Displays

The stress state variation during the fault rupturing is the key issue for the earthquake hazard. However, the modern seismic catalogs exist the huge gap of large earthquake recurrence records. To understand the occurrence, the probabilities and the dynamic processing of large earthquakes, we employed the multi-cycle earthquake simulator, RSQSim, to exam the fundamental aspects of seismicity distribution in spatial and time in western Taiwan. This 3D, boundary element software assembles the Rate and State Friction law (RSF) and initial stress state to simulate the earthquakes distributions in completely, complex seismogenic system. The heterogeneous initial stresses and recurrence seismic events would be estimated in the long sequences. In this research, we focus on the similarity comparison to the CWB earthquake catalog and Taiwan Earthquake Model (TEM) for the RSQSim simulations. Additionally, this information provides the near optimal nucleation locations and seismic events propagation at the stress evolution in Taiwan faulting systems. Through this process, we would like to examine the recurrence time of the significant earthquakes in western Taiwan. RSQsim results include the comprehensive large events in temporal series to understand the key discrepancy between models and simulators, which will bring the mutual input to TEM for update discussion on slip rate, stress accumulation, and fault system. These modifications provide the better understanding of faults slip and stress state evolution to support the fundamental aspects of earthquake cycles.

How to cite: Wu, H.-Y., Ma, K.-F., and Fry, B.: Rate and State Seismicity Simulations for Large Earthquake Cycles in Western Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8641, https://doi.org/10.5194/egusphere-egu2020-8641, 2020.

Ground-Motion Models (GMMs) characterize the random distributions of ground-motions for a combination of earthquake source, wave travel-path, and the effected site’s geological properties. Typically, GMMs are regressed over a compendium of strong ground-motion recordings collected from several earthquakes recorded at multiple sites scattered across a variety of geographical regions. The necessity of compiling such large datasets is to expand the range of magnitude, distance, and site-types; in order to regress a GMM capable of predicting realistic ground-motions for rare earthquake scenarios, e.g. large magnitudes at short distances from a reference rock site. The European Strong-Motion (ESM) dataset is one such compendium of observations from a few hundred shallow crustal earthquakes recorded at a several hundred seismic stations in Europe and Middle-East.

We developed new GMMs from the ESM dataset, capable of predicting both the response spectra and Fourier spectra in a broadband of periods and frequencies, respectively. However, given the clear tectonic and geological diversity of the data, possible regional and site-specific differences in observed ground-motions needed to be quantified; whilst also considering the possible contamination of data from outliers. Quantified regional differences indicate that high-frequency ground-motions attenuate faster with distance in Italy compared to the rest of Europe, as well as systematically weaker ground-motions from central Italian earthquakes. In addition, residual analyses evidence anisotropic attenuation of low frequency ground-motions, imitating the pattern of shear-wave energy radiation. With increasing spatial variability of ground-motion data, the GMM prediction variability apparently increases. Hence, robust mixed-effects regressions and residual analyses are employed to relax the ergodic assumption.

Large datasets, such as the ESM, NGA-West2, and from KiK-Net, provide ample opportunity to identify and evaluate the previously hypothesized event-to-event, region-to-region, and site-to-site differences in ground-motions. With the appropriate statistical methods, these variabilities can be quantified and applied in seismic hazard and risk predictions. We intend to present the new GMMs: their development, performance and applicability, prospective improvements and research needs.

How to cite: Kotha, S. R., Weatherill, G., Bindi, D., and Cotton, F.: Spatial Variability of Source and Attenuation Characteristics in Large Ground-Motion Datasets, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5187, https://doi.org/10.5194/egusphere-egu2020-5187, 2020.

In this study, we use an ergodic ground motion model (GMM) of California of Bayless and Abrahamson (2019) as a backbone and incorporate the varying-coefficient model (VCM) to develop a new French non-ergodic GMM based on the French RESIF data set (1996-2016). Most of the magnitudes of this database are small (Mw = 2.0 – 5.2), so we adopt the Fourier amplitude spectral GMM rather than the spectral acceleration model, which allows the use of small magnitude data to constrain path and site effects without the complication of the scaling being affected by differences in the response spectral shape. For the VCM, the coefficients of GMPE can vary by geographical location and they are estimated using Gaussian process regression. That is, there is a separate set of coefficients for each source and site coordinate, including both the mean coefficients and the epistemic uncertainty in the coefficients. Moreover, the epistemic uncertainty associated with the predicted ground motions also varies spatially: it is small in locations where there are many events or stations and it is large in sparse data regions. Finally, we modify the anelastic attenuation term of a GMM by the cell-specific approach of Kuehn et al. (2019) to allow for azimuth-dependent attenuation for each source which reduces the standard deviation of residuals at long distances. The results show that combining the above two methods (VCM & cell-specific) to lead an aleatory standard deviation of residuals for the GMM that is reduced by ~ 47%, which can have huge implications for seismic-hazard calculations.

How to cite: Sung, C. H., Abrahamson, N., Kuehn, N., Traversa, P., and Zentner, I.: Non-ergodic FAS Ground-Motion Model for France, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5832, https://doi.org/10.5194/egusphere-egu2020-5832, 2020.

We propose a framework for stochastically modelling the Fourier spectrum of the noisy seismic recording, considering that a seismic signal is a random rather than a deterministic quantity. We show that under this assumption, the noisy recording’s periodogram can be modelled as independent Exponential random variables with a frequency-dependent mean. With this model, estimating seismological parameters can be tackled through Maximum Likelihood (ML), allowing a fast, accurate and robust solution. This new approach constitutes a general estimation framework applicable to any parameter estimation that uses spectral analysis. Here we apply it to the high-frequency decay parameter kappa, which is particularly important for estimating and adjusting ground motion on rock. The improved ML performance is shown through a series of experiments on synthetic and recorded seismograms. The biggest advantage of the new method is its ability to account for the noise in the recording instead of just trying to avoid it, as is typically done when any ‘acceptable’ frequency range is isolated through signal-to-noise (SNR) criteria. As a result, our proposed technique can achieve acceptable results even for what would be typically considered very low and often unusable SNR, pushing the boundary of what is considered usable quality in seismic recordings.

