively offshore Distributed Acoustic Sensing, the onshore permanent seismic networks, and all networks combined.</figcaption></figure>

# 4. Earthquake Catalog Management and Visualization

Detecting, locating and characterizing earthquakes is not the final objective in the context of a submarine permanent seismological observatory. Making the catalog as Findable, Accessible, Interoperable and Reusable (FAIR, Wilkinson et al., 2016) as possible over a long time period is an important aspect to make the data set usable by the community. To achieve this, we have built a database model that complies with current standards for seismological data structures and a website that communicates with this database for visualization and to make the data accessible.

## 4.1. Database

All event parameters, including picks, event origins, and magnitudes, are stored in a relational database powered by the open-source PostgreSQL engine. The database model is directly derived from the QuakeML structure, incorporating all its definitions and descriptions to comprehensively represent each component of a seismic event (see the database schema Figure S8 in Supporting Information S1). Data input and retrieval are managed by an API developed in Python, enabling continuous storage and access throughout the workflow. This model also includes built-in SQL methods to seamlessly write data from a QuakeML file that has been pre-converted to JSON. Triggers defined within the model ensure data integrity and consistency by verifying all data insertions and updates. Additionally, we created a standardized method for catalog access using an FDSN-compliant web service written in Golang, which connects directly to the database. This service allows querying of events with standard FDSN-event parameters and outputs the catalog in QuakeML format.

## 4.2. Exploration and Visualization

The events catalog is publicly accessible at https://pisco.unice.fr/. Built with TypeScript and the Vue. js framework, the website offers a dynamic and efficient user experience. It features a map and list view for event previews, each accompanied by a general description. Selecting an event reveals detailed information, including the processing stations, a travel-time chart, and a plot of the raw data with P and S-phase picks. Catalog data is retrieved through the FDSN-event web service, while the station inventory is accessed via the FDSN-station web service. The website also provides in-browser live visualization of data streaming coming from the three inter-rogated fibers. To accommodate the high volume of full-resolution DAS data, a real-time downscaled feed is generated and transmitted in real-time using the ZeroMQ protocol. This feed is then delivered to the browser via a WebSocket-based protocol for seamless web compatibility.
<figure><img src="image_10.png" /></figure>

**Figure 6.** One month of seismic activity recorded in central Chile. The earthquakes (circles of size proportional to their local magnitudes $M_L$) have been detected using either the Distributed Acoustic Sensing (DAS) network (orange), the Onland networks (gray) or a combination of all (blue). Panels (a) and (b) display the global seismicity and panels (c) and (d) focus on the seismicity around La Serena cables. (a) Geographic map including the location of the DAS cables (red lines, solid when interrogated, dotted otherwise), the onland stations (red inverted triangles), the trench (gray dash-dotted line), the coastline (gray solid line), the borders of Chile (gray dotted line) and a delimited geographical area offshore which delineates the data selected for Figure 8 (dashed black square). (b) Vertical profile of the seismicity taken from latitudes 27°S to 37°S in the East–West direction. Markers at the surface indicate the average longitude of the trench (inverted green triangle), the mean DAS cables position (red rectangle) and the coastline (green inverted triangle). The top and bottom of the slab interface from the Slab2.0 model (Hayes et al., 2018) are represented by the solid gray lines. The dashed black square delineates the data shown on panel (d). (c) Zoom on the seismicity around La Serena cables. (d) Zoom on the black dashed square of panel (b) showing seismicity above –31° of latitude.

**5. Results**

When applying this workflow to the test month of data we detect 2811 earthquakes with the DAS network, 1,600 with the Onland networks and 490 earthquakes were found in common. The retrieved seismicity profile by the Onland network in the East–West direction (Figure 6b) clearly displays the Pampean flat slab segment (Alvarado et al., 2009; Isacks & Barazangi, 1977; Kay et al., 2009; Marot et al., 2013; Ramos et al., 2002) between 32.5° and 31° of latitude. The change in orientation of the subducted plate which becomes horizontal around 100 km depth
<figure><img src="image_11.png" /><figcaption>**Figure 7.** Magnitude estimation validation for earthquakes found in common between the Distributed Acoustic Sensing catalog and the Centro Sismológico Nacional web catalog. The gray error bars represent the Median Absolute Deviation multiplied by 1.4826 (the expected value for a normal distribution) computed for each event. (a) Comparison of the $M_L$ of 110 earthquakes. (b) Comparison of the $M_w$ for 107 earthquakes.</figcaption></figure>

after a 25–30° dipping angle is well observed, which confirms the validity of the developed detection and localization process.

