# Automatic Identification of High-Quality Channels in Distributed Acoustic Sensing Through Implementation of a Channel Quality Index

Tatiana Rodriguez, Aleix Seguí, Arantza Ugalde, Melissa Cubas Armas, Hugo Latorre, Sergi Ventosa, Tony Monfret

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Tatiana Rodriguez, Aleix Seguí, Arantza Ugalde, Melissa Cubas Armas, Hugo Latorre, et al.. Automatic Identification of High-Quality Channels in Distributed Acoustic Sensing Through Implementation of a Channel Quality Index. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18, pp.19869-19883. 10.1109/jstars.2025.3593330. hal-05419783

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# Automatic Identification of High-Quality Channels in Distributed Acoustic Sensing Through Implementation of a Channel Quality Index

Tatiana Rodriguez, Aleix Seguí ID, Arantza Ugalde ID, Melissa Cubas Armas ID, Hugo Latorre ID, Sergi Ventosa ID, and Tony Monfret ID

**Abstract**—Using fiber-optic cables, distributed acoustic sensing (DAS) has emerged as a powerful technology for seismic monitoring, particularly in oceanic regions, but the massive amount of data generated presents challenges for its integration into operational seismic networks. However, since the quality of DAS recordings varies along the fiber due to physical and environmental factors, not all data collected from fiber-optic cables contribute meaningfully to earthquake detection and characterization. Identifying and filtering out low-quality channels reduces unnecessary data processing and optimizes resource use. To address this, we developed a channel quality index (CQI), a machine-learning-based approach that automatically identifies high-quality DAS channels based on their ability to record earthquake signals. Trained on data from four offshore DAS experiments in the Spanish Mediterranean and Canary Islands, our model evaluates temporal, frequency, and energy-based features to assign quality scores to individual channels, reliably identifying high-quality channels with high precision (0.92–0.96) and strong performance in terms of area under the receiver operating characteristic curve (ROC–AUC, 0.92–0.98). By prioritizing high-quality data, CQI enhances the efficiency of data transmission and storage in large-scale DAS deployments. It enables the selective retrieval of relevant seismic data from repositories, reducing storage demands and minimizing data transmission time, which is especially critical for large datasets. It also provides a mechanism to quantify information loss in data compression

strategies, ensuring critical seismic signals are preserved. These capabilities of CQI facilitate the practical adoption of DAS for both real-time and long-term seismic monitoring, optimizing data quality and resource management.

**Index Terms**—Fiber-optic sensing, machine learning, offshore seismicity, seismic monitoring.

## I. INTRODUCTION

DISTRIBUTED acoustic sensing (DAS) is a fiber-optic sensing technology built upon the extensive development of fiber-optic communication systems over the past five decades. It utilizes Rayleigh backscattering that naturally occurs when pulses generated by a highly coherent laser source are sent through optical fibers, to detect and locate external perturbations such as vibrations and temperature variations [1]. In the last decade, DAS systems have received increased attention from various fields as an alternative to point sensor arrays for scientific applications ([2], [3], and references therein), and are being developed rapidly. Modern DAS interrogators can achieve dynamic strain sensing with submeter spatial resolution and sampling rates up to kHz over tens of kilometers [4], [5], [6].

Considering the various applications of DAS, its role in seismology stands out due to its ability to address longstanding challenges in seismic monitoring, such as the sparse distribution of instrumentation across the Earth, particularly in oceanic regions [7], [8], [9], [10], [11]. With DAS, existing telecommunication fiber optic cables laid in the ocean floor can be transformed into powerful seismic arrays for real-time, continuous, high-resolution recording of seismic activity. This innovation offers a compelling complement to traditional seismological instruments, which are limited by spatial deployment constraints, cost, and maintenance requirements.

It should be noted that, besides the influence of environmental noise, the quality of seismic signals recorded by DAS depends on physical factors such as the local coupling conditions between the fiber-optic cable and the surrounding medium [12], and the presence of small-scale heterogeneities in the shallow subsurface [13]. Moreover, DAS systems measure only the axial strain along the fiber. As a result, their sensitivity depends on the angle between the seismic wave propagation direction and the fiber orientation, leading to reduced sensitivity to waves with particle motion perpendicular to the fiber [2], [14]. Additionally, DAS exhibits increased sensitivity to slower waves, such as surface

Received 16 February 2025; revised 20 June 2025; accepted 25 July 2025. Date of publication 28 July 2025; date of current version 15 August 2025. This work was supported in part by the “Severo Ochoa Centre of Excellence” program under Grant CEX2019-000928-S-21-2; in part by the European Union Next Generation EU/PRTR Program under projects PSI under Grant PLEC2021-007875 and TREMORS under Grant CPP2021-008869; in part by the MCIN/AEI under project ATLANTIS under Grant PID2019-109559RB-100; and in part by the CSIC-Telxius contract SAFE under Grant 20233069. The work of H. Latorre was supported by the Spanish Ministry of Science and Innovation under Grant PRE2021-098838. CANALINK, Telxius, and ENAGAS facilitated fiber-optic cable access, and Aragon Photonics provided the HDAS interrogator used in all experiments. (Tatiana Rodriguez and Aleix Seguí are co-first authors.) (Corresponding author: Arantza Ugalde.)

Tatiana Rodriguez, Arantza Ugalde, Melissa Cubas Armas, Hugo Latorre, and Sergi Ventosa are with the Institute of Marine Sciences, CSIC, 08003 Barcelona, Spain. (e-mail: tatiana.isabel@icm.csic.es; a.ugalde@icm.csic.es; melania@icm.csic.es; hlatorre@icm.csic.es; sventosa@icm.csic.es).

Aleix Seguí is with the ETH Zurich, 8092 Zürich, Switzerland. (e-mail: asegui@student.ethz.ch).

Tony Monfret is with the Université Côte d'Azur, Observatoire de la Côte d'Azur, CNRS, IRD, Géoazur, 06560 Valbonne, France. (e-mail: tony.monfret@geoazur.unice.fr).

The software for the calculation of the CQI is licensed under GNU GPL-v3 and is published on GitHub at https://github.com/B-CSI/cqi_das.

This article has supplementary downloadable material available at https://doi.org/10.1109/JSTARS.2025.3593330, provided by the authors.

Digital Object Identifier 10.1109/JSTARS.2025.3593330
waves, while showing reduced sensitivity to faster propagating *P* and *S* body waves. This can also lead to the amplification of locally scattered surface waves, potentially reducing the coherence of body wave recordings [15], [16].

