地转经验模态方法在深海声层析中的应用

Application of Gravest Empirical Mode Method to Deep-sea Acoustic Tomography

摘要:

为获取墨西哥湾区域海水的温度、盐度和比容异常数据，采用地转经验模态 GEM 方法，通过将墨西哥湾区域的所有历史实测温盐深数据整合在一起，建立一个散点的拉格朗日矩阵，将已有的温盐深数据投影到此二维空间上，从而建立一个传播时间与温度、盐度和比容异常的经验关系。进而结合 IES 实测的传播时间便可最终反演得到温度、盐度和比容异常，得到流速剖面。最后根据构建的墨西哥湾区域的地转经验模态，便可推知墨西哥湾区域的海水传播时延随着温度、盐度和比容异常的变化关系，为更好地研究该海域深海温盐流场提供了有效根据。

关键词:数据处理;地转经验模态;反演;深海声层析

Abstract:

In order to obtain the temperature，salinity and specific volume anomalies of seawater in the Gulf of Mexico region，the gravest empirical mode（GEM）method is used to establish a scattering Lagrangian matrix by integrating all the historical measured temperature and salinity data in the Gulf of Mexico region. And then an empirical relationship between the propagation time and the temperature，salinity and specific volume anomalies is established by projecting the existing temperature and salinity data onto this two-dimensional space. The temperature，salinity and specific volume anomalies are then inverted by combining with the propagation time measured by IES to obtain the flow profiles. Finally，based on the geostrophic empirical mode in the Gulf of Mexico， the relationship between the propagation time and the temperature，salinity and specific volume anomalies can be deduced，which enables a better study of the area and provides an effective basis for the inversion of deep-sea thermohaline currents.

Key words:data processing;gravest empirical mode;inversion;deep-sea acoustic tomography