# Singing around the volcano: Detecting baleen whales in the Mozambique channel based on their song rhythms, from seismic and hydroacoustic data

Richard Dréo, Wayne Crawford, Guilhem Barruol, Sara Bazin, Jean-Yves Royer, Flore Samaran

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## ► To cite this version:

Richard Dréo, Wayne Crawford, Guilhem Barruol, Sara Bazin, Jean-Yves Royer, et al.. Singing around the volcano: Detecting baleen whales in the Mozambique channel based on their song rhythms, from seismic and hydroacoustic data. Journal of the Acoustical Society of America, 2025, 157 (5), pp.3418-3435. 10.1121/10.0036510. hal-05061872

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# Singing around the volcano: Detecting baleen whales in the Mozambique channel based on their song rhythms, from seismic and hydroacoustic data

Richard Dréo,<fnref n="1" />,<fnref n="2" />,ᵃ) Wayne C. Crawford,<fnref n="1" />,<fnref n="3" />) Guilhem Barruol,<fnref n="4" />) Sara Bazin,<fnref n="5" />) Jean-Yves Royer,<fnref n="5" />) and Flore Samaran<fnref n="6" />)<fnref n="1" />

## ABSTRACT:

Since March 2019 and October 2020, two long-term submarine networks—comprising ocean bottom seismometers and water-column hydrophones—have been deployed in the northern Mozambique Channel (Western Indian Ocean) to monitor the seismo-volcanic crisis that began offshore Mayotte in 2018. These deployments provide a valuable multi-year dataset for environmental monitoring in this remote region, including seasonal variations in the presence of baleen whales. To analyze these patterns, we developed an automated detector for stereotyped and regular signals, focusing on the characteristic inter-call intervals of each whale species, independently of the song structure. The detector’s simplicity makes it computationally efficient and easily adaptable to species vocalizing in the 15–100 Hz frequency range over extended periods. Analysis of nearly six years of continuous seismo-acoustic data highlights the seasonal presence of Antarctic blue whales (May–August), Southwest Indian Ocean pygmy blue whales (bi-modal pattern from April–August and October–January), minke whales (June–December), and fin whales (July–November), and highlights the potential variability over years. Our results enhance and refine our understanding of the seasonal migration patterns of whale populations in this specific area of the Western Indian Ocean.
© 2025 Acoustical Society of America. https://doi.org/10.1121/10.0036510

(Received 5 July 2024; revised 3 April 2025; accepted 8 April 2025; published online 2 May 2025)

[Editor: Shane Guan]

Pages: 3418–3435

## I. INTRODUCTION

Establishing the seasonal presence patterns of large baleen whale species is essential for conservation efforts and large ocean area management. Passive acoustic monitoring (PAM) appears to be the most efficient way to monitor large and remote areas at a relatively low cost. Autonomous hydrophones deployed in the sound fixing and ranging (SOFAR) channel have been widely used to establish whale seasonal presence in remote and unexplored areas Mellinger et al. (2007).

Since the songs of several species of baleen whales are partly emitted in the low-frequency (LF) band (LF) (i.e., less than 50–100 Hz), ocean bottom seismometers (OBS) networks deployed by the seismological community also provide opportunities to monitor the acoustic occurrence of large marine mammals (Dréo et al., 2019; Bouffaut et al., 2018; Pereira et al., 2016; Matias and Harris, 2015; Hilmo and Wilcock, 2024; Harris et al., 2013).

The volcano-tectonic crisis that started in mid-2018 in the vicinity of Mayotte Island, and which gave birth to a new

submarine volcano (Cesca et al., 2020; Lemoine et al., 2020; Saurel et al., 2021), motivated strong efforts to establish a continuous and accurate monitoring of the area, through the deployment of land and marine seismological stations. The creation of the new Reseau de surveillance Volcanologique et Sismologique de Mayotte (REVOSIMA) volcano observatory (Mayotte Volcanological Seismological Monitoring Network et al., 2021; Feuillet et al., 2022) launched an ongoing effort of the French geoscience community to understand the seismic activity and to evaluate the associated hazard for the local population and infrastructures. In addition to terrestrial observations (seismic, geodetic, geochemical), submarine stations including OBSs and hydroacoustic instruments were deployed to characterize the predominantly offshore seismo-volcanic activity. Due to the technical complexity and cost of acquiring real-time marine data, the chosen strategy has been based on successive short-term OBS and water-column hydroacoustic instrument deployments. This allows the scientific community to access marine data on a regular basis, in order to monitor the evolution of the submarine volcanic activity. The successive OBS deployments (detailed in the supplementary material) helped locate two deep swarms

<footnote n="1">¹Université de Paris, Institut de physique du globe de Paris, CNRS, F-75005 Paris, France</footnote>

<footnote n="2">²SAS Boksound, Kernevez, 20590 Rosnoën, France</footnote>

<footnote n="3">³LIENSs, UMR 7266 CNRS-La Rochelle Universite, La Rochelle, France</footnote>

<footnote n="4">⁴CNRS, IRD, Grenoble INP, IGE, Université Grenoble Alpes, Grenoble, France</footnote>

<footnote n="5">⁵Université Brest, CNRS, Ifremer, UMR6538 Geo-Ocean, 29280 Plouzané, France</footnote>

<footnote n="6">⁶Lab-STICC - UMR 6285 CNRS, ENSTA IP Paris, Brest, France</footnote>

<footnote>ᵃEmail: adreo@ipgp.fr</footnote>
of seismic events that revealed the structure of probable magma chambers and the dyke that fed the seafloor volcanic eruption (Foix *et al.*, 2021; Lavayssière *et al.*, 2022). The hydrophone deployments helped detecting active lava flows on the ocean bottom during the submarine eruptions (Bazin *et al.*, 2023; Saurel *et al.*, 2022; Lavayssière *et al.*, 2024).

In both the Southern and Indian Oceans, baleen whale populations were greatly depleted to the limit of extinction by commercial and illegal whaling until the creation of the Indian Ocean Whale Sanctuary (IOWS) in 1979 by the International Whaling Commission (IWC) (IWC, 1980). Their recovery remains slow and their monitoring remains a challenge due to the scarcity of individuals and to the extent and location of their habitat, largely encompassing remote and inaccessible regions of the ocean. Moreover, baleen whales are highly mobile migratory species, between the southern Ocean (feeding grounds) in austral summer and subantarctic and subtropical waters in austral winter (breeding grounds) (Mackintosh, 1966). The recent use of PAM greatly increases our knowledge of the broad-scale seasonal occurrence and distribution of baleen whales in the IOWS this past decade (Stafford *et al.*, 2004; Sousa and Harris, 2015; Dréo *et al.*, 2019; Leroy *et al.*, 2018b; Torterotot *et al.*, 2020; Cerchio *et al.*, 2024).

