We present a new, regionally adjusted local magnitude ($M_{ m L}$) model for Switzerland and surrounding regions. The model is derived based on Wood-Anderson displacement amplitudes ($A_{ m WA}$) calculated from 150 000 high-quality waveforms from 15 000 earthquakes between 2000 and 2025, recorded by more than 700 seismic instruments. This data set is substantially richer than those used in previous $M_{ m L}$ studies in Switzerland, with a large number of near-source recordings and data from low-magnitude events, which were notably sparse in earlier works. $A_{ m WA}$ attenuation over hypocentral distance is parametrized through linear and logarithmic distance terms along with hinge distance points, which allow proper modelling of the attenuation characteristics at long distances and changes in attenuation associated with post-critical reflected phases. Regional differences in attenuation between the Alpine region in southern Switzerland and the northern Foreland are smoothly modelled through a ray-path-specific regional adjustment parameter, allowing the model coefficients and the hinge distances to vary spatially. The coefficients of the parametric attenuation curves are estimated using mixed-effects regressions, and the model is anchored to yield a magnitude 3 for an $A_{ m WA}$ of 10 mm measured at a hypocentral distance of 17 km. The station terms are calculated with respect to Swiss reference rock conditions. The new $M_{ m L}$ model reduces uncertainty by 33 per cent compared to the current $M_{ m L}$ scale used by the Swiss Seismological Service and does not exhibit any residual trends with respect to hypocentral distance, earthquake depth, local site conditions or event magnitude. Empirical radiation pattern corrections are derived, further reducing the uncertainty by 8 per cent for strike-slip events. Alternative models, based on non-parametric and cell-based 2-D approaches, are derived independently to validate the parametrization of the parametric model. The new model-$M_{ m LS26}$-yields lower magnitudes for smaller events (with catalogue magnitudes lower than about 2.5) and for events located in the northern Foreland, whereas the magnitudes of the larger Alpine events remain similar. The reduced magnitudes of smaller events decrease the b value of the input earthquake catalogue from 1.00 to 0.93, corresponding to a reduction of about 7 per cent. $M_{ m LS26}$ scales one-to-one with moment magnitude ($M_{ m W}$) for $M_{ m LS26} \gt 4$, while for smaller events, it scales with the logarithm of the seismic moment.

Underground laboratories provide the ultra-low background and low-vibration environments essential for rare-event searches, gravitational-wave detection, and quantum-sensing technologies. We report a comprehensive environmental characterisation of the Bedretto tunnel in Ticino, Switzerland, a site offering horizontal access, excellent infrastructure, and the potential to be Europe’s second-deepest and quietest underground laboratory. At the prospective physics site, located beneath a granite overburden exceeding 1400 m, we measure fluxes of: cosmic-muon (2.54 ± 0.97) × 10^-8 /s /cm^2 , γ -ray 5.67± 0.37 γ /cm^2/ sec , and neutron fluxes (5.56 ± 0.26)× 10^-5 neutrons/cm^2/ sec , as well as the radon concentration, magnetic-field power spectrum, and seismic backgrounds. The muon flux is suppressed by six orders of magnitude relative to the surface, consistent with an effective depth of about 4000 m water equivalent. Gamma-ray and neutron measurements reflect the local geology and guide shielding requirements for future particle and nuclear physics experiments. Magnetic and seismic noise levels are found to be exceptionally low, meeting or exceeding the criteria for next-generation atom-interferometric gravitational-wave detectors. These results establish the site as a highly competitive, accessible deep-underground location for fundamental-physics experiments.

