ORFEUS (Observatories and Research Facilities for European Seismology, www.orfeus-eu.org; orfeus.readthedocs.io; forum.orfeus-eu.org) is a non-profit foundation that coordinates the collection, archival, and distribution of seismic waveform (meta)data, services and products based on international standards. It serves a broad community of seismological data users, on behalf of the Euro-Mediterranean seismic networks and monitoring agencies (orfeus.readthedocs.io/en/latest/governance.html). ORFEUS core domains comprise: (i) the European Integrated waveform Data Archive (EIDA; orfeus-eu.org/data/eida), providing access to raw seismic waveform data and basic station metadata; (ii) the European Strong-Motion databases (orfeus-eu.org/data/strong), offering automatically/manually processed waveforms, advanced station/site metadata, and associated products ; and iii) the European Mobile Instrument Pools (orfeus-eu.org/data/mobile), facilitating access to seismic instrumentation for temporary deployments. Currently, ORFEUS services distribute waveform data from more than  33,000 stations, including DAS deployments and dense temporary regional experiments (eg., orfeus.readthedocs.io/en/latest/adria_array_main.html), with an emphasis on FAIR principles, open access, and high data quality. ORFEUS services constitute a core component of EPOS (www.epos-eu.org/tcs/seismology) and are seamlessly integrated into the EPOS Data Access Portal (www.ics-c.epos-eu.org). Access to data and products relies on state-of-the-art information and communication technologies, with a strong emphasis on web services (www.orfeus-eu.org/data/eida/webservices; https://esm-db.eu/webservices) enabling programmatic interaction. ORFEUS promotes transparent data policies and licenses and acknowledges the indispensable contribution of data providers. Ongoing activities focus on further development of existing services and on facilitating access to massive and multidisciplinary datasets through collaboration with global and regional initiatives, including the FDSN (www.fdsn.org) and EarthScope (www.earthscope.org),  as well as  through support from EC-funded projects (e.g., www.geo-inquire.eu). ORFEUS implements community-oriented services that include software and travel grants, a sustained training/outreach programme of webinars and workshops (www.orfeus-eu.org/other/workshops), and editorial initiatives supporting best practices in seismological data use and dissemination.

How to cite: Cauzzi, C., Crawford, W., D'Amico, S., Danecek, P., Evangelidis, C., Haberland, C., Kiratzi, A., Mascandola, C., Poggi, V., Roumelioti, Z., Schaeffer, J., Sigloch, K., Sleeman, R., and Strollo, A.: ORFEUS Seismological Data Resources and Community Services for the Euro-Mediterranean Region and Beyond, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12355, https://doi.org/10.5194/egusphere-egu26-12355, 2026.

The seismological community is producing ever more datasets, and datasets themselves increase in size and complexity. To support the community in FAIRly archiving this data, seismological data centers must find solutions for the storage and distribution of such large and diverse data. Different aspects related to the FAIRness of these datasets are being considered, among others during the GeoInquire project: appropriate metadata to describe experiments, access control for both data and metadata, and improved data formats for raw data. For the first two topics, two proposals are currently under review by FDSN Working Groups, and a positive decision is expected in the coming months. Access control is particularly important for distributed acoustic sensing (DAS) data, given the sensitivity of the detailed cable location information.

Concerning improved raw data storage, one possible way forward is to combine cloud storage, already in place at several data centers, with asynchronous services that provide direct links to data. Ideally, this will allow users to load specific segments of data from the cloud storage using high-level languages like Python and massively parallelize their processing. In such a setting, analysis-ready, cloud-optimized data might provide advantages over traditional miniSEED archives; previous studies also suggest advantages over the HDF5 formats commonly output by distributed acoustic sensing (DAS) devices and currently used to store the majority of DAS data.

