Significant progress has been made in obtaining high-resolution images of seismic velocity and other elastic/rock physics properties, identifying and quantifying the sources of various ambient noise wave types, and interpreting variations in seismic properties. However, challenges persist, such as using signals from suboptimally situated sources like urban noise or ambient noise body waves from localized storms, interpreting the seismic ambient field’s polarization, and analyzing ambient noise amplitudes for elastic effects and anelastic attenuation. Additionally, the spatial localization of seismic property changes and the implementation of spatial wavefield gradient measurements using advanced sensors, such as fiber optic or rotational sensors, present ongoing challenges.
This session invites discussions on recent advances in ambient noise seismology and seismic interferometry. Topics include theoretical and numerical developments, novel applications, and observational studies. We welcome studies on topics including, but not limited to, ambient seismic sources, ocean wave quantification through ambient noise, urban seismic noise, interferometric imaging, monitoring subsurface properties, and assessing subsurface deformation under both internal (e.g., earthquake, volcanic, slow movements, etc.) and external forces (e.g., tidal effects, environmental effects, anthropogenic effects, etc.). Additional topics of interest include spatial sensitivity studies for imaging and monitoring under diverse source conditions, quantification of site effects, amplification, and attenuation, AI-based signal processing, and interdisciplinary applications of seismic interferometry.
This session will also feature two-minute-long poster pitches from each of our poster presenters.
Orals: Fri, 8 May, 08:30–12:27 | Room -2.31
Monitoring groundwater and near-surface processes at high temporal resolution remains challenging, particularly in urban environments. Recent advances in passive seismology have demonstrated the potential of anthropogenic seismic sources to act as stable and repeatable signals for monitoring temporal variations in seismic velocity. In this study, we extend these approaches by proposing a method to monitor near-surface seismic attenuation variations, an observable that is highly sensitive to water content and therefore particularly relevant for investigating groundwater dynamics.
We exploit train-generated seismic waves as repetitive anthropogenic sources to track temporal changes in near-surface attenuation. Using a single-station approach, we analyze variations in the frequency content and amplitudes of seismic signals generated by passing trains to infer relative changes in seismic attenuation. These variations are interpreted as proxies for changes in near-surface hydrological conditions, including water saturation and groundwater level fluctuations.
The methodology is applied to a managed water catchment in Lyon (France), where artificial recharge operations and natural hydrological events provide independent constraints on subsurface water storage. We compare the inferred attenuation variations with complementary hydrological and geophysical observations, including rainfall records, infiltration basin water levels, piezometric measurements, and seismic velocity changes derived from autocorrelation of train signals. The results reveal consistent temporal relationships between attenuation variations and groundwater system responses, highlighting the sensitivity of attenuation-based observables to hydrological processes.
Our findings demonstrate that repetitive anthropogenic seismic sources can be used as opportunistic source for monitoring seismic attenuation variations. This passive approach offers new perspectives for continuous monitoring of groundwater dynamics in urban environments. More generally, the methodology can be extended to other sites where stable anthropogenic seismic sources are available, opening new opportunities for investigating near-surface processes using attenuation-based seismic observables.
How to cite: Pinzon-Rincon, L., Nziengui Bâ, D., Mordret, A., Rodríguez Tribaldos, V., Coutant, O., and Brenguier, F.: Tracking Near-Surface Attenuation Changes Using Repetitive Anthropogenic Seismic Sources, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7968, https://doi.org/10.5194/egusphere-egu26-7968, 2026.
The San Jacinto fault is one of the most active faults in Southern California and a major cause of seismic hazard. Estimating its capability to generate large earthquakes requires a detailed understanding of its mechanical properties and of their temporal changes at seismogenic depths, which remain difficult to characterize with standard seismic data and methods. In this work, we use 2.5 years of seismic noise recorded by a dense array of 300 nodes deployed at the Piñon Flat Observatory (PFO) from April 2022 to October 2024 to monitor the San Jacinto fault zone with tremor-like signals generated by freight trains travelling in the nearby Coachella valley. Recent studies have shown that trains are powerful and repeatable sources of high-frequency (> 1 Hz) body waves that can be used for monitoring seismic velocity variations at seismogenic depths (5 - 10 km) by seismic interferometry, but they have only exploited P waves so far. Here we show that cross-correlating train tremors recorded on both sides of the San Jacinto fault zone enables us to retrieve both P and S waves, and therefore to measure relative changes of both VP and VS with time, from which we can derive relative changes of an effective VP/VS ratio in the sampled volume. While the 2.5-year-long observation period does not include significant earthquakes in the target area, our results show fluctuations of seismic velocities and of the derived VP/VS ratio, with long-term trends as well as more rapid changes. The quantitative interpretation of these variations remains to be specified, but they are likely related to changes in stress, porosity, or fluid pore pressure at depth. Combining these results with other observables (e.g., detailed catalogs of micro-seismicity) will provide valuable information on the dynamic mechanical behaviour of the San Jacinto fault, potentially yielding insights into the evolution of cracks and fluids in the fault zone.
How to cite: Lavoué, F., Sheng, Y., Higueret, Q., Brenguier, F., Mordret, A., Aubert, C., Hollis, D., Vernon, F., and Ben-Zion, Y.: Monitoring P- and S-wave velocity changes in the San Jacinto fault zone (Southern California) using train tremors recorded by a long-term, dense nodal array, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14244, https://doi.org/10.5194/egusphere-egu26-14244, 2026.
The magnitude 9.0 Tohoku-oki earthquake occurred on March 11, 2011, leading to a devastating tsunami, causing extensive damage and many casualties. It also triggered seismicity all over Japan, such as in the Nagano region, where the Mw 6.2 Northern Nagano earthquake occurred 13 hours later, or in the Mount Fuji area, where the Mw 6.0 East Shizuoka earthquake occurred on March 15. In this study, we focus on the Northern Nagano region, assessing the temporal evolution of the seismic wave velocity, using ambient noise seismic interferometry. By applying the same approach, we also estimate the seismic wave changes around Mount Fuji. Velocity changes can be caused by coseismic damage, so we compare these results with the distribution of Peak Ground Acceleration (PGA).
