黄海太平洋磷虾回波映像识别与资源密度评估

太平洋磷虾是黄海生态系统中浮游动物的关键种。为准确评估太平洋磷虾的资源密度,基于2010年1月黄海渔业资源调查过程中采集的声学和生物学数据,利用SDWBA目标强度理论模型,研究了太平洋磷虾38和120 k Hz目标的回声散射特性,并根据2个频率平均体积散射强度的差值(简称频差技术),开展了太平洋磷虾回波映像识别及资源密度评估研究。结果显示,太平洋磷虾的目标强度与其倾角和体长密切相关;120k Hz的目标强度明显高于38 k Hz,且两个频率的有效平均目标强度之差随着磷虾体长的增加而减小。数据处理结果显示,两个频率回声数据的平均体积散射强度(MVBS)呈线性关系,120 k Hz的MVBS比38 k Hz高约14.1 d B,与理论仿真结果一致;回声散射层内太平洋磷虾的资源密度为1.8~2531.8尾/m3,均值为255.1尾/m3。本研究对利用渔业声学技术开展浮游动物资源评估具有借鉴意义,未来还需要进一步对太平洋磷虾目标强度模型参数及目标识别方法进行完善,以提高其资源密度声学评估的准确度。

Acoustical identification and density estimation of Euphausia pacifica in the Yellow Sea

Euphausia pacifica is a key species of zooplankton in the Yellow Sea ecosystem. To estimate the biomass and distribution of E. pacifica is essential for better understanding local ecosystem structure and energy flow. However, due to its avoidance to zooplankton net, it is difficult to quantify the krill biomass using traditional vertical net sampling method. Based on acoustic and biological data of E. pacifica swarms collected in the Yellow Sea in January 2010, the target strength (TS) of E. pacifica at 38 and 120 kHz was analyzed using SDWBA theoretical model. Moreover, the E. pacifica swarms were identified using dB-differencing method based on 38 and 120 kHz acoustic data and the krill density in the sound scattering layers (SSL) was estimated subsequently. Numerical simulation showed that the TS of E. pacifica was sensitive to its orientation and length distribution. The TS at 120 kHz was obviously higher than 38 kHz, while the difference decreased with increasing krill length. A linear relationship with an intercept of 14.1 dB was observed for the mean volume backscattering strength (MVBS) between the two frequencies. Based on 120 kHz data, the density of E. pacifica in the SSL was estimated between 1.8 and 2351.8 ind/m³ with a mean of 255.1 ind/m³. This paper preliminarily introduced the species identification and density estimation of E. pacifica based on the dB-differencing method, which provides useful reference for the acoustical estimation of zooplankton biomass. To improve the accuracy and precision, further investigations on the parameters in the TS model and the target discrimination method are needed.