Analog-to-digital conversion of time-history data can lead to amplitude truncation (known as clipping) in cases in which a signal exceeds the full-scale range of the digitizer. When digitizers are configured, it is necessary to balance two competing requirements: maximizing instruments’ full-scale range for recording large amplitudes, while ensuring weak signals are recorded with sufficient resolution. An example of this trade-off is seen when recording earthquake ground motions, which span several orders of magnitude: from weak-motion teleseisms or local microseismicity, through to large-magnitude earthquakes. This can lead to clipped records in which strong ground motions saturate instruments configured to record weak signals. To address this problem, we present a new approach: nonstationary clipped waveform restoration, CWRΔt. The approach is capable of reconstructing even severely clipped waveforms, truncated at as low as 10% of true peak motion. The approach is based on “Projection onto Convex Sets” and builds upon the stationary and deterministic CWR method of Zhang et al. (2016). A key advantage of the method presented herein lies with the characterization of temporal uncertainties that can be propagated into other domains such as Fourier, power spectral density, or damped pseudospectral amplitude. We benchmark the method against artificially clipped ground-motion records from the European Engineering Strong-Motion database. A significant improvement is demonstrated with respect to the original CWR method. Clipped records from the 1992 ML 5.8 Roermond, Netherlands, earthquake are then reconstructed. This key event in northwestern Europe stands as the region’s largest instrumentally recorded earthquake, but from which the majority of records have, until now, been unusable. We successfully verify the CWRΔt approach against ground-truth records through comparison with collocated clipped/unclipped records from high- and low-gain instruments at three sites. Finally, we apply the method to clipped records in the epicentral region, providing the tool to develop a key data set for engineering seismology in Europe. The proposed method has a multitude of applications beyond seismology, being generalizable to any time-history data.

An optimisation-based calibration technique, using the area metric, is applied to determine the input parameters of a stochastic earthquake-waveform simulation method. The calibration algorithm updates a model prior, specifically an estimate of a region’s seismological (source, path and site) parameters, typically developed using waveform data, or existing models, from a wide range of sources. In the absence of calibration, this can result in overestimates of a target region’s ground motion variability, and in some cases, introduce biases. The proposed method simultaneously attains optimum estimates of median, range and distribution (uncertainty) of these seismological parameters, and resultant ground motions, for a specific target region, through calibration of physically constrained parametric models to local ground motion data. We apply the method to Italy, a region of moderate seismicity, using response spectra recorded in the European Strong Motion (ESM) dataset. As a prior, we utilise independent seismological models developed using strong motion data across a wider European context. The calibration obtains values of each seismological parameter considered (such as, but not limited to, quality factor, geometrical spreading and stress drop), to develop a suite of optimal parameters for locally adjusted stochastic ground motion simulation. We consider both the epistemic and aleatory variability associated with the calibration process. We were able to reduce the area metric (misfit) value by up to 88% for the simulations using updated parameters, compared to the initial prior. This framework for the calibration and updating of seismological models can help achieve robust and transparent regionally adjusted estimates of ground motion, and to consider epistemic uncertainty through correlated parameters.

ABSTRACT In August 2019, a multistage hydraulic fracturing (HF) operation was carried out at Preston New Road, United Kingdom. HF caused abundant seismic activity that culminated with an ML 2.9 event. The seismic activity was recorded by a downhole array of 12 sensors located in a nearby monitoring well. About 55,556 events were detected and located in real time during the operation by a service company. In this study, we first improve the number of detections by applying template matching and later calculate the moment magnitude of the associated earthquakes. Then we show that by separately analyzing the periods during and immediately after injection, distinct patterns can be identified. We observe an increase in the delay and decrease in amplitude of peak seismicity during subsequent phases of injection. After injection, the seismicity decay can be described by the Omori–Utsu law. The decay rate tends to slow with each successive injection, in particular during the later injection stages. In addition, the frequency–magnitude distribution evolves from a tapered distribution (lack of large events) to a bilinear distribution (excess of large events). This evolution is gradual, with the corner magnitude increasing with each injection. We interpret these patterns as the result of the combined effect of two factors: (1) the stimulated volume becoming increasingly aseismic and (2) the gradual increase in its size, which increases the probability of triggered events on preexisting faults. More generally, these patterns suggest that seismic activity during injection is strongly influenced by the injection history and is modulated by local conditions such as stress state, fault structure, and permeability.

