In this session, studies related to the following topics, but not limited to, are welcome:
● Seismic hazard analysis
● Seismic source characterization
● Characterization of seismicity in seismic hazard analysis
● Ground motion prediction analysis
● Site effect and microzonation
● Earthquake-induced effects (e.g. liquefaction and landslide)
● 1D, 2D, and/or 3D numerical site effect modeling
● Soil-structure interaction and analysis
● New approaches in seismic hazard characterization
● Machine learning for seismic hazard, site effect, and microzonation
Ground motion records combine source, path, and site effects, and isolating them remains difficult, especially for small earthquakes. We apply a non-parametric generalized inversion technique (GIT) of S-wave spectra to the 2010 MW 8.8 Maule aftershock sequence in south-central Chile. The dataset includes about 7,000 events with ML 2.0–6.5 recorded over approximately ten months. To capture spatial variability across the broad rupture, we perform the inversion in local clusters of ~400 events. This strategy preserves lateral and depth heterogeneity and reduces bias from region-wide simplifications in the path and site terms. From the inverted source spectra we estimate seismic moment, corner frequency, stress drop, source kappa, and evaluate depth dependence and self-similarity. Preliminary results indicate an average stress drop of ~0.85 MPa, with weak depth dependence but higher values for larger events, suggesting a scaling with seismic moment. The mean source kappa is about 0.019 s. Path terms provide a frequency-dependent attenuation factor Q(f), while site terms yield frequency-dependent amplification functions that we compare with horizontal-to-vertical (H/V) spectral ratios. We invert clusters independently, then merge the recovered source, path, and site terms into a single region-wide ensemble to verify consistency across cluster boundaries.
How to cite: Flores Allende, R., Seydoux, L., Bonilla, L. F., Bindi, D., Beaucé, E., and Gueguen, P.: Source Parameters, Attenuation Characteristics and Site Effects Derived From The Non-Parametric Generalized Inversion Technique (GIT) For The MW 8.8 Maule Aftershock Sequence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20333, https://doi.org/10.5194/egusphere-egu26-20333, 2026.
Freestanding and rocking structural systems have long demonstrated remarkable seismic performance owing to their inherent rocking working principle such as self-centering capability and damage-avoidance behavior. In recent years, rocking-based isolation concepts have gained increasing attention in earthquake engineering as low-damage alternatives to conventional fixed-base systems. However, their seismic response remains strongly influenced by soil–structure interaction (SSI), impact phenomena, and near-fault ground motion characteristics, which can significantly affect stability and residual displacements.
This study aimed at exploring the potential role of hybrid soil–structure interaction mechanisms in altering the dynamic response of rocking systems. In particular, the combined influence of supplemental inertial effects and engineered soil layers, such as gravel–rubber mixture (GRM) foundations, is investigated from a conceptual and numerical perspective. These components are expected to alter the effective stiffness, damping, and energy dissipation characteristics of the soil–foundation–structure system, especially under pulse-type ground motions.
A simplified modeling framework is considered, in which rocking kinematics are coupled with soil compliance and additional inertial effects. Parametric numerical simulations are performed to investigate key response quantities, including uplift behavior, re-centering tendencies, and sensitivity to ground motion features and soil properties. The role of SSI in controlling rocking stability and modifying seismic demand is discussed.
The results provide insight into how hybrid soil and inerter-based mechanisms may enhance the seismic performance of rocking systems and highlight key parameters governing their effectiveness. The study aims to support future developments in performance-based design strategies for structures prone to rocking and soil-informed seismic isolation concepts, with potential relevance to both modern applications and the protection of freestanding structural systems.
How to cite: Toy, E.: Hybrid Soil–Structure Interaction Effects on Rocking Systems with Supplemental Inertial and Soil-Based Damping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13340, https://doi.org/10.5194/egusphere-egu26-13340, 2026.
Assessing ground shaking at a high spatial resolution after a recent or future earthquake is crucial for a rapid impact assessment and risk management. This is particularly important in urban areas, where small-scale differences can significantly affect the impact of an earthquake on people and property. However, classical seismological networks are usually too sparse to capture the variability of ground shaking at such a high spatial resolution. In this study, we demonstrate how a multivariate spatial statistical model can enhance ShakeMaps by combining station data (e.g. peak ground accelerations) with information from Earthquake Network citizen science initiatives (e.g. smartphone accelerations). The statistical model accounts for the heterogeneity of the data sources in terms of spatial density, measurement uncertainty, and bias. The model achieves data fusion without the need for calibration relationships or co-located information and naturally provides ShakeMap uncertainty.
We apply our approach to the highly monitored area of Campi Flegrei in Italy, where the Earthquake Network initiative involves around 9,000 participants and smartphones. By combining the data gathered from multiple seismic events, we also demonstrate how to generate a high-resolution amplification map of the area, which is useful for enhancing ground motion models.
How to cite: Finazzi, F., Cotton, F., and Bossu, R.: Enhancing microzonation, ground motion models and ShakeMaps through the spatial statistical modelling of seismological station and crowdsourced smartphone data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9428, https://doi.org/10.5194/egusphere-egu26-9428, 2026.
Ambient seismic noise analysis provides an interesting source of information to characterize the subsoil and to investigate local seismic site effects in urban areas. In this study, we present a polarization analysis of ambient noise data acquired in the Aguere Valley (Tenerife), an infilled basin characterized by soft clay-silt deposits and stacked lava flows with pyroclastic and scoria intercalations. We collected a total of 467 ambient noise measurements, covering the entire valley. This dataset has already been analyzed using the standard HVSR method.
