Marine Mammal Vocalizations Research Articles

Machine-learning based detection of marine mammal vocalizations in snapping-shrimp dominated ambient noise

Passive acoustics is an effective method for monitoring marine mammals, facilitating both detection and population estimation. In warm tropical waters, this technique encounters challenges due to the high persistent level of ambient impulsive noise originating from the snapping shrimp present throughout this region. This study presents the development and application of a neural-network based detector for marine-mammal vocalizations in long term acoustic data recorded by us at ten locations in Singapore waters. The detector’s performance is observed to be impeded by the high shrimp noise activity. To counteract this, we investigate several techniques to improve detection capabilities in shrimp noise including the use of simple nonlinear denoisers and a machine-learning based denoiser to suppress the noise level. These are shown to enhance the detection performance significantly. Using the robust detectors developed, we discuss some of the vocalizations detected over three years of our acoustic recorder deployments.

Soundscape characterization in the Central Arctic Ocean ecosystem (Svalbard) using acoustic indices

An increasingly warmer, less frozen Arctic is opening further to vessel traffic, transforming dominant ambient noise sources in the underwater soundscape. Chief sources may be shifting from wind to ship noise. Collecting data that help explain the changing composition of the soundscape may offer insights to guide regulation of noise in the ocean, furthering management and conservation efforts. Hydrophones connected to field recorders were used in this study to characterize the soundscape and to study marine mammal presence and ship noise. Hydrophones were deployed at 44 locations from a 13.8 m Ovni 445 sailing vessel between 9° E and 19° E and 69° N and 80° N in April 2023. Acoustic indices were utilized to assess soundscape composition. Vessel locations were confirmed using Automatic Identification System (AIS) marine traffic data. Wind, waves, and ice (geophony) dominated the soundscape’s acoustic signature in remote locations, while human-caused sounds (anthrophony) were significant near Arctic shipping routes, fishing areas, and in fjords. Marine mammal vocalizations were detected near the ice edge, at fjord mouths, and in fjords. This acoustic characterization study provides a glimpse into the sonic sources and balance of sounds in the soundscape at present, essential data as the region rapidly transforms.

Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal passive acoustic monitoring datasets.

Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches are best suited for characterizing marine acoustic environments. Here, we describe the application of multiple machine-learning techniques to the analysis of two PAM datasets. We combine pre-trained acoustic classification models (VGGish, NOAA and Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine acoustic environment. The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labeled sounds in the 8 kHz range; however, low- and high-frequency sounds could not be classified using this approach. The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow establishing a link between ecologically relevant information and PAM recordings at multiple scales, ranging from large-scale changes in the environment (i.e., changes in wind speed) to the identification of marine mammal species.

Acoustic twilight: A year‐long seafloor monitoring unveils phenological patterns in the abyssal soundscape

AbstractDespite the perpetual darkness of the deep sea, contrasting the sunlit epipelagic waters, many deep‐sea organisms exhibit rhythmic activities. To discern environmental cues that may serve as entrainment signals for deep‐sea organisms, this study investigated the soundscape of the abyssal plain south of Minamitorishima Island. Our analysis revealed clear diel and seasonal patterns, primarily driven by evening fish choruses and marine mammal vocalizations. These evening choruses, discernible above the background noise, likely serve as a circadian time cue for organisms capable of perceiving them within the aphotic depths. In addition, the frequent detection of whistles and echolocation clicks suggests this region functions as a foraging ground for marine mammals. These acoustic cues might guide organisms with auditory capabilities toward habitats rich in sinking food debris and whale falls. By elucidating the ecological processes shaping abyssal soundscape dynamics, these findings open new directions for further exploration in deep‐sea chronobiology.

Building bridges: Doug Cato’s remarkable career spanning underwater acoustics and animal bioacoustics

Doug Cato’s research includes pioneering studies of the underwater acoustic environment surrounding Australia and numerous contributions to our understanding of marine mammal acoustic behavior. Moreover, his relaxed avuncular manner allowed him to assemble collaborations spanning disparate disciplines while mentoring the next generation of acoustics researchers in Australia. Throughout his career, Doug built bridges between underwater acoustics and animal bioacoustics, and between Australian and international researchers. This talk highlights two international collaborations applying signal processing techniques to humpback whale songs and leopard seal calling bouts. These projects estimated the information entropy and structure of the sequences of sounds in these marine mammal vocalizations. Comparing sliding window entropy estimates with Markov models supports Doug’s earlier observations that Australian humpback song follows the hierarchical structure proposed by Payne & McVay for North Atlantic humpbacks [Miksis-Olds et al., JASA, 2008]. The same comparison of entropy estimators for leopard seal calling bouts reveals that most of the sequential structure of in these animals’ calls is captured by Markov models.