Predicting ground motions due to induced seismicity is a challenging task owing to the scarcity of data and heterogeneity of the uppermost crust. Dealing with this requires a thorough understanding of the underlying physics and consideration of inter-site variability. The most common ground motion model used in practice is the parametric ground motion prediction equation (GMPE), of which hundreds exist in the literature. However, relatively few are developed with a focus on induced seismicity. Developing GMPEs that are specific to an appropriate magnitude-distance range (R < 30 km; 2 < M <6) is important for induced seismicity applications. This paper proposes a framework for the development of physically-based GMPEs to provide more accurate and reliable estimates of the potential induced-seismicity ground motion hazard, allowing for better risk assessment and management strategies. To demonstrate this approach, a new set of GMPEs for the 2018-2019 induced seismicity sequence at the Preston New Road (PNR) shale gas site near Blackpool, United Kingdom, is presented. The physically-based GMPE was developed based on a pseudo-finite-fault stochastic ground motion simulation, calibrated with parameters derived from the spectral analysis of weak-motion records from induced seismic events. An optimization-based calibration technique using the area metric (AM) was subsequently performed to calibrate optimal parameters for simulating ground motion at the PNR site. Finally, using a suite of forward simulations for events with 1 < M < 6 recorded at distances up to 30 km, combined with empirical data, a location-specific GMPE was derived through adjustment of an existing model.

A Bayesian framework to characterize ground motions even in the presence of missing data is developed. This approach features the combination of seismological knowledge ( a priori knowledge ) with empirical observations (even incomplete) via Bayesian inference. At its core is a Bayesian neural network model that probabilistically learns temporal patterns from ground motion data. Uncertainties are accounted for throughout the framework. Performance of the approach has been quantitatively demonstrated via various missing data scenarios. This framework provides a general solution to dealing with missing data in ground motion records by providing various forms of representation of ground motions in a probabilistic manner, allowing it to be adopted for numerous engineering and seismological applications. Notably, it is compatible with the versatile Monte Carlo simulation scheme, such that stochastic dynamic analyses are still achievable even with missing data. Furthermore, it serves as a complementary approach to current stochastic ground‐motion models in data‐scarce regions under the growing interests of PBEE (performance‐based earthquake engineering), mitigating the data‐model dependence dilemma due to the paucity of data, and ultimately, as a fundamental solution to the limited data problem in data scarce regions.

Ground motions from shallow induced earthquakes and tectonic seismicity were investigated in this study by directly modeling the seismic attenuation quality factor (Q) using spectral fitting and coda envelope decay methods. We use data from the Preston New Road (PNR) shale gas induced seismicity sequences near Blackpool, United Kingdom, in 2018 and 2019, in addition to regional tectonic events in the United Kingdom. Our results show that the local Q obtained from the induced seismic sequences at PNR, attributed to shallower layers in the crust, leads to a rapid rate of near-field decay (sudden loss in amplitude of earthquake signal over a short distances), with significantly stronger attenuation than observed for regional events. We furthermore find that estimates of Q are nonunique to a given record, differing both with the method and the analysis windows used, particularly at high frequency. These differences can be attributed to the different modeling methodologies (e.g., different assumptions) or to fundamental differences in physical attenuation processes within the seismic wavefield itself, which traverses multiple ray paths and comprises various phases. Our results indicate that to model ground motions for shallow earthquakes, it is important to consider the composite Q along a specific path rather than an average regional Q. To this extent, a depth-dependent attenuation model is considered crucial to bridge the gap between shallow induced earthquakes and tectonic seismicity.