The analysis examines the directional properties of the seismic wavefield to identify preferential azimuths of ground motion and their possible relationship with local heterogeneities and basin geometry. Polarization characteristics are investigated by evaluating the azimuthal dependence of the Horizontal-to-Vertical Spectral Ratio (HVSR) through systematic rotation of the horizontal components over the 0°–180° azimuthal range. This approach allows assessing the azimuthal variability in the H/V ratio and the identification of frequency-dependent polarization features, providing additional constraints on the directional behaviour in a geological complex valley within an urban area.
The results show that polarization analysis often exhibits: 1) localized azimuthal maxima with high H/V values in a narrow angular range, and 2) broad azimuthal bands in the entire polarization angle range characterized by elevated H/V values without any well-defined preferential direction. In many cases, azimuthal features with elevated H/V values are observed between approximately 50° and 160° at frequencies between 1–3 Hz, forming an eye-shaped pattern in the azimuth–frequency domain. At higher frequencies, between 7 and 20 Hz, the H/V response typically exhibits bands with high values across most of the azimuthal range (0º–180º), indicating weak directional dependence.
These features generally coincide with the main frequency peaks previously identified in the HVSR curves, suggesting a close relationship between polarization patterns and site resonance frequencies. The observed azimuthal variability likely reflects the complexity of the ambient seismic wavefield and its interaction with the local subsurface geology.
How to cite: M. van Dorth, D., Cabrera-Pérez, I., D'Auria, L., Ortega-Ramos, V., Calderón-Delgado, M., de Armas-Rillo, S., López-Díaz, P., García-Hernández, R., Rodríguez, Ó., Álvarez-Hernández, A., and Pérez, N. M.: Polarization analysis of the seismic ambient noise in La Laguna Valley (Tenerife, Spain) and its relationship with the local seismic response, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9740, https://doi.org/10.5194/egusphere-egu26-9740, 2026.
The near-surface can exhibit complex, nonlinear behavior when seismic wavefields interact with unconsolidated materials. Traditional linear site-effect models often fail to explain amplitude-dependent ground response, highlighting the need to resolve the physical mechanisms that control nonlinear processes. Improving this understanding is essential for predicting near-surface behavior during strong ground motions and other seismo-acoustic sources.
Here, we investigate the mechanism of nonlinear ground response using volcanic explosions at Mt. Etna (Sicily) as a natural laboratory. We deployed a multi-parameter network near the summit craters, consisting of broadband seismometers, infrasound sensors, and a buried fiber-optic cable at 30 cm depth for distributed dynamic strain sensing (DDSS). The observatiosn show how aereal explosion waves from Etna’s main vents couple into shallow, unconsolidated scoria deposits. The coupling generates a characteristic ground response signal marked by an amplification of emergent high-frequency energy (10–50 Hz) embedded by the predominantly low-frequency (<10 Hz) explosion waves.
To mechanically characterise the near surface under nonlinear excitation, we compiled a catalog of more than 8,000 volcanic explosions. We analise the relationship between peak-to-peak stress-rate amplitudes measured from infrasound recordings of the explosions, and peak-to-peak strain-rate amplitudes of the associated ground response measured with DDSS. This relationship reveals an hyperelastic behavior of the scoria deposits, expressed by three distinct, consecutive elastic stages: (i) semi-linear elasticity, (ii) softening, and (iii) subsequent stiffening.
The resulting hyperelastic curves allow us to estimate key nonlinear elastic parameters, to model the nonlinearity of the scoria using a lattice mesh. Wave-propagation simulations using this constitutive description reproduce the observed ground response at Mt. Etna. We further validate the approach by modeling explosion–ground interactions for events in which nonlinear ground response is not observed, using the same nonlinear material properties. Our results demonstrate that strain-rate measurements can be used to derive nonlinear near-surface properties of complex geomaterials. Such approach enables an improved modeling of ground behavior that cannot be captured by linear site-effect approaches.
How to cite: Diaz-Meza, S., Celli, N., Jousset, P., Currenti, G., and Krawczyk, C. M.: Understanding nonlinear ground response using air-to-ground wave interactions from explosions; an example from Mt. Etna, Sicily., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20513, https://doi.org/10.5194/egusphere-egu26-20513, 2026.
A probabilistic seismic hazard analysis (PSHA) was carried out for Greenland based on the revised earthquake catalogue of GEUS for the period 1974-2022. The analysis is based on more than 5.000 earthquakes. For the analysis Greenland has been divided into 9 areal zones, identical to the zones used in a previous study by Voss et al (2007). The zones are defined based on seismicity and geological provinces. The seismic network in Greenland is sparse and the configuration of the network changes significantly with time. During periods with large international projects the station network is densified, but hundreds of km between neighbouring stations is not uncommon. Some areas experience frequent earthquakes, especially in SE Greenland around the town of Tasiilaq, but most earthquakes in Greenland are less than Magnitude 4.5. The hazard analysis has been carried out using HAZ45.3 for Windows and the code has been validated against the 2007 study. Lacking local information on attenuation a global reference model for normal faults in hard rocks has been applied. Sufficient data were available to obtain robust hazard levels for 7 out of 9 areal zones. One zone in NW Greenland had too few recorded earthquakes for the analysis, and the zone defined by the inland ice was omitted as well. Most of coastal Greenland has peak ground acceleration (PGA) hazard values around 0.04g, with slightly higher values up to 0.06g in SE Greenland for a return period of 475 years.
How to cite: Larsen, T. B., Voss, P. H., Carlton, B., Dahl-Jensen, T., Mordret, A., Rinds, N., and Fønss Jensen, E.: A New Seismic Hazard Map for Greenland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3507, https://doi.org/10.5194/egusphere-egu26-3507, 2026.
