海洋渔业2026，Vol.48Issue(3)：285-300,16.
基于船位数据的西南大西洋公海鱿钓渔船作业状态特征提取和分析
Feature extraction and analysis of operational state for squid-jigging vessels in the Southwest Atlantic high seas based on vessel position data
摘要
Abstract
To investigate the operational state characteristics and spatiotemporal distribution patterns of squid jigging vessels targeting Argentine shortfin squid(Illex argentinus)in the Southwest Atlantic,vessel monitoring system(VMS)data from 10 representative squid jigging vessels from December 2023 to June 2024 were selected.Using a Gaussian mixture model and a multi-layer filtering method,the operational state of the vessels was classified into four types:fishing,drifting,sailing,and storm avoidance.The VMS-derived operational states were then calibrated using fishing logbook data.The results showed that the fishing duration and daily catch extracted from three data sources(VMS,VMS corrected by logbooks,and fishing logbooks)all exhibited high linear fitting degrees(r values of 0.27,0.30,and 0.25,respectively).Moreover,on over 92%of the operational days,the difference between the fishing positions derived from VMS and those recorded in logbooks was less than 15 nmile,validating the reliability of extracting operational state from VMS data.The peak fishing season for Illex argentinus in the Southwest Atlantic in 2024 was from February to May,with the fishing grounds located in the high seas outside Argentina's exclusive economic zone(60°W-61°W,45°S-47°S and 57°W-59°W,41°S-43°S).The prediction of the four operational states of squid jigging vessels in this area using an attention-based deep neural network(ADNN)model achieved an average accuracy of 95.2%.Notably,the accuracy for storm avoidance reached as high as 99%,while the accuracies for sailing,fishing,and drifting all exceeded 93%.This approach can provide technical support for dynamic monitoring of vessel behavior and fisheries resource management.关键词
阿根廷滑柔鱼/船舶监控系统/高斯混合模型/作业状态/深度神经网络Key words
Illex argentinus/vessel monitoring system/Gaussian mixture model/operational state/deep neural network分类
农业科技引用本文复制引用
张发谋,黄思思,董康忠,朱汉吉,汪建华,高铭,孙煜琰,张衡..基于船位数据的西南大西洋公海鱿钓渔船作业状态特征提取和分析[J].海洋渔业,2026,48(3):285-300,16.基金项目
国家重点研发计划(2022YFC2807504) （2022YFC2807504）
深远海渔业资源调查项目 （）
农业农村部全球渔业资源调查监测评估(公海渔业资源综合科学调查)专项 （公海渔业资源综合科学调查）