The Increasing Earthquake Awareness in Switzerland project set out to connect students, teachers, and the wider public with earthquake science by reviving and extending the nationwide seismo@school initiative. Supported by the Swiss National Science Foundation (SNSF) AGORA programme, the project developed a suite of multilingual teaching resources, deployed near real-time seismic sensors in schools, and created hands-on activities such as the Lambda Slinky Seismometer kit to engage 12- to 18-year-olds. Although Switzerland is exposed to only moderate seismic hazard, earthquakes remain the natural hazard with the highest damage potential. Because most residents have never experienced a damaging earthquake, educational programmes play a crucial role in raising awareness and strengthening preparedness. Moreover, seismo@school initiatives can inspire younger generations to pursue geosciences by helping them appreciate the relevance of the field. This article presents the rationale, implementation, and impact of the project, and may serve as a guide for other countries seeking to develop similar initiatives. It examines how experiential, data-driven educational approaches can improve earthquake awareness and preparedness in moderate-hazard regions, how school-based seismometers benefit both teaching and scientific monitoring while considering the practical challenges of installation and operation, and what institutional and policy conditions are required to sustain such efforts over the long term.

Santiaguito is an active volcano located in Guatemala. Among its many hazards, frequent lahar flows impact the infrastructure and the population living and working downstream among the wide river channel network. Lahars are flows comprising large amounts of water and pyroclastic debris which can rapidly initiate and reach speeds of tens of meters per second down the channels making them highly destructive. Lahars are commonplace in the long rainy season. Recently, the agency responsible for monitoring earthquake and volcanic activity in Guatemala (INSIVUMEH), has built and operates a dense real-time seismic network around the volcano. This study shows how seismic data can be used to provide early warning for lahars. We used the 2022 and 2023 seismic data to create a lahar catalog with a total of 50 lahars. We use the seismic records of lahars from 2022 (91 waveforms from 25 events) to train a Siamese Neural Network (SNN) that uses 5 min waveforms to produce a lahar detection. We use the same data to optimize a classical lahar detector that uses ratios between two different short-term/long-term average (STA/LTA) characteristic functions. We test and compare both approaches on continuous data from 2023. Both algorithms operate as single-station detectors but require detection from at least two stations. The tests show that the STA/LTA method misses fewer events, whereas the SNN provides earlier warnings. We show that the two methods could be combined for an optimal lahar early warning that could be faster than the individual methods and operate on a single station. In a move toward operational use, the STA/LTA method (implemented through SeisComP) is used to operate a real-time early warning messaging system at INSIVUMEH. Furthermore, we demonstrate the usefulness of SNNs for developing deep learning models when little training data is available.

The NASA InSight mission observed over 2000 marsquakes in the course of its three year mission. These quakes varied in magnitude between 1.5 and 4.5, as well as in spectral content. We present a simple framework to describe the spectral characteristics of all observed marsquakes, based on source process; propagation through the mantle or crust; and local, receiver-side amplification. We assign to each quake an objective measure of its amplitude, as well as the spectral decay created by the duration of the rupture and the dampening of high frequencies due to visco-elastic attenuation. Together, this allows us to obtain characteristic patterns of the whole marsquake dataset, e.g. in terms of event magnitudes, source size, and - for quakes caused by meteoritic impacts - crater size. We show that a significant fraction of all marsquakes - the high-frequency quakes - form a swarm that is likely not caused by tectonic processes in rocks. Our analysis allows separation of the whole marsquake catalogue into three event classes, of tectonic quakes, meteoritic impacts, and swarm events. We finally conclude that the largest marsquake, S1222a, most likely belongs to the group of meteoritic impacts.

One major hurdle for understanding earthquake mechanics are observational limitations. Important phenomena like strain localisation, fault dilation, and fault healing are readily studied in rock mechanical laboratory experiments and with numerical models. At the scale of natural earthquakes, however, these phenomena are often unresolvable, even by state-of-the-art observatories. To overcome this limitation, we are currently building the Earthquake Physics Testbed at the Bedretto Underground Laboratory for Geosciences and Geoenergies (BedrettoLab), an experimental testbed where we can activate an extensively instrumented natural fault zone via hydraulic stimulation. The goal of the Fault Activation and Earthquake Rupture (FEAR) project is to induce earthquakes of up to Mw~1.0 on this exceptionally well characterised and instrumented fault zone. Here we summarize the main scientific goals and current FEAR project status, and present first results from conducted experiments. We discuss how this large-scale experimental approach may allow us to tackle both fundamental science as well as practical questions on earthquake physics, induced seismicity and seismic hazard.

