Epidemic-type Aftershock Sequence Research Articles

Forecasting future earthquakes with deep neural networks: Application to California

Summary We use the spatial map of the logarithm of past estimated released earthquake energies as input of Fully Convolutional Networks (FCN) to forecast future earthquakes. This model is applied to California and compared with an elaborated version of the Epidemic Type Aftershock Sequence (ETAS) model. Our long-term earthquake forecast simulations show that the FCN model is close to the ETAS model in predicting earthquakes with $M \ge 3.0,\ 4.0,\ {\rm{and\ }}5.0$ according to the Molchan diagram. Moreover, training and implementing the FCN model is 2000∼4000 times faster than calibrating the ETAS model and generating its probabilistic forecasts. The FCN model is straightforward in terms of its neural network structure and feature engineering. It does not require extensive knowledge of statistical seismology or the analysis of earthquake catalog completeness. Using the earthquake catalog with $M \ge 0$ as FCN input can enhance the model's performance in some time-magnitude prediction windows.

Earthquake Predictability and Forecast Evaluation Using Likelihood-Based Marginal and Conditional Scores

Abstract Earthquake probability forecasts are typically based on simulations of seismicity generated by statistical (point process) models or direct calculation when feasible. To systematically assess various aspects of such forecasts, the Collaborative Studies on Earthquake Predictability testing center has utilized N- (number), M- (magnitude), S- (space), conditional likelihood-, and T- (Student’s t) tests to evaluate earthquake forecasts in a gridded space–time range. This article demonstrates the correct use of point process likelihood to evaluate forecast performance covering marginal and conditional scores, such as numbers, occurrence times, locations, magnitudes, and correlations among space–time–magnitude cells. The results suggest that for models that only rely on the internal history but not on external observation to do simulation, such as the epidemic-type aftershock sequence model, test and scoring can be rigorously implemented via the likelihood function. Specifically, gridding the space domain unnecessarily complicates testing, and evaluating spatial forecasting directly via marginal likelihood might be more promising.

Bayesian Earthquake Forecasting Using Gaussian Process Modeling: GP-ETAS Applications

Abstract Numerous seismicity models are known to simulate different observed characteristics of earthquake occurrence successfully. However, their ability of prospective forecasting future events is a priori not always known. The recently proposed semiparametric model, Gaussian process epidemic-type aftershock sequence (GP-ETAS) model, which combines the ETAS model with GP modeling of the background activity, has led to promising results when applied to synthetic seismicity. In this study, we focus on the ability of GP-ETAS for different forecasting experiments in two case studies: first, the Amatrice, Italy, sequence during 2016 and 2017, and second, long-term seismicity in Southern California. The results indicate that GP-ETAS performs well compared with selected benchmark models. The advantages become particularly visible in cases with sparse data, in which GP-ETAS shows in general a more robust behavior compared to other approaches.

What Do We Know Without the Catalog? Eliciting Prior Beliefs from Experts for Aftershock Models

Abstract Fitting parametric seismological models to earthquake catalogs often comes with numerical challenges, especially when catalogs are small. An alternative way to quantify parameter values for a seismic region is by eliciting expert opinions on the seismological characteristics that each parameter corresponds to. For instance, expert beliefs on aftershock patterns can be formulated into prior distributions for aftershock parameters, for example, for the epidemic-type aftershock sequence (ETAS) model. We illustrate such a method by not only eliciting priors for ETAS parameters for the Pacific Northwest (PNW), a subduction zone with a complex tectonic environment, but also a relatively small catalog. We compare these priors with those suggested by the ETAS literature for global subduction zones, discussing implications for aftershock forecasting for the PNW.