Passive ocean acoustic waveguide remote sensing of vocalization behavior and spatial distribution of diverse marine mammal species in the Norwegian and Barents Sea

Leveraging data acquired using a 160-element coherent hydrophone array deployed in the Norwegian and Barents Seas during spring 2014, and the passive ocean acoustic waveguide remote sensing (POAWRS) technique is employed to enable instantaneous wide-area monitoring of marine mammal vocalizations over expanses exceeding 100 km in diameter. The vocalization behavior of diverse marine mammal species including Fin, Humpback, Minke, Sperm, and Beluga whales are analyzed, quantifying time-frequency characteristics and call patterns from their vocalization signals present in high-resolution beamformed power spectrograms. Previously developed automatic detection and machine learning algorithms are employed for clustering and classification of underwater acoustic events, facilitating the subsequent manual audio-visual inspection of whale calls. Bearing-time trajectories of repetitive species-specific vocalizations signals are utilized to estimate locations of the whale calls and hence their temporo-spatial distributions. Through comparative studies, we assess the presence and abundance of marine mammal species-specific vocalization signals in three distinct coastal regions off Norway, namely, Alesund, Lofoten, and Northern Finmark. In addition, we investigate correspondences of marine mammal distributions with concurrently imaged instantaneous wide-area fish distributions from distinct fish species to elucidate potential predator-prey interactions and habitat preferences.

Underwater acoustic surveys in the eastern Clarion Clipperton Zone

A drifting, underwater sound recording system was developed and deployed for acoustic surveys of vocalizing marine mammals in the eastern Clarion Clipperton Zone (CCZ), Northern Tropical Pacific. The system was designed to position an Ocean Instruments Sound Trap 500HF recorder and hydrophone at a depth of 450 m. A depressor vane was inserted halfway along the drop line, with shock cord, to minimize hydrodynamic noise. The surface line included a Spotter V2 buoy that provided real-time tracking of the location of the surface line. The recording system was deployed ten times in the CCZ, between November 2020 and October 2021. Deployment length was between two and seven days. All deployments were set to continuously record underwater sound with a sample frequency of 288 kHz. Acoustic data were analyzed using the CHORUS software to manually review recordings, and an FM signal detector (in MATLAB) was used to detect potential biological sounds. A variety of marine mammals were detected, including Delphinidae species, Physeter macrocephalus (sperm whales); and clicks likely to be from Ziphiidae species (beaked whales). Beaked whales have been previously visually sighted in the CCZ, but to our knowledge these might be the first acoustic recordings of them in the CCZ.

Acoustic Characterization around the CalWave Wave Energy Converter

Sound generated by marine energy (ME) installations in the ocean environment remains a particular concern for environmental permitting despite the limited evidence showing low levels of ME sounds relative to other anthropogenic sounds. In an effort to increase understanding of potential environmental effects of marine energy projects and help reduce barriers to marine energy deployments, a new acoustic monitoring technology, the NoiseSpotter®, was developed and recently demonstrated around CalWave’s operational scaled xWave™ wave energy converter (WEC). The NoiseSpotter® improves upon traditional acoustic sensing techniques by use of a cost-effective, compact array of acoustic particle velocity sensors that characterizes, classifies, and provides accurate location information for anthropogenic and natural sounds in real time. Results are presented from co-deployments of NoiseSpotter® with the operational CalWave WEC that were conducted over a 9-day period in fall 2021 offshore of Scripps Research Pier in San Diego, California, USA. The multi-day deployment of the NoiseSpotter® at 100 m from the CalWave WEC revealed a rich library of sounds that include: Low-level (~95 dB re 1 µPa relative to an ambient noise floor between 80-90 dB re 1 µPa) sounds from the WEC associated with the deliberate actuation of mechanical components, Sounds from a hovering helicopter, Marine mammal vocalizations, and, Small boat engines. Sound levels from the WEC were placed within the context of ambient sounds, and reveal little deviation from the ambient soundscape. The azimuthal anisotropy of WEC sound was investigated via deployments along four cardinal directions around the WEC. While a noticeable increase was observed along the north-south orientation, the sound levels along all directions still showed little deviation relative to the ambient noise floor. Analysis of low-level WEC sounds in conjunction with exploratory machine learning techniques demonstrate the utility of directional acoustic sensing in distinguishing marine energy sounds from the myriad other sounds in the surrounding ocean environment.

