Geological Disaster Monitoring Based On Sensor Networks
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Geological Disaster Monitoring Based on Sensor Networks
This book presents the outcomes of the workshop sponsored by the National Natural Sciences Foundation of China and the UK Newton Fund, British Council Researcher Links. The Workshop was held in Harbin, China, from 14 to 17 July 2017, and brought together some thirty young (postdoctoral) researchers from China and the UK specializing in geosciences, sensor signal networks and their applications to natural disaster recovery. The Workshop presentations covered the state of the art in the area of disaster recovery and blended wireless sensor systems that act as early warning systems to mitigate the consequences of disasters and function as post-disaster recovery vehicles. This book promotes knowledge transfer and helps readers explore and identify research opportunities by highlighting research outcomes in the internationally relevant area of disaster recovery and mitigation.
Early Warning for Geological Disasters
Author: Friedemann Wenzel
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-08-13
The past years have seen new technologies that could be utilized for early warning and real-time loss estimation. They include self-organizing sensor networks, new satellite imagery with high resolution, multi-sensor observational capacities, and crowd sourcing. From this and improved physical models, data processing and communication methodologies a significant step towards better early warning technologies has been achieved by research. At the same time, early warning systems became part of the disaster management practice for instance in Japan and Indonesia. This book marks the important point where: Research activities continue to improve early warning Experience with applications is expanding At this critical point in development of early warning for geological disasters it is timely to provide a volume that documents the state-of-the-art, provides an overview on recent developments and serves as knowledge resource for researcher and practitioners.
Tsunami Data Assimilation for Early Warning
This book focuses on proposing a tsunami early warning system using data assimilation of offshore data. First, Green’s Function-based Tsunami Data Assimilation (GFTDA) is proposed to reduce the computation time for assimilation. It can forecast the waveform at Points of Interest (PoIs) by superposing Green’s functions between observational stations and PoIs. GFTDA achieves an equivalently high accuracy of tsunami forecasting to the previous approaches, while saving sufficient time to achieve an early warning. Second, a modified tsunami data assimilation method is explored for regions with a sparse observation network. The method uses interpolated waveforms at virtual stations to construct the complete wavefront for tsunami propagation. Its application to the 2009 Dusky Sound, New Zealand earthquake, and the 2015 Illapel earthquake revealed that adopting virtual stations greatly improved the tsunami forecasting accuracy for regions without a dense observation network. Finally, a real-time tsunami detection algorithm using Ensemble Empirical Mode Decomposition (EEMD) is presented. The tsunami signals of the offshore bottom pressure gauge can be automatically separated from the tidal components, seismic waves, and background noise. The algorithm could detect tsunami arrival with a short detection delay and accurately characterize the tsunami amplitude. Furthermore, the tsunami data assimilation approach is combined with the real-time tsunami detection algorithm, which is applied to the tsunami of the 2016 Fukushima earthquake. The proposed tsunami data assimilation approach can be put into practice with the help of the real-time tsunami detection algorithm.