How to cite: Pikoulis, E.-V., Ktenidou, O.-J., Psarakis, E., and Abrahamson, N.: A new approach for arriving at higher frequencies through stochastic modelling: application to site attenuation (kappa), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21645, https://doi.org/10.5194/egusphere-egu2020-21645, 2020.

Including site specific amplification factors in ground motion prediction models represented an advance for PSHA (Atkinson 2006; Rodríguez-Marek et al. 2013; Kotha et al. 2017) that has become nowadays a standard. However, this issue has only recently received attention by the seismological community of earthquake early warning (EEW) (Spallarossa et al., 2019; Zhao and Zhao, 2019), which applications require a real-time prediction of ground motion and the delivery of alert messages to users for mitigating their exposure to seismic risk. Indeed, all EEW systems are high-technological infrastructures devoted to the real-time and automatic detection of earthquakes, rapid assessment of the associated seismic hazard for targets and the prompt delivery of alerts trough fast telecommunication networks. Among them, the on-site approaches are based on seismic networks placed near to the target, indifferently by the location of seismic threats and they issue the alert predicting the ground motion at the target from P-wave parameter. This configuration cause that On-Site EEWS are generally highly affected by site conditions.

In this work, we calibrated ground motion prediction models for on-site EEW considering acceleration response spectra (RSA) and the P-waves EEW parameters Iv2 and Pd, and we investigated the role of site-effects. We considered a dataset of nearly 60 earthquakes belonging to the Central Italy 2016-17 sequence. The high density of stations near to the sequence has allowed us to use a non-ergodic random-effect regression approach to explore and to reduce the contribution of site-effects to the uncertainty of the On-site laws predictions. We grouped the records in two ways: by stations and by EC8 classification. Then, we validated the estimated models by the Leave One Out (L1Out) technique and applied a K-means analysis to assess the performance of the EC8 classification.

The residuals analysis proved that grouping by station provides a set of relations that improves the predictions at many stations. On the contrary, L1Out cross-validation proved that the regressions retrieved grouping by EC8 classification produce higher uncertainties on the predictions than the others. Furthermore, the cross-validation proved that Iv2 is more correlated to RSA than Pd. Finally, the analysis of the random effect vs period curves confirmed that EC8 classification is unrelated to the site effect on RSA even looking only at the trend of these curves.

In conclusion, non-ergodic random-effect regression can be used also in the EEW applications to predict site-specific ground motion. EEWS that use this approach are less dependent by site-effect and able to provide more precise and reliable alerts.

How to cite: Iaccarino, A. G., Picozzi, M., Bindi, D., and Spallarossa, D.: On-site Earthquake Early Warning: Predictive Models for Acceleration Response Spectra Considering Site-Effects, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8939, https://doi.org/10.5194/egusphere-egu2020-8939, 2020.

Over the last two decades, seismic ground motion prediction has been significantly improved thanks to the development of shared, open, worldwide databases (waveform and parametric values). Unlike seismic ground motion, earthquake data recorded in buildings are rarely shared. However, their contribution could be essential for evaluating the performance of structures. Increasing interest in deploying instrumentation in buildings gives hope for new observations, leading to better understanding of behavior. This manuscript presents a flat-file containing information on earthquake responses of instrumented buildings. Herein, we present the structure of the NDE1.0 flat-file containing site and earthquake characteristics (vs30, Magnitude, Distance...), structural response parameters (i.e. drift ratio, peak top values of acceleration, velocity and displacement, pre- and co-seismic fundamental frequencies) computed for several intensity measures characterizing ground motion (peak and spectral values, duration...). The data are from real earthquake recordings collected in buildings over the years. This 1.0 version contains 8,520 strong motion recordings that correspond to 118 buildings and 2,737 events, providing useful information for analyses related to seismic hazard, variability of building responses, structural health monitoring, nonlinear studies, damage prediction, etc. Some specific analysis will be presented concerning seismic structural health monitoring and damage prediction, with a special focus on the engineering demand parameter versus intensity measures variability.

How to cite: Guéguen, P., Astorga, A., and Ghimire, S.: NDE1.0 – A new database of earthquake data recordings from buildings for performance based earthquake engineering, vulnerability assessment and seismic structural health monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6948, https://doi.org/10.5194/egusphere-egu2020-6948, 2020.

    Modeling seismic hazard based on the ground motion scenario through numerical simulation could enhance the earthquake prevention strategy, especially for highly populated urban region. Taiwan as an earthquake prone country, it is important to provide the earthquake awareness through multiple risk (impact and loss) scenarios. These end-to-end hazard and risk scenarios will increase the resilience of society to extreme earthquake events by identifying the factors critical to society in the earthquake hazard and risk scenarios. The results will help to provide resilient urban development and future design by understanding and strengthening societal capacity for resilience.  Taking advantage on the open data policy, we collected the dense seismic data and open exposure data in buildings in the Taipei Metropolitan to develop the task of the end-to-end hazard and risk scenarios. The seismic hazard was made through earthquake scenario from the rupture of the Shanchiao fault, which is to the west of the Taipei basin. The topography and velocity structure of the basin were taken into account in the simulation to explore the long duration of shaking and basin effect, together with thorough evaluation on site amplification of densely populated seismic stations within the basin. We explore the assessment of scenario-based loss estimation with the exposure model of 500x500 meter grid-based data from National Science and Technology Center for Disaster Reduction (NCDR) and the governmental open data consisted of Taipei building user license information and open street map shape file data. For building damage estimation, we developed building damage based fragility curves including 1999 Chi-Chi and 2016 Meinong earthquakes for the ground motion in PGA, PGV and Intensity. We also considered the acceleration response spectrum (Sa) and velocity response spectrum (Sv) in different interval of period. Through the development of the end members, we hope to build the earthquake hazard and risk scenarios to ensure effective disaster response from up-to-date, open, transparent and reliable risk-data.

