Real-time In-situ Seismic Tomography in Sensor Network

Seismic tomography is a technique for illuminating the physical dynamics of the Earth by seismic waves generated by earthquakes or explosions. In both industry and academia, the seismic exploration does not yet have the capability of imaging seismic tomography in real-time and with high resolution. There are two reasons. First, at present raw seismic data are typically recorded on sensor nodes locally then are manually collected to central observatories for post processing, and this process may take months to complete. Second, high resolution tomography requires a large and dense sensor network, the real-time data retrieval from a network of large-amount wireless seismic nodes to a central server is virtually impossible due to the sheer data amount and resource limitations. This limits our ability to understand earthquake zone or volcano dynamics. To obtain the seismic tomography in real-time and high resolution, a new design of sensor network system for raw seismic data processing and distributed tomography computation is demanded. Based on these requirements, three research aspects are addressed in this work. First, a distributed multi-resolution evolving tomography computation algorithm is proposed to compute tomography in the network, while avoiding costly data collections and centralized computations. Second, InsightTomo, an end-to-end sensor network emula-tion platform, is designed to emulate the entire process from data recording to tomography image result delivery. Third, a sensor network testbed is presented to verify the related methods and design in real world. The design of the platform consists of hardware, sensing and data processing components. iv DEDICATION This dissertation is dedicated to Georgia State University. This dissertation is also dedicated to my parents Tingli Shi and Cuizhen Zhang, for their lasting love, sacrifice and support. v ACKNOWLEDGEMENTS