The method used in this study consists in correlating continuous noise recordings to estimate the temporal seismic velocity variations in the medium. We use the NIED Hi-net three components waveform data from 110 stations to compute daily cross-correlation functions, which are then averaged with a moving window of 30 days. We retrieve the components of the Green’s function of the medium between pairs of stations, for all the component combinations. Using the wavelet cross-spectrum analysis, we estimate the seismic wave velocity variations of all stations, for the period between January-August 2011, for 0.1-0.9Hz. The same approach is also applied around Mount Fuji area. We thus obtain the spatio-temporal distribution of seismic velocities for both Northen Nagano and Mount Fuji regions. We compare these velocity variations to the distribution of the PGA observed for both regions. Overall, areas with larger velocity decreases experienced larger PGA during the mainshocks. Nevertheless, some other patterns can also be observed; for example, we observe anomalously large velocity drops for some stations located near volcanoes, in line with previous observations.
Our ongoing work aims to estimate the velocity variations from two events in the Kyūshū region: the 2016 Kumamoto earthquake and the 2024 Hyūga-nada earthquake. We could then compare these non-triggered events, of smaller magnitudes, to events triggered by the Tohoku-oki earthquake, and this analysis may help identify similar patterns comparable with the Nagano area, especially near volcanoes.
How to cite: Bagot, L., Enescu, B., Brenguier, F., Paris, N., Higueret, Q., Kakiuchi, Y., Miyazawa, M., Ohmi, S., Takeda, T., Lavoué, F., and Mordret, A.: Shallow crustal seismic velocity variations in the Nagano region, Japan, imaged by ambient noise seismic interferometry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13369, https://doi.org/10.5194/egusphere-egu26-13369, 2026.
Ambient seismic noise surface waves are routinely used to explore subsurface velocities. In contrast to active seismic exploration, seismic noise methods are cost-efficient, sensitive to shear velocities that can indicate fluid content, and can be repeated to highlight temporal variations.
Three-component ambient noise beamforming (B3AM) uses three-component sensor arrays to determine dominant wave types and their properties in small time windows at individual frequencies. Thus, wavefield composition, wavenumber, propagation direction, and Rayleigh wave ellipticities can be stacked for all times or analysed consecutively. To interpret temporal variations in these properties, the variations of noise sources need to be isolated from variations in the subsurface. We aim to understand the sensitivity of seismic surface waves to subsurface changes by analysing an existing ten-month long dataset in the Lower Rhine Embayment, Germany, where during the recording time no significant subsurface changes can be anticipated.
Analysing the continuous timeseries with B3Am and stacking properties for a rolling five days, we found two prominent noise source directions active at different times during the year. Observing the surface wave properties over time jointly and separated by apparent noise sources revealed different scales of property variations. Small-scale sharp variations of a few days and long-term property changes over a few months were observed. Finally, dispersion curves show significant differences when observing different times of year but not when comparing different noise sources. This enables us to interpret the variations of surface-wave properties in the context of the local geological context and global noise source variations.
How to cite: Finger, C., Neugebauer, S., Dormann, M., and Löer, K.: Temporal variation of seismic surface-wave properties and impact on exploration and monitoring of geothermal resources, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20009, https://doi.org/10.5194/egusphere-egu26-20009, 2026.
Groundwater level variations alter pore pressure within the rock formation and induce measurable changes in seismic velocity. Using continuous passive seismic data from Phase 1 of the WA-Array, Western Australia, we estimated time-lapse velocity changes in the Perth Basin and used it for tracking groundwater level throughout 2023. By analysing early coda waves from ambient noise cross-correlations, we revealed shear-wave velocity changes (dv/v) up to 0.4% with an annual cycle. The velocity increases during summer and decreases in winter, corresponding to Perth’s dry and wet seasons. These temporal velocity variations show a clear inverse correlation with the groundwater level measurement from borehole, as higher groundwater levels increase pore pressure and reduces seismic velocities. Spatial mapping of the velocity changes using coda-wave sensitivity kernels shows in general consistent results, however, the resolution is limited by the large station spacing (~40 km). Complementary to traditional hydraulic-head observations, our results demonstrate the importance of having long-term seismic networks (arrays) in metropolitan area for cost-effective long-term groundwater level monitoring.
How to cite: Saygin, E., Guo, P., and Lin, H.: Tracking groundwater level in the Perth Basin using WA-Array and passive seismic interferometry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21141, https://doi.org/10.5194/egusphere-egu26-21141, 2026.
Shallow subsurface velocity variations provide key constraints for understanding the migration of soil water and associate hydrological processes. In this study, we use a distributed acoustic sensing (DAS) unit to record high-frequency ambient noise and apply ballistic wave seismic monitoring (Mi et al., 2025) to retrieve velocity variations within the upper 10 m of the shallow subsurface in Hefei, Anhui Province, China, over a two-month period. During non-precipitation periods, low-frequency (< 22 Hz) phase velocities exhibit a negative correlation with temperature, whereas high-frequency (>22 Hz) phase velocities show a positive correlation. Following precipitation events, phase velocities decrease significantly across all frequencies. Based on a reference 1-D shear-wave velocity model, we further invert the time-dependent phase velocity perturbations to obtain depth-dependent shear-wave velocity variations (Haney and Tsai, 2017). Integration with borehole observations reveals contrasting responses between shallow (<5 m) and deeper (>5 m) layers to evaporation, infiltration, and loading: diurnal temperature variations regulate soil moisture and thereby control velocity changes during dry periods, while rainfall-induced infiltration becomes the dominant factor during precipitation. Our results demonstrate the effectiveness of DAS-based time-lapse velocity monitoring for characterizing shallow soil water cycling and highlight its potential for high spatiotemporal resolution hydro-geophysical monitoring of the near surface.
Raeferences
Haney, M.M. & Tsai, V.C., 2017. Perturbational and nonperturbational inversion of Rayleigh-wave velocities, Geophysics, 82, F15–F28.
Mi, B., Xia, J. & Li, J., 2025. On the measurement of relative phase velocity changes for ballistic wave seismic monitoring, Geophysical Journal International, 234, 1-9.
How to cite: Guan, B., Yao, H., and Sheng, Y.: Shallow subsurface velocity changes and hydrological responses revealed by distributed acoustic sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7049, https://doi.org/10.5194/egusphere-egu26-7049, 2026.