This variability in sensitivity and response is particularly significant, as it causes variations of the quality of the seismic signal along the fiber-optic cable. Several factors contribute to this variation: poor coupling conditions may result in persistently noisy channels; fluctuating noise levels can occur depending on environmental factors; and variations in earthquake parameters may cause some channels to miss the signal or to record it weakly, even if they otherwise appear to be high-quality channels. While signal quality can often be improved through processing techniques such as filtering or stacking, these methods are typically applied offline and are generally not feasible for real-time monitoring due to their computational cost and time requirements. Therefore, in this article, we propose identifying poor-quality channels for a given event to exclude from analysis, and potentially remove from storage, in order to enhance the overall performance of DAS systems.

Previous articles have approached the issue of DAS channel quality in various ways. In [17], a supervised learning approach is developed to identify noisy channels by calculating ambient noise levels using probabilistic power spectral densities and comparing them with standard seismological noise models. In [18], a method for detecting poorly coupled channels by identifying outliers in waveform energy along the fiber implemented, based on the assumption of spatial continuity in poor coupling.

Here we propose a new approach: ranking the channels based on their capability to record earthquake signals. This approach is designed to reliably identify the channels functioning correctly, while effectively pinpointing those that are underperforming. We chose to apply machine-learning techniques to capture the diverse characteristics of seismic signals across our DAS datasets in varying offshore locations. To achieve this, we initially defined a curated set of 105 features that address the temporal, frequency, and energy properties of each channel. A subset of 17 of these features was later selected during model optimization. This approach required manual channel selection to create the training labels.

While our methodology is based on supervised machine learning, it follows the same goals as traditional seismological practice [19]. For decades, seismologists have relied on well-established metrics, such as signal-to-noise ratio (SNR), root-mean-square (RMS) amplitude, gap count, dead channel and spike detection, timing quality, deviations from reference noise models, and coherence between colocated sensors, to assess data quality and optimize sensor arrays [20], [21]. These tools remain fundamental, but they are not easily scalable to the dense and complex datasets produced by DAS arrays. Our approach aims to achieve the same goal of maximizing array performance, using modern computational methods suited to DAS, and enabling real-time, automated identification of reliable channels.

Ultimately, our goal is to support the integration of DAS arrays into operational seismic monitoring networks, particularly in offshore regions where conventional seismic instrumentation is sparse or absent.

## II. TRAINING DATA

The data consisted of events from four distinct DAS experiments conducted in various offshore locations along the Spanish Mediterranean coast and the Canary Islands. The dataset was divided into training, validation, and evaluation sets to ensure generalization. The training process for the classification models required a well-curated dataset of labeled channels. To facilitate this, we developed a custom software tool that allows the user to visually inspect and classify individual channels as being either high- or low-quality. This software, along with the implementation for the channel quality index (CQI) calculation, is publicly available at https://github.com/B-CSI/cqi_das.

In Fig. S1 in the Supplemental Material, we provide an example of the software displays. The software allows simultaneous visualization of a two-dimensional image of a subset of channels alongside individual channel traces, enabling the visual stacking of channels. This manual selection process ensured high-quality labeling for the training set, which ultimately included almost 50 000 channels collected from 10 different events across four separate DAS experiments: CANDAS [22], CANDAS2 [23], SAFE [24], and CASTOR [25]. The locations of these experiments are shown in Fig. 1, while Table I provides details on the recorded events. The selected time window (TW) duration in seconds is given for each event, and event parameters are sourced from the Spanish National Geographic Institute (IGN) catalog at https://www.ign.es/web/ign/portal/sis-catalogo-terremotos.

The training dataset (publicly available at https://doi.org/10.20350/digitalCSIC/17043) includes local earthquakes with varying characteristics and magnitudes, ranging from 1.3 to 3.5, reflecting the range of events available in our data. This strategy aimed to cover the training data space effectively, enhancing the model's ability to generalize to similar events and make predictions on data from new deployments within comparable or slightly broader magnitude ranges. This generalization capability is evaluated in the Results section, where we report the method's performance on events from experiments that were entirely excluded from the training set in what we termed "Out-of-Distribution (OOD)" scenario. This generalization is also contingent on a few common characteristics of the input data. For instance, all events were recorded at or downsampled to a sample rate of 50 Hz, and filtered to a frequency of between 3 and 20 Hz. The duration window of events varied between 15 and 100 s, with between 5 and 20 s of preseismic signal at the beginning of the window.

## III. METHODS

We developed multiple features for seismic detection and assessed their ability to accurately predict channel quality. We then built a model with the best-performing features to calculate the CQI, which estimates the probability of a channel exhibiting good quality.

### A. Preprocessing and Normalization

Event data must be preprocessed before feature extraction to ensure that calculations are comparable across different settings.
<figure><img src="image_2.png" /><figcaption>Fig. 1. Map showing the locations of the four DAS experiments: (a) CANDAS/CANDAS2, (b) CASTOR, and (c) SAFE. The red lines indicate the fiber-optic cables used in each experiment, while the seismic events listed in Table I are marked with red circles.</figcaption></figure>

