Earthquake disaster mitigation involves different elements, ranging from analysis of hazards (e.g. physical description of ground shaking) to its impact on built and natural environment, from vulnerability and exposure to hazards to capacity building and resilience, from long-term preparedness to post-event response. The scientific base of this process involves various seismic hazard/risk models, developed at different time scales and by different methods, as well as the use of heterogeneous observations and multi-disciplinary information. Accordingly, we welcome contributions about different types of seismic hazards research and assessments, both methodological and practical, and their applications to disaster risk reduction in terms of physical and social vulnerability, capacity and resilience.
This session aims to tackle theoretical and implementation issues, as well as aspects of communication and science policy, which are all essential elements towards effective disasters mitigation, and include:
⇒ earthquake hazard and risk estimation at different time and space scales, including their performance verification against observations (including unconventional seismological observations);
⇒ time-dependent seismic hazard and risk assessments (including contribution of aftershocks), and post-event information (early warning, alerts) for emergency management;
⇒ earthquake-induced cascading effects (e.g. landslides, tsunamis, etc) and multi-risk assessment (e.g. earthquake plus flooding).
Different hazards can combine and mutually enhance their impact, turning into a disaster. The COVID-19 pandemic pointed out the low preparedness of human society to large-scale crises. In particular, there have been several damaging earthquakes during the pandemic (Croatia, Greece, USA, Iran), which highlighted the impacts of concurrent hazards and the complexity in handling such situations.
The interdisciplinary session will provide an opportunity to share lessons learned from recent events, best practices and experience gained with different methods, providing opportunities to advance our understanding of disaster risk in "all its dimensions of vulnerability, capacity, exposure of persons and assets, hazard characteristics and the environment", while simultaneously highlighting existing gaps and future research directions.
vPICO presentations: Mon, 26 Apr
The space concept of the Unified Scaling Law for Earthquakes (USLE), which generalizes the Gutenberg-Richter relationship making use of the fractal distribution of earthquake sources in a seismic region, has been applied to seismicity in Northeastern Italy. In particular, the temporal variations of USLE coefficients have been investigated, with the aim to get new insights in the evolving dynamics of seismicity within different tectonic domains of Friuli-Venezia Giulia region (FVG) and its surroundings.
For this purpose, we resorted to the catalog compiled at the National Institute of Oceanography and Applied Geophysics (OGS), considering earthquakes occurred in the period 1995 – 2019, with epicenters within three sub-regions of the territory under investigation, delimited based on main geological and tectonic features (Bressan et al. 2018, J. Seismol. 22, 1563–1578). To quantify the observed variability of seismic dynamics, a multi-parametric analysis has been carried out for each sub-region by means of several moving averages, including: the inter-event time, τ; the cumulative Benioff strain release, Σ; the USLE control parameter, η and the USLE coefficients, estimated for moving six-years time intervals. The analysis evidenced that the USLE coefficients in FVG region are time-dependent and show up correlated (Nekrasova and Peresan 2021, Frontiers in Earth Science, 8, 624). Moreover, the dynamical features of the considered parameters in the three sub-regions highlighted a number of different seismic regimes; in particular, major changes in the parameters are associated to occurrence of the 12 April 1998 (M5.6) and the 12 July 2004 (M5.1) Kobarid (Slovenia) earthquakes within the corresponding sub-region.
The results obtained for seismicity in Northeastern Italy and surrounding areas confirm similar analysis performed on a global scale, in advance and after the largest earthquakes worldwide. In addition, the analysis evidenced the spatially heterogeneous and non-stationary features of seismicity, in agreement with results from independent analysis of background seismicity within the investigated territory (Benali et al. 2020, Stoch. Environ. Res. Risk. Assess. 34, 775–791), thus suggesting the opportunity of resorting to time-dependent models of earthquakes occurrence for improving local seismic hazard assessment.
How to cite: Peresan, A. and Nekrasova, A.: Space-Time Dependent features of the Unified Scaling Law for Earthquakes in Northeastern Italy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3196, https://doi.org/10.5194/egusphere-egu21-3196, 2021.
We investigate the statistical properties of declustered catalogs as obtained from the application of two different data-driven declustering algorithms, namely the nearest-neighbor method and the stochastic declustering method (Benali et al., Stoch. Environ Res Risk Assess, 2020). The nearest-neighbor method partitions earthquakes into background and clustered components, based on nearest-neighbor distances between earthquakes in the space-time-magnitude domain (Zaliapin and Ben-Zion, J Geophys Res, 2013); the stochastic declustering method classifies earthquakes into background and clustered components through a probabilistic procedure based on the estimation of the space-time ETAS model (Zhuang et al., J Geophys Res, 2004).
Two Italian case studies are considered: North-Eastern Italy (data from OGS Bulletins) and Central Italy (data from ISIDe catalog). For both case studies, the time series of background seismicity are obtained from the two declustering methods. Then we investigate the general assumption according to which the temporal sequence of background seismicity is suitably modelled by the stationary Poisson model. For this purpose, several features and statistical tests are considered to verify the main properties that characterize Poisson processes (e.g. events are independent, exponential inter-arrival times, etc.).
Whenever the Poissonian hypothesis is rejected, we get evidence of certain heterogeneity in the background sequence, which leads us to rule out the simple Poisson process for background seismicity modeling. As a simple and more suitable alternative, we consider here the Markov Modulated Poisson Process (MMPP model), which allows the Poisson seismicity rate to change over time according to a finite (unknown) number of states of the system. The MMPP model turns out suitable for identifying and quantifying heterogeneities in background seismicity, as well as for comparing them against the two considered declustering algorithms.
How to cite: Varini, E., Peresan, A., and Benali, A.: Markov modulated Poisson processes for stochastic modelling of background seismicity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11036, https://doi.org/10.5194/egusphere-egu21-11036, 2021.
Mechanisms of stress transfer and probabilistic models have been widely investigated to explain earthquake clustering features. However, these approaches are still far from being able to link individual events and to determine the number of earthquakes caused by a single event. An alternative approach based on proximity functions allows to generate hierarchical clustering trees and to identify pairs of nearest-neighbours between consecutive levels of hierarchy. Then, the productivity of an earthquake is the number of events of the next level to which it is linked. To account for scale invariance in the triggering process we use a relative magnitude threshold ΔM. Recently it was shown that the relative productivity attached to each event is a random variable that follows an exponential distribution. The exponential rate of this distribution does not depend on the magnitude of triggering events and systematically decreases with depth. Here we test a hypothesis that this stochastic property of the earthquake productivity is a consequence of high spatial heterogeneity of the background event rates. The study was supported by Russian Science Foundation, project no. 20-17-00180.
How to cite: Shebalin, P., Baranov, S., Matochkina, S., and Krushelnitskiy, K.: Exponential earthquake productivity law, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8157, https://doi.org/10.5194/egusphere-egu21-8157, 2021.
We have created a new seismotectonic regionalization for Germany including a 200 km zone around its borders, based on a new concept which initially processes geological information separately from modern seismicity. The identification of a region as a distinct seismotectonic unit is estimated from past deformation, not the present one as represented by earthquakes. This has been done by analyzing fault density and displacements separately for six time slices from 300 Ma to the Present. The final regionalization results from overlaying the six deformation intensity maps and contrasts regions that deformed either repeatedly or very strongly in the geological past with others that suffered very little deformation. The new regionalization is significantly different from existing regionalizations. The existing ones mostly relied on modern seismicity for defining areas while using geological contacts of varying type (surface traces of faults, but also erosional edges of stratigraphic units as represented on geological maps) to trace boundaries.
