dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, advanced studies on spatial and temporal variability of attenuation are now possible  (Bindi et al. 2018a; Dawood and Rodriguez-Marek 2013; Kotha et al. 2019; Landwehr et al. 2016; Piña-Valdés et al. 2018; Sahakian et al. 2019). The data visualized in Fig. 1  are from shallow crustal earthquakes in the ESM dataset. The full dataset contains ground-motions from other tectonic regimes as well, such as subduction interface, subduction in-slab, Vrancea, etc. To filter out these and other records not suitable for a shallow crustal GMM development, we adopt the following selection criteria: 1. To keep only the shallow crustal earthquakes, we select only those events classified as non-subduction events by  Weatherill et al. (2020d). The selection removes inslab, interface, outer-rise, and upper-mantle events from the regression dataset.
The resulting 927 events with event depth 0 < D ≤ 39 km are located in regions with 14 ≤ Moho depth ≤ 49 km , as per the Moho map of  Grad et al. (2009)  2. Only those events with ≥ 3 records in the dataset are used in regression. Also, wherever available, the harmonised M W estimates from the European Mediterranean Earthquake Shaded cell if black indicates a feature included, white for a feature not included, and grey for a feature tested but not included in the final GMM 1 3 Catalogue [EMEC] (Grünthal and Wahlström 2012) are preferred over the ESM default values 3. We keep all sites in the dataset irrespective of whether their V S30 , measured from geo- technical investigations, is provided or not in ESM. This is to estimate the site-specific terms ( S2S s ) at as many sites as possible, and then explore various site-response proxies to characterize them  (Kotha et al.
2018; Weatherill et al. 2020b ) 4. Choice of distance metric is R JB where available, otherwise the epicentral distance R epibut only for events with M W ≤ 6. 2. The distance range is not truncated and extends up to R JB = 545 km 5. Only those records with high-pass filter frequency f hp ≤ 0. 8∕T , where T is the period, for both horizontal components are kept in the regression of spectral accelerations SA(T). This is to ensure that the filter does not significantly affect the response spectral values. As a result the dataset varies with the T  (Abrahamson and Silva 1997). Further detail on waveform processing can be found in  Lanzano et al. (2019a). Following the above criteria, the number of records available for GMM regression is 18,222, from 927 events ( 3. 1 ≤ M w ≤ 7. 4 ) recorded at 1829 stations ( 0 ≤ R JB < 545 km ).
Regionalisation datasets In order to attempt GMM regionalisation, we first need to define regions, classify the data into regions, and quantify the regional variabilities through regression. The mixed-effects regression estimates first the group random-variances, and then the random-effects for individual levels in the group  (Bates et al. 2014). For instance, in estimating the well-known between-event term ( B e ), the preliminary step is to quantify observed event-to-event ground-motion variability as the between-event random-variance (τ 2 ). Following this, the between-event random-effects for each event are estimated from the total-residuals. The procedure is similar for the between-site terms ( S2S s ), and other random-effects in a mixed-effects regression. Essentially, within the event group, individual events are the levels.
The seismotectonic source zonation (referred to as 'TECTO') is designed to be large-scale and does not attempt to resolve smaller scale seismogenic features. As such it is intended to reflect the local tectonics influencing the seismic source generation but not to resolve highly localised features, which makes it a good candidate for our purpose-and more appropriate for source localisation than the TSUMAPS-NEAM model used for attenuation regionalisation. The random-effects group will be referred to as between-locality ( L2L l ) from hereon, with each level (locality l) being a tectonic locality with allotted shallow crustal events. Figure  3  shows the distribution of recordings across the tectonic localities with at least one shallow crustal event associated to them. Once again, this group is introduced to quantify the earthquake locality-to-locality variability of ground-motion in the dataset through the between-locality variance ( 2 L2L ), which if close to zero indicates no regional variability of earthquake characteristics.