Although designed for submarine volcano monitoring, the REVOSIMA network can be also considered as an environmental observatory, particularly for whale monitoring, providing a unique opportunity to assess the presence and long-term seasonal variations of whale species. Among baleen whales that emit songs or part of songs in the LF band and which could occur in the vicinity of the volcano, Antarctic Blue Whale (Balaenoptera musculus intermedia) (ABW), Fin Whale (Balaenoptera physalus) (FW), and Antarctic Minke Whale (Balaenoptera bonaerensis) (AMW) are good candidates and are also seasonally present in the Indian Ocean. ABW and Pygmy Blue Whale (Balaenoptera musculus brevicauda) (PBW) are genetically distinct and differ in size and acoustic signatures (Ljungblad *et al.*, 1998; LeDuc *et al.*, 2023). ABW emit a unique stereotyped song across the Southern Hemisphere whereas PBW present at least four distinct regional populations that do not display morphological differences but that are distinguishable only acoustically by their song-types: the South West Indian Ocean Pygmy Blue Whale (Balaenoptera musculus brevicauda) (PBW-SWIO), the Central Indian Ocean Pygmy Blue Whale (PBW-CIO), the South East Indian Ocean Pygmy Blue Whale (Balaenoptera musculus brevicauda) (PBW-SEIO) (McDonald *et al.*, 2009; Leroy *et al.*, 2021), and the North West Indian Ocean Pygmy Blue Whale (Balaenoptera musculus brevicauda) (PBW-NWIO) (Cerchio *et al.*, 2020a).

In the Southern Ocean, ABW songs are detected year-round, with a lesser presence during austral autumn and winter. In the southern Indian Ocean, detections shift from subantarctic locations in austral autumn to tropical locations in the austral winter and back to subantarctic locations in spring (September to November) (Stafford *et al.*, 2004; Samaran *et al.*, 2010; Samaran *et al.*, 2013; Leroy *et al.*, 2016; Thomisch *et al.*, 2016; Torterotot *et al.*, 2020).

In contrast, PBW present a totally different distribution. There is a longitudinal differentiation of the PBW-SEIO and PBW-SWIO populations with PBW-SEIO mainly present in the eastern Indian Ocean and PBW-SWIO in the western Indian Ocean (Leroy *et al.*, 2018b). These two subspecies are not distributed much further south than 54°S (Branch *et al.*, 2007), indicating that feeding grounds and wintering grounds occur in the Indian Ocean. PBW-SWIO songs were detected seasonally at different acoustic stations in the Indian Ocean ranging from 6°S to 56°S, 037°E to 074°E from Diego Garcia in the north (Stafford *et al.*, 2004), off Kerguelen plateau in the south (Torterotot *et al.*, 2020), off Marion Island in the west (Shabangu *et al.*, 2024) and southwest of Amsterdam Island in the east (Torterotot *et al.*, 2020).

The 20 Hz pulse of the FW is generally detected in all oceans of the globe. In the Southern Hemisphere, previous acoustic studies have detected FW during the feeding period close to Antarctica (Širović *et al.*, 2004). In the Indian Ocean, detections have been reported in mid and low latitudes during the austral winter and spring (Leroy *et al.*, 2018b; Dréo *et al.*, 2019; Shabangu *et al.*, 2024), suggesting that the western Indian Ocean may be part of the migration route and/or breeding area or at least winter location of FW populations.

AMW also exhibit migratory movement between Antarctica and mid and low latitudes but distribution is still poorly known. Only sparse acoustic detections and visual observations attest to the occurrence of the species in the western part of the Indian Ocean (Cerchio *et al.*, 2018).

Understanding the occurrence and distribution of these endangered species in such a remote area offers valuable insights into their range, migration patterns, and seasonality, which are crucial for effective management and conservation efforts. This study highlights the importance of identifying habitats utilized by various baleen whale species, providing critical guidance for their recovery and conservation planning.

In the present work, we take the opportunity of offshore Mayotte seismic and hydroacoustic deployments to monitor the baleen whale presence over nearly six years, from March 2019 to October 2024, when the latest data were recovered. In order to efficiently explore and investigate such large databases, we developed an inter call interval (ICI) -based whale detector, which is well adapted since cadenced signals such as whale songs are quite rare in the underwater LF soundscape and should therefore provide a robust and discriminant characteristic to establish the regional and seasonal presence of whales. This detector aims to assess the presence of a species, without counting individuals or songs.

The paper first describes the recording system, the used datasets, and the characteristics of the different whale songs studied in this analysis. We then describe the new automatic detection tool and detail its validation using manually detected whales as ground truth observations. We then assess the seasonal distribution patterns of baleen whale species that dwell in the area.
## II. SEISMIC AND HYDROACOUSTIC MONITORING DATA

The opportunity to explore the seismo-acoustic sound-scape in the neighborhood of Mayotte Island has been closely related to the submarine volcano-tectonic crisis that started in mid-2018. The REVOSIMA observatory was indeed designed to provide continuous but not real-time monitoring of the potentially dangerous submarine volcanic activity occurring in the area. It involved successive short-time (from three to 12 months) deployments and recoveries of seismological (MAYOBS) and hydroacoustic (MAHY) instruments, respectively, on the ocean bottom and in the water column, both of which are used in our analysis.

### A. MAYOBS seismological networks

The deployment of ocean bottom instruments East of Mayotte Island started less than one year after the beginning of the large seismic activity in April 2018. The first OBS deployment occurred in March 2019, followed by 11 successive deployments—recovery phases such as the one presented (Fig. 1). The complete details of the successive deployments are summarized in the supplementary material. This relatively short-term deployment strategy allowed fast data access and analysis, required to monitor the submarine activity, in a situation where real-time data access was not possible.

Most of the OBSs were equipped with a short-period 3-component seismometer and a hydrophone, providing continuous recordings of four channels at sampling rates

ranging from 62.5 to 250 sample/s, depending on the site and period of deployment. The successive deployments were adjusted in geometry, number, and type of instruments, based on the scientific objectives and the evolution of the volcanic activity. The geometries varied in the first year (2019), and have been fixed since. Continuous data have been validated and will be made available to the community at the Epos-France data archive (https://seismology.resif.fr) under the FDSN network code 1 T.