SUMMARY Strong ground motion prediction is a fundamental topic in the field of engineering seismology, as it provides the input for seismic hazard studies as well as for vulnerability and risk assessment. The spectral modelling approach can provide a realistic representation of ground motion behaviour, possibly including its frequency variability, as the full ground motion spectrum is modelled analytically. In its parametric form, this approach requires a careful calibration of the model, starting from empirical observations and fitting the source, path and the site-specific response assuming a predefined physically constrained functional form. This study explores the use of spectral modelling for a study area in northeast Italy, at the border with Slovenia and Austria. It is based on the parametrization of seismic source and attenuation effects, and it also allows to estimate site effects, as a by-product. The main innovation with respect to standard spectral modelling is the inclusion of dedicated uncertainty estimators in the functional form. Parametric inversion of source and path attenuation is performed on a data set corresponding to 23 events recorded by 24 stations located within the target area. The modular inversion setup allows to properly include a priori constraints in the mathematical solution to reduce trade-off between variables. Spectral amplification at each site is defined with respect to the network average rock condition, and its frequency-dependent component is estimated from residual analysis after the inversion. Inverted source parameters are comparable with reference values for the region available from literature (with seismic moments between ${10}^{13}$ and ${10}^{15}$ N·m, and related stress drop values in the range $1.5 - 15.5\ {\rm{MPa}}$); the same is also true for average attenuation properties (e.g. apparent frequency-independent attenuation quality factor ${Q}_0$ of $1145$). For a selection of stations with available characterization based on different methods, a preliminary comparison of site-specific response functions shows that both the frequency value and amplitude of the main amplification peaks are well recovered. These encouraging results open to the possibility of subsequently using the calibrated model for forward modelling purposes.

A novel Bayesian Augmented-Learning framework, quantifying the uncertainty of spectral representations of stochastic processes in the presence of missing data, is developed. The approach combines additional information (prior domain knowledge) of the physical processes with real, yet incomplete, observations. Bayesian deep learning models are trained to learn the underlying stochastic process, probabilistically capturing temporal dynamics, from the physics-based pre-simulated data. An ensemble of time domain reconstructions are provided through recurrent computations using the learned Bayesian models. Models are characterized by the posterior distribution of model parameters, whereby uncertainties over learned models, reconstructions and spectral representations are all quantified. In particular, three recurrent neural network architectures, (namely long short-term memory, or LSTM, LSTM-Autoencoder, LSTM-Autoencoder with teacher forcing mechanism), which are implemented in a Bayesian framework through stochastic variational inference, are investigated and compared under many missing data scenarios. An example from stochastic dynamics pertaining to the characterization of earthquake-induced stochastic excitations even when the source load data records are incomplete is used to illustrate the framework. Results highlight the superiority of the proposed approach, which adopts additional information, and the versatility of outputting many forms of results in a probabilistic manner.

Stochastic processes are widely adopted in many domains to deal with problems which are stochastic in nature and involve strong nonlinearity, nonstationarity and uncertain system parameters.However, the uncertainties of spectral representation of the underlying stochastic processes have not been adequately acknowledged due to the data problems in practice, for instance, missing data.Therefore, this paper proposes a novel method for uncertainty quantification of spectral representation in the presence of missing data using Bayesian deep learning models.A range of missing levels are tested.An example in stochastic dynamics is employed for illustration.

Induced earthquakes pose a serious hurdle to subsurface energy development. Concerns about induced seismicity led to terminal public opposition of hydraulic fracturing in the UK. Traffic light protocols (TLPs) are typically used to manage these risks, with the red-light designed as the last-possible stopping-point before exceeding a risk tolerance. We simulate trailing earthquake scenarios for the UK, focusing on three risk metrics: nuisance, damage, and local personal risk (LPR) – the likelihood of building collapse fatality for an individual. The severity of these risks can spatially vary (by orders-of-magnitude), depending on exposure. Estimated risks from the Preston New Road earthquakes are used to calibrate our UK earthquake risk tolerances, which we find to be comparable to Albertan (Canadian) tolerances. We find that nuisance and damage concerns supersede those from fatality and that the safest regions for Bowland Shale development would be along the east coast. A retrospective comparison of our TLP result with the Preston New Road case highlights the importance of red-light thresholds that adapt to new information. Overall, our findings provide recommendations for red-light thresholds (ML 2-2.5) and proactive management of induced seismicity – regardless of anthropogenic source.

