The geoscience community is converging on interdisciplinary approaches that use these observations to answer key questions about earthquake rupture mechanics and seismic hazard.
This session invites contributions presenting new approaches in automated and machine learning-based seismic monitoring, developments in real-time and end-to-end workflows for earthquake detection and characterization, and modeling of rupture processes using data from dense infrastructures. We invite contributions that use multiparameter observation integration, and discuss innovations in instrumentation, including DAS and geochemical sensors. We also encourage contributions on Earth observation, fault imaging, as well as data integration and software development from near-fault observatories and induced-seismicity episodes.
Peak Ground Displacement (PGD) plays a fundamental role in the description of strong ground motion and is increasingly important in earthquake early warning systems for real-time estimation of earthquake source parameters, including moment magnitude estimation. In this study, we aim to derive a universal PGD scaling law based on the integration and joint analysis of seismic and GNSS observations, under the explicit assumption that a physically consistent PGD scaling relationship should be valid across different sensor types. In other words, the same PGD scaling law is expected to describe observations from both GNSS receivers and seismometers, providing a unified framework for rapid moment magnitude estimation.
The dataset consists of more than 20,000 observations from strong motion sensors - ESM FlatFiles (Lanzano et al. 2019) and over 3,000 observations from GNSS receivers (Ruhl et al., 2018; DeGrande & Crowell, 2025; INGV, GEONET (GSI)), all thoroughly checked and rigorously filtered to remove erroneous and unreliable records. The data cover 1,802 earthquakes that occurred between 1969 and 2025, with moment magnitudes ranging from Mw 3.0 to 9.1. To our knowledge, this is the largest dataset ever used for the estimation of PGD scaling relationships.
The analysis is based on the PGD functional form originally proposed by Crowell et al. (2013), which serves as a reference model. In addition, two alternative PGD scaling models are introduced and evaluated. Special attention is given to the treatment of observational uncertainties: several observation-weighting strategies are tested, and systematic issues inherent in commonly used PGD weighting approaches are identified and discussed. The results demonstrate that the choice of weighting scheme has a significant impact on the estimated scaling parameters.
By combining high-quality seismic and GNSS data over an unprecedented range of magnitudes and distances, and by enforcing a unified description across sensor types, this study provides new constraints on PGD scaling behaviour and highlights methodological aspects that are critical for the development of robust, physically consistent, and transferable PGD scaling laws. The proposed approach delivers the moment magnitudes (Mw) from GNSS and/or strong motion sensors PGD observations with the average bias less than 0.02 and 0.23 RMSE.
Crowell, B. W., D. Melgar, Y. Bock, J. S. Haase, and J. Geng (2013). Earthquake magnitude scaling using seismogeodetic data, Geophys. Res. Lett. 40, 6089–6094, doi: https://doi.org/10.1002/2013GL058391
DeGrande, J. V., and B. W. Crowell (2025). A Combined PGD–PGV Scaling Law with Rproxy Distance for the G-FAST Earthquake Early Warning Module, Bull. Seismol. Soc. Am. XX, 1–16, doi: https://doi.org/10.1785/0120250168
Lanzano, G., Sgobba, S., Luzi, L. et al. (2019). The pan-European Engineering Strong Motion (ESM) flatfile: compilation criteria and data statistics. Bull Earthquake Eng 17, 561–582. https://doi.org/10.1007/s10518-018-0480-z
Ruhl, C. J., Melgar, D., Geng,et al. (2018). A Global Database of Strong‐Motion Displacement GNSS Recordings and an Example Application to PGD Scaling. Seismological Research Letters 90 (1): 271–279. doi: https://doi.org/10.1785/0220180177
How to cite: Kapłon, J., Kudłacik, I., and Crespi, M.: Towards universal PGD scaling law derived from GNSS and seismic data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8920, https://doi.org/10.5194/egusphere-egu26-8920, 2026.
The Bedretto Underground Laboratory for Geosciences and Geoenergies (BULGG, ETH Zurich) operates a dense, multi-scale seismic monitoring framework for on-fault observation of induced seismicity during underground stimulation experiments. The system integrates permanent seismic stations (500 to 10k samples per second, sps), high-rate experimental deployments (up to 200k sps), and distributed acoustic sensing (DAS; up to 4000 sps), providing high spatial and temporal data coverage at the reservoir and local scale. This contribution documents real-time seismic monitoring during underground experiments and the generation of associated post-processed earthquake catalogues.