### B. MAHY hydroacoustic network

To complement the seafloor seismological observations, hydroacoustic stations were deployed in the water column. The MAHY network consists of four hydrophones deployed in a near square shape encompassing the MAYOBS OBSs and providing a continuous recording at 240 Hz (Fig. 1). The hydroacoustic array was first deployed in October 2020 and has been serviced and redeployed four times since then, to recover data and perform maintenance on the instruments. Table I summarizes the MAHY station locations and recording periods.

### C. Species selection and song description

The terminology used in the present study comes from the literature (Leroy et al., 2021): A “song” is a regularly repeated vocalization, composed of different units. Series are composed of various numbers of repeated songs with a regular and elementary interval called ICI. This ICI corresponds to the interval between the beginning of one song to the beginning of the following song. Series are separated by irregular intervals which are called inter series interval (ISI).

Among the species of baleen whales living in this part of the western Indian Ocean, we selected four of them that emit stereotyped songs with a regular ICI in the frequency band of the instruments and the sampling rate limit of the acquisition systems:

* ABW

* PBW-SWIO

* AMW

* FW

Songs, and particularly their spectral features, have already been described in detail in several studies (Leroy et al., 2018b; Dréo et al., 2019; Torterotot et al., 2020; Dominello and Širović, 2016; Risch et al., 2014;

<table><caption>TABLE I. MAHY network characteristics. The four stations recorded with a FS of 240 Hz.</caption><caption>MAHY network</caption><thead><tr><th>Station</th><th>Lon.</th><th>Lat.</th><th>Ele.</th><th>Start time</th><th>End Time</th></tr></thead><tbody><tr><td>MAHY1</td><td>45.36</td><td>-13.19</td><td>-1303</td><td>2020/10/16</td><td>2024/09/27</td></tr><tr><td>MAHY2</td><td>45.82</td><td>-13.35</td><td>-1342</td><td>2020/10/15</td><td>2024/07/02</td></tr><tr><td>MAHY3</td><td>46.15</td><td>-12.77</td><td>-1366</td><td>2020/10/14</td><td>2024/09/21</td></tr><tr><td>MAHY4</td><td>45.55</td><td>-12.49</td><td>-1331</td><td>2020/10/13</td><td>2024/10/03</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>FIG. 1. Example of MAHY and MAYOBS-H network geometry (from October 2020 to January 2021). OBSs are indicated by red stars and hydrophones deployed in the SOFAR channel by green stars. The white circle locates the new volcano. The successive 2019–2023 OBS deployments are detailed in the supplementary material.</figcaption></figure>
<table><caption>TABLE II. Characteristics of the song unit and ICI targeted by the detector for each considered whale species. Examples of spectrogram of each species are presented in the associated Fig. 2.</caption><thead><tr><td>Species</td><td>Frequency (Hz)</td><td>ICI (s)</td></tr></thead><tbody><tr><td>ABW</td><td>26.2±0.005</td><td>66</td></tr><tr><td>PBW-SWIO</td><td>24.4 to 21.6</td><td>103</td></tr><tr><td>AMW</td><td>120 to 60</td><td>2.9 to 3</td></tr><tr><td>FW</td><td>31.5 to 13</td><td>9.9 ± 0.04</td></tr></tbody></table>

Filun et al., 2020; Cerchio et al., 2018; Shabangu et al., 2024). The spectral complexity of each song is not relevant for this study: we only deal with its repetitive nature. The detector will therefore focus on the frequency band of the song that provides the higher signal-to-noise ratio (SNR) and bandwidth. Table II summarizes the whale song characteristics (dominant frequency and ICI duration) targeted by

the detector. Figure 2 shows that the selected whale species have dominant song frequencies in the “seismological” band, potentially well recorded by OBSs and hydroacoustic stations, and that the ICIs are very different from one species to another. As discussed later, the AMW is at the detection limit because of its high-frequency content, requiring a sampling rate of at least 200 sample/s, and its low level of emission.

### III. A NEW INTER CALL INTERVAL BASED DETECTOR

#### A. Process description

Our goal in developing a new whale detector is to create a robust and discriminant system, able to quickly and efficiently analyze large datasets. The originality of the approach presented in this paper is that it relies not on the spectral shape of the whale song—which can be partially

<figure><img src="image_4.png" /><figcaption>FIG. 2. Examples of time-frequency signatures of the four studied whale species songs recorded at OBS and hydroacoustic stations. They are presented over the same horizontal time window but with different vertical frequency scales for better illustration. (a) ABW recorded by MOSJ (2021/07/07) spectrogram parameters: FFT size = 256, overlap = 90%, FS = 100 Hz; (b) PBW-SWIO recorded by MOAJ (2021/05/25), spectrogram parameters: FFT size = 512, overlap = 95%, FS = 100 Hz; (c) AMW recorded by IF12C (2019/10/04), spectrogram parameters: FFT size = 256, overlap = 75%, FS = 250 Hz; (d) FW recorded by MAHY2 (2023/09/12) spectrogram parameters: FFT size = 128, overlap = 75%, FS = 100 Hz.</figcaption></figure>
hidden by noise and distorted by signal propagation effects—but rather on its repetitive nature, which is a prominent characteristic feature of whale songs. This choice is particularly relevant when dealing with inter-annual variations such as the long-term and seasonal decrease in the song frequency (Leroy et al., 2018a). The process targets non-ambiguous songs, meaning that the potential chorus of distant whales is not considered as useful signal. As the distance of these singers cannot be determined, such detections are difficult to interpret and therefore of limited relevance to our objective of establishing the species' seasonality within the area covered by our network. The four selected whale species emit different but very stereotyped songs, meaning that their patterns in the time-frequency domain are very similar from one song to another. In addition, the ICI is stable over time and characteristic of the considered species. The detector described in the following takes advantage of these two important features to detect the presence of a whale, simply by finding the rhythm of its song. Last but not least, the detector benefits from the fact that other sources of regular and stereotyped signals in the LF band are quite rare.

