Early Warning and Impact Forecasting

The interaction of earthquakes and other natural phenomenon with the complex and vulnerable socio-economic system of exposed communities often results in damage and loss, sometimes with catastrophic consequences. As strongly stated by the Sendai Framework for Disaster Risk Reduction (http://www.unisdr.org/we/coordinate/sendai-framework) understanding disaster risk is the first priority in the pursuit of a global strategy for risk reduction. Furthermore, the implementation of early warning systems is one of the viable solutions to actively prevent or reduce damage to specific structures, or even to whole regions.

Impact forecasting

Understanding risk entails firstly understanding the damaging mechanisms of natural perils, and being able to predict their adverse consequences in quantitative and reliable frameworks.

Our group in particular deals with:

  • modeling the potential impact of earthquakes (and other natural perils) in terms of damage to physical assets, loss of life and livelihoods, economical consequences and functional disruption of infrastructure;
  • understanding the underlying uncertainties in the modeling process, and the role of these uncertainties in the subsequent risk management phase;
  • devise innovative methodologies to efficiently collect and integrate the information needed in order to reliably carry out risk assessment and impact forecasting at different spatial scales.

Early Warning and Rapid Response

Early warning refers to the early detection of an event whose unfolding may result in damage and loss, and the subsequent issuing of an alarm that can be used by civil protection authorities to undertake emergency prevention or mitigation actions. In the case of earthquakes, this usually involves installing a dense network of monitoring stations, an endeavour associated with significant economic and technical investment.

Our group is active in the research and development of innovative solutions for Earthquake Early Warning (EEW) that may be scaled according to the available resources, and which also finds applications in economically developing countries. Furthermore, we are advocating for the more tightly knitted integration of rapid impact forecasting into early warning systems in order to support and complement rapid response activities.


On-site Decentralised Early Warning

We are developing an on-site early warning system for the Kyrgyz capital Bishkek. To this regard, several low cost sensors equipped with MEMS accelerometers have been installed in eight buildings distributed within the urban area of the city. The different sensing units communicate with each other via wireless links and the seismic data are streamed in real-time to data centres at GFZ and the Central Asian Institute for Applied Geoscience (CAIAG) using internet. Since each sensing unit has its own computing capabilities, software for data processing can be installed to perform decentralised actions. In particular, each sensing unit can perform event detection tasks and run software for on-site early warning. If a description for the vulnerability of the building is uploaded to the sensing unit, this can be exploited to introduce the expected probability of damage in the early-warning protocol customized for a specific structure.


  • Across



Picozzi, M., Bindi, D., Brondi, P., DiGiacomo, D., Parolai, S., Zollo, A. (2017): Rapid determination of P wave-based energy magnitude: Insights on source parameter scaling of the 2016 Central Italy earthquake sequence . - Geophysical Research Letters, 44, 9, pp. 4036-4045. DOI: doi.org/10.1002/2017GL073228

Bindi, D., Iervolino, I., Parolai, S. (2016): On-site structure-specific real-time risk assessment: perspectives from the REAKT project. - Bulletin of Earthquake Engineering, 14, 9, pp. 2471-2493. DOI: doi.org/10.1007/s10518-016-9889-4

Parolai, S., Boxberger, T., Pilz, M., Bindi, D., Pittore, M., Wieland, M., Fleming, K., Haas, M., Oth, A., Milkereit, C., Dahm, T., Lauterjung, J. (2016): Auf dem Weg zur Schadensabschätzung in Echtzeit: Dezentralisierte regionale und Vor-Ort-Frühwarnung in ACROSS. - System Erde, 6, 1, pp. 32-37. DOI: doi.org/10.2312/GFZ.syserde.06.01.5

Bindi, D., Boxberger, T., Orunbaev, S., Pilz, M., Stankiewicz, J., Pittore, M., Iervolino, I., Ellguth, E., & Parolai, S. (2015). On-site early-warning system for Bishkek (Kyrgyzstan). Annals of Geophysics, 58(1): S0112. DOI: doi.org/10.4401/ag-6664




Performance-based and Hybrid Early Warning System

Earthquake Early Warning and Rapid Response Systems (EEWRRS) should be a viable complement to other disaster risk reduction strategies, particularly in economically developing countries. Our group is actively involved in the development of novel strategies to develop scientific and technological solutions that may be efficiently applied in the many countries suffering from limited resources.

