dataesr/scientific-paragraphs-categorization huggingface dataset:
Development of V S30 maps following a geology-based approach The spatial distribution of the observed V S30 dataset is quite unbalanced, with more than 80% of the data coming from active tectonic areas (Italy, Greece, Turkey, the Alps and the Pyrenees) (Fig. 2 ). This poor spatial distribution of the V S30 data induces a bias in the analysis since some geological units will be underrepresented in the dataset as, for example, geologies from stable areas in Western and Northern Europe. However, the spatial analysis in Fig. 5  shows that an extrapolation of the V S30 information (coloured polygons) to all the geological polygons characterized by a similar lithology (grey polygons) leads to a rather good spatial coverage of V S30 information all over Europe. We analysed the distributions of measured V S30 values for each of the lithological categories shown in Fig.
Our observed V S30 dataset is limited in terms of rock V S30 values due to a lack of measurements from sites located on these types of geological units. As the V S30 dataset collected for the ESRM20 was not sufficient to build our own geology-based model for estimating V S30 , we opted for a more pragmatic approach that uses an existing model, built on a regional scale, and extrapolated it to the rest of Europe. Several possible approaches adopted in various regions of continental Europe were considered. The first was the model developed by  Stewart et al. (2014)  in Greece, which combined terrain type, surface geology (age) and surface gradient information. This model is not easily reproducible at European scale, however, mainly due to the complexity of terrain classification. The second approach was the model developed by  Di Capua et al.
Comparison between inferred V S30 and observed V S30 We compared the two V S30 models described above to the in-situ observations of V S30 from the compiled database shown in Fig. 2. The comparison was processed in terms of EC8 soil classes and the accuracy of the two different V S30 models to predict the correct soil class for the respective in-situ observations. From these comparisons we found that (i) the two models are better than random chance (accuracy > 25%), (ii) the topographically-inferred model using the approach of  Wald and Allen (2007)  yielded a greater accuracy (51%) than that of the geologically-inferred model using the approach of Vilanova et al. (  2018 ) (38%), (iii) the accuracy of the two models are similar for the Eurocode 8 C class but the topographically inferred model improves on the geologically-inferred model for the A and B site classes.
Structure of the ground motion model and its random effects With the availability of the Engineering Strong Motion flatfile  (Lanzano et al. 2019 ) the number of recording stations reporting multiple observations has increased by an order of magnitude in comparison to the preceding RESORCE strong motion database  (Akkar et al. 2014b ). In the construction of the ground motion model for application to shallow crustal seismicity in Europe,  Kotha et al. (2020)  identify 644 stations with more than five strong motion observations, and more than 1100 with three or more. Of the set of strong motion stations represented in the database used for the development of the Kotha et al. (  2020 ) GMM, just over fifth of the strong motion stations are associated with a measurement of V S30. For the remainder, V S30 values are defined from the topographically inferred model.
As such, the resulting S2S S (T) term refers to the relative station-to-station amplification with respect to the centre of the data set, rather than to a reference rock condition. This follows the approach of  Kotha et al. (2018) , who utilised cluster analysis on the resulting S2S S (T) distribution to identify sites within the Japa- nese Kik-net database sharing common site characteristics, from which the definition reference rock emerges from the stations with similarly low S2S S (T). The centre of the data set will, to some extent, reflect the underlying distribution of V S30 values (or other proxy) in the data, albeit that this does not refer to a single V S30 but rather a reference value that is dependent on period. Being centred on the middle of the data set, S2S S (T) can take negative values, thus describing the de-amplification with respect to the "average" site condition implied by the data, as well as to positive amplification terms.
A general model for site amplification within the GMM The complete set of station-to-station terms allows us to explore the possibility of identifying site properties that can be adopted as predictors of site response at a large geographical scale, as well as their impact on the uncertainties of the GMM. Of particular interest for this purpose is the distinction between measured site properties (i. e. , those known by direct measurement at the site) and inferred site properties (i. e. , those associated to the site by virtue of its location in a large-scale geographical data set, i. e. a proxy). For application in the ESHM20, the distinction between measured and inferred site parameters is of critical importance because of the two contexts being served, namely the engineering application (requiring the characterisation of the hazard on Eurocode 8 class A rock) and the seismic risk application (requiring the characterisation of the seismic ground motion for any location in the European exposure model according to the local soil condition).
Though the quadratic functional form may fit the dataset reasonably well, it lacks a physical basis and deviates from a simple linear model only at the extreme low and high V S30 or slope values, which are themselves poorly constrained by data. It also cannot be safely extrapolated to higher or lower V S30 values outside of the range of data, which may result in unexpectedly high or low amplifications at certain locations. For the purposes of the ESHM20 and ESRM20 a different functional form is adopted, this time using a two segment piecewise linear model: where is the site property of interest, g 1 and ref are period-dependent coefficients fit dur- ing the regression, and C is a period independent cut-off parameter above which f site is constant. S2S S is the resulting site to site residual, which itself should be Gaussian dis- tributed with a mean of zero and standard deviation of S2S.
