dataesr/scientific-paragraphs-categorization huggingface dataset:
In section 3 we will focus on the special cases, explaining what prompted further consideration and how we used insights from other models and data to adapt the GMM logic tree in the final ESHM20 and ESRM20. Section 4, will present a comprehensive overview of the subduction scaled backbone logic tree, covering the classification of subduction ground motions from the ESM data set, the identification and calibration of the scaled backbone model, and the subsequent adjustments that were needed to improve the application of the models in the PSHA calculations. Finally, we finish the paper in Section 5 with a discussion reflecting upon the development of the GMM logic tree, its advantages and limitations, the adaptability of the framework to future models of seismic hazard and the insights gained from the work that will begin to form the areas of research focus for the next generation(s) of European Seismic Hazard and Risk Models.
All of the ground motion models described in this publication are available in the OpenQuake software for seismic hazard and risk calculation (http://github. com/gem/oq-engine)  (Pagani et al. , 2014)  and can be explored by the eGSIM interactive webservice for GMMs (https://egsim. gfz-potsdam. de/home); however, to facilitate their adoption in other applications we have also provided open-source implementations of the GMMs in the Python language in a stand-alone software repository (https://gitlab. seismo. ethz. ch/efehr/eshm20_gmms). The path toward updating the GMM logic tree for ESHM20 began in several preliminary studies that were undertaken early in the SERA project  (circa 2017 -2018). Shortly following the completion of the ESHM13 a new generation of ground motion models emerged, including those from the NGA West 2 Project  (Abrahamson et al.
After the completion of this study the new Engineering Strong Motion (ESM) database and corresponding flatfile were published  (Lanzano et al. , 2019; Bindi et al. 2019) , which allowed us to apply the GMM log-likelihood scoring to the new generation of models using a vastly expanded set of ground motion records  (Weatherill et al. , 2018). This revealed that when evaluated at a European scale the newer models provided improved fits to data than those selected by ESHM13. However, the volume of observations available within ESM was such that country-to-country differences in the model-to-data fits could be observed within areas that had been previously classified uniformly as active shallow crustal regions. Such differences had been observed in recent GMM studies for Europe including  Kale et al.
The Regionalised Backbone Ground Motion Model Logic Tree for Shallow Crustal Seismicity (Non-Craton) The ESM database and associated flatfile  (Lanzano et al. , 2019)  provides more than 20,000 strong motion observations from across Europe, with Italy, Turkey, Switzerland, Greece and Romania especially well represented. For the selection of the backbone GMM we opted to derive a new model for Europe that capitalises on this data, which was published in  Kotha et al. (2020). In their earlier work,  Kotha et al. (2016).
Using 𝑁(0, 𝜏 <4< ) and 𝑁(0, 𝜏 +" ) to describe epistemic uncertainty in the median ground motion requires an important assumption, which is that the seismological properties of target region are represented within the data set used to calibrate the distributions. Specifically in this case, do we believe that among the regions for which we have calculated 𝛿𝐿2𝐿 , and 𝛿𝑐 " using the ESM dataset some may be consistent with the seismological properties of regions for which we do not have data. With few or no available records in a target region, this may need to be inferred from other information, which we will consider in further detail for the case of the craton region. Where we do believe that the seismological properties of the "source region" are consistent with or representative of those of the target region, but cannot calibrate region-specific distributions of 𝛿𝐿2𝐿 , and 𝛿𝑐 " , we apply the full distributions of 𝑁(0, 𝜏 <4< ) and 𝑁(0, 𝜏 +" ) respectively.
After interpreting results such as those shown in Figure  2 , and exploring for dependencies of 𝛿𝐿2𝐿 , on factors such as style-of-faulting, a decision was taken not to attempt to regionalise 𝛿𝐿2𝐿 , on the basis of the data available, but rather to assign 𝑁(0, 𝜏 <4< ) to all of the non-craton crustal seismicity regions. This may be a conservative assumption in certain regions contributing many earthquakes to the ESM database, such as central Italy, but it reflects our current degree of confidence. Naturally, we hope that with the addition of further earthquakes to the database it will be possible to refine this term to a greater extent in future models. For the residual attenuation term, 𝛿𝑐 " , being based on the number of stations available in a region and indicative more of local path and site properties, we had a greater degree of confidence in the regional trends that were emerging.
