The Multi-Parameter Wireless Sensing System (MPwise): Its Description and Application to Earthquake Risk Mitigation

The Multi-Parameter Wireless Sensing (MPwise) system is an innovative instrumental design that allows different sensor types to be combined with relatively high-performance computing and communications components. These units, which incorporate off-the-shelf components, can undertake complex information integration and processing tasks at the individual unit or node level (when used in a network), allowing the establishment of networks that are linked by advanced, robust and rapid communications routing and network topologies. The system (and its predecessors) was originally designed for earthquake risk mitigation, including earthquake early warning (EEW), rapid response actions, structural health monitoring, and site-effect characterization. For EEW, MPwise units are capable of on-site, decentralized, independent analysis of the recorded ground motion and based on this, may issue an appropriate warning, either by the unit itself or transmitted throughout a network by dedicated alarming procedures. The multi-sensor capabilities of the system allow it to be instrumented with standard strong- and weak-motion sensors, broadband sensors, MEMS (namely accelerometers), cameras, temperature and humidity sensors, and GNSS receivers. In this work, the MPwise hardware, software and communications schema are described, as well as an overview of its possible applications. While focusing on earthquake risk mitigation actions, the aim in the future is to expand its capabilities towards a more multi-hazard and risk mitigation role. Overall, MPwise offers considerable flexibility and has great potential in contributing to natural hazard risk mitigation.

[1]  Hiroo Kanamori,et al.  A threshold-based earthquake early warning using dense accelerometer networks , 2010 .

[2]  Paul Rodriguez.  Optical motion tracking in earthquake-simulation shake table testing: Preliminary results , 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.

[3]  Stefano Parolai,et al.  Joint inversion of phase velocity dispersion and H/V ratio curves from seismic noise recordings using a genetic algorithm, considering higher modes , 2005 .

[4]  Chih-Chen Chang,et al.  Flexible Videogrammetric Technique for Three-Dimensional Structural Vibration Measurement , 2007 .

[5]  Luca Manetti,et al.  Using GPS in structural health monitoring , 2001, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[6]  Kohji Tokimatsu,et al.  S-Wave Velocity Profiling by Inversion of Microtremor H/V Spectrum , 2004 .

[7]  Daniel Leonardo B.R. Jurjo,et al.  Experimental methodology for the dynamic analysis of slender structures based on digital image processing techniques , 2010 .

[8]  Antonio F. Gómez-Skarmeta,et al.  A Real-Time Measurement System for Long-Life Flood Monitoring and Warning Applications , 2012, Sensors.

[9]  James L. Beck,et al.  ePAD: Earthquake Probability‐Based Automated Decision‐Making Framework for Earthquake Early Warning , 2013, Comput. Aided Civ. Infrastructure Eng..

[10]  G. Roe,et al.  REAL-TIME SEISMOLOGY AND EARTHQUAKE DAMAGE MITIGATION , 2005 .

[11]  Hyun Myung,et al.  Vision-based displacement measurement method for high-rise building structures using partitioning approach , 2010 .

[12]  Stefano Parolai,et al.  Joint Deconvolution of Building and Downhole Strong‐Motion Recordings: Evidence for the Seismic Wavefield Being Radiated Back into the Shallow Geological Layers , 2016 .

[13]  James L. Beck,et al.  Earthquake early warning application to buildings , 2014 .

[14]  Carlo Rainieri,et al.  STRUCTURAL HEALTH MONITORING SYSTEMS AS A TOOL FOR SEISMIC PROTECTION , 2008 .

[15]  Massimiliano Pittore,et al.  Assessing Earthquake Early Warning Using Sparse Networks in Developing Countries: Case Study of the Kyrgyz Republic , 2017, Front. Earth Sci..

[16]  Palle Andersen,et al.  Modal Identification from Ambient Responses using Frequency Domain Decomposition , 2000 .

[17]  Jin-Hee Ahn,et al.  Structural dynamic displacement vision system using digital image processing , 2011 .

[18]  Iunio Iervolino,et al.  Performance-based earthquake early warning , 2011 .

[19]  Jens-Peter Redlich,et al.  A wireless mesh sensing network for early warning , 2012, J. Netw. Comput. Appl..

[20]  Can Zulfikar,et al.  The Self-organizing Seismic Early Warning Information Network (SOSEWIN) , 2009 .

[21]  E. Safak,et al.  Enhanced Rapid Post-Event Assessment of Buildings , 2014 .

[22]  James L. Beck,et al.  Earthquake Probability-based Automated Decision-making Framework for Earthquake Early Warning Applications , 2012 .

[23]  Dario Albarello,et al.  Combining genetic and linearized algorithms for a two-step joint inversion of Rayleigh wave dispersion and H/V spectral ratio curves , 2007 .

[24]  Bart De Moor,et al.  Subspace Identification for Linear Systems: Theory ― Implementation ― Applications , 2011 .

[25]  Hyun Myung,et al.  Paired Structured Light for Structural Health Monitoring Robot System , 2011 .

[26]  A. Malherbe,et al.  Seismic structural displacement measurement using a high-speed line-scan camera: experimental validation , 2010 .

[27]  Dino Bindi,et al.  Combining Seismic Noise Techniques for Landslide Characterization , 2014, Pure and Applied Geophysics.

[28]  Falko Kuester,et al.  A methodology for image-based tracking of seismic-induced motions , 2005, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[29]  M. Ohori,et al.  A Comparison of ESAC and FK Methods of Estimating Phase Velocity Using Arbitrarily Shaped Microtremor Arrays , 2002 .

[30]  Robert R. Stewart,et al.  Field data comparisons of MEMS accelerometers and analog geophones , 2008 .

[31]  Joachim Fischer,et al.  GFZ Wireless Seismic Array (GFZ-WISE), a Wireless Mesh Network of Seismic Sensors: New Perspectives for Seismic Noise Array Investigations and Site Monitoring , 2010, Sensors.

[32]  William H. Press,et al.  Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing , 1992 .

[33]  Stefano Parolai,et al.  Performance of the GFZ Decentralized On‐Site Earthquake Early Warning Software (GFZ‐Sentry): Application to K‐NET and KiK‐Net Recordings, Japan , 2017 .

[34]  Massimiliano Pittore,et al.  On‐Site Early Warning and Rapid Damage Forecasting Using Single Stations: Outcomes from the REAKT Project , 2015 .