New Marginal Spectrum Feature Information Views of Humpback Whale Vocalization Signals Using the EMD Analysis Methods.

Marginal spectrum (MS) feature information of humpback whale vocalization (HWV) signals is an interesting and significant research topic. Empirical mode decomposition (EMD) is a powerful time-frequency analysis tool for marine mammal vocalizations. In this paper, new MS feature innovation information of HWV signals was extracted using the EMD analysis method. Thirty-six HWV samples with a time duration of 17.2 ms were classified into Classes I, II, and III, which consisted of 15, 5, and 16 samples, respectively. The following ratios were evaluated: the average energy ratios of the 1 first intrinsic mode function (IMF1) and residual function (RF) to the referred total energy for the Class I samples; the average energy ratios of the IMF1, 2nd IMF (IMF2), and RF to the referred total energy for the Class II samples; the average energy ratios of the IMF1, 6th IMF (IMF6), and RF to the referred total energy for the Class III samples. These average energy ratios were all more than 10%. The average energy ratios of IMF1 to the referred total energy were 9.825%, 13.790%, 4.938%, 3.977%, and 3.32% in the 2980-3725, 3725-4470, 4470-5215, 10,430-11,175, and 11,175-11,920 Hz bands, respectively, in the Class I samples; 14.675% and 4.910% in the 745-1490 and 1490-2235 Hz bands, respectively, in the Class II samples; 12.0640%, 6.8850%, and 4.1040% in the 2980-3725, 3725-4470, and 11,175-11,920 Hz bands, respectively, in the Class III samples. The results of this study provide a better understanding, high resolution, and new innovative views on the information obtained from the MS features of the HWV signals.

EMD-Based Energy Spectrum Entropy Distribution Signal Detection Methods for Marine Mammal Vocalizations

To develop a passive acoustic monitoring system for diversity detection and thereby adapt to the challenges of a complex marine environment, this study harnesses the advantages of empirical mode decomposition in analyzing nonstationary signals and introduces energy characteristics analysis and entropy of information theory to detect marine mammal vocalizations. The proposed detection algorithm has five main steps: sampling, energy characteristics analysis, marginal frequency distribution, feature extraction, and detection, which involve four signal feature extraction and analysis algorithms: energy ratio distribution (ERD), energy spectrum distribution (ESD), energy spectrum entropy distribution (ESED), and concentrated energy spectrum entropy distribution (CESED). In an experiment on 500 sampled signals (blue whale vocalizations), in the competent intrinsic mode function (IMF2) signal feature extraction function distribution of ERD, ESD, ESED, and CESED, the areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were 0.4621, 0.6162, 0.3894, and 0.8979, respectively; the Accuracy scores were 49.90%, 60.40%, 47.50%, and 80.84%, respectively; the Precision scores were 31.19%, 44.89%, 29.44%, and 68.20%, respectively; the Recall scores were 42.83%, 57.71%, 36.00%, and 84.57%, respectively; and the F1 scores were 37.41%, 50.50%, 32.39%, and 75.51%, respectively, based on the threshold of the optimal estimated results. It is clear that the CESED detector outperforms the other three detectors in signal detection and achieves efficient sound detection of marine mammals.