The proposed solution includes a risk estimation module that extracts from a portfolio of precomputed impact scenarios those matching the characterization of the event detected by an optimized real-time monitoring network.

The real-time network integrates both local, on-site components based on low-cost, smart sensor platforms, as well as regional, sparse strong-motion stations. This hybrid solution allows for the optimization of the lead-time and is tailored to the seismotectonic features of the considered region.

A prototype EEWRR System is being developed for the Kyrgyz Republic, with the support of the partner CAIAG  and in collaboration with the Ministry of Emergency Solutions of the Government of the Kyrygz Republic  (MES).


  • Across


Picozzi, M., Bindi, D., Brondi, P., DiGiacomo, D., Parolai, S., Zollo, A. (2017): Rapid determination of P wave-based energy magnitude: Insights on source parameter scaling of the 2016 Central Italy earthquake sequence . - Geophysical Research Letters, 44, 9, pp. 4036-4045. DOI: doi.org/10.1002/2017GL073228

Parolai, S., Boxberger, T., Pilz, M., Fleming, K., Haas, M., Pittore, M., Petrovic, B., Moldobekov, B., Zubovich, A., Lauterjung, J. (2017): Assessing Earthquake Early Warning Using Sparse Networks in Developing Countries: Case Study of the Kyrgyz Republic. - Frontiers in Earth Science, 5, 74. DOI: doi.org/10.3389/feart.2017.00074

Clinton, J., Zollo, A., Marmureanu, A., Zulfikar, C., Parolai, S. (2016): State-of-the art and future of earthquake early warning in the European region. - Bulletin of Earthquake Engineering, 14, 9, pp. 2441-2458. DOI: doi.org/10.1007/s10518-016-9922-7

Parolai, S., Bindi, D., Boxberger, T., Milkereit, C., Fleming, K., & Pittore, M. (2015). On‐Site Early Warning and Rapid Damage Forecasting Using Single Stations: Outcomes from the REAKT Project. Seismological Research Letters, 86(5), 1393-1404, DOI: doi.org/10.1785/0220140205

Stankiewicz, J., Bindi, D., Oth, A., Pittore, M., & Parolai, S. (2015). The Use of Spectral Content to Improve Earthquake Early Warning Systems in Central Asia: Case Study of Bishkek, Kyrgyzstan. Bulletin of the Seismological Society of America, 105(5), 2764-2773, DOI: doi.org/10.1785/0120150036

Bindi, D., Schurr, B., Puglia, R., Russo, E., Strollo, A., Cotton, F., Parolai, S. (2014): A Magnitude Attenuation Function Derived for the 2014 Pisagua (Chile) Sequence Using Strong-Motion Data. - Bulletin of the Seismological Society of America, 104, 6, p. 3145-3152, DOI: doi.org/10.1785/0120140152

Stankiewicz, J., Bindi, D., Oth, A., Parolai, S., (2015). Toward a cross-border early-warning system for Central Asia. Annals of Geophysics, 58(1): S0111, DOI: doi.org/10.4401/ag-6667

Pittore, M., Bindi, D., Stankiewicz, J., Oth, A., Wieland, M., Boxberger, T., Parolai, S. (2014): Toward a Loss-Driven Earthquake Early Warning and Rapid Response System for Kyrgyzstan (Central Asia). - Seismological Research Letters, 85, pp. 1328-1340, DOI: doi.org/10.1785/0220140106

Stankiewicz, J., Bindi, D., Oth, A., Parolai, S. (2013): Designing efficient earthquake early warning systems: case study of Almaty, Kazakhstan. - Journal of Seismology, 17, 4, pp. 1125—1137, DOI: doi.org/10.1007/s10950-013-9381-4



Risk Monitoring and On-site Early Warning for Induced and Triggered Seismicity

EEW systems, coupled with non-standard monitoring approaches, provide valuable tools for mitigating the risk associated with induced and triggered seismicity. Our group is carrying out research that includes the rapid and non-invasive characterization of the built-environment, in terms of exposure and vulnerability, and the use of advanced sensor platforms for the implementation of performance-based real-time risk monitoring and early warning.