Instrumental density is a key to increase the detectability of very small local events. This can be observed by the increased number of small events located in a radius of less than 50 km from the interrogated portions of the cables. In particular, compared to on-land stations, DAS detects more small events located near the plate boundary at relatively shallow depths between 10 and 40 km (Figure 6d). The lack of instrumentation of the segment of the cable connecting CCN to SER, which is beyond the reach of both DAS units at either end, creates an instrumentally induced seismicity gap, emphasizing the importance of the spatial coverage of the sensors.

Although the cable has a rectilinear geometry over sections spanning several tens of kilometers (the northern section of the CCN.N cable and the southern section of the SER. S cable), the detected seismicity is not dominated by east–west ambiguity due to the radial symmetry of the localization problem along the cable axis. A horizontal alignment of small events can be observed on Figure 6d around 16 km depth. These events tend to cluster at one of the model's interfaces. This effect could be mitigated by using a 3-D velocity model composed of velocity gradients. As in Trabattoni et al. (2024) accounting for sedimentary corrections sufficiently reduces the residuals to solve the location ambiguity and a majority of the events concentrate on the subducted interface. Even for small seismic events, the DAS arrays easily produce dozens of phase detections, while for the same events only one or two on-land stations would be triggered, an insufficient number for association and localization. In turn, the geometry of the on-land network, combined with the phase picks of the DAS arrays, permits a well-constrained hypocenter location estimate. Hence, the two networks are greatly complementary when it comes to detecting seismicity.

We assessed the performance of the magnitude estimation with the DAS network by using the online official $M_L$ estimates from the CSN as reference (Figure 7). The CSN online catalog contains events with computed $M_L$ above or equal to 2.5. We find a good agreement in terms of $M_L$ for the 110 earthquakes found in common with a magnitude difference rarely exceeding 0.5 point (Figure 7a) except for some earthquakes like the three biggest ones above magnitude 5. This discrepancy in the estimations arises from the differences between the two $M_L$ scaling laws. Indeed, the CSN $M_L$ laws consider site effects and an instrumental response correction for each seismic station which is not part of the $M_L$ equation we use. We then compare the DAS obtained $M_w$ with the CSN web $M_L$ for the 107 earthquakes for which the $M_w$ estimation could be performed (see Section 3.4). We see that the $M_w$ to $M_L$ differences also rarely exceed 0.5 point (Figure 7b) and that the magnitudes of the three biggest events are better estimated. The $M_w$ estimation of the biggest event could not be performed automatically due to an insufficient number of S-phases; thus, we chose to manually pick the waveforms to have more S-phases and
<figure><img src="image_12.png" /><figcaption>**Figure 8.** Number of events as a function of local magnitude representing the cumulative frequency-magnitude distribution for two geographical areas; offshore (open circles) and global area of study (solid circles). The offshore area has the extent of the black rectangle overlain on Figure 6a. The Distributed Acoustic Sensing (DAS) catalog is represented in red and the Onland catalog in green. The catalogs are filtered to keep only events with a depth above 0 km. For each catalog, the Mc of the two areas are equal and are represented by a vertical line. The DAS Mc is equal to 1.5 and the one of the Onland catalog is equal to 2.0. On the figure background, the thin gray dotted lines indicate a b-value equal to 1.</figcaption></figure>

confirm that the biggest events are also in good agreement with the CSN estimates. This lake of S-phases for the biggest earthquakes may be due to a saturation of the recorded wave fields, or to small amplitude ratio between the P coda waves, composed of a local scattered waves, and the S arrival.

**5.1. Network Sensitivities**

To further assess the improvements enabled by the DAS instrumentation we investigate the magnitude of completeness, Mc, representing the minimum magnitude above which all the earthquakes are expected to be captured by the instrument in a given space-time volume. We conduct two analyses: an estimation of the regional catalog Mc and a visualization of the local Mc to highlight spatial variations.