How to cite: Hsu, M.-K., Ma, K.-F., Chan, C.-H., and Schorlemmer, D.: The hazard and risk assessment of Taipei Metropolitan through earthquake scenario from open data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19670, https://doi.org/10.5194/egusphere-egu2020-19670, 2020.

A conditional ground-motion model (GMM) is developed for peak ground displacement (PGD)for Taiwan. The conditional GMM includes the observed pseudo-spectral acceleration (PSA(T)) as an input parameter in addition to magnitude and distance. The conditional PGD model can be combined with the traditional GMMs for PSA values to develop a GMM for PGD without the dependence on PSA. The main advantages of the conditional model approach are that it can be quickly developed, is easily understandable, can fully capture the magnitude, distance, and site scaling of the secondary parameters that are compatible with the design response spectral values, and also has much smaller aleatory variability than traditional GMMs. In this study, we use part of the database of Taiwan SSHAC Level 3 project (13691 strong-motion records from 158 crustal events occurred between 1992 and 2018 with 4.5 ≤ Mw ≤ 7.65) to develop a new conditional scaling model for horizontal PGD consisted from the suite period of the PSA, rupture distance and moment magnitude. Furthermore, we combine this conditional model with each of two SSHAC Level 3 models and NGA-West2 ground-motion models for PSA(T) to derived new GMMs for the median and standard deviation of PGD. The results show that the new PGD GMMs include the more complex ground-motion scaling which capture from the GMMs of PSA, such as hanging-wall effects, sediment-depth effects, soil nonlinearity effects, and regionalization effects.

How to cite: Sung, C. H., Abrahamson, N., and Huang, J. Y.: Taiwan Conditional Prediction Equation for Horizontal PGD for Crustal Sources, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9345, https://doi.org/10.5194/egusphere-egu2020-9345, 2020.

For years, engineering seismologists aim to reduce the epistemic uncertainty related to ground motion prediction. Assuming that simple models with few variables are not sufficient to describe the complex phenomena, there is a trend in present-day science to increase complexity of ground motion models. Therefore, some of the most recent ground motion prediction equations use more than 20 variables to improve the predictive power of the model. However, the legitimate question to ask is whether the inclusion of additional variables leads to an improved predictive power of the model. In other words, what is the smallest number of predictive variables needed to reconstruct the distribution of ground motion induced shaking observed in data? In this study, by taking advantage of the exponential growth of ground motion data and new machine learning methods, we present a data-driven approach to derive the dimensionality of ground motion data in the Fourier amplitude spectrum (FAS) metric. We apply an autoencoder architecture, which is commonly used for mapping high dimensional data to a lower dimensional space (bottleneck) and search for the lowest dimensionality (minimum number of nodes in the bottleneck) required to reconstruct the FAS input data. The approach is tested on synthetic ground motion data with known dimensionality (2D and 4D) and finally applied to the FAS of recorded ground motion data. A simple autoencoder with variable nodes in the bottleneck is used to explore the dimensionality of the ground motion data. We use the relation between the total residual of the network with the number of codes in the bottleneck as an indicator of dimensionality. Its numerical value is estimated based on the reduction of residuals by increasing the number of codes in the bottleneck layer. In addition, we use the low dimensional manifold of the ground motion data to predict the ground motion shaking for a given scenario. The residual analyses between observed and reconstructed data and observed and predicted data are used to validate the training and prediction steps. We applied the method on different scenarios in two synthetic data sets which are simulated by a stochastic simulation method and secondly the Pan-European engineering strong motion data (EMS) to show the performance of the proposed method. The results show that the statistical properties of ground motion data can be captured by using a limited number of three to five parameters. Especially for low frequency data the most dominant features are already captured by two parameters (codes), which roughly correspond to magnitude and distance. For higher frequencies additional parameters, e.g. corresponding to stress drop and kappa, become more relevant. The standard deviation of the residuals can be reduced to its lower bound in comparison with the standard deviations of conventional methods. Finally, we use a two-dimensional manifold to predict the FAS for given magnitude and distance values.

How to cite: Dokht Dolatabadi Esfahani, R., Vogel, K., Cotton, F., Ohrnberger, M., Scherbaum, F., and Kriegerowski, M.: Exploring the dimensionality of ground motion data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6911, https://doi.org/10.5194/egusphere-egu2020-6911, 2020.

Ground response analyses (GRA) model the vertical propagation of SH waves through flat-layered media (1DSH) and are widely carried out to evaluate local site effects in practice. Horizontal-to-vertical spectral ratio (HVSR) technique is a cost-effective approach to extract certain site-specific information, e.g., site resonant frequency, but HVSR values cannot be directly used to approximate the level of S-wave amplification. Motivated by the work of Kawase et al. (2019), we propose a procedure to correct earthquake HVSR amplitude for direct amplification estimation. The empirical correction, in essence, compensates HVSR by generic vertical amplifications grouped by vertical fundamental resonant frequency (f_0v) and 30 m average shear-wave velocity (V_S30) via k-mean clustering. In this investigation, we evaluate the effectiveness of the corrected HVSR in approximating observed amplification in comparison with 1DSH modelling. To the end, we select a total of 90 KiK-net surface-downhole recording sites which are found to have no velocity contrasts below downhole sensor and thus of which surface-to-borehole spectral ratio (SBSR) can be taken as its empirical transfer function (ETF). 1DSH-based theoretical transfer function (TFF) is computed in the linear domain considering the uncertainty in V_S profile through randomization. Five goodness-of-fit metrics are adopted to gauge the closeness between observed (ETF) and predicted (i.e., TTF and corrected HVSR) amplifications in both amplitude and spectral shape. The major finding of this study is that the empirical correction procedure to HVSR is highly effective, and the corrected HVSR has a “good match” in both spectral shape (Pearson’s r > 0.6) and amplitude (Index of agreement d > 0.6) at 74% of the investigated sites, as opposed to 17% for 1DSH modelling. In addition, the HVSR-based empirical correction does not need a site model and thus has great potentials in site-specific seismic hazard assessments.