Time-Dependent Aftershock Probabilistic Forecasting and Ensemble Model Implementation: Insights from the 2021 Ms 7.4 Maduo Earthquake

Abstract On 22 May 2021, an Ms 7.4 earthquake with a focal depth of 17 km struck the Maduo region of Qinghai province, breaking a 3.8-year quiescence of strong earthquakes (magnitude >7.0) in mainland China. This event has increased stress on the Maqin–Maqu segment of the Kunlunshan fault, heightening the potential for a large earthquake in the region. In this study, we employed the epidemic-type aftershock sequence (ETAS) model and Reasenberg–Jones (R–J) model to fit the aftershock sequence following the mainshock, analyzing the temporal response and its ability to influence future generations. Concurrently, ensemble models were explored to leverage the strengths of both the ETAS and R–J models. Short-term forecasts of the probability and occurrence rate of aftershock events with varying magnitudes were conducted for the next three days. Then statistical methods, that is, receiver-operating characteristic diagrams and information gain, were used to evaluate the unconditional and relative performance. Our findings include that the ETAS model indicates a high decay rate with many aftershocks triggered by previous events, whereas the R–J model shows a normal decay rate with a higher proportion of strong aftershocks. The ETAS and R–J models perform better than random guesses for different aftershock magnitudes, but the ETAS model is somewhat affected by the problem of missing small earthquakes for a short period of time after the mainshock. The ensemble model that chooses the minimum strategy shows promise, especially in the early period, suggesting a reasonable and cautious decision approach should be chosen during the unstable stage. This study highlights the importance of short-term aftershock probability estimation for seismic research and decision-making. In the absence of more accurate models, current analytical approaches within the operational earthquake forecasting framework remain valuable. Continuous testing, feedback, and refinement of forecasting models, along with the development of ensemble models, are essential for enhancing seismic risk assessment and mitigation strategies.

A combining earthquake forecasting model between deep learning and epidemic-type aftershock sequence (ETAS) model

SUMMARY The scientific process of earthquake forecasting involves estimating the probability and intensity of earthquakes in a specific area within a certain timeframe, based on seismic activity features and observational data. Among the various methodologies, epidemic-type aftershock sequence (ETAS) models, rooted in seismic empirical laws, stand as widely used tools for earthquake forecasting. In this study, we introduce the CL-ETAS model, a novel approach that integrates convolutional long short-term memory (ConvLSTM), a deep learning model, with the ETAS model. Specifically, we leverage the forecasting outputs of ETAS to enhance both the training and forecasting processes within the ConvLSTM framework. Through forecasting tests, our findings illustrate the effectiveness of the CL-ETAS model in capturing the trends observed in earthquake numbers ($M \ge 3$) in Southern California following three main shocks. Overall, our model outperforms both a simple ETAS model and ConvLSTM in this context.

A Review of 15 Years of Public Earthquake Forecasting in Aotearoa New Zealand

Abstract New Zealand entered a period of increased seismic activity in 2003, which, to date, has included around 20 large or damaging earthquakes. Building on decades of forecast model research and including research into formal model evaluation, GNS Science, the governmental institution tasked with providing natural hazards information, began issuing public earthquake forecasts following the 2009 Mw 7.2 Darfield earthquake. This article provides a review of the public earthquake forecasting methods and outcomes since that time. Initially the release of forecasts was motivated by the scientific team, but interest, use and understanding quickly rose in the public, government, and private sectors which led to regular refinement and additions to the type of forecast information provided. The basic tenet of forecasting has remained the same, with the forecasts being used to inform decisions requiring consideration of timeframes from days to decades. The operational models were all based on the previous 10–20 years of research in model development and testing; this included the development of methods for combining multiple models into a single hybrid model that typically provides increased statistical forecast skill. The hybrid models combine models from three classes of time horizons: (1) short-term aftershock clustering based on the epidemic-type aftershock sequence class of models, (2) medium-term clustering with time horizons of years to decades, and (3) long-term models with time horizons of decades to nominally time independent. Forecasts have been issued in response to 14 large or significant earthquakes with the forecasts and accompanying information regularly updated on the GeoNet webpages. An important component to the successful deployment and uptake of the models was the foundation laid in the previous decades in model development and testing. This provided understanding and confidence in the models’ ability to provide useful forecasts for response and recovery decisions.