A new generation of smart, distributed sensor devices called MP-WISE  has been developed in order to provide the community with a flexible and powerful sensor platform for on-site real-time risk monitoring and early warning.




Picozzi, M., Oth, A., Parolai, S., Bindi, D., De Landro, G., Amoroso, O. (2017): Accurate estimation of seismic source parameters of induced seismicity by a combined approach of generalized inversion and genetic algorithm: Application to The Geysers geothermal area, California. - Journal of Geophysical Research, 122, 5, pp. 3916-3933. DOI: doi.org/10.1002/2016JB013690

Woith, H., Parolai, S., Boxberger, T., Picozzi, M., Özmen, Ö. T., Milkereit, C., Lühr, B., Zschau, J. (2014): Spatio-temporal variability of seismic noise above a geothermal reservoir. - Journal of Applied Geophysics, 106, p. 128-138, DOI: doi.org/10.1016/j.jappgeo.2014.04.012




Probabilistic and Scenario-Based Risk Assessment, Urban Risk

Understanding Disaster Risk strongly depends on how accurately we are able to model the interactions between natural phenomenon such as earthquakes, and the complex infrastructure of the built environment that modern societies rely on. Our group is developing methodologies to quantitatively estimate the physical and economical impact of earthquakes (and other natural hazards) on exposed communities. The assessment of earthquake risk over extended geographical regions is usually carried out probabilistically, employing the information provided by Probabilistic Seismic Hazard Assessment (PSHA) estimates. Over smaller scales, and specifically when considering large towns and urban sprawls, a different approach is followed, based on detailed scenarios. This type of “Urban Risk” assessment requires, however, a more detailed understanding of the local exposure and vulnerability (exposure and vulnerability), as well as a careful consideration of local amplification effects (site effects).



Bindi, D., Parolai, S. (2016): Reply to “Comment on ‘Total Probability Theorem Versus Shakeability: A Comparison between Two Seismic‐Hazard Approaches Used in Central Asia’ by D. Bindi and S. Parolai” by A. A. Gusev. - Seismological Research Letters, 87, 5, pp. 1125-1129. DOI: doi.org/10.1785/0220160052

Haas, M., Agnon, A., Bindi, D., Parolai, S., Pittore, M. (2016): Data‐Driven Seismic‐Hazard Models Prepared for a Seismic Risk Assessment in the Dead Sea Region. - Bulletin of the Seismological Society of America, 106, 6, pp. 2584-2598. DOI: doi.org/10.1785/0120150361

Kottmeier, C., Agnon, A., Al-Halbouni, D., Alpert, P., Corsmeier, U., Dahm, T., Eshel, A., Geyer, S., Haas, M., Holohan, E., Kalthoff, N., Kishcha, P., Krawczyk, C., Lati, J., Laronne, J. B., Lott, F., Mallas, U., Merz, R., Metzger, J., Mohsen, A., Morin, E., Nied, M., Rödiger, T., Salameh, E., Sawarieh, A., Shannak, B., Siebert, C., Weber, M. (2016): New perspectives on interdisciplinary earth science at the Dead Sea: The DESERVE project. - Science of the Total Environment, 544, pp. 1045-1058. DOI: doi.org/10.1016/j.scitotenv.2015.12.003

Ullah, S. (2016): Seismic hazard assessment in Central Asia: combining site effects investigations and probabilistic seismic hazard, PhD Thesis, Berlin : Technische Universität, 163 p.

Wieland, M., Liu, W., Yamazaki, F. (2016): Learning Change from Synthetic Aperture Radar Images: Performance Evaluation of a Support Vector Machine to Detect Earthquake and Tsunami-Induced Changes. - Remote Sensing, 8, 10, 792. DOI: doi.org/10.3390/rs8100792

Bindi, D., Parolai, S., (2015). Total Probability Theorem Versus Shakeability: A Comparison between Two Seismic‐Hazard Approaches Used in Central Asia. Seismological Research Letters, 86(4), 1178-1184. DOI: doi.org/10.1785/0220150039.

Mikhailova, N. N., Mukambayev, A. S., Aristova, I., Kulikova, G., Ullah, S., Pilz, M., Bindi, D., (2015). Central Asia earthquake catalogue from ancient time to 2009. Annals of Geophysics, 58(1): S0102. DOI: doi.org/10.4401/ag-6681.