How to cite: Zhu, C., Pilz, M., and Cotton, F.: When 1D response analysis fails: application of Earthquake HVSR in Site-Specific Amplification Estimation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21607, https://doi.org/10.5194/egusphere-egu2020-21607, 2020.

Ground motions with strong pulses often bring significant damage to structures. The period and the amplitude of the strong-velocity pulses are critical for structural engineering and seismic hazard assessment. The scaling of pulses periods with magnitudes and the within-event variability of pulses is however poorly understood. In this study, we analyze two moderate earthquakes, namely 2016 Meinong earthquake and 2018 Hualien earthquake, using Shahi and Baker’s criteria (2014) to detect pulses. The observations in this study show that the amplitudes of the pulse decay with the distance from the source to the stations, and is also associated with the rupture direction from the asperity instead of the direction from the hypocenter. In addition, we further perform simulations using a simple FK method to clarify the causes of the variability of the pulse periods within and between events. We test the effect of faults dipping angles and the impacts of the asperity location and size. Through our simulations, we reveal that the amplitudes of the pulses in the shallow dipping fault are larger on the hanging wall than on the foot wall, and that the asperity properties has a large impact on the pulses periods and the amplitudes at the nearby stations. The results show that the asperity characteristics are critical for the occurrence of the strong-velocity pulses. The complete understanding of the kinematics of the rupture is then important for clarifying the effects of the strong-velocity pulses and improving ground-motions predictions.

How to cite: Yen, M.-H., Ma, K.-F., Cotton, F., Lin, Y.-Y., and Lee, Y.-T.: Within and Between-Events Variability of Strong-Velocity Pulses , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21146, https://doi.org/10.5194/egusphere-egu2020-21146, 2020.

The problem is considered of unrealistic ground motion estimates, which arise when the Cornell–McGuire method is used to estimate the seismic hazard for extremely low annual probabilities of exceedance. This problem stems from using the normal distribution in the modelling of the variability of the logarithm of ground motion parameters. In this study, the statistical properties of the logarithm of peak ground acceleration (PGA) are analysed by using the database of the strong-motion seismograph networks of Japan. The normal distribution and the generalised extreme value distribution (GEVD) models were considered in the analysis, with the preferred model being selected based on statistical criteria. The results indicate that the GEVD was a more appropriate model in eleven out of twelve instances. The estimates of the shape parameter of the GEVD were negative in every instance, indicating the presence of a finite upper bound of PGA. Therefore, the GEVD provides a model that is more realistic for the scatter of the logarithm of PGA, and the application of this model leads to a bounded seismic hazard curve.

How to cite: Pavlenko, V.: Statistical properties of peak ground acceleration and their effect on results of probabilistic seismic hazard analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21741, https://doi.org/10.5194/egusphere-egu2020-21741, 2020.

The city of Durrës was recentely struck by a Mw 6.2 mainshock event (http://cnt.rm.ingv.it/event/23487611) that caused considerable damage and 51 victims. The city is located on an actively seismotectonic belt where seismic catalogues report few past events with magnitude higher than 6.

Surface geology is generally considered to influence the ground motion recorded on site. The analysis of the influence of local effects on seismic response at ground surface appears relevant also considering that Durrës is a densely populated city prone to high seismic risk and is characterized by several important archeological and cultural heritage sites.

Preliminary results obtained from recent geophysical in-situ measurements and geological surveys, carried out in Durrës after the ML 5.4, 21^st September 2019 event, are presented with the aim of providing new elements for the assessment of local seismic hazard and following a comprehensive approach to the modifications induced by the site.

Twenty-nine single-station noise measurements, processed through the HVSR technique, two MASW surveys and two 2D array measurements were performed. Results from noise measurements define a zone eastward of the historical centre, where the characteristics of surficial soil layers are responsible for modification to the seismic demand. In particular, HVSR curves in this area show amplification higher than 4 at a period higher than 1s. Moreover, on this location a surface waves-velocity profile obtained from a joint inversion of Rayleigh curves from MASW and 2D array with ellipticity individuates a class D soil, EC8 sensu, corresponding to marshy soil of very poor geotechnical quality. These data may be considered as key elements in the site-specific response analyses, i.e. realized according to the international codes (EC8, NEHRP), which allow to quantify the expected ground motion. These results are potentially useful for  correlating  construction typologies and period vibration of the buildings with the site amplification.

In addition, a damage survey was carried out in one of the most damaged zones after the 21^st September 2019 earthquake. Because of the following stronger event of the 26^th November 2019, we think that these preliminary results may provide useful information for the post-earthquake reconstruction and enhancement of the urban resilience.

                The activities are carried out wihin the framework of the CNR/MOES Joint research project “Seismic risk assessment in cultural heritage cities of Albania” in the biennium 2018-2019 (https://www.cnr.it/en/bilateral-agreements/agreement/60/moes-ministry-of-education-and-sport-of-the-republic-of-albania).

