Distributed Sensor Networks Based Shallow Subsurface Imaging and Infrastructure Monitoring

Distributed sensor networks can be used as passive seismic sensors to image and monitor subsurface and underground activities. Passive seismic surface-wave imaging adopts background ambient sounds from a far-field energy source. Because high frequency components decay a lot between the neighboring stations, conventional sparse sensor networks cannot image small-scale and shallow objects. In this article, we propose to use local seismic spatial autocorrelation coefficients, obtained by the combinations of independent dense sensor network measurements and pre-processed readings of its neighbor(s), to perform real-time collaborative imaging of the shallow subsurface objects. First, we derive the high-frequency spectral coefficient based shallow subsurface imaging method. Then, we apply the proposed approach to image a shallowly buried pipeline and obtain promising results. Furthermore, based on a time-lapse manner, the water leakage from the buried pipeline can also be detected using distributed computations between sensors. Comparisons and analysis of field deployments are made to validate the effectiveness and performance of the proposed method.