From the real-time integration of multi-parametric observations is expected the major contribution to the development of operational time-Dependent Assessment of Seismic Hazard (t-DASH) systems, suitable for supporting decision makers with continuously updated seismic hazard scenarios. A very preliminary step in this direction is the identification of those parameters (seismological, chemical, physical, etc.) whose space-time dynamics and/or anomalous variability can be, to some extent, associated with the complex process of preparation of major earthquakes.
This session includes studies on various aspects of seismic risk research and assessment, observations and/or data analysis methods within the t-DASH and Short-term Earthquakes Forecast perspectives:
- Studies on time-dependent seismic hazard and risk assessments
- Development of physical/statistical models and studies based on long-term data analyses, including different conditions of seismic activity
- Application of AI to assess earthquake risk factors (hazard, exposure, and vulnerability). Exploring innovative data collection and processing techniques, such as statistical machine learning
- Estimating earthquake hazard and risk across different temporal and spatial scales and assessing the accuracy of these models against available observations
- Earthquake-induced cascading effects such as landslides and tsunamis, and multi-risk assessments
- Studies devoted to the description of genetic models of earthquake’s precursory phenomena
- Infrastructures devoted to maintain and further develop our present observational capabilities of earthquake related phenomena also contributing to build a global multi-parametric Earthquakes Observing System (EQuOS) to complement the existing GEOSS initiative
Orals: Fri, 2 May | Room 1.15/16
Seismogenic mechanism of strong earthquakes plays a fundamental role in disaster prevention. Electromagnetic methods, which are sensitive to fluid, have been widely adopted in the study on seismogenic structure and earthquake physics. Due to the increasing environmental disturbances and limited understanding on electromagnetic anomalies, electromagnetic data cannot fully show their potential values in disaster prevention. We propose an integrated work on seismogenic structure, identification of electromagnetic disturbances, and mechanism of seismo-electromagnetic anomalies. Based on the tests of synthetic and field data, we demonstrate that the multiple electromagnetic methods can reveal the feature of the multi-scaled seismogenic structure. With the developments of the new methodology based on deep learning and the seismo-electromagnetic coupling model, one can investigate the spatio-temporal characteristics of electromagnetic anomalies and their possible relationship with earthquakes. This study may contribute to the study on earthquake forecast and disaster prevention.
How to cite: Huang, Q.: Seismo-electromagnetism: observations and mechanisms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21907, https://doi.org/10.5194/egusphere-egu25-21907, 2025.
There has long been research on the phenomenon of abnormal microwave radiation emitted from the Earth's surface before a major earthquake. However, the enhanced microwave radiation received by satellite sensors is affected by a combination of factors such as surface vegetation, soil moisture, land surface temperature, and atmospheric environment. So far, it has been difficult to remove non-seismic interference through quantitative physical modeling, leaving only the earthquake-related additional components for earthquake precursor analysis and short-term earthquake prediction. To tackle with this, we developed a knowledge-guided deep learning model that leverages a large amount of remote sensing observation data for training, incorporating prior knowledge of earthquake anomaly analysis. In the modelling process, a large amount of multi-source data, such as surface microwave brightness temperature (MBT), land surface temperature (LST), surface vegetation index, soil moisture index, atmospheric water vapor content, cloud cover, land cover type, digital elevation model (DEM), and geological type, were collected, and a regression model between multiple factors and surface MBT were firstly established through deep learning methods. In the same way, another regression model was developed between non-temperature parameters and LST by using historical records. During the seismic window of one month before and after the target earthquake, the LST was obtained by using non-temperature data through the second regression model, and then was substituted it into the first regression model to get the MBT value that does not include the additional effects of earthquakes. Eventually, we can obtain the additional MBT value due to seismic activity by calculating the difference with the actual observation, which represents the earthquake-related MBT anomaly. Since the deep learning-based modeling is based on long time series data and the output results of the model already include the contribution of multiple factors on the surface to the MBT, the differential results are mainly affected by the additional impacts of the earthquake, so they can be considered 'pollution-free'. In other words, there is no need to use additional auxiliary data to discriminate and separate the non-seismic disturbances. For a specific target area, such as the Tibetan Plateau, after establishing a model based on historical data using the aforementioned method, we can obtain real-time earthquake MBT variations as the input data is continuously updated. This can be used to analyze and identify potential earthquake precursors, and consequently, for short-term earthquake prediction.