In this contribution, we report on ongoing collaborative work to systematically evaluate cloud-optimized formats on commercial and on-premise (university or institute) cloud storage services to evaluate their usability for archival, distribution and analysis-ready access to large datasets. We tested I/O performance and storage aspects in Zarr, tileDB and Apache Iceberg on AWS and self-hosted S3 buckets. We will report on test results and a first scientific use case that utilizes data on an on-premise cloud. We will also compare challenges and opportunities of these storage solutions for DAS and for large-N nodal data.

The zarr format is already used in the Earth Science community and, combined with rich metadata and the xarray library, turns out to provide very user-friendly access and data slicing for DAS data. The TileDB format provides similarly good access and slicing, but is less well known in the Earth Science community and requires careful engineering of data ingestion and maintenance. With this presentation, we aim to provide updates on the ongoing collaboration, show first usage examples for scientific workflows, and to stimulate discussion about future seismological data archives.

How to cite: Schaeffer, J., Lecointre, A., Ermert, L., Hamilton, A., and Quinteros, J.: Testing cloud-optimized formats for future data archival & distribution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12951, https://doi.org/10.5194/egusphere-egu26-12951, 2026.

How to cite: Joo, H., L. Evans, P., Heinloo, A., Quinteros, J., and Strollo, A.: Station Explorer: the new interactive EIDA web tool for seismic data discovery , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16737, https://doi.org/10.5194/egusphere-egu26-16737, 2026.

We introduce SeedPSD, a web service designed to visualize precomputed Power Spectral Densities (PSDs) as PDFs and spectrograms. SeedPSD was initially deployed at the Epos-France EIDA node and has since been deployed across 12 nodes as of 2025.

PSDs are routinely employed to characterize seismological station sites and perform data quality control. Furthermore, seismic noise is now widely used for tomography, crustal monitoring, and investigating ocean-solid Earth coupling. An efficient means of retrieving or plotting spectrograms and PDFs for a given site is valuable for identifying stations that have recorded specific noise events (such as oceanic storms) and, more broadly, for understanding the noise wavefield.

Looking ahead, we are considering proposing jointly with Earthscope a new FDSN standard for a PSD web service. This service would enable users to access PSDs, as well as PSD-derived spectrograms and Probability Density Functions (PDFs), computed from continuous seismic data. This initiative is supported by EIDA, EarthScope, and Geo-INQUIRE.

How to cite: Stehly, L., Panay, S., Bollard, P., Schaeffer, J., and Pedersen, H.: SeedPSD: a web service for analyzing seismological station noise levels, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5057, https://doi.org/10.5194/egusphere-egu26-5057, 2026.

Subbotin Institute of Geophysics of the National Academy of Sciences of Ukraine is currently undertaking efforts to rebuild and modernize the national seismic network, aiming to improve earthquake monitoring and data quality across the country. With the support of international collaborations, including the ORFEUS Integration Grant and the SNEMU project funded by the U.S. Department of Energy, several seismic stations have recently been upgraded, and new stations have been installed. Within the framework of the ORFEUS Integration Grant, five seismic stations in the Carpathian region were modernized, and waveform data from these upgraded stations have been integrated into the European Integrated Data Archive (EIDA). In parallel, four new seismic stations were installed as part of the SNEMU project, expanding the national network coverage. Data from these newly deployed stations are openly available through the EarthScope Data Management Center.

As part of this modernization process, we are implementing routine seismic data processing and event analysis using the SeisComP software framework. This work includes waveform quality control, phase picking, event localization, and the development of a preliminary earthquake catalogue based on data from the upgraded stations. We present first results from a pilot dataset processed using recordings from the newly modernized network. Although the resulting catalogue represents an initial stage of processing and includes a limited number of detected events, it demonstrates the operational capability of the upgraded infrastructure and provides a first overview of recent seismicity in Ukraine.

