NH4.2

Over the last 30 years, the Cosserat continuum has gained an importance in the description of physical properties of tectonic faulting. Using the sine-Gordon equation we show that kink and antikink solitary wave solutions can be used to describe propagation of the couple-stresses through the faulted medium of the Earth’s crust. Recently it was established that the shear-traction exerted on the tectonic faults by the couple-stresses correlates with radon degassing. Degassing is estimated through atmospheric effects due to air ionization expressed in the form of the atmospheric chemical potential (ACP), thus providing a direct and measurable proof for the existence propagating couple-stresses in the Earth’s crust. The positive and negative correlation corresponds to different faulting mechanism and thickness of the Earth’s crust. Positive correlation is observed in the regions characterized by thin crust, as well as in the normal and strike-slip faulting regimes. The negative correlation is observed in the regions characterized by thick crust as well as in the reverse faulting regimes, and along very long transform faults. Using together the shear-traction modeling and ACP measurements, we can identify critical zones prone to the earthquake triggering and calculate the time-dependent probability for the future earthquakes.

How to cite: Pulinets, S., Vičič, B., Budnikov, P., Žalohar, J., Potočnik, M., Komac, M., and Dolenec, M.: Real-time global shear-traction model verification using atmospheric effects of radon activity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4746, https://doi.org/10.5194/egusphere-egu22-4746, 2022.

Abstract
We proposed an earthquake forecasting model for Albania, one of the most seismic
regions in Europe, to give an overview of seismic activity by implementing area
source and smoothing approaches. The earthquake catalogue was firstly declustered
to evaluate the completeness time window and magnitude of completeness for shallow
events. Considering catalogue completeness, the events with M≥4.0 during the period
of time 1960 – 2006 were implemented for forecasting seismicity in 20 area sources
covering the region of study and each grid cell with a size of 0.2 x 0.2 degrees. Our
results from both models show a high seismic rate along the western coastline and
south part of the study area, consistent with previous studies and currently active
regions. To further evaluate the seismicity results from the models, we introduced a
Molchan diagram to investigate the correlation between a model and observations of
earthquake events. The catalogue from 1960 to 2006 is regarded as the “learning
period” for model construction, and the catalogue data covering the period of time
2018-2020 is the “testing period” for comparing and validating the results. The
Molchan diagram suggests that both models are significantly better than random
distributed, confirming their forecasting abilities. Our results could provide crucial
information for subsequent probabilistic seismic hazard assessment.

Keywords: area sources, declustering, earthquake catalogue, Molchan diagram,
probabilistic seismic hazard assessment, smoothing model,.

How to cite: Xhafaj, E., Chan, C.-H., and Ma, K.-F.: Earthquake forecasting model in Albania, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9041, https://doi.org/10.5194/egusphere-egu22-9041, 2022.

Complex network theory has been recently applied to get new insights and a different perspective in the study of earthquake patterns. Several studies (see for instance Daskalaki et al., J Seismol, 2016; Telesca, Phys. Chem. Earth, 2015; Varini et al., J Geophys Res, 2020; Ebrahimi et al., Chaos Solitons Fractals, 2021, and references therein) were based on the preliminary mapping of the time series of earthquakes into networks, by applying visibility graph method or other clustering algorithms. In a second step, the topological properties of the obtained networks were analyze by exploiting tools of complex network theory with the aim of discovering possible precursory signatures of strong earthquakes or other features relevant to hazard assessment.

In this study we investigated the earthquake clusters extracted by two data-driven declustering algorithms: the nearest-neighbor, which classifies the earthquakes on the basis of a nearest-neighbor distance between events in the space-time-energy domain (Zaliapin and Ben-Zion, J Geophys Res, 2013), and the stochastic declustering, which is based on the space-time ETAS point process model (Zhuang et al., J Geophys Res, 2004). Case studies from selected sequences, occurred in Central Italy from 1985 to 2021, are examined in some detail.

