Section 7.1: Centre for Early Warning Systems

The on-site early warning approach (aiming at identifying potential damaging ground shaking from the first few seconds of recording of the generally not damaging P-waves) is particularly useful for target areas (like in most of Europe or Central Asia) where dense strong motion networks are not available. These target areas are affected “only” by moderate to strong events, or they are not configured for real-time actions. The on-site early warning approach also represents an optimal solution for critical facilities. Within this context, the requirement for a single instrument to detect and identify a possibly dangerous event in a reliable and timely manner and to forecast the expected risk for a target structure or several of them is a challenging task. Exploiting the computational power of modern sensing units for building monitoring, the implementation of this concept can be transferred to each sensor, resulting in the creation of a decentralized performance-based early warning system.


The MP-wise is an innovative system that allow several kind of sensors to be combined witha highly performant computing system able to implement complex information integration andprocessing tasks at node (sensor) level and therefore suitable for a wide range of possible applications.The aim of the new multi hazard monitoring and early warning system is to fulfillthe needs for different types of application, mainly regional and on-site earthquake early warningand rapid response systems, structural health monitoring and site-effect estimation by twodimensionalarrays. However, it might be useful also for geotechnical problems like landslides monitoring, or other geohazards like tsunami. The system is designed in a modular way, allowing to design easily different configurations with respect to the used sensors and the communication interfaces depending on the the different application. Referring to the possible sensors,the prototype is capable to be instrumented (and was tested for the connection) with standard strong motion, weak motion sensor, broadband sensors, MEMS sensor including accelerometer and gyroscope, camera, temperature and humidity sensor and a low cost GNSS system.


Layer / Description


  • regional and onsite (also decentralised!) earthquake early warning
  • rapid response systems
  • building and structural health monitoring
  • site-effect estimation by twodimensional arrays

Acquisition: number of channel, external/internal sensors

  • 6 channel
  • external sensor (Broadband, StrongMotion, or Geophone)
  • internal MEMS sensor
  • Full HD camera
  • low cost GPS receiver temperature and humidity sensor

Processing on node level:

  • on-site early warning and assessment of the predicted damage of buildings
  • Computer Vision-based monitoring
  • displacement estimation by joint processingof single-frequency GPS and MEMS-accelerometer data
  • automatic audiovisual alarming system (using the touch display and loudspeakeror external devices)

Communication: Hardware and software application

  • LAN, WLAN including self organizing wireless mesh network topology, UMTS secure data transmission via OpenVPN

Use cases - examples

  • Pre-event seismic monitoring during swarms
  • Earthquake on-site early warningEarthquake on-site damage forecasting
  • Emergency reconnaissance and communication
  • Earthquake post-event building tagging
  • Pre-event landslide monitoring and earlywarning
  • Flood monitoring
  • REM database schema: postgresql / postgis schema based on the SENSUM db schema, which employs the GEM v2.0 taxonomy and forms the basis for most REM tools.
  • REM-RRVS (Remote Rapid Visual Survey) is a web tool for exposure screening based on omnidirectional images
  • REM-SATEX (SATellite EXposure) is a QGIS plugin to process LANDSAT satellite images
  • REM optimized routing: module for optimized routing for mobile mapping campaigns

more information on:



The EMCA (Earthquake Model Central Asia) catalogue (Mikhailova et al., 2015) includes information for 33620 earthquakes that occurred in Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan). The catalogue provides for each event the estimated magnitude in terms of MLH (surface wave magnitude) scale, widely used in former USSR countries.MLH magnitudes range from 1.5 to 8.3.

The catalogue includes the standard parametric information required for seismic hazard studies (i.e., time, location and magnitude values) and has been composed by integrating different sources (using different magnitude scales) and harmonised in terms of MLH scale. The MLH magnitude is determined from the horizontal component of surface waves (Rautian and Khalturin, 1994) and is reported in most of the seismic bulletins issued by seismological observatories in Central Asia. For the instrumental period MLH magnitude was estimated, when not directly measured, either from body wave magnitude (Mb), the energy class (K) or Mpva (regional magnitude by body waves determined by P-wave recorded by short-period instruments) using empirical regression analyses.

The dataset is freely available for scientific use and can be downloaded here.


The area sources for Central Asia within the EMCA model are defined by considering the pattern of crustal seismicity down to 50 km depth. Tectonic and geological information, such as the position and strike distribution of known faults, have also been taken into account when available.

In order to obtain a robust estimation of the necessary parameters for PSHA derived by the statistical analysis of the seismicity, due to the scarcity of data in some of the areas covered by the model, super zones are introduced. These super zones are defined by combining area sources based on similarities in their tectonic regime, and taking into account local expert’s judgments. The super zones are used to estimate:

(1) the completeness time of the earthquake catalogue, (2) the depth distribution of seismicity, (3) the tectonic regime through focal mechanisms analysis, (4) the maximum magnitude and (5) the b values via the GR relationship.

The earthquake catalogue for focal mechanism is extracted from the Harvard Global Centroid Moment Tensor Catalog (Ekström and Nettles, 2013). The Boore et al. (1997) convention is used for the classification of the focal mechanism.


The dataset is freely available for scientific use and can be downloaded here.