<table><caption>TABLE I
SEISMIC EVENTS USED TO CREATE THE MANUALLY LABELED DATASET</caption><thead><tr><th>Experiment name</th><th>Geographic region</th><th>Origin time</th><th>Lat.</th><th>Lon.</th><th>h (km)</th><th>Mag. (m<sub>bLg</sub>)</th><th>TW (s)</th><th>Num. channels</th></tr></thead><tbody><tr><td rowspan="6">CANDAS</td><td rowspan="2">Offshore Gran Canaria, Spain</td><td>2020-08-15 19:10:39</td><td>27.7824</td><td>-15.2736</td><td>38</td><td>3.0</td><td>100</td><td>5574</td></tr><tr><td>2020-08-22 17:13:20</td><td>28.1221</td><td>-15.2559</td><td>35</td><td>2.1</td><td>60</td><td>5669</td></tr><tr><td rowspan="3">Offshore Tenerife, Spain</td><td>2020-08-22 17:13:20</td><td>28.1221</td><td>-15.2559</td><td>35</td><td>2.1</td><td>40</td><td>5239</td></tr><tr><td>2020-07-27 13:05:31</td><td>28.0661</td><td>-16.1642</td><td>10</td><td>2.0</td><td>60</td><td>5239</td></tr><tr><td>2020-07-27 20:44:26</td><td>28.0347</td><td>-16.1244</td><td>34</td><td>3.1</td><td>80</td><td>5239</td></tr><tr><td>CANDAS2</td><td>Offshore Gran Canaria, Spain</td><td>2022-12-27 07:54:54</td><td>27.7306</td><td>-15.2616</td><td>28</td><td>2.5</td><td>60</td><td>6592</td></tr><tr><td rowspan="2">CASTOR</td><td rowspan="2">Gulf of Valencia, Spain</td><td>2023-06-01 06:49:22</td><td>40.1928</td><td>0.8346</td><td>0</td><td>2.0</td><td>15</td><td>2406</td></tr><tr><td>2023-07-20 11:57:04</td><td>40.4083</td><td>0.7463</td><td>0</td><td>1.3</td><td>20</td><td>2406</td></tr><tr><td rowspan="2">SAFE</td><td rowspan="2">Alboran Sea, Spain</td><td>2023-11-13 14:58:30</td><td>35.4569</td><td>-3.6227</td><td>17</td><td>3.5</td><td>35</td><td>5264</td></tr><tr><td>2023-12-15 23:45:11</td><td>35.6539</td><td>-4.6770</td><td>6</td><td>3.4</td><td>25</td><td>5748</td></tr></tbody></table>
<table><caption>TABLE II
FEATURES USED FOR CHANNEL QUALITY ANALYSIS</caption><thead><tr><td>Category</td><td>Feature</td><td>Calculation summary</td></tr></thead><tbody><tr><td>Common signal processing in time-domain</td><td>Mean, median, variance, skew, kurtosis, entropy, root-mean-square, peak, crest factor, average rectified value, standard deviation, root amplitude, peak-to-average power ratio, impulse factor, waveform factor, shape factor, clearance factor, median absolute deviation, detection significance</td><td>Statistical and shape-based metrics on the raw time series. The same metrics are applied to the signal time series and its envelope.</td></tr><tr><td>Fourier transform features</td><td>Frequency average, frequency median, frequency variance, frequency skew, frequency kurtosis, frequency entropy, frequency root-mean-square, frequency peak, frequency crest factor</td><td>Features calculated from the Fast Fourier Transform (FFT) of the signal time series and its envelope.</td></tr><tr><td>Power spectral density (PSD) features</td><td>PSD mean, PSD median, PSD variance, PSD skew, PSD kurtosis, PSD entropy, PSD root-mean-square, PSD peak, PSD crest factor</td><td>Statistics applied to the power spectral density of the signal time series and its envelope.</td></tr><tr><td>Mel-frequency cepstral coefficients (MFCC)</td><td>MFCC (0-12) mean, MFCC (0-12) maximum</td><td>Coefficients derived from the Mel-scale spectrogram.</td></tr><tr><td>Custom features</td><td>Distance from interrogator, distance from signal maximum, gentle distance from signal maximum, three-peak prominence factor, one-peak prominence factor, two-kurtosis</td><td>Specialized metrics developed for DAS-specific signal analysis.</td></tr></tbody></table>

<table><caption>TABLE III
PERFORMANCE METRICS FOR EACH CLASSIFICATION MODEL</caption><thead><tr><td>Model name</td><td>Metric</td><td>Low-quality channel (Test)</td><td>High-quality channel (Test)</td><td>Low-quality channel (OOD)</td><td>High-quality channel (OOD)</td></tr></thead><tbody><tr><td rowspan="5">XGBoost classifier</td><td>Precision</td><td>0.907 ± 0.009</td><td>0.957 ± 0.005</td><td>0.74 (0.13)</td><td>0.92 (0.07)</td></tr><tr><td>Recall</td><td>0.944 ± 0.007</td><td>0.928 ± 0.007</td><td>0.91 (0.08)</td><td>0.67 (0.31)</td></tr><tr><td>F1 score</td><td>0.925 ± 0.006</td><td>0.942 ± 0.005</td><td>0.80 (0.08)</td><td>0.71 (0.28)</td></tr><tr><td>Accuracy</td><td colspan="2">0.935 ± 0.005</td><td colspan="2">0.82 (0.13) [0.59-0.91]</td></tr><tr><td>ROC-AUC</td><td colspan="2">0.984 ± 0.002</td><td colspan="2">0.92 (0.04) [0.84-0.95]</td></tr><tr><td rowspan="5">Logistic regression</td><td>Precision</td><td>0.879 ± 0.010</td><td>0.937 ± 0.006</td><td>0.68 (0.15)</td><td>0.83 (0.17)</td></tr><tr><td>Recall</td><td>0.918 ± 0.008</td><td>0.906 ± 0.008</td><td>0.92 (0.07)</td><td>0.66 (0.21)</td></tr><tr><td>F1 score</td><td>0.898 ± 0.007</td><td>0.921 ± 0.005</td><td>0.77 (0.10)</td><td>0.73 (0.20)</td></tr><tr><td>Accuracy</td><td colspan="2">0.911 ± 0.006</td><td colspan="2">0.78 (0.13) [0.57-0.90]</td></tr><tr><td>ROC-AUC</td><td colspan="2">0.969 ± 0.003</td><td colspan="2">0.83 (0.21) [0.46-0.98]</td></tr><tr><td rowspan="5">Decision tree</td><td>Precision</td><td>0.838 ± 0.011</td><td>0.938 ± 0.007</td><td>0.59 (0.22)</td><td>0.72 (0.25)</td></tr><tr><td>Recall</td><td>0.923 ± 0.009</td><td>0.868 ± 0.009</td><td>0.81 (0.10)</td><td>0.54 (0.25)</td></tr><tr><td>F1 score</td><td>0.879 ± 0.008</td><td>0.902 ± 0.006</td><td>0.67 (0.18)</td><td>0.60 (0.24)</td></tr><tr><td>Accuracy</td><td colspan="2">0.892 ± 0.006</td><td colspan="2">0.67 (0.19) [0.43-0.87]</td></tr><tr><td>ROC-AUC</td><td colspan="2">0.956 ± 0.004</td><td colspan="2">0.76 (0.17) [0.47-0.88]</td></tr><tr><td rowspan="5">Random forest</td><td>Precision</td><td>0.897 ± 0.009</td><td>0.956 ± 0.005</td><td>0.70 (0.14)</td><td>0.87 (0.09)</td></tr><tr><td>Recall</td><td>0.943 ± 0.007</td><td>0.920 ± 0.007</td><td>0.92 (0.05)</td><td>0.63 (0.33)</td></tr><tr><td>F1 score</td><td>0.920 ± 0.006</td><td>0.938 ± 0.005</td><td>0.79 (0.09)</td><td>0.67 (0.32)</td></tr><tr><td>Accuracy</td><td colspan="2">0.930 ± 0.005</td><td colspan="2">0.79 (0.14) [0.56-0.89]</td></tr><tr><td>ROC-AUC</td><td colspan="2">0.978 ± 0.003</td><td colspan="2">0.89 (0.07) [0.77-0.96]</td></tr></tbody></table>

First, a short TW prior to and including the event, capturing both the *P* and *S* waves, is selected. For each event, the same window is applied to all channels. We first apply a median filter for optical noise reduction [26]. Next, raw strain data are filtered between 3 and 20 Hz and decimated to 50 Hz. We focus on this frequency range because small local earthquakes generate energy at high frequencies, and starting at 3 Hz avoids contamination from low-frequency oceanic noise. Finally, each channel is standardized by subtracting its mean and dividing by its standard deviation. Once the data are normalized, feature extraction can be performed.