To evaluate the high seismic risk and structural health monitoring (SHM) on the Tainan tableland, we equipped a P-alert seismometer array in an academic building of the Department of Resources Engineering, National Cheng Kung University (NCKU). With the R-1 rotational seismometer deployed on the 12th floor, the vertical and horizontal arrays help us to resolve the rotation kinematics in seismic events. The SHM system recorded 65 earthquakes from April 2024 to December 2025, including the 2024 Hualien earthquake and the 2025 Dapu earthquakes. These records enable the systematic analysis of the rotation rate comparison of asymmetric high-rise buildings. Rotation rates were estimated from horizontal accelerations using an array-derivative formulation and were validated against the direct measurements from the R-1 rotational seismometer. In this study, the rotation rates are consistent with two equipment, and the maximum torsion was taking place in the location far away from the elevator due to the building asymmetry. Moreover, the varied, position-dependent rotation rates can be determined by the P-alert horizontal array. To address the site effect of the Tainan metropolitan area, two earthquakes recorded by the NCKU Distributed Acoustic Sensing (NCKUDAS) were used to understand the amplification effect that foundations exert on buildings during earthquakes. To utilize these observations, we propose a machine-learning framework to test the vulnerability of building with the event magnitude. This integrated study provides a robust methodology for torsion-aware SHM and performance-based retrofitting decisions in seismically active regions.
How to cite: Wu, H.-Y., Yeh, Y.-T., and Huang, C.-Y.: Integrated Seismic Risk Assessment of Asymmetric High-Rise Structures: Insights from Building Array, Distributed Acoustic Sensing, and Machine Learning-Based Hazard Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2120, https://doi.org/10.5194/egusphere-egu26-2120, 2026.
The mitigation of seismic risk is a fundamental requirement for the successful development of geothermal projects. Fluid injection and extraction alter the subsurface stress field through pore pressure diffusion, poroelastic stressing, and thermal stressing. Historical cases, such as those in Basel (2006) and Pohang (2017), underscore the necessity for robust hazard assessment. However, predicting fault reactivation remains a challenge due to the complex interaction of thermo-hydro-mechanical (THM) processes and inherent uncertainties in subsurface properties.
This study introduces PISA (Probabilistic Induced Seismicity Assessment), an open-source workflow developed to quantify these uncertainties. The tool integrates Gmsh for automated mesh generation and OpenGeoSys (OGS) for multi-physical simulations. Using a Design of Experiments (DoE) approach, we conduct a comprehensive sensitivity analysis involving 27 variable parameters to identify the key drivers for fault reactivation. The model is based on a simplified three-layer stratigraphy (overburden, aquifer, and underburden), focusing on a wide range of geomechanical and thermal properties, including initial stress state, Young’s modulus, Poisson’s ratio, Biot coefficient, specific heat capacity, and thermal expansivity.
The workflow simulates ten years of continuous injection and production within a fully coupled THM framework. A distinct methodological feature is the post-processing assessment of fault stability: fault planes are stochastically inserted into the simulated stress field, where the Mohr-Coulomb failure criterion is applied to evaluate the destabilization of faults. This decoupling allows for a high-throughput screening of various geological scenarios. The primary objective is to identify which parameters, beyond operational variables such as flow rate and injection temperature, exert the greatest influence on fault stability, thereby enabling operators to prioritize critical subsurface characteristics during exploration prior to field development.
How to cite: Decker, S., Khasheei, M., Götzel, G., Buchmann, T., Boncecchio, F., You, T., and Yoshioka, K.: Probabilistic Induced Seismicity Assessment (PISA): A THM-Coupled Sensitivity Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13306, https://doi.org/10.5194/egusphere-egu26-13306, 2026.
Abstract:
This study develops an artificial neural network (ANN)-based ground motion model (GMM) for the Azores Plateau (Portugal) using a dataset generated through stochastic finite-fault simulations. The simulations are performed for both onshore and offshore rock-site scenarios, employing a dynamic corner-frequency algorithm. Randomized source and path parameters are incorporated to capture the aleatory variability of regional seismicity. The simulated ground motions are validated through a comprehensive statistical framework, confirming that the implemented randomization reproduces realistic variance and inter-period correlations observed in recorded data. The ANN-based GMM is trained using the simulated database to predict spectral acceleration across a wide range of magnitudes and source-to-site distances. The developed model and accompanying dataset together provide a reliable foundation for seismic hazard and risk assessments in the Azores Plateau region.
Keywords: Artificial neural network (ANN); Ground motion model (GMM); Stochastic finite-fault simulation; Onshore and offshore scenarios; Spectral acceleration prediction; Azores Plateau (Portugal).
Acknowledgments:
This work is financed by national funds through FCT – Foundation for Science and Technology, under grant agreement [2023.08982.CEECIND/CP2841/CT0033] attributed to the first author (https://doi.org/10.54499/2023.08982.CEECIND/CP2841/CT0033). This work was also supported by FCT/ Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under the references UID/4029/2025 (https://doi.org/10.54499/UID/04029/2025) and UID/PRR/04029/2025 (https://doi.org/10.54499/UID/PRR/04029/2025), and under the Associate Laboratory Advanced Production and Intelligent Systems (ARISE) under reference LA/P/0112/2020. This work is partly financed by national funds through FCT (Foundation for Science and Technology), under grant agreement [UI/BD/153379/2022] attributed to the second author. This work is partly financed by national funds through FCT – Foundation for Science and Technology, under grant agreement [2023.01101.BD] attributed to the third author.
How to cite: Karimzadeh, S., Hussaini, S. M. S., Caicedo, D., Mohammadi, A., Carvalho, A., and Lourenço, P. B.: ANN-Based Ground Motion Model for the Azores Plateau (Portugal) Using Stochastic Ground Motion Simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1533, https://doi.org/10.5194/egusphere-egu26-1533, 2026.