The detector relies on a signal processing operation called the cepstrogram, which highlights the time lags between repetitive events in a signal. This technique has previously been applied to identify echoes generated by multi-path propagation through the water column, in order to calculate the distance between a receiver on the seafloor and a moving ship at the sea surface (Trabattoni et al., 2019; Trabattoni et al., 2023) or a between a glider equipped with an acoustic vector sensor in the water column and a moving ship (Dréo et al., 2022). Considering a unique observation window of a time series (in our case a hydroacoustic instrument), the cepstrum is defined as the inverse Fourier transform of the window's logarithmic spectrum [Eq. (1)]. The cepstrogram is the display of the results of several successive time windows,

$$C(\tau) = F^{-1}(\log \|S(f)\|^2). \quad (1)$$

The unit of the cepstrum is the “quef frequency” (denoted as $q$ in the following tables and equation), expressed in seconds, which can be interpreted as the time difference between two similar signals in the considered time window. If several occurrences of a song repeat with a regular interval in this time window, the cepstrum will show a peak at the quef frequency corresponding to the ICI. If this rhythm continues across several time windows, a line will appear at this quef frequency on the cepstrogram. An important requirement of this processing approach is the window size [fast Fourier transform (FFT) size] that has to be long enough to observe at least two songs (that is to say, longer than the considered ICI). Depending on the species, this ICI can be up to 110 s (in the case of the PBW-SWIO), hence the signal must be pre-processed to avoid extreme FFT sizes.

As previously mentioned, the detection process does not focus on the song shape, meaning that it is not necessary to

listen to the whole frequency band of the whale song to perform an efficient detection. By reducing the size of the analyzed frequency band, we can reduce the amount of data to process. The first signal conditioning step is therefore to reduce the sample rate (FS) as much as possible, focusing only on the relevant frequency band for each studied species. Targeting modulated units rather than tonals, when possible, can also provide better results: for example, we choose the 22 Hz down-sweep of PBW-SWIO instead of its 33 Hz tonal.

Figure 3 illustrates the detection process of singing PBW-SWIO on a 45 mn sample recorded at the OBS station MOBJ. This example also presents the ability of the approach to work in the presence of several individuals, here two whales are singing simultaneously. We start from a raw time series that may contain whales songs, as seen on the spectrogram in Fig. 3(a). We first apply a bandpass filter adapted to the considered species' song to the continuous signal [waveform of Fig. 3(b)]. This isolates the relevant song unit and rejects useless spectral information (such as other potential species songs or boats signatures) that could interfere with the studied species' signal. After the bandpass filtering stage, we shift the signal to zero by multiplying it by a complex exponential and keep the real part of the result (Bingham et al., 1967), and then we down-sample it. In our example, the signal is shifted from the 20–26 Hz band (the main unit of the PBW-SWIO song) to the 0–6 Hz band [Fig. 3(b)], with a resulting FS of 12 Hz.

Table III lists the filter parameters used for each species, and the resulting FS after the frequency shifting/down-sampling process. If working over long periods, like decades, one should account for possible decrease in the song frequency band (Leroy et al., 2018a) and adapt the bandpass filter accordingly.

At this point of the process, for each recording station, we have one temporal waveform per species in which we want to check if a signal repeats at a rate consistent with the ICI of the considered species. The third step is therefore to calculate a long-term cepstrogram, from the spectrogram calculated using the species-adapted parameters (Table IV). To reduce noise, we overlap time windows and integrate (average) several spectra. We choose the overlap and the number of spectra to average in order to obtain 40–50 cepstra per hour. Figure 3(c) shows the calculated cepstrogram, for the PBW-SWIO. The quef frequency line at about 110 s corresponds to the species' ICI.

The fourth step is to automatically search for the existence of a rhythm corresponding to the considered species' ICI. In this step, we de-trend the cepstrogram, and then apply a peak-to-valley ratio (p2vr) analysis along the time axis. The peak value is the cepstrum level around the expected quef frequency (with boundaries $P_{qmin}$ and $P_{qmax}$), and the valley value is the cepstrum level outside of the peak boundaries (with boundaries $V_{qmin}$ and $V_{qmax}$). This step is described by Eq. (2). Figure 3(d) shows the mean cepstrum calculated over the 45 min of the example: one can see that it is affected by a linear noise trend (white line) that must be removed before applying the p2vr equation. The trend is
<figure><img src="image_5.png" /><figcaption>FIG. 3. Illustration of the detection process on a 45 mn sample recorded by the hydrophone channel of OBS MOBJ (same location as MOBH in Fig. 1), containing two distant singing PBW-SWIO. (a) Raw waveform and spectrogram showing PBW-SWIO series of songs. Red dashed lines represent the frequency shifting boundaries $F_{min}$ (20 Hz) and $F_{max}$ (26 Hz) used in the following of the process (see Table III). (b) Waveform and spectrogram after frequency shifting in the temporal domain. (c) Cepstrogram of the shifted signal using the parameters of Table IV showing quef frequency peak at around 110 s. (d) Mean cepstrum of the 45 mn cepstrogram. Green and red rectangles show, respectively, the peak and valley ranges used to compute the p2vr 2. The white line represents the linear trend to be removed before computing the p2vr. (e) De-trended mean cepstrum after removing the noise linear trend. (f) p2vr computed on the successive cepstra along the time axis.</figcaption></figure>

estimated using a linear regression over the quef frequency range from 10% to 90% of the maximum quef frequency. Figure 3(e) shows the resulting de-trended mean cepstrum, on which we apply the p2vr operation. It also shows the peak and valley boundaries. The values of peak and valley boundaries were empirically defined by processing a representative sample of data (at least one year), and are indicated for each species in Table V,

<table><caption>TABLE III. Frequency shifting parameters. $F_{min}$ and $F_{max}$ correspond to the bandpass filter boundaries.</caption><thead><tr><th>Species</th><th><i>F</i><sub>min</sub></th><th><i>F</i><sub>max</sub></th><th>New FS</th></tr></thead><tbody><tr><td>ABW</td><td>24</td><td>26</td><td>4</td></tr><tr><td>PBW-SWIO</td><td>20</td><td>26</td><td>12</td></tr><tr><td>AMW</td><td>100</td><td>117</td><td>34</td></tr><tr><td>FW</td><td>18</td><td>22</td><td>8</td></tr></tbody></table>

$$p2vr(t) = \frac{\left( \sum_{q=Pqmin}^{Pqmax} \right)^3}{\left( \frac{1}{2} * \left( \sum_{q=Vqmin}^{Pqmin} + \sum_{q=Pqmax}^{Vqmax} \right) - 1 \right)^3} \quad (2)$$