## B. Feature Extraction

The analyzed features can be grouped into several categories (see Table II). Some features are calculated for both the

signal time series and its envelope. Common signal processing features include time-domain features (e.g., mean, RMS, and skew) applied to both the signal and its envelope. Frequency-domain features are extracted from the Fourier transform of the signal, capturing its main frequency characteristics. Power spectral density (PSD) features quantify the energy distribution of the signal, offering insights into its spectral composition. Last, custom features include unique metrics developed specifically for this analysis, such as distances from the signal's maximum and prominence factors. Table IV in Appendix A shows the full feature calculation details, where $x_i$ represents each individual data point and *N* is the total number of samples.

Numerous signal processing features used in our analysis are widely employed across various fields, including applications beyond seismology. For instance, we drew inspiration from the strategies employed in the analysis of electroencephalograms
in neurology [27], where similar features are used to character-
ize temporal and spectral properties of signals. Mel-Frequency
Cepstral Coefficients (MFCCs), originally developed for speech
analysis, are a feature extraction method for time signals and
have been adapted for use in other domains, including seismic
signal analysis [28]. Additionally, we found insights from previ-
ous article on DAS [29] that informed our application of various
features. The additional custom features used in our analysis
emerged from the iterative process of manual selection for train-
ing purposes. A close study of the DAS data provided valuable
insights into which characteristics were critical for identifying
high-quality channels. For example, channels located furthest
from the interrogator often exhibited higher levels of noise due to
signal attenuation over the length of the cable. This degradation
is associated with the exponential decay of the optical signal
amplitude as the distance increases, primarily due to fiber losses
[30]. This insight led us to incorporate the distance from the
interrogator as a feature, recognizing it as a potential indicator of
channel quality. Additionally, some channels that contributed to
the visual recognition of the S-wave front had low SNR and could
only be identified by carefully stacking signal peaks aligned to
the same phase onset time. To capture this behavior, we needed
a precise way to identify the phase arrival time. To this end,
we utilized a kurtosis approach, which allowed us to detect the
precise phase arrival and better handle low-SNR channels [31].

C. Classification Model Design

To develop the CQI, we trained selected machine-learning
models using the extracted features, which were normalized us-
ing a method robust to nonstandard distributions by subtracting
the mean and dividing by the interquartile range. Additionally,
outliers were excluded only during model training to more
directly compare results among the different models tested.

We chose to evaluate four algorithms to explore the feature
interaction space without sacrificing interpretability or risking
overfitting. These are: logistic regression, decision trees, random
forest, and XGBoost [32], [33]. We hypothesized that logistic
regression could effectively capture the relationships between
certain features and channel quality, as we noticed that spe-
cific events strongly correlated with a small subset of features.
Decision trees provide interpretable, hierarchical decision rules
and naturally handle nonlinear interactions between features,
but are prone to overfitting and instability due to small changes
in the training data. Random forests mitigate these drawbacks
by averaging predictions from an ensemble of trees, improving
generalization and reducing overfitting while maintaining the
ability to model complex patterns. However, they can be com-
putationally intensive with large datasets. XGBoost excels in
handling complex, nonlinear interactions between features while
maintaining strong generalization capabilities, and reduces the
risk of overfitting compared to other tree-based models.

To refine the feature set, we employed recursive feature elim-
ination (RFE) following [34] to reduce dimensionality while
maintaining model performance. Many of the extracted and
custom features were collinear, making it necessary to identify
the most relevant subset for classification. RFE systematically

removes less significant features, ensuring that the selected
subset maximizes interpretability and computational efficiency
without compromising performance, and improves the model’s
generalization ability, i.e., its capacity to perform well on events
outside the experiments in the training set.

In agreement with other studies, we found binary classifi-
cation models to be overconfident and tend to concentrate the
prediction probabilities around 0 or 1 [35]. To obtain a smoother,
more distinguishing model, we postprocessed the model predic-
tions using a probability calibration method involving isotonic
regression [36]. Finally, motivated by the assumption that neigh-
boring channels often exhibit similar and correlated signals,
predictions were slightly smoothed across channels using an
exponential moving window. Appropriate smoothing parame-
ters can vary depending on the event, but we found good results
with a conservative choice of 0.5. We also found that this final
smoothing is effective to reduce the overconfidence from the
model. Fig. 2 illustrates the processing steps involved in calculat-
ing the CQI for an earthquake signal within a manually selected
TW. User-defined parameters, such as probability smoothing
and thresholding, allow for customization to suit specific cable
configurations and experimental conditions.

IV. EXPERIMENTAL RESULTS

A. Evaluation Design and Model Performance

We used two evaluation methods: first, we defined the “Test”
scenario. In this case, all labeled data from all events is combined
and then split randomly into two sets: a training set containing
70% of the data and an evaluation set with the remaining 30%.
The Test scenario allows us to measure performance and prevent
overfitting during model training. Second, we defined an “OOD”
scenario. In this scenario, we divided our dataset into the four
experiments it comprises, trained on three of those experiments
and evaluated on the fourth. OOD was then performed four
times. In turn, the held-out experiments were CANDAS (Gran
Canaria), CANDAS2, CASTOR, and SAFE. To obtain a more
reliable and stable estimate, the OOD final performance was
computed as the average of the metrics across four different
splits. The OOD detection allows us to assess the generalization
capabilities of our models on new, unseen data.

As evaluation metrics, we chose accuracy, precision, recall,
F1-score, and ROC-AUC. Accuracy measures the proportion of
correct predictions, assuming a threshold of 0.5, reflecting the
overall performance of the classifier. Precision quantifies the
proportion of channels labeled as high (or low) quality that are
actually high (or low) quality. Recall evaluates the proportion of
actual high (or low) quality channels that are correctly labeled as
high (or low) quality. The F1-score, calculated as the harmonic
mean of precision and recall, provides a robust measure for im-
balanced datasets. It prevents bias when one class is significantly
more populated than the other. Last, ROC-AUC represents the
area under the receiver operating characteristic curve, indicating
the probability that a high-quality channel is ranked higher than
a low-quality channel. Alongside precision and accuracy, it is
the most important metric for CQI evaluation.
<figure><img src="image_3.png" /><figcaption>Fig. 2. Processing steps to calculate the CQI for an earthquake signal within a manually selected time window. The model is already fixed and used for inference. Probability smoothing and thresholding are user-defined parameters and can vary for each specific application.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 3. Evaluation of the four models on the in-domain Test scenario (left) and the four out-of-distribution (OOD) splits (right). Left panel: ROC-AUC and accuracy are shown as points with 95% bootstrap confidence-interval error bars. Right panel: each dot is one OOD split; the thick horizontal segment marks the mean. The lowest scoring dot corresponds to using the Castor dataset for validation, indicating that Castor events have very distinct signal statistics.</figcaption></figure>