How to cite: Mancini, M., Gaudiosi, I., Muci, R., Simionato, M., and Skrame, K.: Contribution for seismic hazard assessment with local scale focus on Durrës (Albania) and damage observation after the ML 5.4, 21st September 2019 earthquake, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19528, https://doi.org/10.5194/egusphere-egu2020-19528, 2020.

Site effect is one of the critical factors influencing the seismic hazard evaluation. Among others, the average shear-wave velocity of the upper 30 meters of a soil profile (Vs30) has been widely used for assessing the ground-motion amplification. However, spatial resolution of shear wave velocity data is usually poor for reginal- or national-wise evaluation. Standard Penetration Test N-value, the most abundant geotechnical data, was then used to estimate the shear wave velocity (Vs) empirically and the uncertainty of the Vs30 map can be reduced. In this study, we use the state variables of soils (void ratio and effective stress) to evaluate the shear wave velocity and to map the Vs30 in Taiwan. Engineering Geological Database for TSMIP (EGDT) comprises soil profile, shear wave velocity measurements, groundwater table, and soil physical properties (such as void ratio, water content, specific gravity, and unit weight), was used to construct the correlation between Vs, void ratio, and effective stress. The drilling database of Taiwan CGS was then used to estimate the spatial distribution of Vs30, where the Vs is un-available. The results were compared with the previous version of Vs30 map of Taiwan. The uncertainty of the new Vs30 map was evaluated and the propagation of uncertainty to the seismic hazard can be evaluated accordingly.

How to cite: Gao, J. C., Guo, J. L., Dong, J. J., and Lee, C. T.: Correlation between shear wave velocity, void ratio and effective stress: Mapping Vs30 in Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6555, https://doi.org/10.5194/egusphere-egu2020-6555, 2020.

There are two kinds of bedrocks that are widely used in seismology and earthquake engineering respectively. The seismology field uses the “seismic bedrock” to define an interface that has a practically lateral extent. The strata deeper than this interface is much more homogeneous in comparison with the shallower one. It is common to set the seismic bedrock within the upper crust has 3000 m/sec of the shear wave velocity. In contrast, the earthquake engineering prefers the shallower interface which dominates the main seismic site amplification, especially the predominant frequency of ground motion. The interface is called “Engineering Bedrock”, which the underlying stratum has the shear wave velocity from 300 to 1000 m/sec for different purposes. But, the reference shear wave velocity of the engineering bedrock is mostly defined as 760 m/sec for ground motion prediction and simulation. In Taiwan, the Central Weather Bureau (CWB) constructed and operates a dense strong-motion network called TSMIP (Taiwan Strong Motion Instrument Program), which provides numerous ground motion data for seismology and earthquake engineering. In our previous studies, the shallow shear wave velocity profiles of over 700 TSMIP stations were estimated by the Receiver Function method. The velocity profiles are from the ground surface to the depth with the shear wave velocity of at least 2000 m/sec. It allows us to compare the theoretical site amplification of the velocity profile of TSMIP stations with their observed one from the seismic records. The variance of fitness between theoretical and observed amplifications through shear wave velocity is analyzed to evaluate which reference velocity can appropriately define the depth of engineering bedrock, where the most site amplification occur beneath, in all of Taiwan. The difference between local geology is also discussed. Finally, an engineering bedrock map is proposed for further applications in earthquake engineering.

How to cite: Lin, C.-M., Huang, J.-Y., Kuo, C.-H., and Wen, K.-L.: Identification of Engineering Bedrock in Taiwan based on Site Amplification and Velocity Structures of Strong-motion Stations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11988, https://doi.org/10.5194/egusphere-egu2020-11988, 2020.

Marmara region located on the western end of the North Anatolian Fault Zone is a tectonically active region in Turkey. There have been frequent severe earthquakes in the region and will continue to occur. There was no serious earthquake in the region after the 1999 Mw7.4 Kocaeli and Mw7.2 Düzce earthquakes. A Marmara Sea offshore earthquake Mw5.8 close to Silivri Town of Istanbul Metropolitan City has occurred on September 26, 2019 daytime at 13:59. The earthquake happened at the coordinate of 40.87N – 28.19E with a depth of 7.0km on the Kumburgaz segment of the North Anatolian Fault line. It was felt in almost all Marmara region. In some settlements in Istanbul City, slight to moderate damages were observed. A foreshock earthquake of Mw4.8 occurred on the same segment on 24 September, 2019. 150 aftershock events ranging from M1.0 to M4.1 have been recorded within the 24 hours after the mainshock. The ground motions have been recorded in the region by the several institutions including AFAD (Disaster and Emergency Management Presidency), KOERI (Kandilli Observatory and Earthquake Research Institute) and IGDAS (Istanbul Gas Distribution Industry and Trade Inc.). The ground motion records and selected parameters have been examined in this study. The ground motion parameters (MMI, PGA, PGV, Sa, Sv, Sd) distribution have been achieved and checked by the recent NGA-West2 ground motion prediction equations (GMPEs); ASK2014, CY2014 and BSSA2014. The compatibility of the GMPEs for a moderate size Marmara Sea earthquake has been examined.

How to cite: Okuyan Akcan, S. and Zulfikar, C.: SEPTEMBER 26, 2019 Mw5.8 MARMARA SEA-SILIVRI (ISTANBUL) EARTHQUAKE: ANALYSIS OF GROUND MOTION RECORDS, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7315, https://doi.org/10.5194/egusphere-egu2020-7315, 2020.