Ullah, S., Bindi, D., Pilz, M., Danciu, L., Weatherill, G., Zuccolo, E., Ischuk, A., Mikhailova, N. N., Abdrakhmatov, K., Parolai, S., (2015). Probabilistic seismic hazard assessment for Central Asia. Annals of Geophysics, 58(1): S0103. DOI: doi.org/10.4401/ag-6687.

Ullah, S., Bindi, D., Pilz, M., Parolai, S., & 2.1 Physics of Earthquakes and Volcanoes, 2.0 Physics of the Earth, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum (2015). Probabilistic seismic hazard assessment of Bishkek, Kyrgyzstan, considering empirically estimated site effects. Annals of Geophysics, 58(1): S0105. DOI: doi.org/10.4401/ag-6682.

Bindi, D., Parolai, S., Gómez-Capera, A., Locati, M., Kalmetyeva, Z., Mikhailova, N. (2014): Locations and magnitudes of earthquakes in Central Asia from seismic intensity data. - Journal of Seismology, 18, 1, p. 1-21. DOI: doi.org/10.1007/s10950-013-9392-1

Tyagunov, S., Pittore, M., Wieland, M., Parolai, S., Bindi, D., Fleming, K., Zschau, J. (2014): Uncertainty and sensitivity analyses in seismic risk assessments on the example of Cologne, Germany. - Natural Hazards and Earth System Sciences (NHESS), 14, 6, p. 1625-1640. DOI: doi.org/10.5194/nhess-14-1625-2014




Multi-hazard and Compound Risk Assessment

In a world progressively more exposed and vulnerable to natural phenomenons, the quantitative assessment of risk and the related estimation of impact should be considered in a more holistic perspective. Our group is involved in several projects tackling different hazards and their individual and combined impact models.

This includes:

  • the consideration of different, possibly concurrent hazards, and their cascading effects;
  • the complex interactions that frequently occur at the vulnerability level;
  • the individual and joint contribution of each peril to the overall impact.



Hu, K., Awange, J. L., Khandu, ., Forootan, E., Goncalves, R. M., Fleming, K. (2017): Hydrogeological characterisation of groundwater over Brazil using remotely sensed and model products. - Science of the Total Environment, 599, pp. 372-386. DOI: doi.org/10.1016/j.scitotenv.2017.04.188

Petrovic, B., Dikmen, S. U., Parolai, S. (2017 online first): Real data and numerical simulations-based approaches for estimating the dynamic characteristics of a tunnel formwork building. - Bulletin of Earthquake Engineering. DOI: doi.org/10.1007/s10518-017-0250-3

Fleming, K., Parolai, S., Garcia-Aristizabal, A., Tyagunov, S., Vorogushyn, S., Kreibich, H., Mahlke, H. (2016): Harmonizing and comparing single-type natural hazard risk estimations. - Annals of Geophysics, 59, 2, S0216. DOI: doi.org/10.4401/ag-6987

Khan, S., Sasgen, I., Bevis, M., Van Dam, T., Bamber, J., Wahr, J., Willis, M., Kjaer, K., Wouters, B., Helm, V., Csatho, B., Fleming, K., Björk, A., Aschwanden, A., Knudsen, P., Munneke, P. (2016): Geodetic measurements reveal similarities between post-Last glacial Maximum and present-day mass loss from the Greenland ice sheet. - Science Advances, 2, 9, e1600931. DOI: doi.org/10.1126/sciadv.1600931

Komendantova, N., Scolobig, A., Garcia-Aristizabal, A., Monfort, D., Fleming, K. (2016): Multi-risk approach and urban resilience. - International Journal of Disaster Resilience in the Built Environment, 7, 2, pp. 114-132. DOI: doi.org/10.1108/IJDRBE-03-2015-0013

Kottmeier, C., Agnon, A., Al-Halbouni, D., Alpert, P., Corsmeier, U., Dahm, T., Eshel, A., Geyer, S., Haas, M., Holohan, E., Kalthoff, N., Kishcha, P., Krawczyk, C., Lati, J., Laronne, J. B., Lott, F., Mallas, U., Merz, R., Metzger, J., Mohsen, A., Morin, E., Nied, M., Rödiger, T., Salameh, E., Sawarieh, A., Shannak, B., Siebert, C., Weber, M. (2016): New perspectives on interdisciplinary earth science at the Dead Sea: The DESERVE project. - Science of the Total Environment, 544, pp. 1045-1058. DOI: doi.org/10.1016/j.scitotenv.2015.12.003

Liu, Z., Nadim, F., Garcia-Aristizabal, A., Mignan, A., Fleming, K., Luna, B. Q., (2015). A three-level framework for multi-risk assessment. Georisk, 9(2), 59-74. DOI: doi.org/10.1080/17499518.2015.1041989.