Postseismic healing generally involves nonlinear mechanical deformations characterized by the strain-dependent changes of seismic velocities. Brenguier et al. (2008) documented postseismic seismic-velocity changes after the 2004 Parkfield earthquake and primarily compared dv/v with along-fault displacement; GPS-derived strain was discussed mainly at an order-of-magnitude level, suggesting a potential link between dv/v and strain without providing a detailed quantitative analysis. Here ambient noise studies using seismic interferometry reveal the stress-dependent change of seismic velocities during the fault healing. To quantify this coupling, we develop acoustoelastic seismic interferometry that couples ambient-noise Green’s-function reconstruction with an acoustoelastic stress–velocity mapping to convert interferometric dv/v into the spatiotemporal evolution of stress changes during healing. The mapping is evaluated using second- and third-order elastic constants taken from experimental studies with Snake River Plain Basalt (Wang and Schmitt, 2024). We validate the approach independently using coseismic stress-drop and postseismic stress-recovery constraints with active-source benchmarks reported by Niu et al. (2008). Applied to the Parkfield sequence, we analyze the dv/v recovery trend with the corresponding stress-recovery pattern. This provides a physics-based route from phenomenological dv/v monitoring to quantitative inference of fault-zone stress evolution. The theoretical framework can be extended to other fault systems to continuously image stress transfer and healing from ambient noise and to inform earthquake-cycle models.
References
Niu, F., Silver, P. G., Daley, T. M., Cheng, X., & Majer, E. L. (2008). Preseismic velocity changes observed from active source monitoring at the Parkfield SAFOD drill site. Nature, 454(7201), 204-208.
Wang, W., & Schmitt, D. R. (2024). Measurement of the static nonlinear third-order elastic moduli of rocks: Problems and applicability. Journal of Geophysical Research: Solid Earth, 129(10), e2024JB028784.
Brenguier, F., Campillo, M., Hadziioannou, C., Shapiro, N. M., Nadeau, R. M., & Larose, É. (2008). Postseismic relaxation along the San Andreas fault at Parkfield from continuous seismological observations. science, 321(5895), 1478-1481.
How to cite: Yang, H., Fu, L.-Y., Wang, Q.-Y., and Campillo, M.: Nonlinear stress dependence from seismic interferometry for postseismic healing of the 2004 Parkfield earthquake, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19450, https://doi.org/10.5194/egusphere-egu26-19450, 2026.
Ambient seismic noise, traditionally viewed as undesired signal in seismic records, has increasingly gained importance as a source of information for site characterization and seismic monitoring. With the growing demand for exploitation of alternative energy resources (e.g. geothermal projects) in urban and suburban environments, understanding the spatial distribution and seismic noise levels - generated by both natural sources and anthropogenic sources - is critical for subsurface characterization as well as for optimizing microseismic monitoring networks.
In this work, we analyzed eight days of continuous recordings from a temporary seismic monitoring network in Switzerland. The main noise sources in the study area were identified through the analysis of satellite maps and their corresponding spectral characteristics were extracted from the passive seismic records. Seismic noise from the most powerful sources (trains) was used to derive the frequency-dependent attenuation coefficient (α). Moreover, seismic interferometry was applied to a subset of stations to estimate Rayleigh waves dispersion. These two pieces of information were combined to estimate the seismic quality factor (Q) of the subsurface.
We will highlight how the noise spectra database we built is a step toward optimizing several seismological applications. Specifically, it will reduce interpolation-related uncertainty in probabilistic power spectral density noise maps and will provide a first-order approximation of expected noise levels acting as a predictive tool in unmonitored areas.
This study was developed in the frame of “The Geosciences for Sustainable Development” project (Budget Ministero dell’Università e della Ricerca–Dipartimenti di Eccellenza 2023–2027 C93C23002690001).
How to cite: Abdulghany, A. A., Fuggi, A., Brovelli, A., Cassiani, G., and Barone, I.: Ambient Seismic Noise: From Characterization to Simulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13022, https://doi.org/10.5194/egusphere-egu26-13022, 2026.
Industrial facilities can act as persistent, high-energy seismic noise sources and are increasingly exploited for passive time-lapse monitoring. In geothermal settings, vibrations generated by power plants provide spatially stable sources that are well suited for long-term interferometric analyses. However, temporal variations in operational conditions may lead to changes in source spectral content, potentially biasing time-lapse measurements derived from ambient noise cross-correlation. Assessing and mitigating the effects of such source nonstationarity is therefore essential for reliable monitoring.
We investigate continuous seismic noise recorded in 2008 at the Salton Sea geothermal field (southern California) and apply a Variational Autoencoder (VAE) to characterize temporal variability in source spectra. The VAE is trained on frequency spectra from relatively stable periods and subsequently used to identify time intervals exhibiting anomalous time–frequency behavior, interpreted as changes in the industrial noise source. We then compute noise cross-correlation functions and corresponding travel-time variations (dt) for both normal and anomalous periods.
Our analysis reveals systematic differences in dt behavior associated with source spectral changes, including abrupt offsets at transitions between normal and anomalous intervals and increased high-frequency fluctuations during anomalous periods. These effects occur despite stable source locations, demonstrating that spectral variability alone can significantly contaminate time-lapse measurements.
To reduce these biases, we construct separate correlation reference functions for distinct source regimes. This adaptive strategy suppresses spurious dt fluctuations during anomalous intervals and yields more physically interpretable travel-time variations. The results highlight the importance of explicit source characterization in passive seismic monitoring and demonstrate how machine learning–based approaches can enhance the robustness of time-lapse interferometry in geothermal fields and other environments dominated by industrial noise sources.
How to cite: Sheng, Y. and Cai, K.: Source Variability as a Limiting Factor in Seismic Velocity Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4798, https://doi.org/10.5194/egusphere-egu26-4798, 2026.
Primary and secondary microseisms are both generated by ocean wave interactions. It has been observed that, in some regions, the secondary microseism splits into long- and short-period bands. Short-period secondary microseism (SPSM) sources have traditionally been associated with nearby coastal segments, local storms or wave heights, and relatively constant offshore distances. However, the parameters controlling this split, as well as the precise spatial distribution of SPSM sources, remain poorly understood.
