Distributed travel-time seismic tomography in large-scale sensor networks

• We present a distributed method to obtain seismic image on wireless sensor networks.
• We perform in-network computing and avoid data gathering.
• High resolution image can be obtained using proposed multi-resolution scheme.
• We evaluate the method using synthetic seismic data in a CORE emulator.
• Our method is able to recover velocity model of Mt. St. Helens.

Current geophysical techniques for visualizing seismic activity employ image reconstruction methods that rely on a centralized approach for processing the raw data captured by seismic sensors. The data is either gathered manually, or relayed by expensive broadband stations, and then processed at a base station. This approach is time-consuming (weeks to months) and hazardous as the task involves manual data gathering in extreme conditions. Also, raw seismic samples are typically in the range of 16–24 bit, sampled at 50–200 Hz and transferring this high fidelity sample from large number of sensors to a centralized station results in a bottleneck due to bandwidth limitations. To avoid these issues, a new distributed method is required which processes raw seismic samples inside each node and obtains a high-resolution seismic tomography in real time. In this paper, we present a component-averaged distributed multi-resolution evolving tomography algorithm for processing data and inverting volcano tomography in the network while avoiding centralized computation and costly data collection. The algorithm is first evaluated for the correctness using a synthetic model in a CORE emulator. Later, our proposed algorithm runs using the real data obtained from Mt. St. Helens, WA, USA. The results validate that our distributed algorithm is able to obtain a satisfactory image similar to centralized computation under constraints of network resources, while distributing the computational burden to sensor nodes.