The new, geology-based regionalization comprises comparatively few regions. Ubiquitous small faults (cm- to m-displacements) in the geological record suggest that earthquakes of low magnitude can occur anywhere and need not be tied to large faults. Our regionalization concurs with earlier ones in identifying the Cenozoic rifts – Upper Rhine Graben, Lower Rhine Graben and Eger Rift – as zones of increased hazard. A 100-150 km wide, NW-SE-trending belt of intense Mesozoic deformation runs across northwestern and central Germany from the Emsland to the Erzgebirge where it bifurcates into two branches that continue along the borders of the Bohemian Massif. This belt coincides reasonably well with the relatively sparse earthquakes in central and northern Germany. The Tornquist Fault Zone running NW-SE from northern Denmark to Bornholm is another belt of increased past deformation and elevated seismic activity on the northeastern border of our region. Areas of particularly low past deformation comprise the Brabant Massif, the Rhenish Massif and Münsterland Basin east of the Lower Rhine Graben, the Alpine foreland south of the Danube river and the Bohemian Massif southeast of the Eger Rift. Earthquake clusters occurring in stable areas such as the Brabant Massif or the Swabian Jura highlight geologically unexpected events. They can be added to the regionalization as separate zones or accounted for via a logic tree. They should not be used to assign increased hazard to the larger regions of the geology-based regionalization.
How to cite: Spies, T., Hahn, T., Kley, J., and Kaiser, D.: New seismotectonic regionalization for Germany: comparison with existing regionalizations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14356, https://doi.org/10.5194/egusphere-egu21-14356, 2021.
The Zagros mountains is a tectonically active Arabian-Eurasian plate convergence zone. The convergence direction changes along the strike of the belt, results in oblique faulting in the North-Western Zagros (NWZ) and the prevalence of pure reverse faulting in the South-Eastern Zagros (SEZ). The two regions undergo different convergence rates, (4 ± 2 mm yr −1) in NWZ and (9 ± 2 mm yr −1) in SEZ. These differences is partially accommodated by right-lateral strike-slip faulting throughout the Central Zagros (CZ), resulting in catastrophic earthquakes like 1972 Mw = 6.7 Qir and 1934 Mw = 6.3 Kazerun. This study presents the Probabilistic Seismic Hazard Assessment (PSHA) for the CZ region by integrating fault sources and seismological data. The seismological catalog data consists of 6504 events (2.5 < Mw < 6.7) during 1925-2020 and was compiled from the International Seismological Center (ISC) and the Iranian Seismological Center (IRSC). The faults with the history of Mw > 5.5 or geometrical potential of producing such an event were modeled. A Truncated Gutenberg–Richter (TGR) Magnitude-Frequency Distribution (MFD) for a range of magnitudes (5.5 < Mw < Mmax ) is evaluated by processing the geometrical parameters and slip rate of each fault source using the FiSH code. The Mmax is computed for each source by combining various Mmax estimates based on the faults geometry and observed Mmax if it is available. The catalog data was modeled as a grid source. A unique set of seismic activity rate parameters (for Mw > 4) in each grid is obtained by applying a modified smoothed seismicity approach. More precisely, a penalized likelihood-based methodwas utilized for the spatial estimation of the b-values, and a weighted smoothing method was implemented to calculate the spatial distribution of the a-values. The catalog events with Mw > 5.5 were excluded to avoid duplicated hazard estimation (modified earthquake catalog). Compiling the source models, the hazard computations were performed using the OpenQuake Engine. The Peak Ground Acceleration (PGA) is computed for the Probability Of Exceedance (POE) of 10% over 50 years for distributed seismicity obtained by the full catalog, and an aggregated model of active faults and distributed seismicity with the modified earthquake catalog. The distributed model produces an approximately uniform PGA with a maximum value of 0.185 g over CZ, while the aggregated model accents the PGA in the vicinity of the faults the maximum of 0.319 g observed around the Kazerun fault. The results show the competence of aggregating fault-based and distributed seismicity hazard assessments for applying comprehensive PSHA studies.
How to cite: Dolatabadi, N., Tavakolizadeh, N., Mohammadigheymasi, H., and Valentini, A.: A combined fault- and catalog-based hazard assessment for Central Zagros, Iran, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14411, https://doi.org/10.5194/egusphere-egu21-14411, 2021.
We investigate the source parameters of 87 local earthquakes (3.5 ≤ ML ≤ 5.0) that occurred in West Brahmaputra basin and its neighbouring area, using body wave displacement spectra. Seismic moment, corner frequency, source dimension and static stress drop are estimated using a grid search method based on the model of circular source. The measured seismic moments, corner frequency and moment magnitude ranges from to N-m, 0.7 to 12.1 and 3.0 to 4.8, respectively. The average ratio of corner frequency of P - and S - waves is 2.21. The scaling relationship of seismic moment against corner frequency is also studied for various tectonics regimes separately. Median stress drop values of individual earthquake vary from ~ 0.1 to 38.5 MPa, with an average value of about ~ 6 MPa. Spatial variation of stress drop observed for different tectonic unit reveals a higher stress drop values associated with West Brahmaputra basin, Shillong-Mikir plateau and Indo-Myanmar subduction zone suggesting a higher stress accumulation that may increase the probability of higher magnitude earthquake. The empirical relationship between ML and MW scale is also derived for hazard assessment.
How to cite: Khan, P. K. and Baruah, B.: Estimation of Source Parameters and their scaling relationship of small to moderate magnitude earthquakes for northeast India, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1636, https://doi.org/10.5194/egusphere-egu21-1636, 2021.
The Mexican subduction zone, the Gulf of California spreading center, as well as the triple junction point around the Jalisco and the Michoacán Blocks, represents the most active seismogenic belts inducing seismic hazard in the Jalisco-Colima-Michoacán region. Herein, considering such seismotectonic setting, we have developed a new seismic source model for the surrounding of this zone to be used as an input to the assessment of the seismic hazard of the region.
This new model is based on revised Poissonian earthquake (1787-2018) and focal mechanism (1963-2015) catalogs, as well as crustal thickness data and all information about the geometry of the subducting slabs. The proposed model consists of a total of 37 area sources, comprising the three different possible categories of seismicity: shallow crustal, interface subduction, and inslab earthquakes. A special care was taken during the delimitation of the boundaries for each area source to ensure that they represent a relatively homogeneous seismotectonic region, and to include a relatively large number of earthquakes that enable us to compute, as reliable as possible, seismicity parameters.
Actually, the sources zones were delimited following the standard criteria of assessing a probabilistic seismic hazard, being characterized in terms of their seismicity parameters (annual rate of earthquakes above Mw 4.0, b-value, and maximum expected magnitude), mean seismogenic depth, as well as the predominant stress regime. The proposed seismic source model defines and characterizes regionalized potential seismic sources that can contribute to the seismic hazard at the Jalisco-Colima-Michoacán region, providing the necessary information for seismic hazard estimates.
How to cite: Peláez, J. A., Sawires, R., Santoyo, M. A., and Henares, J.: Development and characterization of a seismic source model for the Jalisco-Colima-Michoacán region, Western Mexico, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-411, https://doi.org/10.5194/egusphere-egu21-411, 2021.