Motivated by the observation of a frequency bimodality in seismic noise cross-correlations in NE Iberia, we find evidence that the secondary microseism split occurs more broadly across the Mediterranean region. Using a combination of complementary and independent approaches, such as polarization analysis, source mechanism modeling, and attenuation assessment, we investigate possible variations in the location of source regions within the microseismic band. As a result, distinct source regions are consistently identified across all the methodologies employed, providing insight into the microseismic sources.
We further demonstrate that SPSM sources are well constrained, spatially localized, and highly dynamic. In this study, we reveal key variables directly related to the generation of SPSM sources, enhancing our understanding of the parameters controlling their spatial distribution. These findings are likely applicable to other regions with similar conditions.
How to cite: Jorde, S., Schimmel, M., Stutzmann, E., Xu, Z., Sánchez-Pastor, P., Seivane, H., and Dı́az, J.: Locating Source Regions of Short-Period Secondary Microseisms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13967, https://doi.org/10.5194/egusphere-egu26-13967, 2026.
Full-waveform modeling plays a critical role in many areas of seismology. After decades of research and technological progress, fundamentally, it remains computationally expensive. The largest computational models, on the biggest clusters, can take hours to simulate. This still puts full-waveform modeling out of reach for the most time-critical applications. It also means that whenever large regional or community datasets are computed, difficult choices have to be made regarding probable and preferred source and receiver locations.
For applications that rely on elastic models that are updated only every few months or years, the status quo does not have to be this way. We revisit an early interferometry idea that makes it possible to exhaustively compute full-waveforms for any given model, and to store and recall those waveforms efficiently. Based on elastodynamic reciprocity theorems of the correlation type, we present two variants of the approach; opportunistic and exhaustive modeling, both discussed in detail below. The former allows any user to benefit, indirectly, from simulations carried by any other user, by turning every source point ever used into a potential receiver location, while the latter could enable fast (sub-second) computation of full-waveforms for earthquakes with complex rupture types occurring anywhere in a 3D model.
Opportunistic full-waveform modeling exploits expressions for elastodynamic Green’s function retrieval between source points in the volume. Whenever a user submits a simulation for one or more sources in the interior, in addition to storing the wavefield at the user-designated receiver locations, the wavefield on the surrounding surface, just inside the PMLs, is also separately stored. By consistently and opportunistically doing this whenever users submit such computations, full-waveform Green’s functions can later be computed between any pair of source points for which the wavefield on the surrounding surface was stored. Over time, the number of full-waveforms that can be retrieved this way grows quadratically. The advantage of this approach is that it requires much less disk, and it does not require any sorting. The disadvantage is: only full-waveforms between previously visited source points can be computed.
Exhaustive modeling exploits the reciprocal expressions for elastodynamic Green’s function retrieval, i.e., between receivers in the volume. When discretising the wave equation using a full-waveform method, the wavefield in the interior is evaluated at every location and at every time step of the simulation. Thus, the cost of a receiver is mainly disk. By systematically illuminating the model from the surrounding surface and storing the wavefield in as many points as possible, it becomes possible to retrieve full-waveform data between any pair of points at which the wavefield was stored, using only crosscorrelations and summations. This makes sub-second full-waveform computation feasible for any pair of points, even in 3D.
We demonstrate the exhaustive approach on the 2D elastic Marmousi model and the opportunistic approach on a 3D version of the same model. Among other things, we show, how a comprehensive 3.8 TB dataset allows retrieving an exhaustive 1B full-waveforms in the 2D model.
How to cite: van Manen, D.-J., Aloisi, G., and Robertsson, J.: Opportunistic and Exhaustive Full-Waveform Modeling using Seismic Interferometry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15483, https://doi.org/10.5194/egusphere-egu26-15483, 2026.
We deployed two dense nodal arrays in the area comprising the Alto Tiberina Fault (ATF) system, Northern Apennines of Italy. This area is characterized by high geodetic strain-rates, with ~3 mm/yr of SW-NE extension partially accommodated by aseismic creep on the low angle ATF and partially by seismic swarm occurrences on the synthetic and antithetic faults in the hanging wall. High angle hanging wall faults play a role in seismogenic ruptures and during the occurrence of seismic swarms. The two almost linear arrays were roughly pointing towards a cement factory whose mills (while operating) could produce a seismic noise in the range [3 10] Hz. Thanks to this continuous (at intervals) source we could reconstruct the Green function of the medium from cross-correlations, and thus identify the arrival of the body waves, firstly in the nodal, but successively also in the permanent station recordings.
Measuring temporal changes of seismic velocities on body-waves reconstructed from noise cross-correlations between stations separated by tens of km should allow us to gain new insights into the evolution of the ATF and its surrounding faults at seismogenic depths, and to improve our understanding of the earthquakes preparatory phase.
How to cite: Zaccarelli, L., Brenguier, F., Mandler, E., and Serpelloni, E.: Body wave reconstruction from the cross-correlation of high frequency seismic noise in the Alto Tiberina Fault zone, Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6427, https://doi.org/10.5194/egusphere-egu26-6427, 2026.
Fiber-optic telecommunication cables are commonly installed along major transport corridors such as highways and railways. Traffic along these infrastructures continuously excites the shallow subsurface, generating surface waves whose dispersive behavior can be analyzed to investigate near-surface structures. Applying Distributed Acoustic Sensing (DAS) to dark fiber deployed along such corridors makes it possible to record these signals with dense spatial sampling over large distances, thereby offering new opportunities for passive imaging and/or monitoring of the shallow subsurface.
In this study, we investigate the potential of DAS recordings acquired on dark fiber to perform Multi-Channel Analysis of Surface Waves (MASW) using traffic-induced seismic sources. Our analysis is based on two independent datasets: one collected along a main road crossing the Karlsruhe Institute of Technology (KIT) campus, and another acquired along a railway line. We introduce a comprehensive processing framework including (i) automated vehicle detection and tracking along the fiber, independent of prior knowledge of vehicle trajectories or source locations; (ii) surface-wave analysis based on cross-correlation to retrieve virtual shot gathers (VSGs); and (iii) a stacking strategy designed to enhance coherent surface-wave energy while suppressing noise, improving the resolution of dispersion spectra and enabling robust dispersion-curve estimation even in challenging, high-noise environments. The stacking strategy enables the analysis of temporal variations in the retrieved dispersion characteristics, beside resolving spatial variations along the cable.