Modeling the amplitude spectra based on the source term, the path one and site ones for 54 sites located in and around the Taipei basin is the aim of this study. The site term includes the amplification function varied with frequency and the site-specific parameter (k0). The amplification functions for Class-C, -D, and -E site are from Huang et al. (2007) for the central Taiwan. Meanwhile, the amplification function for Class-B site can be referred to Boore and Joyner (1997). The root-mean-squared spectral amplitudes of two horizontal shear waves after three-point smoothing from the observed seismograms are compared to the synthetic amplitude spectra. The goodness of fit coefficient (GFC) and the residual errors (ERR) are calculated for concluding the fitness of the modeling amplitude spectra. Results show both the GFC and ERR of stations are varied with the earthquake magnitude and hypo-central distance. The averaged GFC are larger than 0.8 for 42 stations. Meanwhile, there are 12 station with averaged GFC smaller than 0.8. Besides, the ERRs of 28 stations are less than 0.5. Meanwhile, there are 18 stations with ERRs in the range of 0.5-0.6. The obtained results may be used for modeling the amplitude spectra for the Taipei area. The more accurate amplitude spectra can be improved by updating the parameters utilized in the source-, the path- and the site terms.

How to cite: Huang, M.-W., Chang, C.-L., and Liu, S.-Y.: The synthetic spectra of potential earthquakes for Taipei basin, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12333, https://doi.org/10.5194/egusphere-egu2020-12333, 2020.

For seismic analysis of complex and non-linear structure system, a seismic code recommends a dynamic time history analysis. In these cases, the input earthquake seismograms should be needed, and these input earthquake seismograms must be compatible with design response spectrum and reflect the site seismic characteristics including information about a fault and wave travel path between the fault and the site. The foreshocks, main shock and aftershocks of earthquake measured in the target area can be assumed to be the output signals of the system consisting of the fault and the wave travel path between the fault and the site. Each earthquake seismogram is considered as a amplitude modulated (AM) signal defined by the magnitude (or energy) and phase function with time. The probability distribution function (PDF) of the magnitude and phase function can be evaluated through the statistical and harmonic wavelet analysis of the measured output signals and these magnitude and phase PDFs include sufficient information to generate the possible output earthquake seismograms of the fault and travel path system for a site, which mean the phase and magnitude PDFs represent a site seismic characteristics.

In this paper, the method to generate the possible design response spectrum compatible earthquake seismograms based on the measured foreshocks, main shock and aftershocks of earthquake is proposed. At first the proposed method generate possible earthquake signals reflecting the phase characteristic of a site, and then modify the magnitude of these earthquake seismograms to determine the response spectrum compatible earthquake motions.  

How to cite: Park, H.-C. and Oh, H.-J.: Generation of response spectrum compatible earthquake seismogram considering phase characteristics of possible earthquake motions of site, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12182, https://doi.org/10.5194/egusphere-egu2020-12182, 2020.

A fundamental feature of any Earthquake Early Warning System is the ability of rapidly broadcast earthquake information to a wide audience of potential end users and stakeholders, in an intuitive, customizable way. Smartphones and other mobile devices are nowadays continuously connected to the internet and represent the ideal tools for earthquake alerts dissemination, to inform a large number of users about the potential damaging shaking of an impending earthquake.

Here we present a mobile App (named ISNet EWApp) for Android devices which can receive the alerts generated by a network-based Early Warning system. Specifically, the app receives the earthquake alerts generated by the PRESTo EWS, which is currently running on the accelerometric stations of the Irpinia Seismic Network (ISNet) in Southern Italy. In the absence of alerts, the EWApp displays the standard bulletin of seismic events occurred within the network. In the event of a relevant earthquake, instead, the app has a dedicated module to predict the expected ground shaking intensity and the available lead-time at the user position and to provide customized messages to inform the user about the proper reaction during the alert.

We first present the architecture of both network-based system and EWApp, and then and describe its essential operational modes. The app is designed in a way that is easily exportable to any other network-based early warning system.

How to cite: Colombelli, S., Carotenuto, F., Elia, L., and Zollo, A.: Design and implementation of a mobile device APP for network-based EEW systems: application to PRESTo EEWS in Southern Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19404, https://doi.org/10.5194/egusphere-egu2020-19404, 2020.

Seismic hazard assessment of urban areas is an important and extremely challenging task. It is so important because without the knowledge of the influence of local soil conditions and properties, of the changing layer thickness in urban areas, and without considering multiple possible scenario earthquakes for this territory, engineers do not have enough information on how to design and construct seismically safe buildings. The particular challenge of this task is due to the great uncertainty affecting the prediction of the spatially (and sometimes even temporally) changing seismic properties of soils with respect to urban development.
Dushanbe is the capital of Tajikistan, a mountainous country marked by high to very high seismic hazard. The reason for the high seismic hazard specifically near Dushanbe is related to its location between two fault systems: South Gissar fault and Ilek-Vaksh fault.  Estimation of the seismic hazard of the urban areas in Tajikistan is very important because they had developed in a very short time and many high buildings are being constructed now Existing seismic action estimations are based on the old approaches when the main factors of the local soil conditions only consider general engineering-geological features of the territory as well as macro-seismic observations data. An additional problem is the building code in Tajikistan; it uses the estimation of the ground motions in terms of the MSK-64 scale, but does not enough take into account the variety of the soil conditions in the Dushanbe city area. Existing seismic hazard estimation of the area of Tajikistan is based on the so-called “The map of general seismic zoning of the territory of Tajikistan”, that was produced in 1978 in terms of MSK-64 scale. The seismic microzonation map of the Dushanbe city area was made in 1975 in terms of MSK-64 scale as well and was based on the engineering-geological approach mostly. This map does not represent the highly variable soil conditions of the Dushanbe city area which are partly due to the anthropogenic influence of the large city. Therefore, earlier seismic zonation maps assigned an intensity of IX to most districts of the city. However, those previous studies did not sufficiently quantify the local effects of soils on the seismic hazard, mainly the macro-seismic conditions (the relative distance of districts to fault lines) were considered for the zonation. 
This study describes and implements a number of new approaches to the evaluation of maximum seismic impact and site effect values. 