In the present work, we examine the stress parameter values for the stochastic simulation modeling of shallow interface (h<45km) earthquakes in the southern Aegean Sea subduction zone. Using the extended-source model (EXSIM code), the stochastic stress parameter is estimated for several of these earthquakes, which are typically associated with thrust faulting. The assessment is performed using a Monte Carlo parametric search (non-linear optimization) of the stress parameter values, realized through an adapted neighborhood algorithm (Wathelet, 2008). In this approach, we estimate the stress parameter which minimizes the total root mean square (rms) misfit between observed and simulated Fourier Amplitude Spectra (FAS) for all records of each event available in the strong motion database. We also employ appropriate source and path parameters (e.g., moment magnitude, fault dimensions, high-frequency spectral attenuation, etc.), from previous works on strong-motion simulations, considering earthquakes in the range M4.4 to M6.6. For several recording stations, we employed site-specific transfer functions, derived from a generalized inversion of strong motion records, considering the seismic source and propagation path of the seismic events in terms of their frequency content (Drouet et al., 2008; Grendas et al., 2018). For the remaining stations, the assessment of site-effects on seismic motions was performed based on the Vs30 values available for all recording stations. Using these values, soil classes according to NEHRP (1994) have been assigned and we employed generic transfer functions for NEHRP site conditions A/B, C and D (together with the corresponding κ0 values), as these were available from previous work for Greece by several authors (Margaris and Boore, 1998, Margaris and Hatzidimitriou, 2002; Klimis et al., 1999, 2006). The final comparisons show that the FAS of the strong motion data can be adequately matched (in most cases) by the synthetic data from the EXSIM simulations, using stress parameter values less than 100bars. This value is quite different from results obtained for larger depth interface and inslab events of the Aegean Sea and Vrancea subduction zones (e.g., Sokolov et al., 2005; Kkallas et al., 2018), which show much larger stress parameters (>200bar) for M>6 events. These findings suggest that the event hypocentral depth is a critical factor regarding the observed stress parameter affecting accordingly the seismic hazard estimation. Strong Intermediate-depth events (h>45km) require large stress parameters, while shallow interface thrust events show rather similar stress parameter values with the typical shallow back-arc normal and strike-slip events of the Aegean region. This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project “Reinforcement of Postdoctoral Researchers - 2nd Cycle” (MIS-5033021), implemented by the State Scholarships Foundation (ΙΚΥ).
How to cite: Kkallas, C., Papazachos, C., Margaris, B., Grendas, I., Theodoulidis, N., and Hatzidimitriou, P.: Investigation of the stress parameter values for the stochastic simulation of shallow (h<45km) interface earthquakes of the southern Aegean Sea subduction zone, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8034, https://doi.org/10.5194/egusphere-egu21-8034, 2021.
Probabilistic fault displacement hazard analysis (PFDHA) estimates the probability of occurrence and the expected exceedance of on-fault (principal fault rupturing; PF) and off-fault (dist ributed rupturing; DR) surface displacement during an earthquake. Here we concent rate on off-fault rupturing on dip-slip earthquakes, and present an original probability model for the occurrence of DR and for the expected exceedance of displacement dist ribution based on an approach named “slicing” (an alternative to the “gridding” approach commonly used). The method is developed based on the compilation and reappraisal of surface ruptures from 32 historical crustal dip-slip earthquakes, with magnitudes ranging from Mw 4.9 to 7.9. A ranking scheme is applied to distinguish PF (rank 1) from simple DR (rank 2) and t riggered faulting (rank 3). Thus modellers can use prediction equations based on or excluding ruptures st rongly related to local st ructural setting depending on the site of concern. In the case of a st ructural setting at a site where large-scale bending (rank 21, 22) and pre-existing faults (rank 1.5, 3) is considered irrelevant, modelling can be performed considering only the unpredictable DR (rank 2). To minimize bias due to the incomplete nature of the database, we int roduce the “slicing” approach, which considers that the probability of having a surface rupture within slices parallel to the PF is homogeneous along the st rike of each slice. “Slicing” probabilities, computed as a function of magnitude of the earthquake and distance from the PF, are then combined with Monte Carlo simulations that model the dependence of the probability of occurrence of rupture and exceedance of displacement with the dimensions and position of the site of interest with respect to the PF. Finally, both probabilities are combined with existing predictive equations of exceedance of displacement on the PF to calculate fault-displacement hazard curves for sites of interest.
How to cite: Nurminen, F., Visini, F., Baize, S., Boncio, P., Pace, B., Scotti, O., and Valentini, A.: Probability of distributed surface rupturing occurrence and displacement regression for dip-slip earthquakes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12792, https://doi.org/10.5194/egusphere-egu21-12792, 2021.
Fault displacement hazard assessment is based on empirical relationships derived from data of historical surface rupturing earthquakes. This approach is used for land use planning, sizing of lifelines or major sensitive infrastructures located in the proximity of active faults. These relationships provide the probability of occurrence of surface rupture and predict the amount of displacement, both for the main ruptures (principal) and for distributed ones appearing beyond.
Following the first version of the global database SURE 1.0 (Baize et al., 2019), we are continuing the effort to compile observations from well-documented historical and recent surface faulting events in order to feed and improve empirical relationships. The new SURE2.0 global database consolidates the previous version SURE 1.0 data, rejecting some poorly constrained cases, reviewing some cases already in, and adding well-documented new ones (e.g. Ridgecrest sequence, USA, 2019). In total, the SURE 2.0 database has 46 earthquakes, including 15 normal fault cases, 16 reverse fault cases and 15 strike-slip cases from 1872 to 2019. The magnitude range is from M4.9 to 7.9, with ruptures from 5 to 300 km long.
SURE 2.0 provides the geometric location and attribute information of rupture segments in a GIS environment and a spreadsheet reports the amplitude and characteristics of deformation, including data sources and its eventual geometric refinement during analysis. In this new version, we completed an essential task to derive attenuation relationships, by classifying each rupture segment and each slip measurement point, using a ranking scheme based on the pattern and amplitude of the observed rupture traces, and considering the structural context and the long-term geomorphology. This distinguishes the principal rupture (class 1), which is the main surface expression of the source of the earthquake. Typically, in the siting study, this class is assigned to the identified active fault. Class 2 features (distributed ruptures) are characterized by shorter lengths and smaller displacements that appear randomly close and around the main rupture. We introduced the distributed main fracture category (class 1.5), which corresponds to the relatively long minor fractures recognized on cumulative structures secondary to the main fault. Class 3 represents triggered slip evidences on remote active faults, clearly not connected with the earthquake causative fault (sympathetic ruptures).
As was done with reverse fault cases (Nurminen et al., 2020), this new SURE 2.0 version will be used to derive probabilities associated with the rupture distribution during any type of earthquake.
How to cite: Baize, S., Blumetti, A. M., Boncio, P., Cinti, F. R., Civico, R., Guerrieri, L., and Nurminen, F.: A new release of the SURE database of earthquake surface ruptures suited to Fault Displacement Hazard Analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14182, https://doi.org/10.5194/egusphere-egu21-14182, 2021.
Seismic hazard assessment requires an adequate understanding the earthquake distribution in magnitude, space, and time ranges. Laking data for a period of several thousand years makes probabilistic approach to estimating the recurrence time of hazardous ground shaking unreliable and misleading. In spite of theoretical flaws and actual failures on practice, the probabilistic seismic hazard assessment (PSHA) maps keep being actively used both at global and national scales. In recent decades, alternative methodologies have been developed to improve the reliability and accuracy of reproducible seismic hazard maps that pass intensive testing by historical evidence and realistic modelling of scenario earthquakes. In particular, the neo-deterministic seismic hazard assessment (NDSHA) confirms providing reliable and effective input for mitigating object-oriented earthquake risks. The unified scaling law for earthquakes (USLE) is a basic part of NDSHA that generalizes application of the Gutenberg-Richter law (G-RL). The USLE states that the logarithm of expected annual number of earthquakes of magnitude M in an area of linear size L within the magnitude range [M– , M+] follows the relationship log N(M, L) = A + B×(5 − M) + C×log L, where A, B, and C are constants. Naturally, A and B are analogous to the classical a- and b-values, while C compliments to G-RL with the estimate of local fractal dimension of earthquake epicentres allowing for realistic rescaling seismic hazard to the size of exposure at risk. USLE implies that the maximum magnitude MX expected with p% chance in T years can be obtained from N(MX, L) = p%, then used for estimating and mapping ground shaking parameters by means of the NDSHA algorithms. So far, the reliable USLE based seismic hazard maps tested by historical evidence have been plotted for a number of regions worldwide. We present the USLE based maps of MX computed at earthquake-prone cells of a regular grid, as well as the adapted NDSHA estimates of seismic hazard and risks for social and infrastructure exposures in the regions adjacent to the Russian Federation Baikal–Amur Mainline. The study supported by the Russian Science Foundation Grant No. 20-17-00180.
How to cite: Kossobokov, V. and Nekrasova, A.: Seismic hazard and risks for social and infrastructure exposures adjacent to the Baikal–Amur Mainline, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6254, https://doi.org/10.5194/egusphere-egu21-6254, 2021.
