This paper presents a linearized Bayesian approach to invert acoustic arrival-time data for high-precision estimation of experiment geometry and uncertainties for geoacoustic inversion applications. The data considered here were collected as part of the 2017 Seabed Characterization Experiment at the New England Mud Patch for the purpose of carrying out broadband reflection-coefficient inversion. The calculation of reflection coefficients requires accurate knowledge of the survey geometry. To provide this, a Bayesian ray-based inversion is developed here that estimates source–receiver ranges, source depths, receiver depths, and water depths at reflection points along the track to much higher precision than prior information based on GPS and bathymetry measurements. Near the closest point of approach, where rays are near vertical, data information is low and inaccurate range estimates are improved using priors from analytic predictions based on nearby sections of the track. Uncertainties are obtained using analytic linearized estimates, and verified with nonlinear analysis. The high-precision experiment geometry is subsequently used to calculate grazing angles, with angle uncertainties computed using Monte Carlo methods.

中文翻译：

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新英格兰泥滩实验几何的线性贝叶斯反演

本文提出了一种线性化贝叶斯方法来反演声波到达时间数据，用于高精度估计实验几何形状和地声反演应用的不确定性。这里考虑的数据是作为 2017 年新英格兰泥滩海底特征实验的一部分收集的，目的是进行宽带反射系数反演。反射系数的计算需要准确的测量几何知识。为了提供这一点，这里开发了一种基于贝叶斯射线的反演，它可以估计源 - 接收器范围、源深度、接收器深度和沿轨道反射点的水深，其精度比基于 GPS 和水深测量的先验信息高得多。在最近的接近点附近，射线接近垂直，数据信息较少，并且使用基于轨道附近部分的分析预测的先验来改进不准确的距离估计。不确定性是使用分析线性化估计获得的，并通过非线性分析进行验证。高精度实验几何随后用于计算掠射角，角度不确定性使用蒙特卡罗方法计算。