Saponaro, A., Pilz, M., Bindi, D., Parolai, S., (2015). The contribution of EMCA to landslide susceptibility mapping in Central Asia. Annals of Geophysics, 58(1): S0113. DOI: doi.org/10.4401/ag-6668.

Saponaro, A., Pilz, M., Wieland, M., Bindi, D., Moldobekov, B., Parolai, S., (2015). Landslide susceptibility analysis in data-scarce regions: the case of Kyrgyzstan. Bulletin of Engineering Geology and the Environment, 74(4), 1117-1136. DOI: doi.org/10.1007/s10064-014-0709-2.

Komendantova, N., Mrzyglocki, R., Mignan, A., Khazai, B., Wenzel, F., Patt, A., Fleming, K. (2014): Multi-hazard and multi-risk decision-support tools as a part of participatory risk governance: Feedback from civil protection stakeholders. - International Journal of Disaster Risk Reduction, 8, p. 50-67. DOI: doi.org/10.1016/j.ijdrr.2013.12.006

Kreibich, H., Bubeck, P., Kunz, M., Mahlke, H., Parolai, S., Khazai, B., Daniell, J., Lakes, T., Schröter, K. (2014): A review of multiple natural hazards and risks in Germany. - Natural Hazards, 74, 3, p. 2279-2304. DOI: doi.org/10.1007/s11069-014-1265-6




Multi-parameter Monitoring of Buildings and Critical Infrastructure

The aim of these activities is concentrated on the development of ad-hoc multi-parameters sensors (and software) for Decentralized Onsite Early Warning and Building Health Monitoring. These sensors are the backbone of several recording systems that GFZ is operating in different parts of the world (Central Asia, Turkey, Greece etc.).



Boxberger, T., Fleming, K., Pittore, M., Parolai, S., Pilz, M., Mikulla, S. (2017): The Multi-Parameter Wireless Sensing System (MPwise): Its Description and Application to Earthquake Risk Mitigation. - Sensors, 17, 10, 2400. DOI: doi.org/10.3390/s17102400

Boxberger, T., Fleming, K., Pittore, M., Parolai, S., Pilz, M., Mikulla, S. (2017): The Multi-Parameter Wireless Sensing System (MPwise): Its Description and Application to Earthquake Risk Mitigation. - Sensors, 17, 10, 2400. DOI: doi.org/10.3390/s17102400

Karapetrou, S., Manakou, M., Bindi, D., Petrovic, B., Pitilakis, K. (2016): “Time-building specific” seismic vulnerability assessment of a hospital RC building using field monitoring data. - Engineering Structures, 112, pp. 114-132. DOI: doi.org/10.1016/j.engstruct.2016.01.009

Petrovic, B., Parolai, S. (2016): Joint Deconvolution of Building and Downhole Strong‐Motion Recordings: Evidence for the Seismic Wavefield Being Radiated Back into the Shallow Geological Layers. - Bulletin of the Seismological Society of America, 106, 4, pp. 1720-1732. DOI: doi.org/10.1785/0120150326

Pitilakis, K., Karapetrou, S., Bindi, D., Manakou, M., Petrovic, B., Roumelioti, Z., Boxberger, T., Parolai, S. (2016): Structural monitoring and earthquake early warning systems for the AHEPA hospital in Thessaloniki. - Bulletin of Earthquake Engineering, 14, 9, pp. 2543-2563. DOI: doi.org/10.1007/s10518-016-9916-5

Raub, C., Bohnhoff, M., Petrovic, B., Parolai, S., Malin, P., Yanik, K., Kartal, R. F., Kiliç, T. (2016): Seismic‐Wave Propagation in Shallow Layers at the GONAF‐Tuzla Site, Istanbul, Turkey. - Bulletin of the Seismological Society of America, 106, 3, pp. 912-927. DOI: doi.org/10.1785/0120150216

Bindi, D., Petrovic, B., Karapetrou, S., Manakou, M., Boxberger, T., Raptakis, D., Pitilakis, K. D., Parolai, S., (2015). Seismic response of an 8-story RC-building from ambient vibration analysis. Bulletin of Earthquake Engineering, 13(7), 2095-2120. DOI: doi.org/10.1007/s10518-014-9713-y.