The broader Aegean area is one of the highest seismicity regions in Europe, with almost half of the European seismicity released in this region, often with damaging mainshocks, such as the recent M7.0 Samos event. While several Probabilistic Seismic Hazard Assessment (PSHA) studies have been performed for this area, an attempt to quantify the main factors controlling PSHA has not been performed. To study the effect that each input factor (seismic source model, GMPE, seismicity parameters, etc.) has on the seismic hazard calculations, an OFAT (One Factor at A Time) analysis has been conducted. For this analysis we considered two standard peak ground motion parameters, PGA and PGV, for a typical PSHA scenario, namely 10% probability of exceedance for a mean return period of 50 years (equivalent to a 476 yr return period). For the analysis the following factors were considered: a) Four (4) seismicity area-type source models for the broader Aegean area (Papazachos, 1990; Papaioannou and Papazachos, 2000; Woessner et al., 2015; Vamvakaris et al., 2016), as well as various uncertainties for the associated G-R seismicity parameters and active fault geometries of each seismic source, b) ten (10) Ground Motion Prediction Equations (GMPEs), which contain four NGA-West2 (Abrahamson et al., 2014; Boore et al., 2014; Campbell and Bozorgnia, 2014; Chiou and Youngs, 2014), two European (Bindi et al., 2011; Cauzzi and Faccioli, 2008) and four “Greek” (Theodulidis and Papazachos, 1992; Skarlatoudis et al., 2003; Danciu and Tselentis, 2007; Chousianitis et al., 2018) equations, as well as a variable number of sigma for each equation and, c) the minimum (Mmin) and maximum (Mmax) source magnitude of each seismic source. Tornado diagrams (Howard, 1988) were generated for 42 selected sites of seismological interest that span the study area, allowing to explore the extent of each factor’s effect on the PSHA results. The sensitivity analysis results suggest that the GMPE selection, as well as uncertainties in the G-R parameters a and b are the most critical factors, significantly affecting the PGA/PGV levels for all sites. They also reveal a strong correlation of PSHA sensitivity with other seismicity parameters. For example, the employed source model and Mmax play a more critical role for regions of low seismicity, while the least important factor is the selected Mmin. The spatial distribution of the PSHA sensitivity on the various factors considered was also examined through the generation of several maps, exposing regions of high and of low PSHA uncertainty. The results can be efficiently employed by scientists and engineers in order to focus research and application efforts for a targeted uncertainty minimization of the most critical factors (which may not be the same for all sub-regions of the examined Aegean area), as well as to evaluate the reliability and uncertainty of the current PSHA estimates that are employed in seismic design.
How to cite: Kerkenou, A., Papazachos, C., Margaris, B., and Papaioannou, C.: Identifying the main factors that control Probabilistic Seismic Hazard Assessment (PSHA) in the Aegean area: Results from OFAT (One Factor at A Time) analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7967, https://doi.org/10.5194/egusphere-egu21-7967, 2021.
Probabilistic seismic hazard assessments forecast levels of earthquake shaking that should be exceeded with only a certain probability over a given period of time are important for earthquake hazard mitigation. These rely on assumptions about when and where earthquakes will occur, their size, and the resulting shaking as a function of distance as described by ground-motion models (GMMs) that cover broad geologic regions. Seismic hazard maps are used to develop building codes.
To explore the robustness of maps’ shaking forecasts, we consider how maps hindcast past shaking. We have compiled the California Historical Intensity Mapping Project (CHIMP) dataset of the maximum observed seismic intensity of shaking from the largest Californian earthquakes over the past 162 years. Previous comparisons between the maps for a constant VS30 (shear-wave velcoity in the top 30 m of soil) of 760 m/s and CHIMP based on several metrics suggested that current maps overpredict shaking.
The differences between the VS30 at the CHIMP sites and the reference value of 760 m/s could amplify or deamplify the ground motions relative to the mapped values. We evaluate whether the VS30 at the CHIMP sites could cause a possible bias in the models. By comparison with the intensity data in CHIMP, we find that using site-specific VS30 does not improve map performance, because the site corrections cause only minor differences from the original 2018 USGS hazard maps at the short periods (high frequencies) relevant to peak ground acceleration and hence MMI. The minimal differences reflect the fact that the nonlinear deamplification due to increased soil damping largely offsets the linear amplification due to low VS30. The net effects will be larger for longer periods relevant to tall buildings, where net amplification occurs.
Possible reasons for this discrepancy include limitations of the dataset, a bias in the hazard models, an over-estimation of the aleatory variability of the ground motion or that seismicity throughout the historical period has been lower than the long-term average, perhaps by chance due to the variability of earthquake recurrence. Resolving this discrepancy, which is also observed in Italy and Japan, could improve the performance of seismic hazard maps and thus earthquake safety for California and, by extension, worldwide. We also explore whether new nonergodic GMMs, with reduced aleatory variability, perform better than presently used ergodic GMMs compared to historical data.
How to cite: Gallahue, M., Salditch, L., Lucas, M., Neely, J., Hough, S., Stein, S., Abrahamson, N., and Williams, T.: Probabilistic seismic hazard maps for California do not perform better relative to historical shaking data when site-specific VS30 is considered, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6633, https://doi.org/10.5194/egusphere-egu21-6633, 2021.
Probabilistic Seismic Hazard Assessment (PSHA) attempts to forecast the fraction of sites on a hazard map where ground shaking will exceed the mapped value within some time period. Because the maps are probabilistic forecasts, they explicitly assume that shaking will exceed the mapped value some of the time. At a point on a PSHA map, the probability p that during t years of observations shaking will exceed the value on a map with a T-year return period is assumed to be described by the exponential cumulative density function: p = 1 – exp(-t/T). The fraction of sites, f, where observed shaking exceeds the mapped value should behave the same way. To assess the 2018 USGS National Seismic Hazard Model maps for California, we created the California Historical Intensity Mapping Project (CHIMP), a 162-yr long dataset that combines and consistently reinterprets seismic intensity information that has been stored in disparate and sometimes hard-to-access locations (Salditch et al., 2020). We use two performance metrics; M0 based on the fraction of sites where modeled ground motion is exceeded, and M1 based on of the difference between the mapped and observed ground motion at all sites. M0 is implicit in PSHA because it measures the difference between the predicted and observed fraction of site exceedances and is therefore a key indicator of map performance.
We explore these metrics for CHIMP. Assuming the dataset to be correct, it appears that the hazard maps overpredicted shaking even correcting for the time period involved. Assuming the model is also correct, a shaking deficit exists between the model and observations. Possible reasons for this apparent overprediction/shaking deficit include: 1) the observations in CHIMP are biased low; 2) the observation period has been less seismically active than typical – either by chance or temporal variability due to stress shadow effects; 3) the model overpredicts due to either the earthquake rupture forecast or the ground motion models. Similar overpredictions appear for past shaking data in Italy, Japan, and Nepal, implying that seismic hazards are often overestimated. Whether this reflects too-high models and/or biased data remains an important question.
How to cite: Salditch, L. and Stein, S.: Do PSHA maps overpredict or are there shaking deficits in the historic record?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12450, https://doi.org/10.5194/egusphere-egu21-12450, 2021.
