The 2013 European Seismic Hazard Model: key components and results

2.1 Compiling homogeneous datasets

A milestone of the SHARE project was to compile harmonised datasets suitable for assessing the seismic hazard of the entire Euro-Mediterranean region. Harmonised datasets are the best way to handle the inherent heterogeneity in the tectonic environments and data availability as they help remove differences due to country-specific approaches and emphasize the differences due to actual seismotectonic diversity. Such datasets were compiled during the initial phases of the project (Fig. 1) and are described in the following sections. All data is available on-line through the European Facility for Earthquake Hazard and Risk website (http://www.efehr.org/).

2.1.1 The SHARE European Earthquake Catalogue

For the scope of the project we compiled a new homogeneous earthquake catalogue (SHEEC—the “SHARE European Earthquake Catalogue”; Stucchi et al. 2012; Grünthal et al. 2013). The catalogue consists of two parts: (a) 1000–1899, compiled from historical earthquake data and from information gathered in the Archive of Historical EArthquake Data (AHEAD) (Locati et al. 2014) after reassessing all earthquake parameters, based either on raw macroseismic data or on existing, selected regional catalogues (Gomez-Capera et al. 2014); and (b) 1900–2006, harmonised from existing national catalogues and numerous special studies as described in Grünthal and Wahlström (2012) and Grünthal et al. (2013). The catalogue was extended to cover Central and Eastern Turkey for a more homogeneous hazard analysis. The assessment is documented as the SHARE earthquake catalogue for Central and Eastern Turkey (SHARE-CET, available at http://www.emidius.eu/SHEEC/docs/SHARE_CET.pdf). For the time-window 1000–2006 the catalogue lists over 30,000 earthquakes in the magnitude range 1.7 ≤ M _W  ≤ 8.5 (Fig. 2).

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The completeness of the SHEEC catalogue was assessed from a historical perspective within 22 completeness superzones. The superzones were defined as groups of single area sources within the AS-model, covering zones that exhibit a fairly homogeneous capacity of documenting the earthquake occurrence through history. The completeness time intervals defined at the scale of each superzone were reviewed for individual area sources to take into account local variations in seismicity and data availability. The results of the completeness assessment and the differences in the superzones are described in Appendix #3 of Stucchi et al. (2012). Superzones are groups of Area Sources (unrelated to country borders) also defined taking into account data homogeneity.

The catalogue was declustered with the windowing-approach using the window paramters of Burkhard and Grünthal (2009). Slightly more than 8000 of the original 30,000 events survived after declustering and completeness cut-off, implying specific challenges in some low-seismicity areas.

2.1.2 The European Database of Seismogenic Faults (EDSF)

The European Database of Seismogenic Faults (EDSF; Basili et al. 2013; Fig. 3), was specifically designed to fulfill the project requirements in terms of exploiting the wealth of geologic, tectonic, and paleoseismic knowledge available in the scientific community. The database includes faults that are deemed capable of generating earthquakes of M _W  ≥ 5.5 and ensures homogenous input for use in probabilistic ground-shaking hazard assessment in the Euro-Mediterranean area. The chosen minimum magnitude threshold corresponds to a fault size of about 5 km and is assumed to define the resolution limit for identifying a seismogenic fault through geological investigations.

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The EDSF hosts two types of formalized seismogenic faults stored in two separate GIS layers: crustal faults and subduction zones.

For the parameterisation of crustal faults the EDSF adopts the Composite Seismogenic Source (CSS) model defined in previous works (Basili et al. 2008, 2009) with standards used in other seismogenic fault databases (Haller and Basili 2011). The CSS is a simplified 3D model of a fault or fault system containing an unspecified number of aligned earthquake ruptures that could not—or simply were not—singled out. The fault is represented with a geo-referenced plane having variable strike but fixed dip along its entire length. Its surface projection is confined between two horizontal lines. In addition to this geographic feature, each fault is characterised by geometric parameters (minimum and maximum depth, strike, and dip) and behaviour parameters (rake, slip rate, and maximum earthquake magnitude).

For the parameterisation of subduction zones (SUBD) the EDSF adopts a newly defined model, partly following common definitions established in recent global efforts (Berryman et al. 2013). SUBD is a simplified 3D model of a subducting slab; similarly to the CSS model, the number and size of potential earthquake ruptures is unspecified. The slab is a complex surface represented by a georeferenced set of depth contours. In addition to its geographic attributes, each subduction zone is characterised by geometric parameters (minimum and maximum depth of the seismic interface and prevalent slab dip direction) and behaviour parameters (convergence azimuth, convergence rate, and maximum earthquake magnitude).

In order to capture the natural variability of the data (aleatory uncertainty) for both, crustal faults and subduction zones, all parameters are given as a min–max range. For each source the compilers also provide short explanations and qualified evidence on how each parameter was estimated. A list of pertinent references to scientific literature is also annexed to each seismogenic fault. For simplicity, we will in the following refer to seismogenic faults as fault sources, as opposed to area sources.

