From the acoustic data acquired by the RHUM-RUM (Réunion Hotspot and Upper Mantle Réunions Unterer Mantel) Ocean Bottom Seismometer (OBS) network between October 2012 and November 2013, this study revealed baleen whale occurrence in the western Indian Ocean (IO). Low-frequency songs from three species (Antarctic Blue Whales, Pygmy Blue Whales and Fin Whales) as well as P-calls (or Spot-calls) from an unknown species were recorded on the dataset. The wide arrangement of the OBS network (2000 km × 2000 km) provided valuable information to draw seasonal patterns of occurrence and distribution all over the area. These species occurred sympatrically in the western IO, at least during austral autumn months emphasizing the importance of this region for these populations. This data set helped to refine the knowledge on their spatio-temporal distribution and complete the picture built by previous studies. A tighter sub-network of 8 OBSs deployed on the South West Indian Ridge provided ideal inter-sensor spacing for whale tracking. We demonstrated the capability of such array of detecting and tracking the three different whale species up to 50 km and for several hours. As a result and to understand the effect of acoustic wave propagation, songs from the tracking were described at a close and remote distance of the sensor. This work could also help to understand the local behavior of these species during austral autumn months in this area of the western Indian Ocean.
In a post-industrial whaling world, flagship and charismatic baleen whale species are indicators of the health of our oceans. However, traditional monitoring methods provide spatially and temporally undersampled data to evaluate and mitigate the impacts of increasing climatic and anthropogenic pressures for conservation. Here we present the first case of wildlife monitoring using distributed acoustic sensing (DAS). By repurposing the globally-available infrastructure of sub-sea telecommunication fiber optic (FO) cables, DAS can (1) record vocalizing baleen whales along a 120 km FO cable with a sensing point every 4 m, from a protected fjord area out to the open ocean; (2) estimate the 3D position of a vocalizing whale for animal density estimation; and (3) exploit whale non-stereotyped vocalizations to provide fully-passive conventional seismic records for subsurface exploration. This first example’s success in the Arctic suggests DAS’s potential for real-time and low-cost monitoring of whales worldwide with unprecedented coverage and spatial resolution.
Abstract-As a first step to Antarctic Blue Whale monitoring, a new method based on a passive application of the Stochastic Matched Filter (SMF) is developed. To perform Z-call detection in noisy environment, improvements on the classical SMF requirements are proposed. The signal's reference is adjusted, the background noise estimation is reevaluated to avoid operator's selection, and the time-dependent Signal to Noise Ratio (SNR) estimation is revised by time-frequency analysis. To highlight the SMF's robustness against noise, it is applied on a Ocean Bottom Seismometers hydrophone-recorded data and compared to the classical Matched Filter: the output's SNR is maximized and the false alarm drastically decreased.
Our oceans are critical to the health of our planet and its inhabitants. Increasing pressures on our marine environment are triggering an urgent need for continuous and comprehensive monitoring of the oceans and stressors, including anthropogenic activity. Current ocean observational systems are expensive and have limited temporal and spatial coverage. However, there exists a dense network of fibre-optic (FO) telecommunication cables, covering both deep ocean and coastal areas around the globe. FO cables have an untapped potential for advanced acoustic sensing that, with recent technological break-throughs, can now fill many gaps in quantitative ocean monitoring. Here we show for the first time that an advanced distributed acoustic sensing (DAS) interrogator can be used to capture a broad range of acoustic phenomena with unprecedented signal-to-noise ratios and distances. We have detected, tracked, and identified whales, storms, ships, and earthquakes. We live-streamed 250 TB of DAS data from Svalbard to mid-Norway via Uninett’s research network over 44 days; a first step towards real-time processing and distribution. Our findings demonstrate the potential for a global Earth-Ocean-Atmosphere-Space DAS monitoring network with multiple applications, e.g. marine mammal forecasting combined with ship tracking, to avoid ship strikes. By including automated processing and fusion with other remote-sensing data (automated identification systems, satellites, etc.), a low-cost ubiquitous real-time monitoring network with vastly improved coverage and resolution is within reach. We anticipate that this is a game-changer in establishing a global observatory for Ocean-Earth sciences that will mitigate current spatial sampling gaps. Our pilot test confirms the viability of this ‘cloud-observatory’ concept.