To compute the Mc, for the two analyses we used the non-cumulative frequency-magnitude distribution (FMD) with the maximum curvature-method proposed by Wiemer and Wyss (2000) with bin sizes of 0.1 in magnitude units assuming that the FMD above the Mc follows the Gutenberg-Richter law (Richter, 1935). We estimate the Mc for both networks at two geographical scales (Figure 8): at the regional scale of the networks by taking the entire catalog (filtered by keeping events with depth above 0 km), and at a more local scale by selecting earthquakes located offshore within the black square delimitations seen in Figure 6a. The DAS catalog and the Onland catalog exhibit an Mc difference of 0.5 for both the offshore and global areas with an Mc equal to 1.5 and 2.0 respectively. These values are the same for the two selected areas. The two networks follow the Gutenberg-Richter law and their FMD are superimposed for the regional area and are very close above ML = 2.5 for the offshore area. Below this magnitude value, the FMD diverges due to the presence of more small magnitude events in the DAS catalog. For ML < 1.8, the FMD of the DAS offshore catalog joins the one of the global area which is not the case for the CSN catalogs, showing the capability of the DAS network to record small earthquakes in the offshore area (owing to its offshore location). The Onland catalog has no earthquakes with an ML below 1 whereas the smallest magnitude of the DAS catalog is 0.26.

To better characterize the networks' spatial sensitivities, we then compute local Mc maps for the DAS, Onland and the Combined catalogs (Figure 9). As Geffers et al. (2022) showed, 200 events with an ML above the Mc are sufficient to estimate a reliable Mc and b-value. For each earthquake, we estimate Mc taking the 250 closest events around the earthquake and also store the distance of the farthest event used in the estimate. This defines a node that is characterized by a value of Mc and a value of distance. We then divide our study area in a grid of 0.2° cell size in latitude and longitude and attribute to each cell the median Mc of all the nodes present in the cell along with the maximum search distance to retain this spatial information.

On the resulting local Mc maps the DAS network exhibits low Mc values ranging from 1.3 to 2.1, with maximum search distances of less than 50 km around the cables (Figure 9a). The lowest Mc value of 1.3 is recorded between the SER. S and SER. N fibers. The Onland catalog shows Mc values that are more than one magnitude unit higher in the offshore region (Figure 9b). The lowest values are spread across a broad area around Santiago and to the northeast of Santiago. Lastly, the Combined catalog displays the overall lower Mc values showing the benefits of combining an offshore DAS network with an onshore network.

The capability of DAS to record small events near the trench clearly outperforms that of the Onland network, with the Mc improvement reaching 0.8 close to the cable. More interestingly, we observe that offshore DAS contributes to the decrease in Mc onland, in areas between 50 and 100 km from the coast. This demonstrates the value of offshore DAS in complementing a sparse permanent onshore network for seismic monitoring not only offshore areas but also coastal regions.
<figure><img src="image_13.png" /><figcaption>**Figure 9.** Magnitude of completeness maps using different sub networks. The heatmap colors encode the $M_c$ values for each 0.2° while the thin black contours represent the maximum search distance, in km, used at each earthquake point to include 250 events in the computation of the $M_c$ value. The following geographic features are also displayed: The Chilean coastline (solid white line), the Chile-Argentina border (dashed white line), the Chile-Peru trench (dashed-dotted white line), interrogated cable parts (black thick lines), non interrogated part (black dashed line), and the onland stations (inverted black triangles). (a) Distributed Acoustic Sensing catalog. (b) Onland catalog. (c) Combination of DAS and Onland catalogs.</figcaption></figure>

## 6. Discussion

We have developed a workflow for seismic monitoring using both DAS and conventional seismic networks, leveraging state-of-the-art tools designed for traditional seismology and showed how it improves the completeness offshore. While this emphasizes the possibility to integrate velocity converted DAS recordings into a conventional network and to apply standard tools, using DAS-specific methods should lead to further significant improvements.

Because underwater telecom cables serve other purposes than being used as a seismic array, their mostly rectilinear geometries prevent the precise localization of small-magnitude (<1.5) seismic events that are only detected by some parts of the offshore DAS cables. The azimuthal gap can be significant and very accurate arrival time measurement and modeling are required to avoid localization ambiguity. We have shown that accounting for sedimentary corrections is an effective strategy toward that direction. But we have also observed that the standard PhaseNet is unable to distinguish direct and converted phases leading to important picking bias. The training of 2D deep learning pickers on marine DAS recordings that take advantage of the spatial coherence of phase arrivals within the continuous space-time DAS recordings in the picking method and by able to distinguish secondary phases should enhance the picking precision and robustness and result in significant improvements. Moreover, small events could be relocated relatively to better-constrained bigger earthquakes using double-difference relocation methods like HypoDD by Waldhauser (2001) or GrowClust by Trugman and Shearer (2017). Another important source of errors when it comes to travel time modeling can be attributed to the positioning uncertainties of offshore telecommunication cables. Those positioning errors are expected to reach up to a kilometer at the end of the cable, and to locally produce biased modeled arrivals as much as 0.5 s for the S-waves. A possible solution, as proposed by Papotto et al. (2022), would be to relocate the cables using ships traversing along or across the cables and utilizing their precise trajectories broadcasted through the Automatic Identification System.