To determine the optimal hyperparameters for each model, we performed a cross-validation grid search over an extensive parameter grid using the training set from the Test scenario. From the resulting ranking of model parameters, we selected the most conservative option among those within one point of the highest ROC-AUC score. This approach ensures that the chosen models have parameters providing reliable regularization while still achieving good performance. Table III presents the evaluation metrics for the two proposed scenarios. To show statistical significance, Test scenario metrics are reported with 95% confidence intervals, calculated using bootstrapping over the evaluation set. OOD scenario metrics are shown with the standard deviation across the four different experiments, which can serve as validation folds. For completeness, training metrics calculated using five-fold cross-validation are reported in Table SI in the Supplemental Material. For visual clarity, we plot ROC-AUC and Accuracy, two relevant metrics, in Fig. 3, showing a selection of the best models of different types. We observe that, while most models perform well within the Test

scenario, the OOD scenario highlights the struggle with generalization, particularly for an experiment like CASTOR, which has seismic characteristics that differ considerably from the other three. Based on the results, we selected the XGBoost Classifier as it demonstrates the best performance across both the Test and OOD scenarios. Selected XGBoost model parameters and their description are provided in Table SII in the Supplemental Material.

## B. Feature Elimination

After selecting XGBoost as the best model, we conducted an RFE analysis to reduce dimensionality while maintaining model performance. The model was evaluated for progressively fewer features, and the results are shown in Fig. 4. The AUC and accuracy metrics for the Test set gradually increase with the number of features. However, the OOD scenario highlights how an elevated number of features can lead to overfitting, resulting in a drop in accuracy. Indeed, dimensionality reduction acts as a
<figure><img src="image_5.png" /><figcaption>Fig. 4. ROC-AUC and accuracy for the Test and OOD settings as a function of the number of features. The selected final number of features is indicated with a vertical dashed line.</figcaption></figure>

regularization method. Hence, considering all curves in the plot, we selected a reduced set of 17 features from our initial pool of 105 as the final input for our model (see Table V in Appendix B). Note that the top five features have tied ranks, as the XGBoost model with the selected parameters needed at least five features to fit. This selection also reveals what signal characteristics are the most important for predicting channel quality, which we further analyze in the discussion.

### C. Channel Quality Evaluation on Seismic Events

With the prediction model selected, we could execute all processing steps to obtain a CQI. Fig. 5 shows the CQI results for new events in the CANDAS2 (panels a and b) and SAFE (panels c and d) experiments. All panels show submarine events except for panel (c), which represents a land event recorded on the submarine cable. Visually, the results demonstrate the robustness of our method. Using a moderate threshold, noisy channels are correctly identified as low quality and isolated from the rest of the channels. When a higher CQI threshold is applied, some good channels may be discarded, but only the clearly high-quality channels remain, an outcome that aligns with the intended behavior. This approach provides users with the flexibility to adjust the threshold based on their specific precision requirements. Additional examples of this analysis can be found in Fig. S2 of the Supplemental Material, illustrating the consistent performance of CQI across multiple events.

## V. DISCUSSION

### A. Analysis of Important Features

Feature engineering is a particularly creative step in machine-learning model development. In our case, since the dataset labels

were generated through close manual inspection of each channel's waveform, we crafted features that aligned with waveform characteristics that visually indicate good quality to a human expert. To understand how well the model captures these intuitive signal traits, we performed an in-depth feature importance analysis. In analyzing the list of the 17 most important ranked features (Table V in Appendix B), we noted that many are related to the RMS envelope and the PSD of the signal. These include statistics about the peaks, noise, and the statistical distribution of the signals in both the time and frequency domains. From the 105 engineered features, the RFE reveals that indicators such as impulse factor, clearance factor, peak, frequency amplitudes, and variances are fundamental for predicting waveform quality.

To illustrate this, Fig. 6 shows two examples of waveforms categorized as high quality and low quality, respectively. On the left, the time-domain signals and the RMS envelope and PSD of each waveform are displayed. On the right, SHAP values [37] are used to measure the predictive power of each feature in the model for assessing the quality of these two specific waveforms. For the high-quality signal, clear peaks are observed, making it reasonable that features such as the envelope's peak, the impulse factor, and the prominence factor have strong predictive power for high quality. Conversely, for the low-quality signal, the corresponding values of prominence factors and peak contribute to the prediction of low quality. The strong, distinct peaks in the high-quality waveform's RMS envelope and its concentrated energy in the frequency spectrum suggest that the signal likely represents a meaningful seismic event rather than noise. In contrast, the diffuse, low-amplitude nature of the low-quality signal's envelope and PSD results in negative contributions for features that capture a lack of distinct peaks.

Additionally, our results indicate that the first significant MFCC coefficient (MFCC-1) is an important predictor of seismic signal quality, while the 0th coefficient, which represents
<figure><img src="image_6.png" /><figcaption>Fig. 5. CQI results for new events in: (a) CANDAS2 (2023-02-22 07:02:14, 2.7 $m_bL_g$); (b) CANDAS (2020-09-13 09:33:38, 1.3 $m_bL_g$); (c) SAFE (2023-06-16 04:22:00, 3.2 $m_bL_g$); and (d) SAFE (2023/10/05 09:00:45, 3.5 $m_bL_g$). For each experiment, channels identified as bad or good are displayed, along with the probability for each channel. The chosen probability thresholds (all 0.5 except 0.6 for panel b) are indicated by dashed vertical lines.</figcaption></figure>

overall signal energy, is not relevant and can be discarded. This exclusion is consistent with observation from many MFCC analysis studies [28], [38]. In speech processing, the first coefficients capture the broad spectral shape and loudness, whereas for seismic signals, MFCC-1 likely reflects the dominant energy modes or primary oscillatory characteristics. This suggests that the relative energy distribution across frequencies, rather than absolute energy, plays an important role in determining seismic signal quality.

This feature analysis shows that the model relies on signal characteristics that are meaningful to human experts, which helps build trust in its decisions. In addition, the analysis offers

insight into which aspects of the waveform are most relevant for detecting good quality signals, in particular the shape of the RMS envelope, which stands as a strong indicator. This aligns with the common expectation that features related to SNR are well suited for this task. However, the need to combine multiple features and the limitations of single-feature thresholds are further discussed in Section V-D.