The Himalayan region is known as an earthquake-triggered landslides prone area. It is characterized by high seismicity, large relative relief, steep slopes, and dense precipitation. These seismically triggered landslides are likely to affect substantial societal impacts, including loss of life, damage to houses, public buildings, various lifeline structures like highways, railways tracks, etc. Further, they obstruct post-earthquake emergency response efforts. A past study by Martha et al. 2014 reported that an earthquake of Mw 6.9 in 2011 triggered 1196 landslides in Sikkim which is a part of the eastern Himalayas. The slope failure events are controlled by several factors, which can be grouped into four main classes: seismology, topography, lithology, and hydrology. Each class contains several sub-factors. Having in-depth knowledge of these factors and their influence on the density of landslide events in the affected area due to the 2011 Sikkim earthquake is essential to realize the level of threat of co-seismic landslide due to future earthquakes. Eight landslide controlling factors is considered in this analysis including peak ground acceleration (PGA), slope, aspect, elevation, curvature, lithology, distance from rivers, and topographic wetness index (TWI). Further, the frequency ratio model using the GIS framework is applied to evaluate the contribution of each landslide controlling factor to landslide occurrence. Scatter plots between the number of landslides per km2 (LN) and percentage of landslide area (LA) and causative factors indicate that distance from the river, slope angle, and PGA are the dominant factors that control the landslides. The results of the above analysis showed that the majority of co-seismic landslides occurred at slope >30°, preferably in East, Southeast, and South directions and near river within a distance of 1500 m. The detailed study of interactions among these factors can improve the understanding of the mechanisms of co-seismic landslide occurrence in Sikkim and will be useful for producing a co-seismic landslide susceptibility map of the area.
How to cite: Kumar, S. and Aniruddha, S.: Analysis of factors affecting the Spatial Distribution of co-seismic landslides triggered by the 2011 (Mw 6.9) Sikkim earthquake, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16369, https://doi.org/10.5194/egusphere-egu21-16369, 2021.
The coastal settlements in the Aegean Sea coast have experienced numerous tsunamis throughout history due to the frequent earthquakes of different magnitudes. Three normal-faulting events have been recorded over the last four years, confirming the tsunami threat in the NEAM region. The June 12th, 2017 (Mw 6.3) and July 20th, 2017 (Mw 6.6) events in the Eastern Aegean affected the nearby coastal areas and served as reminders, the latter causing remarkable loss of property and boat damage in Bodrum, Turkey and Kos Island, Greece.
On October 30th, 2020, a strong earthquake (Mw 6.6, AFAD, 2020) caused substantial structural damage at 75 km epicentral distance in the Bayraklı region resulting in 117 casualties. A tsunami was also generated, causing very strong motion in the nearshore shallow areas and small craft harbors along 130 km shoreline from Alaçatı (North) to Gümüldür (South) in Seferihisar and Çeşme districts of İzmir Province. The tsunami also caused one casualty and several injured people. Learning from previous events, such as the October 30th, 2020 tsunami event, is a key issue in mitigation and future preparedness. Understanding the regional effects of this tsunami will definitely help in developing necessary tools for tsunami risk reduction in the Eastern Aegean region. In this regard, post-tsunami field surveys provide invaluable information to enable the enhancement of tsunami disaster risk management practices. Two different post-tsunami field surveys were performed after the October 30th, 2020 tsunami to document the tsunami effects along the affected coast in Turkey, considering the observed coastal amplitudes and inundation extent. The combined results of the field surveys include flow depth, runup, and inundation measurements, as well as arrival time information and coastal damage observations. Furthermore, we discuss the survey findings to better understand the tsunami behavior and its effects on the nearby coastal areas.
Another important point is that the public tsunami awareness in the Bodrum region in Turkey was extremely low, with no evacuation practices in July 2017 tsunami. There is a considerable increase in people’s response to tsunami hazard in the Eastern Aegean region, as acquired from the eyewitness interviews during the October 30th tsunami field survey. However, considering the high seismicity, the public awareness about tsunamis that might take place around the Aegean coast and response to natural and official warnings should be raised and supported with evacuation practices.
In the light of lessons learned from the most recent Aegean tsunami, using the recent measurement techniques and computational tools in tsunami hazard assessment has become extremely important to improve mitigation. In the framework of disaster risk reduction, high-resolution inundation maps through high-resolution vulnerability analysis and evacuation mapping are the essential requirements for the development of tsunami action plan for the coastal communities, which will help to achieve a successful tsunami risk reduction. In this work, additionally, the examples of new achievements in this direction from megacity İstanbul and high-resolution numerical modeling of tsunamis in the İzmir metropolitan are presented with discussions.
How to cite: Dogan, G. G. and Yalciner, A. C.: Analysis of the October 30th, 2020 Aegean Sea Tsunami towards Future Tsunami Preparedness, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15882, https://doi.org/10.5194/egusphere-egu21-15882, 2021.
In seismic hazard assessment studies, the estimation of site effects plays an important role. In recent years, using seismic noise has become increasingly popular because of their simplicity, low cost, and non-destructively. Seismic ambient noise wavefield investigation can be fulfilled by both single-station and array methods. The RayDec single station method is used to estimate ellipticity curve of Rayleigh wave based on Random Decrement (RD) technique by putting more emphasis on Rayleigh waves in compare to other participant waves in the seismic noise wavefield. In this study, to assess measuring the ellipticity of Rayleigh waves in an array of stations, Vector Random Decrement (VRD) technique is applied. The main idea is applying vector triggering condition on vertical components in an array of stations and selecting common triggering points. Those parts of signals where common points of all stations are detected would be included in further processing. It may lead to a lower number of obtained triggering points and insufficient convergence. To control the convergence, the vector of triggering conditions could be divided into some subsets. The maximum number of subsets can be estimated as the lowest integer of N/2 in which N is the number of stations in the array. Wherever, the common triggering points are detected on three components of the stations, the time windows with the same length are extracted. In the following, the signals in the mentioned windows are stacked and the ellipticity ratio is estimated by analyzing the energy content of the horizontal and vertical signals. In order to verify the method, synthetic circular array data are simulated using the FD code including five stations regularly placed on the circumference and a station in the center. Furthemore, the real array data recorded in Ramsar site (North of Iran) are used to study the method. The data included six Nanometrics trillium 40 seismic stations in which five stations placed on the circumference as well as a station at the center regarding to array aperture of about 15m. The retrieved ellipticity curves are evaluated and compared with the results of high resolution Rayleigh three component beam-forming (RTBF) method. The RTBF and VRD methods show good performance in recognizing the right flank of peak frequency while, the peak frequency and the left flank are better retrived using VRD method. Finally, the retrieved ellipticity curve from VRD alongside with the dispersion curves obtained from RTBF for both synthetic and real array data are used as targets in a joint inversion process to validate the shear wave velocity profile.
How to cite: Moghadasi, N. S. and Shabani, E.: Rayleigh wave ellipticity in seismic noise studies based on vector random decrement technique (VRD), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4485, https://doi.org/10.5194/egusphere-egu21-4485, 2021.
The selection and ranking of Ground Motion Models (GMMs) for scenario earthquakes is a crucial element in seismic hazard analysis. Typically model testing and ranking do not appropriately account for uncertainties, thus leading to improper ranking. We introduce the stochastic area metric (AM) as a scoring metric for GMMs, which not only informs the analyst of the degree to which observed or test data fit the model but also considers the uncertainties without the assumption of how data are distributed. The AM can be used as a scoring metric or cost function, whose minimum value identifies the model that best fits a given dataset. We apply this metric along with existing testing methods to recent and commonly used European ground motion prediction equations: Bindi et al. (2014, B014), Akkar et al. (2014, A014) and Cauzzi et al. (2015, C015). The GMMs are ranked and their performance analysed against the European Engineering Strong Motion (ESM) dataset. We focus on the ranking of models for ranges of magnitude and distance with sparse data, which pose a specific problem with other statistical testing methods. The performance of models over different ranges of magnitude and distance were analysed using AM, revealing the importance of considering different models for specific applications (e.g., tectonic, induced). We find the A014 model displays good performance with complete dataset while B014 appears to be best for small magnitudes and distances. In addition, we calibrated GMMs derived from a compendium of data and generated a suite of models for the given region through an optimisation technique utilising the concept of AM and ground motion variability. This novel framework for ranking and calibration guides the informed selection of models and helps develop regionally adjusted and application-specific GMMs for better prediction.
How to cite: Sunny, J., De Angelis, M., and Edwards, B.: Ranking and calibration of ground-motion models using the stochastic area metric. , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11143, https://doi.org/10.5194/egusphere-egu21-11143, 2021.