Time-dependent seismic hazard assessments require ground-motion models that capture source complexity, rupture timing, and the spatial variability of intensity measures, while remaining applicable to engineering practice. Here, we present a simulation framework that combines empirical models for the Effective Amplitude Spectrum (EAS) and the Group Delay Time (GDT) with physics-informed rupture scenarios to generate broadband ground-motion time histories for large interface earthquakes and potential future events based on interseismic coupling models. The empirical EAS and GDT models are derived from a curated strong-motion dataset from the Chilean subduction zone, encompassing relatively small events with magnitudes ranging from 4.6 to 7.0. To extend the approach to megathrust earthquakes, we adopt a rupture-decomposition strategy in which the total seismic moment is distributed among subevents with prescribed rupture and travel times. We first apply the framework to the 2010 Mw 8.8 Maule, 2014 Mw 8.1 Iquique, and 2015 Mw 8.3 Illapel earthquakes, using coseismic slip models and also interseismic coupling distributions, to examine whether coupling can serve as a proxy for earthquake ruptures. The observed-versus-predicted comparison of seismic intensities includes Fourier amplitudes, Arias intensity, pseudo-spectral acceleration ordinates, PGA, and PGV. Despite its relative simplicity, the approach reproduces the main amplitude and temporal characteristics of observed ground motions. Slip-based simulations tend to slightly overestimate shaking amplitudes, whereas coupling-based scenarios produce lower, more conservative ground motions while preserving realistic durations. Residual analyses show improved temporal coherence and spatial variability compared to commonly used predictive ground-motion models. In light of these results, we finally apply this approach to mature seismic gaps identified from geodetic coupling models along the Chilean margin, including the Atacama and Central Chile segments, last ruptured in 1922 (Mw~8.5) and 1730 (Mw~9.0), respectively. Simulations at virtual stations reveal high seismic intensities in densely populated cities such as Valparaíso and Santiago, underscoring the importance of integrating time-dependent exposure and vulnerability models to compute the seismic risk associated with the 1730-type scenario. These findings highlight the value of including coupling information into time-dependent ground-motion simulations and demonstrate how rupture timing and fault loading influence seismic hazard assessments. The proposed framework provides a physically consistent and engineering-relevant tool for seismic hazard analysis in subduction environments.
How to cite: Ojeda, J., Montalva, G., Osses-Valenzuela, M., Bastías, N., Leyton, F., Heresi, P., Jünemann, R., and Calderón, S.: From Interseismic Coupling to Ground Motions: An Empirical Amplitude and Phase Approach for Megathrust Earthquake Simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16008, https://doi.org/10.5194/egusphere-egu26-16008, 2026.
Assessing the influence of local site conditions on seismic ground motion is crucial for seismic hazard analysis and earthquake engineering research and applications. This study analyzes site effects in the Kumamoto area, Japan, using 985 high-quality horizontal strong-motion records from 45 aftershocks (Mj = 2.7-4.9) recorded within 24 hours following the 2016 Kumamoto Mj 7.3 earthquake, as observed by 51 K-NET and KiK-net stations. For the generalized inversion technique (GIT), a reference station is required as a standard. In the GIT process, the number of events available for analysis is limited to those recorded by the reference station, and the stations whose site effects can be estimated are restricted to those that record common events with the reference station. To overcome this limitation, we apply the “transfer-station generalized inversion method (TSGI),” a modified GIT, to obtain the site responses for all stations and the average S-wave quality factor (QS) in the study area. It is found that QS is proportional to frequency in the 0.4-3 Hz range, while at frequencies above approximately 3 Hz, the dependence of QS on frequency becomes weak and QS can be regarded as constant. However, the results of GIT and TSGI are relative to the reference station, which may itself exhibit site effects. Therefore, we additionally apply a reference-independent technique, i.e., genetic algorithm (GA), to obtain the absolute site amplifications. Our result shows that at frequencies greater than about 1 Hz, the site response of the reference station is substantially lower than the theoretical amplification factor of 2, resulting in an overestimation of the site responses at other stations. When the results of GIT are corrected with the site response of the reference station obtained from GA, these two results agree very well for most of the stations. This indicates that the results of GIT are reliable if the reference station is an ideal surface rock station. The GA method yields accurate absolute site amplification factors for the stations investigated this study, demonstrating the effectiveness of GA in site effect analysis. In addition, we analyze the characteristics of S-wave high-frequency attenuation parameter (κ) in the Kumamoto area, and establish κ models for different site conditions and an empirical κ0-VS30 relationship.
How to cite: Zhang, W. and Zhou, T.: Estimation of site effects in the Kumamoto area, Japan, using aftershock acceleration records of the 2016 Kumamoto Mj 7.3 earthquake, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-222, https://doi.org/10.5194/egusphere-egu26-222, 2026.
The 2020 earthquake sequence in Croatia caused significant damage, particularly to cultural assets and older masonry buildings in areas of pronounced topography in Northern Croatia (EMS intensity VI). The observed damage distribution aligns closely with topographical features, with higher intensities recorded in hilly areas—such as Hrvatsko Zagorje, Ivanščica, Kalnik, and Međimurje—compared to adjacent alluvial basins.
To investigate these phenomena, this study presents results from microtremor measurements using the Horizontal-to-Vertical Spectral Ratio (HVSR) method across five localities characterised by distinct geological and morphological configurations. We integrated HVSR fundamental frequencies with local geological data to derive detailed seismic microzonation maps that quantify the terrain's resonance potential. These maps illustrate critical correlations between the slope/height of the dominant hill axis and the measured site frequencies.
