Several seismic techniques, both passive and active, exist for estimating the shear-wave velocity structure of shallow sedimentary structures. In particular, passive compliance signals recorded by broadband ocean-bottom seismometers (OBSs) can be used to invert for structure. While compliance-based imaging studies have been carried out at several locations across the Cascadia Subduction Zone, such an approach has not been extensively applied to OBSs deployed on the continental shelf and slope. In this study, we measure compliance and coherence signals at 13 broadband OBSs deployed along Cascadia's continental shelf and upper slope. We then use a recently developed technique to probabilistically invert compliance signals for shallow structure that makes use of mixture density neural networks. Finally, we compare and contrast our inverted profiles and derived properties obtained using this method with previous studies focused on the properties of basin sediments.

中文翻译：

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来自海底柔度数据的概率反演的卡斯卡迪亚大陆边缘沉积物的剪切波速度结构

有几种地震技术，包括被动和主动，用于估计浅层沉积结构的横波速度结构。特别是，宽带海底地震仪 (OBS) 记录的被动顺应性信号可用于结构反演。虽然已经在卡斯卡迪亚俯冲带的多个位置进行了基于顺应性的成像研究，但这种方法尚未广泛应用于部署在大陆架和斜坡上的 OBS。在这项研究中，我们测量了沿卡斯卡迪亚大陆架和上坡部署的 13 个宽带 OBS 的合规性和相干性信号。然后，我们使用最近开发的技术对浅层的顺应性信号进行概率反演。利用混合密度神经网络的结构。最后，我们将使用这种方法获得的倒置剖面和派生的特性与之前专注于盆地沉积物特性的研究进行比较和对比。