Until recently, seismic data acquisition within the IGPH network was operational, while systematic data processing was limited due to the ongoing situation in the country. Data from upgraded stations were primarily archived without routine analysis. Prior to 2022, earthquake detection at IGPH relied on continuous manual processing using outdated software. As part of the current modernization efforts, we have initiated the transition toward a modern, automated seismic data processing workflow for earthquake detection and cataloging, with SeisComP selected as the core processing framework. In parallel, further expansion of the seismic network through the installation of additional stations is planned for the coming years, although specific timelines remain uncertain.

Subbotin Institute of Geophysics acknowledges funding support from the Data Integration Grant (ORFEUS, Geo-INQUIRE, Grant Agreement 101058518). Instruments and technical support were provided by GFZ German Research Centre for Geosciences and GIPP-GEOFON, GaiaCode and Université Côte d’Azur, CNRS, Laboratoire Géoazur. The SNEMU project is implemented in partnership with the Science and Technology Center of Ukraine, the U.S. Department of Energy, Lawrence Livermore National Laboratory, Michigan State University, and the EarthScope Consortium. T. Amashukeli is supported by the Philipp Schwartz Initiative for Researchers at Risk.

How to cite: Amashukeli, T., Farfuliak, L., Haniiev, O., Kuplovskyi, B., Petrenko, K., and Levon, D.: Upgrading the Ukrainian seismic network: first results of data processing and earthquake monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7421, https://doi.org/10.5194/egusphere-egu26-7421, 2026.

Seismic monitoring in areas of low natural seismicity is often complicated by anthropogenic signals which can have waveform characteristics similar to small magnitude earthquakes. In Ireland, quarry blasts make up the majority of signals detected by the Irish National Seismic Network (INSN), increasing analyst workload and making it difficult to produce reliable seismic catalogues.

Here we present an automated classification workflow for seismic events based on supervised machine learning methods that is suitable for operational use in discriminating between earthquakes, quarry blasts, and false detections. Instead of relying solely on waveform data, the classifier uses as input a combination of features derived from seismic waveforms plus event information, such as source parameters (e.g. depth, origin time, magnitude, number of phases) and location (e g. distance to the nearest known quarry).

Gradient-boosting classifiers, including XGBoost and CatBoost, were trained on a catalogue of more than 7,000 labelled events. Synthetic oversampling and hyperparameter optimisation were used to enhance robustness and reduce overfitting, and combining probabilistic outputs from multiple models was also explored. Our results show that by incorporating event information along with waveform features the accuracy and reliability of the classification was improved, with the final model achieving accuracies of more than 99% for quarry blasts and 95% for earthquakes, based on testing with unseen event data.

The trained classification tool has now been integrated into the INSN processing environment (SeisComP), and is currently being used to provide classification information for real-time alerts of automatically triggered events, as well as assisting in manual analysis. Although this work relies on the use of network-specific information, it demonstrates a transferable approach that can be used to integrate data-driven classifiers into operational geophysical monitoring systems. It also highlights the effectiveness of modern machine-learning techniques, supports the development of next-generation seismic data services and provides a practical example of how such tools can augment and complement traditional seismic monitoring workflows.

How to cite: Smith, P., Grannell, J., Moelhoff, M., and Bean, C.: Real-time classification of Irish seismic events using supervised machine learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18191, https://doi.org/10.5194/egusphere-egu26-18191, 2026.

Modern seismic networks have taken upon the crucial task of identifying geological risks to our increasingly dense civilization. Unfortunately, risk prevention and mitigation are still pending issues in many regions, specially in developing countries prone to geological disasters. This lack of proper monitoring infrastructure has been traditionally caused by the elevated costs of seismic equipment, mainly sustained due to commercial interests for its use in the oil&gas industry.

With the advent of the Open Seismometer design with performance comparable to state-of-the-art broadband stations, there is now the possibility to vastly increase seismic monitoring density in previously underrepresented regions. However, there were still unavoidable complexities regarding sensor deployment and infrastructure requirements that in practice limited their use to experts and large organizations.