Acoustic indices respond to specific marine mammal vocalizations and sources of anthropogenic noise

Using passive acoustic methods for biodiversity conservation and effective ecosystem monitoring is hindered by laborious, human-mediated processes of accurately identifying biologic and anthropogenic sounds within large datasets. Soundscape ecology provides a potential means of addressing this need through the use of automated acoustic-based biodiversity indices, which show promise in representing biodiversity in terrestrial environments. However, the direct relationship between specific underwater sounds and acoustic index measurements are largely unexplored. Using passive acoustic data collected from three broadband hydrophones within the Ocean Observatories Initiative’s cabled arrays in the Pacific northwest, we identified periods of vocalizing marine mammals and sources of anthropogenic noise. Automated calculations of seven acoustic indices were compared across biologic and anthropogenic sound type and call parameters. Although several index measurements did not vary significantly, the Acoustic Complexity Index (ACI) measurements increased in response to echolocation clicks from sperm whales (Physeter macrocephalus) and burst pulses originating from unidentified delphinid species. Measurements of the Bioacoustic Index (BI) decreased dramatically in response to sperm whale echolocation clicks, a more obvious trend when loud clicks were parsed from moderate and quiet clicks. Correlations coefficient and confidence interval values between ACI and BI measurements and call characteristics from sperm whales indicate a moderate to strong relationship, which was not found in correlations with delphinid calls. A generalized linear mixed-effect model indicated multiple species and sound types contribute significantly to the variation of several index measurements. Noise generated by passing ships consistently resulted in decreased values for the Normalized Difference Soundscape Index (NDSI) and Total Entropy (H) as compared to quiet periods and periods with vocalizing marine mammals. These findings provide information on the relationship between several acoustic indices and specific underwater sounds produced by marine mammals and anthropogenic sources. This ground-truthing endeavor expands the understanding of acoustic indices and their potential use as a tool for conservation and ecosystem health management purposes.

Recent occurrence of marine mammals and sea turtles off Angola and first report of right whales since the whaling era

AbstractMarine megafauna occurrence was recorded in the deep-sea region bordering the abyssal plain ~400 km north-west of Luanda, Angola. The survey took place during an Environmental Baseline Study (EBS), prior to drilling exploration activities, with the goal of characterizing the habitat and biodiversity of the region. Offshore shipboard surveys were conducted during September 2018 in water depths ranging from 2350–3850 m. We recorded daytime sightings of marine mammals and sea turtles and at night made audio recordings using passive acoustic monitoring (PAM) methods focused on capturing the sounds of vocalizing marine mammals. A variety of species were visually detected, including the humpback whale (Megaptera novaeangliae), sperm whale (Physeter macrocephalus), common dolphin (Delphinusspp.), striped dolphin (Stenella coeruleoalba), Atlantic spotted dolphin (S. frontalis), and olive ridley turtle (Lepidochelys olivacea). Acoustic click bouts similar to those made by several odontocete species, possibly including beaked whales, were recorded within the 25–48 kHz range. The humpback whale was the most frequently sighted species, accounting for 56% of mammal sightings, indicating a potential far offshore migratory habitat in this region. Most notably, right whales (probableEubalaena australis) were visually observed. This is the first confirmed record of right whales in Angolan waters since the early 1900s. As development expands in this offshore region, these data can usefully inform future monitoring and mitigation strategies focused on minimizing impacts to wildlife.

Real-time instantaneous wide-area ocean acoustic monitoring in the shallow Great South Channel and deep ocean south of Rhode Island with Northeastern University coherent hydrophone array

A large-aperture 160-element coherent hydrophone array system, developed in-house at Northeastern University, was deployed and tested at sea in shallow water Great South Channel and deep ocean south of Rhode Island during September 2021. The system implements the passive ocean acoustic waveguide remote sensing technology for real-time instantaneous wide area ocean monitoring. The array data sampled at 100 kHz per element were processed in real-time at sea to provide beamformed spectrogram imagery in multiple frequency subbands from 10 Hz to 50 kHz and spanning 360 degree horizontal azimuths about the receiver array. A wide variety of acoustic detections were made in real time, including marine mammal vocalizations and motion-related signals, fish grunts and other fish-generated signals, tonal and broadband signals from distant ships and own tow ship. Real-time detections of marine mammal vocalizations include fin whale 20 Hz pulses, humpback whale songs and social sounds, minke whale buzz sequences, sperm whale echolocation and social clicks, dolphin whistles and echolocation clicks, as well as calls from unidentified baleen and toothed whale species. A database of automatically detected signals with bearing-time information along with extracted time-frequency characteristics was generated in real-time at sea.