How to cite: Qi, Y., Mao, W., Wu, L., and Huang, B.: A Knowledge-Guided Deep Learning Model for Extracting Pollution-free Seismic Microwave Brightness Temperature Anomalies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1408, https://doi.org/10.5194/egusphere-egu25-1408, 2025.
In the last decades, the scientific community has been focused on searching earthquake signatures in the Earth's atmosphere, ionosphere, and magnetosphere. This work investigates an offshore Mw 5.5 earthquake that struck off the Marche region's coast (Italy) on November 9, 2022, with a focus on the potential coupling between the Earth's lithosphere, atmosphere, and magnetosphere triggered by the seismic event. Analysis of atmospheric temperature data from ERA5 reveals a significant increase in potential energy (Ep) at the earthquake's epicenter, consistent with the generation of Atmospheric Gravity Waves (AGWs). This finding is further corroborated by the MILC analytical model, which accurately simulates the observed Ep trends (within 5%), supporting the theory of Lithosphere-Atmosphere-Ionosphere-Magnetosphere Coupling. The study also examines the vertical Total Electron Content (vTEC) and finds notable fluctuations at the epicenter, exhibiting periodicities (7-12 minutes) characteristic of AGWs and traveling ionospheric disturbances. The correlation between ERA5 observations and MILC model predictions, particularly in temperature deviations and Ep distributions, strengthens the hypothesis that earthquake-generated AGWs impacted atmospheric conditions at high altitudes, leading to observable ionospheric perturbations. This research contributes to a deeper understanding of Lithosphere-Atmosphere-Ionosphere-Magnetosphere Coupling mechanisms and the potential for developing reliable earthquake prediction tools.
How to cite: Piersanti, M., D'Angelo, G., Recchiuti, D., Lepreti, F., Cusano, P., De Lauro, E., Carbone, V., Ubertini, P., and Falanga, M.: On the Ionosphere-Atmosphere-Lithosphere coupling during theNovember 9, 2022 Italian Earthquake, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2283, https://doi.org/10.5194/egusphere-egu25-2283, 2025.
This study conducted a systematic analysis of the 2022 Chihshang earthquake sequence in eastern Taiwan, integrating multidimensional observational parameters related to the lithosphere, atmosphere, and ionosphere. High-resolution data from the MAGIC (Multidimensional Active fault of Geo-Inclusive observatory - Chihshang) at the Chihshang fault area provided a comprehensive and diverse dataset. The analysis revealed significant pre-earthquake anomalies across various parameters. These include a marked increase in soil radon concentration one month prior to the earthquake, concurrent anomalies in hydrogeochemical parameters (e.g., elevated groundwater temperature, reduced pH, and decreased chloride ion concentration), and active foreshock activity detected by a dense microseismic network starting mid-August, suggesting the development of microfractures within the lithosphere. Additionally, persistent OLR (Outgoing Longwave Radiation) anomalies, indicating hotspots near the epicenter, were observed from September 5 to 7. Pre-earthquake signals in TEC (Total Electron Content) were identified between August 20 and September 13 in two independent datasets, GIM-TEC and CWA-TEC.
Post-earthquake observations revealed a significant increase in CO2 flux in the region, likely attributable to the release of deep-seated gas sources or enhanced permeability of the fault system. These combined observations suggest that all anomalies can be classified as short-term precursors, which can be interpreted within the theoretical framework of lithosphere-atmosphere-ionosphere coupling (LAIC). The findings also contribute to a deeper understanding of the earthquake preparation process. This study underscores the critical importance of real-time integration of multi-parameter observations, offering new insights and improvements for seismic hazard assessment and advancing the predictive capability of earthquake precursors.
How to cite: Fu, C.-C., Kuo-Chen, H., Mu, C.-H., Jhuang, H.-K., Lee, L.-C., Walia, V., and Tsai, T.-C.: Multiparameter observations of Lithosphere–Atmosphere–Ionosphere pre-seismic anomalies: Insights from the 2022 M6.8 Chihshang earthquake in southeastern Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8052, https://doi.org/10.5194/egusphere-egu25-8052, 2025.
Various types of changes in the characteristics of very low frequency (VLF) signals before earthquakes have been presented during the past few decades. Most of these changes have been observed on data with time sampling of the order of a few tenths of a second or of the order of minutes. Improvements in this sampling in recent years have indicated three new types of changes whose onsets have been observed a few minutes or tens of minutes before the earthquake. These changes manifest themselves as reductions in the VLF signal amplitude and phase noises, and excitation and attenuation of waves with small wave periods in both of these signal characteristics [1-5].