The study of strong historical and early instrumental earthquakes is based almost exclusively on the use of their macroseismic data, which usually constrain the area that has suffered the heaviest damage. In the recent decades, strong-motion data have been also employed for the same purpose. We present a stochastic simulation approach to jointly model macroseismic and strong motion data for selected shallow strong (M≥6.0) earthquakes that occurred in the broader Aegean region between 1978 and 1995. For the simulations we employed the finite-fault stochastic simulation method, as realized by the EXSIM algorithm. We calibrated several parameters for the stochastic simulation modeling using a priori published information (e.g., moment magnitude, stress parameter). Other rupture zone information were collected from published works, such as fault plane solutions, relocated seismicity, etc. A Monte Carlo approach was adopted to perform a parametric search for the stress parameter and the modelling both independently and jointly the available macroseismic data and the strong motion instrumental recordings. The validity and the reliability of this semi-automated simulation approach was examined, to test if this method could be applied either in a fully automated manner, or for the study of the source properties of historical earthquakes. The results suggest that a joint-misfit minimization from the simultaneous simulation of macroseismic and strong motion data is a feasible target, that can be potentially employed for the simulation of older events, for which a limited number of instrumental data is often available. In general, a good agreement of the spatial distribution of the original and modeled macroseismic intensities is observed, showing that can reliably reconstruct the main features of the damage distribution for strong shallow mainshocks in the Aegean area using the proposed joint interpretation approach.
How to cite: Ravnalis, M., Kkallas, C., Papazachos, C., Margaris, B., and Papaioannou, C.: Joint interpretation of macroseismic and strong motion data for recent large shallow mainshocks of the Aegean area using a Monte Carlo optimization of finite-fault stochastic simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7969, https://doi.org/10.5194/egusphere-egu21-7969, 2021.
Loss estimation for buildings that experienced earthquake shaking is an important step in Performance Based Earthquake Engineering (PBEE), comprising four major components – seismic hazard, building response, probability of damage, and the costs incurred in losses and repair works. The implementation of PBEE strongly depends on the ability to predict Engineering Demand Parameters (EDPs) that are usually defined in terms of maximum story drifts, plastic hinge rotations, and floor accelerations.
In this study, we compute building responses for large sets of recorded ground motions considering frames with different natural periods (0.1-1.5s). The ground motion data used in our analysis comprise near field records from moderate-to-large earthquakes; these may generate shaking levels high enough to be of concern for the design and safety of buildings. We select the frames by varying the number of storys and bays to obtain a wide range of natural building periods. We compute ground motion intensity measures (IM) from the recorded dataset and extract engineering demand parameters (EDP) from building response analyses. Our results indicate that the inter-story drift correlates strongly with spectral measures of ground motion intensity (correlation coefficient above 0.85). We also investigate the effect of natural period on the estimated correlations. We find that the correlations with spectral intensity measures do not strongly depend on Vs30 and epicentral distance. Our results are useful in the context of applied performance-based design of structures, especially if uncertainties in seismological parameters due to limited knowledge of source, site or path effects play an important role in earthquake ground motions.
How to cite: Aquib, T. A., Sivasubramonian, J., and Mai, P. M.: Analysis of correlation between structural response and ground motion intensity measures., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11016, https://doi.org/10.5194/egusphere-egu21-11016, 2021.
Marmara region is a tectonically active part of Turkey. Over the history, the Marmara region has been the site of numerous destructive earthquakes such as the 1509 Istanbul earthquake (Mw=7.5), 1766 Istanbul earthquake (Mw=5.63), 1953 Yenice-Gönen Depremi (Ms=7.2), 1999 Kocaeli (Mw=7.4) and Düzce (Mw=7.2) earthquakes. Many Electric power systems located in the Marmara region are exposed to the destructive effects of potential earthquakes. The serviceability and functionality of the electric power systems after a major earthquake are major concerns for people's wealth. Thus, the design of the electric power system requires site-specific seismic hazard assessment. Site-specific hazard analysis provides a uniform hazard spectrum used for the design of power structures. Response spectrums are presented for the seismically resistant design of the structures according to the Turkey Building Earthquake Regulation 2018 (TBDY2018) and Turkish Seismic Code 2007 (TSC2007) regulations.
In this study, seismic hazard assessment of the Marmara region has been studied using the Openquake platform. Earthquake hazard has been investigated using the time-independent probabilistic (Poisson) models. Probabilistic seismic hazard assessment (PSHA) is conducted based on SHARE project ESHM13 model characteristics. The SHARE project has presented the 2013 European –Mediterranean seismic hazard model (ESHM13). ESHM13 models consist of all events with magnitudes Mw>=4.5 in the computation of seismic hazard and it covers the whole European territory including Turkey. The probabilistic seismic hazard assessment calculations take into account SHARE seismic source characterization. Akkar&Bommer(2010), Cauzzi&Faccioli(2008), Chiou&Youngs(2008), and Zhao et.al (2006) ground motion prediction models have been considered for active shallow crustal tectonic region. The study has developed uniform hazard spectrum and hazard maps of the Marmara Region with peak ground acceleration (PGA) and spectral accelerations (SA)’s at 0.2s and 1s periods corresponding to 10% and 2% probabilities of exceedance in 50 years. Obtained uniform hazard spectrums of electric power systems in the Marmara region have been compared with response spectrums of TBDY2018 and TSC-2007. The compatibility of SHARE model hazard analysis results with TBDY 2018 and TSC2007 has been assessed.
How to cite: Zulfikar, A. C. and Okuyan Akcan, S.: Seismic Hazard Assessment for the Energy Facilities in Marmara Region, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15353, https://doi.org/10.5194/egusphere-egu21-15353, 2021.
Earthquakes play a major role worldwide regarding economic and social consequences. In the event of an earthquake, many lives are at risk and the impact on the built and natural environment may be significant. Until now, estimations of damage and losses and the assessment of the stability of buildings are, however, only available several days to months after the event and are often based on the subjective assessment of experienced engineers.
For the effective planning of rescue measures and the best possible use of available resources, a fast, (semi-)automatic and accurate detection of the situation and an objective assessment of damage to critical infrastructures is indispensable. This requires a combination of innovative methods and technologies (UAVs, Machine Learning and Crowdsourcing combined with earthquake engineering knowledge) covering a wide range of spatial and temporal scales.
The interdisciplinary system LOKI (www.uni-heidelberg.de/loki) consists of the following procedure: After the occurrence of an earthquake, an initial damage forecast is made within a few minutes based on the Global Dynamic Exposure model and integrated vulnerability functions in combination with the ground-motion field to identify areas with potential high/low damage. Missing building footprints and required building information are recorded via a crowdsourcing approach to complete the OpenStreetMap building database, which serves as input to the exposure model. In parallel, mission plans for overview flights are created and transferred to fixed-wing UAVs, which record low to medium-resolution photos and 3D point clouds of the entire affected area. These data are used for damage detection, in which a binary distinction is made at building level between visible and non-visible damage using Machine Learning approaches. Thus, after a few hours, first orthophotos and the location of potentially damaged buildings can already be transmitted to emergency response teams. Thereafter, mission planning focuses on the capture of high-resolution 3D information of individual buildings. Fleets of multicopter drones provide highly detailed 3D imagery following mission plans that can be modified in real time by the emergency response teams. The mission planning algorithms support prioritization of specific areas or buildings for data acquisition, so that rescue measures can be optimally supported. The acquired high-resolution images and point clouds serve as input for damage classification, which is carried out per building using a combination of automatic procedures and Micro-Mapping. This offers the possibility to combine the advantages of fast automated procedures with the human ability to visually interpret details. Potential global and building material-related damage characteristics, which are based on observations of previous earthquakes, are included in a damage catalogue and allow building damage to be classified into five damage grades. In an iterative process, a timely and objective building-level classification of damage with an indication of the reliability of the specified degree of damage is achieved.
