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14 results for “underwater acoustics”
An Acoustic and Optical Dataset for the Perception of Underwater Unexploded Ordnance (UXO)
<p>We present a dataset for acoustic and optical sensing of unexploded ordnance (UXO) underwater.</p> <p>UXO in the sea pose an environmental problem and a challenge for the growing offshore economy. It is best practice to perform the recovery of ammunition without explosions to protect anthropogenic structures and marine mammals. During explosive ordnance disposal (EOD), experts often rely on optical images. However, visibility underwater may be limited in harbor areas, after storm events or in waters with very mobile sediments. Thus, visual inspection is not always possible. EOD experts therefore use high-frequency sonars with large vertical apertures like the ARIS Explorer 3000 for acoustic imaging. While efforts have been made to use the available information for 3D reconstruction, existing solutions can be limited to predefined motion patterns. </p> <p>The topic is inherently sensitive, and most of the data is acquired by and for private companies and not made available to the public, which impedes research in this area. Additionally, in-situ data often lacks sufficient pose information. To facilitate further research, we created a validation dataset that was recorded in a controlled experimental environment. It has the following properties:</p> <ul> <li>Close to 100 recordings of 3 different UXO.</li> <li>More than 74000 matched and annotated imaging sonar and camera frames.</li> <li>UXO ground truths in the form of photogrammetric 3D models.</li> <li>Precise position and attitude sensor data with respect to the targets.</li> <li>Realistic motion trajectories achievable in non-experimental environments.</li> </ul> <p>This dataset allows quantitative analysis with different algorithms. 3D models and trajectories can be compared against each other to evaluate different solutions.</p> <p> </p> <p><strong>The accompanying paper is:</strong></p> <blockquote> <p>@INPROCEEDINGS{dahn2024uxo,<br> author={Dahn, Nikolas and Firvida, Miguel Bande and Sharma, Proneet and Christensen, Leif and Geisle, Oliver and Mohrmann, Jochen and Frey, Torsten and Kumar Sanghamreddy, Prithvi and Kirchner, Frank},<br> booktitle={OCEANS 2024 - Halifax}, <br> title={An Acoustic and Optical Dataset for the Perception of Underwater Unexploded Ordnance (UXO)}, <br> year={2024},<br> doi={10.1109/OCEANS55160.2024.10754316}}</p> </blockquote> <p>The paper is available on <a href="https://www.researchgate.net/publication/386124306_An_Acoustic_and_Optical_Dataset_for_the_Perception_of_Underwater_Unexploded_Ordnance_UXO">researchgate</a>.</p> <p> </p> <p><strong>Notes:</strong></p> <ul> <li>Labels have been (unfortunately) generated for the SD camera frames. To get the correct coordinates on the included FHD images, multiply all coordinates by 3.</li> </ul> <p> </p> <p><strong>Files</strong>:</p> <ul> <li>data_export_recordings.7z: main dataset</li> <li>data_export_polar.7z: contains only the polar-transformed sonar frames</li> <li>data_export_3dmodels.7z: 3d models of the UXO</li> <li>data_processed.7z: extracted and cut unmatched raw data</li> </ul>
Test experiments with distributed acoustic sensing and hydrophone arrays for locating underwater sounds.
<p>Whales and dolphins rely on sound for navigation and communication, making them an intriguing subject for studying language evolution. Traditional hydrophone arrays have been used to record their acoustic behavior, but optical fibers have emerged as a promising alternative. This study explores the use of distributed acoustic sensing (DAS), a technique that detects local stress in optical fibers, for underwater sound recording. An experiment was conducted in Lake Zurich, where a fiber-optic cable and a self-made hydrophone array were deployed. A test signal was broadcasted at various locations, and the resulting data was synchronized and consolidated into files. Analysis revealed distinct frequency responses in the DAS channels and provided insights into sound propagation in the lake. Challenges related to cable sensitivity, sample rate, and broadcast fidelity were identified. This dataset serves as a valuable resource for advancing acoustic sensing techniques in underwater environments, especially for studying marine mammal vocal behavior.</p>
Palmer Deep Underwater Acoustic Mooring Deployments 2021-2024
Marine mammals play a crucial role in the ocean ecosystem, yet monitoring their occurrence, distribution, and abundance poses a challenge in remote areas, such as the Antarctic. Traditionally, human observers have conducted visual surveys to detect marine mammals, relying on the animals' need to surface periodically for air. This method is often costly, requiring a large team of observers and the use of ships or aircraft. Additionally, visual surveys are limited by weather and sighting conditions, such as fog, rain, heavy seas, and darkness. Despite their expense, visual surveys are often inefficient for continuous real-time monitoring of marine mammal presence. However, they remain essential for tasks like photo identification, health assessment, and abundance estimation for many species. In recent decades, passive acoustic recorders have become extremely popular for detecting vocally active marine mammals, as they can operate continuously for periods of months to years. This enables a passive method of observation of marine mammals during periods where visual surveys are untenable. We developed a long term mooring system equipped with an underwater acoustic recorder that is capable of recording at 60% for up to 200 days at a time. Since 2022, we have been maintaining these moorings to provide for annual acoustic coverage, and will continue to under the Palmer LTER. This data can be used to compose the underwater soundscape of the region, and a general assessment of year-round acoustic presence of sound producing animals. Further analysis could examine the impacts of inter- and intra- seasonal environmental conditions (e.g., sea ice advance and retreat, sea ice extent, storminess) on the phenology of acoustic presence in the region.
