Determining the detection function of passive acoustic data loggers for porpoises using a large hydrophone array

AbstractHumpback whales are thought to undertake annual migrations between their low latitude breeding grounds and high latitude feeding grounds. However, under specific conditions, humpback whales sometimes change their migratory destination or skip migration overall. Here we document the surprising persistent presence of humpback whales in the Atlantic sector of the Southern Ocean during five years (2011, 2012, 2013, 2017, and 2018) using passive acoustic data. However, in the El Niño years 2015 and 2016, humpback whales were virtually absent. Our data show that humpback whales are systematically present in the Atlantic sector of the Southern Ocean and suggest that these whales are particularly sensitive to climate oscillations which have profound effects on winds, sea ice extent, primary production, and especially krill productivity.

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Holistic Approach in Integrating Passive Acoustic Logs with Static and Dynamic Data Improved Wells Integrity Understanding at Field Scale

10.2118/207060-ms ◽

2021 ◽

Author(s):

Fuad Atakishiyev ◽

Rizvan Ramazanov ◽

Fergus Allan ◽

Adrian Zett

Keyword(s):

Risk Mitigation ◽

Single Point ◽

Optimal Decision ◽

Dynamic Data ◽

Safe Delivery ◽

Acoustic Data ◽

Dynamic Events ◽

Well Integrity ◽

Conventional Methods ◽

Passive Acoustic

Abstract Proactive well diagnostic surveillance helps with safe delivery of production by effective well management and risk mitigation. The objective of the paper is to demonstrate the data analytics approach utilizing passive acoustic technology in combination with conventional methods of detecting low magnitude dynamic events behind single or multiple casing strings. The results of integrated interpretation of passive acoustic wireline technology with the data from different sources helped to make optimal decision. Traditional well integrity diagnostic includes temperature and passive acoustic data analysis that are associated with high uncertainty. A newer generation of array passive acoustic technology with enhanced sensitivity capabilities was deployed offshore Azerbaijan. A combination of array passive acoustics data, single point temperature and distributed fiber optic data have been acquired during a multi-well campaign. Extensive review of well integrity history, downhole and surface gauge data incorporated with passive acoustic data from arrays of spectral sensors in time and depth domain helped to refine the process and evolve into a unique interpretation methodology. The comprehensive interpretation accounted for integration of all available static and dynamic data such as: fluids and formation pressure distribution along the borehole, cement bond logs evaluation, annuli pressure and temperature, production and downhole gauge measurements, fibre optic data, temperature and passive acoustic logs. This helped to understand the low scale dynamic events behind the casing and make an informed decision on safe and reliable well operations. The sensitivity of array passive acoustic technology proved successful in detecting subtle acoustic events where conventional methods failed or had limited success. Successful results have been achieved by customizing the logging program using a multiple well evolutionary approach that improved data quality and saved rig time. Interpretation and decisions derived from each well involved multi-disciplinary well review panel sessions with specialists from subsurface & geohazards, drilling & completions, production & operations departments. Case studies presented in this paper describe the interpretation approach of highly sensitive array passive acoustic sensors in combination with available static and dynamic point and distributed data. The logging program and interpretation approach used in this article could be considered as a basis for future applications in wells with similar design.

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Instantaneous wide-area passive acoustic monitoring of surface ships and submerged vehicles using a coherent hydrophone array

The Journal of the Acoustical Society of America ◽

10.1121/1.4988089 ◽

2017 ◽

Vol 141 (5) ◽

pp. 3704-3704 ◽

Cited By ~ 1

Author(s):

Wei Huang ◽

Purnima R. Makris ◽

Heriberto A. Garcia

Keyword(s):

Acoustic Monitoring ◽

Wide Area ◽

Passive Acoustic Monitoring ◽

Passive Acoustic ◽

Hydrophone Array

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Use of a novel acoustic 'listening' method for detecting pump impellor strike on downstream migrating eels

Marine and Freshwater Research ◽

10.1071/mf19205 ◽

2020 ◽

Vol 71 (6) ◽

pp. 571

Author(s):

B. O. David ◽

M. Lake ◽

M. K. Pine ◽

J. Smith ◽

J. A. T. Boubée

Keyword(s):