The EDSF was compiled through successive stages of update, revision, and homogenisation between June 2009 and May 2012, thanks to the contributions of over one hundred scientists from several European research institutions. It incorporated data from regional initiatives such as the well established DISS for Italy (DISS Working Group 2010) and surrounding areas, and the more recent GREDASS for Greece (Pavlides et al. 2010; Caputo et al. 2012, 2013) and QAFI for Spain and Portugal (Garcia-Mayordomo et al. 2012). It also incorporated data from the sibling project EMME (Earthquake Model of the Middle East) for Turkey and from a number of local and regional studies. The final version of the database contains 1128 records for ~63,775 km of crustal faults, from the Iberain peninsula to Turkey, and from three subduction zones known as Calabrian Arc, Hellenic Arc, and Cyprus Arc in the central and eastern Mediterranean Sea.

2.2 Tectonic regionalisation

The tectonic regionalisation exploits various large-scale public datasets, including the International Geological Map of Europe (IGME 5000: Asch 2005) and the crustal model CRUST 2.0 (Bassin et al. 2000), as well as datasets of more localized geological and tectonic features such as volcanoes (Siebert and Simkin 2002), spreading ridges (Müller et al. 2008), plate boundaries (Bird 2003), earthquake focal mechanisms (Dziewonski et al. 1981; Pondrelli et al. 2002), and the SHARE earthquake and fault databases described earlier (Figs. 2, 3).

2.3 Modelling maximum magnitudes

For many hazard practitioners M _max is best handled by using multiple values in a logic-tree with weights decreasing with increasing magnitude. Unfortunately it is impossible to test rigorously this parameter from a statistical point of view as this would require observation periods that extend far beyond human lifetime (Holschneider et al. 2014), thus M _max remains a matter of choice, depending on the applied methodology.

For the ESHM13 M _max is defined as the time-independent, ultimately largest earthquake that can occur in a region. The methods to assess M _max are different for each model and explained in the following. In short, standard methods to assess the maximum magnitude were used for the AS-model (Melletti et al. 2009; Wheeler 2009). The SEIFA-model includes a single M _max separately for crustal and subduction zone seismicity, and allows the largest event to occur anywhere geographically, however, with a model-driven cut-off leading to an actual spatial variation of M _max . For the FSBG-model, M _max is determined from the mean of multiple scaling relations that convert the rupture area to moment magnitude (Fig. 4).

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For the AS-model, the general approach was to give priority to actual data everywhere they are available and reliable, rather than to models. We adopted different approaches in low/moderate and in high seismicity regions. To this end we first defined four groups of area sources: (1) stable continental regions (SCR), (2) oceanic crust and mid-Atlantic ridge (OC), (3) Azores-Gibraltar zone (AG) and (4) generic active zones. For each source zone we defined four values of maximum magnitude which were then entered in a logic tree with decreasing weight values. The smallest and the largest values are displayed in Fig. 4a, b.

In low-to-moderate seismicity regions (mainly those in the stable continental regions) a single distribution was assumed, in analogy with the global analog approach (Wheeler 2009, 2011): the magnitude of the largest observed earthquake, with proper consideration of its uncertainty, was taken as the lower value for the distribution of maximum magnitude, i.e. M _W  = 6.5, whereas the other values were obtained by 0.2 increments (i.e. 6.7, 6.9, 7.1).

In the remaining seismogenic areas, normally characterised by high seismicity level and by a better understanding of historical seismicity and seismogenic sources, the distribution of M _max was anchored to the larger value between the largest earthquake reported in the catalogue and the maximum magnitude expected based on the fault database, again with consideration of its uncertainty; this procedure was repeated in each AS in the active regions or in each superzone for oceanic crust, mid-Atlantic ridge and Azores-Gibraltar zones. Larger values of the M _max distribution were obtained by 0.2 increments. The values obtained for the AS-model are shown in Fig. 5 together with their weights.

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For the SEIFA-model, we defined M _max  = 8.6 for crustal seismicity as the truncation magnitude accounting for the largest moment magnitude earthquake listed in the SHEEC catalogue, i.e. the 1755 M _W 8.5 Lisbon earthquake. For subduction zones we defined M _max  = 9.0 based on historically documented earthquakes (i.e. the 365AD, M8.5+ event in the Hellenic Arc; Stiros 2010), the overall size of the Hellenic Arc (e.g. Strasser et al. 2010), and subduction zone characteristics in the Mediterranean. However, it is important to note that magnitude bins with an annual rate smaller than 10⁻⁶ are not considered in the hazard calculation. This leads to an apparent spatial variation of the maximum magnitude which is model-driven and not defined by a separate analysis, and thus constitutes a novel philosophy (Fig. 4c).

For the fault sources of the FSBG-model (Fig. 4d) M _max is calculated as the arithmetic mean of the estimates obtained from multiple fault scaling relations (Wells and Coppersmith 1994; Hanks and Bakun 2002; Kagan 2002; Leonard 2010). This additional assessment of M _max , with respect to that given by compilers of the EDSF, was necessary to further homogenize the fault source dataset.