We therefore have many observations of site-to-site variability with respect to the median ground motion predicted by Equation  1 , but significantly fewer direct measures of predictor variables of site amplification. Additionally, the expansion of the number of recording stations integrated into the ESM database with respect to previous generations of ground motion records means that the data now contain records from a broader variety of geological and geotechnical conditions than before. To include a site amplification scaling term into their model,  Kotha et al. (2020)  two alternative functions: one is dependent on measured 𝑉 !"# and this is fit to the subset of 419 sites for which this parameter is available, the other is dependent on scalar 30" slope at the site, which is available for all 1,829 stations using Shuttle Radar Topography Mission (SRTM) digital elevation data.
Both models adopt a quadratic site scaling function:  𝑓 ! 6𝑥 G52C , 𝑇9 = 𝛿𝑆2𝑆 ! (𝑇) = 𝑔 # (𝑇) + 𝑔 -(𝑇) ⋅ 𝑙𝑛 O H *%&+ H %&' Q + 𝑔 4 (𝑇) ⋅ O H *%&+ H %&' Q 4 + 𝜀 ⋅ 𝜙 !4! # (𝑇) (6) Where 𝑥 G52C is the predictor variable (i. e. 𝑉 !"# (m/s) or slope (m/m)), 𝑥 526 a period-independent reference condition (𝑉 !"# = 800 m/s or slope = 0. 1 m/s), 𝑔 # , 𝑔 -and 𝑔 4 are period-dependent coefficients, and 𝜙 !4! # the corrected site-to-site variability, which is shown to be smaller for the measured 𝑉 !"# case. By limiting the dataset to only those stations with measured 𝑉 !"# and applying robust regression analysis,  Kotha et al. (2020)  return a lower 𝜙 !4! # compared to that of  Kotha et al. (2016). In the development of the ESRM20,  Weatherill et al. (2023)  addressed the issue of calibrating the site amplification for the context of the seismic risk calculations, with the requirement that ground motion must be predicting accounting for site response across all of Europe at a 30" scale.
Starting with an updated topography/bathymetry model a new pan-European map of 𝑉 !"# inferred from slope was created using the approach of Wald & Allen (  2007 ), which is complemented by a harmonised geology database for Europe compiled by BRGM. Using the regression outputs from  Kotha et al. (2020)  and selecting around 1,100 stations for which 𝛿𝑆2𝑆 ! was constrained by three or more records,  Weatherill et al. (2023)  defined two different amplification models, one based on 𝑉 !"# but whose coefficients and variability are dependent on whether the 𝑉 !"# is measured or inferred from the slope proxy, the other using slope as the main predictor but introducing a geological era classification as a random effect and thus making the implied amplification (but not its variability) dependent on both slope and geological era.
Adaptations to the Backbone GMM (Kotha et al. , 2022) The backbone GMM as it is described in  Kotha et al. (2020)  applies a hinge magnitude, 𝑀 3 , to capture the break in magnitude scaling from quadratic (for 𝑀 $ < 𝑀 3 ) to linear (for 𝑀 $ ≥ 𝑀 3 ) at magnitudes greater than 𝑀 3 = 6. 2. This number was originally based on non-parametric analysis of the magnitude scaling implied by the ESM dataset; however, data from larger earthquakes was limited to a relatively small number of large events from Italy and Turkey. At the same time, when compared to its predecessor European database of strong motions, the ESM contains more than 10,000 new records from earthquakes with magnitudes M ≤ 4. 5, for which very few direct moment magnitudes (𝑀 $ ) are present. Although  Kotha et al. (2020)  used a harmonised moment magnitude from the updated European-Mediterranean Earthquake Catalogue (EMEC), these magnitudes are equivalent proxy magnitudes converted from their originally recorded scales.
When fitting the GMM these factors, and potentially others, resulted in a positive 𝑏 4 coefficient in their model for short periods, which in turn produced a flattening of the magnitude scaling at low magnitudes, a "kink" in the magnitude scaling at magnitudes 𝑀 $ ≈ 𝑀 3 and a more strongly negative trend in the ground motions for 𝑀 $ > 𝑀 3 (the result of a negative 𝑏 " term in equation 2). These effects contributed to a more significant weaking of ground motions for larger magnitudes at short distances ("apparent oversaturation"), along with a higher 𝜎 '()(*'(*+), , compared to other GMMs. Following the  Kotha et al. (2020)  GMM and based on further exploration and comparison of the models using the Near Source Strong Motion dataset NESS  (Pacor et al. , 2018; Sgobba et al. , 2021) , it was found that the negative scaling of 𝑏 " was yielding a moderate bias in between event residuals for large magnitude earthquakes.
