Sensing whales, storms, ships and earthquakes using an Arctic fibre optic cable.

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.

Source level and vocalizing depth estimation of two blue whale subspecies in the western Indian Ocean from single sensor observations.

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.

A performance comparison of tonal detectors for low-frequency vocalizations of Antarctic blue whales.

Extraction of tonal signals embedded in background noise is a crucial step before classification and separation of low-frequency sounds of baleen whales. This work reports results of comparing five tonal detectors, namely the instantaneous frequency estimator, YIN estimator, harmonic product spectrum, cost-function-based detector, and ridge detector. Comparisons, based on a low-frequency adaptation of the Silbido scoring feature, employ five metrics, which quantify the effectiveness of these detectors to retrieve tonal signals that have a wide range of signal to noise ratios (SNRs) and the quality of the detection results. Ground-truth data were generated by embedding 20 synthetic Antarctic blue whale (Balaenoptera musculus intermedia) calls in randomly extracted 30-min noise segments from a 79 h-library recorded by an Ocean Bottom Seismometer in the Indian Ocean during 2012-2013. Monte-Carlo simulations were performed using 20 trials per SNR, ranging from 0 dB to 15 dB. Overall, the tonal detection results show the superiority of the cost-function-based and the ridge detectors, over the other detectors, for all SNR values. More particularly, for lower SNRs (⩽3 dB), these two methods outperformed the other three with high recall, low fragmentation, and high coverage scores. For SNRs ⩾7 dB, the five methods performed similarly.

Passive stochastic matched filter for Antarctic blue whale call detection.

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 offline based on a priori knowledge of the signal second order statistics and simulated colored sea-noise. Then, the detection relies on online 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.