In this work, we present these changes and list the parameters in the time and frequency domains that are significant for statistical analyses. A central issue is the relationship of the changes with the characteristics of earthquakes, the observed signals, and their spread in the surrounding area. The presented analyses were conducted on data recorded by a VLF receiver in Belgrade, Serbia.
References:
[1] A. Nina, S. Pulinets, P.F. Biagi, G. Nico, S.T. Mitrović, M. Radovanović, L.Č. Popović, “Variation in natural short-period ionospheric noise, and acoustic and gravity waves revealed by the amplitude analysis of a VLF radio signal on the occasion of the Kraljevo earthquake (Mw = 5.4)”, Science of The Total Environment, 710, 136406, 2020.
[2] A. Nina, P. F. Biagi, S. T. Mitrović, S. Pulinets, G. Nico, M. Radovanović, L. Č. Popović, “Reduction of the VLF signal phase noise before earthquakes”, Atmosphere 12 (4), 444, 2021.
[3] A. Nina, P. F. Biagi, S. A. Pulinets, G. Nico, S. T. Mitrović, V. M. Čadež, M. Radovanović, M. Urošev, L. Č. Popović, “Variation in the VLF signal noise amplitude during the period of intense seismic activity in Central Italy from 25 October to 3 November 2016”, Frontiers in Environmental Science, 10, 10:1005575, 2022.
[4] A. Nina, “Analysis of VLF Signal Noise Changes in the Time Domain and Excitations/Attenuations of Short-Period Waves in the Frequency Domain as Potential Earthquake Precursors”, Remote Sensing, 16(2), 397, (2024)
[5] A. Nina “VLF Signal Noise Reduction during Intense Seismic Activity: First Study of Wave Excitations and Attenuations in the VLF Signal Amplitude”, Remote Sensing, 16(8), 1330, 2024.
How to cite: Nico, G., Nina, A., Biagi, P., Eichelberger, H. U., Boudjada, M. Y., and Popović, L. Č.: Noise reductions of VLF signals and excitation/attenuation of waves with small wave periods before earthquakes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8809, https://doi.org/10.5194/egusphere-egu25-8809, 2025.
A critical review of geoelectrical monitoring activities carried out in seismically active areas is presented and discussed. The electrical resistivity of rocks is one of the geophysical parameters of greatest interest in the study of possible seismic precursors, and it is strongly influenced by the presence of highly fractured zones with high permeability and fluid levels. The analysis in this study was based on results obtained over the last 50 years in seismic zones in China, Japan, the USA and Russia. These previous works made it possible to classify the different monitoring strategies, to analyze the theoretical models for interpreting possible correlations between anomalies in resistivity signals and local seismicity, and to identify the main scientific and technological gaps. In addition, much attention is given to some recent work on the study of correlations between focal mechanisms and the shapes of anomalous patterns in resistivity time series, and to the new possibilities offered by the AI-based methods for geophysical data processing. Finally, new strategies and activities for investigating the spatial and temporal dynamics of the electrical resistivity changes in seismically active areas were identified.
How to cite: Lapenna, V.: Detecting DC Electrical Resistivity Changes in Seismic Active Areas: State-of-the-Art and Future Directions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9250, https://doi.org/10.5194/egusphere-egu25-9250, 2025.
Electric field amplitude and phase measurements between narrowband VLF/LF transmitters and receivers in the sub-ionospheric waveguide are affected and altered by man-made and natural sources (Nina 2024; Boudjada et al., 2024a,b). In this study we investigate Mw≥5.0 earthquakes (EQs) which occurred in Europe during the year 2024 based on data from the INFREP receiver network (Biagi et al., 2019; Moldovan et al., 2015; Galopeau et al., 2023). In the selected Mediterranean area with geographical longitude [-10°E, 40°E] and latitude [30°N, 50°N] the United States Geological Survey EQ catalog (USGS, 2025) provides 20 events with Mw≥5.0. For these EQs we apply the night-time amplitude method and consider variations in the terminator times (Hayakawa et al., 2010). The main radio links that cross the EQ prone areas are from transmitters localized in the southern part of Europe, including TBB (26.70 kHz, Bafa, Turkey), ITS (45.90 kHz, Niscemi, Sicily, Italy), and ICV (20.27 kHz, Tavolara, Italy).