Insights from Implementation and Special Cases for Application Although the general framework for the GMM logic tree has been laid out in the previous section and in the preceding publications, in certain regions there have been additional adaptations to this that were implemented in response to feedback from local scientists and engineers. In each of these cases the adaptations have been based on analysis of additional data that was not contained within the ESM database used by  Kotha et al. (2020; 2022)  to define the backbone GMM.
This same explanation does not apply to the Pyrenees, however, and the transition from slightly slower than average attenuation in the Alps to particularly slow attenuation in the Pyrenees occurs over a relatively short Motion (SIGMA 2) project, which undertook further investigation of seismic ground motion in France using the Réseau Sismologique et Géodésique Français (RESIF) ground motion data set  (Traversa et al. , 2020; Kotha, 2020). The RESIF database contains more than 6,500 records from 468 earthquakes recorded at 379 stations within metropolitan France. The magnitude distribution of these earthquakes is between 2. 0 ≤ 𝑀 $ ≤ 5. 2, and is generally skewed toward a lower range of magnitudes than that of the ESM dataset. Kotha (2020)  extends the analysis presented in  Kotha et al. (2020)  to include the RESIF data, providing both 𝛿𝐿2𝐿 , and 𝛿 =" for regions in France that were not well constrained by the ESM data (Figure  8 ).
(2020) , but given the importance of this region for seismic hazard and risk in southern Portugal, further analysis was warranted. We returned to the earlier RESORCE database of strong ground motions  (Akkar et al. 2014b) , where more Portuguese strong motion records were available, and identified 42 records from which we attempt to make comparisons against 1) the "default" shallow crustal seismicity logic tree, 2) the shallow crustal seismicity logic tree for the "very slow attenuation" cluster, 3) the GMM logic tree for the parametric craton model  (Weatherill & Cotton, 2020). Figure  10  shows the magnitude-distance-𝑉 !"# composition of the data set (where 𝑉 !"# is largely assigned from topographic proxy), indicating that all but one record is recorded at a distance greater than 100 km.
, 2011; Kowsari et al. 2019). This was acknowledged, to a certain extent, in the original formulation of the regionalised backbone GMM logic tree by  Weatherill et al. (2020) , who identified a predominance of ground motion records with partially volcanic travel paths (or paths through regions of higher heatflow) as a common factor behind the low 𝛿 +" values in the regions assigned to the "fast attenuation" cluster. It was originally proposed to apply the "fast attenuation" 𝛿 +" adjustments also to onshore source zones in Iceland too, even though Icelandic data was not present in ESM. Feedback from local experts in the region, along with recently published GMMs for Iceland  (Kowsari et al. 2019) , prompted us to re-visit the GMM logic tree here. While the Icelandic strong motion data was not available to ESM, records had been made available in the earlier RESORCE database  (Akkar et al.
This reduces the calculation to only the 225 GMM logic tree branches (15 from the Iceland-adjusted GMM logic tree applied to onshore and mid-Atlantic Ridge sources, 15 from the original default shallow GMM applied to the stable offshore sources), allowing for faster and more convenient computation. The three cases discussed in this section (France, Portugal and Iceland) illustrate how it was possible to take decisions and make modifications to the logic tree on the basis of other ground motion data not contained within the original ESM dataset used to fit the shallow backbone GMM and its regionalisation. This shows that the approach can be flexible and that the backbone GMM can accommodate further regional adjustments when there is a sufficient basis to do so. Further discussion around the issue of adaptability of the regionalised GMM logic tree approach can be found in the concluding section 5.