<table><caption>TABLE IV. ICI characteristics observed in the REVOSIMA dataset and spectrogram parameters chosen for each considered species. Are indicated the length of the FFT window (in points), the overlap (percentage of the FFT size) and the number of spectra used in the integration.</caption><thead><tr><th>Species</th><th>ICI (s)</th><th>FFT size</th><th>Rec. (%)</th><th>Int. (sp)</th></tr></thead><tbody><tr><td>ABW</td><td>67–77</td><td>1024</td><td>0.95</td><td>5</td></tr><tr><td>PBW-SWIO</td><td>100–120</td><td>4096</td><td>0.95</td><td>5</td></tr><tr><td>AMW</td><td>2.7–3.3</td><td>512</td><td>0.8</td><td>20</td></tr><tr><td>FW</td><td>9–11</td><td>512</td><td>0.75</td><td>5</td></tr></tbody></table>
<table><caption>TABLE V. Peak-to-valley ratio parameters: $V_{qmin}$ and $V_{qmax}$ represent the minimum and maximum quefrencies of the Valley range, respectively. $P_{qmin}$ and $P_{qmax}$ represent the minimum and maximum quefrencies of the peak range, respectively.</caption><thead><tr><th>Species</th><th>P<sub>qmin</sub></th><th>P<sub>qmax</sub></th><th>V<sub>qmin</sub></th><th>V<sub>qmax</sub></th></tr></thead><tbody><tr><td>ABW</td><td>67</td><td>77</td><td>57</td><td>87</td></tr><tr><td>PBW-SWIO</td><td>100</td><td>120</td><td>80</td><td>140</td></tr><tr><td>AMW</td><td>2.7</td><td>3.3</td><td>2</td><td>4</td></tr><tr><td>FW</td><td>9</td><td>11</td><td>8</td><td>12</td></tr></tbody></table>

Applying the p2vr equation provides the red curve shown in Fig. 3(f).

The fifth and final step of the process is to translate this p2vr curve into the presence or absence of the studied

species in a given time period. To align with our study's goal of establishing regional seasonality patterns of whale presence, we chose a rate of 1 point per hour. We average the output of the p2vr step [Fig. 3(f)] over 1 h time windows, and apply the species' threshold defined during the evaluation phase (see Sec. III C) to get hourly detections. We then group these hourly detections into daily detections [detection positive hour (DPH) per day] at each station. Similarly, the hourly station-based detections are combined into network-level detections, with one or more station detections considered to be a positive network detection.

Figure 4 summarizes the process, using a nine-month ABW analysis on data from hydrophone station MAHY4. The initial spectrogram [Fig. 4(a)] shows the complex signature of the whale songs as discontinuous horizontal bands

<figure><img src="image_6.png" /><figcaption>FIG. 4. Illustration of a long-term detection process for the period January to October 2023. (a) Spectrogram of MAHY4 hydrophone. The ABW signature is the thin horizontal line at 26 Hz, surrounded by the PBW-SWIO units at 22–23 Hz and 36 Hz. (b) Cepstrogram dedicated to ABW ICI detection. Quefrencies corresponding to the ICI are clearly visible between 65 and 80 s. (c) Output of the peak to valley ratio step (red curve) and detection threshold that will be applied (blue horizontal line) to extract hourly detections. (d) Histogram of the daily detection rate (DPH per day).</figcaption></figure>
within the broader soundscape: the ABW signature can be identified by the thin horizontal line at 26 Hz, surrounded by PBW-SWIO songs at 22–23 Hz and 36 Hz. The cepstrogram [Fig. 4(b)] is calculated using the parameters specific to the ABW: the quefrencies corresponding to the ICI are diffuse but clearly visible between 65 and 80 s. The p2vr red curve is then obtained by applying Eq. (2) to the cepstrogram, and downsampled to one measurement per hour [Fig. 4(c)]. The p2vr is then compared to the species’ threshold (blue hori- zontal line), and positive detections are converted into DPH per day [Fig. 4(d)].

## B. On the choice of the OBS channel

The REVOSIMA multi-sensor dataset represents an impressive volume of accumulated data to process: 240 000 h for each of the three major species (ABW,

PBW-SWIO, and FW), and 125 000 h for the AMW (less data because the 200 Hz minimum FS requirement was not met for several OBS acquisitions). Considering only OBS data, these amounts are, respectively, 141 000 h and 27 000 h. To reduce computation time, we only run our detection algo- rithm on one of the four OBS channels, opening the question of the choice of the channel. Figure 5 compares the PBW- SWIO detection curve for each of the four channels of the MOAH OBS station, using a 60-d time window (the curves are normalized for comparison). The curves representing the different OBS channels have very similar shapes, highlight- ing the power of OBSs to be used for monitoring purposes. The diagrams show, however, second-order differences, likely due to the fact that the hydrophone is omni-directional and measures the pressure scalar value, while each seismom- eter component provides a vectorial measure of the ground displacement along a given direction. Table VI presents

<figure><img src="image_7.png" /><figcaption>FIG. 5. The PBW-SWIO detection curves for the different channels of the OBS MOAH over 60 days. HDH is the hydrophone, EH3 is the seismometer verti- cal component and EH1 and EH2 are the seismometer horizontal components. The curves are normalized for comparison.</figcaption></figure>
<table><caption>TABLE VI. Correlation coefficients between the hydrophone and the three seismometer channels' detection curves. Z is the vertical component and H1 and H2 are the two horizontal components.</caption><thead><tr><th></th><th>H1</th><th>H2</th><th>Z</th></tr></thead><tbody><tr><td>Hydrophone</td><td>0.86</td><td>0.84</td><td>0.95</td></tr></tbody></table>

correlation coefficients between the hydrophone and the seismometer channels detection curves depicted in Fig. 5. A strong correlation (0.95) is observed between the hydrophone and the vertical channel, and a good but slightly lower correlation (0.84–0.86) is observed between the hydrophone and the horizontal seismological channels. We, therefore, selected the hydrophone channel in our study for its omnidirectional sensitivity and because it uses the same sensor technology as the water column instruments, facilitating the simultaneous use of different instruments among a heterogeneous network and simplifying the comparison and fusion of measurements and detection parameters. This analysis confirms the interesting potential for OBSs to perform whale detection in the absence of a colocated hydrophone

### C. Validation / performance

Validating and evaluating this new ICI detector performances requires comparing its automatic detections with a ground truth. For this purpose, an extensive manual annotation of a large sample of seismo-hydroacoustic continuous data was performed by two experienced analysts (Maëlle Torterotot and Julie Beesau, ENSTA Bretagne, Lab-STICC) using the web-based annotation platform APLOSE (Annotation Platform for Ocean Sound Explorers) (Dubus et al., 2025). This annotation work was carried out independently of the detector's development. The annotation stage consisted of flagging each whale species on one-hour-long segments (window size = 512, FFT size = 1024, overlap = 98% for FS = 240 Hz—adjusted for lower FS) of data taken from either ocean bottom seismic station (hydrophone channel) or a hydroacoustic sensor in the water column. A positive tag indicates the presence of a given species during a one-hour-long window and does not indicate the timing of individual songs, nor the possible number of individuals, which is beyond the goal of this study. Even in the presence of a unique song, the full hour was flagged as positive. The chorus generated by distant whales was not considered positive. In order to minimize the annotation time and to preserve a good seasonal representativeness of these annotations, 24 h of data were visually inspected every two days. The final sample represented 7528 h of data, distributed among the MOSA (152 h), MOSI (746 h), MOSO (840 h), IF14E (865 h), and MAHY2 (1484 h) stations. These annotations were then compared to the automatic detections at these same stations and during the same time windows with various detection thresholds.