The fixed-effects component of this GMM remains similar to that of K16, but with a few minor changes based on non-parametric analyses. In Fig. 4 , the top row of the plot shows non-parametric scaling of SA(T = 0. 1s) with R JB ; wherein, for clarity, the data is split into magnitude bins and plotted in separate panels. The bottom row of the plot shows non-parametric scaling of SA(T = 0. 1 s) with M W ; where, the data is split into distance bins and plotted in separate panels. The smooth (coloured) curves are the loess fits  (Jacoby 2000)  illustrating non-parametric scaling of SA(T = 0. 1 s) with mag- nitude and distance. Given the variety of hypocentral depths in the dataset, and that we are using the depth-insensitive R JB , we categorized the events into three depth bins ( D < 10 km, 10 km ≤ D < 20 km, 20 km ≤ D ), and plotted unique non-parametric attenua- tion curves for each bin.
The generic fixed-effect coefficient c 3 in Eq. (  4 ) can be adjusted with a region's c 3,r to achieve region-specific coefficient for that region r, as in c 3,r = c 3 + c 3,r. Unlike the generic c 3 , c 3,r is the apparent anelastic attenuation term that varies with region (subscript r identifies regions in Fig. 2 ). In K16, r identified the regions Italy, Turkey, and the rest of pan-Europe as Other. This grouping ensured that each of the regions had sufficient data to estimate statistically reliable c 3,r. Accordingly, c3 quantified the regional variability of anelastic attenuation when RESORCE dataset is grouped into Italy, Turkey, and Other. ESM contains much more data from Italy and Turkey, and also several other nations. Since the number of regions is 15 times (46 regions) that of K16 (three regions), the quantified regional variability in anelastic attenuation, in terms of c3 , is also larger than that of K16.
Regression method adapted to large datasets Unlike K16, where the GMM is regressed using an ordinary least-squares mixed-effects regression algorithm  (Bates et al. 2014) , in this study we employ a robust mixed-effects regression algorithm  (Koller 2016). As any other real-life data, ground-motion data may contain outliers and other contaminations. Even minor contamination may drive the classical ordinary least-square mixed-effect estimates away from those without contamination. Robust linear mixed-effects (rlmm) regression is then quite useful in limiting the influence of outlier events, sites, and records on the GMM median and variances. It is important to note that, these outlier data points are not entirely removed from the GMM regression but are simply down-weighted. A feature of rlmm relevant to this study is that the random-effects (attenuation regions, events, event localities, and sites) and residuals (records) with values beyond ± 1.
Which of the 46 regions in pan- Europe apparently attenuate faster or slower than pan-European average (with c 3,r = 0 ) will be discussed in the following sections, and elaborated in a follow-up study. Effect of In Fig. 5 , we also show the epistemic uncertainty on median predictions. The orange ribbon is almost too thin to be noticeable for M4 and M5. 5 predictions. Only for M7 and larger events, the ribbon is visibly wide because of the limited data from large magnitude events at near-source distances in the ESM dataset, and the uncertainty on the coefficient b 3 , which describes the magnitude scaling beyond M h = 6. 2.
A few previous GMMs observed the same with various datasets. Boore et al. (2014)  and K16 allowed oversaturation at large magnitudes ( M W ≥ 6. 75) , but whether this is realistic or not needs to be verified with specially compiled near-source ground-motion datasets, e. g. the one recently made available by  Pacor et al. (2018). The oversaturation effect, however, disappears at longer periods, as shown here for SA(T = 1 s). Figure  6  shows the predicted response spectra for the same scenarios shown in Fig. 5. The effect of event depth and regionalized anelastic attenuation are the same as in Fig. 5. An interesting feature is that the peak of the response spectra shifts towards longer periods for larger magnitude events, reflecting the decreasing corner-frequency with increasing magnitude of a Brune (1970) source spectra.
Random-effects and residuals Figure  7  shows the random-effect and residual standard-deviations of the GMM. The total ergodic standard-deviation of the GMM is = √ 2 L2L + 2 0 + 2 S2S + 2 , when considering all source and site variabilities as aleatory. The solid lines in this plot correspond to the variance estimates of the GMM from ESM dataset (this study), while the dashed lines indicate those of the K16 GMM from RESORCE dataset. Note that the K16 GMM does not regionalise the events, and thus between-locality variability component L2L is absent. For the sake of comparison between the two models, we combine the two source related random-variances of this study into the traditional between-event variability = √ 2 L2L + 2 0 We show that the total standard-deviation σ of the new GMM is considerably larger than that of K16 GMM.