Earthquake catalogs are built from continuous seismic recordings through a series of steps, including signal detection, phase arrival picking, arrival association, event localization, and amplitude-based magnitude determination. Although each step can be automated, everpresent noise in the waveforms often limits reliable processing to larger events or quieter stations or necessitates manual data review to ensure high-quality results. In this study, we introduce Earthquake Seismogram Denoiser (EQS-denoiser), a deep learning-based denoising model trained on earthquake and noise signals recorded in Switzerland and preprocessed to refine label quality. Our objective is to improve automated analysis of continuous seismic waveform streams across the full network, enhancing earthquake monitoring and characterization. We demonstrate its performance in three key tasks essential for catalog curation: (1) signal detection, in comparison with deep learning pickers; (2) signal denoising, against conventional digital filters; and (3) phase arrival picking using denoised data versus raw data. In all cases, the denoiser outperforms baseline methods, particularly under low signal-tonoise ratio conditions. We integrate EQS-denoiser into an end-to-end earthquake monitoring framework to maximize detections and optimally recover signals from continuous data. Furthermore, we assess how existing phase pickers can best leverage denoised data to improve identification of arrival times and estimate associated timing uncertainty using test-time augmentation. We demonstrate through a case study of a small alpine sequence how EQS-denoiser significantly advances the generation of seismicity catalogs compared with both a deep learning picker-based catalog and the manually reviewed Swiss catalog. In automated processing, denoising enables more reliable signal detection, a greater number of phase picks with fewer false picks, more accurate automated peak amplitude estimation for magnitude determination, and enhanced waveforms for other types of data analysis. This results in a deeper seismicity catalog that, after relative relocation, achieves location quality comparable to the manual-reviewed catalog while extending to lower-magnitude earthquakes.

Abstract Earthquake early warning (EEW) systems that deliver real-time alerts to the public are now operating in Guatemala, El Salvador, Nicaragua, and Costa Rica. An independent system is operated by national seismological agencies in each country. The EEW systems were developed through the Alerta Temprana de Terremotos en América Central (ATTAC) project, a multi-year collaboration (2016–2024) led by ETH Zurich and funded by the Swiss Development Agency. These landmark systems are unique as they provide operational EEW in several low- and middle-income countries. Each system uses the same ETHZ-SED SeisComP EEW software that implements the virtual seismologist (VS) and finite-fault rupture detector (FinDer) algorithms. The national seismic monitoring infrastructures are supported by 72 EEW-ready strong-motion stations. Public alerts are disseminated primarily via a mobile application that has over one million users. EEW performance is regionally consistent due to shared real-time seismic data and common system architecture. Our evaluation period started when the software was installed and included periods of network and software optimization. The VS algorithm achieved an overall accuracy of 89%. Including FinDer reduced accuracy to 77%, mainly due to false positives in Nicaragua and Costa Rica that occurred early in the evaluation window. First alerts are generally available 10 s after origin time for shallow onshore events, with location and magnitude errors typically below 30 km and 0.5 units, respectively. Mobile alerting has proven effective in providing low-latency alerts to increasingly large numbers of users, with most users receiving red alerts within 2–8 s after issuance, though some variability can occur. Maintaining and enhancing EEW in the region requires sustained investment in seismic infrastructure, trained personnel, and dissemination solutions that, for the short term at least, will include the mobile application. Long-term viability depends on stable governance, consistent funding, and strengthened and coordinated public guidance on how to respond to alerts.