The integration of various disciplines and the combination of different concepts and technologies allows supporting disaster relief in different temporal and spatial resolutions with timely and reliable information on earthquake-induced damage.
How to cite: Kohns, J., Zahs, V., Ullah, T., Schorlemmer, D., Nievas, C., Glock, K., Meyer, F., Mey, H., Stempniewski, L., Herfort, B., Zipf, A., and Höfle, B.: Innovative methods for earthquake damage detection and classification using airborne observation of critical infrastructures (project LOKI), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2712, https://doi.org/10.5194/egusphere-egu21-2712, 2021.
Magnitude estimation for earthquake early warning has been shown that it can be achieved by utilizing the relationship among the first three seconds P-wave amplitude, hypocentral distance and magnitude. However, the regression models in previous studies about P-Alert didn't include station correction factors, which may cause non-negligible effects. Thus, to improve the precision of magnitude estimation, we take station corrections into consideration when building the regression model. For the reason that station corrections are the unobserved latent variables of the model, we adopt the iteration regression method, which is based on the expectation-maximization algorithm, to determine them. By using this method, we are able to approach the values of both the station corrections and the coefficients of the regression model after several iterations. Our preliminary results show that after utilizing the iteration regression method, the standard deviation reduces from 0.30 to 0.26, and the station corrections we get range from -0.70 to 0.66.
How to cite: Wu, Y.-T. and Wu, Y.-M.: Station Correction of P-Alert Network to Improve Magnitude Estimation for Earthquake Early Warning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13876, https://doi.org/10.5194/egusphere-egu21-13876, 2021.
Real-time magnitude determination is one of the critical issues for earthquake early warning (EEW). Magnitude determination may have saturation situation using initial seismic signals after an earthquake occurrence. Previous studies utilized eventual cumulative absolute velocity (eCAV) to determine magnitude up to 9.0 without any saturation. However, to determine eCAV will be too late for EEW application. In order to shorten time to obtain eCAV, 4,754 strong motion records from 64 events with ML large than 5.5 in Taiwan are used to establish the relationship between eCAV and initial shaking parameters (initial CAV, initial cumulative absolute displacement, initial cumulative absolute integral displacement, Pd and τc) from 1 s to 20 s after P arrival. Our preliminary results show that eCAV can be estimated using initial shaking parameters. Logarithm linear correlation coefficients vary from 0.78 to 0.97 with standard deviations from 0.27 to 0.10 for time windows from 1 s to 20 s after P arrival. Eventually, we can timely estimate eCAV for magnitude determination as well as or on-site EEW purpose.
How to cite: Huang, H.-Y. and Wu, Y.-M.: Magnitude Estimation and Onsite Earthquake Early Warning using Cumulative Absolute Velocity in Taiwan, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8570, https://doi.org/10.5194/egusphere-egu21-8570, 2021.
We present the SESAR funded project ALARM. The overall objective of ALARM is to develop a prototype global multi-hazard monitoring and Early Warning System for different hazards affecting aviation. Continuous global Earth observations from satellite, ground-based systems, and atmospheric forecasts will be used to feed models capable of observing and predicting (nowcasting/forecasting) the displacement of particles in suspension and gas derived from natural hazards (volcanic ash and SO2, dust clouds from sandstorms, and smoke from a forest fire); severe weather situations such as deep convection and extreme weather; exposure to increased levels of solar radiation during flight; and environmental hotspots potentially contributing to global warming in a large extent. Specifically, the aim is to enhance situational awareness of all stakeholders in case of multiple hazard crisis by facilitating the transfer of required relevant information to end-users, presenting such information in a user-friendly manner to ATM stakeholders. In summary, anticipating severe hazards and fostering better decision-making.
- ALARM will enhance an existing alert system –– with additional observations coming from geostationary satellites, improving the capabilities of observing natural hazards such as volcanic ash, SO2 plumes, sandstorms, and forest fire.
- ALARM will tailor alert products (based on observations from satellites) of volcanic ash, SO2 plumes, sandstorms, and forest fire to aviation stakeholders, including its severity, geographical location, and altitude.
- ALARM will develop nowcasting [up to 2 hours] and short-term forecasting [up to 6 hours] of SO2 plumes at a regional scale.
- ALARM will develop nowcasting [up to 2 hours] and short-term forecasting [up to 6 hours] of severe thunderstorms at a local scale (airport).
- ALARM will develop short-term forecasting [up to 6 hours] and medium-term forecasting [up to 48 hours] of climatic hotspots at a European scale.
- ALARM will draft the requirements of all these alert products to be included in the SWIM Yellow profile.
How to cite: Soler, M., Brenot, H., Biondi, R., Bannister, D., Grewe, V., Bolic, T., and García-Heras, J.: ALARM Project -multi-hAzard monitoring andearLy wARning systeM-, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7664, https://doi.org/10.5194/egusphere-egu21-7664, 2021.
School education constitutes one of the strategic functions to be recovered after an earthquake. The structural improvement of school buildings together with the strengthening of the administrators’ capacity to react positively following an earthquake are key factors that contribute to social vulnerability’s reduction. Nevertheless, in Italy, the issue of risk reduction policies related to school sector is not yet consolidated in the institutional agendas. Observing the last major Italian earthquakes what remains predominant is school buildings’ damage degree with consequent interruption of the system functionality. Among the causes: the building heritage vulnerability and the lack of risk mitigation policies, capable of building a resilient community for future earthquakes. That of resilience is considered a relevant paradigm to address the issue of how to strengthen the school sector’s capacity to ensure the buildings physical safety and to guarantee the maintenance of the school function, looking at pre and post-event phases.
The paper proposes a set of indicators and a methodology for a preliminary assessment of the educational sector’s seismic resilience, in terms of initial conditions. The method has been tested on a first case study: Calabria Region, Southern Italy. The results show that spatial differences in the educational sector’s seismic resilience are evident. Except for some large urban areas, the less resilient areas are grouped mainly in the southern part of the Region, while the most resilient ones are located mostly in the central-northern sector. The ambition is to identify a repeatable approach, useful as guidelines for school seismic prevention policies.
How to cite: Fontana, C., Cianci, E., and Moscatelli, M.: Assessing Seismic Resilience of School Educational Sector. An attempt to establish the initial conditions in Calabria Region, Southern Italy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12678, https://doi.org/10.5194/egusphere-egu21-12678, 2021.
On October 30, 2020, an Mw=6.9 earthquake struck the eastern Aegean Sea. It was the largest earthquake in Europe and the deadliest worldwide in 2020, as it resulted in 119 fatalities (117 in Turkey, 2 in Greece) from partial or total building collapse. Moreover, it generated environmental effects and damage to the built environment in both countries. The primary earthquake environmental effects included permanent surface deformation and coseismic surface ruptures, while the secondary effects comprised tsunami, slope failures, liquefaction phenomena, hydrological anomalies and ground cracks.
Every time a strong earthquake strikes, disaster management plans for emergency response tested in drills are applied under real conditions and on large scale. Immediately after the 2020 Samos earthquake, Greek authorities launched the largest mobilization of resources for assisting the affected population since the initiation of the COVID-19 pandemic in Greece.
Public authorities from all administration levels, civil protection agencies as well as security and armed forces were mobilized. All emergency plans for protection of life, health and property of the affected population were applied according to the existing legislation framework. The immediate response comprised search and rescue operations, first-aid treatment and medical care, provision of emergency supplies, establishment of emergency shelters, building inspections and assessment of damage extent. Moreover, the Greek government announced immediate relief measures and financial assistance for reconstruction and repairs.
The local population and responders were exposed to geohazards including the earthquake, the subsequent tsunami and aftershocks among other effects and to the evolving COVID-19 pandemic. The situation was more serious as there were many contradicting issues in the emergency response phase. Actions usually applied in the pre-pandemic period are in contradiction with the main measures for preventing SARS-CoV-2 transmission. The novel coronavirus adds extra risk to these life-saving activities. Thus, these actions had to adapt to the newly introduced conditions and adopt provisional measures for mitigation and elimination of COVID-19 consequences.