SB-ETAS: using simulation based inference for scalable, likelihood-free inference for the ETAS model of earthquake occurrences

The rapid growth of earthquake catalogs, driven by machine learning-based phase picking and denser seismic networks, calls for the application of a broader range of models to determine whether the new data enhances earthquake forecasting capabilities. Additionally, this growth demands that existing forecasting models efficiently scale to handle the increased data volume. Approximate inference methods such as inlabru, which is based on the Integrated nested Laplace approximation, offer improved computational efficiencies and the ability to perform inference on more complex point-process models compared to traditional MCMC approaches. We present SB-ETAS: a simulation based inference procedure for the epidemic-type aftershock sequence (ETAS) model. This approximate Bayesian method uses sequential neural posterior estimation (SNPE) to learn posterior distributions from simulations, rather than typical MCMC sampling using the likelihood. On synthetic earthquake catalogs, SB-ETAS provides better coverage of ETAS posterior distributions compared with inlabru. Furthermore, we demonstrate that using a simulation based procedure for inference improves the scalability from O(n2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(n^2)$$\\end{document} to O(nlogn)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(n\\log n)$$\\end{document}. This makes it feasible to fit to very large earthquake catalogs, such as one for Southern California dating back to 1981. SB-ETAS can find Bayesian estimates of ETAS parameters for this catalog in less than 10 h on a standard laptop, a task that would have taken over 2 weeks using MCMC. Beyond the standard ETAS model, this simulation based framework allows earthquake modellers to define and infer parameters for much more complex models by removing the need to define a likelihood function.

Non-Stationary ETAS Model: How It Works for External Forcing

Abstract The stationary epidemic-type aftershock sequence (ETAS) model is based on the important empirical laws in aftershock statistics with a self-similar feature, and is therefore useful for the statistical analysis of many common earthquake occurrence series. However, because earthquake catalogs become richer, the non-stationarity arising from geophysical heterogeneity becomes more pronounced. This article discusses the utility of a model that assumes time dependence of the first two ETAS parameters, which are sensitive to the short-term prediction. The inversion analyses of the non-stationary ETAS model in this article show heuristic results for swarm earthquakes and complex mainshock–aftershock-type seismicity. The case studies demonstrate how these parameters change quantitatively in swarm seismicity associated with slow slips, increases in pore-fluid pressure such as magma and hydrothermal fluids, stress changes associated with an earthquake motion, and interseismic-induced effects due to geological properties.

Estimating spatio-temporal variable parameters of Epidemic Type Aftershock Sequence model in a region with limited seismic network coverage: a case study of the East African Rift System

SUMMARY The Epidemic-Type Aftershock Sequence (ETAS) model is currently the most powerful statistical seismicity model that reproduces the general characteristics of earthquake clustering in space and time. However, its application can be hampered by biased parameter estimations related to earthquake catalogue deficiencies, particularly in regions where the spatial coverage of local recording networks is relatively poor. Here, we systematically investigate the possible influences of the effect introduced by data truncation through the choice of the cut-off magnitude (${{m}_{\rm cut}})$ and missing events due to heterogeneity of the seismic network on ETAS parameter estimates along the East African Rift System (EARS). After dividing the region into six source zones based on rheological and mechanical behaviours, the ETAS model is fitted to the earthquakes within each zone using the Davidon–Fletcher–Powell optimization algorithm. The fits and variations in parameter estimates are compared for each zone to the others and the seismological implications are discussed. We found that some parameters vary as a function of ${{m}_{\rm cut}}$ primarily driven by changes in catalogue size. Additionally, a systematic regional dependency of ETAS parameters is found across source zones. Furthermore, a median heat flow value for each analysed source zone in the EARS is calculated. In contrast to previous findings in other tectonic settings, the results reveal no significant correlations between the crustal heat flows and the ETAS parameters describing earthquake productivity (${{K}_0}$) and the relative efficiency of an earthquake with magnitude M to produce aftershocks ($\alpha $). Our findings have significant implications for understanding the mechanisms of earthquake interaction and, therefore, provide tight constraints on the model's parameters that may serve as a testbed for existing earthquake forecasting models in this region where the vulnerability of local buildings and structures exacerbate seismic risk.