Public earthquake early warning (EEW) in Taiwan has been operating for more than a decade with a single point-source algorithm, existing Earthworm-Based Earthquake Alarm Reporting (eBEAR). For large earthquakes with finite rupture, point-source EEW algorithms will fail to accurately predict ground motion for all affected regions, even when the magnitude is correctly estimated. Further, single-algorithm EEW systems are more likely to fail than those that operate multiple independent algorithms. Since mid-2023, the Central Weather Administration (CWA) has been testing the Swiss Seismological Service (SED) at ETH Zurich SeisComP EEW system, developed at the Swiss Seismological Service at ETH Z & uuml;rich, to explore the benefits of integrating Finite-fault rupture Detector (FinDer) and virtual seismologist (VS) using the SeisComP platform. This study aims to evaluate whether combining line-source and point-source models can overcome limitations observed in the existing EEW strategy, particularly during large earthquakes. During the recent ML 7.2 earthquake on 3 April 2024, in Hualien County, the original eBEAR faced challenges with its alert updating mechanism, issuing high-level Public Warning System alerts to only 12 counties and cities, notably missing five highly populated northern areas, including the capital, Taipei. To address this issue, CWA lowered the magnitude change threshold for updating alerts from 0.5 to 0.1 magnitude units. This adjustment improved the responsiveness of initial alerts; however, its point-source assumption still resulted in limited spatial coverage. In contrast, FinDer accurately captured rupture geometry and achieved the highest accuracy in ground-motion prediction as well as broader spatial alert coverage. Although the VS algorithm initially suffered from a configuration issue, the offline-corrected version yielded results similar to eBEAR, reflecting shared limitations of point-source methods. Our results demonstrate the complementary characteristics of point-source and finite-fault models in EEW applications. We suggest that integrating rapid point-source methods such as eBEAR or VS with finite-fault approaches like FinDer may significantly enhance both ground-motion prediction and reliability of EEW systems in future large earthquakes.

Earthquake catalogues are derived from continuous seismic recordings through signal detection, phase picking, association, location, and magnitude estimation, but pervasive noise still limits reliable automation, especially for small events and noisy stations, and often requires manual review. Building on the demonstration that deep-learning denoising can be applied to continuous data to improve network-wide earthquake monitoring (Dahmen et al., 2026), we advance toward operational deployment by (i) implementing denoising within the SeisComP ecosystem (Helmholtz Centre Potsdam, 2008), (ii) evaluating on larger continuous datasets, and (iii) systematically comparing multiple denoising approaches and testing monitoring-driven methodological refinements.We train and compare multiple denoising models using a dedicated, curated training and benchmarking dataset composed of earthquake signals and noise recordings from Switzerland and its border regions, with event waveforms pre-cleaned to enhance label quality. Denoised waveforms are then propagated through an end-to-end monitoring workflow spanning signal detection, continuous waveform denoising, phase picking with arrival-time uncertainty estimation and peak amplitude estimation, and final catalog generation. The performance is benchmarked with monitoring-relevant metrics such as signal detection capability, waveform fidelity, phase-pick quality, and the reliability of amplitude estimation, thereby quantifying, for each denoiser, the trade-offs and improvements relative to standard digital filters and relative to applying common phase pickers to raw versus denoised data.A case study using continuous data in realistic settings shows that catalogues based on denoised data can contain significantly more detected events with more associated phase picks, improved location quality, and more reliable magnitude estimates than catalogs derived from raw data, ultimately extending catalogue depth toward smaller magnitudes while preserving reliability.This work is carried out within TRANSFORM², funded by the European Commission under project number 101188365 within the HORIZON-INFRA-2024-DEV-01-01 call. References:Dahmen, N., J. Clinton, M.-A. Meier, and L. Scarabello, 2026, Toward Operational Earthquake Seismogram Denoising, Bull. Seismol. Soc. Am., XX, 1–23, doi: 10.1785/0120250198Helmholtz Centre Potsdam (2008). The SeisComP seismological software package, GFZ Data Services.