How to cite: Proenca, T. and Azzola, J.: Using vehicle-induced DAS signals on dark fiber for MASW and monitoring of spatio-temporal variations of near-surface ground properties, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5539, https://doi.org/10.5194/egusphere-egu26-5539, 2026.
Seismic velocity monitoring using body waves has recently emerged as a promising approach to investigate temporal variations in crustal structures. In particular, body waves generated by specific, high-frequency (> 1 Hz) anthropic sources, such as train traffic, have been shown to provide stable and repeatable illumination of the crust at seismogenic depths (5 – 10 km). However, the applicability of this approach is geographically limited to targets located in the vicinity of these strong and continuous train-induced seismic sources. This limitation motivates the search for other opportune sources of continuous, high-frequency body waves for seismic monitoring.
To this end, we revisit the dense array data of the FaultScan experiment, originally deployed at the Piñon Flat Observatory (Southern California) to study the San Jacinto fault zone via seismicity analysis and velocity monitoring using train-induced signals. In this work, we look at quiet periods, in the absence of strong seismic events (no earthquakes, no train signals), in order to characterize the remaining ambient noise at high frequency (1 – 10 Hz), using beamforming and time-frequency analysis. During these quiet periods, we consistently observe body waves (both P and S waves) coming from the direction of the Los Angeles and Riverside urbanized sedimentary basins. The amplitude of these body waves exhibits a clear weekly pattern, suggesting that anthropogenic activity is their primary source mechanism. We hypothesize that anthropic sources within the basin may excite basin resonance and lead to the scattering of body waves at the basin-bedrock interface. We further demonstrate that these basin-radiated body waves can be successfully extracted by cross-correlation in the frequency band 2 – 6 Hz.
This wave field provides a good opportunity to investigate the characteristics of the basin resonance through seismic observations from outside the basins. Besides, by characterizing the spatiotemporal properties of body-wave energy from sedimentary basins, this study could extend the applicability of passive monitoring techniques using body waves to a wider range of tectonic and urban environments, including regions lacking strong specific anthropogenic sources such as trains.
How to cite: Kakiuchi, Y., Brenguier, F., Lavoué, F., Higueret, Q., Mordret, A., Paris, N., Bagot, L., and Vignon-Livache, M.: Investigating body waves radiated by urbanized sedimentary basins for seismic monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15840, https://doi.org/10.5194/egusphere-egu26-15840, 2026.
Seismic monitoring in active mining environments is strongly conditioned by the presence of intense and highly variable anthropogenic noise sources, which are often considered a limitation for conventional ambient-noise–based approaches. However, these sources also represent a persistent and information-rich wavefield that can be exploited if properly characterized and handled.
In this contribution, we focus on mining settings where seismic noise is dominated by anthropogenic activity (e.g., machinery, blasting-related processes, and operational cycles) and present a methodology specifically designed to turn these coherent but non-stationary sources into a useful signal for subsurface monitoring. The approach is based on seismic noise interferometry, combined with tailored preprocessing and source-selection strategies that enhance the stability and interpretability of the retrieved waveforms under strongly time-varying noise conditions.
We show how this method allows us to extract robust information on subsurface dynamics in mining-related contexts, with particular emphasis on applications such as tailings dam monitoring and near-surface mechanical stability. The results demonstrate that, rather than being an obstacle, anthropogenic noise can be systematically leveraged to improve seismic monitoring in active resource-extraction environments, opening new perspectives for environmental risk assessment and sustainable mining practices.
How to cite: Sánchez Sánchez-Pastor, P., Schimmel, M., Jorde, S., Seivane, H., Díaz, J., Balaguera, A., and Torné, M.: Seismic interferometry under dominant anthropogenic noise, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11685, https://doi.org/10.5194/egusphere-egu26-11685, 2026.
Seismic While Drilling (SWD) offers the possibility of imaging the subsurface in real time by using the drill bit as a seismic source. This research is part of the Drilling the Ivrea-Verbano zonE (DIVE) project, run as ICDP expedition 5071, and it examines the feasibility of using a diamond core drill bit for SWD in two boreholes (5071_1_A and 5071_1_B) in hard-rock conditions. Continuous seismic data were recorded with three-component MEMS sensor arrays. The aim was to determine the detectability of weak seismic signals from the drill bit at the surface. Advanced processing techniques, such as noise suppression, wavefield separation and cross-coherence interferometry were deployed, but no drill-bit signals could be reliably detected. Noise from the drill rig, generators, mud pumps, and general site activity dominated the recordings. This result highlights the fundamental challenges of SWD with weak sources in hard-rock environments. It also provides important lessons for future SWD campaigns, such as the quantification of detection limits for diamond core drilling, the need for noise mitigation, and the likely requirement of near-bit or downhole sensors.
How to cite: Trabi, B., Bleibinhaus, F., and Greenwood, A.: Lessons Learned from Seismic While Drilling with Diamond Core Drill Bits from ICDP Expedition 5071 in the Ivrea-Verbano Zone (Western Alps, Italy), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19248, https://doi.org/10.5194/egusphere-egu26-19248, 2026.
Monitoring temporal variations of seismic velocities (dv/v) is a key tool for investigating stress changes and damage processes in active tectonic regions. Traditional dv/v studies rely on dense seismic networks or on repeating earthquakes, which can limit their spatial resolution and applicability. Distributed acoustic sensing (DAS), by providing continuous and densely sampled measurements of the seismic wavefield, offers new opportunities to overcome these limitations and to develop high-resolution velocity monitoring strategies.
In this study, we investigate how dv/v can be estimated from DAS data by focusing on the analysis of seismic swarms. We develop a processing workflow and apply it to DAS data acquired from three submarine telecommunication fiber-optic cables of ~150 km each, with ~10,000 sensing points per cable, deployed in the central part of Chile (Abyss network). We first identify seismic swarms and quantify waveform similarity between events using multi-channel cross-correlation analysis. We then select event pairs exhibiting high waveform similarity across multiple DAS channels for further analysis. We analyze coda waves using a cross-spectral approach to estimate coherence and phase delays between events, and we infer relative seismic velocity variations from a linear regression of the measured time delays over selected coda time windows starting a few seconds after the S-wave arrival.