Test Site ◽

Acoustic Properties ◽

Fish Mortality ◽

Management Options ◽

Acoustic Data ◽

Goldfish Carassius Auratus ◽

Pump Station ◽

Pumping Stations ◽

Global Issue ◽

Passive Acoustic

Fish mortality through floodplain pumping stations is a recognised global issue, but few studies have quantified the degree of mortality that occurs during pumping. We investigated the potential of passive acoustic monitoring (PAM) as a tool to record sounds made by fish and their likely mortality as they passed through pumps during downstream migration. The acoustic properties made by freshly killed eels that were fed through an existing pump station were compared to those made by goldfish (Carassius auratus). Processing and analysis of acoustic data enabled the development of an ‘eel-specific’ algorithm for detecting eels passing through the pumping station. The duration of sound and filtered intensity were useful characteristics enabling reliable separation of the two fish species. The algorithm was then applied retrospectively to soundscape recordings obtained during a typical eel migration period at the test site. Although the tool is unlikely to be able to differentiate the sound of goldfish from ‘other’ potential sounds of short duration (e.g. sticks), differentiating eels from other sounds was demonstrated. We conclude that this tool has considerable potential for improving the understanding of the timing of eel migrations and likely mortality through pumping stations. The tool may also be used to inform the development of both remote and manual pump management options for reducing pump-related eel mortality.

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Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data

Global Change Biology ◽

10.1111/gcb.15191 ◽

2020 ◽

Vol 26 (9) ◽

pp. 4812-4840 ◽

Cited By ~ 2

Author(s):

Genevieve E. Davis ◽

Mark F. Baumgartner ◽

Peter J. Corkeron ◽

Joel Bell ◽

Catherine Berchok ◽

...

Keyword(s):

North Atlantic ◽

Movement Patterns ◽

Baleen Whales ◽

Acoustic Data ◽

Passive Acoustic

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Passive acoustic monitoring using a towed hydrophone array results in identification of a previously unknown beaked whale habitat

The Journal of the Acoustical Society of America ◽

10.1121/1.4816585 ◽

2013 ◽

Vol 134 (3) ◽

pp. 2589-2595 ◽

Cited By ~ 15

Author(s):

Tina M. Yack ◽

Jay Barlow ◽

John Calambokidis ◽

Brandon Southall ◽

Shannon Coates

Keyword(s):

Acoustic Monitoring ◽

Beaked Whale ◽

Passive Acoustic Monitoring ◽

Passive Acoustic ◽

Hydrophone Array

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A Convolutional Neural Network for Automated Detection of Humpback Whale Song in a Diverse, Long-Term Passive Acoustic Dataset

Frontiers in Marine Science ◽

10.3389/fmars.2021.607321 ◽

2021 ◽

Vol 8 ◽

Author(s):

Ann N. Allen ◽

Matt Harvey ◽

Lauren Harrell ◽

Aren Jansen ◽

Karlina P. Merkens ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Characteristic Curve ◽

Humpback Whale ◽

Learning Approaches ◽

Spatial And Temporal Patterns ◽

Mammal Species ◽

Diverse Range ◽

Acoustic Data ◽

Passive Acoustic

Passive acoustic monitoring is a well-established tool for researching the occurrence, movements, and ecology of a wide variety of marine mammal species. Advances in hardware and data collection have exponentially increased the volumes of passive acoustic data collected, such that discoveries are now limited by the time required to analyze rather than collect the data. In order to address this limitation, we trained a deep convolutional neural network (CNN) to identify humpback whale song in over 187,000 h of acoustic data collected at 13 different monitoring sites in the North Pacific over a 14-year period. The model successfully detected 75 s audio segments containing humpback song with an average precision of 0.97 and average area under the receiver operating characteristic curve (AUC-ROC) of 0.992. The model output was used to analyze spatial and temporal patterns of humpback song, corroborating known seasonal patterns in the Hawaiian and Mariana Islands, including occurrence at remote monitoring sites beyond well-studied aggregations, as well as novel discovery of humpback whale song at Kingman Reef, at 5∘ North latitude. This study demonstrates the ability of a CNN trained on a small dataset to generalize well to a highly variable signal type across a diverse range of recording and noise conditions. We demonstrate the utility of active learning approaches for creating high-quality models in specialized domains where annotations are rare. These results validate the feasibility of applying deep learning models to identify highly variable signals across broad spatial and temporal scales, enabling new discoveries through combining large datasets with cutting edge tools.

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