A spectral decomposition of the Fourier amplitude spectra is applied to determine the source parameters of earthquakes (source spectral shape, stress drop) that have occurred in central-southern Europe. About 52 million waveforms recorded in the target area since the late ‘90s have been downloaded from the European Integrated Data Archive (EIDA) within the tool stream2segment (Zaccarelli et al., 2019), by using the event catalog of the International Seismic Centre (ISC) and innovative data quality assessment. A non-parametric decomposition approach in this study introduced a regionalization for the attenuation models into two spatial domains (southern and “active” Europe, northern and “stable” Europe). For each domain, a spectral attenuation with hypocentral distance model is simultaneously determined and used to remove regional specific propagation effects from the spectra of recordings. Once isolated from local site effects, the obtained source spectra of 4380 earthquakes of magnitude larger than 2.5 are fitted to a standard -model to determine the seismic moment, corner frequency and Brune stress drop. The scaling relationship, spatial variation and variability of these source parameters are finally derived and discussed.

SUMMARY Seismic damping of near-surface deposits is an important input to site-response analysis for seismic hazard assessment. In Groningen, the Netherlands, gas production from a reservoir at 3 km depth causes seismicity. Above the gas field, an 800 m thick layer of unconsolidated sediments exist, which consists of a mixture of sand, gravel, clay and peat strata. Shear waves induced at 3 km depth experience most of their anelastic attenuation in these loose sediments. A good estimate of damping is therefore crucial for modelling realistic ground-motion levels. In Groningen, we take advantage of a large network of 200 m deep vertical arrays to estimate damping from recordings of the induced events. As a first step, we apply seismic interferometry by deconvolution to estimate local transfer functions over these vertical arrays. Subsequently, two different methods are employed. The first is the ‘upgoing’ method, where the amplitude decay of the retrieved upgoing wave is used. The second is the ‘up-down’ method, where the amplitude difference between retrieved up- and downgoing waves is utilized. For the upgoing method, the amplitude of the upgoing direct wave is affected by both elastic and anelastic effects. In order to estimate the anelastic attenuation, it is necessary to remove the elastic amplification first. Despite the fact that elastic compensation could be determined quite accurately, non-physical damping values were estimated for a number of boreholes. Likely, the underlying cause was small differences in effective response functions of geophones at different depths. It was found that the up-down method is more robust. With this method, elastic propagation corrections are not needed. In addition, small differences in in situ geophone response are irrelevant because the up- and downgoing waves retrieved at the same geophone are used. For the 1-D case, we showed that for estimating the local transfer function, the complex reverberations need to be included in the interferometric process. Only when this is done, the transfer function does not contain elastic transmission loss and Q estimation can be made without knowing the soil profile in detail. Uncertainty in the estimated damping was found from the signal-to-noise ratio of the estimated transfer function. The Q profiles estimated with the up-down method were used to derive a damping model for the top 200 m of the entire Groningen field. A scaling relation was derived by comparing estimated Q profiles with low-strain damping profiles that were constructed using published models for low-strain damping linked to soil properties. This scaling relation, together with the soil-property-based damping model, allowed up-scaling of the model to each grid-cell in the Groningen field. For depths below 200 m, damping was derived from the attenuation of the microseism over Groningen. The mean damping model, over a frequency band between 2 and 20 Hz, was estimated to be 2.0 per cent (0–50 m depth), 1.3 per cent (50–100 m), 0.66 per cent (100–150 m), 0.57 per cent (150–200 m) and 0.5 per cent (200–580 m).