Development of deep neural networks for marine mammal call detection using an open-source, user friendly tool

As the collection of large acoustic datasets used to monitor marine mammals increases, so too does the need for expedited and reliable detection of accurately classified bioacoustic signals. Deep learning methods of detection and classification are increasingly proposed as a means of addressing this processing need. These image recognition and classification methods include the use of a neural networks that independently determine important features of bioacoustic signals from spectrograms. Recent marine mammal call detection studies report consistent performance even when used with datasets that were not included in the network training. We present here the use of DeepSqueak, a novel open-source tool originally developed to detect and classify ultrasonic vocalizations from rodents in a low-noise, laboratory setting. We have trained networks in DeepSqueak to detect marine mammal vocalizations in comparatively noisy, natural acoustic environments. DeepSqueak utilizes a regional convolutional neural network architecture within an intuitive graphical user interface that provides automated detection results independent of acoustician expertise. Using passive acoustic data from two hydrophones on the Ocean Observatories Initiative’s Coastal Endurance Array, we developed networks for humpback whales, delphinids, and fin whales. We report performance and limitations for use of this detection method for each species.

An overview of ambient sound using Ocean Observatories Initiative hydrophones.

The Ocean Observatories Initiative (OOI) sensor network provides a unique opportunity to study ambient sound in the north-east Pacific Ocean. The OOI sensor network has five low frequency (Fs = 200 Hz) and six broadband (Fs = 64 kHz) hydrophones that have been recording ambient sound since 2015. In this paper, we analyze acoustic data from 2015 to 2020 to identify prominent features that are present in the OOI acoustic dataset. Notable features in the acoustic dataset that are highlighted in this paper include volcanic and seismic activity, rain and wind noise, marine mammal vocalizations, and anthropogenic sound, such as shipping noise. For all low frequency hydrophones and four of the six broadband hydrophones, we will present long-term spectrograms, median time-series trends for different spectral bands, and different statistical metrics about the acoustic environment. We find that 6-yr acoustic trends vary, depending on the location of the hydrophone and the spectral band that is observed. Over the course of six years, increases in spectral levels are seen in some locations and spectral bands, while decreases are seen in other locations and spectral bands. Last, we discuss future areas of research to which the OOI dataset lends itself.

Residual Learning for Marine Mammal Classification

The passive acoustic monitoring of marine mammals is an essential tool for researchers tracking the populations of individual species in threatened environments. Given the large quantity of audio data generated by passive acoustic arrays, it is desirable to automate the process of identifying marine mammals present in the recordings. Utilizing acoustic data from the William A. Watkins Marine Mammal Sounds Database, we present an approach using residual learning networks (ResNets) for classifying the marine mammal vocalizations of up to 32 species. We first determine the optimal methods for converting acoustic recordings into discrete spectrograms suitable for input into neural networks. A series of configurations for spectrographic window functions, preprocessing augmentations, and multi-channel spectrogram generation are examined. Each configuration’s spectrographic output is used to train a residual learning network. Its multi-class classification performance is ranked using the harmonic mean of precision and recall to calculate a weighted F1-score. Configurations specifying 512×256 spectrograms created with a Hann window of 1024 and utilizing horizontal roll demonstrate superior performance. We use the top-performing configurations to generate training data as input for a series of single and multi-channel residual neural networks. These networks are trained to high precision before evaluating their multi-class classification performance. A single-channel network performed the best, obtaining an F1-score of 0.867 with an AUC of 0.9281 on a 32-class classification task. Our multi-channel configuration obtained an F1-score of 0.846 with an AUC of 0.9169. While we demonstrate that networks may learn more information from multi-channel spectrographic inputs, we find that single-channel spectrograms offer superior classification performance overall.