## B. Model Performance

We initially hypothesized that logistic regression could effectively capture the relationships between certain features and
<figure><img src="image_7.png" /><figcaption>Fig. 6. Analysis of a high-quality (black lines) and low-quality (gray lines) waveform. The events are extracted from CANDAS, Tenerife on 2020-07-27. On the left, the time domain, RMS envelope, and PSD are shown, illustrating the different characteristics of the two signals. On the right, the SHAP feature value contributions highlight the importance of each feature in determining the quality of the signal.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 7. Evaluation of classifier performance using ROC and threshold-based metrics for an event in SAFE, Alboran, on 2023-11-13. (a) ROC curve illustrating the tradeoff between true positive rate (TPR) and false positive rate (FPR), with the optimal threshold marked in red. (b) TPR, FPR, and their difference (TPR-FPR) plotted as functions of the decision threshold. The optimal threshold ($\tau^*$) maximizes the separation between TPR and FPR, while a vertical line at 0.5 indicates the default decision threshold.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 8. Channel quality calculation on SAFE, Alboran, 2023-12-15, using (a) only the RMS envelope clearance factor and (b) full CQI model. For a fair comparison, the decision threshold is chosen to be the median value of the indicator for both cases; that is, there are the same number of low- and high-quality channels with the selected threshold.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 9. Schematic representation of the integration of CQI into the seismic monitoring workflow: (a) earthquake detection and initial location estimation are performed using land-based seismic stations; (b) corresponding time window of DAS data is extracted based on the estimated earthquake location; (c) CQI algorithm is applied to assess the quality of individual DAS channels; (d) only high-quality (HQ) channels are selected for further analysis; and (e) refined earthquake location is determined by integrating selected DAS data with land-based seismic observations.</figcaption></figure>

channel quality, as we noticed that specific events strongly correlated with a small subset of features. Upon further study, we found capturing nonlinear interactions among variables was essential for robust performance. This led us to compare logistic regression, decision trees, random forest, and XGBoost. Among these, XGBoost stood out for its rich set of hyperparameters, regularization techniques, and fast training, all of which were critical for achieving excellent generalization in OOD scenarios. Meanwhile, random forest, while closer in performance to XGBoost, proved less versatile, with lengthier training times

and fewer options for fine-tuning, resulting in weaker OOD performance.

Finally, recognizing that consecutive DAS channels (separated by 10 m in our case) are naturally correlated, we applied a mild smoothing using a conservative exponential moving average. This smoothing effectively reduced the impact of occasional misclassifications by moderating any outlier predictions and ensuring more consistent quality estimates across neighboring channels. We used a parameter of 0.5 for the exponential averaging window. For applications with tighter channel spacing, a smaller smoothing factor might be beneficial, whereas larger spacing could justify a larger factor or possibly omitting smoothing altogether.

### C. Decision Threshold

CQI produces a continuous score between 0 and 1 that reflects the confidence that a given channel is high quality. A threshold ($\tau$) is then chosen to separate low- and high-quality channels, enabling a binary decision. The choice of $\tau$ controls the tradeoff between precision and recall: a high threshold is conservative, reducing false positives but potentially missing good data, while a low threshold is more permissive but risks admitting low-quality points.

Fig. 7 illustrates this behavior using a real example from the SAFE experiment. Subfigure (a) presents the ROC curve, showing how the true positive rate (TPR) and false positive rate (FPR) evolve across different threshold values. The point of maximum separation between TPR and FPR, which we label $\tau^*$, represents the best compromise between the two metrics. Subfigure (b)
plots TPR, FPR, and their difference across thresholds, showing
how performance varies with $\tau$.

While $\tau*$ serves as a proxy for an automatic threshold choice,
its value can vary significantly between events, for example,
depending on whether the event signal is clean or noisy. In
practice, visual exploration by the user remains essential. To
support this, CQI-DAS offers an interactive interface that allows
users to rapidly assess the impact of different thresholds on the
data.

### D. Other Channel Quality Indicators

In [39], a channel reliability indicator ($\beta$) was proposed to estimate the level of similarity between a reference channel and all other channels using phase cross-correlation functions (PCCF) [40]. We evaluated the effect of adding PCCF-related features to our full feature set and observed no measurable gain in classification accuracy or other metrics (see details about the reliability indicator and its derivations in Table VI in Appendix A). During feature elimination, these features ranked lower compared to other indicators, underscoring their limited predictive power for channel quality. In addition, computing six PCCF-related features can take 2 to 4 h on standard desktop hardware for a dataset of 5000 to 6000 channels over a 60-s window, whereas the rest of the full feature set can be processed in less than 3 min.

We acknowledge that $\beta$ and its related features may offer benefits for certain monitoring tasks. However, in our context, shifting noise patterns made it difficult to identify consistent thresholds for distinguishing high- from low-quality channels, especially for weaker seismic events. While $\beta$ was effective in some scenarios, it did not deliver consistent channel rankings according to quality when compared to visual inspection. Given these findings, and considering the substantial computational effort involved, we decided to exclude $\beta$ from our final feature set. This decision helped reduce processing time while preserving classification performance, thereby better supporting operational needs that require both efficiency and reliability.

A natural hypothesis for the quality indicator is that it serves as a proxy for the SNR. Indeed, many key features used in CQI can be interpreted as approximations of SNR, assessing the strength of the event signal relative to background noise. However, CQI offers a more effective model by integrating multiple signal attributes, thereby capturing richer characteristics than any single SNR-based metric.

To illustrate this, Fig. 8 compares the use of the RMS envelope clearance factor (panel a), which can be interpreted as a simple proxy for SNR, against the composite CQI metric (panel b). For consistency and fairness, thresholds for both methods are set at the median of their respective predicted quality value distributions. While the RMS-based method achieves a reasonable ROC-AUC score of 0.85, the CQI approach significantly outperforms it, reaching an ROC-AUC of 0.96 in this instance. To visually compare the two methods, a threshold is selected such that the number of low- and high-quality channels is identical in both examples. The visual contrast highlights how,

in noisy conditions, the RMS envelope method wrongly labels
very low-quality channels as high, further underscoring CQI's
superior capacity to identify high-quality signals.

### E. Integrating CQI for Enhanced Earthquake Monitoring

We propose using offshore fiber-optic cables as an extension of existing seismic monitoring networks, treating them as dense arrays of virtual seismic stations. In our workflow, land-based stations provide the initial earthquake location. Based on this estimate, a corresponding TW is extracted from the DAS recordings. The CQI algorithm is then applied to evaluate and select the most informative DAS channels, ensuring that only high-quality data contribute to refining the earthquake location. Fig. 9 illustrates how CQI fits into the broader seismic monitoring workflow.

