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Animal songs can change within and between populations as the result of different evolutionary processes. When these processes include cultural transmission, the social learning of information or behaviours from conspecifics, songs can undergo rapid evolutions because cultural novelties can emerge more frequently than genetic mutations. Understanding these song variations over large temporal and spatial scales can provide insights into the patterns, drivers and limits of song evolution that can ultimately inform on the species’ capacity to adapt to rapidly changing acoustic environments. Here, we analysed changes in fin whale (Balaenoptera physalus) songs recorded over two decades across the central and eastern North Atlantic Ocean. We document a rapid replacement of song INIs (inter-note intervals) over just four singing seasons, that co-occurred with hybrid songs (with both INIs), and a clear geographic gradient in the occurrence of different song INIs during the transition period. We also found gradual changes in INIs and note frequencies over more than a decade with fin whales adopting song changes. These results provide evidence of vocal learning in fin whales and reveal patterns of song evolution that raise questions on the limits of song variation in this species.

Animal songs can change within and between populations as the result of different evolutionary processes. When these processes include cultural transmission, the social learning of information or behaviours from conspecifics, songs can undergo rapid evolutions because cultural novelties can emerge more frequently than genetic mutations. Understanding these song variations over large temporal and spatial scales can provide insights into the patterns, drivers and limits of song evolution that can ultimately inform on the species’ capacity to adapt to rapidly changing acoustic environments. Here, we analysed changes in fin whale (Balaenoptera physalus) songs recorded over two decades across the central and eastern North Atlantic Ocean. We document a rapid replacement of song INIs (inter-note intervals) over just four singing seasons, that co-occurred with hybrid songs (with both INIs), and a clear geographic gradient in the occurrence of different song INIs during the transition period. We also found gradual changes in INIs and note frequencies over more than a decade with fin whales adopting song changes. These results provide evidence of vocal learning in fin whales and reveal patterns of song evolution that raise questions on the limits of song variation in this species

Oceans cover more than 70 percent of the Earth's surface making it difficult and costly to deploy modern seismological instruments here. The rapidly expanding global network of submarine telecom cables offers tremendous possibilities for seismological monitoring using laser light. Recent pioneer studies have demonstrated earthquake detection using lasers in onland and submarine fiber optic cables. However, permanent strain at the seafloor has never before been measured directly as it happens. With this aim, we deployed a dedicated 6-km-long fiber optic strain cable, offshore Catania Sicily, in 2000 m water depth, and connected it to a 29-km long electro-optical cable for science use. We report here that deformation of the cable equivalent to a total elongation of 2.5 cm was observed over a 21-month period (from Oct. 2020 to Jul. 2022). Brillouin laser reflectometry observations over the first 10 months indicate significant strain (+25 to +40 microstrain) at two locations where the cable crosses an active strike-slip fault on the seafloor, with most of the change occurring between 19 and 21 Nov. 2020. The cause of the strain could be fault slip or seabottom currents. During the following 11 months, the strain amplitude increased to +45 to +55 microstrain, affecting a longer portion of the cable up to 500 m to either side of the first fault crossing. A sandbag experiment performed on the distal portion of the cable (3.2–6.0 km) starting Sept. 2021 demonstrates how the fiber optic cable deforms in response to an applied load and how the deformation signal partially dissipates over time due to the elastic properties of the cable. These preliminary results are highly encouraging for the use of BOTDR (Brillouin Optical Time Domain Reflectometry) laser reflectometry as a technique to detect strain at the seafloor in near real time and to monitor the structural health of submarine cables.

Monitoring the presence of blue whale (Balaenoptera musculus ssp.) stereotyped calls has been a widely used method to assess the different populations' distribution worldwide. All blue whale populations also produce nonstereotyped vocalizations, or D-calls. Here, we monitored the presence of D-calls in long-term records from a large hydrophone array located in the open southern Indian Ocean, using an automated detection method and manual validation of the detections. D-calls were detected at all sites of the array, which extends from 24°S to 56°S, but the majority of them were detected at the two southernmost sites. We observed a latitudinal shift in their seasonal occurrence, with more D-calls in the north during austral autumn and winter and more in the south during austral spring. The geographical occurrence of D-calls compared to that of songs indicates that blue whale acoustic behavior switches from a song-intensive and sparse-D-call emission in the north to song-moderate and more intensive D-call emissions in the south. These findings support the hypothesis that both call types are used for different purposes, as D-calls are mainly detected around foraging grounds and songs in wintering grounds. Monitoring both call types might therefore be a relevant acoustic indicator of blue whale behavior.