ABSTRACT Generalized inversion techniques (GITs) have become popular for determining seismological parameters (e.g., source, attenuation, and site response), particularly in low-to-moderate seismicity regions. Indeed, GITs can potentially provide reliable site-response estimates when a minimum number of recordings is available, as well as valuable information about source parameters and regional attenuation characteristics. Significant advances have been made on GITs in which different approaches and hypotheses were investigated, such as the application of “nonparametric” and “parametric” inversion schemes. In this context, several scientific questions have arisen that depend on the final scope of the GITs: What is the optimal inversion strategy for a given dataset configuration? What is the impact of the different choices, assumptions, and implementations on the reliability of the results? Is it possible to quantify the associated epistemic uncertainties? Here, we have considered and compared the different approaches of GITs to improve the understanding of each for use in different applications. A methodological benchmark that includes different GIT methods and dataset configurations is set up to fulfill the objective, using a simple synthetic dataset, a French regional sparse dataset, and an Italian national dense dataset. The benchmark is developed in two phases: (1) phase I: a free phase with no common constraints; and (2) phase II: a constrained phase with unified reference conditions. Despite unifying the reference conditions in the different inversions, the variability was not reduced. Discrepancies are observed between different terms of GITs. Site responses appear to be the most robust estimates, compared to source and attenuation terms. The way that stress drops of earthquakes and quality factors for crustal attenuation are parameterized appears to lead to significant variability between different approaches. Finally, uncertainties are addressed by quantification of the inter-method variability for the different terms and parameters.

Small-magnitude earthquakes induced by gas production in the Groningen field in the Netherlands have prompted the development of seismic risk models that serve both to estimate the impact of these events and to explore the efficacy of different risk mitigation strategies. A core element of the risk modelling is ground-motion prediction models (GMPM) derived from an extensive database of recordings obtained from a dense network of accelerographs installed in the field. For the verification of damage claims, an empirical GMPM for peak ground velocity (PGV) has been developed, which predicts horizontal PGV as a function of local magnitude, M L ; hypocentral distance, R hyp ; and the time-averaged shear-wave velocity over the upper 30 m, V S30 . For modelling the risk due to potential induced and triggered earthquakes of larger magnitude, a GMPM for response spectral accelerations has been developed from regressions on the outputs from finite-rupture simulations of motions at a deeply buried rock horizon. The GMPM for rock motions is coupled with a zonation map defining frequency-dependent non-linear amplification factors to obtain estimates of surface motions in the region of thick deposits of soft soils. The GMPM for spectral accelerations is formulated within a logic-tree framework to capture the epistemic uncertainty associated with extrapolation from recordings of events of M L ≤ 3.6 to much larger magnitudes.

We introduce the cumulative-distribution-based area metric (AM)—also known as stochastic AM—as a scoring metric for earthquake ground-motion models (GMMs). The AM quantitatively informs the user of the degree to which observed or test data fit with a given model, providing a rankable absolute measure of misfit. The AM considers underlying data distributions and model uncertainties without any assumption of form. We apply this metric, along with existing testing methods, to four GMMs in order to test their performance using earthquake ground-motion data from the Preston New Road (United Kingdom) induced seismicity sequences in 2018 and 2019. An advantage of the proposed approach is its applicability to sparse datasets. We, therefore, focus on the ranking of models for discrete ranges of magnitude and distance, some of which have few data points. The variable performance of models in different ranges of the data reveals the importance of considering alternative models. We extend the ranking of GMMs through analysis of intermodel variations of the candidate models over different ranges of magnitude and distance using the AM. We find the intermodel AM can be a useful tool for selection of models for the logic-tree framework in seismic-hazard analysis. Overall, the AM is shown to be efficient and robust in the process of selection and ranking of GMMs for various applications, particularly for sparse and small-sized datasets.

