Redirigiendo al acceso original de articulo en 24 segundos...
ARTÍCULO
TITULO

Characterizing the Sound-Scattering Layer and Its Environmental Drivers in the North Equatorial Current of the Central and Western Pacific Ocean

Tianji Gao    
Jianfeng Tong    
Minghua Xue    
Zhenhong Zhu    
Yue Qiu    
Richard Kindong    
Qiuyun Ma and Jun Li    

Resumen

Acoustic technology is an essential tool for detecting marine biological resources and has been widely used in sound-scattering layer (SSL) research. The North Equatorial Current (NEC) warm pool region of the Central and Western Pacific Ocean has a vast distribution of micronekton and zooplankton; analyzing the SSL characteristics in this region is vital for monitoring the marine environment and studying the marine ecosystem. In this study, we statistically analyzed the spatiotemporal factors of 10?200 m SSL in the NEC of the Central and Western Pacific Ocean using acoustic survey data collected by the ?Songhang? research vessel (RV) in 2022, and the influence of environmental factors on the scattering layer distribution was analyzed using the Generalized Additive Model (GAM). The results showed that the SSL in the warm pool area of the NEC is distributed in shallow waters above 100 m. The primary scatterers are micronekton and zooplankton, and this SSL had diel vertical migration behavior. By comparing Akaike?s Information Criterion of different GAMs, the model consisting of six factors, namely, temperature, current velocity, turbidity, solar altitude angle, longitude, and latitude, was remarkable. Each model?s factor effects primarily influence the contribution of the volume-backscatter strength (Sv). The cumulative deviation explanation rate of the Sv was 67.2%, among which the highest explanation rate of solar altitude angle variance was 35.4%, the most critical environmental factor. The results of this study can provide a reference for long-term studies on ecological changes and their effects on micronekton and zooplankton distribution.

 Artículos similares

       
 
Yongkun Deng, Chenghao Zhang, Nan Yang and Huaming Chen    
Semi-supervised learning (SSL) is a popular research area in machine learning which utilizes both labeled and unlabeled data. As an important method for the generation of artificial hard labels for unlabeled data, the pseudo-labeling method is introduced... ver más
Revista: Applied Sciences

 
Hansoo Kim, Garam Kim, Mira Kim and Donhyug Kang    
The Yellow Sea Bottom Cold Water (YSBCW) refers to seawater with a water temperature of 10 °C or less found at the bottom of the center of the Yellow Sea. The spatiotemporal variability of the YSBCW directly affects the distribution of organisms in the m... ver más

 
Ali Reza Sajun and Imran Zualkernan    
Given recent advances in deep learning, semi-supervised techniques have seen a rise in interest. Generative adversarial networks (GANs) represent one recent approach to semi-supervised learning (SSL). This paper presents a survey method using GANs for SS... ver más
Revista: Applied Sciences

 
Hyunkyung Shin, Hyeonung Shin, Wonje Choi, Jaesung Park, Minjae Park, Euiyul Koh and Honguk Woo    
The automatic analysis of medical data and images to help diagnosis has recently become a major area in the application of deep learning. In general, deep learning techniques can be effective when a large high-quality dataset is available for model train... ver más
Revista: Applied Sciences

 
Ahmed Abdelmoamen Ahmed and Gbenga Agunsoye    
The increasing ubiquity of network traffic and the new online applications? deployment has increased traffic analysis complexity. Traditionally, network administrators rely on recognizing well-known static ports for classifying the traffic flowing their ... ver más
Revista: Algorithms