On‐Site Early Warning and Rapid Damage Forecasting Using Single Stations: Outcomes from the REAKT Project

Parolai, Stefano; Bindi, Dino; Boxberger, Tobias; Milkereit, Claus; Fleming, Kevin; Pittore, Massimiliano

doi:10.1785/0220140205

Cited by 32 publications

(25 citation statements)

References 46 publications

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“…The relationship seems to perform reasonably well in the region lying within the range of Pd and PGV values for which it was calibrated (gray-shaded area), although when it is applied outside of the range of PGV and Pd values for which it was calibrated, it tends to overestimate the PGV for larger events, which were recorded at regional distances (i.e., for Pd values ranging between 0.05 and 0.1 cm in Figure 4), and underestimate them (the majority) for smaller magnitude events. However, within the range of low Pd values relevant to this paper, the estimated PGV values are still well below the thresholds proposed in Parolai et al (2015) for a declaration of an ORANGE (3.4 cm/s) or RED (8.1 cm/s) status. Only one recording was observed to overstep the values that would have led to an ORANGE status (note this was the yellow point in the lower panel in Figure 3), but it was classified incorrectly as a no Alarm.…”

Section: Decentralized Earthquake Early Warning Performancementioning

confidence: 49%

“…This software, following the concept of Decentralized Earthquake Early Warning System (i.e., the computations are carried out on-site), detects a possible seismic event and, based on the first few seconds (with a maximum of 3 s) of recordings after the triggering of the event, forecasts the expected peak ground velocity (PGV) at the recording site (Zollo et al, 2010;Parolai et al, 2015Parolai et al, , 2017. The software considers a combination of the expected median plus/minus the standard deviation of the PGV values with respect to pre-selected thresholds for declaring a certain alarm status .…”

Section: Decentralized Earthquake Early Warning Performancementioning

confidence: 99%

“…The software considers a combination of the expected median plus/minus the standard deviation of the PGV values with respect to pre-selected thresholds for declaring a certain alarm status . The alarm status is defined as being either RED (i.e., the expected PGV is likely to be >8.1 cm/s), meaning an Alarm will be issued since at least light damage is to be expected, i.e., an intensity ≥VI, ORANGE (i.e., the expected PGV is likely to lie between 8.1 cm/s and 3.4 cm/s), when only very light damage is expected, i.e., an intensity equal to V, and GREEN (i.e., the expected PGV is likely to be <3.4 cm/s), when no damage is expected, i.e., an intensity ≤IV (see Figure 4 in Parolai et al, 2015Parolai et al, , 2017. Figure 3 shows the results obtained during the analyzed period, where the software carried out the analysis correctly for 99.8% of the events.…”

Section: Decentralized Earthquake Early Warning Performancementioning

confidence: 99%

“…Only one recording was observed to overstep the values that would have led to an ORANGE status (note this was the yellow point in the lower panel in Figure 3), but it was classified incorrectly as a no Alarm. Please note that this is due to the fact that both the median and 16th percentile of the expected PGV for a Pd at this level are below the 3.4 cm/s threshold chosen by Parolai et al (2015), hence a GREEN (No Alarm) status was declared, although the subsequently observed was above this threshold.…”

Section: Decentralized Earthquake Early Warning Performancementioning

confidence: 99%

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Assessing Earthquake Early Warning Using Sparse Networks in Developing Countries: Case Study of the Kyrgyz Republic

et al. 2017

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The first real-time digital strong-motion network in Central Asia has been installed in the Kyrgyz Republic since 2014. Although this network consists of only 19 strong-motion stations, they are located in near-optimal locations for earthquake early warning and rapid response purposes. In fact, it is expected that this network, which utilizes the GFZ-Sentry software, allowing decentralized event assessment calculations, not only will provide useful strong motion data useful for improving future seismic hazard and risk assessment, but will serve as the backbone for regional and on-site earthquake early warning operations. Based on the location of these stations, and travel-time estimates for P-and S-waves, we have determined potential lead times for several major urban areas in Kyrgyzstan (i.e., Bishkek, Osh, and Karakol) and Kazakhstan (Almaty), where we find the implementation of an efficient earthquake early warning system would provide lead times outside the blind zone ranging from several seconds up to several tens of seconds. This was confirmed by the simulation of the possible shaking (and intensity) that would arise considering a series of scenarios based on historical and expected events, and how they affect the major urban centers. Such lead times would allow the instigation of automatic mitigation procedures, while the system as a whole would support prompt and efficient actions to be undertaken over large areas.