The combined instrumentation is used for monitoring of hydraulic stimulation and geothermal project experiments, including the Fault Activation and Earthquake Rupture (FEAR) ERC project and Geothermal Test-Bed (GTB) activities. Permanent monitoring provides manually reviewed earthquake locations and magnitudes in a continuous reference earthquake catalogue, continuously updated since 2021. Experimental deployments enable near real-time event detection, characterization, and Traffic Light System (TLS) alerting, including borehole DAS observations since 2024.
For the FEAR and GTB experiments, additional post-processed catalogues provide optimal yet conventional event parameters based on the final instrumentation configuration, optimized phase picking and hypocenter locations. The resulting catalogues provide consistent absolute locations and high-resolution double-difference relocations based on fully repicked waveform data.
Event magnitudes are estimated using multiple complementary approaches. The backbone national network provides a Swiss-specific local magnitude (Mlhc) using station corrections, while the permanent BedrettoLab network applies conventional local magnitude (MLc) from Wood–Anderson amplitudes. Experimental catalogues include relative moment magnitudes derived from acoustic emission amplitude ratios (MwA) calibrated against collocated accelerometers. Ongoing developments include spectral moment magnitudes and DAS strain-rate-based magnitude estimates to improve characterization of small-magnitude events.
The BedrettoLab monitoring framework, implemented in SeisComP, addresses challenges such as electromagnetic noise, tunnel operations, and heterogeneous sensor coverage while ensuring continuous acquisition, archiving, and FDSN-compliant data access. Beyond Bedretto, this on-fault, densely instrumented approach demonstrates how integrated DAS, permanent, and experimental monitoring can substantially improve event detection, location accuracy, and magnitude estimation, providing a transferable framework for induced seismicity monitoring, operational decision-making, and risk mitigation in geothermal and underground engineering applications.
How to cite: Massin, F., Scarabello, L., Rosskopf, M., Clinton, J., Meier, M.-A., Gischig, V., Wimez, M., Tuinstra, K., Edme, P., Rinaldi, A., Poggiali, G., Tinti, E., Supino, M., Giardini, D., and Wiemer, S. and the BULGG team: Multi-Scale On-Fault Seismic Monitoring at the Bedretto Underground Laboratory: An Operational Framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10187, https://doi.org/10.5194/egusphere-egu26-10187, 2026.
We benchmark several pre-trained deep learning models for automatic phase picking and discrimination of volcano-tectonic earthquakes from long-period events in the complex volcanic setting of Mount Etna, Italy. We used SeisBench, an open-source framework to evaluate PhaseNet and EQTransformer models trained on different datasets from both tectonic and volcanic environments. These configurations are integrated into an autonomous workflow for phase picking, event association, and event classification.
The tests use a dataset of seismic waveforms recorded between January 2019 and June 2020 by INGV – Osservatorio Etneo network. Performance is assessed throughout the workflow, using two human-compiled catalogs of volcano-tectonic earthquakes and long-period events as reference benchmarks. Event classification combines signal-to-noise analysis, network geometry, and the frequency content associated with each event.
Among the tested configurations, models trained on volcanic datasets achieved the highest accuracy in both phase picking and events association. Furthermore, the spatial and temporal distribution of classified events closely matches the patterns observed in the reference catalogs.
How to cite: Carducci, A., Cocina, O., Sciotto, M., Cannata, A., Di Gioia, S., Vuan, A., Saraò, A., Tanaka Hernández, K., and Sugan, M.: Performance assessment of Deep Learning picking models at Mount Etna volcano, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11585, https://doi.org/10.5194/egusphere-egu26-11585, 2026.
How to cite: Fornasari, S. F. and Costa, G.: A Feasibility Study of Earthquake Early Warning at the NITRO Near-Fault Observatory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12472, https://doi.org/10.5194/egusphere-egu26-12472, 2026.
Distributed Acoustic Sensing (DAS) is an emerging technology that turns optical fibers into dense seismic arrays, offering dense spatial coverage for ground motion monitoring. However, DAS recordings are limited by instrumental noise, which can obscure weak seismic signals and ambient noise, and complicate interpretation. Understanding the characteristics of this noise, its variability, and its potential informational content is therefore a crucial step toward establishing DAS as a robust tool for seismology.