The majority of the stations with measured V S30 < 250 m/s are located in the Po Plain, while the rest are located in mostly Quaternary basins or valleys within the Appenines and the Alpine foreland. When exploring the specific ground motions at those stations with both low V S30 and lower than expected S2S S , however, we find that in several cases the S2S S term is constrained exclu- sively by low intensity shaking from smaller magnitude events (less than 0. 02 g), which does not necessarily support the assumption that nonlinearity is playing the dominant role here. We note here too that the location and housing of the strong motion sensor (e. g. , freefield, in-building, close to large structures etc. ) may contribute to the overall variability, especially at short periods. For 55% of the stations considered, no information is available in the ESM database regarding the location of installation and proximity to structures, while for those that are reported nearly 70% are identified as free-field or "free-field close to structure".
The remainder are reported as located in basements or ground floors of buildings (predominantly one-storey masonry). While we cannot rule out the possibility of such site-specific factors influencing the S2S S terms at short periods, we do not have enough information to identify specific trends or regional variation that could be attributed to these factors, and therefore cannot quantify the extent to which this contributes to the total aleatory variability. Though we hope that more information on housing and installation will be 1 3 included in future versions of the ESM database, we do not find any compelling reason to believe that these factors would impact significantly on the models calibrated here. The site amplifications predicted by both the inferred and measured V S30 models here are compared in Fig.
4 (dashed black line) 1 3 of the measured V S30 on soft soil (V S30 < 250 m/s), where it predicts higher amplifications than all of the models except the Kotha et al. (  2016 ) "Turkey" model. These comparisons would suggest that while the method adopted here for the calibration of a V S30 site amplification model may differ from those fit to previous European strong motion databases, the resulting level of amplification is mostly consistent with the range predicted by such models, even in the case of inferred V S30.
Incorporating geology into the site amplification model The dual objectives of the ESHM20 in terms of describing hazard on reference rock for engineering application and on soil for risk applications provide a basis for devising separate models for inferred and measured site condition. As the seismic risk application requires the definition of the site condition across the entire extent of the area covered within the exposure model, it is inevitable that V S30 inferred from proxy will come to play the dominant role. The topographically and geologically inferred V S30 maps shown in Sects. 4. 1 and 4. 2 form the primary data set for defining the site parameterisation for input into the seismic risk calculations. Adopting the inferred V S30 classification in the GMMs should ensure that an appropriate increase in uncertainty is assigned to the ground motion for using the proxy.
Weatherill et al. (2020b)  explored the relation between S2S S , as determined from a GMM fit using data from the Japanese Kik-net data set in a manner similar to that described here, and the properties of the site including those measured directly for the station and those inferred from topography and geology. They find that the correlation between S2S S (T) and both measured and inferred site properties to be dependent upon the geol- ogy at the location of the station. Specifically, in regions where older surficial geological units of pre-Cenozoic are present there is little to no correlation between inferred V S30 and S2S S , while in Quaternary environments the correlations and their dependence on spectral period are clear. They also demonstrate that in terms of predicting site amplification, there is no discernible reduction in uncertainty for using V S30 inferred from topographic slope rather than using the topographic slope as the predictor variable in itself.
The dependence of f site ( ) on geology, whether in this case refers to measured V S30 , inferred V S30 or slope itself, was integrated into the amplification using a mixed effects regression process, which calibrated the coefficients of the model depending on the geological environment. This was utilised by  Weatherill et al. (2020b)  to produce a 30 arc-second site amplification model of Japan. Given both the absolute number of stations in the ESM database for which S2S S (T) has been determined, and the relatively low proportion with measured site properties, there is 1 3 a case for adopting the approach of  Weatherill et al. (2020b)  for application to Europe. The 30 arc-second topographic slope and geology are assigned to each station in the database for which S2S S (T) is determined, using the topography and geology data sets described previously.
(2020) GMM with- out a specific site term, we cannot separate easily the respective contributions of different effects. For example, short period S2S S at certain sites may contain records from smaller 1 3 Fig. 18  Spatial pattern of mean amplification at a spectral period of T = 0. 2 s with respect to a slope of 0. 3 m/m on Precambrian rock. Amplification is not determined for areas classified as "Ice" or "Lake/Inland Water" Fig. 19  As Fig. 18 , for a spectral period of T = 1. 0 s 1 3 earthquakes producing predominantly linear amplification, as well as from larger earthquakes for which nonlinear amplification is present. In the underlying ESM dataset, there are few observations from large earthquakes on soft soil sites; an observation confirmed by  Guéguen et al. (2019)  who found less evidence of soil nonlinearity in ESM with respect to the other databases.