We find statistically significant electric field anomalies for various VLF/LF paths, particularly for events with higher magnitudes. The continuous VLF/LF electric field amplitude and phase datasets can be important parameters for real-time observations and services to assess seismic hazards and disturbing physical phenomena within the waveguide.
References:
Biagi, P.F., et al., The INFREP network: Present situation and recent results, OJER, 8, 101-115, 2019. https://doi.org/10.4236/ojer.2019.82007
Boudjada, M.Y., et al., Unusual sunrise and sunset terminator variations in the behavior of sub-ionospheric VLF phase and amplitude signals prior to the Mw7.8 Turkey Syria earthquake of 6 February 2023, Remote Sens., 16, 4448, 2024. https://doi.org/10.3390/rs16234448
Boudjada, M.Y., et al., Analysis of pre-seismic ionospheric disturbances prior to 2020 Croatian earthquakes, Remote Sens., 16, 529, 2024. https://doi.org/10.3390/rs16030529
Galopeau, P.H.M., et al., A VLF/LF facility network for preseismic electromagnetic investigations, Geosci. Instrum. Method. Data Syst., 12, 231–237, 2023. https://doi.org/10.5194/gi-12-231-2023
Hayakawa, M., et al., A statistical study on the correlation between lower ionospheric perturbations as seen by subionospheric VLF/LF propagation and earthquakes, JGR Space Physics, 115(A9), 09305, 2010. https://doi.org/10.1029/2009JA015143
Moldovan, I.A., et al., The development of the Romanian VLF/LF monitoring system as part of the International Network for Frontier Research on Earthquake Precursors (INFREP), Romanian Journal of Physics, 60 (7-8), 1203-1217, 2015. Bibcode: 2015RoJPh..60.1203M https://rjp.nipne.ro/2015_60_7-8/RomJPhys.60.p1203.pdf
Nina, A., VLF signal noise reduction during intense seismic activity: First study of wave excitations and attenuations in the VLF signal amplitude, Remote Sens., 16, 1330, 2024. https://doi.org/10.3390/rs16081330
USGS, United States Geological Survey earthquake catalog, https://www.usgs.gov/programs/earthquake-hazards, as of Jan 2025.
How to cite: Eichelberger, H. U., Boudjada, M. Y., Nina, A., Besser, B. P., Wolbang, D., Solovieva, M., Biagi, P. F., Galopeau, P. H. M., Schirninger, C., Moldovan, I.-A., Nico, G., Stachel, M., Aydogar, Ö., Muck, C., Wilfinger, J., and Jernej, I.: Sub-ionospheric VLF/LF waveguide electric field investigation from Mw≥5.0 earthquake events with multiple receivers in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13142, https://doi.org/10.5194/egusphere-egu25-13142, 2025.
Despite advances in satellite remote sensing, predicting large earthquakes, remains a significant challenge due to the unpredictable nature of these events. To address this challenge, our study, building upon the achievements of the French DEMETER satellite, focuses on atmospheric and space electrical variations as potential indicators of ionospheric D-region precursors to earthquakes. This approach is expected to contribute to the enhancement of short-term prediction capabilities. For this purpose, we would like to introduce our CubeSat PRELUDE (Precursory electric field observation CubeSat Demonstrator), a tiny satellite dedicated to the earthquake precursor detection and elucidated the physical mechanism. PRELUDE is scheduled for launch in JFY2025 as part of JAXA’s Innovative Satellite Technology Demonstration Program. This study presents the results of the system design, development, and mission planning of the PRELUDE, aimed at clarifying the physical mechanisms behind the statistically significant earthquake precursor ionospheric phenomena. PRELUDE is a 6U CubeSat specialized in VLF electromagnetic wave intensity observation, weighing 8 kg. To achieve miniaturization, it incorporates a drive recording function to downlink only the data surrounding the EQ epicenter to ground stations, reducing data storage and transmission requirements. Additionally, it hybridizes the Langmuir and electric field probes, typically found on satellites weighing over 100 kg like DEMETER, into a compact design suitable for CubeSats weighing just a few kilograms. The hybrid sensor unit extends booms bidirectionally by 1.5 m from the satellite using a folding extension mechanism, In this presentation, we show the satellite design requirements for elucidating the mechanism of earthquake precursor phenomena.