The OHASISBIO network of autonomous hydrophones moored in the SOFAR channel of the southern Indian Ocean detected many short-duration and energetic acoustic events originating at mid-oceanic spreading ridge axes. Their average duration of 10 to 15s makes them “impulsive” relative to the 80 to 200s-long T-waves generated by submarine earthquakes. Their frequency goes up to 50-60 Hz and their source level ranges from 200 to 233 dB. Most of them are detected as far as 3700 km away from their source where received levels can reach up to 108 dB. They are associated with seismic swarms located on ridge axes where new seafloor is produced. Along the Southwest Indian Ridge (ultra-slow spreading rate at 14 mm/yr), such impulsive events (IMP) were found near Novara transform fault (58˚20’E), with 69 IMP among 1109 seismic events, near Melville transform fault (61˚00’E), with 118 IMP among 27624 events, and at 67˚45’E, with 58 IMP among 4880 events. Along the Central Indian Ridge (slow spreading rate at 40 mm/yr), 711 IMP among 2015 seismic events were found near an active hydrothermal field at 23˚52’S, 69˚30’E. The short duration, high energy and frequency content of IMP suggest that their source is punctual and generates water-borne waves, thus not resulting from the conversion of seismic waves on the seafloor. IMP sources are often located at the foot or slopes of bathymetric highs and occur in clusters after large-magnitude earthquakes nearby. All these clues point to a volcanic origin, perhaps to hot lava and seawater interactions during magmatic eruptions, after dike-intrusion triggered earthquakes. This hypothesis calls for in-situ inspections with a submarine vehicle to be confirmed. Except for their locations, these events could be mistaken for man-made underwater explosions. These narrow and energetic events could also provide useful sources to investigate the effects of long-distance propagation of acoustic waves in the ocean or to monitor sound-speed or temperature

Hydroacoustic monitoring has become particularly efficient for studying the low‐magnitude seismicity occurring at mid‐ocean ridges. In 2016–2017, a seismic swarm occurred near the Melville transform fault of the ultraslow‐spreading Southwest Indian Ridge in the Indian Ocean. It comprised 258 events in the land‐based International Seismological Centre catalog, extending from June 2016 to March 2017. We examined this seismicity using hydroacoustic records from three to nine hydrophones moored in the southern Indian Ocean from the Hydroacoustic Observatory of Seismicity and Biodiversity in the Indian Ocean (OHASISBIO) temporary network and the International Monitoring System of the Comprehensive Nuclear‐Test‐Ban Treaty Organization. We detected 27,624 hydroacoustic events spanning 298 days (1 June 2016–25 March 2017) with lower localization and origin time errors. These include several energetic, short‐duration impulsive events, which we interpret as due to lava–water interactions on the seafloor. The spatio‐temporal distribution of all the detected events does not show clear tectonic mainshock–aftershock sequences and suggests a magmatic origin of the swarm with bursts of seismicity likely caused by dike emplacements.

This paper discusses the nature of the low-frequency seismo-acoustic waves generated by submarine earthquakes in the ocean. In a finite-depth homogeneous ocean over a semi-infinite solid crust, the derivation of the acoustic equations shows that waves propagate as modes. The waves propagating with the speed of sound in water (T waves) are preceded by waves with frequencies below the Airy phase. Furthermore, the group speeds of these modes are sensitive to the environmental setting. As a test, we applied the spectral finite-element code SPECFEM2D in a simplified configuration with an ocean layer overlaying a solid crust, and a seismic source below a Gaussian seamount surrounded by a flat seafloor. The simulations confirm that the generated T waves and their precursors follow the theoretical dispersion curves. A more realistic environment with a seismically-layered crust and a sound-speed profile in the ocean is then used to predict the expected acoustic modes. Although noisy, recordings by ocean bottom seismometers from the southwest Indian Ocean show T waves preceded by ultra-low frequency waves, which display two modes comparable to the theoretical ones. They are in good agreement for mode 1, whereas, for mode 0, a slight offset in frequency has yet to be explained.