Through this work, we present a systematic framework for estimating dv/v from DAS-recorded seismic swarms and assess its sensitivity to event similarity, frequency band, and coda window selection. This work shows that seismic swarms, when recorded by dense DAS arrays, provide a promising basis for developing high-resolution seismic velocity monitoring strategies.
How to cite: Mamfoumbi Ozoumet, F. W., Rivet, D., Baillet, M., and Trabattoni, A.: Monitoring seismic velocity variations using DAS data: a workflow based on seismic swarm analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19161, https://doi.org/10.5194/egusphere-egu26-19161, 2026.
Extracting reliable long-period surface waves from ambient noise cross correlation functions (CCFs) remains challenging for small-aperture dense arrays, where the interstation distances are commonly shorter than the target wavelengths. This geometric limitation leads to unstable phase-delay estimates and labor-intensive, manual dispersion analysis. Here we present a beamforming-enabled Eikonal tomography framework that integrates array beamforming with interferometric surface-wave fields to stabilize and automate phase-velocity estimation at both short and long periods. Beamforming is first applied to the CCF wavefield at each period to estimate its dominant propagation direction and associated slowness, and the individual CCFs are subsequently aligned using the estimated slowness. Relative arrival-time fields derived from these aligned CCFs are then used as input for Eikonal tomography to recover spatially continuous phase-velocity maps across the array. This approach effectively extends the measurable period range beyond the conventional aperture-limited regime while preserving computational efficiency and minimizing subjective dispersion picking. Application to a ~96-station dense seismic network in Taiwan demonstrates the retrieval of coherent long-period phase-delay wavefields and substantially improved phase-velocity maps that more clearly delineate crustal and seismogenic structures compared with conventional ambient noise tomography. Our results indicate coupling beamforming with automated Eikonal tomography provides a transferable and robust pathway for long-period imaging using existing dense arrays, yielding better constraints on deep crustal structures that are essential for tectonic interpretation and regional seismic hazard assessment in complex geological settings.
How to cite: Liao, C.-F., Chen, Y.-N., Gung, Y., and Hung, S.-H.: Extending Long-Period Ambient Noise Surface-Wave Imaging in Small-Aperture Dense Arrays With Beamforming-Assisted Eikonal Tomography, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4217, https://doi.org/10.5194/egusphere-egu26-4217, 2026.
Accurately mapping buried fault structures is essential for assessing seismic hazards and characterizing subsurface complexity. Many small or concealed faults lack surface expression or recorded seismicity, creating hidden risks that are difficult to identify with traditional methods. Consequently, there is a vital need for reliable detection tools in diverse environments, ranging from geothermal fields to urban centers. The analysis of back-scattered surface waves from ambient seismic noise has emerged as a promising method to fill this gap. However, while synthetic models often exhibit distinct V-shaped patterns in virtual source gathers, applying these techniques to complex geological settings like the Southern Vienna Basin presents significant practical challenges.
We conducted an comprehensive, gather-by-gather analysis of virtual shot gathers from a dense linear nodal array. By manually inspecting binned plots across multiple frequency bands, we identified distinct wavefield anomalies at suspected fault locations. Preliminary results reveal two distinct wavefield anomalies: a transition from normal moveout to nearly flat moveout in the 0.5–1 Hz range, and a transition of energy from the causal to the acausal time lag in the 2–3 Hz range at suspected fault locations.
While these "kinks" and moveout of surface waves appear to correlate with known geological faults, their proximity to urban infrastructure introduces significant interpretive ambiguity. At this preliminary stage, it remains unclear whether these features represent true structural back-scattering or are artifacts induced by localized anthropogenic noise sources acting as stationary phase points. This study highlights the inherent difficulties in urban seismic imaging and underscores the necessity of distinguishing between structural scattering and source-induced artifacts to reliably identify hidden fault risks.
How to cite: Aiman, Y. A., Kramer, R., Estève, C., Lu, Y., and Bokelmann, G.: Challenges in fault detection using ambient noise in urban environments: Distinguishing structural scattering from source-induced artifacts through moveout and back-scattering analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7199, https://doi.org/10.5194/egusphere-egu26-7199, 2026.
Saxon Lusatia (eastern Germany) is considered a region with particularly low seismic background noise. It is therefore earmarked as a possible location for a so-called Low Seismic Lab as part of the newly founded German Center for Astrophysics (DZA) and for the Einstein Telescope, the new generation of gravitational wave detectors.
As part of the preliminary site investigations, several temporary seismic networks (a total of almost 400 stations) were operated in the area between Bautzen, Kamenz, and Hoyerswerda in 2024 and 2025. The main objectives were to create a 3D model of the subsurface (shear wave velocity; ambient noise tomography) using the seismic ambient noise field, and to investigate the spatial-temporal distribution of seismic noise (and noise sources).
Following the general approaches to analyzing ambient seismic noise, we started with a division of the data sets (vertical component data) into hourly segments, followed by bias removal and trend correction, as well as spectral brightening and 1-bit normalization. These pre-processed hourly segments were then used to calculate cross-correlations. Finally, these individual hourly cross-correlations were stacked to obtain the final empirical Green's functions for every station pair.
In the next step, the Rayleigh dispersion curves were determined interactively for a large number of cross-correlations. A general observation for the FTAN displays was that in almost all cases, the energy content of the selectable dispersion curves is very frequency-limited (typically 1.5–4 Hz) and that the data is noisy. This suggests that the tomographic resolution of the subsurface structures will be quite limited. Given the expected model complexity with a strongly varying layer of unconsolidated sediments of variable thickness (1–200 m) on top of high-velocity granodiorite, we focused our dispersion curve analysis on traces with offsets < 2 km.
The inversion was performed using a Bayesian statistical method, namely a transdimensional hierarchical Monte Carlo search using Markov chains and a Metropolis/Hastings sampler. This is a full tomographic inversion technique that can be used to derive the 3D distribution of shear wave velocity and the associated uncertainty. Given the difficult initial situation with regard to the data (noise, band-limited), we extended the inversion of the dispersion curves to include H/V data from 128 three-component stations.
Using seismic ambient noise data (dispersion curve and H/V data), we were able to successfully create a three-dimensional model of the shallow (<1 km) shear wave velocity structure beneath the Lausitz region. Lower velocities generally indicate softer, less consolidated, or more saturated (e.g., water-bearing) sediments near the surface. Higher velocities typically occur at greater depths, where the sediments are more compacted or transition into bedrock. The spatial distribution of the low-velocity layer corresponds very well with the distribution of granodiorite and greywacke outcrops, and the depth extent fits well with information from boreholes.