Anelastic attenuation variability Figure  8  shows the region-specific c 3,r adjustments (coloured lines and ribbons) of the 45 regions for T = 0. 01 -8 s. Most of these curves lay within the ± c3 (red lines) bounds. As indicated by c3 , the region-to-region variability of apparent anelastic attenuation is the highest at short periods, and decreases gradually towards longer periods. High-frequency ground-motions are more susceptible to strong anelastic decay in the crust, which could be related to the crustal properties. Long period SAs are not effected as much by anelastic attenuation, therefore regional differences are relatively smaller, and c3 is smaller by ~ 50% at T ≥ 1 s. In RESORCE dataset, K16 observed that short period SAs attenuate faster in Italy than in Turkey, which was observed earlier in NGA-West2 dataset by  Boore et al.
Similarly, there is an apparent distinction between the central Apennines and Po-plain earthquakes. The central Apennines tectonic locality contains the recent M6. 5 Norcia earthquake (2016) and associated shocks, and several historic earthquakes in that region. The Po-plain locality contains data from the substantially stronger M6. 45 Friuli earthquake (1976) and a few recent earthquakes. At a first glance, it may appear as if the spatial patterns are due to the predominant focal mechanisms in the regions, but the diversity of focal mechanisms within each region (especially among smaller events) dissuades this hypothesis. Spectral decomposition of the ESM dataset by Bindi and Kotha (2020) revealed a much larger stress-drop of the M6. 45 Friuli earthquake compared to M6. 5 Norcia earthquake-the δL2L l of regions containing these earthquakes also con- trast similarly  (Bindi et al.
2019). Once again, correlating the random-effects obtained in response spectra domain (as here) to the earthquake parameters (such as stress-drop) Fig. 11  Variation of L2L l across the 56 tectonic locality (l) for T = 0. 1 s (top panel) and T = 1 s (bottom panel). Blue polygons identify tectonic localities that produced earthquakes less energetic than the pan-European average, red polygons identify localities with more energetic earthquakes, and white polygons are localities close to the average. Tectonic localities with fewer ground-motion observations, thereby larger epistemic error on L2L l , are more transparent and appear white. Note that the color scale is limited to vary between ±3 * L2L (T) for each period T 1 3 may not hold well at short periods. We reserve further elaboration of random-effects with the Fourier version of the ESM dataset.
In this study, we use the typical style-of-faulting classification provided in the ESM dataset: Normal (NF), Thrust (TF), Strike-slip (SS), and Unknown (U). However, instead of introducing a style-of-faulting random-effect in the GMM regression, we queried the ΔB 0 e and ΔL2L distributions and found no systematic differences. This result is consistent with the fact that style-of-faulting factors cannot be quantified using isotropic factors with no azimuthal variations  (Kotha et al. 2019). Centroid Moment Tensor (CMT) solutions are however available for too few events to treat style-of-faulting with azimuth dependent factors as in  Kotha et al. (2019). Even if available, the diversity of crustal structure across pan-Europe makes it difficult, without substantially more metadata (e. g. take-off angles) in the ESM dataset The left column of Fig.
2019; Weatherill et al. 2020a, b). Given the importance of site-response in hazard and risk assessments, the complexities in finding a compromise between a site-response proxy, its availability at different regional scales, and the propagation of uncertainties from GMMs to risk assessments, we intend to perform a separate investigation. Nevertheless, a database of S2S s (T = 0. 01 -8s) for the 1829 ESM sites along with their robust regression weights and standard-errors is provided. Note that we always use the measured V S30 in the analysis shown in this study, and never the inferred values. Early analyses of S2S s (T = 0. 0, 0. 1, 1. 0 s) trends with measured V S30 and topographic slope are presented in Fig. 13. The left column of Fig. 13  plots the mean and MAD (robust standard-deviation estimate) of S2S s (T = 0.
0, 0. 1, 1. 0 s) within ranges of V S30 coinciding with Eurocode8 site classes A, B, C, and D, and slope divided into 9 bins of equal width between 0. 001 and 1. 000 m m -1. Trend with V S30 (not measured but) inferred from a V S30 ∼ slope correlation model of Wald and Allen (2007) is comparable to the trend with slope from which it is inferred in the right column of Fig. 13. The correlation S2S s ∼ V S30 (measured only) at short-periods T = 0. 0, 0. 1 s is rather poor, as indicated by similar mean and MAD (the robust standard-deviation) for V S30 < 800 m/s in the left column of Fig. 13. It appears that short-period site-responses of soft rock, stiff, and soft soils (EC8 class B, C, D) in this dataset are not adequately distinguishable. However, it is interesting to see that the mean of EC8 class A 'rock' sites with V S30 ≥ 800 m/s is much lower than the rest, along with a considerably larger vari- ability.