The performance of the detector for the four species is illustrated by the receiver operating characteristic (ROC) presented in Fig. 6. They represent the true positive rate

(TPR) as a function of the false positive rate (FPR) obtained for thresholds ranging from the minimum to the maximum of the detection curve, considering the manual annotations as the ground truth. Setting the threshold to its minimum value implies that “everything is a detection.” As a consequence, the TPR is 1 (we do not miss any detections), but the FPR is also close to 1 (measurement noise is considered as a detection). On the other hand, setting the threshold to the maximum value leads to a TPR of 0 (we do not detect anything), and a FPR of 0 (we do not make any mistakes). In other words, detecting low- SNR signals involves lowering the threshold, which consequently increases the risk of false detections.

A detector has a good performance if the ROC curve reaches a TPR close to 1 for a FPR close to 0. Figure 6 provides the ROC curves obtained for the four considered species. In our study, they show a generally good performance, especially for the ABW, PBW-SWIO, and FW. The lower performance for the AMW is likely due to ship noise: Ships can be indeed detected at large distances (several hundreds of km; Trabattoni et al., 2023) and may generate signals in the 0–5 s quefrency band through their acoustic signatures and through multi-path echoes across the water column, impacting the overall quality of the AMW detections. In this study, we chose thresholds that limit the FPR to 5%, as indicated by the vertical red dashed line in Fig. 6.

### IV. RESULTS

Figures 7–10 show the number of positive detection hours per day for the four species. These results are obtained by aggregating all occurrences across all stations, such that any positive detection at any station is considered to be present for the corresponding hour. In this study, the austral seasons are defined as follows: summer includes December, January, and February; autumn includes March, April, and May; winter includes June, July, and August; and spring includes September, October, and November.

In 2019, exceptionally high seismo-volcanic activity in the area (Saurel et al., 2021; Lavayssière et al., 2022) generated a high noise level that interfered with the detection of ABW, PBW-SWIO, and FW. Therefore, the 2019 patterns of whale presences of these three species should be considered with care. The detection of the AMW species, involving a much higher frequency band than the three other species, appears not to be affected by this high seismo-volcanic noise in 2019.

In addition to these natural perturbing activities, two active seismic surveys were conducted from 2020/12/27 to 2021/02/04 and from 2021/02/20 to 2021/03/07 and are indicated by gray rectangles in Figs. 7–10. The strong and repetitive airgun signals during these periods impeded any reliable whale detection.

### A. The ABW (Balaenoptera musculus intermedia)

Figure 7 illustrates the detection results for the ABW, revealing a fluctuating seasonal pattern characterized by a presence in late autumn and winter.
<figure><img src="image_8.png" /><figcaption>FIG. 6. ROC curves for (a) ABW, (b) PBW-SWIO, (c) AMW, (d) FW. The red star represents the performance of the detector for a FPR of 5%.</figcaption></figure>

In 2020 and 2021, few detections are made, mainly in May and June. The widest presence pattern is observed in 2022, ranging from late April/early May to the end of August. Finally, in 2023 and 2024, ABW are primarily detected in June and July.

An unknown event (a probable remote seismic experiment) took place in late 2021, generating noise detected by the algorithm with the ABW parameters. These wrong-detections are marked by a red rectangle in Fig. 7.

## B. The PBW-SWIO (*Balaenoptera musculus brevicauda*)

Figure 8 reveals a strongly fluctuating seasonal pattern, underscoring the importance of long-term (several years) observations for obtaining a realistic overview of the whale presence in a given region. Each year presents two main periods of presence from mid-autumn and early winter and the end of spring to mid-summer with slight variations from one year to the other. In 2020–2021, the detection pattern extends

from May to June) and from November to early January. The acoustic activity significantly increases in the subsequent 2022–2023 period, characterized by both a broader pattern and an increased daily rate. The initial period extends from mid-March to mid-July, while the second period spans from November to February. In 2024, the detection pattern for the initial period started later than the previous years (mid-May).

## C. The AMW (*Balaenoptera bonaerensis*)

Figure 9 shows the seasonal presence of the AMW from 2019 to 2024. Despite a lack of data from March to October 2020, due to a too-low sampling frequency of the deployed sensors impeding the detection of the high-frequency songs of the AMW, it appears that they are present in the area during the second part of the year in winter and spring (June–July to early December).

Before the 2020 data gap, the detection was done using OBSs hydrophones; MAHY hydrophones became available after October 2020. Note that the detection of this species is
<figure><img src="image_9.png" /><figcaption>FIG. 7. ABW detection result. Detection threshold set for FPR = 5%. The year 2019, is not considered representative due to the presence of high seismic noise in the frequency band of the ABW song. Detection is considered impossible during the two successive seismic experiments (gray rectangles).</figcaption></figure>

not affected by seismic noise under 60 Hz, which affects the
detection of the other species.

D. The FW (Balaenoptera physalus)

Figure 10 shows the detection results for the FW spe-
cies. Excluding the data from 2019 that are likely perturbed

by the excessive seismological noise in the region, we
observe a large variability in the FW seasonal pattern over
the years. In 2020, this species was detected for only a few
days in August. It was entirely absent in 2021, then observed
from July to October 2022, and from July 2023 until
November. In 2024, FW song was detected for only a few
<figure><img src="image_10.png" /><figcaption>FIG. 8. PBW-SWIO detection result. Other factors and comments as in Fig. 7. Red rectangle represents wrong detections generated by an unknown event.</figcaption></figure>

sparse days in August and October the end of the last recovered MAHY deployment (end of October 2024).

Note that the detections visible from the end of December 2020 until March 2021 are identified as false detections corresponding to successive seismic experiments involving airgun shots covering the same frequency band at rates close to the FW song.