We propose a new approach for a public Open Seismometer Network that simplifies the steps needed to deploy these optimized stations. The technical complexities of seismic infrastructure can be minimized now there is a base open-source seismometer design, as calibration is similar between units, and improvements are easily transferred back to all stations. Also, by leveraging open-source seismic software and P2P internet communication protocols it is possible to provide a distributed and scalable service without depending on a single server. And additionally, the transition to modern seismic formats such as Mseed3 and the cost of maintaining legacy Mseed2 streams can be simplified and reduced through an efficient data distribution and network design.

Building on open-source seismic solutions such as RingServer, combined with a novel P2P architecture, can finally provide a unified Seedlink service that transparently englobes a network of interconnected and independent servers. This enables field recovery of seismometer data in a reliable way, and at the same time can provide the real-time waveforms to a large body of users.

In today’s big-tech world, where it seems every technological solution is pushed to become a centralized subscription service, we believe it is specially crucial to make a stand and design a truly open seismic research environment. For this, the Open Seismometer Network aims to bridge the gap between novel low-cost seismic instrumentation and effective seismic networks.

How to cite: García-Saura, C. and Méndez-Chazarra, N.: The Open Seismometer Network as a Collaborative P2P Infrastructure that is Scalable, Distributed and Robust, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6008, https://doi.org/10.5194/egusphere-egu26-6008, 2026.

Operational seismic networks are required to deliver reliable, low-latency data and timely earthquake information while managing heterogeneous instrumentation, long-term data archiving, and evolving network and operational demands. Beyond waveform acquisition, network operators require comprehensive visibility of network state-of-health (SOH), latency, and data continuity in order to maintain catalogue completeness and rapid response capabilities.

We present the Güralp Data Centre (GDC), a data acquisition and archiving platform designed to support operational network monitoring by providing centralized access to real-time and archived seismic data alongside integrated SOH analysis tools. GDC aggregates SEEDlink data streams from distributed stations and provides time-based data interrogation, long-term performance metrics, and automated SOH reporting to support both day-to-day operations and long-term network assessment. GDC can be deployed either on Güralp-hosted cloud infrastructure or within a customer-managed cloud environment, allowing users to balance redundancy, scalability, and data control requirements. When accessed through the Güralp Discovery platform, GDC enables automated instrument registration, remote firmware and configuration updates across networks, and traffic-light dashboards for rapid assessment of station health, latency, and outages. We discuss how these capabilities address common challenges in network seismology, including remote station management, near-real-time data availability, and long-term network performance monitoring, and consider lessons learned for improving the reliability and responsiveness of seismic monitoring systems.

How to cite: Kerkenyakova, A., Lindsey, J., Calver, J., Watkiss, N., Kitka, K., Hill, P., and Restelli, F.: Güralp Data Centre: Cloud-Based Data Acquisition and State-of-Health Monitoring for Seismic Networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12662, https://doi.org/10.5194/egusphere-egu26-12662, 2026.

Orientation of a seismic sensor is a key item of metadata required for analysis, however this is often inaccurate or missing from stations where the operator cannot directly access the sensor to see its orientation, for example, in boreholes or on the ocean bottom.  Nanometrics has developed a unique, patented system incorporating a miniature MEMS gyrocompass in the seismometer to determine its orientation with an accuracy of +/-0.5 degrees, which is generally not achievable by other methods.  Software in the data recorder automatically queries this orientation and incorporates it in the channel metadata.  Seismometers incorporating this gyrocompass also have self-leveling functionality, so the tilt of the data is known as well as the orientation.  This North-finding capability was initially developed for ocean bottom use and is now being integrated into down-hole systems using the Trillium 120 Borehole or Posthole seismometer with the Centaur data recorder.