How to cite: Hakimov, F., Havenith, H.-B., Ischuk, A., Pilz, M., and Reicherter, K.: Seismic Hazard Assessment and Numerical Modeling for Seismic Microzonation purpose of Dushanbe, Tajikistan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21712, https://doi.org/10.5194/egusphere-egu2020-21712, 2020.

Site amplification behavior are important in ground motion prediction. Seismic waves were amplified and caused significant building damages in the Taipei Basin by the 1986 Hualien offshore (subduction interface) and the 1999 Chi-Chi earthquakes (crustal), for which both of the epicentral distances were nearly 100 km. To understand local site amplifications in Taiwan, empirical site amplification factors for both horizontal and vertical ground motions are studied using recently constructed strong motion and site databases for the free-field TSMIP stations. Records of large magnitude earthquakes of M_W larger than 5.5 from 1991 to 2016 were selected for this study. Site amplification factors at site conditions with Vs30 between 120 m/s to 1600 m/s and bedrock accelerations up to 0.8 g were evaluated using ratios of spectral accelerations at different periods. The reference site condition, i.e. the engineering bedrock, is assumed as Vs30 of 760 m/s (B/C boundary) in this study. Our empirical site amplification form are borrowed from the site response function of ASK14 and CY14 ground motion models in NGA-West2 project with slight modification. Therefore our site amplification model includes a linear amplification term and a nonlinear deamplification term. The coefficients of the empirical models were obtained by a nonlinear regression analysis using the selected Taiwan data. Site amplification factor is a function of Vs30 and spectral intensity in the model. Similar linear site amplification factor to the NGA models is derived in our model; however, more significant soil nonlinearity behavior than the NGA models is likely captured from the empirical data. The amplification factor in vertical component is smaller than that in horizontal.

How to cite: Kuo, C.-H., Chao, S.-H., Lin, C.-M., Huang, J.-Y., and Wen, K.-L.: Empirical Site Amplification Modelling for Horizontal and Vertical Ground Motions in Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2935, https://doi.org/10.5194/egusphere-egu2020-2935, 2020.

Characterisation of seismic risk within a probabilistic framework is dependent upon well-constrained models of the seismic source, the ground motion scaling and the local site response, in addition to both their aleatory variability and epistemic uncertainty. When assessing risk as a large geographical scale such as that of a country or continent, however, complex models of site response that require detailed parameterization of the site conditions are seldom feasible to constrain. Instead, the use of simpler proxies, such as the well-known topographically inferred 30 m averaged shear-wave velocity (V_S30), have become widely adopted for this purpose. In practice, the inference of V_S30 from topographic and/or geological proxies have substantial limitations in terms of both the geological environments for which they are appropriate and the increased uncertainty in the prediction of site response; limitations that are not always accounted for in existing seismic risk models.

The volume of data reported by both new and well-established stations is increasing at an exponential rate, with hundreds of thousands of strong motion records now available from thousands of stations. Through this enormous and ever-expanding data set it is possible to constrain thousands of station-specific amplifications and utilize this dataset to calibrate the site amplification directly upon regionally mappable parameters, which can be applied across large spatial scales needed for regional seismic risk analysis. In doing so, it is possible not only to adapt the model of site amplification to different geological environments, but also to adjust the uncertainty in the ground motion characterization to ensure that this is captured appropriately in the seismic risk analysis when using the mappable site proxies. Applications of this approach have been made for two case study regions: i) Japan, where detailed station metadata are available and the relative increase in uncertainty from using regionally-mappable parameters instead of well-constrained site properties can be constrained, and ii) Europe, where station metadata more limited but a large number of stations with repeated observations are available. The implications for the estimates of seismic losses when adopting this new approach in place of the existing methodology are illustrated using examples from the 2020 European Seismic Risk model.

How to cite: Weatherill, G., Cotton, F., and Kotha, S. R.: Using Large Strong Motion Datasets to Model Regional Site Response in Seismic Risk Assessment: Examples from Japan and Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6801, https://doi.org/10.5194/egusphere-egu2020-6801, 2020.

The substantial reduction of disaster risk and loss of life, a major goal of the Sendai Framework by the United Nations Office for Disaster Risk Reduction (UNISDR), requires a clear understanding of the dynamics of the built environment and how they affect, in the case of natural disasters, the life of communities, represented by local governments and individuals. These dynamics can be best understood and captured by the local communities themselves, following two of the guiding principles formulated by the UNISDR: "empowerment of local authorities and communities" and "engagement from all of society". The two lead to societies increasing their understanding of efficient risk mitigation measures.

Our Global Dynamic Exposure model and its technical infrastructure build on the involvement of communities in a citizen-science approach. We are employing a crowd-sourced exposure capturing based on OpenStreetMap (OSM), an ideal foundation with already more than 375 million building footprints (growing daily by ~150,000), and a plethora of information about school, hospital, and other critical facilities. We are harvesting this dataset with our OpenBuildingMap system by processing the information associated with every building in near-real-time. We are enriching this dataset in a truly big-data approach by including built-up area detection from remote sensing with satellite and radar imagery combined with different sources of road networks, as well as various open datasets and aggregated exposure models that provide relevant additional information on, buildings and land use. 

A task of such a scale does not come without challenges, particularly in matters of data completeness, privacy and the merging and homogenizing of different datasets. We are thus investing a large effort on the development of strategies to tackle these in a transparent and consistent way.