Future research in this area towards the calibration of a nonlinear amplification model is certainly warranted. For longer period motion the amplification model here cannot distinguish which effects are due to amplification from shallow soils and which may be due to deep basin effects. In contrast to other databases such as those of the NGA West 2  (Ancheta et al. 2014)  or  Kik-net (NIED 2019) , the ESM contains no information about the depth to bedrock for the stations present in the database. Though it was subsequently possible to retrieve the full V S profiles for several hundred stations in Italy, Turkey and Greece, we find that in many cases the available profiles for stations in the deep basin regions, particularly in Northern Italy, do not reach the engineering bedrock (e. g. V S 800 m/s-1000 m/s).
2018) , further details of which can be found in  Weatherill and Cotton (2020). For the subduction and deep seismicity earthquakes, while the ESM database did provide a sufficient set of records to identify and calibrate a backbone GMM and its epistemic uncertainties, in this case the Abrahamson et al. (  2016 ) "BC Hydro" model, the number of stations was too small to attempt the sort of site model calibration shown here. In both the stable shield region of the Baltic sea and surrounding countries as well as the subduction/deep seismicity regions of the Hellenic, Calabria, Cypriot arcs and the Vrancea deep seismic zone, we adopt the site amplification terms directly from the models themselves and apply them to the topographically inferred V S30 values for all the cells within the regions for which these GMMs influence.
(2019)  showed that the two were similar in terms of absolute value and the relative change with period. For subduction and deep seismicity regions we find that total within event-variability ( ) of the  Abrahamson et al. (2016)  subduction model is fixed for all periods at 0. 6, a value lower than that of both the shallow crustal seismicity and craton model estimates found here. A corresponding S2S term for this model is provided in the original BC Hydro report, which is given as 0. 43. This value is comparable to the measured V S30 case used here, which is unsurprising given that the derivation of the S2S term was based on the subset of stations in the BC Hydro database with more than 10 records, which is mostly limited to data from Taiwan and Japan where the vast majority V S30 values for the sites were taken from direct measurement.
Recent analysis by Abrahamson and Gulurce (2020) of within-event residuals from the more extensive NGA Subduction database finds not only a degree of region-to-region variability in S2S but also a distance dependence in 0 that results in higher within-event variability for longer distance records than for the previous  Abrahamson et al. (2016)  model. Their resulting estimates of yield values comparable to the present inferred V S30 or slope and geology models here for regions such as South America, Central America and Japan, but closer 1 3 to the original  Abrahamson et al. (2016)  models for New Zealand, Taiwan, Alaska and Cascadia. When combined, the more recent investigations build a more complex picture of S2S globally than the previous models would infer and suggest there is a case for adopting higher values than those assumed by the  Abrahamson et al.
(2016)  GMM in some regions. In spite of this, we cannot calibrate a more appropriate S2S from the data available and attempts to increase it to bring it closer to the inferred V S30 case do not have a sufficiently rigorous basis and may overestimate site-to-site variability from subduction earthquakes in Europe. Therefore, for subduction and deep seismicity events we adopt the V S30 values implied from topography in Sect. 3 and apply no further modification of the GMM. We recognise that this may underestimate the within-event variability from subduction earthquakes, however, and suggest this as an area that warrants further research in light of the publication of the NGA Subduction database  (Bozorgnia and Stewart 2020 ) and associated GMMs.
Essentially, we wish to know if the complete site characterisation approach we are adopting for regional scale application, with the uncertainties fully propagated into the hazard and risk calculations, yields estimates of hazard or loss that would be significantly different from those determined using approaches that could be applied, were better data available. For this purpose, we have assembled a data set of locations across Europe for which we have complete V s profiles. At these locations we can run seismic hazard and risk computations adopting both the regional scale approaches used for the ESRM20 (i. e. , amplification based on slope and geology or amplification based on topographically inferred V S30 ) with some that could be achieved at a site-specific level. The complete database of V S profiles covers Italy (212 locations), Turkey (219 locations) and Greece (19 locations) and is taken from the European Strong Motion Database (https:// esmdb.
eu/#/ home) and the European Geotechnical Database (http:// egd-epos. civil. auth. gr/). Though a measured V S30 can be identified from each of these profiles, not all profiles are sufficiently deep to identify 800 m/s V S layer or the 1000 m/s V S layer. We therefore limited analysis to just the subset of 172 profiles for which an observed V S30 and depth to 1000 m/s V S could be obtained. This will obviously exclude more sites in deeper sedimentary basins, particularly in the lower basin of the River Po, Northern Italy, than on stiffer soil and rock; however, in terms of geological composition the resulting database still provides an adequate sample of profiles on different geological conditions.