The goal of this project is to use acoustic signatures to detect, classify, and count the calls of four acoustic populations of blue whales so that, ultimately, the conservation status of each population can be better assessed. We used manual annotations from 350 h of audio recordings from the underwater hydrophones in the Indian Ocean to build a deep learning model to detect, classify, and count the calls from four acoustic song types. The method we used was Siamese neural networks (SNN), a class of neural network architectures that are used to find the similarity of the inputs by comparing their feature vectors, finding that they outperformed the more widely used convolutional neural networks (CNN). Specifically, the SNN outperform a CNN with 2% accuracy improvement in population classification and 1.7%–6.4% accuracy improvement in call count estimation for each blue whale population. In addition, even though we treat the call count estimation problem as a classification task and encode the number of calls in each spectrogram as a categorical variable, SNN surprisingly learned the ordinal relationship among them. SNN are robust and are shown here to be an effective way to automatically mine large acoustic datasets for blue whale calls.

Blue whales were brought to the edge of extinction by commercial whaling in the twentieth century and their recovery rate in the Southern Hemisphere has been slow; they remain endangered. Blue whales, although the largest animals on Earth, are difficult to study in the Southern Hemisphere, thus their population structure, distribution and migration remain poorly known. Fortunately, blue whales produce powerful and stereotyped songs, which prove an effective clue for monitoring their different ‘acoustic populations.’ The DGD-Chagos song has been previously reported in the central Indian Ocean. A comparison of this song with the pygmy blue and Omura’s whale songs shows that the Chagos song are likely produced by a distinct previously unknown pygmy blue whale population. These songs are a large part of the underwater soundscape in the tropical Indian Ocean and have been so for nearly two decades. Seasonal differences in song detections among our six recording sites suggest that the Chagos whales migrate from the eastern to western central Indian Ocean, around the Chagos Archipelago, then further east, up to the north of Western Australia, and possibly further north, as far as Sri Lanka. The Indian Ocean holds a greater diversity of blue whale populations than thought previously.

Networks of autonomous underwater hydrophones (AUHs) are successfully employed for monitoring the low‐level seismicity of mid‐oceanic ridges by detecting hydroacoustic phases known as T waves. For a precise localization of a seismic event from T‐wave arrival times, all AUHs must be synchronized. To this effect, at the beginning of the experiment, all instrument clocks are set to GPS time, which serves as a common reference. However, during the experiment, the instrument clock often deviates from GPS time, and, because the amount of deviation differs from one instrument to another, the synchronization of the AUHs deteriorates, as the experiment progresses in time. Just after the instrument recovery, the time difference (called “skew”) between the instrument and the GPS clocks is measured. Assuming that the skew varies linearly with time, the correction of a time series for the clock drift is a straightforward procedure. When the final skew cannot be determined, correcting for the clock drift is not possible, and any event localization becomes problematic. In this article, we demonstrate that the clock‐drift rate (assumed to be time‐independent) can be successfully estimated from arrival times of teleseismic P waves, commonly recorded by AUHs. Using a ray‐tracing code, and accounting for the uncertainties in event hypocenter locations, origin times, and the Earth seismic‐velocity model, confidence intervals of the estimated drift rates are deduced. The validity of the approach is tested on data from two AUHs with known clock drifts. Our results show that a reliable estimation is possible for skews as small as 4 s per two years (corresponding to a drift rate of about 5.5 ms⋅day−1⁠). This method can also be applied to correct data of other recording instruments subject to internal‐clock drift, such as ocean‐bottom seismometers, when the skew is unknown.

