
NSF Org: |
CCF Division of Computing and Communication Foundations |
Recipient: |
|
Initial Amendment Date: | November 2, 2016 |
Latest Amendment Date: | November 2, 2016 |
Award Number: | 1663709 |
Award Instrument: | Standard Grant |
Program Manager: |
David Corman
CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
Start Date: | August 5, 2016 |
End Date: | December 31, 2019 (Estimated) |
Total Intended Award Amount: | $444,028.00 |
Total Awarded Amount to Date: | $444,028.00 |
Funds Obligated to Date: |
|
History of Investigator: |
|
Recipient Sponsored Research Office: |
310 E CAMPUS RD RM 409 ATHENS GA US 30602-1589 (706)542-5939 |
Sponsor Congressional District: |
|
Primary Place of Performance: |
310 East Campus Rd Athens GA US 30602-1589 |
Primary Place of
Performance Congressional District: |
|
Unique Entity Identifier (UEI): |
|
Parent UEI: |
|
NSF Program(s): | CyberSEES |
Primary Program Source: |
|
Program Reference Code(s): |
|
Program Element Code(s): |
|
Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project creates a real-time Ambient Noise Seismic Imaging system in sensor networks, which enables the study and monitoring of the subsurface sustainability and potential hazards. The system is comprised of a self-sustaining seismic sensor network that can autonomously perform in-network computing of the 3D shallow earth structure images based on ambient seismic noises. We have developed distributed communication-efficient algorithms for in-situ imaging, including new fog computing architectures and non-uniform subsampling of Fourier transform of the raw signal; delay map recovery algorithm from local cross-correlation using matrix-completion; statistical change-point detection based algorithms for event picking; and multiple new ambient noise imaging methods were developed to best utilize the dense array configuration and adopt to the unique noise environment. We have also successfully demonstrated that high-resolution subsurface imaging can be achieved by combining rapid deployment of geophone dense arrays and passive seismic imaging. For example, double beamforming tomography was developed to image detailed 2D velocity structure across linear dense arrays and interferometry based polarization analysis was developed to resolve spatial-temporal hydrothermal tremor source migration beneath iconic Yellowstone geysers. Throughout this project, we have applied our techniques to better understand a variety of interesting geological structures in the US, including the subsurface infrastructure imaging and security, the magmatic and hydrothermal system of Yellowstone, the subduction structure associated with the Juan de Fuca Plate, fault zone structure in Southern California, and the continental scale lithospheric structure across the US. The results of the studies are published in multiple prestigious peer reviewed journals and conferences of computer, electrical and geological fields. Other broader impacts include the support of six PhD students (four females) and one postdoc. Some have graduated as early career faculty and scientists.
Last Modified: 02/17/2020
Modified by: Wenzhan Song
Please report errors in award information by writing to: awardsearch@nsf.gov.