We systematically analyse ambient seismic noise recorded by a ~22 km DAS array deployed in the Irpinia region of Southern Italy, under the Italian project MEET – Monitoring Earth Evolution and Tectonics. Using Power Spectral Density (PSD) analysis of continuous strain rate data, we quantify the spatial and temporal variability of noise along the fibre. A primary objective of this work is to characterise the noise floor of our DAS system and its dependence on frequency and channel positions. Our analysis reveals that noise levels even in the quietest fiber sections approach the converted Peterson High Noise Model, indicating that the DAS array along commercial cables exhibits higher noise levels than traditional seismometers, usually deployed in remote areas. Spatial variability in PSD shows that some sections exhibit a flat, low noise response (down to -200 dB), dominated by instrumental noise, whereas others are more sensitive to ambient noise. This substantial variation in noise levels, governed by cable deployment conditions, directly controls the sensitivity of individual DAS channels.
Based on the statistical characterization of ambient noise, we estimate the detection threshold of the DAS array by comparing the observed noise spectra with the theoretical strain rate spectra. From this channel level analysis, we derived an integrated detection threshold curve for the entire array. We found that the DAS fiber can detect events with a minimum magnitude of Mw 1.75 at a hypocentral distance of 25 km, with the threshold increasing to Mw 2.5 at 60 km. This empirically derived threshold is validated by real seismicity.
How to cite: Suresh, A., Strumia, C., Scotto Di Uccio, F., Carotenuto, F., Elia, L., Pegna, R., Saccorotti, G., and Festa, G.: Ambient Noise Characterisation and Detection Threshold of the DAS System at the Irpinia Near Fault Observatory , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12887, https://doi.org/10.5194/egusphere-egu26-12887, 2026.
The Irpinia Near Fault Observatory is a multiparametric infrastructure in the Southern Apennines dedicated to monitoring the seismicity associated with the fault system responsible for the destructive 1980, Mw 6.9 Irpinia earthquake. The observatory integrates seismic, geodetic, and geochemical measurements and has recently been enhanced with two Distributed Acoustic Sensing (DAS) systems within the framework of the national PNRR MEET project (Monitoring Earth Evolution and Tectonics).
A first DAS interrogator has been operating for one year and half and is connected to a 20 km fiber-optic cable deployed in the southern Campania–Lucania Apennines. Eleven months later, a second 60 km–long cable was connected to the same interrogator, covering the central sector of the region and crossing the surface projection of the main fault segments of the 1980 event. Since its installation, the DAS network has recorded about 150 earthquakes, many of which are clearly observed along large portions of one or both cables.
We developed an automatic workflow for DAS data analysis that enables earthquake detection and characterization. Event detection and phase picking are performed using the deep-learning model PhaseNet, demonstrating the effective transferability of conventional seismic models to DAS data. Event association is carried out using at least 50 phase picks, and absolute locations are obtained with the NNLoc software. Local magnitude is estimated from strain-rate data converted to short-wavelength displacement and convolved with the Wood–Anderson response, while moment magnitude and source parameters are derived directly from native strain data. Estimated magnitudes are consistent with those obtained from the conventional seismic network. The ability of the DAS system to detect and characterize earthquakes is controlled by the signal-to-noise ratio variability along the cable and agrees with detection thresholds inferred from power spectral density (PSD) analysis.
How to cite: Festa, G., Strumia, C., Scotto di Uccio, F., Suresh, A., Trabattoni, A., Elia, L., Saccorotti, G., Piana Agostinetti, N., Carotenuto, F., and Pegna, R.: Earthquake detection and characterization with Distributed Acoustic Sensing at the Irpinia Near Fault Observatory, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13733, https://doi.org/10.5194/egusphere-egu26-13733, 2026.
As part of the Fault Activation and Earthquake Rupture (FEAR) experiment [1] at the Bedretto Underground Laboratory for Geosciences and Geoenergies (BULGG), a comprehensive monitoring network has been installed to observe fault activation and rupture processes during controlled, in-situ experiments. Within this framework, we planned and installed a dense fibre-optic sensing network that complements a wide range of geophysical, hydraulic, and geomechanical instrumentation. Our contribution focuses on providing continuous, high-resolution measurements of seismic and deformation signals with fibre-optic sensing techniques across and around the target fault zone.
The fibre-optic network consists of 1 km of sensing fibre cemented six boreholes intersecting and surrounding a well-characterised target fault zone, enabling distributed acoustic, temperature, and strain sensing (DAS, DTS, and DSS). To ensure mechanical coupling and long-term monitoring, fibre-optic cables were cemented along the borehole walls and inside the tunnel floor. This combined borehole–tunnel geometry enables dense spatial coverage of the fault zone, allowing the observation of both localized fault slip and more distributed deformation.