The earthquake clusters extracted by the two declustering algorithms are compared by different tools, so as to assess the similarities and differences in their classification and characterization (Varini et al., J Geophys Res, 2020). The connections between events forming a cluster, as defined by the considered declustering method, allow us representing its hierarchical structure by means of a tree graph. The topological structure of the clusters is then investigated by means of centrality measures in the frame of Network analysis.

How to cite: Varini, E. and Peresan, A.: Investigating earthquake clusters complexity in Central Italy by network theory tools, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2700, https://doi.org/10.5194/egusphere-egu22-2700, 2022.

Over the years numerous attempts have been made to obtain the distribution of earthquake numbers. The most popular distribution that has been widely used to describe earthquake numbers is the Poisson distribution due to its simplicity and relative ease of application. Another distribution that has been used to approximate the earthquake number distribution is the Negative Binomial. However, for small-time intervals, both the Poisson and Negative binomial distributions fail to fit observed earthquake frequencies. We propose an extension of mixture models that is Hidden Markov Models (HMMs) with Poisson and Negative Binomial state-specific probability distributions and thus derive Poisson (P-HMMs) and Negative Binomial Hidden Markov Models (NB-HMMs), respectively. We use the parametrization of the Negative Binomial distribution in which the probability density function is expressed in terms of the mean and the shape parameter. In this parametrization, a variance is a quadratic form of the mean and the Negative Binomial distribution tends to the Poisson distribution when the shape parameter tends to infinity. Three-time units have been selected to count the number of earthquakes, namely 1-day, 5-day, and 10-days counting intervals resulting in daily, 5-day, and 10-day time series.

The region of Killini, Western Greece has been selected to apply the proposed methodology. All earthquakes with Local Magnitude ML 3:2 have been selected in the time interval from 1990 to 2007, inclusive. This time interval is divided into two sub-intervals that correspond to the learning and the testing periods. In the learning period from 1990 to 2004, inclusive the parameters of the models are estimated while in the testing period from 2005 to 2007, inclusive the ability of the models is tested to extrapolate past states of seismicity into the future. We applied both models with a different number of states to the daily, 5-day and 10-day time series of earthquakes that occurred in the Killini region during the learning period. Based on the Bayesian Information Criterion (BIC) for all three counting intervals the NB-HMMs model with three components was selected. The best-fitting model was used to estimate through simulations the number of earthquakes expected to occur in the study area during 1-day, 5-day, and 10-day intervals for the testing period. From the results obtained it appears that regardless of the selected time unit the models are able to capture the future variations
of seismic activity.

How to cite: Orfanogiannaki, K. and Karlis, D.: Using Negative Binomial Hidden Markov models to extrapolate past states of seismicity into the future, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4035, https://doi.org/10.5194/egusphere-egu22-4035, 2022.

Investigation into possible precursors of strong earthquakes constitutes a challenging research topic which is carried out mainly in two directions: the one based on the analysis of physical parameters and the one based on statistical methodologies. In the first, recent studies have shown significant correlation between major earthquakes and anomalies of different physical parameters measured in the atmosphere/ionosphere which cover time intervals of months.

On the contrary in this presentation we focus on the statistical modelling of the parameters that constitute an earthquake record in a catalog (location, time, magnitude) and we show that significant variations are observed in the months/years preceding a strong earthquake. In particular we consider the spatial distribution of a set of earthquakes and its temporal variations by modelling the area of Voronoi cells generated by the epicenters through a generalized Pareto (GP) distribution. Following the Bayesian paradigm we analyze the recent seismicity of the central Italy and we compare the posterior marginal likelihood of the most promising distributions in shifting time windows. We point out that the best fitting distribution varies over time and the trend of the GP distribution and of other distributions among the most studied in the literature converges to that of the exponential distribution a few months before the start of the preparatory phase to the main shock.

How to cite: Rotondi, R.: Statistical methods for middle-term forecast of earthquake occurrences, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13415, https://doi.org/10.5194/egusphere-egu22-13415, 2022.