Monitoring small magnitude induced seismicity requires a dense network of seismic stations and high-quality recordings in order to precisely determine events’ hypocentral parameters and mechanisms. However, microseismicity (e.g. swarm activity) can also occur in an area where a dense network is unavailable and recordings are limited to a few seismic stations at the surface. In this case, using advanced event detection techniques such as template matching can help to detect small magnitude shallow seismic events and give insights about the ongoing process at the subsurface giving rise to microseismicity. In this paper, we study shallow microseismic events caused by hydrofracking of the PNR-2 well near Blackpool, UK, in 2019 using recordings of a seismic network which was not designed to detect and locate such small events. By utilizing a sparse network of surface stations, small seismic events are detected using template matching technique. In addition, we apply a full-waveform moment tensor inversion to study the focal mechanisms of larger events ( M L > 1) and used the double-difference location technique for events with high-quality and similar waveforms to obtain accurate relative locations. During the stimulation period, temporal changes in event detection rate were in agreement with injection times. Focal mechanisms of the events with high-quality recordings at multiple stations indicate a strike-slip mechanism, while a cross-section of 34 relocated events matches the dip angle of the active fault.

To better characterize seismic hazard, particularly, for induced seismicity, there is an increasing interest in methods to estimate moment magnitude (M-w) for small earthquakes. M-w is generally preferred over other magnitude types, but, it is difficult to estimateMw for earthquakes with localmagnitude (M-L) < 3-3:5, using conventionalmoment tensor (MT) inversion. The 2020 M-ww 5.7 Magna, Utah, seismic sequence provides an opportunity to illustrate and evaluate the value of spectral methods for this purpose. Starting with a high-quality seismic catalog of 2103 earthquakes (M-L < 5.6), we estimate M-w using two independent spectral methods-one based on direct waves, yielding M-w;direct, and the other based on coda waves, yielding M-w,M-coda. For the direct-wave method, we present a non-parametric (NP) inversion scheme that solves for apparent geometrical spreading, G(R), and site effects (S), similar to other NP procedures that have been used to calibrate regional M-L scales. The NP inversion is constrained using M(w)s derived from MTs for nine events in the Magna sequence. We recover statistically robust and physically reasonable G(R) and S and compute M-w,M-direct for 635 Magna earthquakes down to M-L 0.7. For the coda-wave method, we consider two separate calibration schemes involving previous MTsolutions and computeMw; coda for 311 earthquakes down to M-L 1.0. For 280 of the events that were processed with both methods-Mw; direct and M-w,M-coda-are strongly correlated (r = 0.98), with a mean difference of only 0.05. We compareMw; direct and M-w,M-coda withML and find reasonably good agreement for M-L < 3.6with the theoretically predicted relationship of M-w = (2/3)M-L + C, in which C is a regional constant. Our results imply that seismic network operators can use spectral-based Mw estimates to replace M-L estimates for events with M-L >= 1.0, and possibly smaller. The main requirement is the existence of a small number of MT solutions for calibration purposes.

Seismic hazard and risk analyses are increasingly tapping into the previously underused resource of local weak-motion records. This is facilitating the development of local- or even application-specific models for the characterisation of earthquake ground motion. In turn, this offers the opportunity to derive non- or partially non-ergodic models and significantly reduce bias and uncertainty. However, weak-motion data, while carrying important information about local earthquake source, path and site effects, are susceptible to noise. We show that high-frequency noise has a record-, or region-specific, impact on pseudo-spectral acceleration (PSA). This impact depends on the shape of the records’ Fourier amplitude spectrum (FAS): PSA from moderately to highly damped ‘soil’ records (e.g. Groningen, the Netherlands) is much less susceptible to high-frequency noise than PSA from weakly damped ‘rock’ records (e.g. Eastern North America). We make use of simulated ground motion records to develop a parametric model for the lower usable period of PSA ( T min ). The model accounts for the impact of high-frequency noise on PSA, conditional on easily measured parameters characterising the shape of a record’s FAS. We then present a workflow, describing processing undertaken for records of induced seismicity from the Groningen gas field. The workflow includes the definition of maximum and minimum usable frequencies and periods of FAS and PSA, respectively. As part of the workflow, we present an approach that considers multiple estimates of T min . These include the parametric model and, additionally, record-specific hybrid simulations that artificially extend or modify time series’ FAS beyond the noise floor to assess subsequent impacts on PSA.