Although DAS sensors may be located closer to offshore epip centers, our approach prioritizes operational feasibility. National alert systems, such as that of the Spanish National Geographic Institute (IGN), are built around land-based stations, which typically offer higher sensitivity and lower noise levels, especially for low-magnitude earthquakes. These networks are also already well integrated into real-time detection and warning pipelines. Integrating DAS as a secondary step triggered by land-based detections enables a seamless, low-latency enhancement without disrupting existing workflows.

Detailed relocations using the full DAS dataset can still be performed offline during postprocessing. However, CQI is designed to support real-time, automatic integration of DAS data by selecting only high-quality segments. This reduces computational cost and false inputs while maintaining compatibility with current infrastructures. The software runs efficiently on standard systems, making it suitable for national and regional networks. Once the dataset is read, processing the 17 selected features and performing model inference to compute the CQI requires approximately 10 s on standard desktop hardware.

Looking forward, CQI can be incorporated as an independent module that continuously monitors offshore DAS arrays and feeds reliable data into centralized systems. This architecture is scalable: multiple fiber-optic cables could be interrogated in parallel, with CQI filtering and forwarding only the most relevant signals. Furthermore, the method supports incremental learning, allowing the model to adapt over time as new data are acquired and conditions evolve. However, since CQI relies on a specific set of engineered features, its generalizability may vary across regions or instrumentation. Significant changes in event characteristics, noise levels, or environmental conditions may require periodic recalibration or retraining to maintain high performance.

Real-time data processing at large scales, particularly when monitoring multiple cables, is challenging due to the large volumes of data generated by DAS systems. This can strain computational resources, and requires efficient data handling strategies to maintain system performance. Similarly, data storage becomes increasingly complex when multiple cables are deployed within the network. To address this challenge, the
<table><caption>TABLE IV
LIST OF FEATURES EVALUATED AND THEIR DETAILED CALCULATIONS</caption><thead><tr><th>Feature name</th><th>Calculation/formula</th></tr></thead><tbody><tr><td>Mean (μ)</td><td><sup>1</sup>/<sub>N</sub> &sum;<sub>i=1</sub><sup>N</sup> x<sub>i</sub></td></tr><tr><td>Median</td><td>x<sub>(N+1)/2</sub>, N odd<br>x<sub>N/2</sub> + x<sub>N/2+1</sub>, N even</td></tr><tr><td>Variance (σ<sup>2</sup>)</td><td><sup>1</sup>/<sub>N</sub> &sum;<sub>i=1</sub><sup>N</sup> (x<sub>i</sub> - μ)<sup>2</sup></td></tr><tr><td>Skewness</td><td><sup>1</sup>/<sub>N</sub> &sum;<sub>i=1</sub><sup>N</sup> (<sup>x<sub>i</sub> - μ</sup>/<sub>σ</sub>)<sup>3</sup></td></tr><tr><td>Kurtosis (κ)</td><td><sup>1</sup>/<sub>N</sub> &sum;<sub>i=1</sub><sup>N</sup> (<sup>x<sub>i</sub> - μ</sup>/<sub>σ</sub>)<sup>4</sup> - 3</td></tr><tr><td>Shannon entropy</td><td>- &sum;<sub>i=1</sub><sup>M</sup> P(x<sub>i</sub>)log<sub>2</sub>P(x<sub>i</sub>)</td></tr><tr><td>Root-mean-square (rms)</td><td>&radic;(<sup>1</sup>/<sub>N</sub> &sum;<sub>i=1</sub><sup>N</sup> x<sub>i</sub><sup>2</sup>)</td></tr><tr><td>Peak</td><td>abs(max(x<sub>1</sub>, x<sub>2</sub>, ..., x<sub>N</sub>))</td></tr><tr><td>Crest factor</td><td>Peak / RMS</td></tr><tr><td>Average rectified value</td><td><sup>1</sup>/<sub>N</sub> &sum;<sub>i=1</sub><sup>N</sup> |x<sub>i</sub>|</td></tr><tr><td>Standard deviation (σ)</td><td>&radic;(<sup>1</sup>/<sub>N</sub> &sum;<sub>i=1</sub><sup>N</sup> (x<sub>i</sub> - μ)<sup>2</sup>)</td></tr><tr><td>Root amplitude</td><td>(<sup>1</sup>/<sub>N</sub> &sum;<sub>i=1</sub><sup>N</sup> &radic;|x<sub>i</sub>|)<sup>2</sup></td></tr><tr><td>Peak-to-average power ratio</td><td>(Crest factor)<sup>2</sup></td></tr><tr><td>Impulse factor</td><td>Peak / average rectified value</td></tr><tr><td>Waveform factor</td><td>rms/ mean</td></tr><tr><td>Shape factor</td><td>rms/ average rectified value</td></tr><tr><td>Clearance factor</td><td>Peak / root amplitude</td></tr><tr><td>Median absolute deviation</td><td>median(|x<sub>i</sub> - median(x)|)</td></tr><tr><td>Detection significance</td><td>(Peak - median) / median absolute deviation</td></tr><tr><td>Distance from interrogator</td><td>Spatial distance (in meters) between the channel position and the location of the interrogator</td></tr><tr><td>Distance from signal maximum</td><td>Minimum distance between the top three peaks of the signal and the location of maximum positive gradient of kurtosis, stacked over the 100 nearest channels</td></tr><tr><td>Gentle distance from signal maximum</td><td>Minimum distance between the top three peaks of the signal and the location of maximum positive slope of kurtosis, stacked over the 100 nearest channels.</td></tr><tr><td>One-peak prominence factor (k)</td><td>min(<sup>1</sup>/<sub>N</sub> &sum;<sub>i=1</sub><sup>N</sup> (<sup>max(|envelope(x<sub>i</sub)|)</sup>/<sub>k</sub>))</td></tr><tr><td>Three-peak prominence factor (k)</td><td>min(<sup>1</sup>/<sub>N</sub> &sum;<sub>i=1</sub><sup>N</sup> (<sup>max(|envelope(x<sub>i</sub)|)</sup>/<sub>k</sub>))</td></tr><tr><td>Two Kurtoses</td><td>κ(FFT(envelope(x))) &times; κ(x)</td></tr></tbody></table>
<table><caption>TABLE V
LIST OF FEATURES DERIVED FROM PHASE CROSS-CORRELATION FUNCTIONS (PCCFs)</caption><thead><tr><th>Feature name</th><th>Calculation/Formula</th></tr></thead><tbody><tr><td>Beta (β)</td><td>Computed as per [39] and computing PCCF as per [40].</td></tr><tr><td>Modified- β</td><td>Computed as per [39], but using an rms window excluding the maximum PCCF and a 4 s surrounding region, to better quantify the PCCF.</td></tr><tr><td>Mean PCCF</td><td>Mean of the computed PCCFs per channel</td></tr><tr><td>Median PCCF</td><td>Median of the computed PCCFs per channel</td></tr><tr><td>Median- absolute- deviation PCCF</td><td>Median absolute deviations of the computed PCCFs per channel</td></tr><tr><td>Lags</td><td>The distance of the absolute maximum of the signal from the midpoint of the signal<br>|argmax(|x|) - N/2|</td></tr></tbody></table>