The blue line represents the parametric forms (Eqs. 6 and 7) fit to the data For completeness, along with a database of S2S s (T = 0. 0 -8. 0 s) for the 1829 ESM sites, we derive a continuous empirical models for both S2S s ∼ V S30 (measured) and S2S s ∼ slope correlations seen in Fig. 13. We chose a quadratic functional form instead of the traditional piecewise linear function; as shown in Eqs. (  6 ) and (  7 ). Here, the measured V S30 is in m s -1 and slope in m m -1 , the regression coefficients g 0 , g 1 , g 2 are different for SR V s30 and SR slope , and change with period. The residuals from S2S s ∼ V S30 (measured) and S2S s ∼ slope regressions are S2S Robust linear fits using an M estimator  (Venables and Ripley 2002) , at each of 34 periods between T = 0. 01s -8 s, PGA(T = 0 s) and PGV, are derived for δS2S s ∼ V S30 cor- relation of 419 sites with measured V S30 available, and δS2S s ∼ slope of the 1829 sites with slope derived from digital elevation models provided by Shuttle Radar Topography Mission.
1 + g 2 ⋅ ln slope 0. 1 2 Fig. 14  Between-site and total variance estimates ( S2S , ) of the GMM for T = 0. 01-8 s (solid lines) compared with those of K16 GMM from RESORCE dataset (dashed lines). Reduction of ( S2S , ) to ( V S30 S2S , V S30 ) using V S30 , and to ( slope S2S , slope ) using Slope as site-response proxies is shown here. Note that the ( S2S , ) of K16 GMM are those using V S30 as site-response proxy, and are smaller than those shown in Fig. 7 , which are from the K16 GMM version without a site-response parameter A significant reduction in between-site standard-deviation can be achieved using an efficient site-response proxy or a combination of proxies. Since only a few sites are provided with measured V S30 values, V S30 S2S is substantially smaller than slope S2S. For a new site with V S30 or slope available, Eq.
Aleatory variability The last component of the GMM is the apparent aleatory variability, quantifying the natural randomness of the ground-motion data-that which is not captured by the mixedeffects. The aleatory = N 0, 2 , are tested for event depth and recording site distance dependencies in Fig. 15. For all periods ( T = 0. 0, 0. 1, 1. 0 s ), we observe no significant trends in binned means and MAD, which implies the f R,g M W , R JB and f R,a R JB of the GMM (Eqs. 2 and 3) explain the distance scaling of ground-motion suf- ficiently well. Heteroscedasticity of is not evident either in these plots. The moderate-long period residuals of this dataset show a clear evidence of anisotropic shear-wave radiation pattern in the near and intermediate distance ranges of R JB ≤ 80 km , similar to that reported by Kotha et al.
(2019) with the KiK-net (Dawood  et al. 2016; Kotha et al. 2018) and NGA-W2  (Ancheta et al. 2014; Boore et al. 2014)  datasets and their GMMs. In addition, there is a strong evidence of SmS phases from Moho reflection in the 60 km < R JB ≤ 200 km distance ranges, e. g. Bindi et al. (2006), especially from events deeper than 10 km and close to Moho boundary. However, we chose not to discuss these features in the response spectral domain in which the GMM residuals are estimated, but instead with those in the Fourier domain as a follow-up.
Region-and site-specific application Partially non-ergodic region-and site-specific ground-motion predictions are possible for those sites with S2S s provided with this GMM or for new sites with sufficient ground- motion data. S2S s for the 1829 sites in the ESM dataset are provided, along with the c 3,r Fig. 16  Reduction in total standard deviation estimates (σ) of the GMM for T = 0. 01-8 s (solid lines) from ergodic with and without site-response proxy, region-specific with and without site-response proxy, and region-and site-specific values. Standard deviation estimates when using site-response proxies are indicated by the annotations with corresponding superscript, i. e. ( V S30 ) with V S30 , and to ( slope ) with slope. Annotations with subscript r correspond to the variances for regionalized predictions, i.