This study focuses on the emergency response actions taken shortly after the earthquake amid the COVID-19 pandemic. They comprised establishment of the operational centres and emergency shelters in outdoor places, mandatory mask wearing indoors and outdoors at all times by all responders, immediate housing of homeless in hotels and touristic facilities in order to maintain social distancing, provision of protective equipment against COVID-19 transmission in responders and the affected population among others.
Based on the officially reported laboratory-confirmed daily COVID-19 cases in the earthquake-affected area during the pre- and post- disaster period, it is concluded that the impact of the natural hazards on the evolution of the pandemic in the affected area was negligible. The viral load was low and no increase of the infection rate was recorded.
From the aforementioned, it is concluded that the disaster management policy amid pandemic in Greece proved to be more efficient than thought with a well-planned and well-structured procedure for dealing not only with earthquakes amid pandemic, but also with other types of disasters induced by natural hazards. This approach could be used as a guide for similar compound emergencies worldwide.
How to cite: Mavroulis, S., Mavrouli, M., Thoma, T., Kourou, A., Manousaki, M., Karveleas, N., and Lekkas, E.: Lessons on COVID-19 pandemic and Earthquake Response: What we have learned from the October 30, 2020, Mw 6.9 Samos (Eastern Aegean, Greece) earthquake, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1722, https://doi.org/10.5194/egusphere-egu21-1722, 2021.
The first confirmed COVID-19 case was reported in December 2019. Over the first months of 2020, the novel SARS-CoV-2 virus was spread worldwide resulting in the declaration on March 11, 2020 of a global COVID-19 pandemic by the World Health Organization. The evolving pandemic has resulted in over 1900000 fatalities worldwide (as of January 8, 2021), while all sectors of the everyday life has been affected in numerous and varied ways. Natural hazards did not stop for the novel coronavirus. When the natural hazards cross the path of an evolving pandemic, compound emergencies emerge and are characterized by various effects and new unprecedented challenges.
Greece was no exception. Geological, hydrological and meteorological hazards took place in several parts of the country and they affected the local population, the natural and the built environment including buildings, infrastructures and lifelines. Among the most destructive effects in terms of human and economic losses was the March 21, 2020, Mw=5.7, Epirus (northwestern Greece) earthquake, the August 9, 2020, Evia (central Greece) flood, the September 17, 2020, Ianos medicane and the October 30, 2020, Mw=7.0, Samos (Eastern Aegean Sea) earthquake.
In order to identify the potential impact of the aforementioned disasters on the evolution of the COVID-19 pandemic in the disaster-affected areas, the officially reported laboratory-confirmed daily COVID-19 cases for the pre- and post- disaster periods from the disaster-affected areas were used. The impact of disasters in the evolution of the pandemic in the studied disaster-affected areas comprises increasing and decreasing trends and stability of the COVID-19 cases during the post-disaster period. More specifically, the geological and the hydrological hazards and the induced disasters negligibly affected the evolution of pandemic in the affected areas, while the hydrometeorological hazards resulted in increasing trends of the post-disaster reported COVID-19 cases in various affected areas.
The detected trends are strongly associated with the pre-existing viral load and infection rate in the disaster-affected areas, to the emergency response actions adapted to adopt provisional measures for the mitigation and elimination of COVID-19 consequences, to demographic features of the affected areas and to the intensity of the induced disasters and their effects on the local population (fatalities and injuries), the natural environment (primary and secondary environmental effects) and the built environment (structural damage to buildings, infrastructures and lifelines).
How to cite: Mavrouli, M., Mavroulis, S., and Lekkas, E.: Impact of natural hazards on the evolving COVID-19 pandemic: cases from Greece, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1731, https://doi.org/10.5194/egusphere-egu21-1731, 2021.
Across the world, health and disaster managers face the challenge of responding to natural hazards such as cyclones, floods, and droughts while minimizing the impacts of Covid-19. The tropical cyclones and floods affect vulnerable communities and result in losses of life and damages. The drought situations can weaken the agricultural economy and local livelihoods. How these impacts could be amplified by the Covid-19, mainly during the monsoon season, is of great importance for informed-planning. The present study aims to assess exposure to hydro-meteorological hazards (tropical cyclones, floods, and droughts) in terms of the number of people affected, economic activities exposed, and how these hazards superimposed over the Covid-19 pandemic could impact the different phases of disaster risk management cycle. The study focuses on three deltas, namely, Ganges-Brahmaputra-Meghna (GBM) delta spanning over India and Bangladesh, and Red River (RR) and Mekong River (MK) deltas in Vietnam.
Present research found that the GBM delta suffers from frequent cyclones and floods and less with coastal floods and droughts, whereas the MK delta suffers from riverine and coastal floods and droughts. The RR delta faces frequent tropical cyclones, riverine and coastal floods, and droughts. Populations living in Red delta (100%) exposed more to tropical cyclone as compared to GBM (2.22%) and the Mekong delta (0%) with 50-year return period (RP). Similarly, about 36.46 (0.28), 83.24 (47.23), and 72.76 (33.49) % population of the GBM, RR, and MK deltas are exposed to riverine (coastal) flood hazards with 10-year RP, respectively. During May-Aug 2020, a maximum of 0.76, 100, and 33.49 % population in a month was exposed to meteorological drought (SPI3 below -1) in the GBM, RR, and MK deltas, respectively.
The results include probabilistic exposure of urban area, cropland, livestock, and GDP to major hydro-meteorological hazards on a similar line. In the second part, the study explores the number of Covid-19 cases reported at the administrative level 2 and draws qualitative inferences on how tackling multi-hazards in the deltas could have become more challenging during the ongoing pandemic and vice versa. The study recommends that the pandemic has resulted in an urgent need to incorporate health emergency disasters while designing hydro-meteorological disaster management plans.
How to cite: Pal, I., Udmale, P., Szabo, S., Pramanik, M., and Ganni, S. V. S. A. B.: Multi-hazard mitigation challenges during the Covid-19 crisis? Evidence from the tropical regions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13930, https://doi.org/10.5194/egusphere-egu21-13930, 2021.
In 2020, central part of Croatia was struck by two major earthquakes: on 22 March in Zagreb and on 29 December near Petrinja. Both earthquakes happened while country was in COVID-19 „lockdown“.
Magnitude ML5.5 earthquake occurred on Sunday morning at 5:24 UTC (6:24 CET) with the epicentre at Medvednica Mt., in the Zagreb's outskirts, just 7 km to the north of the centre of the Croatian capital. The intensity in the epicentre and in the historic centre was estimated as VII EMS, and a young girl lost her life. The earthquake struck just a day after public transport was suspended for 30 days, three days after public gatherings of more than five people were forbidden, the restaurants, shops (except for groceries, hygienic and other necessary items) and cultural institutions were closed, and six days after closure of schools and universities. A day after the main event, people were forbidden to leave their city/town/municipality of residence without written permission of local government. At that time, Croatian “lockdown” was described as one of the strictest ones in EU.
On Tuesday 29 December 2020 at 11:19 UTC (12:19 CET) a magnitude ML6.2 (MW6.4) earthquake occurred in rural area of central Croatia, near town of Petrinja. It was preceeded by magnitude ML5.0 and ML4.7 events a day before. These events caused significant damage to buildings in Petrinja and Glina and the surrounding villages. The highest intensity was estimated as VIII–IX EMS and seven people lost their lives. This sequence happened also during the “lockdown” due to COVID-19 pandemics with strict measures imposed on 21 December 2020, some of which were cancelled after the mainshock.
We will discuss events and processes that followed these strong earthquakes and how having to deal with two damaging events only nine months apart and in the unusual pandemic-related circumstances affected our work as seismologists but also our “everyday” lives.
How to cite: Dasović, I. and Herak, M.: ML5.5 and ML6.2 earthquakes in central Croatia during COVID-19 pandemics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13418, https://doi.org/10.5194/egusphere-egu21-13418, 2021.
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