We present the design and installation strategy of the fibre-optic network, discuss coupling conditions in different installation environments, and evaluate data quality in relation to other co-located sensors at Bedretto, installed both in the tunnel and in boreholes. The resulting multi-borehole fibre-optic array is deployed directly on and around the target fault, forming one of the most densely and closely monitored fault zones instrumented to date. Integrated within a vast multi-sensor observatory, this network provides an exceptional in-situ experimental setting to observe seismicity and related processes at close range.
[1] Meier, Men-Andrin, et al. Activating a natural fault zone in the Swiss Alps, Seismica, 2024 (doi: 10.26443/seismica.v5i1.2065).
How to cite: Tuinstra, K., Rinaldi, A. P., Edme, P., Massin, F., Clinton, J., Wimez, M., Meier, M.-A., Hertrich, M., Selvadurai, P., Giardini, D., and Wiemer, S. and the BULGG team: On-fault monitoring using Fibre-Optic Sensing: a multi-borehole network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14412, https://doi.org/10.5194/egusphere-egu26-14412, 2026.
Conventional catalogs are limited in size, since many small events are hidden in the noise. Therefore, discovering such events requires advances in both earthquake detection techniques and monitoring infrastructures. Dense permanent multidisciplinary observatories have been deploying near active seismogenic areas (Near-Fault Observatories, NFO) collecting seismological, geodetic and geochemical data with the goal of understanding the physical processes governing earthquake rupture. Moreover, temporary integration of dense seismic arrays has been proposed to further decrease the detection threshold. Finally, the progressive adoption of fiber optic systems for earthquake monitoring offers a novel decametric resolution, providing a significantly larger number of observations for earthquake characterization. With the growth of data quality and quantity, advanced machine learning models and similarity-based approaches have been developed to systematically identify low-magnitude earthquakes, reconciling the needing of efficient and reliable strategies.
Here, we apply innovative strategies for generating enhanced microseismic catalogs within the Irpinia Near-Fault Observatory (Southern Italy), which monitors the area struck by the 1980 M6.9 Irpinia earthquake, collecting high-resolution seismic observations from the kilometric-scale of the permanent seismic network (ISNet) to the decametric resolution offered by two DAS systems.
We showcase how the integration of machine learning and similarity-based detection techniques can increase the content of seismic catalogs both for background seismicity and seismic sequences. Characterization of the seismic source reveals the activated fault patches, while constraining evolutive models for the seismic sequences. We confirmed the effectiveness of the integrated detection strategy with the integration of 200 stations in dense arrays for one year. We demonstrated the possibility to consistently detect small magnitude earthquakes with the use of dense arrays lowering the magnitude of completeness of seismic catalogs down to M 0. The new catalog enables to downscale the seismicity characteristics to small, decametric-size events, illuminating active seismogenic structures capable to generate events up to M 7.
To exploit the novel resolution offered by DAS systems, we exported machine learning models for the identification of P and S waves on native DAS records, showing that existing models can effectively recognize phase arrival times on DAS records, with the integration of 2D cross-correlation techniques identifying lower magnitude earthquakes. We integrated machine learning models for an automatic characterization of the earthquakes recorded by DAS operating in the Irpinia region, tackling earthquake detection, phase association and local magnitude estimation.
How to cite: Scotto di Uccio, F., Scala, A., Strumia, C., Picozzi, M., Muzellec, T., De Landro, G., Beroza, G., and Festa, G.: From dense monitoring seismic infrastructures to DAS: bridging detection techniques over different monitoring scales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16237, https://doi.org/10.5194/egusphere-egu26-16237, 2026.
Near-Fault Observatories (NFOs) are natural laboratories located at or near active faults undergoing complex geophysical processes, often in proximity to densely populated urban areas.
Covering relatively small areas, NFOs provide researchers from multiple disciplines (e.g., geophysics, geodesy, geochemistry) accessing rich, reusable datasets for generating scientific outputss. This enables improved understanding of the multi-scale physical and chemical processes driving earthquake generation—a goal achieveable only through continuous, long-term, high-resolution multidisciplinary data acquisition and consistent application of state-of-the-art processing techniques.
Eight NFOs in Europe have been identified by the European Plate Observing System (EPOS) as long-term Research Infrastructures (RIs); one additional is in observer status. NFOs aim to enhance understanding of earthquake mechanics to unravel the anatomy of complex seismogenic faults.