A somewhat different challenge arises from the impressive data density of DAS lies in its correct data fusion with the rather sparse traditional networks. In very active areas such as the Chilean margin, the process of associating phases to unique events can be a daunting task, in particular because of the possible overlap of several small earthquakes simultaneously occurring at different locations.
A final challenge arises from distant earthquakes (>150 km) of magnitude greater than 4.5. In some cases, the picker fails to correctly identify the rather incoherent S-wavefront, leading to a decreased precision in our estimations of the moment magnitude, as the method of Strumia et al. (2024) relies on the quality and number of S-picks. The very small number of S-phases for a single event is a limiting factor, and even for an acceptable number of detected S-phases, some of them may be picked on noisy or poorly coupled portions of the cables. One way to account for this would be to compute $M_w$ only on channels known for their good coupling or high signal-to-noise ratio. Moreover, the saturation of S-waves for high-amplitude ground motions (large magnitude or closeby events) is one of the primary limiting factors in computing magnitude (van den Ende et al., 2024). This is a significant limitation for Earthquake Early Warning. However, for the purpose of catalog creation, the magnitude can be correctly estimated by using the onland network.

## 7. Conclusions

We have developed a processing pipeline that supports a permanent DAS-based observatory for monitoring offshore and coastal seismicity, leveraging submarine telecommunication cables deployed off the central coast of Chile. Using three DAS instruments simultaneously and the national onshore seismological network, we have successfully implemented a fully automatic system that integrates both data sources. The workflow we present here outlines the various steps involved, from processing continuous data in near real-time, picking, association, and magnitude estimation, to generating results in QuakeML format and visualization in a dedicated website.

For the data processing, we utilize state-of-the-art tools developed for traditional seismometer data, making the deployment of this workflow adaptable for any network combining DAS and classical instrumentation, in this case forming an amphibious network. For optimal performance, some specificities of DAS must be taken into account. For instance, accounting for the presence of shallow sediments is essential, as the P-phases are often converted to S-waves at the interface between the bedrock and sediments.

Lastly, the workflow we have deployed is highly modular and allows for the easy integration of other components that may be better suited to different case studies.

## Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

## Data Availability Statement

The full DAS data set is too large to be stored in a public repository, but it is available upon request. However, for reproducibility needs, the workflow, figure scripts and a subset of earthquakes representative of our data set are available online in a first repository (Baillet, 2025a). In a second repository we share 20 hours of continuous DAS data recorded on the CCN.N fiber during the month covered by this study (Baillet, 2025b). The relational database scripts, the two 3D tomography models used in this study and the codes used to compute the FMD and $M_c$ are published respectively by Peix (2025) on GitLab, Potin, Barrientos, and Ruiz (2024) on GitHub and Lapins (2021) on GitHub. For the data management, analysis and to make the figures, we used Visual Studio Code software version 1.101.2 (Microsoft, 2019). We used Python version 3.10.6 (Python Software Foundation, 2022) with many libraries like: Pandas version 1.5.0 (McKinney, 2010), Numpy version 1.23.5 (Harris et al., 2020), Scipy version 1.14.0 (Virtanen et al., 2020), Xdas version 0.2.1 (Trabattoni et al., 2025), Matplotlib version 3.8.0 (Hunter, 2007), h5py version 3.12.1 (Collette, 2013), ObsPy version 1.4.1 (Beyreuther et al., 2010), cartopy version 0.21.0 (Elson et al., 2024), tqdm version 4.64.1 (Costa-Luis et al., 2022).

## Acknowledgments

This project is supported by the European Research Council under the European Union's Horizon 2020 research and innovation program (grant agreement Number 101041092 - ABYSS). Bertrand Potin thank the Programa de Riesgo Sísmico (PRS) of the University of Chile.

