LAUSR

Understanding earthquakes and volcano eruptions phenomena require the analysis of large amounts of subsurface data. Currently, data collected from these events need post-processing in an off-line mode due to hardware limitations and strong bandwidth constraints. Furthermore, analytics may take days, even weeks, in being processed after collection. In some cases, underground structures require previous studies and subsurface knowledge from experts to establish certain input parameters. By leveraging current IoT technologies, we introduce an autonomous seismic imaging system (ASIS), a real-time system for monitoring and generating analytics of seismic data based on a sensor network. ASIS processes real-time analytics near where the events are generated (fog computing) to mitigate bandwidth limitations. Calculations for earthquake detection, location, and magnitude are processed in situ. ASIS allows monitoring sensor status, visualizing the data of each sensor, and generating two dimensional/three dimensional (2-D/3-D) subsurface structures. The system is able to learn the underground structure by taking advantage of ambient noise data analysis, which significantly reduces the need of initial parameters. We incorporated a renewable energy source to extend sensor functionality, and we made the system self-healing and fault-tolerant. Indoor and outdoor evaluation of the system showed the error of earthquake detection is 13.7 ms and the spatial location accuracy is 5.1 ft.