Towards non-ergodic ground-motion predictions For tectonic localities, attenuating regions, and sites with sufficient amount of recordings the epistemic uncertainty on the random-effect adjustments are negligible with respect to the random-effect and standard-deviations. Collecting more ground-motion recordings is principal in moving towards non-ergodic predictions. The benefits in resolving the ergodic assumption and progressing towards region-and site-specific in ground-motion prediction is demonstrated Fig. 17. In this plot, predictions for the M6. 5 Norcia event of the central Italy sequence, which occurred on 30th October, 2016, are compared to the response spectra recorded at three sites covered by the Italian strong motion network  (Gorini et al. 2010 ). These sites are identified by the network code IT in the ESM dataset: (1) permanent, freefield station LSS (Leonessa) with V S30 = 1091 m/s located 25 km from the event epicentre, (2) permanent, free-field station MVB (Marsciano Monte Vibiano) with V S30 = 1046 m/s located 65 km from the event epicentre and, (3) permanent, free-field station PSC (Pescasseroli) with V S30 = 1000 m/s located 110 km from the event epicentre.
e. , regionalisation of anelastic attenuation, localisation event terms, and the site-to-site random-effect To perform the cross-validation, the dataset is split into 10 parts with non-overlapping events. Meaning, earthquakes (and records) are exclusive to their subsets and do not feature in any other subset. We perform regression of the three models on any nine subsets combined, and test the predictions on the tenth subset. Root-mean-squarederrors are estimated for each trial and then averaged over the ten trials. This exercise is repeated for IMs:PGA, PGV, SA(T = 0. 1, 0. 2, 0. 5, 1, 2, 4 s). Figure  18  shows a histogram of RMSE for each IM, from the three models. The reduction in RMSE from ergodic to region-specific GMMs is clear and most prominent at short periods. Towards longer periods, the improvement is less pronounced but is still substantial.
Summary In this study, we present the update of the  Kotha et al. (2016)  region-and site-specific GMM, using the recently published European Strong Motion dataset (ESM) by  Lanzano et al. (2019a)  and  Bindi et al. (2018b). The update derived from a dataset 15 times larger than the older RESORCE dataset  (Akkar et al. 2014b) , with only minor changes to fixedeffects formula, and one additional random-effect grouping. Leveraging on the recently developed regionalisation datasets, TSUMAPS-NEAM by  Basili et al. (2019)  and the tectonic zonation defined in the purview of European Seismic Hazard Model 2020 project, we have regionalised the ground-motion data to capture spatial variation of anelastic attenuation and earthquake characteristics, respectively. Due to the increased spatial and temporal diversity of the data, the random-effect and residual variances are larger than the older pan-European GMMs.
This increased variance motivates the development of region-dependent ground-motion models since a larger ergodic σ will severely affect the probabilistic seismic hazard and risk assessments. We propose making use of the attenuation region, tectonic locality, and site-specific terms (random-effects), along with their epistemic uncertainty, to develop partially non-ergodic ground-motion predictions with a reduced σ. The improved predictive power of the regionand site-specific version of the GMM is substantiated with a 10-fold cross-validation. All the random-effect values will be provided on request, along with the fixed-effects Only the customary random-effect and residual analyses are presented here, leaving out their physical evaluation for a follow-up study. A similar exercise is actually carried out on the Fourier amplitude version of the ESM dataset.
Random-effects and residuals of the Fourier GMM are easier to associate to geophysical and geological parameters that are not involved in the GMM regression. If indeed the spatial variability of source, path, and site random-effects can be attributed to a globally available parameter, e. g. a crustal velocity map, we can attempt migrating the GMM developed from seismically active regions to the less active regions with sparse ground-motion recordings. The GMM is developed with the intention of perpetual updates, when and wherever new datasets become available. Further elaboration on random-effect analyses, GMM update and application procedures are ongoing, and will complement this study. Meanwhile, this new GMM has the widest applicability yet for the pan-European region; as it is derived from a dataset stretching from Pyrenees in the west to Iran in the east, Rhine-Graben in the north to Hellenic arc in the south, and constituted of manually processed ground-motion data shallow crustal earthquakes of 3 ≤ M W ≤ 7.