6, iii) the degree of membership to the next highest membership class is < 0. 4. From ESM flatfile the earthquake parameters available are the location and depth estimate for nearly every event. Focal mechanisms were not included for the events initially (only a qualitative style of faulting where available); however, corresponding focal mechanisms for events were retrieved from national and regional databases. This provided focal mechanisms for nearly all earthquakes with magnitude 𝑀 $ > 4. 8; however, for many earthquakes no moment tensor was available. For the slab geometry, we had available slab upper surface definitions from Slab 2. 0  (Hayes et al. , 2018)  for the Hellenic/Cypriot and Calabrian arcs. The ESM lacked any records from deep seismicity in the Gibraltar arc, so this region is not considered any further.
Identifying the Best Suited GMM for use as a Backbone GMM The distribution of records with respect to magnitude and distance shown in Figure  14   significance for hazard from subduction earthquakes (typically 𝑀 $ ≥ 6. 5) is virtually unconstrained. Vacareanu et al. (2015)  addressed this issue by supplementing their dataset with ground motions from well recorded deep earthquakes in other regions of the globe. This may be a valid approach; however, for ESHM20 we preferred to leverage upon existing ground motion models that would comply with the quality selection criteria proposed by  Bommer et al. (2010)  that are constrained by global subduction data, before then attempting a recalibration of the attenuation coefficient of the model based on the Hellenic and Calabrian data. Here, the distance range covered by travel paths from the small-to-moderate magnitude earthquake ground motions is sufficient to provide some valid inference on ground motion.
Although the interface and in-slab record sets contain records from three different subduction zones (Hellenic, Cypriot and Calabrian) further subdivision by zone was not attempted as this would yield too few records from the Calabrian and Cypriot arcs from which to make any inference on attenuation. The selection of the backbone model is based on a two-step process that first selects a set of candidate models based on quality criteria, and then apply likelihood scoring compare the candidate models against the observed data. We note that the development of the subduction model for ESHM20 was undertaken prior to publication of GMMs from the NGA Subduction project NGA-SUB  (Hassani and Atkinson, 2020; Parker et al. 2022; Kuehn et al. 2020; Abrahamson and Gülerce, 2022; Si et al, 2020). The publication of the NGA-SUB models would have inevitably increased the available suite of candidate models for application to the eastern Mediterranean and we expect this to be an   2018 ) might be considered more as an intermediate update to the original "BC Hydro" model using the NGA-SUB database  (Bozorgnia & Stewart, 2020) ; the more complete update eventually being  Abrahamson & Gülerce (2022).
To calibrate the coefficients for these terms we retrieve between-and within-event residuals (𝛿𝐵 and 𝛿𝑊 for the observations with respect to the model in which 𝑓 ) (𝑅) = 0. The trends in 𝛿𝑊 are inspected with each period and a linear model fit such that the slope (𝜃 F is calculated and constrained to be ≤ 0. 0. The variation in fitted 𝜃 F with spectral period is shown in Figure  16  for the interface ground motions (black) and in-slab ground motions (red). The attenuation terms from the two datasets show divergent behaviour at short periods, with slower attenuation for in-slab earthquakes than interface earthquakes, gradually converging at T ≥ 1. 0 s. As a point of comparison, we also show the anelastic attenuation coefficients from several other regions of the globe, as presented by  Abrahamson et al.
Here the volcanic front passes through the Aeolian Islands and follows the azimuth of the Calabrian subduction westward to cross northern Sicily and north-eastward to come onshore in southern Campania. Though the volcanic fronts are delineated by judgement they are also contrasted against maps of heatflow such that regions of higher heatflow are predominantly found in the backarc regions. For Vrancea we retained a previous delineation used by  With the boundaries delineated, the records in our ground motion databases are classified either as forearc or backarc, as shown in Figure  21. As our data come predominantly from small to moderate magnitude events, the proportion of usable strong motion records in the backarc regions is generally low, particularly for interface events. To determine if differences in attenuation between forearc and backarc records are present we retrieve the within-event residual with respect to the  Abrahamson et al.
(2018)  GMM, which itself does not include 𝑓 OS&S. For sites with 𝑅 /?@ ≥ 120 km, we compare the mean and standard deviations of 𝛿𝑊 for the forearc residuals with those for the backarc residuals and apply a two-sided Kolmogorov Smirnov test to indicate the significance of the hypothesis that both are drawn from the same distribution. This is applied to each of the three data sets and for each period. We found that for interface earthquakes the null hypothesis that the backarc residuals are drawn from the same distribution as the forearc residuals cannot be rejected at probability level p = 0. 05. For the in-slab and Vrancea databases, the distributions are significantly different p << 0. 05 for spectral periods less than 0. 75 s, indicting clear evidence of a difference in attenuation scaling between forearc and backarc sites.