How to cite: Ryberg, T., Haberland, C., Rietbrock, A., Lindner, M., and Körschner, S.: Combined ambient seismic noise tomography and H/V analysis to decipher the shallow subsurface in Saxon Lusatia (eastern Germany), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18764, https://doi.org/10.5194/egusphere-egu26-18764, 2026.
Ambient noise interferometry enables the retrieval of inter-station surface wave responses through cross-correlation and linear averaging of continuous seismic response, under the assumption that the seismic wavefield is equipartitioned, with energy uniformly distributed over all propagation directions. In practice, however, ambient noise sources are highly non-uniform in both space and time, leading to biased estimates of the inter-station response between station pairs. If sources located within the stationary-phase zone can be identified and only the corresponding cross-correlation windows are selected for averaging, the inter-station response can be more accurately approximated, resulting in improved causal–acausal symmetry. Deep learning-based coherent source subsampling has been shown to effectively identify stationary-phase noise sources, thereby enhancing the recovery of physically meaningful inter-station surface wave responses.
In the Himalayan region, linear averaging of ambient-noise cross-correlations often does not yield causal–acausal symmetry and fails to recover inter-station surface wave response. In this study, we use data driven coherent source subsampling approach to systematically identify ambient-noise cross-correlations associated with stationary-zone sources prior to averaging. In this study, data from 19 stations deployed along a linear profile in the Kumaon–Garhwal Himalaya, spanning the periods 2005–2008 and 2011–2012, are analyzed. The continuous seismograms during the mentioned period were divided into 30-minute windows, and inter-station cross-correlations were computed for 167 station pairs. Using a symmetric variational autoencoder with discrete latent variables, we subsampled cross-correlation windows into distinct source states and select those corresponding to the stationary-phase zone, characterized by pronounced causal-acausal symmetry and maximum time lag. Averaging cross-correlations associated with the stationary-phase source state enhances the inter-station surface-wave dispersions, with causal and acausal branches yielding similar dispersions. These results show that coherent source subsampling provides an effective framework for improving ambient-noise interferometry in complex Himalayan geological settings.
How to cite: Bharadwaj, P., Bajad, S., and Bharadwaj, P.: Coherent Source Subsampling for Ambient Noise Correlation Analysis: A Himalayan Case Study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10297, https://doi.org/10.5194/egusphere-egu26-10297, 2026.
Passive seismic methods have become increasingly important for investigating crustal structures in tectonically active regions. The Asal Rift, located in central Djibouti, is characterized by pronounced lithospheric thinning resulting from extensional processes accommodated by major normal faults and a high geothermal flux associated with mantle upwelling beneath the region.
In this study, we evaluate the contribution of passive seismology to the assessment of the geothermal potential of the Asal Rift. The dataset comes from a network of 31 short-period and broadband seismic stations deployed between 2009 and 2011 as part of the Dynamics of Rifting in Asal (DORA) Project. We performed ambient seismic noise cross-correlations, applied Frequency–Time Analysis to extract Rayleigh-wave dispersion curves within the 1–5 s period band and constructed group velocity maps of the region. From those group velocity maps we constructed a 3D shear-wave velocity model around the rift.
The results reveal a significant decrease in seismic velocities within the rift zone, where several geothermal development projects are ongoing. These findings are interpreted as thermal anomalies that provide valuable insights for guiding future geothermal exploration and improving the understanding of crustal dynamics in the Asal Rift. Updated results will be presented at the meeting.
How to cite: Hassan, I., Zigone, D., Doubre, C., Jalludin, M., and Leroy, S.: Noise based tomography around the Asal Rift, Djibouti, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9258, https://doi.org/10.5194/egusphere-egu26-9258, 2026.
Botswana is situated in central Southern Africa and is characterized by diverse geology, including prominent cratons such as the Congo and Kalahari cratons, as well as two sedimentary basins. Previous studies of the crustal structure beneath Botswana have primarily relied on traditional regional and teleseismic earthquake tomography. In this study, we use ambient seismic noise tomography to image the crustal structure of Botswana and its surrounding region. Using two years of seismic data (2019–2020) from 40 broadband stations including stations from the Botswana Seismological Network (BSN) and neighbouring regions, cross-correlation functions (CCFs) are computed and used to reconstruct surface waves propagating between station pairs. Dispersions of the surface waves are extracted and used in a 2-D inversion to produce Rayleigh-wave group velocity maps of the region. Here, we present results including 2-D group-velocity maps across multiple periods and 1-D inversion results in the form of shear-velocity depth profiles derived from the dispersion measurements. We discuss these results and their implications for imaging crustal structure in this region and for developing detailed 3-D velocity models of Botswana's crust, providing new insights into the region's subsurface structure and geodynamics.
How to cite: Ntibinyane, O., Qorbani Chegeni, E., Estève, C., Kramer, R., and Bokelmann, G.: Crustal Structure Beneath Botswana from Ambient Seismic Noise Tomography, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9882, https://doi.org/10.5194/egusphere-egu26-9882, 2026.
Primary microseisms are long-period seismic waves generated by the interaction of ocean surface gravity waves with the seafloor. Yet, the spatial distribution and excitation mechanisms of their sources remain poorly resolved. Here, we investigate the heterogeneous generation of primary microseisms associated with seasonal tropical cyclone activity and Cyclone Ita in North-east Australia (2014). Combining continuous seismic data from a regional network with high-resolution (30 m) bathymetric maps, we show that primary microseism excitation is highly localised in both time and space, and critically dependent on fine-scale seafloor roughness. Our analysis reveals that the most energetic Rayleigh wave bursts arise from regions with pronounced bathymetric variability, where coupling between ocean waves and the solid Earth is most efficient. The finding provides observational evidence to confirm the theoretical conjecture that topographic undulations at scales comparable to ocean wave wavelengths govern the strength of microseism sources in the 10 – 20 s period band. Our findings highlight the critical role of nearshore bathymetric roughness in shaping the spatial coherence of primary microseism excitation. The understandings are essential for guiding the future use of persistent seismic sources in seismic imaging of Earth’s near-surface structures and in monitoring the evolution of the seismic wavefield in near-future applications.
