School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada V8W 3P6.
J Acoust Soc Am. 2011 Apr;129(4):1794-806. doi: 10.1121/1.3557052.
This paper develops a sequential trans-dimensional Monte Carlo algorithm for geoacoustic inversion in a strongly range-dependent environment. The algorithm applies advanced Markov chain Monte Carlo methods in combination with sequential techniques (particle filters) to carry out geoacoustic inversions for consecutive data sets acquired along a track. Changes in model parametrization along the track (e.g., number of sediment layers) are accounted for with trans-dimensional partition modeling, which intrinsically determines the amount of structure supported by the data information content. Challenging issues of rapid environmental change between consecutive data sets and high information content (peaked likelihood) are addressed by bridging distributions implemented using annealed importance sampling. This provides an efficient method to locate high-likelihood regions for new data which are distant and ∕ or disjoint from previous high-likelihood regions. The algorithm is applied to simulated reflection-coefficient data along a track, such as can be collected using a towed array close to the seabed. The simulated environment varies rapidly along the track, with changes in the number of layers, layer thicknesses, and geoacoustic parameters within layers. In addition, the seabed contains a geologic fault, where all layers are offset abruptly, and an erosional channel. Changes in noise level are also considered.
本文提出了一种用于强距离相关环境中水声反演的序贯跨维蒙特卡罗算法。该算法将先进的马尔可夫链蒙特卡罗方法与序贯技术（粒子滤波器）相结合，对沿航迹连续采集的数据集进行水声反演。沿航迹的模型参数化变化（例如，沉积物层的数量）通过跨维分区建模来考虑，该建模内在地确定了数据信息含量支持的结构数量。通过使用退火重要性采样实现的桥接分布来解决连续数据集之间快速环境变化和高信息量（峰值似然）的挑战性问题。这为远离先前高似然区域或与先前高似然区域不相交的新数据提供了一种有效定位高似然区域的方法。该算法应用于沿航迹的反射系数模拟数据，例如可以使用拖曳式阵列在靠近海底的地方进行采集。模拟环境沿航迹迅速变化，包括层数量、层厚度以及层内水声参数的变化。此外，海底还包含一个地质断层，所有层在此突然错开，以及一个侵蚀性通道。还考虑了噪声水平的变化。