How to cite: Kamogawa, M., Yamazaki, M., and Sone, N.: Design of the PRELUDE CubeSat for investigating ionospheric D-region earthquake precursor, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14706, https://doi.org/10.5194/egusphere-egu25-14706, 2025.
In this study, we compared the results of multiparametric and multilayers investigations of three doublet earthquakes that occurred around the Arabian plate (M6.2 + M6.0 on 18 August 2014 close to Dehloran, Iran; M6.0 + M6.0 occurred on 15 July 2018 offshore Kilmia, Yemen and M6.0 + M6.0 occurred on 1 July 2022 close to Bandar-e Lengeh). We applied identical methods to the same dataset for all three cases. In particular, we investigated lithospheric, atmospheric, and ionospheric data six months before the three events. The lithosphere was investigated by calculating the cumulative Benioff strain with the USGS earthquake catalogue. Several atmospheric parameters (aerosol, SO2, CO, surface air temperature, surface latent heat flux humidity, and dimethyl sulphide) have been monitored using the homogeneous data from the MERRA-2 climatological archive. We used the three-satellite Swarm constellation for magnetic data, analysing the residuals after removing a geomagnetic model. All the cases present some patterns of anomalies, and when comparing them, we noticed some similarities but also differences. We pointed out that the released energy by the three events is very similar and occurred around the same plate. Still, they involved two different tectonic contexts (compressional on the Iranian side and extensional and transcurrent on the African Plate border). For the above reasons, their comparison is very interesting. Some similarities seem to be explainable in the tectonic context, and some are caused by the ocean's influence at the epicentre location. However, we also identified some differences that still require further investigation and comparison with other case studies.
Finally, this work can be considered a preliminary test of an extensive investigation and systematical search of LAIC patterns before the earthquake occurrences and the study of the possible influence of focal mechanism, location, geological factors, and other constraints.
References :
Ghamry Essam; Marchetti Dedalo; Metwaly Mohamed. Geophysical Coupling Before Three Earthquake Doublets Around the Arabian Plate. Atmosphere 2024, 15, 1318. https://doi.org/10.3390/atmos15111318
How to cite: Ghamry, E., Marchetti, D., and Metwaly, M.: Similarities and differences of the preparation of three (M≈6) earthquake doublets around the Arabian Plate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5269, https://doi.org/10.5194/egusphere-egu25-5269, 2025.
Let us focus on a specific question that may has an ability to build an efficient method toward extracting significantly major ingredients of pre-active events going ahead of significant seismic activities. What is the common point at the state spaces of significant earthquakes of Türkiye in 1999 and 2023? The answer comes from some live but non-instrumented observations, those are devised privately. Those observations are related to both waveguide and cavity effects of natural and/or manmade significant structures replaced in both underground and/or atmosphere. The effects are studied on the electromagnetic wave propagation at significant pre-seismic activities of both circularly cylindrical wave guide and cavity structures meshed in underground and/or atmosphere by considering the extended wave equations in irregularly deviating environs1. Those structures have excessive dimensions as in subway tunnels2 and/or layered guiding pathways in atmosphere3.
The answer comes from two big tunnels excavated before abovesaid two earthquakes of Türkiye. First is Mount Bolu Tunnel, that is almost finished in 2007 and begun in 1993 and second is New Mount Zigana Tunnel, that is finished in 2023 and begun in 2016. Why? First of all, both tunnels are into mounts area of Northern Anatolia. The reason is related to the changing character of seismic activities after 5.9 R (included) magnitude that converts the seismic activities to electromagnetic activities majorantly4.
There is one more tunnel process that still continues for constructions: Between Bahce (37° 12′ 0″ N, 36° 35′ 0″ E) and Nurdagi (37° 10′ 44″ N, 36° 44′ 23″ E) districts of Gaziantep Province, Türkiye. This tunnel construction may have a potential on future seismic activities as two tunnel constructions said in previous paragraph.
The cavities and tunnels behave as layered guiding pathways for propagating waves either homogeneous and/or inhomogeneous fillings; therefore, the activities of waveforms may propagate along long distances under the Earth; i.e., between NAF and SAF by suitable transmissions, propagations, and guiding of waves. The majorant contributions come through Casimir and Casimir-like activities from the boundary interfaces between different materials with specific conditions under stochastic processes. The propagating waves create similar effects among transmitters and receivers through atmosphere layers. Author calls transmission effect by the cavity tunneling and layered guiding pathways these effects.