All faults in EDSF were classified according to their primary style of faulting and then the relevant fault scaling relations were applied. Considering that seismogenic faults in the EDSF may span more than one rupture in length, the maximum length of a single rupture was constrained based on aspect ratio for dip-slip faulting; conversely, no limitation was applied for strike-slip faulting. Given the fault classification and using the expected value for each fault scaling law (i.e. incorporating the epistemic uncertainty between relations), we obtained two to seven different moment magnitude estimates for each fault source. To capture also the internal variability of relations, being interested mainly in the positive side of the uncertainty, we augmented the mean M _max with an uncertainty of ∆M _W  = 0.3 that represents a reasonable scatter of the relevant regressions.

3.1 Area source model

For several years the evaluation of seismic hazard has largely relied on area source models (Grünthal et al. 1998; Sleijko et al. 1998; Grünthal and GSHAP Working Group Region 3 1999; Musson 1999; Sleijko et al. 1999; Grünthal and Wahlström 2000; Musson 2000; Jimenez et al. 2001; Demircioglu et al. 2004; Meletti et al. 2008; Wiemer et al. 2009b; Papaioannou and Papazachos 2010), both in Europe and worldwide (e.g. Giardini 1999). Area source zones are designed with the assumption that seismicity may occur anywhere within each zone. The delineation considers seismicity, tectonics, geology and geodesy upon expert evaluation, generally without assigning uncertainties to the zone boundaries and only infrequently documenting the importance of the data used for the zonation (Burkhard and Grünthal 2009; Musson et al. 2009; Schmid and Slejko 2009; Wiemer et al. 2009a; Vilanova et al. 2014). Our zonation approach, based on the ESHM13 AS-model previously described, was applied to crustal seismicity down to a depth of 40 km.

The ESHM13 AS-model is based on the latest national area source models and on the former Euro-Mediterranean model (Jimenez et al. 2001) that have been merged and harmonised at national borders (Arvidsson and Grünthal 2010). Besides a number of political issues, challenges in the construction phase included the variability of seismotectonic environments, the differences in the nature of documentation, data availability and philosophies of the former models, and the need to harmonise the AS-model with the other datasets being compiled simultaneously within SHARE. In a final harmonisation step, small area sources that turned out to be devoid of seismicity were removed for consistency with the final datasets compiled within the project. The final area model consists of 432 area sources depicted as black lines in Fig. 7b.

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The considerable differences in the tectonic environments across Europe are reflected in the final delineation of the area sources. In the Mediterranean area, and especially in the Balkans, Greece, Turkey, and Italy, mapped active faults played a major role in defining the area sources. The knowledge on active faults of southern Spain, which was improved during the SHARE project, allowed for a redefinition of existing source models. In contrast, very few active structures are known in the continental shield of northern Europe. In these areas, seismicity was the main basis for the designing the area source model. Only a few major tectonic structures, such as the Rhine Graben in Central Europe, are represented in the zonation with a higher level of detail than large structures like the Baltic Shield. The AS-model was matched with the tectonic regionalisation (Fig. 7a, b) in order to define, for each area source, the prevailing style-of-faulting, the upper and lower bounds of the characteristic seismogenic depth, and the distribution of hypocentral depth.

As the declustered catalogue contains about 8000 events, a large number of the area sources contain only a few data; for instance, 196 zones contain 10 events or less. Those that did not contain any seismicity were assigned an activity rate by regional experts, or a water-level annual activity for M _W  ≥ 4.5 of 5 × 10⁻⁸ events per km².

We estimated activity rate parameters using a Bayesian penalized maximum likelihood (PML) approach (Johnston et al. 1994; Coppersmith et al. 2012) that updates the prior b-value only if it is supported by local data. The prior b-values are estimated in the specific b-value superzones with the method by Weichert (1980). The PML-estimates for the a–b-value pairs and their associated uncertainty recover well the distribution of seismicity, also for sources with little data. The estimates are, however, guided by the more abundant small magnitude data. Two observations were made based on the PML-results as shown in the examples in Fig. 8—an apparent underestimation of rates in the moderate to large magnitude events in the range 5.5 ≤ M _W  ≤ 7.0, and a relatively large variation in b-values.

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Like any maximum likelihood estimator, the PML-estimator is largely driven by the more abundant small magnitude seismicity. Thus, underestimating the rate of moderate to large events may result from how the catalogue completeness is assessed, from the variability of magnitude determinations for different time periods (e.g. historical vs. instrumental) and, more generally, from spatio-temporal variations of the observed seismicity. As such, this problem may be rooted both into the data and into the selected methods of data analysis. In many regions the rates of small magnitude events are mainly governed by recent instrumental data, whereas the larger magnitude events are mainly historical earthquakes. Due to their origin from intensity assessments, the magnitudes of historical earthquakes can only assume a limited number of discrete values; as such they are binned differently from instrumental earthquakes, which may cause artificial perturbations in the seismicity rates.

We carefully analyzed each individual fit to the data and replaced the PML-results by subjective judgment in case b-values deviated more than ∆b = 0.4 from the global estimate of the SHEEC catalogue (b = 0.9: Hiemer et al. 2014). The b-values obtained using the PML-approach for area sources characterised by very few data were constrained in the range 0.8–1.2, with the only exception of volcanic and swarm-type seismicity area sources, for which the b-value was allowed to increase up to 1.5 based on observed data.