Petrovic, B., Bindi, D., Pilz, M., Serio, M., Orunbaev, S., Niyazov, J., Hakimov, F., Yasunov, P., Begaliev, U. T., Parolai, S., (2015). Building monitoring in Bishkek and Dushanbe by the use of ambient vibration analysis. Annals of Geophysics, 58(1): S0110. DOI: doi.org/10.4401/ag-6679.




Time- and State-dependent Earthquake Risk Models

Two classical assumptions in seismic risk analysis over larger scales (e.g., urban) are that 1) damage from fore and aftershocks is negligible and 2) the time between consequent events is sufficient to repair damaged structures to their intact state. Considering the need to relax these assumptions, we are developing stochastic models that are able to deal more realistically over urban scales. The central parts of this research therefore deal with the development of stochastic models for complete seismic sequences, the derivation of state-dependent fragility models, and time-dependent repair functions.






Multi-resolution Dynamic Exposure Modelling

Different natural (e.g., earthquakes, tsunamis, tornadoes) and anthropogenic (e.g., industrial accidents) hazards threaten millions of people every day all over the world. Yet, while these hazards may be so different from each other, the exposed assets are mostly the same: populations, buildings, infrastructure and the environment. Exposure should be regarded as a dynamic process, as best exemplified by rapid urbanisation, the depopulation of rural areas, and all of the changes associated with the evolution of the settlements themselves, in turn all dependent upon the economic development of the region of concern. The challenge is thus to find innovative, efficient methods to collect, organize, store and communicate exposure data at different spatial scales, while also accounting for its inherent spatio-temporal dynamics.

Our group develops innovative methodologies for exposure data collection, integration and modeling. In particular, we focus on the following:

  • use of medium-resolution multispectral satellite images to infer land-use/land-cover and for the characterization of built-up areas;
  • methodologies for spatial sampling and data collection through rapid remote visual screening based on mobile mapping (link to RRVS);
  • incremental assimilation of building-specific data into statistical exposure models using machine learning and geo-statistical methodologies.



Pittore, M., Wieland, M., Fleming, K. (2017): Perspectives on global dynamic exposure modelling for geo-risk assessment. - Natural Hazards, 86, pp. 7-30. DOI: doi.org/10.1007/s11069-016-2437-3

Geiß, C., Thoma, M., Pittore, M., Wieland, M., Dech, S. W., Taubenböck, H. (2017): Multitask Active Learning for Characterization of Built Environments With Multisensor Earth Observation Data. - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 12, pp. 5583-5597. DOI: doi.org/10.1109/JSTARS.2017.2748339

Wieland, M., Pittore, M. (2017): A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution. - ISPRS International Journal of Geo-Information, 6, 4, 114. DOI: doi.org/10.3390/ijgi6040114

Wieland, M., Pittore, M. (2016): Large-area settlement pattern recognition from Landsat-8 data. - ISPRS Journal of Photogrammetry and Remote Sensing, 119, pp. 294-308. DOI: doi.org/10.1016/j.isprsjprs.2016.06.010

Wieland, M., Torres, Y., Pittore, M., Benito, B. (2016): Object-based urban structure type pattern recognition from Landsat TM with a Support Vector Machine. - International Journal of Remote Sensing, 37, 17, pp. 4059-4083. DOI: doi.org/10.1080/01431161.2016.1207261

Pittore, M., (2015). Focus maps: a means of prioritizing data collection for efficient geo-risk assessment. Annals of Geophysics, 58(1): S0107. doi:10.4401/ag-6692.