Abstract Improving our understanding of induced and natural earthquakes benefits from controlled experiments in insitu laboratories. To investigate the processes during an Mw∼0 earthquake, we performed the “ Mzero ” experiments in a densely instrumented testbed of the Bedretto Underground Laboratory. These multi‐day hydraulic stimulation experiments went the opposite direction to typical induced seismicity research in that they were designed to enhance seismic rupture. In the first experiment, MzeroA, the rock mass was preconditioned by injecting water for 4 days at a pressure just below fracture reactivation pressure followed by a hydraulic stimulation above reactivation pressure. This strategy aimed at increasing the fractures area near critical stress conditions for shear failure, thus facilitating larger ruptures. During MzeroA, an event with Mw–0.54 was induced, followed by a distinct aftershock sequence. This event was one magnitude unit larger than any preceding event. In the second experiment, MzeroB, stimulation was initiated directly, without preconditioning. Compared to MzeroA, MzeroB exhibited higher seismicity rates, a larger seismicity cloud, and a pronounced migrating seismicity front. Combining seismological and hydromechanical observations, we discuss mechanisms that may have influenced the contrasting seismicity responses. In addition to fluid preconditiong, stress transfer from the Mw–0.54 mainshock, and natural seasonal pressure changes, may have contributed to modulating the seismogenic response. However, the relative importance of these processes remains uncertain given experimental limitations. Our results highlight both the potential and challenge of designing hydraulic stimulations to enhance or suppress seismic rupture, with implications for earthquake physics and induced seismicity.

Machine learning (ML) has seen widespread use in seismology recently, with a significant focus on earthquake monitoring. ML models are now available for phase picking, first motion polarity determination, etc. Implementing them in standard monitoring software (e.g. SeisComP) could significantly improve the automatic earthquake monitoring and save time for human analysts, whilst leveraging all the existing benefits of existing mature monitoring frameworks. An important first step for moving the ML models from research into production has been the Python package SeisBench (Woollam et al., 2022; DOI: 10.1785/0220210324), which allows users to benchmark and access ML models and datasets. The scdlpicker SeisComP module (Tillman et al., 2023; DOI: 10.5194/egusphere-egu23-10046) created an interface between SeisComP and the trained ML pickers in SeisBench to allow event-based re-picking (i.e., not real-time phase onset detection) as demonstrated using teleseismic earthquakes and the GEOFON network. Here, we build on top of the existing scdlpicker module to provide both P and S picks at local distances, and add pick uncertainty and P-pick first motion polarity. We demonstrate the performance of this extended module in routine earthquake monitoring at the Swiss Seismological Service (SED) and show the improvements over classical pickers currently in use. We show that the ML pickers improve the automatic monitoring in both the number and the quality of the picks, leading to better automatic locations and magnitudes. We show that the ML picker’s characteristic function provides a good proxy of the human analyst assigned pick uncertainty. Additionally, this extended SeisComP module provides the ML-determined first-motion polarity for each pick, fully characterizing the pick itself (pick time, pick uncertainty, first motion polarity) in the same way a manual analyst would do. This allows the adoption of streamlined workflows in which the automatic (i.e. ML) picks would only be reviewed (and in most cases accepted) rather than re-picked from scratch by the human analyst (as currently done at SED).

located in south-central Switzerland in the middle of a 5.2-km-long tunnel, which connects the Bedretto valley to the Furka railway tunnel. BULGG is a multidisciplinary laboratory that facilitates experiments and research across various fields. From a seismological perspective, a dense seismic network is deployed that allows real-time monitoring of both natural and induced seismicity occurring in the tunnel and the surroundings. In addition, a multilevel monitoring approach during experiments leads to the generation of real-time high-resolution earthquake catalogs issuing event-based alerts and is the input for a simple traffic light system (magnitude and/or groundmotion based), which provides essential information for the advanced traffic-light system (probabilistic approach). We have set up two separate real-time monitoring systems that monitor the background seismicity, as well as injection experiments, with both systems built on the SeisComP framework. The background monitoring, serving as the backbone network, includes broadband sensors at the surface and along the tunnel, as well as strong-motion sensors and high-frequency geophones along the tunnel and in boreholes. The sampling rate is divergent and depends on sensor type and proximity to faults (200-2000 Hz). Acoustic emission sensors and high-frequency accelerometers sampled at 200 kHz constitute the experimental setup that locates in multiple experimental volumes, which include fluid injections, extractions, and tunneling activities. All sensors transmit real-time data to a common server (SeedLink), which serves multiple clients for processing, real-time visualization, archiving via SeisComP, and risk control via dedicated software. A standardized workflow is applied to both background and experimental monitoring, encompassing automatic picking, automatic phase association and location, and magnitude estimation. Advanced methods are implemented in real time that include double-difference relocation and earthquake detection based on waveform cross correlation. BULGG provides a unique environment to implement novel methods in observational and network seismology across scales.