The a–b-value pairs, with the a-value defined at M _W  = 0, were then used to calculate a weighted mean frequency–magnitude distribution (FMD) considering the weights of the maximum magnitude distribution (Fig. 8, solid black line). We calculated the incremental FMDs truncated at the different maximum magnitudes and then stacked them with the weights of the M _max . Uncertainties are plotted for the 2.5 %/15 %/85 %/97.5 % percentiles of the distribution (Fig. 8, red and green dashed lines).

We illustrate our results for two area sources, one in southwestern Switzerland (CHAS089) and one in the Central Apennines (ITAS308), containing 8 and 52 events above completeness, respectively (Fig. 8). For CHAS089, the difference between the two estimates is substantial (a = 2.4, b = 0.9; a _pml  = 2.71, b _pml  = 1.03; Fig. 8a). In contrast, for ITAS308 the difference between the estimates taken from the current Italian Seismic Hazard Model (a = 4.2, b = 1; Stucchi et al. 2011) and the PML (a _pml  = 3.92, b _pml  = 0.98) is negligible. Figure 8 also illustrates the difference in the uncertainties when estimating rates for magnitudes in the small and in the large magnitude ranges: for CHAS089 these uncertainties are much larger in the upper magnitude range than for ITAS308 due to the limited amount of data.

The AS-model activity rates are fully defined by the a–b-values and by the distribution of M _max . The time-independent annual rate forecast for M _W  ≥ 4.5 varies across the continent and highlights the higher activity rates along the active fault systems of Southern Europe, Turkey and Iceland in comparison to the lower activity rates observed in stable continental regions (Fig. 9a). The rates are computed from the productivity parameter (a-value), that varies strongly across the continent, and from the b-value, that by construction varies only in a limited range (Fig. 9b, c).

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3.2 Kernel-smoothed stochastic rate model considering seismicity and fault moment release (SEIFA-model)

In contrast to the other source models, this refined smoothed seismicity model uses estimates of the total productivity and frequency–magnitude distribution by fitting the entire declustered SHEEC catalogue using the catalogue completeness levels of the superzones simultaneously first. In a second step, earthquakes rates are spatially distributed according to a weighted combination of two spatial probability density functions (Hiemer et al. 2014). These are estimated from past seismicity and from accumulated moment release along faults, inferred from their slip rates and geometry. The relative weights of these two spatial density functions depend on the earthquake magnitude and are determined by pseudo-prospective forecasting experiments. The model, initially developed for California (Hiemer et al. 2013), is applied at the European scale: we generated a crustal model using earthquakes shallower than 40 km depth and active faults of the European Database of Seismogenic Faults (EDSF; Basili et al. 2013). We separately modelled seismicity in subduction zones in combination with the models of the Calabrian, Hellenic and Cyprus arcs with their complex 3D geometry. We treat active faults and subduction zones in the same way, as they are all characterised by their size, geometry and slip rate converted to moment release per unit area (discretized to 5 km² patches). The following sections provide a general description of the model; full details are given in Hiemer et al. (2014).

The SEIFA-model assumes that (1) the frequency–magnitude distribution of past earthquakes is the best estimate of productivity and size distribution, (2) future earthquakes will occur in the vicinity of past events, (3) larger earthquakes occur more likely on mapped faults, and more likely along fast-slipping than along slow-slipping faults. These assumptions are parameterised to estimate the annual earthquake rate as a function of space and magnitude. The total productivity and the Gutenberg–Richter relations are estimated from the entire catalogue considering the space–time completeness histories as defined for the superzones (Hiemer et al. 2014). The cumulative annual earthquake rate forecast log₁₀ λ(M _W  ≥ 4.5) for crustal seismicity depicts a relatively smooth image with higher rates in currently active regions and along active fault systems (Fig. 10) as compared to the stable continental areas. This earthquake rate forecast shows more smaller scale variability in regions of frequent seismicity, a feature that can also be observed in the hazard map calculated only from this model (Fig. 16b).

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We estimate the activity rate parameters (a-, b-value) for the entire catalogue including the space–time completeness variations (Hiemer et al. 2014). The combined maximum likelihood estimate for crustal seismicity is a = 5.87 ± 0.04, b = 0.9 ± 0.01, equaling a total annual number of events N ₀ (M _W  ≥ 4.5) = 65.6. For the subduction zones, these values are a = 5.21 ± 0.36/0.39 and b = 0.95 ± 0.07/0.08, yielding N ₀ (M _W  ≥ 4.5) = 8.6 per year.

The model allows for a maximum magnitude of M _W  = 8.6 for crustal events throughout Europe and Turkey. As the annual rates of larger magnitude earthquakes are locally very small, however, the actual maximum magnitude that entered the hazard calculations varies as we disregard magnitude bins for which the inferred annual rates are smaller than 10⁻⁶ (Fig. 5c). According to our sensitivity tests, the contribution of these very low rate events to PGA hazard up to return periods of 5000 years is negligible, thus they were removed.