ABSTRACTHydraulic fracturing of the first shale gas well at Preston New Road (PNR), Blackpool, United Kingdom, in late 2018, marked the end of a 7 yr United Kingdom-wide moratorium on fracking. Despite a strict traffic-light system being in place, seismic events up to ML 2.9 were induced. The ML 2.9 event was accompanied by reports of damage and was assigned European Macroseismic Scale 1998 (EMS-98) intensity VI by the British Geological Survey. The moratorium was subsequently reinstated in late 2019. The study here presents a pseudo-probabilistic seismic risk analysis and is applied to the larger of the induced events at PNR, in addition to hypothetical larger events. Initially, site characterization analysis is undertaken using direct and indirect methods. These analyses show low-velocity deposits dominate the region (VS30‾=227 m/s). We test existing ground-motion prediction equations using spatially dependent VS30 to determine applicability to the recorded waveform data and produce a referenced empirical model. Predicting median and 84th percentile peak ground velocity fields, we subsequently determine macroseismic intensities. Epicentral intensities of IV, IV–V, and VI–VII are predicted for the observed ML 2.9, and hypothetical ML 3.5 and 4.5 scenarios, respectively. A probabilistic analysis of damage is performed for 3500 ground-motion realizations (2.1≤ML≤4.5) using the OpenQuake-engine, with nonlinear dynamic analysis undertaken to define building fragility. Based on these analyses, the onset of cosmetic damage (DS1) in terms of median risk is observed for the ML 2.9 event. Mean modeled occurrences of DS1 and DS2 (minor structural damage), 75 and 10 instances, respectively, are consistent with reported damage (DS1:97, DS2:50). Significant occurrences (median≥30 buildings) of DS2, DS3, and DS4 (minor to major structural damage) are likely for ML 3.5, 4.0, and 4.5 events, respectively. However, by comparing reported damage with modeled damage due to the ML 2.9 event and considering the fact that low macroseismic intensities (EMS-98 <4) are often not reported by the public, we conclude that the previously assigned intensity of VI is too high, with V being more appropriate.

The Swiss Seismological Service (SED; http://www.seismo.ethz.ch) at ETH Zürich is the federal agency in charge of monitoring earthquakes in Switzerland and neighboring areas, and for the assessment of seismic hazard and risk for the region. The SED seismic network largely relies on software and databases integrated in the SeisComP3 monitoring suite for waveform acquisition, automatic and manual event processing, event alerting, web infrastructure, data archiving and dissemination. Data from all digital seismic stations acquired by the SED over the last 30 years - broadband (presently ~230), strong-motion (~185), short-period (~65), permanent and temporary - are homogeneously integrated in the seismic network processing tools and products. Waveform data from the Swiss National Seismic Networks are openly available through the SED website and ORFEUS EIDA / Strong-Motion (http://orfeus-eu.org/data/) data gateways. The SED earthquake catalogue is publicly available through FDSN Event web services at the SED (http://arclink.ethz.ch/fdsnws/event/1/). The Swiss seismic hazard maps are integrated in the EFEHR portal (http://www.efehr.org). The SED is updating its strategy for magnitude determination to make it fully consistent with the state-of-the-art in engineering seismology and seismic hazard studies in Switzerland, and to optimise the use of its dense seismic monitoring infrastructure. Among the planned changes are the: (a) adoption of a new ML relationship applicable in the near-source region at epicentral distances smaller than 15-20 km; (b) inclusion of ML station corrections based on empirically observed (de)amplification with respect to the Swiss reference rock velocity model and associated predictions; (c) seamless computation of Mw based on spectral fitting of recorded FAS using a Swiss specific model. In this contribution we present and discuss the updated magnitude computations for a playback dataset of thousands of recorded earthquakes, and compare them with the current official estimates. We discuss the expected impacts of the new magnitude determination strategy on the SED event processing chain in SeisComP3, the SED catalogues and other seismological products. We welcome community feedback on our planned transition strategy.