3 research outputs found

    Monitoring and early warning detection of collapse and subsidence sinkholes using an optical fibre seismic sensor

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    AbstractWe present and experimentally demonstrate a seismic ambient noise monitoring optical fibre sensor for early warning detection of sinkholes. The developed optical fibre sensor is designed for warning alert of subsidence and cover collapse sinkholes. The progressive process of sinkhole development causes structural change in the subterranean surface. The impact of this change and its influence on the subsurface acoustic modes was detected in the form of variations in the spectral content of the ambient noise signals monitored in the subsurface. Structural surface integrity was monitored through frequency response as the void increased. Vibrational states relating to unsteady structural conditions were identified. Significant instability events were captured giving timely warnings before collapse. The polarisation based single mode fibre sensor and monitoring method is proposed for implementation in a phase sensitive distributed acoustic sensor setup. Peak frequencies in the micro-seismic noise band of 0.1 Hz to 1.0 Hz were observed through cavity development and growth. Extended peak frequency shifts and bandwidth in the band >1Hz were recorded, indicating weakness and imminence of collapse. Early warning detection by the structural field model was achieved prior to the sudden subsurface failure which results in collapse sinkholes. By monitoring variations in the vibrating frequency modes when a subsurface cavity develops within the structure, trigger events and collapse precursor conditions are identified. We have successfully demonstrated an early response warning annunciator by using an algorithm to analyse combinational characteristics of the spectral components of the detected signals. The fibre sensor reduces the risk and socio-economic impact of infrastructural damage due to sudden collapse of sinkholes and has extended potential of monitoring earthquakes and landslides
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