## References

Alvarado, P., Pardo, M., Gilbert, H., Miranda, S., Anderson, M., Saez, M., & Beck, S. (2009). Flat-slab subduction and crustal models for the seismically active Sierras Pampeanas region of Argentina. In *Backbone of the Americas: Shallow Subduction, Plateau Uplift, and Ridge and Terrane Collision*, S. M. Kay, V. A. Ramos, and W. R. Dickinson, eds. Geological Society of America Memoir (Vol. 204). https://doi.org/10.1130/2009.1204(12)

Baillet, M. (2025a). Codes and data for the publication "Automatic earthquake catalogs from a permanent DAS offshore network" [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.15849254

Baillet, M. (2025b). Continuous data used in the publication "Automatic earthquake catalogs from a permanent DAS offshore network" [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.17143738
Beyreuther, M., Barsch, R., Krischer, L., Megies, T., Behr, Y., & Wassermann, J. (2010). ObsPy: A python toolbox for seismology. *Seismological Research Letters*, 81(3), 530–533. https://doi.org/10.1785/gssrl.81.3.530

Calais, E., Symithe, S., Monfret, T., Delouis, B., Lomax, A., Courboulex, F., et al. (2022). Citizen seismology helps decipher the 2021 Haiti earthquake. *Science*, 376(6590), 283–287. https://doi.org/10.1126/science.abn1045

Collette, A. (2013). *Python and HDF5: Unlocking scientific data*. O'Reilly Media, Inc.

Constantinou, A., Hartog, A., Dickenson, P., & Lees, G. (2017). The merits of DAS acquisition on multimode optical fiber. *Proceedings*, 2017(1), 1–5. https://doi.org/10.3997/2214-4609.201702478

da Costa-Luis, C., Larroque, S. K., Altendorf, K., Mary, H., richardsheridan, Korobov, M., et al. (2022). tqdm: A fast, extensible progress bar for python and CLI (version v4.64.1) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.7046742

Danciu, L., Nandan, S., Reyes, C., Basili, R., Weatherill, G., Beauval, C., et al. (2021). The 2020 update of the european Seismic Hazard model: Model overview. *EFEHR Technical Report 001*, v1.0.0. https://doi.org/10.12686/a15

Elson, P., Andrade, E. S. de, Lucas, G., May, R., Hattersley, R., Campbell, E., et al. (2024). *SciTools/cartopy: REL: v0.24.1* (Version v0.24.1) [Software]. Zenodo. https://doi.org/10.5281/zenodo.13905945

Geffers, G.-M., Main, I. G., & Naylor, M. (2022). Biases in estimating b-values from small earthquake catalogues: How high are high b-values? *Geophysical Journal International*, 229(3), 1840–1855. https://doi.org/10.1093/gji/ggac028

González-Vidal, D., Moreno, M., Sippl, C., Baez, J. C., Ortega-Culaciati, F., Lange, D., et al. (2023). Relation between Oceanic plate structure, patterns of interplate locking and microseismicity in the 1922 Atacama seismic gap. *Geophysical Research Letters*, 50(15), e2023GL103565. https://doi.org/10.1029/2023GL103565

Harris, C. R., Millman, K. J., Van Der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., et al. (2020). Array programming with NumPy. *Nature*, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2

Hayes, G. P., Moore, G. L., Portner, D. E., Hearne, M., Flamme, H., Furtney, M., & Smoczyk, G. M. (2018). Slab2, a comprehensive subduction zone geometry model. *Science*, 362(6410), 58–61. https://doi.org/10.1126/science.aat4723

Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. *Computing in Science and Engineering*, 9(3), 90–95. https://doi.org/10.1109/ MCSE.2007.55

Hutton, L. K., & Boore, D. M. (1987). The MLscale in Southern California. *Bulletin of the Seismological Society of America*, 77(6), 2074–2094. https://doi.org/10.1785/BSSA0770062074

Isacks, B. L., & Barazangi, M. (1977). Geometry of Benioff zones. In *Island arcs, deep Sea trenches and back-arc basins* (pp. 99–114). American Geophysical Union (AGU). https://doi.org/10.1029/ME001p0099

Jousset, P., Reinsch, T., Ryberg, T., Blanck, H., Clarke, A., Aghayev, R., et al. (2018). Dynamic strain determination using fibre-optic cables allows imaging of seismological and structural features. *Nature Communications*, 9(1), 2509. https://doi.org/10.1038/s41467-018-04860-y

Kay, S. M., Ramos, V. A., & Dickinson, W. R. (2009). Preface. In Backbone of the americas: Shallow subduction, Plateau uplift, and Ridge and terrane collision (p. 0). Geological Society of America. https://doi.org/10.1130/978-0-8137-2446-1-204.0.v

Kissling, E., Kradolfer, U., & Maurer, H. (1995). *VELEST user's guide – Short introduction*. Institute of Geophysics.