Number of Aftershocks in Epidemic-type Seismicity Models

Summary The Epidemic Type Aftershock Sequence (ETAS) model describes how an earthquake generates its own aftershocks. The regular ETAS model assumes that distribution F of the number of direct aftershocks is Poissonian, however there is evidence suggesting that a geometric distribution might be more adequate. Let ${V}_M({m}_ \bullet )$ be the number of $m > M$ aftershocks generated by ${m}_ \bullet $event. In this study we consider the ${V}_M({m}_ \bullet )$ distribution within Epidemic-type Seismicity models, ETAS(F). These models include the Gutenberg-Richter law for magnitude and Utsu law for average ${m}_ \bullet $- productivity, but differ in the type of F distribution for the number $v({m}_ \bullet )$ of direct aftershocks. The class of F is quite broad and includes both the Poisson distribution, which is the basis for the regular ETAS model, and its possible alternative, the Geometric distribution. We replace the traditional $M = {m}_ \bullet - \Delta $ threshold in $\Delta $-analysis with $M = {m}_a - \Delta $ where ${m}_a$ is the distribution mode of the strongest aftershocks. Under these conditions we find the limit ${V}_M({m}_ \bullet )$ distribution at ${m}_ \bullet > > 1$. In the subcritical case, the limit distribution is extremely simple and identical to the $v({m}_\Delta )$ distribution with a suitable magnitude ${m}_\Delta $. This result allows us to validate both the priority of the geometric distribution of F for direct aftershocks and the very concept of epidemic-type clustering on global seismicity data.

Toward Real-Time Ground-Shaking-Intensity Forecasting Using ETAS and GMM: Insights from the Analysis of the 2022 Taitung Earthquake Sequence

Abstract Earthquake forecasting, combined with precise ground-shaking estimations, plays a pivotal role in safeguarding public safety, fortifying infrastructure, and bolstering the preparedness of emergency services. This study introduces a comprehensive workflow that integrates the epidemic-type aftershock sequence (ETAS) model with a preselected ground-motion model (GMM), facilitating accurate short-term forecasting of ground-shaking intensity (GSI), which is crucial for effective earthquake warning. First, an analysis was conducted on an earthquake catalog spanning from 1994 to 2022 to optimize the ETAS parameters. The dataset used in this analysis allowed for the further calculation of total, background, and clustering seismicity rates, which are crucial for understanding spatiotemporal earthquake occurrence. Subsequently, short-term earthquake activity simulations were performed using these up-to-date seismicity rates to generate synthetic catalogs. The ground-shaking impact on the target sites from each synthetic catalog was assessed by determining the maximum intensity using a selected GMM. This simulation process was repeated to enhance the reliability of the forecasts. Through this process, a probability distribution was created, serving as a robust forecasting for GSI at sites. The performance of the forecasting model was validated through an example of the Taitung earthquake sequence in September 2022, showing its effectiveness in forecasting earthquake activity and site-specific GSI. The proposed forecasting model can quickly deliver short-term seismic hazard curves and warning messages, facilitating timely decision making.

A fractional Hawkes process model for earthquake aftershock sequences

Abstract A new type of Hawkes process, known as the fractional Hawkes Process (FHP), has been recently introduced. This process uses a Mittag-Leffler density as the kernel function which is asymptotically a power law and so similar to the Omori–Utsu law, suggesting the FHP may be an appropriate earthquake model. However, it is currently an unmarked point process meaning it is independent of an earthquake’s magnitude. We extend the existing FHP, by incorporating Utsu’s aftershock productivity law and a time-scaling parameter from the fractional Zener Model to a marked version so that it may better model earthquake aftershock sequences. We call this model the ‘Seismic Fractional Hawkes Process’ (SFHP). We then estimate parameters via maximum likelihood and provide evidence for these estimates being consistent and asymptotically normal via a simulation study. The SFHP is then compared to the epidemic type aftershock sequence and FHP models on four aftershock sequences from Southern California and New Zealand. While it is inconclusive if the seismic fractional Hawkes process performs better in a retrospective predictive performance experiment, it does perform favourably against both models in terms of information criteria and residual diagnostics especially when the aftershock clustering is stronger.