Earthquake Early Warning (EEW) systems aim to alert users in advance of imminent shaking, enabling them to take action. In collaboration with local seismic agencies, the Swiss Seismological Service (SED), has developed national EEW systems across Central America. Public EEW alerts are now available, considering the frequent seismic activity and the vulnerability of the building stock, EEW has the potential to reduce casualties (i.e. fatalities and injuries). In this study, we build upon a probabilistic framework to quantify the potential benefits of EEW systems in reducing casualties. For each event generated in the stochastic catalog (100,000 event sets), we estimate the number of casualties in the absence of EEW. The framework evaluates the potential casualty reduction attributable to an operational EEW system, considering the expected warning times in each event at the target site, the subsequent actions taken upon receiving the alert, and system performance. For a return period of 475 years, the fatality reduction could reach ∼14% to 17% corresponding to hundreds fewer fatalities in Costa Rica and Nicaragua, and thousands fewer fatalities in El Salvador and Guatemala. From this baseline scenario, we explore strategies to improve casualty reduction: (1) increase warning time by densifying the seismic network; and (2) compare the effectiveness of Drop, Cover, And Hold On (DCHO) versus evacuation as recommended protective actions. Our results suggest that evacuation is a suitable strategy for reducing fatalities in this region, given the prevalence of single-story structures. Given the available warning time, evacuation is advised for occupants on the first floor, and those on upper floors should adopt DCHO. Our findings indicate that the implementation of EEW leads to a ∼10% reduction in average annual fatalities. A cost–benefit analysis reveals that the economic benefits of public EEW systems significantly outweigh the associated costs, making EEW a cost-effective mitigation strategy.

In the last 5 years, the seismological community has experienced an impressive growth in the novel Distributed Acoustic Sensing (DAS) technique, in terms of both the number of experiments and the generated data volume. DAS experiments can generate data at a much finer resolution in space and time, than is seen with standard acquisition techniques. This creates challenges not only for data centres regarding the data management, but also for users that need to access and process this data. The current seismological standards for data and metadata formats, as well as community services specifications, are not capable of handling these datasets in an effective way - not unexpected considering that data volumes and ‘station’ numbers are orders of magnitude larger than typical broad-band experiments. Within the context of the “Geosphere INfrastructures for QUestions into Integrated REsearch” project (Geo-INQUIRE, https://www.geo-inquire.eu/), we have defined a roadmap to advance towards community standards for some of these aspects. The main objective of improving the FAIRness of these datasets was separated in 3 steps. First, we defined how to archive downsampled versions of the datasets in standard community formats (i.e. miniseed and StationXML). Second, we wanted to support the definition (and foster the adoption) of a new metadata standard for DAS experiments based on the outcome of the DAS Research Coordination Network group (DAS-RCN), an initiative led by US researchers. And finally, we wanted to work on the definition of a data format capable of providing fast processing on the data centre side, as well as being able to provide the data to the user to be processed elsewhere. We worked with 3 datasets from the Global DAS Month (February 2023), acquired by INGV, ETH and GFZ. These datasets had been published and made available in different non-standard formats. We used these experiments as test cases to later apply this workflow to the datasets generated by the Transnational Access Calls of this project at a variety of Research Infrastructures across Europe (e.g. at Etna, Bedretto, Ligurian, Madeira, Irpinia, and others). Regarding the data volume and lack of standardisation, we have improved “dastools”, a software package developed at GFZ, to read DAS data in proprietary formats from different manufacturers and convert it to standard miniseed. Downsampling in time and space it provides a reduced version ready to be archived in seismological data centres. Regarding metadata formats, we included in “dastools” the support for the DAS-RCN proposal, discussed and agreed within the community during the last 3 years. We can generate a first draft version of the metadata based on the information available in the raw data of the experiment. We also added a converter to StationXML (still beta) in order to support each step of the archival of a downsampled version of the DAS data. We plan to work soon on the definition of a data format for this type of experiment as it is a key part of our project. In parallel, we’ve just started the development of a Seedlink plugin (real-time transmission) to be deployed and tested at interrogators.

