Katrina H Johnson, Vanessa M ZoBell, Lynne E W Hodge, Melissa S Soldevilla, John A Hildebrand, Kaitlin E Frasier
{"title":"表征和模拟墨西哥湾商业船只的源水平。","authors":"Katrina H Johnson, Vanessa M ZoBell, Lynne E W Hodge, Melissa S Soldevilla, John A Hildebrand, Kaitlin E Frasier","doi":"10.1121/10.0039379","DOIUrl":null,"url":null,"abstract":"<p><p>The Gulf of Mexico is among the noisiest marine regions globally, primarily due to widespread seismic airgun operations and vessel traffic. While airguns dominate the low-frequency soundscape, vessel traffic also contributes substantial high-amplitude noise in the same range low-frequency band (<500 Hz). Between August 2020 and July 2022, two underwater acoustic recording stations documented 13 930 vessel transits from five major ship types operating within commercial shipping lanes. Tankers and cargo ships were the most common, followed by tug-tows, passenger ships, and special crafts. Cargo ships and tankers had average broadband (20-1000 Hz) monopole source levels (MSLs) of ∼183 dB re 1 μPa m, while tug-tows were 2-3 dB lower, and passenger ships/special craft were 4-5 dB lower. To investigate factors influencing low-frequency sound production, this study analyzed the relationship between vessel MSLs and ship characteristics, transit conditions, and oceanographic parameters. For this study, machine-learning models were trained to predict MSLs and their performance was compared to that of generalized additive models. Vessel speed was the most influential predictor, with additional contributions from deadweight, gross tonnage, length, and environmental parameters. This machine learning approach provides a tool to estimate MSLs in other regions and simulate the effects of noise reduction solutions, such as speed reduction or vessel design modifications.</p>","PeriodicalId":17168,"journal":{"name":"Journal of the Acoustical Society of America","volume":"158 3","pages":"2250-2268"},"PeriodicalIF":2.3000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Characterization and modeling source levels of commercial vessels in the Gulf of Mexico.\",\"authors\":\"Katrina H Johnson, Vanessa M ZoBell, Lynne E W Hodge, Melissa S Soldevilla, John A Hildebrand, Kaitlin E Frasier\",\"doi\":\"10.1121/10.0039379\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The Gulf of Mexico is among the noisiest marine regions globally, primarily due to widespread seismic airgun operations and vessel traffic. While airguns dominate the low-frequency soundscape, vessel traffic also contributes substantial high-amplitude noise in the same range low-frequency band (<500 Hz). Between August 2020 and July 2022, two underwater acoustic recording stations documented 13 930 vessel transits from five major ship types operating within commercial shipping lanes. Tankers and cargo ships were the most common, followed by tug-tows, passenger ships, and special crafts. Cargo ships and tankers had average broadband (20-1000 Hz) monopole source levels (MSLs) of ∼183 dB re 1 μPa m, while tug-tows were 2-3 dB lower, and passenger ships/special craft were 4-5 dB lower. To investigate factors influencing low-frequency sound production, this study analyzed the relationship between vessel MSLs and ship characteristics, transit conditions, and oceanographic parameters. For this study, machine-learning models were trained to predict MSLs and their performance was compared to that of generalized additive models. Vessel speed was the most influential predictor, with additional contributions from deadweight, gross tonnage, length, and environmental parameters. This machine learning approach provides a tool to estimate MSLs in other regions and simulate the effects of noise reduction solutions, such as speed reduction or vessel design modifications.</p>\",\"PeriodicalId\":17168,\"journal\":{\"name\":\"Journal of the Acoustical Society of America\",\"volume\":\"158 3\",\"pages\":\"2250-2268\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Acoustical Society of America\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1121/10.0039379\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ACOUSTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Acoustical Society of America","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1121/10.0039379","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ACOUSTICS","Score":null,"Total":0}
Characterization and modeling source levels of commercial vessels in the Gulf of Mexico.
The Gulf of Mexico is among the noisiest marine regions globally, primarily due to widespread seismic airgun operations and vessel traffic. While airguns dominate the low-frequency soundscape, vessel traffic also contributes substantial high-amplitude noise in the same range low-frequency band (<500 Hz). Between August 2020 and July 2022, two underwater acoustic recording stations documented 13 930 vessel transits from five major ship types operating within commercial shipping lanes. Tankers and cargo ships were the most common, followed by tug-tows, passenger ships, and special crafts. Cargo ships and tankers had average broadband (20-1000 Hz) monopole source levels (MSLs) of ∼183 dB re 1 μPa m, while tug-tows were 2-3 dB lower, and passenger ships/special craft were 4-5 dB lower. To investigate factors influencing low-frequency sound production, this study analyzed the relationship between vessel MSLs and ship characteristics, transit conditions, and oceanographic parameters. For this study, machine-learning models were trained to predict MSLs and their performance was compared to that of generalized additive models. Vessel speed was the most influential predictor, with additional contributions from deadweight, gross tonnage, length, and environmental parameters. This machine learning approach provides a tool to estimate MSLs in other regions and simulate the effects of noise reduction solutions, such as speed reduction or vessel design modifications.
期刊介绍:
Since 1929 The Journal of the Acoustical Society of America has been the leading source of theoretical and experimental research results in the broad interdisciplinary study of sound. Subject coverage includes: linear and nonlinear acoustics; aeroacoustics, underwater sound and acoustical oceanography; ultrasonics and quantum acoustics; architectural and structural acoustics and vibration; speech, music and noise; psychology and physiology of hearing; engineering acoustics, transduction; bioacoustics, animal bioacoustics.