Our analysis confirms that topographic site effects are primarily driven by the focusing of seismic waves at ridge crests, a process governed by diffraction, reflection, and wave type conversions. It is observed that amplification is highly frequency-dependent; resonance is strongest when the incoming wavelength aligns with the ridge’s frequency characteristics. Furthermore, the steepness of the topography plays a major role, with the uppermost portions of hills consistently showing stronger resonant motion than lower slopes.
Preliminary site amplification factors calculated for the 2020 earthquake scenarios (Zagreb Mw 5.4 and Petrinja Mw 6.4) reveal complex interactions between topographic irregularities and wave propagation. These findings underscore the necessity of explicitly incorporating topographic site effects into seismic microzonation studies. This approach is essential for producing reliable ground-shaking models and refining the local seismic-hazard assessment, particularly for preserving vulnerable historical structures in seismically active regions of Northern Croatia.
How to cite: Stanko, D., Novak, L., Jug, J., Hrnčić, N., Markušić, S., and Kovačić, M.: Assessing Topographic Site Effects and Seismic Microzonation in Northern Croatia: Case Study Insights from the 2020 Earthquake Sequence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6376, https://doi.org/10.5194/egusphere-egu26-6376, 2026.
Reliable shear-wave velocity (VS) profiles and their quantified uncertainties are essential for the robust characterization of site conditions and the accurate interpretation of seismic waveforms. This study presents uncertainty-quantified VS profiles extending from the surface to depths of approximately 1 km for 100 seismic strong-motion stations across the southern Korean Peninsula. At each site, active and passive surface-wave dispersion data were acquired via microtremor array measurements and multichannel analysis of surface waves, spanning a broad frequency range from ~1 to 10 Hz and ~5 to over 50 Hz, respectively. These datasets were jointly inverted using a trans-dimensional and hierarchical Bayesian framework, which treats the number of layers and dataset-specific error levels as unknown parameters. This approach yields an ensemble of VS profiles for each station, which inherently captures depth-dependent uncertainties. From these ensembles, key seismic site parameters, including VS30, bedrock depth, and resonance frequency, were estimated with rigorous uncertainty bounds to construct a comprehensive site flatfile. The estimated profiles and parameters were validated against independent in-situ borehole data, showing high consistency within the quantified uncertainty intervals. Furthermore, we derived region-specific regression equations among the site parameters, facilitating the generation of high-resolution maps for site parameters and their associated uncertainties. These outputs provide a foundation for correcting site effects in seismic waveforms, refining site terms in ground-motion prediction equations, and supporting regional seismic hazard assessments.
How to cite: Jeon, Y. and Kim, S.: Uncertainty-Quantified VS Profiles for 100 Strong-Motion Stations and Regional Site-Parameter Maps in the Southern Korean Peninsula, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16331, https://doi.org/10.5194/egusphere-egu26-16331, 2026.
We present the development of a nationwide seismic soil class map for Switzerland that implements the revised soil classification introduced in the new Eurocode 8 (EC8). In contrast to the current fragmented cantonal products, which are largely based on geological criteria and not directly compatible with EC8, the new scheme prioritizes geophysical descriptors of the subsurface, in particular average shear-wave velocity of the near surface (Vs30, vs,H) and the depth to engineering bedrock (H800). The resulting map will also support the updated national seismic hazard model.
To enable a consistent national study, we assembled large-scale datasets. These include shear-wave profiles estimated at seismic stations and other sites, horizontal-to-vertical spectral ratios (HVSR) analyses, standard- and cone penetration test datasets, and complementary geological information such as lithology, digital bedrock models, and borehole data. Together, these resources would allow mapping of shear-wave velocity structure and sediment thickness, which form the basis for the new EC8 classification.
The project is currently in its initial stage. We design a workflow that integrates measured velocity and HVSR information, where available, and use lithological and geological classifications as proxies where coverage is sparse. Thereby shifting from a purely geological classification to one that privileges geophysical parameters prescribed by EC8. This contribution outlines the conceptual design, data resources, and preliminary implementation of this geophysics-driven national soil class map and highlights its relevance for seismic hazard assessment, engineering practice, and future updates as new data become available.
How to cite: van Ginkel, J., Janusz, P., Shynkarenko, A., and Bergamo, P.: Developing a seismic soil class map for Switzerland using geophysical parameters , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6651, https://doi.org/10.5194/egusphere-egu26-6651, 2026.
Gas extraction from the Groningen field has induced a substantial number of earthquakes that, despite their typically low magnitudes, produce notable ground motions at the surface due to their shallow depths of approximately 3 km. These ground motions pose risks to society and infrastructure. Therefore, an accurate ground motion simulation is essential for seismic hazard assessment. Previous studies have demonstrated that near-surface unconsolidated layers significantly influence ground motion amplification. However, less attention has been devoted to understanding the role of deeper structures. In the Groningen region, significant amplification and de-amplification effects are anticipated due to the complex subsurface, thickness variations across relatively short lateral distances, compositional heterogeneity within sedimentary sequences, and the presence of the Zechstein salt layer overlying the reservoir formation.
This study investigates how subsurface heterogeneity, both shallow and deep, affects seismic wave propagation and the corresponding ground motion observed at the surface. To analyze this, we employ 3D full waveform modeling using the spectral element method (SEM). First, to optimize mesh resolution, determined by the local S-wave velocity and the target design frequency, we conduct simulations across a range of frequencies and corresponding spatial resolutions to analyze their impact on wavefield accuracy and computational cost. Second, we simulate seismic wave propagation through a synthetic velocity model representative of the Groningen subsurface and compute Peak Ground Acceleration (PGA) for different earthquake scenarios using various Centroid Moment Tensor (CMT) source solutions. Since amplification effects are highly location-dependent, we evaluate multiple earthquake scenarios with varying source characteristics and locations. We then compare these results with PGA values computed for a homogeneous half-space model that preserves the bulk elastic properties of the realistic heterogeneous model, using identical earthquake sources. This comparison produces amplification factor maps that reveal distinct spatial patterns of amplification and de-amplification across the study region. To isolate the contributions of individual factors, we examine the influence of source frequency, the depth and thickness of velocity layers, the presence of velocity inversions within the stratigraphic sequence, and subsurface interface topography.