Replacement of 𝐹 OS&S with a path-through-backarc distance term in the GMM may be preferable from a pure physical perspective, but such a term requires that the PSHA software trace the path between the source and site to measure the proportion through the backarc. The path-tracing is not a trivial calculation when considering tens of thousands of sources and thousands of sites, as we are in the ESHM20, and would incur substantial computational cost. This option was not available to us in the current version of the OpenQuake-engine software. Instead, we attempt to affect a smoother transition between forearc and backarc attenuation regime by turning 𝐹 OS&S into a continuous function that varies over source-to-site distance. To do this we define for each site the distance to the line of the nearest volcanic front (𝑅 PYO , which takes a positive value on the forearc side and negative on the backarc.
The Next Generation of Subduction and Deep Seismicity GMMs for Europe At several points throughout this section is has been noted that the efforts to develop the backbone GMM logic tree for noncrustal earthquakes preceded the publication of the NGA Subduction GMMs. Much like their NGA-West and NGA-East counterparts, the NGA Subduction GMMs are transformative in how they approach problems of regionalization, uncertainty (epistemic and aleatory), and source-, path-and site scaling for subduction earthquake hazard. The implications of this for hazard in Europe need to be understood, and a more extensive analysis of the models and their comparison to ground motions in the eastern Mediterranean and Romania would be an important starting point for subduction GMMs in Europe. No ground motions from Europe were included in the NGA Subduction database  (Bozorgnia & Stewart, 2020 ), yet the volume of records from non-crustal seismicity in Europe is growing  (Manea et al.
, 2021; Luzi et al. , 2020)  and the site characterisation of recording stations improving. While we are unlikely to witness many large subduction or deep seismicity earthquakes in Europe within the following few years, there remains scope to use this growing data to attempt to calibrate certain parameters the NGA Subduction GMMs for application to the Hellenic Arc and to explore the issue of regionalisation in more detail. Likewise, as will be discussed in the following section, non-ergodic GMMs are likely to play a significantly greater role in the next generation ESHM than was possible for ESHM20. The implementation issue of path-tracing inside the PSHA calculation software will inevitably need to be addressed in the PSHA software, and if supported in future versions of OpenQuake then this opens up the possibility to move beyond the forearc/backarc scaling undertaken here and into more flexible and physicallysound characterisation of travel path differences.
The question of regionalisation could be revisited in future, if one does not adopt directly full-scale non-ergodic models based on regular grids. Another factor that may have potential influence on regionalisation, and one that requires further investigation and refinement, is variability in the definition of the magnitude. The ESM flatfile did not contain a harmonised 𝑀 $ for all the events, and therefore the magnitudes used in the regression were assigned from the harmonised European-Mediterranean Earthquake Catalogue, which had been shown to reduce the between-event variability compared to the use of the heterogeneous magnitudes in the original ESM flatfile  (Bindi et al. , 2019). For many earthquakes with 𝑀 < 5. 0, moment magnitudes were either taken directly from different local moment tensor databases (sometimes without adjustment) or were converted from other magnitude scales by locally or regionally calibrated conversion equations  (Grünthal & Wahlström, 2012).
(2022)  and that relates to the large magnitude and near-fault scaling. While the ESM dataset boasts a significant increase in the number of ground motion records compared to its predecessor, this increase has come mostly from small events. For 𝑀 $ ≥ 6. 0 generally it is the 2012 Emilia-Romagna sequence and the 2016 Central Italy earthquake sequence that contributes the largest volume of records from large magnitude shallow crustal events. For larger earthquakes with 𝑀 $ ≥ 7. 0, the database is dominated by a few well recorded large Turkish earthquakes (e. g. , Izmit 1999; Duzce 1999, Van 2011). These are not necessarily rich in near-source records, and it required careful scrutiny of the  Kotha et al. (2020)  model and comparison against the subsequent NESS data sets to identify the apparent over-saturation issue that was addressed by reducing the breakpoint in magnitude scaling 𝑀 3 from 𝑀 \ 6.