How to cite: Pandey, A., Pham, T.-S., and Tkalčić, H.: Seafloor Topography Controls Primary Microseism Generation: New Insights from Cyclone-Forced Seismic Observations in Northeastern Australia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8474, https://doi.org/10.5194/egusphere-egu26-8474, 2026.
On 27 November 2025, Tropical Cyclone (TC) Senyar impacted northeastern Sumatra, providing the first opportunity to study cyclone-induced microseismic signals in Indonesia. The characteristics of the cyclone were analyzed using continuous seismic data recorded between 24 and 30 November 2025 from the seismic network across Sumatra. Recent advances in noise interferometry allow the estimation of empirical Green’s functions through the calculation of cross-correlation tensors. These functions are dominated by surface waves, enabling the monitoring of seismic velocity and anisotropy through Horizontal Polarization Anomaly (HPA). In this study, we evaluate the seismic effects of TC Senyar on ambient seismic noise. The results show an amplification of ambient seismic noise dominated by microseismic activity in the frequency range of 0.10 to 0.25 Hz, characterized by an enveloped waveform associated with TC Senyar. Seismic displacement and energy were detected several hours after the cyclone passed near the observation area, at distances of approximately 20 to 30 km from the cyclone location. The cyclone reorganized the ocean wave field, and ocean wave–wave interactions likely generated multiple second order microseism sources. The observed HPA pulse reflects a temporary change in the ambient noise source, demonstrating the strong coupling between tropical cyclone dynamics and near-surface seismic observations in the Indonesian region.
How to cite: Palgunadi, K. H., Simanjuntak, A. V., Warnana, D. D., Supendi, P., Pranata, B., Daryono, D., Florida, N., and Montagner, J.-P.: Cyclone-Induced Microseismic Signals Recorded in Sumatra During Tropical Cyclone Senyar, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20526, https://doi.org/10.5194/egusphere-egu26-20526, 2026.
How to cite: Becker, D., Michel, H., Kiehn, M., Shani-Kadmiel, S., Tomassen, M., Koley, S., Nguyen, F., Hammer, C., and Hadziioannou, C.: Trade-off between signal coherence and ambient noise (3C) beamforming results in a complex noise environment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21232, https://doi.org/10.5194/egusphere-egu26-21232, 2026.
The North Atlantic Ocean has a significant role in the Earth’s climate and local weather conditions. Ocean wave-wave interactions generate seismo-acoustic noise known as secondary microseisms, which provide valuable information on storm activity, long-term climate variability, and ocean-land-atmosphere coupling. While terrestrial seismic stations have been shown to successfully detect and localize deep-ocean secondary microseism sources, the recorded wavefields are inevitably influenced by propagation effects along the source-receiver path, including attenuation, scattering, and interactions with complex Earth structures. The extent to which major tectonic features modify these signals remains an active area of research.
This study investigates secondary microseism propagation effects in the North Atlantic with a focus on the Mid-Atlantic Ridge, a major tectonic structure located along the path of microseisms generated south of Greenland as they propagate towards Europe. A combined approach is adopted using numerical simulations and ocean-bottom seismometer (OBS) observations from the SEA-SEIS project in order to provide new constraints on microseism wave propagation in structurally complex oceanic environments that define mid-ocean ridges. A 3D seismo-acoustic model of the northern Mid-Atlantic Ridge region is generated, incorporating realistic bathymetry and crustal structure. Synthetic seismograms reveal that the ridge strongly modifies seismic wave amplitudes and frequency content, with pronounced scattering and mode conversions observed near the ridge axis.
The simulations further suggest a partial screening effect across the ridge, whereby signals from synthetic microseism sources located on one side of the ridge show reduced amplitudes at stations on the opposite side. This effect is likely associated with scattering from shallow bathymetric structure linked to the ridge’s complex morphology, while deeper structural heterogeneity and velocity variations also contribute. In addition, the analysis of the OBS stations in the Eastern Atlantic region reveal interesting patterns in ambient noise cross-correlations when combined with the expected microseisms source regions derived from ocean wave models. Preliminary results show that there seems to be no clear dominant propagation direction towards the East when sources are located south of Greenland, west of the ridge. These observations are consistent with the simulation results and indicate the significant influence of bathymetry on microseism propagation.
How to cite: Vijayan, A., Le Pape, F., Bean, C. J., and Lebedev, S.: Secondary Microseism Propagation Across the Mid-Atlantic Ridge: Insights from 3D Seismo-Acoustic Modelling and OBS Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13619, https://doi.org/10.5194/egusphere-egu26-13619, 2026.
With the continuous growth of urban populations, monitoring human activity in urban areas is increasingly important for social stability, infrastructure management, and sustainable urban development. Conventional approaches for monitoring human activity, such as wearable devices, survey sensor networks, and satellite remote sensing, are often constrained by privacy concerns, data accessibility, or weather conditions (Chen and Xia, 2023). In this context, ambient seismic noise recorded by seismometers has emerged as a promising alternative for monitoring urban activity, offering high temporal resolution and robust, privacy-preserving observations (Lecocq et al., 2020; Poli et al., 2020). We analyze ambient seismic noise recorded at stations across multiple European cities and countries to investigate the relationship between human activity and seismic noise characteristics. The analysis focuses on frequency bands dominated by anthropogenic signals and examines temporal and spatial variations in seismic noise levels. The results demonstrate that urban ambient seismic noise contains rich information related to human activity and exhibits pronounced diurnal and weekly cycles, as well as variability associated with holidays, weather changes, and major societal disruptions such as the COVID-19 pandemic. Our findings indicate that seismic noise analysis can reveal how multiple factors jointly influence the spatiotemporal patterns of human activity in large urban regions. The results demonstrate the efficacy of ambient seismic observations in facilitating near-real-time monitoring of urban dynamics. Such an approach may provide valuable complementary information for governmental agencies and policymakers, supporting dynamic urban management and decision-making from a geophysical perspective.
How to cite: Hong, Y., Xia, J., Boschi, L., and Poli, P.: Urban Activity Monitoring in Multiple European Cities Using Ambient Seismic Noise, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16367, https://doi.org/10.5194/egusphere-egu26-16367, 2026.