Leveraging the ETAS model to forecast mining microseismicity

SUMMARY Mining operations result in changes of the subsurface stress field that can lead to the occurrence of microseismic events. The development of strategies for forecasting and avoidance of significant events is crucial for safe and efficient operations of mines. One such example, discussed here is the observed induced microseismicity in soft rock potash mines. It is primarily driven by the rock excavations but can also be triggered by preceding events or can result from the delayed effects of plastic creep of soft rocks. Therefore, it is important from seismic hazard assessment and risk mitigation points of view to understand the statistical aspects of microseismicity in potash or other types of mines. In this study, the temporal evolution of the induced microseismicity from a potash mine in Saskatchewan is analysed and modelled. Specifically, the epidemic type aftershock sequence model is used to approximate the occurrence rate of the induced mining microseismicity. The estimated parameters signify that the microseismicity displays swarm-type characteristics with limited inter-event triggering. Moreover, the Bayesian predictive framework is used to compute the probabilities of the occurrences of the largest expected events above a certain magnitude for prescribed forecasting time intervals during the evolution of the sequence. This approach for computing the probabilities allows one to incorporate fully the uncertainties of the model parameters. The Markov Chain Monte Carlo sampling of the posterior distribution are used to generate parameter chains to quantify their variability. Furthermore, several statistical tests are conducted to assess the credibility of the obtained retrospective forecasts compared to the observed microseismicity. The obtained results show that the developed approach can accurately forecast the number of events and intensity of the sequence. It also provides a framework for computing the probabilities for the largest expected events.

Fluids-Triggered Swarm Sequence Supported by a Nonstationary Epidemic-Like Description of Seismicity

Abstract The variation in Coulomb failure stress (CFS) plays a crucial role in either increasing or decreasing seismic activity. In cases in which the standard epidemic-type aftershock sequence (ETAS) model does not adequately fit seismicity data, the potential deviations from empirical laws are explored. These deviations may arise from stress changes imparted by aseismic transients that lead swarm-like earthquake sequences to occur. The time-dependent background rate of seismicity serves as an indicator for detecting changes in CFS or the presence of transient aseismic forcing. We investigate seismic anomalies in the slow deforming Mt. Pollino, Italy seismogenic area, where a 4-yr-long swarm-like sequence partially filled a previously hypothesized seismic gap. The primary process of this seismic swarm is still under debate. Employing a nonstationary ETAS model on a new template-matching high-resolution catalog, we suggest a slow-slip event and fluid interplay as the main aseismic forces in triggering and developing this swarm-like sequence.

Seismic fragility analysis of girder bridges under mainshock‐aftershock sequences based on input‐output hidden Markov model

AbstractCurrent seismic design codes for bridge structures do not account for the influence of aftershock sequences, which, to some extent, overestimate the seismic performance for bridges subjected to mainshock‐aftershock (MS‐AS) scenarios. To address the great need for ground motion sequences tailored to specific research sites for fragility analysis, this study proposes a method for generating artificial MS‐AS ground motion sequences based on the evolutional bimodal Kanai–Tajimi model and the Epidemic–Type Aftershock Sequence model. We establish a framework for MS‐AS fragility analysis using an input–output Hidden Markov Model (IOHMM), where the damage states (DS) of bridge piers are considered unobservable and are inferred statistically through damage indices in an unsupervised manner. Model parameters are trained using intensity measure (IM) sequences and damage index (DI) sequences. Fragility curves for both the mainshock and state‐dependent aftershocks considering multiple aftershocks are formulated based on the initial state probability and state transition probabilities of the proposed IOHMM. The fragility analysis results reveal that as the initial seismic damage level increases, the probability of aftershocks causing higher damage levels in the structure also increases, highlighting the significant impact of aftershocks on structural damage increments. Furthermore, we extend the proposed model to a bivariate seismic intensity measure and develop fragility surfaces. The proposed framework provides a novel approach and insights for tackling seismic fragility under multiple aftershocks.

Distribution Related to All Samples and Extreme Events in the ETAS Cluster

Abstract The probabilistic approach of statistical seismology plays a fundamental role in analyzing the evolution of an earthquake sequence at different scales to deliver reliable forecasts of complex earthquake phenomena. This study contributes to the field by extending previous results relative to the extreme events probability. We derive the explicit formulation of the probability of extreme events in any seismic cluster generated by the epidemic-type aftershock sequence model, as a function of time, space, and magnitude. The results give insights into understanding the distinguishing features between mainshocks and foreshocks, thus helping to shed light on earthquake prediction.