The computation of probable occurrence of future large earthquakes is the prime objective of the present study in the Northeast Himalaya India. For this purpose, the physics based rate-and-state friction law is adopted for forecasting the seismicity rate changes for M_W ≥ 5.0 during the period 2016-2020. The coulomb stress changes (ΔCFF) is consider as a principle component which associated with the earthquake ruptured from the receiver’s fault. The reason behind considering the coulomb stress changes lies on the fact that the seismicity rate increases where the stress increase and decrease where the stress decreases. Here, it has been observed that high ΔCFF values are found widespread along the Main Central Thrust. Moreover, highest b-value is found to be in and around the Sikkim Himalaya. However, the highest background seismicity rate is also obtained in the vicinity of Sikkim and Bhutan with values ranging from 0 to 3.6. Finally, we have considered the consecutive fault parameter (Aσ = 0.05 MPa) for computing the forecast model with variable ΔCFF and heterogeneous b-value. The different value of the constitutive parameter (Aσ = 0.01, 0.02, 0.09, and 0.30 MPa) is adopted to understand the contribution of this parameter in a sudden change of seismicity rate due to stress perturbations. Also, various friction coefficient values (μ' = 0.2, 0.5, 0.6 and 0.8) are considered to find out the variation of seismicity rate changes. Then, CSEP model have been explored to check the consistency between the observed earthquakes and forecasted seismicity rates. The result from the CSEP model approves that the observed earthquakes matches well with the forecasted seismicity rates, thereby showing the consistency and efficiency of our forecast model.

How to cite: Pandey, A. K., Chingtham, P., and Roy, P. N. S.: Physics Based Seismicity Rate Computation For Northeast Himalaya, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9029, https://doi.org/10.5194/egusphere-egu22-9029, 2022.

The northern Chile region of the Nazca subduction zone has hosted a Mw 8.5-9.0 earthquake in 1977 which induced a tsunami. The different magnitude estimates of this event are based on the evaluation of seismic intensities, tide gauge information and/or on the event’s inferred length, however, its actual along-strike extent is still under discussion. Based on geodetic data, former studies have also suggested this region awaits a Mw 8.6-8.8. In our study, we propose to revisit the evaluation of the seismic potential of the region, accounting for the fault’s moment deficit rate, earthquake magnitude-frequency distribution and earthquake physics. To do so, we combine an improved probabilistic estimate of moment deficit rate with results from dynamic models of the earthquake cycle, taking into account the influence of a potential barrier which could control the extent and therefore the magnitude of large events.

How to cite: Michel, S., Jolivet, R., Rollins, C., and Jara, J.: Seismic potential of the northern Chile region of the Nazca subduction zone, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9327, https://doi.org/10.5194/egusphere-egu22-9327, 2022.

Seismic hazard levels used as reference for the French Lesser Antilles are derived from probabilistic seismic hazard assessment studies performed in 2002. However, our scientific knowledge has greatly increased over the past 20 years in this area. As part of a project linking the French Ministry of Ecological Transition and Solidarity, and the Seismicity Transverse Action of RéSiF (French seismological and geodetic network), we are developing a new seismotectonic model of the Lesser Antilles Subduction Zone (LASZ). The LASZ results from the subduction of the North and South American plates beneath the Caribbean plate since the Eocene. The boundary extends along 850 km in an ENE-WSW convergence direction at 18-20 mm/yr. Significant N-S variations in tectonic, seismic and volcanic activities raise questions on the undergoing geodynamic processes. Fractures and ridges entering into the subduction deform the trench, adding seismotectonic complexities. Several controversial hypothesis remain, such as the origins of the 1839 (Mw 7.5-8) and 1843 (Mw 8-8.5) earthquakes and the long term interseismic coupling, which is currently interpreted as being low. New seismic imageries and more complete seismic catalogs help to better constrain the slab and Moho shapes, as well as the hydrological behavior of the plate interface. In this study, we propose a compilation of existing data and hypothesis, completed by an analysis of focal mechanisms rupture types averaged on grid and strain tensor derived from GPS. For the first time, we add a particular attention in the role and influence of the mantle wedge seismicity, observed in only few subduction zones.