In 2018, two earthquake swarms occurred along spreading ridge segments of the ultra-slow Southwest Indian Ridge (SWIR). The first swarm was located at the spreading-ridge intersection with the Novara Fracture Zone, comprising 231 events (ISC catalogue) and spanning over 6 days (10 July to 15 July). The second swarm was more of a cluster of events focusing near a discontinuity, 220 km west of the Rodrigues Triple Junction, composed of 92 events and spanning over 31 days (27 September to 27 October). We examined these two swarms using hydroacoustic records from the OHASISBIO network with seven to nine autonomous hydrophones moored on either side of the SWIR. We detected 1109 hydroacoustic events spanning over 13 days (6 July to 18 July) in the first swarm and 4880 events spanning over 33 days in the second swarm (25 September to 27 October). The number of events per day was larger, and the hydroacoustic magnitude (source level) was, on average, smaller during the second swarm than the first. The spatio-temporal distribution of events from both swarms indicates a magmatic origin initiated by dike intrusions and followed by a readjustment of stresses in the surrounding crust.

Precise underwater geodetic positioning remains a challenge. Measurements combining surface positioning (GNSS) with underwater acoustic positioning are generally performed from research vessels. Here we tested an alternative approach using a small Unmanned Surface Vehicle (USV) with a compact GNSS/Acoustic experimental set-up, easier to deploy, and more cost-effective. The positioning system included a GNSS receiver directly mounted above an Ultra Short Baseline (USBL) module integrated with an inertial system (INS) to correct for the USV motions. Different acquisition protocols, including box-in circles around transponders and two static positions of the USV, were tested. The experiment conducted in the shallow waters (40 m) of the Bay of Brest, France, provided a data set to derive the coordinates of individual transponders from two-way-travel times, and direction of arrival (DOA) of acoustic rays from the transponders to the USV. Using a least-squares inversion, we show that DOAs improve single transponder positioning both in box-in and static acquisitions. From a series of short positioning sessions (20 min) over 2 days, we achieved a repeatability of ~5 cm in the locations of the transponders. Post-processing of the GNSS data also significantly improved the two-way-travel times residuals compared to the real-time solution.

Seismic rays traveling just below the Moho provide insights into the thermal and compositional properties of the upper mantle and can be detected as Pn phases from regional earthquakes. Such phases are routinely identified in the continents, but in the oceans, detection of Pn phases is limited by a lack of long-term instrument deployments. We present estimates of upper-mantle velocity in the equatorial Atlantic Ocean from Pn arrivals beneath, and flanking, the Mid-Atlantic Ridge and across several transform faults. We analyzed waveforms from 50 earthquakes with magnitude Mw>3.5, recorded over 12 months in 2012–2013 by five autonomous hydrophones and a broadband seismograph located on the St. Peter and St. Paul archipelago. The resulting catalog of 152 ray paths allows us to resolve spatial variations in upper-mantle velocities, which are consistent with estimates from nearby wide-angle seismic experiments. We find relatively high velocities near the St. Paul transform system (∼8.4 km s−1), compared with lower ridge-parallel velocities (∼7.7 km s−1). Hence, this method is able to resolve ridge-transform scale velocity variations. Ray paths in the lithosphere younger than 10 Ma have mean velocities of 7.9±0.5 km s−1, which is slightly lower than those sampled in the lithosphere older than 20 Ma (8.1 km±0.3 s−1). There is no apparent systematic relationship between velocity and ray azimuth, which could be due to a thickened lithosphere or complex mantle upwelling, although uncertainties in our velocity estimates may obscure such patterns. We also do not find any correlation between Pn velocity and shear-wave speeds from the global SL2013sv model at depths <150 km. Our results demonstrate that data from long-term deployments of autonomous hydrophones can be used to obtain rare and insightful estimates of uppermost mantle velocities over hundreds of kilometers in otherwise inaccessible parts of the deep oceans.

Monitoring deformation on the seafloor is a major challenge for modern geodesy and a key to better understanding tectonic processes and assess related hazards. The extension of the geodetic networks offshore can be achieved by combining satellite positioning (GNSS) of a surface platform with acoustic ranging to seafloor transponders. This approach is called GNSS-Acoustic (GNSS-A). The scope of this work is to provide a tool to identify and quantify key points in the error budget of such experiment. For this purpose, we present a least-squares inversion method to determine the absolute position of a seafloor transponder array. Assuming the surface platform is accurately positioned by GNSS, the main observables are the two-way travel time in water between the transponders on the seafloor and the surface platform acoustic head. To better constrain transponder positions, we also consider the baseline lengths and the relative depth-differences between different pairs of them. We illustrate the usefulness of our forward modeling approach and least-square inversion by simulating different experimental protocols (i.e., platform trajectories, with or without information on the distance and depth between transponders). We find that the overall accuracy of a GNSS-A experiment is significantly improved with additional information about the relative depths of the instruments. Baseline lengths also improve the accuracy, but only when combined with depth differences. The codes in Python3 used in this article are freely available online.

