This paper discusses the value added by using a single vector sensor over a conventional pressure-only hydrophone for geoacoustic inversions. Inversion methods based on genetic algorithms are used to estimate the seabed properties. Synthetic signals of impulsive arrivals first are modeled using KRAKEN and RAM propagation models, each being modified to predict components of the vector field. While KRAKEN is utilized to directly compute dispersion curves, RAM provides full-field results that require the application of time warping to separate the modal arrivals. Combinations of dispersion curves utilizing all vector sensor channels are compared to curves estimated with the pressure-only channel. Within the time warping analysis, both binary masking and band-pass filter masking methods are applied to compare stability of results. The environment modeled for the synthetic analysis and inversion method utilize sound speed profiles measured during the Monterey Bay 2019 at-sea experiment and assume a sediment layer of constant thickness overlying a deeper sub-bottom type. White noise is added to the synthetic data at different signal-to-noise ratios to evaluate the impact of signal excess on the results. A hybrid optimization approach is used to improve the results of the genetic algorithm method. The analysis with synthetic data is consistent with the analysis of broadband, impulsive data collected from the experiment, indicating that the additional information from the vertical velocity channel further improves the geoacoustic parameter estimates.

本文讨论了使用单个矢量传感器相对于传统的纯压力水听器进行地声反演所带来的附加值。基于遗传算法的反演方法用于估计海底特性。首先使用 KRAKEN 和 RAM 传播模型对脉冲到达的合成信号进行建模，每个模型都经过修改以预测矢量场的分量。虽然 KRAKEN 用于直接计算色散曲线，但 RAM 提供全场结果，需要应用时间扭曲来分离模态到达。将利用所有矢量传感器通道的色散曲线组合与仅使用压力通道估计的曲线进行比较。在时间扭曲分析中，应用二元掩蔽和带通滤波器掩蔽方法来比较结果的稳定性。综合分析和反演方法建模的环境利用 2019 年蒙特利湾海上实验期间测量的声速剖面，并假设沉积层厚度恒定，覆盖在更深的海底类型上。将白噪声添加到不同信噪比的合成数据中，以评估信号过量对结果的影响。混合优化方法用于改进遗传算法方法的结果。合成数据的分析与从实验中收集的宽带脉冲数据的分析一致，表明来自垂直速度通道的附加信息进一步改进了地声参数估计。