Those circumstances are studied in above paragraphs by considering the state space formulation of equivalent electrical circuits models through the possible mechanical circuits into the Earth.
The equivalent circuit model governs the significant Seismic Activities, sSAs, by the interactions among source and sink structures available in the distributed networks of equivalent circuits. New constructions have the ability to trigger and produce sSAs close to both specific domains of sSAs and their neighbor domains even if they never generated sSAs in past, of tunnel projects in paragraph 3 and similar ones. Temporal intervals may not coincide with the time spans of excavations of sSAs processes and their triggering effects may either decrease, mostly and/or increase, asymptotically as depending to coupling activities in environ.
1https://doi.org/10.1109/APS.1996.549734
2https://doi.org/10.5194/egusphere-egu2020-22589.
3 https://doi.org/10.1109/RAST.2003.1303999.
4 https://doi.org/10.5194/egusphere-egu2020-21121.
How to cite: Sengor, T.: The Cavity Tunneling and Layered Guiding Pathways in Significant Seismic Activities: Pre-fingerprints in Significant Earthquakes of Türkiye in 1999 and 2023, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-988, https://doi.org/10.5194/egusphere-egu25-988, 2025.
We discuss the potential impact of the Geospace environment on the significant earthquake preparation processes. In this work, we investigate the response of major seismic activity to geomagnetic storms with a joint analysis of solar wind, geomagnetic field, and earthquake catalog. As a test case, we processed the seven strongest earthquakes in Italy for the period 1980 - 2016: Amatrice M6.2 of Aug 24, 2016; Visso M6.1 of 26 Oct 2016; Norcia M6.6 of 30 Oct 2016; Emilia-Romagnia M6 of May 20, 2012; L’Aquila M6.3 of Apr 6, 2009; Foligno M6 of Sep 26,1997 and Irpina of M6.9 of 23 Nov 1980. All of the seismic events were preceded by geomagnetic storms, which satisfied a given criterion: at the time of geomagnetic storm onset, the high-latitude part of the longitudinal region, where in the future an earthquake occur, was located under the polar cusp, where the solar wind plasma would directly access the Earth’s environment [Ouzounov and Khachikyan, 2024]. The number of preceded storms varied for different earthquakes from two to five. This results in different time delays between the day of the magnetic storm onset and the day of earthquake occurrence; it ranges between 9-80 days. Because of the existing delay between a shocked solar wind arrival and earthquake occurrence up to some months, this may suggest that solar wind energy does not trigger earthquakes immediately (as it is believed at present); instead, it may accelerate the processes of lithosphere dynamics, such as fluid and gas upwelling, which are active participants in tectonic earthquakes. For comparison, we present the results of the same analysis applied to other territories of the Mediterranean region: the Anatolian Plate (Turkey) and Crete Island (Greece), which look strikingly similar.
How to cite: Ouzounov, D. and Khachikyan, G.: The impact of the geospace environment on earthquake preparation processes. Case studies for M>6 in Italy for 1980-2016, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3686, https://doi.org/10.5194/egusphere-egu25-3686, 2025.
Intense earthquakes have been natural phenomena that produce enormous disasters, mainly in large urban areas, due to the intense energy released in a very short period. Earthquakes are inevitable natural phenomena, and up to now, they cannot be predicted. On February 6, 2023, a M 7.8 earthquake occurred in southern Türkiye, near the northern border of Syria. This earthquake was followed by a M 7.5 earthquake to the north. The relative motions of three major tectonic plates (Arabian, Eurasian, and African) and one smaller tectonic block (Anatolian) are responsible for the seismicity in Türkiye. Recently, Onur investigated the aftershock distribution and its relation to energy release on the faults and Coulomb stress change areas, his study allowed the relocation of two-catastrophic earthquakes. In the present work we analyze the behavior of multifractality and its complexity parameters calculated from the catalog of seismic magnitudes during a period of 14 years monitored within two regions of Türkiye: the first one (west) between (35-42) Latitude, (25-34) Longitude and the second one (East) between (35-42) Latitude and (34-42) Longitude, being this area where the doublet occurred. Our results show differences in both multifractality and its complexity measures between the two regions. These findings may be indicators of expected seismicity in each region.