We are fully automatically collecting exposure and vulnerability indicators from explicitly provided data (e.g., hospital locations), implicitly provided data (e.g., building shapes and positions), and semantically derived data, that is, interpretation applying expert knowledge. The latter allows for the translation of simple building properties as captured by OpenStreetMap users or taken from open datasets into vulnerability and exposure indicators and subsequently into building classifications as defined in the Building Taxonomy 2.0 developed by the Global Earthquake Model (GEM) and in the European Macroseismic Scale (EMS98). A task of such a scale does not come without challenges, particularly in matters of data completeness, privacy and the merging and homogenizing of different datasets. We are thus investing a large effort on the development of strategies to tackle these in a transparent and consistent way. With our open approach, we increase the resolution of existing exposure models minute by minute through data updates and step by step with each added building, as we move forward from aggregated to building-by-building descriptions of exposure. 

We expect the quality of near-real-time estimates of the extent of natural disasters to increase by an order of magnitude, based on the data we are collecting. We envision authorities and first responders greatly benefitting form maps pinpointing the greatest trouble spots in disasters and from detailed quantitative estimates of the likely damage and human losses.

How to cite: Schorlemmer, D., Beutin, T., Cotton, F., Garcia Ospina, N., Hirata, N., Ma, K.-F., Nievas, C., Prehn, K., and Wyss, M.: Global Dynamic Exposure and the OpenBuildingMap - A Big-Data and Crowd-Sourcing Approach to Exposure Modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18920, https://doi.org/10.5194/egusphere-egu2020-18920, 2020.

Synthetic shaking-intensity maps provide the necessary information about the detailed shaking distribution for scenario-based seismic risk assessment as well as post-disaster rapid loss estimates. These ShakeMaps allow to identify areas heavily affected by an earthquakes and are becoming, combined with an exposure/vulnerability model, the underlying data for a risk or loss model. Such computations deliver decision makers the data for informed policy decisions for precautionary measures for increasing resilience, or, in case of post-disaster analyses, rapid estimates for disaster mitigation.

We present a new web engine for synthetic ShakeMaps harnessing the OpenQuake engine of the Global Earthquake Model (GEM) foundation. The back-end asynchronously digests requests parameterizing earthquake sources in terms of source depth, epicentral location, moment magnitude and focal mechanism. The back-end returns shaking in user definable ground-motion measures (e.g. PGA or IMS) and can be retrieved in various formats such as ASCII, GeoJSON, among others. This tool implements an open and documented API that users and other services can query systematically and automatically. It integrates into the LEXIS framework, a Horizon 2020 funded project aiming at improving rapid loss assessments and emergency decision support systems.
An interactive interface allows to explore the expected shaking in the spatial domain by selecting locations of interest on a map and defining the earthquake source interactively within a web browser. Besides the interactive mode, this service now provides, through HTTP requests, a simple interface for any type of ShakeMap to be used in automated systems that require rapid ShakeMap computations without the need to run local instances of OpenQuake.

How to cite: Kriegerowski, M., Schorlemmer, D., Goubier, T., and Cotton, F.: A Web Tool for Interactive Generation and Visualization of Synthetic ShakeMaps, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20735, https://doi.org/10.5194/egusphere-egu2020-20735, 2020.

Residential building exposure models for risk and loss estimations related to natural hazards are usually defined in terms of specific schemas describing mutually exclusive, collectively exhaustive (MECE) classes of buildings. These models are derived from: (1) the analysis of census data or (2) by means of individual observations in the field. In the first case, expert elicitation has been conventionally used to classify the building inventory into particular schemas, usually aggregated over geographical administrative units whose size area and shape are country-specific. In the second case, especially for large urban areas, performing a visual inspection of every building in order to assign a class according to the specific schema used is a highly time- and resource intensive task, often simply unfeasible.

Remote sensing data based on the analysis of satellite imagery has proved successful in integrating large-scale information on the built environment and as such can provide valuable vulnerability-related information, although often lacking the level of spatial and thematic resolution requested by multi-hazard applications. Volunteered Geo Information (VGI) data can also prove useful in this context, although in most cases only geometric attributes (shape of the building footprint) and some occupancy information are recorded thus leaving out most of the building attributes controlling the vulnerability of the structures to the different hazards. An additional drawback of VGI is the incompleteness of the information, which is based on the unstructured efforts of voluntary mappers.

Former efforts have been proposing a top-down/bottom-up approach moving from regional scale to neighbourhood and per-building scale, based on the analysis and integration of different data sources at increasing spatial resolutions and thematic detail. Following the same principle, this work focuses on the downscaling of already existing building exposure models based on census data making use of a probabilistic approach based on Bayesian updating. Different aggregation models can be taken into account to increase the spatial resolution of the building exposure model, also including variable-resolution models based on geostatistical approaches. Land-use masks are first generated after a supervised classification of Sentinel-2 images, in order to better relate the built- up area to meaningful geographical entities. Two independent statistical models are then created based on prior input information. Maximum likelihood estimations are obtained for each model. Two types of auxiliary data have been employed in order to constrain the downscaling via a specific likelihood term in the Bayesian updating: 1) building footprints area from the open-source-volunteered geo-information OpenStreetMaps  and 2) built-up height and density estimators based on remote sensing developed by the DLR (the German Aerospace Agency).

This approach, developed within the scope of the RIESGOS, was tested in Valparaiso and Viña del Mar (Chile) where the residential building exposure model proposed by the GEM-SARA project has been downscaled. The performance of the different auxiliary data were separately tested and compared. An independent building survey has also been carried out by experts from CIGIDEN (Chile) using a Rapid Remote Visual Screening Survey and used for preliminary validation of the approach.

How to cite: Zafrir, R., Pittore, M., Gomez- Zapata, J. C., Aravena, P., and Geiß, C.: Bayesian downscaling of building exposure models with remote sensing and ancillary information, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18240, https://doi.org/10.5194/egusphere-egu2020-18240, 2020.