Tagging ocean soundscapes with machine learning and topological approaches

With a multitude of sources simultaneously contributing to the ambient ocean soundscape, it is important to disaggregate noise source contributions for better monitoring and assessing noise impacts. Our work adopts a modular set of neural networks and topological pipelines to analyze multiple representations of time-series samples of the ambient ocean noise. Specifically, a convolutional neural network (CNN) is used to broadly tag the presence of local shipping and marine mammal vocalizations from spectrogram representations. Multiple approaches are investigated using topological data analysis to predict a class label of the ship hull type or specific marine mammal species using the audio data as input. The datasets used in training, testing, and validation comprise hydrophone recordings across varying sensors and ocean environments to address the wide variation in ambient background noise and propagation paths. Algorithm performance is characterized by classification accuracy on labeled data. This approach demonstrates a generalized tagging cabilitility of the presence of marine mammals and local shipping activity.

Evaluating empirical and variational mode decomposition capabilities on Risso’s and Pacific white-sided dolphin pulsed signals in a sparse and noisy dataset

Tracking marine species is critical for conversation efforts, therefore automatic detectors and classifiers play a significant role to facilitate such efforts. Previous work demonstrated the efficacy of the Empirical Mode Decomposition (EMD) classification capabilities in differentiating marine mammal vocalizations. However, EMD failed to separate Risso’s and Pacific white-sided dolphin pulsed signals due to their high spectral similarity and limitations of EMD filtering response. The Variational Mode Decomposition (VMD), a more advanced version of EMD, paired with an automated detector, provided a promising tool to tell such dolphins apart with accuracy up to 81.3% even under severe channel conditions. This level of accuracy on a sparse dataset that does not contain whistles is important for automatically classifying pulsed signals from dolphins that do not whistle, live in noisy environments, and/or are recorded in datasets with low duty cycles. Because many datasets collected to date in the Arctic Ocean are sparse due to conserving battery power over long deployments, the VMD method can help add to our ability to track dolphins using just their pulsed signals as they expand northward with warming waters.

Boundary Pass underwater listening station cabled hydrophone array system

An advanced cabled real-time underwater listening station was deployed in May 2020 in Boundary Pass, British Columbia, Canada, to measure underwater radiated noise of commercial vessels and to detect and track vocalizing marine mammals. This system was sponsored by Transport Canada and is operated jointly by Vancouver Fraser Port Authority and JASCO Applied Sciences (Canada) Ltd. The system consists of two synchronized tetrahedral hydrophone arrays, each with 1.65 m hydrophone spacings, deployed 300 m apart on the seabed in 190 m water depth. The hydrophone frames are connected to shore by two 2.8 km fiber-optic cables, and also support ADCP and CTD sensors, sound projectors for calibrations, and video cameras. The Boundary Pass location was chosen because of its relatively deep water and because the major inbound and outbound commercial shipping lanes leading to Vancouver become narrow and adjacent here, allowing acoustic measurements of ships passing in both directions. In this presentation we will discuss the system technical design and sensor specifications. We will also discuss its deployment and the data processing and presentation systems, with a brief overview of the substantial measurements obtained in the first full year of operation.

Ocean-Bottom Seismology of Glacial Earthquakes: The Concept, Lessons Learned, and Mind the Sediments

Abstract About 70% of Earth’s surface is covered by ocean, for which seismic observations are challenging. Seafloor seismology overcame this fundamental difficulty and radically transformed the earth sciences, as it expanded the coverage of seismic networks and revealed otherwise inaccessible features. At the same time, there has been a recent increase in the number of studies on cryoseismology. These have yielded multiple discoveries but are limited primarily to land and ice-surface receivers. Near ice calving fronts, such surface stations are noisy, primarily due to crevassing and wind, are hazardous to maintain, and can be lost due to iceberg calving. To circumvent these issues, we have applied ocean-bottom seismology to the calving front of a tidewater glacier in northwest Greenland. We present details of this experiment, and describe the technical challenges, noise analysis, and examples of recorded data. This includes tide-modulated seismicity with thousands of icequakes per day and the first near-source (∼200–640 m) underwater record of a major kilometer-scale calving event in Greenland, which generated a glacial earthquake that was detectable ∼420 km away. We also identified a decrease in bottom-water temperature, presumably due to modified water stratification driven by extreme Greenland glacial melting, at the end of July 2019. Importantly, we identify glacial sediments as the key reason for the anomalously long (∼9.7 hr) delay in the sensor release from the fjord seafloor. Our study demonstrates a methodology to undertake innovative, interdisciplinary, near-source studies on glacier basal sliding, calving, and marine-mammal vocalizations.