These tests allow us to identify how each parameter contributes to the resulting amplification and de-amplification patterns. This framework can provide physical explanations for the spatial distribution of observed ground motion variations, offering valuable insights that are instrumental for current and future seismic hazard assessments in areas of subsurface resource exploitation throughout the Netherlands.
How to cite: Khoveiledy, T., Fadel, I., Dahal, A., and van der Meijde, M.: Effects of subsurface heterogeneity on ground motion amplification in Groningen, the Netherlands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13455, https://doi.org/10.5194/egusphere-egu26-13455, 2026.
The seismic response of the Taipei Basin is heavily influenced by its basin geometry and thick sedimentary deposits. These conditions focus seismic energy and govern surface shaking, resulting in prolonged durations and enhanced long-period content that pose significant risks to high-rise buildings and critical infrastructure. In structural design, the corner period (T0) of the response spectrum is a concise measure of frequency content that integrates source, path, and site effects. With the basin's dense strong-motion network, this study directly derives T0 from observations to refine seismic microzonation and design evaluations.
This study compiled a comprehensive dataset of moderate-to-large earthquakes (MW ≥ 5 or local PGA ≥ 10 gal) recorded in Taiwan from 1992 to 2024. Records underwent a unified processing workflow of baseline correction, filtering, and 5%-damped response spectra generation, after which events were categorized into crustal, subduction-interface, and intraslab types. For each category, the T0 was determined using the mean plus one standard deviation spectrum as the target. Results indicate pronounced spatial variations in T0 for crustal and subduction-interface earthquakes. Values are longest in the northwestern to north-northeastern regions (exceeding 1.5 sec) and shortest along the southeastern region. In contrast, intraslab events exhibit minimal spatial variation. Correlation analysis confirms that T0 distribution is strongly controlled by geological conditions, specifically bedrock depth and sediment thickness. By incorporating these geological parameters into spatial interpolation, this study enhances the resolution and physical interpretability of the microzonation, providing a more robust and detailed reference for seismic design in the Taipei Basin.
How to cite: Yang, C.-H., Gao, J.-C., Dong, J.-J., and Lee, C.-T.: Improving Seismic Microzonation of the Taipei Basin Using Response Spectra and Geology Informed Interpolation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4355, https://doi.org/10.5194/egusphere-egu26-4355, 2026.
Soil liquefaction occurs when saturated soil loses strength due to excess pore pressure generated by seismic activity, often resulting in severe structural failures. Recent earthquakes have highlighted the need for accurate prediction and mitigation, especially in geotechnical engineering, where many interconnected parameters are difficult to define or model mathematically. Triggered by intense ground shaking, liquefaction can undermine the seismic response of urban infrastructure, making early prediction crucial for disaster resilience in densely populated areas. To address these challenges, Artificial Intelligence (AI) techniques—particularly machine learning (ML) and deep learning (DL)—offer a powerful alternative to traditional methods by effectively capturing complex, high-dimensional data patterns. In this study, we propose a hybrid framework combining the Jellyfish Search (JS) algorithm for hyperparameter optimization within an ensemble learning architecture. The model combines the feature-extraction capabilities of Convolutional Neural Networks (CNNs) with the classification performance of eXtreme Gradient Boosting (XGB). Data from Cone Penetration Tests (CPT) obtained from the literature are converted into image-like formats to leverage CNN capabilities before classification by XGB. Performance evaluations compared the proposed models against both standalone and hybrid models documented in previous studies. Among individual machine learning models, XGB outperformed others, followed by Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN). The CNN model slightly exceeded existing standalone and hybrid ML-based models, including the Smart Firefly Algorithm with Least Squares SVM (SFA-LSSVM). When combined, the CNN-XGB model demonstrated superior predictive accuracy compared to either model used alone, highlighting the effectiveness of deep machine learning integration. The proposed JS-CNN-XGB model achieved the highest overall performance, with an additional 2.0% accuracy gain over the CNN-XGB model. These results indicate that XGB is the most robust predictive classification model, with CNN capturing complex features effectively, and that JS further enhances overall performance. Collectively, the JS-CNN-XGB model provides accurate and generalized predictions of liquefaction. Designed for civil engineers and construction risk managers, the system—featuring an embedded JS-CNN-XGB model—offers an intuitive interface and reliable analytical tools, functioning as a practical decision-support system for liquefaction risk assessment. Overall, these contributions emphasize the importance of integrating bio-inspired optimization with deep machine learning to address complex geotechnical challenges and turn research into practical solutions.
How to cite: Chou, J.-S. and Pham, T.-B.-Q.: A Hybrid Deep-Machine Learning Model with Bio-Inspired Optimization for Improved Soil Liquefaction Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2793, https://doi.org/10.5194/egusphere-egu26-2793, 2026.