<table><caption>TABLE VI
RANK AND DESCRIPTION OF THE FIRST 17 FEATURES SELECTED USING FEATURE ELIMINATION</caption><thead><tr><th>Rank</th><th>N-th feature as selected using feature elimination</th></tr></thead><tbody><tr><td>1</td><td>Impulse factor of rms envelope</td></tr><tr><td>1</td><td>Clearance factor of rms envelope</td></tr><tr><td>1</td><td>Peak time of rms envelope</td></tr><tr><td>1</td><td>Average frequency amplitude of rms envelope</td></tr><tr><td>1</td><td>Kurtosis of the PSD</td></tr><tr><td>6</td><td>Amplitude variance of rms envelope</td></tr><tr><td>7</td><td>Prominence factor of the first highest peak</td></tr><tr><td>8</td><td>Entropy of the PSD of the rms envelope</td></tr><tr><td>9</td><td>Entropy of the PSD</td></tr><tr><td>10</td><td>Prominence factor of the third highest peak</td></tr><tr><td>11</td><td>Skewness of the PSD</td></tr><tr><td>12</td><td>Median amplitude of rms envelope</td></tr><tr><td>13</td><td>Peak-to-average power ratio of rms envelope</td></tr><tr><td>14</td><td>rms of the amplitude of the rms envelope</td></tr><tr><td>15</td><td>Skewness of the frequency signal</td></tr><tr><td>16</td><td>Mean of first significant MFCC coefficient</td></tr><tr><td>17</td><td>Maximum of first significant MFCC coefficient</td></tr></tbody></table>

proposed method offers two solutions: 1) for data repositories maintained by agencies, CQI allows researchers to selectively download data segments, automatically filtering out poor-quality channels, and 2) in the context of lossy data compression, the method provides a means to assess and quantify the information loss, optimizing storage management while preserving critical seismic data.

## VI. CONCLUSION

This article highlights the potential of machine learning to enhance DAS data processing for seismic applications. By introducing the CQI, we enable the efficient selection of high-quality channels, improving earthquake detection and characterization in offshore environments. The proposed approach demonstrates robust performance across diverse datasets and has the potential to integrate with existing seismic monitoring infrastructures and operate in near-real-time with minimal computational cost. This methodology enables further research into adaptive DAS data filtering and its flexibility and scalability make it a valuable tool for optimizing DAS-based observations across a wide range of geophysical and environmental domains.

## APPENDIX A

Tables IV and VI are placed at the end of the document.

## APPENDIX B

Table IV is placed at the end of the document.

## ACKNOWLEDGMENT

Figures were created using Matplotlib which is available under the Matplotlib license at https:// matplotlib.org/. Maps were created using Generic Mapping Tools version 6 [41], [42]. The bathymetric data used are available from [43] and [44]. Data processing was realized using Numpy under Numpy license at https:// numpy.org/, SciPy under BSD 3-Clause License and Librosa under ISC License.

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

Tatiana Rodriguez received the Ph.D. degree in experimental high-energy physics from the UPenn, Philadelphia, PA, USA, in 2011.

She worked as a Data Scientist with the JP Morgan Chase before returning to academia to pursue a master's in reservoir geology and geophysics with the University of Barcelona, Barcelona, Spain. Since 2020, she has been a Postdoctoral Researcher in seismology with the Institute for Marine Sciences, Barcelona, Spain, focusing on distributed acoustic sensing.

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

Aleix Seguí received the double B.S. degree in engineering physics and data science from the Polytechnic University of Catalonia, Barcelona, Spain, in 2023. He is currently working toward the master's degree in electrical engineering with the ETH Zurich, Zurich, Switzerland.

His research interests include data compression and applications of machine learning to physical sciences.
<figure><img src="image_13.png" /></figure>

**Arantza Ugalde** received the Ph.D. degree in physics from the Polytechnic University of Catalonia, Barcelona, Spain, in 1997, specializing in seismology and earthquake engineering.

She is currently a Senior Researcher with the Institute of Marine Sciences (ICM-CSIC), Barcelona, Spain. She leads the research line on fiber-optic sensing with the ICM. Her research interests include natural hazard studies, earthquake and tsunami monitoring, induced seismicity, and distributed acoustic sensing in seismology, oceanography, and bioacoustics.

<figure><img src="image_14.png" /></figure>

**Sergi Ventosa** received the engineering degrees in electronics and telecommunication engineering and the Ph.D. degree in telecommunication engineering from the Polytechnic University of Catalonia, Barcelona, Spain, in 1999, 2002, and 2010, respectively.

He was a Postdoctoral Researcher with the IFP Energies Nouvelles and the Institut de Physique du Globe de Paris, Paris, France; and the Geosciences Barcelona-CSIC and Institute of Marine Sciences (ICM-CSIC), Barcelona, Spain. He currently works with the Department of Marine Geosciences, ICM, Barcelona, Spain. His research interests include seismic signal detection, ambient noise tomography, deep and shallow Earth's structure, signal processing, inversion theory, and distributed acoustic sensing.

<figure><img src="image_15.png" /></figure>

**Melania Cubas Armas** received the Ph.D. degree in physics from the University of La Laguna, La Laguna, Spain, in 2019.

She is currently a Postdoctoral Researcher with the Marine Geosciences Department, Institute of Marine Sciences, Barcelona, Spain. Her research interests include distributed acoustic sensing for the detection of submarine earthquakes.

<figure><img src="image_16.png" /></figure>

**Tony Monfret** received the Ph.D. degree in geophysics from the University of Paris 7, Paris, France, in 1988.

He is currently a Researcher with the Institut de Recherche pour le Développement, Géoazur, France. His research interests include seismic data analysis and seismotectonics.

<figure><img src="image_17.png" /></figure>

**Hugo Latorre** received the B.S. degree in physics from the University of Zaragoza, Zaragoza, Spain, in 2019, and the M.Sc. degree in geophysics from the University of Granada, Granada, Spain, in 2020.

He is currently a Predoctoral Fellow with the Institute of Marine Sciences, Barcelona, Spain. His research interests include seismology and distributed acoustic sensing.