The TRANSFORM² project has the ambitious goal of improving and transforming the existing NFOs, by integrating cutting-edge methodological and technological solutions, paving the way for the next generation of NFOs across Europe. This will be achieved by:
- Evaluating state-of-the-art sensors through testing, horizon scanning, gap analysis, and user needs assessment for NFO deployment.
- Accelerating development and field-testing of promising new sensors.
- Developping ML-powered workflows for real-time detection, location, and characterization of seismicity.
- Creating next-generation Earthquake Early Warning (EEW) paradigms, optimized for dense NFO networks and validating their societal impact.
- Strengthening stakeholder & decision-maker engagement by better understanding their needs and demonstrating clear benefits from NFO data/products.
- Positioning existing NFOs as open, high-quality test-beds for calibration and validation of new geophysical instruments and systems.
- Identifying sustainable funding pathways and providing recommendations to national authorities and the European Commission for long-term RI support.
Following the first year of the project, the design and testing of cutting-edge sensors, along with the development of automatic workflows for the detection and characterisation of seismic events and sequences, and the implementation of Earthquake Early Warning systems, are actively being carried out and extended to a growing number of NFOs. Concurrent deployment of the principal sensors, vital for supporting and enabling these advancements, is already in progress.
Finally, a ‘white book’ will be made public to document how data, products and services from the next-generation RIs can be exploited for the benefit of different target stakeholders, such as the research community, local authorities, and society, and to propose ways for ensuring sustainable funding of the RIs in the future.
TRANSFORM² is funded by the European Union under project number 101188365 within the HORIZON-INFRA-2024-DEV-01-01 call.
How to cite: Elias, P., Clinton, J., Colombelli, S., Supino, M., Marmureanu, A., Paronis, D., Kaviris, G., Sokos, E., Karastathis, V., Bernard, P., Festa, G., Evangelidis, C., Vuan, A., Kaplon, J., Plicka, V., Ergintav, S., Costa, G., Šebela, S., Theodoulidis, N., and Aliferis, I. and the rest TRANSFORM² team: From Current to Next-Generation NFOs: First-Year Achievements and Strategic Goals of the TRANSFORM² Horizon Europe Project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16851, https://doi.org/10.5194/egusphere-egu26-16851, 2026.
Distributed Acoustic Sensing (DAS) is rapidly emerging as a powerful complement to conventional seismic networks, enabling dense and continuous ground-motion monitoring by exploiting existing fiber-optic telecommunication infrastructures. In this contribution we present the design, implementation, and first results of a regional-scale DAS seismic monitoring network deployed in Friuli Venezia Giulia (NE Italy), developed through a close collaboration between the regional authorities, the public ICT provider INSIEL, and the scientific community.
The DAS infrastructure is an integral part of the SMINO network, the multi-sensor seismic and geodetic monitoring system for north-eastern Italy operated by OGS, and is fully integrated within its operational framework. The network is based on five strategically distributed interrogation points across the region, collectively instrumenting nearly 250 km of telecommunication fibers belonging to the regional backbone. This configuration provides several tens of thousands of virtual sensing channels with meter-scale spatial sampling, covering urban areas, transportation corridors, and heterogeneous geological settings. To our knowledge, this represents one of the most extensive—and likely the largest—operational regional DAS seismic monitoring networks currently deployed in Europe.
We describe the criteria adopted for fiber selection, network geometry, and acquisition parameters, with particular emphasis on signal optimization, performance, and long-term operational robustness. Examples of recordings from local and regional earthquakes demonstrate the capability of the system to capture coherent seismic wavefields, clear phase arrivals, and spatial variability of ground motion. The network also systematically records anthropogenic signals, which are discussed both as a challenge for data quality control and as an opportunity for multi-purpose environmental monitoring.
Finally, we address the integration of DAS data into existing seismic and civil protection workflows, including real-time data streaming, event detection, and rapid situational awareness. The Friuli Venezia Giulia experience demonstrates the maturity and added value of fiber-optic sensing for operational seismology at regional scale.
How to cite: Chiappetta, G. D., Poggi, V., Cuzzolin, D., Fabbro, F., Perucci, P., Comelli, P., Parolai, S., Chalari, A., and Picozzi, M.: Regional-scale seismic monitoring using distributed acoustic sensing on telecommunication fibers: the Friuli Venezia Giulia experience, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17233, https://doi.org/10.5194/egusphere-egu26-17233, 2026.