Figure 4 in Underwater acoustic behavior of bearded seals (Erignathus barbatus) in the northeastern Chukchi Sea, 2007-2010
Figure 4. Monthly variation of bearded seal call types across all recording stations among three different years (2008, 2009, and 2010). Bar shading indicate year. Error bars are + SE. See Table S2 for call type definitions.
Figure 7 in Underwater acoustic behavior of bearded seals (Erignathus barbatus) in the northeastern Chukchi Sea, 2007-2010
Figure 7. Proportion of bearded seal calls for all overwinter 2007–2008 (A) and overwinter 2008–2009 (B) recording stations (samples were 10 min long and recorded every 10th day).
Figure 6 in Underwater acoustic behavior of bearded seals (Erignathus barbatus) in the northeastern Chukchi Sea, 2007-2010
Figure 6. Diel pattern of bearded seal calls combined across all summer 2009 (A) and summer 2010 (B) recording stations. Vertical bars represent the percentage of time bins that have calls present in each 1 h time bin. Horizontal bars indicate periods of daylight (white), periods of darkness (dark gray), and periods of daylight or darkness depending on the time of recording (light gray).
Figure 2 in Underwater acoustic behavior of bearded seals (Erignathus barbatus) in the northeastern Chukchi Sea, 2007-2010
Figure 2. Bearded seal calls representing the major call types found in the Chukchi Sea dataset. Call types are modified from Risch et al. (2007). See Table 2 for call type definitions.
Acoustic Current Profiler data from Multi-Year (2020-2022) Autonomous Underwater Glider Surveys in the Anegada Passage
<p>This dataset contains glider acoustic doppler profiler observations from four glider deployments in the Anegada Passage region from 2020-2022. The 2020-2021 data are from a Nortek AD2CP and the 2022 data are from a Teledyne RDI Pathfinder. The RDI Pathfinder .PD0 files can be read directly in the code that is used for this analysis. The Nortek AD2CP .ad2cp files are processed using Nortek's MIDAS software to generate NetCDFs. <br> </p>
Example 3D Underwater Acoustic Pressure Data Sampled Over 24 Hours
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Carcharhinus amblyrhynchos and Carcharhinus albimarginatus underwater acoustic telemetry data - Chagos Archipelago 2014-2018
<p>A wide array of technologies are available for gaining insight into the movement of wild aquatic animals. Although acoustic telemetry may lack the fine-scale resolution of some satellite tracking technologies, the substantially longer battery life can yield important long-term data on behaviour and movement for low per-unit cost. Typically, however, receiver arrays are designed to maximise spatial coverage at the cost of positional accuracy leading to potentially longer detection gaps as individuals move out of range between monitored locations. This is particularly true when these technologies are deployed to monitor species in hard-to-access locations. We develop a novel approach to analysing acoustic telemetry data, using the timing and duration of gaps between animal detections to classify movement behaviours into 'restricted' or potential wider 'out of range' movements synonymous with longer distance dispersal. We apply this method to investigate spatial and temporal segregation of inferred movement patterns in two sympatric species of reef shark within a large, remote Marine Protected Area. Drivers of these movements were identified using generalised linear mixed models and multi-model inference. Species, diel period and season were significant predictors of 'out of range' movements. Silvertip sharks were overall more likely to undertake 'out of range' movements, compared to grey reef sharks, indicating spatial segregation, and corroborating previous stable isotope work between these two species. High individual variability in 'out of range' movements in both species was also identified. We present a novel gap analysis of telemetry data to help infer differential movement and space use patterns where acoustic coverage is imperfect and other tracking methods impractical. In remote locations, inference may be the best available tool and this approach shows that acoustic telemetry gap analysis can be used for comparative studies in fish ecology, or combined with other research techniques to better understand functional mechanisms driving behaviour.</p>
Figure 5 in Underwater acoustic behavior of bearded seals (Erignathus barbatus) in the northeastern Chukchi Sea, 2007-2010
Figure 5. Notched box and whisker plots comparing monthly variation of the duration (s) of each bearded seal call type (A: AL1(T), B: AL1i(T), C: AL2(T), D: AL3(M), E: AL4(T), F: AL5(T), G: AL6(T) and H: AL7(A)) among three different years (2008: white, 2009: light gray, and 2010: dark gray). The notch represents the 95% simultaneous confidence interval on the means; dots indicate outliers. Nonoverlapping notches denote a significant difference.
Figure 1 in Underwater acoustic behavior of bearded seals (Erignathus barbatus) in the northeastern Chukchi Sea, 2007-2010
Figure 1. Recording station locations in the northeastern Chukchi Sea (Alaska, U.S.A.) for the 2007–2008, 2008–2009, and 2009–2010 overwinter programs (A) and for the summer 2009 and 2010 programs (B). Specifics on sampling effort are given in Table 1.
Figure 3 in Underwater acoustic behavior of bearded seals (Erignathus barbatus) in the northeastern Chukchi Sea, 2007-2010
Figure 3. Number of male bearded seal calls per 20 min sample for each overwinter recording station. Red lines indicate recording start and end.
Carcharhinus amblyrhynchos and Carcharhinus albimarginatus underwater acoustic telemetry data - Chagos Archipelago 2014-2018
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