Funded by NSF CyberSEES Program (NSF-1663709, $1.2M, 1/2015-1/2020)
This project creates a real-time Ambient Noise Seismic Imaging system, to study and monitor the subsurface sustainability and potential hazards of geological structures. Understanding and addressing the subsurface sustainability has significant impact on the natural, social, and economic issues of the region and across the globe. The system is comprised of a self-sustainable sensor network of geophones that can autonomously perform in-network computing of the 3D shallow earth structure images based on ambient noise alone. The project will study the subsurface sustainability of Long Beach, California and Yellowstone using their existing seismic array datasets and design the imaging system accordingly. In the late stages of the project, a field demonstration of the prototype system in Yellowstone expects to image the subsurface of some geysers. The techniques developed find further utility in monitoring and understanding the dynamics of subsurface oil, mine and geothermal resources, alongside concomitant hazards in oil exploration, mining, hydrothermal eruption, and volcanic eruption).
Real-time imaging of shallow earth structures is essential to assess the sustainability and potential hazards of geological structures. The ability to deploy large wireless sensor arrays in challenging environments is significant for any real-time hazard monitoring and early warning system. The new approach taken is general, and can be implemented as a new field network paradigm for real-time imaging of highly dynamic and complex environments, including both natural and man-made structures. Results from this research will be shared with Yellowstone National Park management (NPS), rangers, and staff. The real-time subsurface images can be used in visitor education centers, official handouts, ranger led field trips, and for public safety management. The educational activities of this project include enhancing undergraduate and graduate curricula and research programs at the three collaborative universities, and the project provides many opportunities for a collaborative cross-disciplinary exchange of ideas among them.
During the first stages, the USArray Transportable Array has been used to obtain seismic-data. The Transportable Array is a network of 400-high quality broadband seismographs and atmospheric sensors that have been operated at temporary sites across the conterminous United States from west to east in regular grid pattern.
In the following map, it can be visualized 1211 stations used for this project stage. (Click on each station to visualize station names).
- WenZhan Song, University of Georgia (UGA)
- Yao Xie, Georgia Institute of Technology (GT)
- Fan-Chi Lin, University of Utah (UU)
- Maria Valero (UGA)
- Sili Wang (UGA)
- Sin-Mei Wu (UU)
- Shixiang Zhu (GT)
- Hongao Yang (GT)
Maria Valero; Fangyu Li; Sili Wang; Fan-Chi Lin; WenZhan Song. Real-time Cooperative Analytics for Ambient Noise Tomography in Sensor Networks. IEEE Transactions on Signal and Information Processing over Networks, 2018
Maria Valero; Fangyu Li; Xiangyang Li; WenZhan Song. Imaging Subsurface Civil Infrastructure with Smart Seismic Network. 37th IEEE International Performance Computing and Communications Conference (IPCCC) 2018
Maria Valero, Goutham Kamath, Jose Clemente, Fan-Chi Lin, Yao Xie, and WenZhan Song. Real-time Ambient Noise Subsurface Imaging in Distributed Sensor Networks. The 3rd IEEE International Conference on Smart Computing (SMARTCOMP 2017), 2017.
Sufri, O., Xie, Y., Lin, F-C., and W. Song, 2015. Optimization of Ambient Noise Cross-Correlation Imaging Across Large Dense Array. American Geophysical Union 2015 Fall Meeting, San Francisco.