This self-orienting seismic system requires only a single software command to find its orientation and automatically incorporate it in the metadata.  It removes any concern about the accuracy of the orientation, since the gyrocompass measures the direction of the Earth’s axis of rotation, which is the definition of geographic North.  This is an improvement over a magnetic compass, which measures magnetic, not geographic, North and may not accurately measure magnetic North due to disturbances from nearby magnetic material, such as the borehole casing.  Furthermore, this North-finding capability eliminates the complex operations currently used to control the orientation of borehole sensors, such as installing a “bishop’s hat” fixture with expensive equipment or cross-correlating seismic data between the borehole sensor and a surface sensor.  The orientation determined by the gyrocompass will automatically accompany the seismic data to the data center, so it will always be available to the user.  Optionally, a rotation transform can be applied in the Centaur data recorder, to supply data that is already aligned to North.

How to cite: Uhlmann, S., Jusko, M., Bainbridge, G., Perlin, M., and Allardice, S.: Solving the Down-hole Installation Problem: A Self-Orienting Seismic System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13508, https://doi.org/10.5194/egusphere-egu26-13508, 2026.

As cross-disciplinary science becomes increasingly critical to understanding geophysical phenomena, a multidisciplinary approach is essential for integrating instrumentation and ensuring reliable and efficient data acquisition for successful scientific outcomes.

The scientific community requires adaptable solutions for the co-location of diverse sensor types. Deploying such instruments in remote, volatile environments while ensuring reliable, continuous data acquisition presents additional challenges. The complexity and cost associated with deploying, operating and maintaining remote stations are significantly increased if using multiple independent sensors, each with dedicated acquisition infrastructure. Recent efforts, such as the European Plate Observing System, aim to address this by integrating multidisciplinary geophysical applications into unified and efficient deployments.

Modern seismic data loggers, such as the Nanometrics Centaur Gen5, support integration of a wide range of sensing elements, while maintaining ultra-low power consumption, precise timing, local data storage and reliable real-time data transmission. Enhanced capabilities regarding customization and edge computing allow the implementation of functionality tailored to meet specific monitoring objectives for unique station configurations.

A case study is presented for a multidisciplinary geophysical monitoring station that leverages these capabilities to enable comprehensive, reliable and efficient data collection. The multidisciplinary station configuration and end-to-end data pipeline, from remote sensing to science doorstep in the data center, are discussed.

How to cite: Allardice, S., Jusko, M., and Perlin, M.: Multidisciplinary Stations: A Next Generation Tool Kit for Geoscience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13809, https://doi.org/10.5194/egusphere-egu26-13809, 2026.

Co-locating weak-motion seismometers with strong-motion accelerometers enables monitoring of seismicity at all scales, from the largest earthquakes to background-level microtremors.  However, a combined analysis depends on having comparable data from both instruments, installed at the same depth, accurately aligned in the same package, and precisely calibrated so they can produce equivalent data, i.e. the same ground motion velocity or acceleration signals after response correction.

We present data from recent earthquake sequences in the Hualien region of Taiwan, captured by dual downhole sensors (Cascadia Slim Posthole) in the Downhole Seismic Observation Network of Taiwan CWA.  In this example the seismometer and accelerometer signals match within 0.5% on average after response correction, allowing for the synthesis of a combined data stream with an unprecedented dynamic range of 220 dB.  Algorithms for optimal combination of the data are discussed and demonstrated.

This processing also enables a new quality assurance metric for calibration accuracy.  Previously, it has only been possible to verify this by running a calibration test procedure, typically no more often than once a year, since the test process is laborious and interrupts normal data collection.  However, in analyzing data from a dual instrument, the response-corrected amplitude ratio of the strong and weak-motion data streams can be continuously measured and reported as a state-of-health metric, to verify that the two instruments are operating correctly and measuring ground motion with the same accuracy in terms of sensitivity and frequency response.

How to cite: Catalano, N., Jusko, M., Bainbridge, G., Perlin, M., Somerville, T., and Allardice, S.: Processing Strong and Weak Motion Data from a Combined Instrument to Verify Calibration and Maximize Dynamic Range, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14367, https://doi.org/10.5194/egusphere-egu26-14367, 2026.