Pittore, M., Wieland, M., Errize, M., Kariptas, C., Güngör, I. (2015): Improving Post-Earthquake Insurance Claim Management: A Novel Approach to Prioritize Geospatial Data Collection. - ISPRS International Journal of Geo-Information, 4, 4, pp. 2401—2427. DOI: doi.org/10.3390/ijgi4042401

Wieland, M., Pittore, M., Parolai, S., Begaliev, U., Yasunov, P., Niyazov, J., Tyagunov, S., Moldobekov, B., Saidiy, S., Ilyasov, I., Abakanov, T., (2015). Towards a cross-border exposure model for the Earthquake Model Central Asia. Annals of Geophysics, 58(1): S0106. DOI: doi.org/10.4401/ag-6663.

Geiß, C., Taubenböck, H., Tyagunov, S., Tisch, A., Post, J., Lakes, T. (2014): Assessment of Seismic Building Vulnerability from Space. - Earthquake Spectra, 30, 4, p. 1553-1583. DOI: doi.org/10.1193/121812EQS350M

Wieland, M., Pittore, M. (2014): Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images. - Remote Sensing, 6, 4, p. 2912-2939. DOI: doi.org/10.3390/rs6042912

Wieland, M., Pittore, M., Parolai, S., Begaliev, U., Yasunov, P., Tyagunov, S., Moldobekov, B., Saidiy, S., Ilyasov, I., & Abakanov, T. (2015). A Multiscale Exposure Model for Seismic Risk Assessment in Central Asia. Seismological Research Letters, 86, 210-222. DOI: doi.org/10.1785/0220140130.




Real‐time correction of frequency‐dependent site‐response

The distribution of damage due to recent earthquakes has shown that the effects of shallow geological structures on the level of ground shaking represents an important factor in engineering seismology. Whereas it is standard practice to estimate site amplification factors in the frequency domain, their application to the real‐time modeling of ground motion is not yet fully established. We are developing methodologies for the real‐time correction of frequency‐dependent site‐response factors, which accounts not only for the modulus, but also for the changes in the signal phase related to local site conditions. The transformation of the complex standard spectral ratios to a causal recursive filter in the time domain allows for the forecasting of the waveforms for soft‐soil sites almost in real time when the signal is recorded earlier at a reference site. Such an approach will allow the level of ground motion at soft‐soil sites with respect to arrival time, energy, duration, and frequency content to be well constrained, even in cases of highly spatially variable amplification patterns.


Boxberger, T., Pilz, M., Parolai, S. (2017): Shear wave velocity versus quality factor: results from seismic noise recordings. - Geophysical Journal International, 210, 2, pp. 660-670. DOI: doi.org/10.1093/gji/ggx161

Pilz, M., Parolai, S., Woith, H. (2017): A 3-D algorithm based on the combined inversion of Rayleigh and Love waves for imaging and monitoring of shallow structures . - Geophysical Journal International, 209, 1, pp. 152-166. DOI: doi.org/10.1093/gji/ggx005

Pilz, M., Fäh, D. (2017): The contribution of scattering to near-surface attenuation. - Journal of Seismology, 21, 4, pp. 837-855. DOI: doi.org/10.1007/s10950-017-9638-4

Pilz, M., Parolai, S. (2016): Ground‐Motion Forecasting Using a Reference Station and Complex Site‐Response Functions Accounting for the Shallow Geology. - Bulletin of the Seismological Society of America, 106, 4, pp. 1570-1583. DOI: doi.org/10.1785/0120150281

Pilz, M., Parolai, S. (2016): On the use of the autocorrelation function: the constraint of using frequency band-limited signals for monitoring relative velocity changes. - Journal of Seismology, 20, 3, pp. 921-934. DOI: doi.org/10.1007/s10950-016-9571-y

Parolai, S., Bindi, D., Pilz, M., (2015). k0: The role of Intrinsic and Scattering Attenuation. Bulletin of the Seismological Society of America, 105(2A), 1049-1052. DOI: doi.org/10.1785/0120140305.

Pilz, M., Abakanov, T., Abdrakhmatov, K., Bindi, D., Boxberger, T., Moldobekov, B., Orunbaev, S., Silacheva, N., Ullah, S., Usupaev, U., Yasunov, P., Parolai, S., (2015). An overview on the seismic microzonation and site effect studies in Central Asia. Annals of Geophysics, 58(1): S0104. DOI: doi.org/10.4401/ag-6662.