Lapins, S. (2021). Sachalapins/bvalues: Version master branch. Python notebook for calculating completeness magnitudes and b-values for earthquake catalogues [Software]. GitHub. https://github.com/sachalapins/bvalues

Lindsey, N. J., Dawe, T. C., & Ajo-Franklin, J. B. (2019). Illuminating seafloor faults and ocean dynamics with dark fiber distributed acoustic sensing. *Science*, 366(6469), 1103–1107. https://doi.org/10.1126/science.aay5881

Lindsey, N. J., Martin, E. R., Dreger, D. S., Freifeld, B., Cole, S., James, S. R., et al. (2017). Fiber-Optic network observations of earthquake wavefields. *Geophysical Research Letters*, 44(23), 11792–11799. https://doi.org/10.1002/2017GL075722

Lior, I., Sladen, A., Rivet, D., Ampuero, J., Hello, Y., Becerril, C., et al. (2021). On the detection capabilities of underwater distributed acoustic sensing. *Journal of Geophysical Research: Solid Earth*, 126(3), e2020JB020925. https://doi.org/10.1029/2020JB020925

Lomax, A., Virieux, J., Volant, P., & Berge-Thierry, C. (2000). Probabilistic earthquake location in 3D and layered models: Introduction of a Metropolis-Gibbs method and comparison with linear locations (pp. 101–134).

Marot, M., Monfret, T., Gerbault, M., Nolet, G., Ranalli, G., & Pardo, M. (2014). Flat versus normal subduction zones: A comparison based on 3-D regional traveltime tomography and petrological modelling of central Chile and Western Argentina (29°–35°S). *Geophysical Journal International*, 199(3), 1633–1654. https://doi.org/10.1093/gji/ggu355

Marot, M., Monfret, T., Pardo, M., Ranalli, G., & Nolet, G. (2013). A double seismic zone in the subducting Juan Fernandez Ridge of the Nazca Plate (32°S), central Chile. *Journal of Geophysical Research: Solid Earth*, 118(7), 3462–3475. https://doi.org/10.1002/jgrb.50240

McBrearty, I. W., Mousavi, S. M., Ellsworth, W. L., & Beroza, G. C. (2022). Earthquake phase association using a bayesian Gaussian mixture model. *Journal of Geophysical Research: Solid Earth*, 127(5), e2021JB023249. https://doi.org/10.1029/2021JB023249

McKinney, W. (2010). Data structures for statistical computing in python. *Proceedings of the Python in Science Conference*, 56–61. https://doi.org/10.25080/Majora-92bf1922-00a

Microsoft. (2019). Visual studio Code (Version 1.101.2) [Software]. GitHub. Retrieved from https://github.com/microsoft/vscode/tree/release/1.101

Münchmeyer, J., Bindi, D., Sippl, C., Leser, U., & Tilmann, F. (2020). Low uncertainty multifeature magnitude estimation with 3-D corrections and boosting tree regression: Application to North Chile. *Geophysical Journal International*, 220(1), 142–159. https://doi.org/10.1093/gji/ggz416

Münchmeyer, J., Molina, D., Radiguet, M., Marsan, D., Baez, J.-C., Ortega-Culaciati, F., et al. (2024). Seismic swarms unveil the mechanisms driving shallow slow slip dynamics in the Copiapó ridge, Northern Chile. *arXiv. arXiv:2411.18490*. https://doi.org/10.48550/arXiv.2411.18490

Nayak, A., & Ajo-Franklin, J., & the Imperial Valley Dark Fiber Team. (2021). Distributed acoustic sensing using dark fiber for array detection of regional earthquakes. *Seismological Research Letters*, 92(4), 2441–2452. https://doi.org/10.1785/0220200416

Papotto, L., DeCacqueray, B., & Rivet, D. (2022). Calibration and repositioning of an optical fibre cable from acoustic noise obtained by DAS technology (EGU22-7311). EGU22. Copernicus Meetings. https://doi.org/10.5194/egusphere-egu22-7311