Extensive microtremor surveys can provide valuable constraints on the deep structure of seismically active Quaternary intramontane basins. This study investigates two study areas, namely the Colfiorito and Annifo basins, which were affected by a seismic sequence during the years 1997–1998 [1]. Within the framework of the First ILGE Transnational Access (TNA) and National Open Access (NOA) Call (within the PNRR MEET WP3 project), a dense microtremors dataset was acquired in 2024 to improve the geological characterization of the two Quaternary basins. A total of 160 single-station microtremor measurements were collected over six days using 48 seismic nodes equipped with 4.5 Hz triaxial sensors, with recording time windows ranging from a few hours to two days. Two helicoidal nodal arrays were deployed in the northern Colfiorito plain and an additional one was installed in the southern area of the Annifo basin, to derive detailed shear-wave velocity profiles [2]. Moreover, two temporary stations equipped with 5-s Lennartz sensors and Reftek dataloggers were also operated for a few days. H/V spectral ratio analysis was carried out and revealed contrasting behaviors between the two basins. In Annifo, H/V peaks exceed 1.0 Hz, with the main depocenter located in the southern part of the basin. In Colfiorito, two main H/V frequency ranges have been observed: one is characterized by low-frequency peaks, between 0.6 and 1.0 Hz, located between the central and northeastern sectors of the basin, whilst a second, with frequencies between 1.0 and 6.0 Hz, characterizes the rest of the basin. In Colfiorito, the spatial distribution of resonant frequencies is consistent with a recent and independent gravimetric survey results [3], which identifyed two significant gravity minima in the central sector of the basin.
Acknowledgments
This publication results from work carried out under transnational /national open access (TNA/NOA) action under the support of WP3 ILGE - MEET project, PNRR - UE Next Generation Europe program, MUR grant number D53C22001400005.
References
[1] Messina, P.; Galadini, F.; Galli, P.; Sposato, A. Quaternary Basin Evolution and Present Tectonic Regime in the Area of the 1997–1998 Umbria–Marche Seismic Sequence (Central Italy). Geomorphology 2002, 42, 97–116, doi:10.1016/S0169-555X(01)00077-0.
[2] Di Giulio, G.; Cornou, C.; Ohrnberger, M.; Wathelet, M.; Rovelli, A. Deriving Wavefield Characteristics and Shear-Velocity Profiles from Two-Dimensional Small-Aperture Arrays Analysis of Ambient Vibrations in a Small-Size Alluvial Basin, Colfiorito, Italy. Bulletin of the Seismological Society of America 2006, 96, 1915–1933.
[3] Di Filippo, M.; Mancinelli, P.; Cavinato, G.P.; Pauselli, C.; Sabatini, A.; Mirabella, F.; De Franco, R.; Barchi, M.R. Bouguer Gravity Anomaly in the Colfiorito Quaternary Continental Basin, Northern Apennines, Central Italy. Journal of Maps 2025, 21, 2503244, doi:10.1080/17445647.2025.2503244.
How to cite: Ercoli, M., Di Giulio, G., Porreca, M., Safarzadeh, E., Alaia, G., and Brunori, C. A.: Revealing the buried structure of Apennines intermontane basins through dense nodal microtremor surveys: the case of Colfiorito and Annifo basins (central Italy)., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3884, https://doi.org/10.5194/egusphere-egu26-3884, 2026.
Seismic-wave attenuation in near-surface deposits is a key factor in site-effect modelling and local seismic hazard assessment. We investigate S-wave attenuation at station CTL8 of the Italian National Seismic Network, located in the Po Plain (northern Italy), by applying the borehole-to-surface deconvolution technique and comparing the results with estimates obtained using the kappa-based approach. The station is equipped with a surface accelerometer and a borehole velocimeter installed at 162 m depth, providing a suitable configuration for near-surface attenuation studies.
The analysed dataset consists of 109 pairs of surface and borehole recordings selected for their high signal-to-noise ratio, associated with local earthquakes with magnitudes between 3.0 and 5.8 and epicentral distances ranging from 36 to 256 km. Assuming predominantly vertical S-wave propagation between the borehole and the surface, identical time windows around the S-wave arrival were selected on the transverse component. The orientation of the borehole sensor was determined using tele-seismic events and corrected prior to the analysis.
Following the deconvolution procedure, up-going and down-going S-wave pulses were successfully isolated in the time domain. The spectral ratio between these pulses was used to estimate attenuation, yielding a surface-borehole kappa difference of Δκ162= (11.3 ± 1.1) ms. The time separation between the pulses also allowed the estimation of the time-averaged S-wave velocity between the borehole and the surface, resulting in Vs162 = (364 ± 7) m/s.
The results are consistent with previous estimates obtained at the same site using standard kappa-based methods and with synthetic deconvolution signals derived from a previously developed velocity profile. These findings indicate that borehole-to-surface deconvolution is a reliable and complementary tool for estimating near-surface attenuation and average S-wave velocity, provided that sufficient borehole depth and data quality allow a clear separation of the up-going and down-going wavefields.
How to cite: Franceschina, G. and Tento, A.: Estimating S-Wave Attenuation in Sediments by Deconvolution Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13214, https://doi.org/10.5194/egusphere-egu26-13214, 2026.
The Alhama de Murcia Fault (AMF), located in southeastern Spain, is one of the most active and hazardous fault systems in the region due to its elevated tectonic activity and its capacity to generate damaging earthquakes. The most recent significant event, the 11 May 2011 Lorca earthquake (Mw 5.1), resulted in nine fatalities, numerous injuries, and substantial material losses. While some authors interpret this earthquake as a purely natural occurrence, others suggest that its rupture may have been influenced by crustal unloading processes associated with groundwater extraction, potentially affecting the timing of the event. This debate underscores the importance of distinguishing between natural and induced seismicity in regions with high societal vulnerability.
Previous studies on the AMF have focused on its structural characteristics, seismic activity, and hazard potential through various methodologies, including paleoseismology and satellite data. However, integrated multidisciplinary analyses remain limited.