How to cite: Flores-Marquez, E. L., Ramirez Rojas, A., and Pérez-Oregon, J.: Comparative multifractal study of seismicity in two seismic zones of Türkiye in the period from 2010 to 2024., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13210, https://doi.org/10.5194/egusphere-egu25-13210, 2025.
Earthquakes are among the most frequent yet unpredictable natural hazards, posing substantial risk to human safety and infrastructure globally, particularly, when large-magnitude earthquakes occur. This highlights the urgent need to develop innovative and alternative methodologies for rapidly assessing the intensity of ground shaking following an earthquake.
This study explores the application of the Machine Learning Estimator for Ground Shaking Maps (MLESmap) methodology in New Zealand, a region characterized by high seismic activity.
MLESmap utilizes extensive datasets of high-fidelity, physics-based seismic scenarios to rapidly estimate ground-shaking intensity in near real-time following an earthquake. This methodology has demonstrated evaluation times similar to those of empirical ground motion models, while offering superior predictive accuracy in the two previously tested regions: the Los Angeles basin and the South Iceland Seismic Zone (SISZ).
To adapt MLESmap for New Zealand’s seismicity, seismic simulations tailored to the unique geological and tectonic context of the region are implemented. Specifically, we use the dataset generated by CyberShake NZ, a probabilistic seismic hazard analysis (PSHA) software developed by the University of Canterbury. Using this software, a total of 11,362 finite-fault rupture simulations were performed across the region and seismic hazard results were calculated on a grid of 27,481 synthetic seismic stations. A ‘forward’ simulation approach was adopted due to the large number of output locations relative to rupture locations, the optimisation of the grid for each rupture and the intention to include plasticity.
The expected results aim to demonstrate the applicability of MLESmap to New Zealand, providing ML-based tools for rapid response actions. This study also takes the first steps in applying cascading effects to MLESmap, in order to improve the overall risk assessment and to advance prevention efforts through innovative and multidisciplinary methodologies.
©2023 ChEESE-2P Funded by the European Union. This work has received funding from the European High Performance Computing Joint Undertaking (JU) and Spain, Italy, Iceland, Germany, Norway, France, Finland and Croatia under grant agreement No 101093038.
How to cite: Blanco Prieto, R., Monterrubio Velasco, M., Bradley, B., Schill, C., and de la Puente, J.: Machine Learning based EStimator for ground shaking maps workflow applied to New Zealand, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16143, https://doi.org/10.5194/egusphere-egu25-16143, 2025.
Recent seismic hazard assessments in Mayotte have highlighted the island's significant exposure to site effects during earthquakes. These effects are closely linked to its complex geological setting, characterized by altered volcanic formations whose heterogeneous geometry leads to strong spatial variations in ground motion. In response to governmental requests, a site effects map is being developed to raise public awareness and support risk-informed urban planning.
A novel methodology for site effects mapping has recently been developed at BRGM, integrating airborne electromagnetic (AEM) data with borehole logs, geological maps, and seismic data (MASW and H/V measurements). This approach was tested on three test sites covering 12 km² of Mayotte surface, and it has demonstrated its potential in imaging the geological interfaces responsible for site effects. However, the current methodology relies on expert-driven data interpretation, making its large-scale application highly labour-intensive and costly. To overcome this limitation, partial automation of the data processing is required in order to handle larger datasets efficiently.
Machine learning techniques offer a promising solution to address this challenge. The test sites provided a unique training dataset, associating resistivity profiles derived from AEM data with the position of geological interfaces responsible for site effects within the soil column. These interface locations were determined through the integration and interpretation of all available geological and geophysical data, including MASW, H/V measurements, and borehole logs. Using this dataset, we trained various models, including Random Forest and Convolutional Neural Networks (CNN), to predict the localization of geological interfaces responsible for site effects based on AEM data.
Preliminary results indicate that the CNN model shows good performances on this task. Nevertheless, further improvements require the expansion of training datasets, underscoring the significant investment needed to generalize this approach to other regions. Future research will focus on refining predictive models and optimizing data acquisition to support large-scale implementation.
How to cite: Gracianne, C., Breuillard, H., Mato, C., Reninger, P.-A., Roullé, A., Raingeard, A., and Rusch, R.: Automated Site Effects Mapping in Mayotte Using Airborne Electromagnetic Data and Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17662, https://doi.org/10.5194/egusphere-egu25-17662, 2025.