Pastén-Araya, F., Potin, B., Azúa, K., Sáez, M., Aden-Antoniów, F., Ruiz, S., et al. (2022). Along-Dip segmentation of the slip behavior and rheology of the copiapó Ridge subducted in north-central Chile. *Geophysical Research Letters*, 49(4), e2021GL095471. https://doi.org/10.1029/2021GL095471

Peix, F. (2025). Sismo/pachamama: Version master [Software]. GitLab. https://gitlab.oca.ca/eismo/pachamama

Potin, B., Barrientos, S., & Ruiz, S. (2024a). Chilean Tomography—Csn version—2019 [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.13146424

Potin, B., Ruiz, S., Aden-Antoniow, F., Madariaga, R., & Barrientos, S. (2024). A Revised Chilean Seismic Catalog from 1982 to Mid-2020. *Seismological Research Letters*, 96(1), 484–498. https://doi.org/10.1785/0220240047
Python Software Foundation. (2022). Python/cpython: Version 3.10.6 [Software]. GitHub. Retrieved from https://github.com/python/cpython/tree/3.10

Quinteros, J., Carter, J. A., Schaeffer, J., Trabant, C., & Pedersen, H. A. (2021). Exploring approaches for large data in seismology: User and data repository perspectives. *Seismological Research Letters*, *92*(3), 1531–1540. https://doi.org/10.1785/0220200390

Ramos, V. A., Cristallini, E. O., & Pérez, D. J. (2002). The Pampean flat-slab of the Central Andes. *Journal of South American Earth Sciences*, *15*(1), 59–78. https://doi.org/10.1016/S0895-9811(02)00006-8

Richter, C. F. (1935). An instrumental earthquake magnitude scale. *Bulletin of the Seismological Society of America*, *25*(1), 1–32. https://doi.org/10.1785/BSSA0250010001

Rivet, D., Barrientos, S., Sánchez-Olavarría, R., Ampuero, J.-P., Lior, I., Prado, J.-A. B., & Opazo, G.-A. V. (2022). *Building a new type of seafloor observatory on submarine telecom fiber optic cables in Chile (EGU22-11508)*. EGU22. Copernicus Meetings. https://doi.org/10.5194/egusphere-egu22-11508

Ruiz, S., & Madariaga, R. (2018). Historical and recent large megathrust earthquakes in Chile. *Tectonophysics*, *733*, 37–56. https://doi.org/10.1016/j.tecto.2018.01.015

Ruiz, S., Metois, M., Fuenzalida, A., Ruiz, J., Leyton, F., Grandin, R., et al. (2014). *Intense foreshocks and a slow slip event preceded the 2014 Iquique Mw 8.1 earthquake*. *Science*, *345*(6201), 1165–1169. https://doi.org/10.1126/science.1256074

Sippl, C., Schurr, B., Asch, G., & Kummerow, J. (2018). Seismicity structure of the Northern Chile Forearc from >100,000 double-difference relocated hypocenters. *Journal of Geophysical Research: Solid Earth*, *123*(5), 4063–4087. https://doi.org/10.1002/2017JB015384

Sladen, A., Rivet, D., Ampuero, J. P., De Barros, L., Hello, Y., Calbris, G., & Lamare, P. (2019). Distributed sensing of earthquakes and ocean-solid Earth interactions on seafloor telecom cables. *Nature Communications*, *10*(1), 5777. https://doi.org/10.1038/s41467-019-13793-z

Strumia, C., Trabattoni, A., Supino, M., Baillet, M., Rivet, D., & Festa, G. (2024). Sensing optical fibers for earthquake source characterization using raw DAS records. *Journal of Geophysical Research: Solid Earth*, *129*(1), e2023JB027860. https://doi.org/10.1029/2023JB027860

Trabattoni, A., Baillet, M., van den Ende, M., Rivet, D., Stutzmann, E., Strumia, C., & Biagioli, F. (2025). Xdas: A python framework for distributed acoustic sensing. *Seismological Research Letters*, *96*(5), 3221–3230. https://doi.org/10.1785/0220240366

Trabattoni, A., Biagioli, F., Strumia, C., van den Ende, M., Scotto di Uccio, F., Festa, G., et al. (2023). From strain to displacement: Using deformation to enhance distributed acoustic sensing applications. *Geophysical Journal International*, *235*(3), 2372–2384. https://doi.org/10.1093/gji/ggad365