How to cite: Foix, O., Mazzotti, S., and Jomard, H.: A New Seismic Source Zone Model for Lesser Antilles Seismic Hazard Assessment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5838, https://doi.org/10.5194/egusphere-egu22-5838, 2022.

We present here the first results of the KUK-AHPAN Project: INTEGRATED REGIONAL STUDY OF STRUCTURE AND EVOLUTION 4D OF CENTRAL AMERICAN LITHOSPHERE. IMPLICATIONS IN SEISMIC HAZARD AND RISK CALCULATION). One of the main purposes of this project is to improve the knowledge of the seismic hazard in Central American countries, as well as the seismic risk in populations of the region.

An initial phase is addressed to define deterministic scenarios in the capital cities, giving the expected strong motion due to possible ruptures in local faults which may be critical for the risk of the population.

Preliminary results have already been found in the metropolitan area of San Jose (Costa Rica), affected by moderate-high seismicity due to a complex system of faults in the Valle Central in a local frame. In a regional context, the seismicity of the country is explained by the tectonic interaction between the Cocos and Caribbean plates.

We have identified three critical scenarios corresponding to events located in the Belo Horizonte, Rio Azul, and Cipreses faults. The strong motion for these scenarios has been estimated firstly in rock conditions, by application of different Ground Motion Prediction Equations. (GMPEs). In the second place, a microzonation map for San Jose is proposed, derived from data of isoperiods, lithology and other geotechnical information.  The amplification factor for the different soils has been extracted from NEHRP. Finally, we estimated the peak ground acceleration (PGA) and other spectral accelerations SA(T) including the local effects for each rupture scenario defined.

These deterministic scenarios will be compared with other results obtained with probabilistic approaches including modelization of faults in the definition of source models. A final goal is to improve the knowledge of the influence of the source models based on faults, not only in zones, in the hazard estimates.

How to cite: Ornelas Agrela, A. F., Benito Oterino, B., Franco Blanco, R., García Lanchares, C., Marchamalo Sacristan, M., Alvarado, G., Climent, A., Montero, W., and Schmidt, V.: Deterministic scenarios for seismic hazard assessment in the metropolitan area of San Jose, Costa Rica. First results of the Kuk-Ahpan Project., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-604, https://doi.org/10.5194/egusphere-egu22-604, 2022.

This paper investigates the predominant effects of sub-surface geological characteristics on the earthquake-induced ground-motion properties relevant to the design of infrastructure systems in urban environments. By considering ensembles of different earthquake scenarios and conducting numerical simulations to generate surface ground motion realizations, the contributing factors of earthquake source and earth properties in shaping the spatial pattern of ground motion amplitudes are scrutinized. Physics-based ground-motion simulations are conducted for 28 earthquake scenarios with moment magnitudes of 5.0 and 6.0 triggered with different azimuthal and geometrical properties. The earth wave-propagation properties are defined by considering data and empirical relationships that represent a typical geological setting with depth crustal rock and soft sedimentary basin (including a river channel). The spatial pattern of ground motion intensity measures (defined as the geometrical mean of the two horizontal pseudo-spectral accelerations) is used to show the average spatial pattern of ground motion severity. The results demonstrate that, even though the spatial ground motion patterns for a specific scenario earthquake depend on both the sub-surface geology and the source properties, the sub-surface geological characteristics impose a deterministic impact on the average spatial pattern of ground motions regardless of the earthquake location, azimuthal and geometrical properties. This clearly indicates that the regional seismic hazard assessments should allocate further resources for determining the sub-surface earth properties as they can disproportionally alter urban designs in contrast to the conventional concern on determining the location of probable future earthquakes and their small-scale characteristics.

How to cite: Tarbali, K., McCloskey, J., Agrawal, H., and Galasso, C.: The effects of large-scale geological characteristics on the average spatial pattern of earthquake-induced ground motions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12835, https://doi.org/10.5194/egusphere-egu22-12835, 2022.

