Seismic Tomography in Sensor Networks

Tomography imaging, applied to seismology, requires a new, decentralized approach if high resolution calculations are to be performed in a sensor network configuration. The real-time data retrieval from a network of large-amount wireless seismic stations to a central server is virtually impossible due to the sheer data amount and resource limitations. In this paper, we propose and design a distributed algorithm for processing data and inverting tomography in the network, while avoiding costly data collections and centralized computations. Based on a partition of the tomographic inversion problem, the new algorithms distribute the computational burden to sensor nodes and perform real-time tomographic inversion in the network, so that we can recover a high resolution tomographic model in real-time under the constraints of network resources. Our emulation results indicate that the distributed algorithms successfully reconstruct the synthetic models, while reducing and balancing the communication and computation cost to a large extent.