Trabattoni, A., Festa, G., Longo, R., Bernard, P., Plantier, G., Zollo, A., & Strollo, A. (2022). Microseismicity monitoring and site characterization with Distributed Acoustic Sensing (DAS): The case of the Irpinia fault System (Southern Italy). *Journal of Geophysical Research: Solid Earth*, *127*(9), e2022JB024529. https://doi.org/10.1029/2022JB024529

Trabattoni, A., Vernet, C., Van Den Ende, M., Baillet, M., Potin, B., & Rivet, D. (2024). Sediment corrections for distributed acoustic sensing. *Journal of Geophysical Research: Solid Earth*, *129*(10), e2024JB029054. https://doi.org/10.1029/2024JB029054

Trugman, D. T., & Shearer, P. M. (2017). GrowClust: A hierarchical clustering Algorithm for relative earthquake relocation, with application to the Spanish springs and Sheldon, Nevada, earthquake sequences. *Seismological Research Letters*, *88*(2A), 379–391. https://doi.org/10.1785/0220160188

Universidad de Chile. (2012). Red Sismologica nacional [Dataset]. *International Federation of Digital Seismograph Networks*. https://doi.org/10.7914/SN/C1

van den Ende, M., Trabattoni, A., Baillet, M., & Rivet, D. (2024). An analysis of the dynamic range of distributed acoustic sensing for Earthquake early warning. https://eartharxiv.org/repository/view/7048/

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*(7), e2024JB030507. https://doi.org/10.1029/2024JB030507

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., et al. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. *Nature Methods*, *17*(3), 261–272. https://doi.org/10.1038/s41592-019-0686-2

Waagaard, O. H., Rønnekleiv, E., Haukanes, A., Stabo-Eeg, F., Thingbø, D., Forbord, S., et al. (2021). Real-time low noise distributed acoustic sensing in 171 km low loss fiber. *OSA Continuum*, *4*(2), 688. https://doi.org/10.1364/OSAC.408761

Waldhauser, F. (2001). hypoDD-A Program to compute double-difference hypocenter locations. *Open-File Report*. Article 2001-113. https://doi.org/10.3133/ofr01113

Wang, K., & Tréhu, A. M. (2016). Invited review paper: Some outstanding issues in the study of great megathrust earthquakes—The Cascadia example. *Journal of Geodynamics*, *98*, 1–18. https://doi.org/10.1016/j.jog.2016.03.010

Wiemer, S., & Wyss, M. (2000). Minimum magnitude of completeness in earthquake catalogs: Examples from Alaska, the Western United States, and Japan. *Bulletin of the Seismological Society of America*, *90*(4), 859–869. https://doi.org/10.1785/011990114

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. *Scientific Data*, *3*(1), 160018. https://doi.org/10.1038/sdata.2016.18

Williams, E. F., Ugalde, A., Martins, H. F., Becerril, C. E., Callies, J., Claret, M., et al. (2023). Fiber-Optic observations of internal waves and tides. *Journal of Geophysical Research: Oceans*, *128*(9), e2023JC019980. https://doi.org/10.1029/2023JC019980

Woollam, J., Münchmeyer, J., Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., et al. (2021). SeisBench—A toolbox for machine learning in seismology. *arXiv*. (arXiv:2111.00786) http://arxiv.org/abs/2111.00786

Yin, J., Zhu, W., Li, J., Biondi, E., Miao, Y., Spica, Z. J., et al. (2023). Earthquake magnitude with DAS: A transferable data-based scaling relation. *Geophysical Research Letters*, *50*(10), e2023GL103045. https://doi.org/10.1029/2023GL103045

Zhao, M., Xiao, Z., Chen, S., & Fang, L. (2023). DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology. *Earthquake Science*, *36*(2), 84–94. https://doi.org/10.1016/j.eqs.2022.01.022

Zhu, W., & Beroza, G. C. (2018). PhaseNet: A deep-neural-network-based seismic arrival time picking method. *Geophysical Journal International*. https://doi.org/10.1093/gji/ggy423

Zhu, W., Biondi, E., Li, J., Yin, J., Ross, Z. E., & Zhan, Z. (2023). Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning. *Nature Communications*, *14*(1), 8192. https://doi.org/10.1038/s41467-023-43355-3