In the area around Belgium, the Hainaut region is one of the most seismically active zones, behind the Roer Valley Graben (where seismicity is linked to known active faults) and the Eastern Ardennes (where the largest historical earthquake in NW Europe occurred). As a result, this comparatively small area stands out on most seismic hazard maps made during the past two decades. However, seismicity only started at the end of the 19^th century and seems to decline gradually since the late 20^th century. Historical earthquakes are not known in this area. This evolution is very similar to the history of coal mining in the area, which started in the 19^th century, culminated in the 20^th century and ceased in 1984, suggesting that the Hainaut seismicity may be induced. This seismicity is characterized by low to moderate magnitudes, up to M_W= 4.1, but due to their shallow focal depth (< 6 km), many earthquakes caused damage with corresponding maximum intensities up to VII on the EMS-98 scale, as indicated by a recent compilation of all available macroseismic intensity data (Camelbeeck et al., 2021). This reassessment also showed that intensities in this region attenuate much faster with distance than in other parts of Belgium. This highlights the importance of selecting appropriate ground-motion prediction equations (GMPEs) for seismic hazard assessment (SHA), which is the main objective of this study.

The past two decades, several metrics have been proposed to evaluate the goodness of fit between a GMPE and observed ground motion, such as the LH and LLH measures (Scherbaum et al., 2004; 2009) and Euclidean-based Distance Ranking (Kale & Akkar, 2013). Using macroseismic data to rank GMPEs requires an additional conversion of predicted ground motions to intensities using a ground-motion-to-intensity conversion equation (GMICE). Normalized residuals between observed and predicted intensities are then computed using the combined uncertainty of GMPE and GMICE (Villani et al., 2019). We evaluated different GMICEs and selected the relation by Atkinson & Kaka (2007) because it includes magnitude- and distance-dependent terms that result in better consistency between PGA and PGV than with the other relations. We made a selection of 20 recent GMPEs for the analysis, including newer versions of GMPEs used earlier in Belgium, GMPEs applied in recent SHAs in France, Germany and the UK, as well as two GMPEs developed specifically for induced earthquakes. Our preliminary results indicate that the latter GMPEs, in addition to the NGA-East GMPE by Atkinson & Boore (2006), show the best agreement with the data, although it should be noted that none of the tested GMPEs provides a really good match and none of the ranking methods considers the trend of residuals with distance. We also find that the scores based on PGV are significantly better than those based on PGA. The ranking results will be used to guide our selection of GMPEs for the new seismic hazard map of Belgium.

How to cite: Vanneste, K. and Camelbeeck, T.: Testing the applicability of GMPEs for the Hainaut region (Belgium) using macroseismic intensity data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4093, https://doi.org/10.5194/egusphere-egu22-4093, 2022.

We present an original method for determining the seismic impact outside the elliptical focal zone of an earthquake. The technique is based on the research of N.V. Shebalin (1927-1996) and generalizes the work of Russian and foreign seismologists, taking into account anisotropic concentration of seismic impact observed in nature in the direction of the source stretch. The macroseismic intensity is approximated by the function of magnitude, hypocentral distance and direction of the main axis of the focal zone taking into account the existing regional characteristics including information on active faults and earthquake focal mechanisms in the study area. The methodology can be used both in the operational assessment of damage from an earthquake immediately after its occurrence, and for the purposes of long-term general seismic zoning. The study was carried out as part of the Russian Federation State task of Scientific Research Works on "Seismic hazard assessment, development and testing of earthquake prediction methods" (No. 0143-2019-0006).

How to cite: Nekrasova, A. and Kossobokov, V.: Seismic intensity outside the earthquake focal zone, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-114, https://doi.org/10.5194/egusphere-egu22-114, 2022.

Earthquake Early Warning System (EEW) is a technology that calculates earthquake parameter using P-wave that arrives earlier and warns the expected damage area before the arrival of destructive S wave. Therefore, many countries are operating EEW to mitigate damage from earthquake shaking. Especially an on-site EEW is drawn attention as it can reduce blind zones due to using only a single or minimum station. In the on-site EEW, it is important to quickly predict the seismic intensity, which indicates the degree of ground damage, response of structures and ground shaking felt by people at a given location, rather than information on the magnitude or distance of the earthquake.