Globally, the Indian Ocean appears to have the greatest blue whale (Balaenoptera musculus ssp) acoustic diversity, with at least four acoustic populations from three defined sub-species. To understand how these different populations use this region as habitat, we first need to characterize their spatial and seasonal distributions. Here, we build on previous passive acoustic monitoring studies and analyze a passive acoustic dataset spanning large temporal (9 years) and spatial (3 to 9 sites covering more than 12 million km2 of potential acoustic habitat in the southwest Indian Ocean) scales. A novel detection algorithm was employed to investigate the long-term presence of Antarctic blue whale and SEIO and SWIO pygmy blue whale calls. We found that Antarctic and pygmy blue whales have completely different spatial and seasonal distribution in the southern Indian Ocean. Antarctic blue whales are heard almost year-round on the whole array, with great inter-annual variability. The two pygmy blue whales share a highly stable seasonal acoustic presence, but their geographical distributions overlap at only a few central Indian Ocean sites. However, Antarctic and pygmy blue whale acoustic co-occurrence is common, especially in sub-tropical waters. These temporal and spatial distributions strengthen our understanding of seasonal occurrence and habitat use of distinct populations of blue whales in the southern Indian Ocean. A better comprehension of the ecology of Indian Ocean blue whales will require interdisciplinary studies to examine the drivers of the variability seen from passive acoustic studies.

The current known distribution of Omura’s whale includes the tropical and warm temperate waters of the western Pacific, Indian, and Atlantic Oceans. Evidence of their presence in the Atlantic Ocean is based on beach cast specimens found on the coasts of Mauritania (North Atlantic) and Northeastern Brazil (South Atlantic). The present study characterizes the occurrence of this species in the São Pedro and São Paulo Archipelago (SPSPA), on the mid-Atlantic ridge between South America and Africa, based on autonomous recording systems. Acoustic signals were similar, but not identical, to B. omurai vocalizations recorded off the coast of Madagascar. Although these signals were recorded for only 11 months, there are peaks in vocal activity between May and June in the vicinities of SPSPA, suggesting either a shift in distribution within the Atlantic equatorial waters or seasonality in the species’ vocal behavior in this region. The first acoustic records of Omura’s whales in the Equatorial Atlantic suggest that these animals may also use deep-water habitats, in addition to the shallow-water habitat use observed in other regions., A atual distribuição conhecida da baleia-de-Omura (Balaenoptera omurai) inclui as águas tropicais e temperadas quentes dos oceanos Pacífico ocidental, Índico e Atlântico. A evidência de sua presença no oceano Atlântico baseia-se em espécimes encalhados encontrados em praias da Mauritânia (Atlântico Norte) e do Nordeste do Brasil (Atlântico Sul). O presente estudo caracteriza a ocorrência dessa espécie no Arquipélago de São Pedro e São Paulo (ASPSP), localizado sobre a Dorsal Mesoatlântica entre a América do Sul e a África, com base em sistemas de monitoramento acústico passivo. Os sinais acústicos detectados foram similares, mas não idênticos, às vocalizações de B. omurai gravadas na costa de Madagascar. Embora esses sinais tenham sido registrados por apenas 11 meses, há picos na atividade vocal entre maio e junho nas proximidades do ASPSP, sugerindo uma mudança na distribuição da espécie nas águas equatoriais do Atlântico ou uma sazonalidade no comportamento vocal nessa região. Os primeiros registros acústicos das baleias-de-Omura no Atlântico Equatorial sugerem que esses animais também podem utilizar habitats de águas profundas, além de habitats de águas rasas como observado em outras regiões.