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Section: Decentralized Earthquake Early Warning Performancementioning

confidence: 49%

Section: Decentralized Earthquake Early Warning Performancementioning

confidence: 99%

Section: Decentralized Earthquake Early Warning Performancementioning

confidence: 99%

Section: Decentralized Earthquake Early Warning Performancementioning

confidence: 99%

See 2 more Smart Citations

Assessing Earthquake Early Warning Using Sparse Networks in Developing Countries: Case Study of the Kyrgyz Republic

et al. 2017

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“…Based on the performance level, a customized rapid damage forecasting system (Parolai et al 2015Bindi et al 2016) (Fig. 2) may then be developed.…”

Section: Methodsmentioning

confidence: 99%

Toward performance-driven seismic risk monitoring for geothermal platforms: development of ad hoc fragility curves

2018

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IntroductionEarthquakes are mostly natural phenomena that threaten millions of people worldwide. However, they may also be induced, or triggered, by a wide range of anthropogenic activities. Some examples of those are mining, fluid injection and extraction, hydraulic fracturing (Rubinstein and Mahani 2015) and geothermal reservoir processes (Majer et al. 2007). Triggered earthquakes refer to cases where seismogenic areas already close to failure are affected by industrial activities that, despite being relatively small, can perturb this critical state enough to initiate the rupture process. AbstractEarthquakes, despite being a mostly natural phenomenon, may also be induced by a wide range of anthropogenic activities such as mining, fluid injection and extraction, hydraulic fracturing and geothermal reservoir processes. In recent years, the occurrence of induced and triggered seismicity and its potential impact on the built environment have heightened both public concern and regulatory scrutiny, motivating the need for an integrated risk management framework. Non-standard monitoring approaches provide valuable tools for mitigating the risk associated with earthquakes. These solutions include the use of advanced sensors and the implementation of performance-based rapid response systems for infrastructure, as well as monitoring the structural response of buildings and infrastructure in real time. Such technical solutions can be further used for validating damage forecasts determined by probabilistic approaches. The goal of this study is to establish a performance-driven monitoring system for induced seismicity. For this purpose, it is necessary to integrate analytical fragility curves in real time. These fragility curves can be derived by simplified vulnerability models that require input obtained from advanced exposure-monitoring techniques. Considering the case of induced seismicity, this also requires the expected damage to refer to non-structural components. Hence, the derived fragility curves are based on the non-structural damage criteria of typical residences. Therefore, a new approach is presented for defining analytical fragility curves of traditional or historic masonry structures, which can be found in large numbers near the geothermal platforms considered in this work.Keywords: Non-standard monitoring, On-site monitoring system, Fragility curve, Nonstructural components, Induced seismicity Open Access© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Megalooikonomou et al. Geotherm Energy (2018) et al. Geotherm Energy (2018) 6:8 Such earthquakes may reach a relatively high magnitude and are potentially dangerous. Induced earthquakes, wh...

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Predicting the macroseismic intensity from early radiated P wave energy for on‐site earthquake early warning in Italy

et al. 2015

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Earthquake Early Warning Systems (EEWS) are potentially effective tools for risk mitigation in active seismic regions. The present study explores the possibility of predicting the macroseismic intensity within EEW timeframes using the squared velocity integral (IV2) measured on the early P wave signals, a proxy for the P wave radiated energy of earthquakes. This study shows that IV2 correlates better than the peak displacement measured on P waves with both the peak ground velocity and the Housner Intensity, with the latter being recognized by engineers as a reliable proxy for damage assessment. Therefore, using the strong motion recordings of the Italian Accelerometric Archive, a novel relationship between the parameter IV2 and the macroseismic intensity (IM) has been derived. The validity of this relationship has been assessed using the strong motion recordings of the Istituto Nazionale di Geofisica e Vulcanologia Strong Motion Data and Osservatorio Sismico delle Strutture databases, as well as, in the case of the MW 6, 29 May 2012 Emilia earthquake (Italy), comparing the predicted intensities with the ones observed after a macroseismic survey. Our results indicate that P wave IV2 can become a key parameter for the design of on‐site EEWS, capable of proving real‐time predictions of the IM at target sites.

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On‐Site Early Warning and Rapid Damage Forecasting Using Single Stations: Outcomes from the REAKT Project

Cited by 32 publications

References 46 publications

Assessing Earthquake Early Warning Using Sparse Networks in Developing Countries: Case Study of the Kyrgyz Republic

Assessing Earthquake Early Warning Using Sparse Networks in Developing Countries: Case Study of the Kyrgyz Republic

Toward performance-driven seismic risk monitoring for geothermal platforms: development of ad hoc fragility curves

Predicting the macroseismic intensity from early radiated P wave energy for on‐site earthquake early warning in Italy

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