3.3 Fault sources and background (FSBG) model

In the FSBG-model, activity rates of the mapped fault sources are determined from the available deformation data as inferred from geodetic and geological methods (Basili et al. 2008, 2009; Haller and Basili 2011; Basili et al. 2013). The activity rates in this model are primarily dependent on the slip rate and the maximum magnitude of the fault sources. As crustal seismogenic faults are not known over the entire area covered by SHARE, and more specifically lack in regions of low tectonic deformation far from plate boundaries, the sources comprising the AS-model are used and adjusted in size and productivity at the edges of the background sources.

Background sources were delineated with consideration for the distribution of fault sources. A background source polygon includes an entire fault system and does not cut across any fault source. A background zone is assumed to include a complete set of fault sources, i.e. one or several sources are embedded in one background source. Within one background zone we assume that all fault sources capable of generating M _W  ≥ 6.5 earthquakes are known and dominate the seismic moment release. Events with magnitudes M _W  ≥ 6.5 are modelled on the 3D geometry of the fault sources; their exact location varies depending on the modelled hypocentral location (Pagani et al. 2014), magnitude, and determined area, however, never extends beyond the mapped structures. Earthquakes in the magnitude range 4.5 ≤ M _W  ≤ 6.4 can occur throughout the background source and are homogeneously distributed.

The earthquake potential of each fault source is evaluated by converting the long-term tectonic moment rate into an earthquake activity rate. The tectonic moment rate is obtained from Ṁ ₀ = µAḊc, where µ is rigidity, A is the area of the fault source, Ḋ is the slip rate and c is the seismic coupling, which indicates how much of the tectonic moment goes into the seismic moment. The earthquake rates are calculated assuming that all the accumulated tectonic moment is released seismically, thus the seismic coupling was set to c = 1 everywhere. We also adopted the maximum slip rate assigned to fault sources, thereby providing an upper bound of the cumulative tectonic moment.

After considering several methods for obtaining earthquake activity rates from slip rates (e.g. Anderson and Luco 1983; Youngs and Coppersmith 1985; Frankel et al. 2002; Stirling et al. 2002; Field et al. 2009; Stirling et al. 2012) we opted for the original contribution proposed by Anderson and Luco (1983), which generates activity rates that conform to a double-truncated Gutenberg–Richter distribution (Anderson and Luco 1983; Youngs and Coppersmith 1985; Bungum 2007).

The scale-invariant, power-law decay of the distribution is controlled by the b-value. Within a background zone the b-value is assumed to be the same everywhere, though it varies between background zones. Similarly to the AS-model, it is computed with the PML-method (Johnston et al. 1994; Coppersmith et al. 2012). The right-hand tail of the distribution, instead, is controlled by the maximum magnitude of the fault source. The total productivity within one background zone is the sum of the contribution of all fault sources. As a whole, the distribution complies with the moment conservation principle by summing up to the calculated seismic moment rate.

The model is illustrated for a background zone covering the Northern Apennines, Italy, that contains eight fault sources, all contributing to the overall productivity with their maximum slip rate (Fig. 11, source ITBG068, dashed grey lines). The b-value is estimated using the penalised maximum likelihood approach and equals b _pml  = 1.3. The summed activity using the maximum slip rates (Fig. 11, red line) defines well an upper bound of the cumulative frequency–magnitude distribution of the observed seismicity. Summing the productivity from the minimum slip rates matches the observed seismicity (Fig. 11, dashed green line) and overlies the PML-estimate. The tail of the distribution resembles a staircase function emerging through the summation of the activity rates calculated for each fault source that all have individual maximum magnitudes (Fig. 11, red line). We consistently used the maximum slip rates to estimate activity rates on fault sources which, as illustrated, often defined an upper bound of activity compared to the data observed in the catalogue.

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The FSBG-model covers mostly southern Europe and Turkey and some structures in Northern Europe such as the Lower Rhine Embayment (Fig. 12; Vanneste et al. 2013). Not all faults mapped and included in the EDSF could be used, however. For example, we excluded all mapped faults that represent only a small portion of a large background zone, thereby assuming that the fault coverage is not complete. The resulting earthquake rate forecast model for the cumulative annual rate [log₁₀ λ(M _W  ≥ 4.5)/km²] is shown in Fig. 12 together with the tectonic association of the background zones and maximum slip rates of the fault sources.

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3.4 Comparing the total rate forecasts for crustal seismicity

To obtain a global overview of the three crustal seismicity rate forecasts we calculated the total expected average annual earthquake rates and compared them with the cumulative frequency–magnitude distribution of the declustered earthquake catalogue using the superzone catalogue completeness (Fig. 13). For the AS- and the FSBG-models this involves summing up the rates of all individual zones. In contrast, by construction the SEIFA-model uses the combined maximum likelihood fit to the entire declustered catalogue (Hiemer et al. 2014).

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3.5 Modelling subduction zones and deep seismicity in Vrancea

We separately modelled the deep seismicity of the Calabrian, Hellenic, and Cyprus Arcs subduction zones as well as the non-subduction deep seismicity of the Vrancea Region (Romania). The activity rates of the three subduction zones has been jointly computed for interface and in-slab seismicity as depth uncertainties did not allow for a separation.