V. DISCUSSION

The analysis of acoustic records from OBSs (MAYOBS network) and water-column hydrophones (MAHY network) in the northern Mozambique Channel provides insights into the presence of large whales in the area. Initially deployed to monitor seismo-volcanic activity, these instruments are
<figure><img src="image_11.png" /><figcaption>FIG. 9. AMW detection result. Detection threshold set for FPR = 5%. Detection is considered impossible during the two successive seismic experiments (gray rectangle).</figcaption></figure>

used in the present study to provide information about the
biodiversity in the Mayotte Marine Natural Park over sev-
eral seasons. The PAM data presented here highlight the
value of data sharing among scientific communities and
emphasize the importance of long-term observations,

allowing for more accurate interpretations of whale presence
in a given area.

Preliminary data analyses revealed the presence of
numerous stereotyped signals emitted by several species,
subspecies, or subpopulations of mysticetes. The repetitive
<figure><img src="image_12.png" /><figcaption>FIG. 10. FW detection result. Other factors and comments as in Fig. 7.</figcaption></figure>

nature of their songs motivated the development of an algo-
rithm allowing an efficient detection of vocalizations of sev-
eral baleen whale species, that can rapidly process multiple
years of multi-site recordings. This new method proved to
be effective when compared to automatic or manual annota-
tions performed by bioacousticians.

The OBSs used in this study were equipped with both a
hydrophone and a three-component seismometer. For data
processing simplicity and to limit computational cost, we
chose to use the hydrophone for the detection task. The
hydrophone offers the advantage of being an omnidirec-
tional sensor and to share the same technology as the
MAHY hydrophones deployed in the water column, facilitating comparison of detection curves issued from ocean-bottom and water column measurements. Nevertheless, we have shown that if a hydrophone is unavailable, the same process can be applied to one of the three seismic components. In such cases, it may be necessary to evaluate ROC curves to accommodate this alternative configuration.

The Indian Ocean has been remarkably well equipped with autonomous recorders for the past 15 years, resulting in a significant improvement in our understanding of the migratory patterns of blue whales and FW species that were targeted during whaling in the first half of the 20th century (Torterotot et al., 2020). However, in addition to the long-term deployment of a hydrophone off the coasts of Nosy Be (from 2016 to 2021) (Cerchio et al., 2024), REVOSIMA is the first network maintained for such a long duration in this poorly sampled region of the northern Mozambique Channel, providing a new perspective on multi-year whale monitoring. This geographic region, despite dense human activities around the Comoros Islands, is highly frequented by various populations of large whales. The simple DPH metric used in this study should allow for efficient comparison of species seasonality using the numerous PAM studies conducted in other places of the Indian Ocean. The study presented here complements and expands our knowledge of the seasonal migration patterns of ABW, PBW-SWIO, and FW, and reveals a yearly variability that remains to be explained.

The wintering area of ABW encompasses a large part of the IO. During the austral winter, they likely spread out over a broader range in the IO where they remain for an extended period of time. ABW songs were detected at various locations in the central/western Indian Ocean, ranging from 26°S to 46°S and 037°E to 083°E, peak detections during the winter months of May to August (Torterotot et al., 2020; Shabangu et al., 2024). The same seasonal distribution was reported from OBSs deployed at various locations in the western Indian Ocean (16°S to 34°S, 048°E to 070°E) (Dréo et al., 2019). Off Diego Garcia (6°S to 7°S, 71°S to 72°E) few detections were identified from May to July (Stafford et al., 2004), and no detections were reported at the northernmost acoustic station deployed in the Indian Ocean, located at 4°S (Leroy et al., 2016). In the Mozambique Channel (13°S), seasonal acoustic detections have been reported in its southern part from March to October and in its northern part from May to September, suggesting that this area is part of an extended winter destination, potentially representing the northern portion of breeding area for this region (Cerchio et al., 2022; Cerchio et al., 2024).

Our results confirm that the northern Mozambique Channel is clearly frequented by ABW from early May to mid-July, indicating their presence during the austral autumn and winter. Unlike observations made in the greater Indian Ocean using the Observatoire HydroAcoustique de la Sismicité et de la Biodiversité de l'océan Indien (OHASIS-BIO) hydrophone network (Royer, 2009), the period of

ABW presence in the northern Mozambique Channel is well defined and covers a relatively short period of time, indicating that we are probably approaching the northern limit of their seasonal habitat. While some sensors in Antarctic or sub-Antarctic regions detect this species year-round, there is no detection in the Mayotte area for several months, suggesting that individuals are only passing through our studied area. As a reminder, the ABW is a protected species classified as "Critically Endangered" on the IUCN Red List of Threatened Species (IUCN, 2018), as the entire population was nearly brought to extinction during whaling. Therefore, knowledge of their seasonal and spatial distribution, as well as migratory movements, is essential and the present study clearly improves our capacity to map their presence.

Acoustic observations made using the OHASIS-BIO, Réunion Hotspot and Upper Mantle-Réunions Unterer Mantel (RHUM-RUM), and Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) seismological and hydroacoustic networks in the Indian Ocean (Samaran et al., 2010; Leroy et al., 2018b; Dréo et al., 2019; Torterotot et al., 2020) attest to the distribution of PBW-SWIO across the northwest of the SWIO. PBW-SWIO songs were detected at different acoustic stations ranging from 6°S to 56°S, 037°E to 074°E from Diego Garcia in the north (Stafford et al., 2004), off the Kerguelen plateau in the south (Torterotot et al., 2020), off Marion Island in the west (Shabangu et al., 2024) and southwest of Amsterdam island in the east (Torterotot et al., 2020). It is therefore not surprising to detect this subpopulation in the northern Mozambique Channel. The migration pattern of PBW-SWIO is still unclear and the two periods of occurrence when PBW-SWIO are detected in our study (also reported in previous monitoring (Cerchio et al., 2024) are unusual. For the major locations in the western Indian Ocean, PBW-SWIO acoustic presence is highly seasonal with one peak of occurrence from the early summer months to the end of autumn months at the southern sites (below 30°S) and only between autumn and winter months in the northern sites. These seasonal detections suggest movements of populations from the summer feeding ground to the winter breeding ground. Visual observations of PBW-SWIO have been reported on the Madagascar Plateau during the summer and past Soviet whaling catches south of Madagascar reinforce that the Madagascar Ridge region is recognized as summer feeding habitat (Best, 2003; Mikhalev et al., 2000). However, all acoustic stations reveal an absence of call during mid-winter to mid-spring (from August to October), which might indicate a change in PBW-SWIO vocal behavior or other population movements in a more western area. The numerous whale catches south of South Africa during the whaling era also support this hypothesis, but this cannot be confirmed due to a lack of PAM sensors in this region (Branch et al., 2007). Acoustic detections off Mayotte reveal a bimodal pattern, similar to the one observed at Nosy Be, located 200 km from our recording sites and encompassing three stations on Madagascar's west coasts (from 13°S to 23°S, 43°E to 48°E) (Cerchio et al., 2024). As in our results,
the first detection peak starts in mid-spring until the beginning of summer (from October to January) and a second in mid-autumn until mid-winter (from April to July). These pieces of information do not tell us where the PBW-SWIO reside during the full winter and full summer, but they reveal that they are present in the northern Mozambique Channel by mid-spring. This study thus completes the partial vision we had of the habitat and dynamics of PBW-SWIO in the Indian Ocean.