As a part of the MADRIZ project, this study aims to advance the seismic characterization of the AMF by compiling and reanalyzing seismic data from the nearest stations of the Spanish National Seismic Network, accessible through the EPOS Data Portal, together with open-access data from additional seismic networks that operate in the region. By applying both one-dimensional and three-dimensional location methods in conjunction with digital waveform analysis, we obtain highly precise hypocentral locations. These solutions form the basis for calculating focal mechanisms to better constrain the geometry and kinematics of active faults in the study area.
This integrated approach provides new insights into the seismic behavior of the AMF, contributing to the ongoing discussion on the interplay between natural tectonic processes and potential anthropogenic influences, and ultimately supporting more refined seismic hazard assessments for southeastern Spain.
How to cite: Núñez, D., Roman, D., Martínez, C., Córdoba, D., Carrillo, R., and Fernández, J.: Active Faulting in Southeastern Spain: New Evidence from the Seismic Characterization of the Alhama de Murcia Fault, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18842, https://doi.org/10.5194/egusphere-egu26-18842, 2026.
The 3D bedrock geometry in the Firenze area was reconstructed from seismic noise measurements and borehole data. A total of ~ 300 measurements of seismic noise, collected since 2002 with various instruments (Le3D-Lite, Le3D-5sec, Tromino) were used to derive the fundamental frequency using the HVSR methodology. The fundamental frequencies obtained range from 0.4 to 12 Hz and provide robust constraints for site effect characterization. Using borehole data, the relationship between frequency and sediment thickness was quantified through nonlinear regression, yielding h= 137 f -1.147. Among the investigated locations, the Mantignano area in western Florence was selected for detailed study with an array of 13 seismic stations equipped with Le3D-Lite seismometers, where inversion of HVSR spectra was performed and dispersion curve of surface wave was measured. For the HVSR inversion we employed the MATLAB- based OpenHVSR program. The inversion workflow incorporates an integrated misfit- minimization algorithm, allowing detailed reconstruction of the 3D subsurface structure at the Mantignano site. The results show that the bedrock position in Mantignano governs the stable low-frequency peak of all the HVSR curve, whereas the higher- frequency peaks reflect the near-surface horizons.
Additionally, phase-velocity information from surface waves, obtained using both CPSD measurements and the theoretical Bessel J0 model, provides consistent constraints on frequency–velocity pairs, improving the reliability of the dispersion characteristics obtained from Cross Spectral phase data.
How to cite: Ayoqi, N. and Marchetti, E.: Detailed 3D bedrock geometry in the Firenze area from HVSR seismic noise measurements, seismic noise inversion and dispersion curves of surface waves, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2237, https://doi.org/10.5194/egusphere-egu26-2237, 2026.
Accurate estimation of strong ground motion is important for seismic hazard assessment and for quickly evaluating earthquake impacts after an earthquake. In this study, data-driven ground-motion prediction models are developed using Japanese data to estimate peak ground acceleration (PGA), peak ground velocity (PGV), peak ground displacement (PGD), and spectral acceleration (SA) using machine-learning methods. Ensemble regression techniques, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), are trained using strong-motion records from the Kiban Kyoshin Network (KiK-net) and the Kyoshin Network (K-NET) collected between 1997 and
2025.
For comparison, PGA is also estimated using a conventional ground-motion prediction equation (GMPE). The functional form of Shoushtari et al. (2018) is adopted, and its coefficients are recalibrated using the same Japan dataset. The data are divided into training, validation, and testing sets, and model performance is evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and logarithmic residuals. Additional analyses, such as observed-versus-predicted comparisons and residual trends with distance, magnitude, focal depth, and VS30, are carried out to assess model behavior and identify possible biases.
The Random Forest model shows performance comparable to the recalibrated GMPE, suggesting that both approaches effectively capture the key effects of magnitude, distance, and site conditions on ground motion in Japan. Although the overall accuracy is similar, machine-learning models provide added advantages, including data-adaptive learning, stable residual patterns, and flexibility in predicting multiple ground-motion parameters. Therefore, machine learning can be considered a useful complementary approach that improves the robustness and applicability of ground-motion prediction for seismic hazard assessment.
How to cite: Taya, R., Mittal, H., Saini, A., and Kumar, R.: Comparative Evaluation of Machine-Learning Models and Recalibrated GMPEs for Ground-Motion Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17735, https://doi.org/10.5194/egusphere-egu26-17735, 2026.
The Uttarakhand Himalaya, situated in the central seismic gap, is one of India’s most active earthquake zones. Although a state-specific Uttarakhand Earthquake Early Warning System (UEEWS) is currently operational, its dependence on generic magnitude scaling relations and the conventional STA/LTA algorithm for P-wave detection leaves room for enhancement in accuracy and speed—especially given the complex tectonic and site conditions of the Garhwal and Kumaon regions. This study presents a two-pronged strategy to strengthen the UEEWS. First, we develop region-specific magnitude scaling relations using a mixed dataset of observed and simulated seismograms, thereby reducing real-time magnitude estimation uncertainties by accounting for local attenuation and source properties. Second, we propose APPNA (Auto Picking of P-wave Onset using Next-Gen Algorithm), a novel computational method designed to improve onset detection accuracy, increase noise resilience, and reduce false triggers compared to the STA/LTA approach. Validated on both real and synthetic data, these advancements demonstrate that integrating tailored scaling relations with an improved picking algorithm can significantly optimize the performance of an earthquake early warning system in high-hazard regions. Our findings underscore the potential of leveraging UEEWS data, regionally calibrated relations, and innovative algorithms like APPNA to enhance the operational effectiveness of the Uttarakhand warning system
How to cite: Sandeep, S., Monika, M., Kumar, P., Srivastava, N., Shaju, C., and Pal, S. K.: Optimising the Uttarakhand EEWS: A Hybrid Data and Next-Generation Algorithm Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16063, https://doi.org/10.5194/egusphere-egu26-16063, 2026.