The subduction interface is modelled as a complex fault as defined by depth contours in the EDSF. Seismicity on the interface is concentrated between 20 and 50 km depth for the Hellenic and 10–50 km depth for the Cyprus Arcs. The surface projection of the subduction interface is shown in Fig. 14, coloured according to the assigned maximum magnitudes. Activity rates are taken to be uniform across the complex structure. In-slab seismicity is modelled in depth volumes located at 40–160 km depth in all subduction zones. In both instances seismicity is homogeneously distributed throughout the area. Maximum magnitudes as well as the b-value are the same for subduction interface and in-slab seismicity. Maximum magnitudes are estimated to range between M _W  = 8.3 and M _W  = 8.9 with increments of 0.2 and decreasing weights (0.5, 0.2, 0.2, 0.1). The estimated b-value is 0.9 with no difference for in-slab and interface events.

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The SEIFA-model uses the same structure for interface and in-slab events. However, in contrast to the model used in combination with the crustal AS-and FSBG-model, the SEIFA-model uses a heterogeneous distribution of the activity rate parameters determined from a combined probability density function determined from smoothing the earthquake density and the moment release on the subduction interface (see Hiemer et al. 2014 for details). Within this model, both the maximum magnitude (M _W  = 9.0) and the b-value (b = 0.95) are the same for subduction interface and in-slab seismicity.

In the Vrancea region, situated beneath the southern Carpathian Arc, the most hazardous seismicity falls in the depth range 70–150 km with earthquakes occurring in the intervals 70–90 and 110–150 km. Lacking a well-defined structural description of the seismogenic processes of the area, we model the deep seismicity with four volumes at depth. The shape differs from the delineation of the crustal area sources (Fig. 14). This specific model is used in combination with all crustal seismicity models.

Earthquake density, depth levels and maximum magnitudes vary within these zones as well as the parameterisation of the preferred rupture orientation. The maximum magnitude expected for Vrancea earthquakes was assigned based on observed seismicity. We allowed for larger magnitudes by adding four magnitude increments with decreasing weights as we did for crustal sources. The M _max values in the 70–90 km depth range are 7.5 and 7.8; the seismicity between 110 and 150 km is limited to M _max values of 7.8–8.1 (Fig. 14).

Attenuation of seismic wave amplitudes exhibits azimuthal dependence—a characteristic that is likely due to the rupture characteristics of the deep events and wave propagation effects rather than amplification effects. This characteristic has been implemented in intensity attenuation relations (Sokolov et al. 2009; Sørensen et al. 2010) but is not considered in GMPEs that consider several spectral ordinates, as required for a harmonised European assessment. To overcome this limitation the deep seismicity is modelled to mimic the azimuthal dependence with a set of sources at depth, a scheme similar to that adopted by Sokolov et al. (2009).

5.1 Properties of the source models

The PGA values calculated from individual source models for a 10 % exceedance probability in 50 years highlight the impact of the different assumptions underlying each model (Fig. 16). The AS-model displays relatively smooth transitions across the full range of hazard values, showing the highest values along the main fault systems of Turkey, along the coasts of Greece, along the entire length of the Italian Apennines and across Iceland. Some high hazard spots are spread across Europe, such as in the Lisbon area, in the western Pyrenees and in the Swabian Alb (Fig. 16a). As a consequence of the zonation approach, some small zones delineate hazard regions that experienced moderate-to-large historic events with relatively sharp transitions.

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The SEIFA-model concentrates hazard where seismicity occurred in the past, a consequence of the model assumptions (Fig. 16b). By using an adaptive kernel and considering the accumulated fault moment for spatially distributing seismicity rates, the SEIFA-model achieves a smoother representation of seismic hazard in comparison to smoothed seismicity models that use fixed kernel widths (e.g. Frankel 1995). The width of the adaptive kernels in the SEIFA-model have been optimized through pseudo-prospective likelihood tests for 5 and 10 years intervals, and hence they are more representative for these periods. Using longer optimisation periods was not possible due to the scarcity of earthquake data. Due to clustering of seismicity around some rare historical earthquakes, a few localized features remain in regions with relatively low seismic activity. The hazard computed with this model exhibits larger variations and also sharp transitions. An interesting feature is that the ground-shaking hazard varies along known fault systems such as the North Anatolian Fault Zone—a feature that may convey additional information on exceedance probabilities far larger than 10 % in 50 years.

The FSBG-model concentrates its contribution to the northern Mediterranean area and the Lower Rhine Embayment. This model predicts the highest ground shaking hazard along the fast-slipping fault systems of Turkey, whereas the slower-slipping faults of south-eastern Europe and Italy return moderate-to-low hazard estimates. Hazard transitions along the slow-slipping faults are smooth; stronger gradients are found along the North Anatolian Fault system. These regional differences are mainly a consequence of the rate modelling procedure: while earthquake rates calculated for the fastest strike-slip faults may exceed the observed seismicity by a factor of up to ten, the rates calculated for slower faults—such as the normal faults of the Appennines—better match the observed rates. The comparison of the overall forecast activity (Figs. 8, 9, 12) shows clearly that the expected rates vary regionally and that the fault model for the Appennines forecasts smaller rates, and thus smaller hazard.