The FW have been detected during the feeding period close to Antarctica (Širović et al., 2004) and during the breeding period in the western Indian Ocean (Leroy et al., 2018b; Dréo et al., 2019). In the Mayotte area, contrary to ABW and PBW-SWIO, the acoustic behavior of FW appears to change with time. We found no detections in 2019 and 2021, and there were only a few days of detection in 2020 and 2024 (August). However, the species is clearly present and singing in 2022 and 2023, from July to October. The sensor network deployed east of our study area, along the length of the west coast of Madagascar (Cerchio et al., 2024) also sporadically detected this species at the very end of winter. The reason for this pattern variation is not yet fully understood. This relatively short period of time may indicate that we are probably approaching the northern limit of their seasonal habitat, at least for this FW population but because knowledge about FW populations is very sparse, this assumption remains difficult to assess.

AMW have been previously reported in this sector of the Indian Ocean, from early July to early December (Cerchio et al., 2018; Cerchio et al., 2022). Despite lower detection performance due to the influence of ship noise and a lack of data sampled at a sufficiently high rate in 2019 and 2020, we show that the species is present in the Mayotte area regularly from late June to early December, confirming the 2017 observations. This pattern is repeated in the other years even though the presence rate is less significant for these years. The reason for this lower presence may be due to the fact that only three or four MAHY sensors were available for AMW detection, and that the detection range of this species is likely lower than for the three other species. With an inter-sensor spacing on the order of 90 km, this array may not be dense enough to provide a clear view of the AMW presence in this area. Our observations confirm the complexity of the distribution of this polar species, whose seasonal patterns are potentially highly fluctuating from year to year. It appears to be present in Antarctica at the end of winter and beginning of spring (Dominello and Širović, 2016) and in the sub-Antarctic zone (Shabangu et al., 2020), but our study shows that it is also present at low latitudes in winter and spring. It is therefore difficult to define a clear migratory pattern at this stage.

Manual annotations of a large sample of data from the REVOSIMA network revealed the exceptional richness of the area, which hosts numerous species of baleen whales throughout the year. In addition to the four species described in this paper, we also observed signals corresponding to the Central Indian Ocean Pygmy Blue Whale (Leroy et al.,

2021), the Omura's whale (Cerchio et al., 2019; Leroy et al., 2021), the North West Indian Ocean Blue Whale (Cerchio et al., 2020b), the P-calls (Leroy et al., 2017), the unstereotyped D-calls (Barlow et al., 2023), and three unidentified songs. Further dedicated research is needed to establish the presence patterns of these other species through a deeper analysis of the REVOSIMA network data.

## VI. CONCLUSION

In this paper, we presented a PAM study of baleen whales based on hydrophones and seismometers initially deployed to monitor submarine seismo-volcanic activity offshore Mayotte in the Northern Mozambique Channel, as part of the REVOSIMA observatory. This allowed us to establish a multi-year overview of the presence of four large whale species in the poorly studied offshore Mayotte Island area. A new detector based on the automatic search for ICI in hydroacoustic and seismic data was developed to establish the whales' regional and seasonal presence patterns and was validated through extensive manual annotation as ground truthing. Our results show that the ABW (Balaenoptera musculus intermedia) is mainly present in this area in late autumn and winter with inter-annual variability. The PBW-SWIO (Balaenoptera musculus brevicauda) shows a bi-modal pattern: late autumn to early winter and mid-spring to mid-summer with significant variations in 2022 and 2023. The FW (Balaenoptera physalus) presence extends from July to October with a high variability, characterized by a complete absence of detections in 2021. Finally, AMW (Balaenoptera bonaerensis) are present from June to December. This work improves our understanding of the migratory patterns of the studied species in the southwest Indian Ocean basin. The new detector we developed shows encouraging performance, motivating further research involving this simple, fast, and computational light detection solution. This pilot study could now easily be applied to other ocean basins, opening perspectives for systematic global detections, particularly by analyzing the numerous existing and under-explored submarine seismo-hydroacoustic datasets. This work emphasizes the importance of exploiting marine geophysical data to explore other disciplinary fields and of combining geoscience and bioacoustic objectives for future multidisciplinary instrumental deployments.

## SUPPLEMENTARY MATERIAL

See the supplementary material for a full description of successive OBSs sub-networks utilized in this study. Characteristics and corresponding maps of deployed instruments are provided.

## ACKNOWLEDGMENTS

This work was co-funded by the French Biodiversity Agency and Boksound (Contract No. Boksound/OFB-22-1051), with the support of the project BRUIT-FM (ANR-21-CE01-0031-01) funded by the French ANR
(Agence Nationale de Recherche). It benefited from the REVOSIMA observatory (doi:10.18715/MAYOTTE.REVOSIMA) supported by CNRS/INSU, IPGP, IFREMER, and BRGM, which provided continuous seismic and hydroacoustic data. We thank the Geoscope network and the Epos-France archive systems (https://seismology.resif.fr) for providing data access. Many thanks to the numerous MAYOBS cruises (https://doi.org/10.18142/291) that involved technical and scientific colleagues from IPGP, CNRS, and BRGM and to the Flotte Océanographique Française (FOF). Thanks to Maëlle Torterotot and Julie Béesau for manually annotating a large subset of the data, and to the three anonymous reviewers who provided constructive comments. This study contributes to the IdEx Université de Paris ANR-18-IDEX-0001.

## AUTHOR DECLARATIONS

### Conflict of Interest

The authors declare that they have no conflict of interest.

## DATA AVAILABILITY

The data that support the findings of this study are available from the corresponding author upon reasonable request. MAYOBS data will be available at the French EPOS-FR seismological archive center under the FDSN code 1T (doi:10.15778/RESIF.1T2018). A database of available open access OBS networks is presented at the following link (https://www.bruit-fm.org/datasets.html) as a map and tables with all the necessary metadata.

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