The InSight lander represents a unique opportunity to correlate seismic data with impact events identified in orbital images, enabling the characterization of the physical properties of the martian crust and mantle. Here, we present the first comprehensive catalog of impacts that occurred during the InSight mission within a 50 degrees radius around the lander. We use a machine learning-enabled approach to identify 123 date-constrained impacts with diameters between similar to 1 and 22.5 m. We estimate an impact rate of 2.7 x 10(-6)/km(2)/year for >3.9 m effective diameter, which is similar to 1.6-similar to 2.5 times higher than previously derived for Mars. We identify 49 seismic events with one or several potential impact match(es) including a 21.5 m crater located near Cerberus Fossae. Our catalog will enable a more accurate characterization of the propagation of seismic body waves at intermediate distances to InSight (5-50 degrees), with major implications for estimates of other seismic event distances.

The base data for any seismotectonic study consist of accurate and precise hypocenter information, consistent magnitude estimates, and focal mechanisms derived either from the analysis of first-motion (FM) polarities or moment-tensor (MT) inversions. In this study, we present a new baseline seismotectonic earthquake catalog of Switzerland and surrounding regions (SECOS24), which covers the Central Alps (CA) region between 45.4°N/5.6°E and 48.4°N/11.1°E. The SECOS24 catalog includes instrumental seismicity routinely detected and located by the Swiss Seismological Service (SED) between 1975 and 2024 (about 49 years). For the digital era of the SED bulletin (phase picks and seismograms available in digital form) starting in 1984, hypocenters were consistently relocated in absolute terms using a recent Pg and Sg 3-D velocity model. Starting from these improved hypocenters, double-difference relative relocations were performed at different scales (single clusters as well as at regional scales), combining differential times from manual picks and waveform cross correlations. Based on available solutions and resulting location quality, a preferred solution was selected for each hypocenter of the SECOS24 catalog, in order to provide the maximum possible hypocenter accuracy and precision for each event. The SECOS24 catalog contains about 36,000 earthquakes with magnitudes ranging between ML -0.7 to 5.3. In addition to ML, the catalog reports complementary magnitudes for a subset of events. For 71 events, an MW magnitude was derived from a revised MT inversion for events starting in 1999. For events since 2009, a spectral MW was calculated if possible. This magnitude compilation allows for the assessment and improvement of existing ML-MW scaling relations. Finally, we linked each hypocenter with the revised MT catalog as well as solutions of an augmented FM catalog, which contains 492 high-quality, manually reviewed mechanisms based on P-wave first-motion polarities. The SECOS24 catalog is used for down-stream seismotectonic analysis of the CA region. In this presentation, we show updated maps of seismicity and moment release in the CA and their foreland. In addition, we provide updated maps of deformation regimes and stress orientations derived from the analysis and inversion of the FM data. Besides previously known features, the SECOS24 catalog reveals several new features in the CA and their foreland like newly imaged seismogenic fault zones, lateral changes in the deformation regime along the Alpine Front of the CA, and ongoing shortening at shallow crustal levels in the Jura fold-and-thrust belt. In addition, the updated stress inversion provides more stable results and, in several places, higher spatial resolution in comparison to previous studies. The SECOS24 catalog therefore contributes to an improved understanding of present-day tectonic processes in the CA region and is crucial input for next-generation seismic hazard models of the region.