5.2 Assigning weights to individual source models

The modelling team and the consortium members agreed on weights for the individual models by judging their assumptions and main ingredients. The weighting scheme varies with the probability of exceedance levels (Fig. 6; Table 1). Note that the combination of weights 0.5/0.3/0.2 (AS/SEIFA/FSBG) is the preferred weighting scheme for exceedance probabilities between 2 and 10 % in 50 years. This weighting scheme is used for aggregating the seismic hazard curves that are released on the website (www.efehr.org), while maps are aggregated as appropriate according to the scheme in Table 1. The rationale of using different weighting schemes originates from the assumptions of the methods applied to calculate activity rates. Using slip rates to calculate activity rates in the FSBG-model is likely to be more representative of the long-term characteristics, while the assumptions used in the SEIFA-model give a better picture of the closer future (Hiemer et al. 2014). Thus we suggested variations for hazard mapping.

The AS-model received the largest weight for all exceedance probability levels. Its weight increases slightly for increasing return periods, due to its qualitatively good match to the observed data and to the complex elaboration and elicitation procedure. This included the largest community-based effort aimed at combining local knowledge and expertise from all over the continent. The model incorporates information from tectonic and geological features, reflects the observed seismicity and includes specific local knowledge not present in the other models in such detail. It also represents the most widely discussed and reviewed part of the model.

In contrast, the weight of the SEIFA-model decreases for increasing return periods. This is motivated by the fact that the kernels were optimized for periods of 5 and 10 years in pseudo-retrospective likelihood-based tests (Hiemer et al. 2014). The distribution of events from the instrumental earthquake record plays a major role here. The SEIFA-model performs significantly better in the likelihood-based consistency testing suite of the Collaboratory for the Study of Earthquake Predictability (CSEP) compared to the AS-model in 5- and 10-year pseudo-prospective tests; thus we believe it is justified to vary the weights of the SEIFA, starting with higher ones for the shorter return periods. This is done in recognition of the fact that optimizing and testing is performed for intervals in the order of 10 or less years; in other words, the implications of the 5- and 10-year pseudo-prospective testing period may convey only limited information content, whereas the AS-model is designed to deliver reliable information over much longer intervals.

Finally, the weight of the FSBG-model increases with respect to the other models for increasing return periods because the earthquake rates are estimated from geological slip rates that are likely to be more representative of long-term activity. The activity rates of the FSBG-model represent an upper limit of what is currently expected as we use the maximum slip rate for each fault source and we assume all energy to be released seismically. Thus it is expected that the FSBG-model predicts seismicity rates that are higher than those observed in the historical record.

6.1 Results for the 10 % exceedance probability in 50 years

The ESHM13 map for the mean PGA at the 10 % probability of exceedance in 50 years (Fig. 17) shows the highest hazard estimates (PGA ≥ 0.25 g) along the North Anatolian Fault Zone (NAFZ), with values up to 0.75 g, in the northern Aegean and in the Marmara Sea, from the southwestern coast of Turkey through Rhodes to eastern Crete, throughout the Gulf of Corinth, and along the western coast of Greece from the Cephalonia fault zone to the northern coasts of Albania. Similar hazard values are obtained for Iceland along the plate boundary of the Mid-Atlantic ridge transform faults, from the South Icelandic Seismic Zone (SISZ) to the Tönjes fracture zone in the north. Slightly lower PGA-values, though still corresponding to comparatively high seismic hazard, are mapped throughout the Appennines, in Calabria and in Sicily (southern Italy). The deep seismicity in the Vrancea zone causes an oval-shaped patch of high hazard values in northeastern Romania, declining eastward towards Moldavia and the Black Sea and also west-northwestward away from the Carpathian Arc.

Moderate hazard levels (0.1 g ≤ PGA < 0.25 g) characterise most areas around the Mediterranean coasts, including large parts of western Turkey, much of Greece and the eastern and western shores of the Adriatic Sea, with the sole exception of the northern coasts of Croatia, where lower hazard levels are predicted. Comparable PGA values are obtained for the Eastern Alps from Trentino (Italy) to Slovenia. Similar hazard levels are also assessed for well defined tectonic structures such as the Upper Rhine Graben (Germany/France/Switzerland), the Rhone valley in the Valais (southern Switzerland) and the northern foothills of the Pyrenees (France/Spain), where the Western Pyrenees exhibit larger hazard than their eastern counterpart. The entire southern coast of the Iberian Peninsula shows moderate hazard values along fault structures and in the greater Lisbon region, along the Lower Tagus Valley. Scattered spots of moderate to high hazard are found south of Belgrade (Serbia), northeast of Budapest (Hungary), south of Bruxelles (Belgium), in the region of Clermont-Ferrand (southeastern France) and in the Swabian Alb (Germany/Switzerland).