Impact on seismic risk analysis The comparison of the seismic hazard curves in Fig. 20  illustrate the differences in probabilities of exceedance of ground motion when adopting different site amplification models, but for the ESRM20 the target is not the hazard but ultimately the probability of exceedance of losses. The complete set of seismic risk results based on the site amplification model developed here can be found in  Crowley et al. (2021)  and will not be repeated here. Instead, we extend the seismic hazard analysis for the three aforementioned sites into the domain of seismic risk by comparing the differences between the site amplification models in terms of loss exceedance curves for three structures. The selected structure is reinforced concrete, with a load bearing wall lateral load resisting system with moderate ductility: CR-LWAL-DUM according to the Global Earthquake Model (GEM) Building Taxonomy (https:// github.
com/ gem/ gem_ taxon omy). To understand how the different periods of spectral acceleration are affected we consider three structures whose fragility models depend on different intensity measures: 3-storey (IM = PGA), 7-storey (IM = Sa (0. 6 s)) and 12-storey (IM = Sa(1. 0 s)). These cover a range of low-to medium-rise reinforced concrete structures with a "moderate" level of seismic resistance. The CR-LWAL-DUM is selected as the periods of the desired IMs over the range of building heights span well the spectral period range we are considering here. The general results we show here would be generalisable to any other fragility and vulnerability models considered within the ESRM20 (available from https:// doi. org/ 10. 5281/ zenodo. 40624 10). Loss curves in terms of annual probability of exceeding a given loss ratio of the structure are calculated using the classical seismic risk calculator of OpenQuake  (Silva et al.
Conclusions The process implemented for the development of the site response model within the ESHM20 and ESRM20 primarily occupies itself with the challenge of application at scale, rather than at a site-specific level. The key outcome is that we are able to characterise ground shaking at the surface at every 30 arc-second grid cell in the ESRM20 target countries and have implemented this in a manner that is practical for the seismic risk calculations and integrates uncertainties that have until now commonly been neglected. Several standalone products have emerged from this effort, which may be of value to the seismology and engineering communities in general: (i) a harmonised map of surficial geology, (ii) a new European model of V S30 constructed from slope and geology, (iii) a database of V S30 observations compiled from multiple sources, (iv) ground motion models (and a ground motion model logic tree) for Europe with site scaling terms calibrated on the largest data set of strong motions and site metadata available across the region, which can be adapted to the appropriate context of the calculation.
3 The data sets themselves are released as part of the complete suite of products to emerge from the ESRM20 and are available for download from http:// risk. efehr. org/ site-model/. The ground motion models and their corresponding site scaling terms have been implemented in the OpenQuake-engine (http:// github. com/ gem/ oq-engine), a state-of-the-art open source software for seismic hazard and risk analysis that is the primary calculation tool for the ESHM20 and ESRM20 calculations. In addition, as the input data for seismic hazard and risk calculations requires a more complete suite of information, a dedicated open-source Python toolkit is available to help users access all of the site input data and construct the site model for their calculation needs. Further information on how the site data can be optimised to best suit the exposure model in a seismic risk calculation can be found in  Dabbeek et al.
(2021) , and the toolkit ("exposure2site") is available for download from https:// gitlab. seismo. ethz. ch/ efehr/ esrm20_ sitem odel. The various calibrations of the f SITE and S2S terms for the shallow earthquake ground motion model pertain to different applications, and care must be taken to ensure that the most appropriate is used. To summarise these briefly: 1. For the development of the seismic hazard maps with respect to the Eurocode 8 reference rock, or similar applications in national seismic hazard maps that may form the input for seismic design codes, the amplification model f SITE V OBS S30 , T from Eq. 3, and its corresponding OBS S2S , should be used. This is the case for the seismic hazard products on reference rock for ESHM20. In this case we expect the V S30 to be known at the site, and when adopting soil amplification factors from the design code itself a pre-determined level of conservatism is already accounted for, so any further inflation of S2S would likely overestimate uncertainty.
The implementation of the site characterisation methodology for ESRM20 serves to highlight some of the future developments that would be needed in order to reconcile local scale and regional scale approaches for the future generations of seismic risk analysis in Europe. The most fundamental of these would be a harmonised database of microzonation studies and a greatly expanded effort to improve the site metadata for current (and future) seismic recording stations, which themselves would benefit from enhanced resolution and availability of digital geological data sets. While the benefits of improved site metadata would be seen in improved calibrations of the amplification terms in the manner implemented here, the microzonation studies would likely have a greater impact in terms of the resulting loss estimates owing to a reduction of siteto-site variability in regions of highest economic exposure.