Background and clustering characteristics of recent seismicity in Southwestern China

SUMMARY This study analysed seismicity in southwestern China (1 January 2008 to 30 June 2021) using the earthquake catalogue compiled by the China Earthquake Network Center and four different space–time Epidemic-Type Aftershock Sequence models: the 2-D point-source (PS) model, the 2-D finite-source (FS) model, the 3-D PS model and the 3-D FS model. Our objective was to understand the features of the background seismicity and the patterns of earthquake clusters to better evaluate the regional seismic hazard. We carefully investigated the aftershock sequences that followed 7 of the 10 MS ≥ 6.0 earthquakes that have struck this region since the occurrence of the 2008 Wenchuan MS 8.0 earthquake [i.e. the Panzhihua (31 August 2008; MS 6.0), Yaoan (9 July 2009; MS 6.0), Lushan (20 April 2013; MS 7.0), Ludian (3 August 2014; MS 6.5), Jinggu (7 October 2014; MS 6.6), Kangding (11 November 2014; MS 6.3) and Yangbi (21 May 2021; MS 6.4) earthquakes]. Our results revealed the following. (1) The background seismicity level for natural earthquakes is usually stable but can experience sudden change due to major events, such as the 2014 Ludian MS 6.5, and the 2014 Jinggu MS 6.6 events. Such changes in the background rate can reach 50 per cent. (2) Reservoir-induced earthquakes substantially increase the level of regional seismicity, indicating that they cannot be ignored when analysing natural seismicity and evaluating regional earthquake hazards. (3) Events triggered directly by the main shock occur mostly in regions adjacent to areas with large coseismic slip, showing a pattern complementary to the main shock ruptures.

Reduced seismic activity after mega earthquakes

Mainshocks are often followed by increased earthquake activity (aftershocks). According to the Omori-Utsu law, the rate of aftershocks decays as a power law over time. While aftershocks typically occur in the vicinity of the mainshock, previous studies have suggested that mainshocks can also trigger earthquakes in remote locations, beyond the range of aftershocks. Here we analyze the rate of earthquakes that occurred after mega-earthquakes (with a magnitude of 7.5 or higher) and show that there is a significantly higher occurrence of mega-earthquakes that are followed by reduced activity beyond a certain distance from the epicenter compared to the expected frequency; the results are based on statistical tests we developed. However, the remote earthquake rate after the strongest earthquakes (magnitude ≥8) can also be significantly higher than the expected rate. Comparing our findings to the global Epidemic-Type Aftershock Sequence model, we find that the model does not capture the above findings, hinting at a potential missing mechanism. We suggest that the reduced earthquake rate is due to the release of global energy/tension after substantial mainshock events. This conjecture holds the potential to enhance our comprehension of the intricacies governing post-seismic activity.

SuiETAS: Developing and Testing ETAS-Based Earthquake Forecasting Models for Switzerland

ABSTRACT We present the development and testing of multiple epidemic-type aftershock sequence (ETAS)-based earthquake forecasting models for Switzerland, aiming to identify suitable candidate models for operational earthquake forecasting (OEF) at the Swiss Seismological Service. We consider seven model variants: four variants use parameters obtained through fitting the ETAS model to the Swiss earthquake catalog, and three use generic parameters that were fit to Californian seismicity or global seismicity from regions tectonically similar to Switzerland. The model variants use different pieces of information from the current state-of-the-art time-independent earthquake rate forecast underlying the Swiss seismic hazard model (SUIhaz2015), and one is calibrated on a larger local data set that includes smaller earthquakes by allowing a time-dependent estimate of the completeness magnitude. We test all variants using pseudoprospective short-term (7-day) forecasting experiments and retrospective long-term (30-year) consistency tests. Our results suggest that all ETAS-based models outperform the time-independent SUIhaz2015 forecast in the short term, but two of the model variants overestimate event numbers in the long term. ETAS parameters are found not to be universally transferrable across tectonic regimes, and region-specific calibration is found to add value over generic parameters. Finally, we rank all model variants based on six criteria, including the results of the pseudoprospective and retrospective tests, as well as other criteria such as model run time or consistency with the existing long-term model, using a multicriteria decision analysis approach. Based on this ranking, we propose the ETAS model calibrated on the Swiss catalog, and with the spatial background seismicity distribution of SUIhaz2015 as the ideal candidate for the first Swiss OEF system. All procedures and choices involved in the development and testing of the Swiss ETAS model follow recently established expert recommendations and can act as a reference in the establishment of time-variant earthquake forecasting models for other regions.