This project is basically imaging what’s underground in a situation where there’s no active source, like an earthquake. We’re using background noise.
(PRWEB) October 02, 2014
Dr. WenZhan Song, a professor in the Department of Computer Science at Georgia State University, has received a four-year, $1.2 million grant from the National Science Foundation to create a real-time seismic imaging system using ambient noise.
This imaging system for shallow earth structures could be used to study and monitor the sustainability of the subsurface, or area below the surface, and potential hazards of geological structures. Song and his collaborators, Yao Xie of the Georgia Institute of Technology and Fan-Chi Lin of the University of Utah, will use ambient noise to image the subsurface of geysers in Yellowstone National Park.
“This project is basically imaging what’s underground in a situation where there’s no active source, like an earthquake. We’re using background noise,” Song said. “At Yellowstone, for instance, people visit there and cars drive by. All that could generate signals that are penetrating through the ground. We essentially use that type of information to tap into a very weak signal to infer the image of underground. This is very frontier technology today.”
The system will be made up of a large network of wireless sensors that can perform in-network computing of 3-D images of the shallow earth structure that are based solely on ambient noise.
Real-time ambient noise seismic imaging technology could also inform homeowners if the subsurface below their home, which can change over time, is stable or will sink beneath them.
This technology can also be used in circumstances that don’t need to rely on ambient noise but have an active source that produces signals that can be detected by wireless sensors. It could be used for real-time monitoring and developing early warning systems for natural hazards, such as volcanoes, by determining how close magma is to the surface. It could also benefit oil exploration, which uses methods such as hydrofracturing, in which high-pressure water breaks rocks and allows natural gas to flow more freely from underground.
“As they do that, it’s critical to monitor that in real time so you can know what’s going on under the ground and not cause damage,” Song said. “It’s a very promising technology, and we’re helping this industry reduce costs significantly because previously they only knew what was going on under the subsurface many days and even months later. We could reduce this to seconds.”
Until now, data from oil exploration instruments had to be manually retrieved and uploaded into a centralized database, and it could take days or months to process and analyze the data.
The research team plans to have a field demonstration of the system in Yellowstone and image the subsurface of some of the park’s geysers. The results will be shared with Yellowstone management, rangers and staff. Yellowstone, a popular tourist attraction, is a big volcano that has been dormant for a long time, but scientists are concerned it could one day pose potential hazards.
In the past several years, Song has been developing a Real-time In-situ Seismic Imaging (RISI) system using active sources, under the support of another $1.8 million NSF grant. His lab has built a RISI system prototype that is ready for deployment. The RISI system can be implemented as a general field instrumentation platform for various geophysical imaging applications and incorporate new geophysical data processing and imaging algorithms.
The RISI system can be applied to a wide range of geophysical exploration topics, such as hydrothermal circulation, oil exploration, mining safety and mining resource monitoring, to monitor the uncertainty inherent to the exploration and production process, reduce operation costs and mitigate the environmental risks. The business and social impact is broad and significant. Song is seeking business investors and partners to commercialize this technology.
For more information about the project, visit http://sensorweb.cs.gsu.edu/?q=ANSI.