This report summarizes the seismicity in Switzerland and surrounding regions in the years 2019 and 2020. In 2019 and 2020, the Swiss Seismological Service detected and located 1660 and 1407 earthquakes in the region under consideration, respectively. The strongest event in the analysed period was the ML 4.3 Elm/Steinibach earthquake, which occurred in the Glarus Alps in eastern Switzerland on October 25, 2020. Received felt reports suggest intensities up to degree V for this earthquake. Modelled and instrumentally measured ground motions, however, hint at intensities approaching degree VI–VII at the epicentre. Derived focal mechanisms and relative hypocentre relocations of fore- and aftershocks image a dextral WSW–ENE to W–E striking multi-segment strike-slip fault zone with a total length of about 3.5 km. Well-constrained focal depths of 1–2 km indicate that the fault zone likely locates in the uppermost part of the crystalline basement of the eastern Aar Massif. Another exceptional earthquake sequence occurred between Anzère and Sanetschpass in the Rawil Depression in November 2019. Within 10 days, more than 300 earthquakes occurred in this cluster and 16 of those events reached ML magnitudes between 2.5 and 3.3. Focal mechanisms and relative hypocentre relocations derived for this sequence image the reactivation of a contractional stepover. The imaged stepover confirms the previously proposed segmented nature of the Rawil Fault Zone north of the Rhône valley in SW Switzerland. The ML 4.2 Novel earthquake, which occurred in the Préalpes region south of Lake Geneva on May 28, 2019, provides additional evidence for the recently proposed domain of NE–SW oriented extensional to transtensional deformation along the Alpine Front in the transition zone between Central and Western Alps. Evidence for transtensional deformation along the SW edge of the Mont-Blanc Massif is provided by another remarkable earthquake cluster near the Grandes Jorasses Mountain in the border region between France and Italy. The transtensional deformation of the Hegau-Bodensee Graben in the northern foreland is revealed by a vigorous earthquake sequence on the Bodanrück Peninsula in southern Germany in 2019. Finally, evidence for unusually shallow seismicity in the domain of the Dent-Blanche nappe is provided by the ML 3.5 Arolla earthquake. In conclusion, the seismic activity during the period 2019–2020 is exceptional in terms of absolute numbers of earthquakes as well as number of events with ML ≥ 2.5.

SeisComP (Seismological Communication Processor) is an open-source, free seismic monitoring software that features an automated seismic data processing workflow, flexible database integration, and data interface capabilities. This study integrates SeisComP with the scanloc module, ETHZ-SED SeisComP Earthquake Early Warning (EEW) system algorithms, and SeisBench to develop three distinct seismic monitoring systems, optimizing three key tasks for the Central Weather Administration (CWA): earthquake early warning, seismic activity analysis, and global earthquake data acquisition. During the ML 7.2 Hualien earthquake on April 3, 2024, at 7:58 AM (UTC+8) CWA, in collaboration with ETHZ-SED, applied the EEW algorithms, including the Virtual Seismologist (VS) and Finite-Fault Rupture Detector (FinDer). Both algorithms, tested in parallel at the time, successfully generated complete results within 26 seconds of the earthquake’s origin. Based on these results, Public Warning System (PWS) alerts would have been issued for 17 out of 19 counties in Taiwan, thereby supporting CWA’s existing system. For the seismic activity analysis system, which integrates SeisComP, SeisBench, and the scanloc module, 3,789 automatic location results were produced within three days of the event. Compared to 604 official earthquake reports from CWA, the horizontal location error was approximately 4 km, the depth error 5 km, and the magnitude error 0.17. These results demonstrate the system’s ability to quickly assess seismic activity and estimate subsequent disaster risks. It also has the potential to automate earthquake catalog creation and reduce manual workload. In the global earthquake monitoring system, data is received from IRIS and GEOFON, currently generating results for earthquakes with magnitudes of 6.0 or larger and depths of 30 km or less in the Pacific region. In addition to providing valuable data for tsunami simulations, the system utilizes the global network to calculate Moment Magnitude Mw, which is derived from broadband P-wave amplitudes. For example, the system calculated a Mw of 7.4 for the 2024 Hualien event, which closely matched the magnitude result reported by the USGS. This helps avoid saturation issues with CWA’s ML estimation, particularly for larger earthquakes, and provides a more accurate measurement of earthquake size and dynamics, ultimately enhancing the system’s ability to monitor and assess earthquake risk. This study successfully tested the use of SeisComP in the aforementioned tasks. Although discrepancies remain between automatic results and the official catalog, ongoing testing and parameter optimization are expected to significantly enhance Taiwan’s earthquake monitoring capabilities and integrate more seismic data, ultimately improving the quality of earthquake monitoring services.Keywords: SeisComP, earthquake early warning, earthquake monitoring