In this study, we suggest a machine learning (ML) model that can directly estimate the seismic intensity scale from initial P-waveforms of three-component acceleration data measured at a single station. We used 1D-Convolutional Neural Networks (1D-CNN), which have been shown good performance in signal processing of speech and medical data which are similar to earthquake signals. K-Net and KiK-net datasets, recorded at stations in Japan, were used for training the ML model. Since the amount of data is enough and all of data are labeled with Japan Meteorological Agency Seismic Intensity Scale (I_JMA), the datasets were used as training data in this study. The developed model produced fast and accurate results using only the three-component acceleration field data at a single station.

In order to test applicability of the trained model to the new dataset acquired from other regions, the trained model was applied to the STEAD data which were recorded at stations distributed globally. When the trained model was applied to STEAD data directly, the prediction results were worse than those of K-Net and KiK-net data. The reason is that the characteristics of the ground and waveforms are different depending on the region. Therefore, to solve this problem, transfer learning was applied, and only the parameters of a fully connected layer of pretrained ML model were fine-tuned using small number of both labeled target dataset and training dataset used for pretraining. Moreover, by considering the imbalance problem of the training data for transfer learning, it was able to obtain better prediction results. Ultimately, this study shows the pretrained model with a specific region dataset can provide reasonable prediction of seismic intensity to new dataset acquired from other regions using transfer learning.

How to cite: Bae, S., Choi, Y., Song, Y., Byun, J., and Seol, S. J.: Seismic Intensity Estimation using Machine Learning for on-site Earthquake Early Warning (EEW), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3695, https://doi.org/10.5194/egusphere-egu22-3695, 2022.

During the last two decades, an operational procedure for time-dependent seismic hazard scenarios has been developed, which integrates fully formalized and validated earthquake forecasting information from pattern recognition analysis (e.g. by CN algorithm), with the realistic modelling of earthquake ground motion by the neo-deterministic approach (NDSHA). The proposed methodology permits to define, both at regional and local scale, a set of scenarios of ground motion for the time interval for the time interval in which a strong event is likely to occur within the alerted areas. When dealing with offshore large earthquakes occurrence, this integrated approach can be naturally extended to the definition of time-dependent tsunami scenarios, based on physical scenario models of tsunami waves.

CN forecasts for the Italian territory and its surroundings, as well as the corresponding time-dependent ground motion scenarios associated with the alarmed areas, are regularly updated since about two decades (Peresan, 2018, Geophysical Monograph Series, 234, pp. 149–172 and references therein). We review the results obtained so far by rigorous prospective testing of the developed procedure, including analysis of the statistical significance of issued forecasts. Special emphasis is placed on the recent earthquakes that occurred in the Adriatic region, which support validation of the applied methodologies, and evidence the opportunity of developing time-dependent tsunamis scenarios, by integrating forecast information with the modelling of tsunami waves propagation.

In this study, tsunami modelling is performed by the NAMI DANCE software (Yalciner et al., 2006, Middle East Technical University, Ankara, Turkey), which allows us accounting for seismic source properties, variable bathymetry, and non-linear effects in waves propagation. Urban scale hazard scenarios for selected coastal sites are developed considering different potential tsunamigenic sources of tectonic origin, located in the Central and Southern Adriatic Sea. The results from parametric studies accounting for possible sources related to historical events, as well as for yet unobserved extreme events, are considered for this purpose. Their verification against recent earthquakes, allows us assessing the relevance of space-time information and uncertainties on source parameters, and their possible operational contribution towards effective tsunami warning system for the Adriatic Sea and in the whole Mediterranean area. 

How to cite: Peresan, A. and Hassan, H. M.: Time-dependent earthquake and tsunami hazard scenarios for the Adriatic region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2303, https://doi.org/10.5194/egusphere-egu22-2303, 2022.