Near-surface imaging with distributed sensor networks (DSN) is promising for planet exploration, which affordably generates a near-surface velocity model. Recently, an Eikonal tomography-based ambient noise seismic imaging (ANSI) algorithm was implemented in a DSN to realize real-time and in-situ near-surface imaging. However, only using data from neighbors to generate a velocity map cannot have enough stacking samples to generate high-quality results. Also, the neighbor range increase will result in the exponential rise of communication costs. To overcome this problem, we propose a new decentralized Eikonal tomography algorithm in the DSN. The main idea is to change the source-based algorithm to a receiver-based one, which we call common receiver decentralized Eikonal tomography (CR-TomoEK). With CR-TomoEK, nodes fully utilize signals from neighbors to generate partial velocity maps, when combined, lead to the final output. When compared with the original Eikonal algorithm, the stacking number is significantly increased, output quality is higher than before, and there is a significant reduction in communication cost. We performed experiments on both synthetic data and real data from the USArray Transportable Array. Both imaging quality and communication cost are considered in the algorithm validation. The result shows that our algorithm significantly increases the output quality while keeping the communication cost safe to generate a real-time result.

中文翻译：

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分布式传感器网络中具有通用接收器簇的环境噪声层析成像

具有分布式传感器网络（DSN）的近地表成像技术有望用于行星探测，并能够负担得起地产生近地表速度模型。最近，在DSN中实现了基于Eikonal层析成像的环境噪声地震成像（ANSI）算法，以实现实时和原位近地表成像。但是，仅使用来自邻居的数据来生成速度图就不能具有足够的堆叠样本来生成高质量的结果。而且，邻居范围的增加将导致通信成本的指数增长。为了克服这个问题，我们在DSN中提出了一种新的分散式Eikonal层析成像算法。主要思想是将基于源的算法更改为基于接收器的算法，我们将其称为普通接收器分散式Eikonal层析成像（CR-TomoEK）。使用CR-TomoEK，节点充分利用来自邻居的信号来生成局部速度图，这些速度图结合起来可产生最终输出。与原始Eikonal算法相比，堆叠数量大大增加，输出质量比以前更高，并且通信成本大大降低。我们对来自USArray可移动阵列的合成数据和实际数据都进行了实验。算法验证中同时考虑了成像质量和通信成本。结果表明，我们的算法显着提高了输出质量，同时保持了通信成本的安全，可以生成实时结果。输出质量比以前更高，并且通信成本大大降低。我们对来自USArray可移动阵列的合成数据和实际数据都进行了实验。算法验证中同时考虑了成像质量和通信成本。结果表明，我们的算法显着提高了输出质量，同时保持了通信成本的安全，可以生成实时结果。输出质量比以前更高，并且通信成本大大降低。我们对来自USArray可移动阵列的合成数据和实际数据都进行了实验。算法验证中同时考虑了成像质量和通信成本。结果表明，我们的算法显着提高了输出质量，同时保持了通信成本的安全，从而可以生成实时结果。