Figure 19 shows the quantile estimates (5, 15, 85 and 95 %) of the hazard for the 10 % exceedance probability in 50 years, illustrating the range of uncertainties resulting from our approach (Fig. 19).

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6.2 Site-specific hazard results

Hazard maps are aggregated from site-specific calculations. For each site we computed hazard curves for spectral periods (from PGA up to 10 s) and the associated uncertainties. This results in the full range of information needed for engineering applications at each site. In the following we show a few examples from cities across Europe: the reader is encouraged to refer to the online resource (http://www.efehr.org/) to explore more sites.

We display hazard curves and UHS for the cities of Rhodes, L’Aquila, Lisbon and Cologne (Fig. 20, 21; exact locations listed in Table 2). These cities lie in different tectonic settings, their activity rates are on quite different and the estimated ground shaking levels originate from varying combinations of ground-motion prediction equations. The city of Rhodes is the capital of the largest Dodecanese island, located northeast of Crete atop the Hellenic arc. L’Aquila lies in the central Appennines next to large and very active normal faults. Lisbon, located on the Atlantic shore of Portugal, suffered from the 1 November 1755 earthquake, the largest in the SHEEC catalogue (M _W  = 8.5 ± 0.3); in spite of its size, however, the exact location of its causative fault is still disputed (Fonseca 2006). Cologne is situated in the Lower Rhine Embayment and experiences ground shaking only from a set of well-identified active normal faults (Vanneste et al. 2013). Figures 20 and 21 illustrate the variability of the expected ground motions, showing the mean hazard level (black line) and the uncertainties of four quantiles (5, 15, 85, 95 %). We prefer to show quantiles instead of standard deviations as the underlying distributions are not necessarily Gaussian.

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Table 2

Locations of selected sites and names of city or region

City	Latitude/(º)	Longitude/(º)
Rhodes	27.9821339	36.2
L’Aquila	13.3821339	42.3
Lisbon	−9.2178661	38.7
Cologne	6.8821339	50.9
Bucharest	26.88213	45.8
Pelentri	32.982	34.8999
Tarbes	−0.317866	43.2
Makarska	17.08213389	43.29999
Thessaloniki	22.7821339	40.8
Haugesund	5.582133	59.5
Vogtland	12.2821339	50.7999
Murcia	−2.017865	38.2

UHS have been computed for a range of periods of vibration (PGA to 4 s) and for probabilities of exceedance ranging from 1 to 50 % in 50 years (corresponding to return periods from 72 to 4975 years). Figure 21 shows the mean, 15th and 85th percentile UHS for a 10 % probability of exceedance in 50 years for the four selected cities. The Eurocode 8 spectral shapes applied to the locations in these cities, anchored to the SHARE mean PGA-values, are also plotted for comparison (dashed-dotted lines). The Type II spectral shape, currently used in areas where the hazard is controlled by earthquakes smaller than M _W  = 5.5, is seen to match fairly well the mean UHS in Cologne, but in the other cities the mean UHS is seen to be much lower than the Eurocode 8 Type I design spectrum. This difference is believed to be at least partly due to significant improvements in ground-motion prediction equations since the Eurocode spectral shapes were first proposed in the 1990s. Hence, wherever a comparison of ESHM13 maps with previous hazard studies shows an increase in the level of PGA, it is important to consider that at higher periods of vibration the ground motions from the ESHM13 UHS might actually be lower than those stipulated in Eurocode 8. The reader may refer to Weatherill et al. (2013) for further discussion on the variation of spectral shapes from ESHM13 across Europe.

Another interesting feature of the ESHM13 is the impact of epistemic uncertainty on the UHS. As shown by Figs. 20 and 21, this uncertainty in the hazard has a large influence on the spectra. Despite this influence, the modelling of epistemic uncertainty in seismic design has not received much attention to date. The authors feel that the possibility of using the epistemic uncertainty at a given return period to constrain the ground motions for more important structures such as hospitals and schools deserves further consideration, potentially by calibrating importance factors using the aforementioned epistemic uncertainty.

Figure 22 shows the UHS for a number of locations with low (PGA = 0.1 g), medium (PGA = 0.2 g) and high (PGA = 0.32 g) levels of hazard. The spectral shapes are surprisingly similar, with the only significant difference found in the spectral shape of Bucharest in the high hazard plot. Differences in spectral shapes are expected when scenarios with varying earthquake magnitude are found to control the hazard at each spectral ordinate. Therefore, in order to explain the similarities and differences in the spectral shapes it would be necessary to disaggregate the hazard at each location and at each spectral ordinate, a yet outstanding task that is also necessary for the selection of appropriate accelerograms for seismic design and assessment.

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Changes in the shape of uniform hazard spectra are also expected for different return periods. Figure 23 shows the UHS for a number of return periods for sites in three cities in Europe. The spectral shape clearly varies with the return period, and as expected this variation is location-specific. The current assumption in Eurocode 8 that a given spectral ordinate can be scaled to another return period of interest using a single fixed factor is thus not reasonable. Weatherill et al. (2013) have shown how this factor varies for different spectral ordinates across Europe.

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