The source level (SL) and vocalizing source depth (SD) of individuals from two blue whale (BW) subspecies, an Antarctic blue whale (Balaenoptera musculus intermedia; ABW) and a Madagascar pygmy blue whale (Balaenoptera musculus brevicauda; MPBW) are estimated from a single bottom-mounted hydrophone in the western Indian Ocean. Stereotyped units (male) are automatically detected and the range is estimated from the time delay between the direct and lowest-order multiply-reflected acoustic paths (multipath-ranging). Allowing for geometric spreading and the Lloyd's mirror effect (range-, depth-, and frequency-dependent) SL and SD are estimated by minimizing the SL variance over a series of units from the same individual over time (and hence also range). The average estimated SL of 188.5 ± 2.1 dB re 1μPa measured between [25–30] Hz for the ABW and 176.8 ± 1.8 dB re. 1μPa measured between [22–27] Hz for the MPBW agree with values published for other geographical areas. Units were vocalized at estimated depths of 25.0 ± 3.7 and 32.7 ± 5.7 m for the ABW Unit A and C and, ≃20 m for the MPBW. The measurements show that these BW calls series are stereotyped in frequency, amplitude, and depth.
As a first step to Antarctic blue whale (ABW) monitoring using passive acoustics, a method based on the stochastic matched filter (SMF) is proposed. Derived from the matched filter (MF), this filter-based denoising method enhances stochastic signals embedded in an additive colored noise by maximizing its output signal to noise ratio (SNR). These assumptions are well adapted to the passive detection of ABW calls where emitted signals are modified by the unknown impulse response of the propagation channel. A filter bank is computed and stored based on knowledge of the signal second order statistics and simulated colored sea-noise. Then, the detection relies on background noise and SNR estimation, realized using time-frequency analysis. The SMF output is cross-correlated with the signal's reference (SMF + MF). Its performances are assessed on an ccean bottom seismometer-recorded ground truth dataset of 845 ABW calls, where the location of the whale is known. This dataset provides great SNR variations in diverse soundscapes. The SMF + MF performances are compared to the commonly used MF and to the Z-detector (a sub-space detector for ABW calls). Mostly, the benefits of the use of the SMF + MF are revealed on low signal to noise observations: in comparison to the MF with identical detection threshold, the false alarm rate drastically decreases while the detection rate stays high. Compared to the Z-detector, it allows the extension of the detection range of 30 km in presence of ship noise with equivalent false discovery rate.
Our oceans are critical to the health of our planet and its inhabitants. Increasing pressures on our marine environment are triggering an urgent need for continuous and comprehensive monitoring of the oceans and stressors, including anthropogenic activity. Current ocean observational systems are expensive and have limited temporal and spatial coverage. However, there exists a dense network of Fibre-Optic (FO) telecommunication cables, covering
Distributed acoustic sensing (DAS) leverages an ocean-bottom telecommunication fiber-optic cable into a densely sampled array of strain sensors. We demonstrate DAS applications to passive acoustic monitoring (PAM) through an experiment on a submarine fiber-optic cable in Longyearbyen, Svalbard, Norway. We show that DAS can measure many types of signals in the frequency range from 0.01 to 20 Hz generated by dynamics in the atmosphere, ocean, and solid earth. These include ocean-bottom loading pressure fluctuation of ocean surface waves generated by storms, winds and airflow turbulence, shear-wave resonances in low-velocity near-surface sediments, acoustic resonances in the water column, and propagating seismic waves. We show that DAS can record high-quality, low-frequency seismo-acoustic waves down to 0.01 Hz, which could be used for subsurface exploration. Using the shear-wave resonances recorded by DAS, we can determine the subsurface structure of near-surface sediments with low velocity. In addition, we can trace ocean swells back to their origins of distant storms as far as 13,000 km away from the cable. Because DAS is capable of seismo-acoustic monitoring with high spatial resolution of ~ 1 m over the cable of ~ 100 km long and with a broadband sensitivity down to 0.01 Hz on the low end, it can deliver great scientific value to ocean observation and geophysics community.
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