Parolai, S. (2014): Shear wave quality factor Qs profiling using seismic noise data from microarrays. - Journal of Seismology, 18, 3, p. 695-704. DOI: doi.org/10.1007/s10950-014-9440-5

Pilz, M., Parolai, S., Bindi, D., Saponaro, A., Abdybachaev, U. (2014): Combining Seismic Noise Techniques for Landslide Characterization. - Pure and Applied Geophysics, 171, 8, p. 1729-1745. DOI: doi.org/10.1007/s00024-013-0733-3

Pilz, M., Parolai, S. (2014): Statistical properties of the seismic noise field: influence of soil heterogeneities. - Geophysical Journal International, 199, 1, p. 430-440. DOI: doi.org/10.1093/gji/ggu273




The Risk Assessment Model Simulation for Emergency Training Exercises


One of the most important aspects of natural disaster risk reduction (DRR) and developing better means of climate change adaption (CCA) is for the various stakeholders (e.g., civil protection practitioners, land-use planners, the insurance industry, infrastructure operators and ultimately, the general public) to communicate and exchange information with each other. This involves understanding what the main concerns and goals of each group are, and what resources (financial, knowledge, etc.) each requires? Furthermore, it needs to be considered how each sector can help and complement the others in terms of achieving their aims. One means of gaining such understanding is by so-called scenario training exercises. This requires a group of stakeholders to participate in a table-top exercise where each has a defined "role", and where a (admittedly simplified) scenario is presented. This leads the participants to interact with each other in order to deal with the presented crisis or situation as effectively as possibly, with the resources made available.

Such exercises should not be considered "operational" as it is a case of the participation and interactions between participants that is of importance, not the final result. These exercises are being developed within the ESPREssO project, where three major "challenges" are being considered, dealing with

  • proposing ways to create coherent and integrated DRR and CCA national and European policies,
  • enhancing risk management capabilities by bridging the gap between scientific and legal/policy issues, and
  • issues surrounding the effective management of cross-border crises. In each case, a different style of exercise is being developed, where the issues surrounding each challenge are explored.






Capacity Building and Technology Transfer

The “Risk Team” organizes and deploys capacity building and training workshops on advanced exposure data collection and modeling. Several one or two-week workshops have been carried out in Chile and Peru, and Costa Rica. The group is also involved in the International Training Course in Seismology that GFZ organizes every year , as well as in other training activities.




Damage Mapping and Bayesian estimation of Macroseismic Intensity

The seismological community acknowledges the essential contribution of macroseismic assessment to the compilation of seismic catalogues used for seismic hazard assessment. Furthermore, macroseismic observations are routinely employed by Civil Protection authorities in the aftermath of damaging events to improve their  decision making capacity. The EMS-98 scale allows a better consideration of the type and vulnerability of buildings in seismic areas. We are developing a novel methodology for the rapid mapping of damage to built structures, and for the probabilistic estimation of the Macroseismic Intensity in the epicentral area of a major event, according to the EMS-98 scale. The methodology includes the use of mobile mapping and a collaborative on-line platform for rapid post-earthquake reconnaissance, which allows the rapid and safe collection of building-by-building damage data in the hours and days immediately following a destructive event. A Bayesian updating scheme is proposed to integrate direct damage observations and prior information, hence allowing the consideration of ancillary data and expert judgment.





Datasets and software tools

  • Rapid Environmental mapping (REM)
  • Rapid Remote Visual Screening (RRVS)
  • Rapid Remote Damage Assessment (RRDA)

Early Warning Team in Section 2.6 Seismic Hazard and Risk Dynamics

Dr.Massimiliano Pittore+49 331 288-28668H7/103
Dr.  Marco Pilz      +49 331 288-28661   H7/106
Dr.Kevin Fleming   +49 331 288-28662H7/102
Dr. Dipl.-Ing.Konstantinos Megalooikonomou+49 331 288-28671       H7/102 
Dipl.-Geophys.Tobias Boxberger+49 331 288-28674H7/109
M.Sc.  Michael Haas    +49 331 288-28930H7/104
M.Sc.Akhmad Haifani+49 331 288-28664H7/101



Massimiliano Pittore
Group Leader
Dr. Massimiliano Pittore
Seismic Hazard and Risk Dynamics
Helmholtzstraße 6/7
Building H 7, Room 103
14467 Potsdam
+49 331 288-28668