Ambient Noise Tomography With Common Receiver Clusters in Distributed Sensor Networks

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.