The region near the India-Eurasia plate boundary has a long history of large earthquakes. Over the past century, more than 50 earthquakes with magnitudes of 7 or greater have occurred within 500 km of the Indo-Eurasian collision zone. These include the 2015 M7.8 Nepal earthquake, the 1934 M8.0 Bihar-Nepal earthquake, the 1950 M8.6 Assam earthquake, and the 1905 M7.9 Kangra earthquake. The January 7, 2025, M7.1 earthquake in the southern Tibetan Plateau further underscores the seismic significance of this region. This study examines the temporal variation in seismicity within the Indo-Eurasian collision zone and its adjacent areas by utilizing historical records and instrumentally recorded earthquake data from 1900 to 2024. Based on seismic behaviour, clustering of events, and tectonic structures, the collision zone is divided into 26 distinct seismic zones. The temporal variation in seismicity for each zone is analyzed, and a susceptibility index, ESI6, is calculated. This index considers the return period of earthquakes with Mw ≥ 6 and the time elapsed since the last Mw ≥ 6 earthquake in each zone. The ESI6 represents the number of pending Mw ≥ 6 earthquakes in each seismic zone. Ten zones with high ESI6 values (>2.5) have been identified; these zones were seismically active in the past but have remained without major earthquakes for the last three decades. To mitigate potential losses and raise awareness, it is critical to implement GPS monitoring of plate movements, satellite-based deformation monitoring, and seismic health assessments of crucial infrastructure in these silent zones.
How to cite: Kumar, S.: Spatio Temporal Analysis of Earthquake Potential in the Indo-Eurasian Collision Zone: Identifying Future Seismic Hotspots Using the Earthquake Susceptibility Index, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16591, https://doi.org/10.5194/egusphere-egu25-16591, 2025.
Earthquake forecasting, combined with precise ground-shaking estimations, plays a pivotal role in safeguarding public safety, fortifying infrastructure, and bolstering the preparedness of emergency services. This study introduces a comprehensive workflow that integrates the epidemic-type aftershock sequence (ETAS) model with a preselected ground-motion model (GMM), facilitating accurate short-term forecasting of ground-shaking intensity (GSI), which is crucial for adequate earthquake warning for earthquake-prone regions like Taiwan. First, an analysis was conducted on a Taiwanese earthquake catalog from 1994 to 2022 to optimize the ETAS parameters. The dataset used in this analysis allowed for the further calculation of total, background, and clustering seismicity rates, which are crucial for understanding spatiotemporal earthquake occurrence. Subsequently, short-term earthquake activity simulations were performed using these up-to-date seismicity rates to generate synthetic catalogs. The ground-shaking impact on the target sites from each synthetic catalog was assessed by determining the maximum intensity using a selected GMM. This simulation process was repeated to enhance the reliability of the forecasts. Through this process, a probability distribution was created, serving as a robust forecasting for GSI at sites. The performance of the forecasting model was validated through an example of the Taitung, Taiwan earthquake sequence in September 2022, showing its effectiveness in forecasting earthquake activity and site-specific GSI. The other example is the Hualien, Taiwan earthquake sequence from April 2024, which serves as an excellent demonstration of a workflow designed to provide real-time aftershock forecasting following an M7.2 event. The proposed forecasting model can quickly deliver short-term seismic hazard curves and warning messages, facilitating timely decision-making.
How to cite: Hsieh, M.-C., Chan, C.-H., Ma, K.-F., Yen, Y.-T., Chen, C.-T., Chen, D.-Y., and Liao, Y.-W.: Toward Real-Time Forecasting of Earthquake Occurrence and Ground-Shaking Intensity Using ETAS and GMM: Insights from Recent Large Earthquakes in Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5493, https://doi.org/10.5194/egusphere-egu25-5493, 2025.
Accurate assessment of seismic vulnerability and fragility is essential for robust risk evaluation. However, uncertainty in the estimated ground-motion intensity often complicates this task. Traditional modeling approaches frequently rely on fixed, best-estimate intensity values. As a result, these methods may over- or underestimate damage probabilities at both lower and higher ground-motion intensities, leading to distorted risk estimates.
The proposed method is applied to two Icelandic building-by-building datasets collected from seismic events in 2000 and 2008, enabling a direct comparison of structural performance across distinct earthquake scenarios. Through Markov Chain Monte Carlo (MCMC) simulations, we derive posterior distributions for the vulnerability parameters, effectively capturing the inherent variability in the Ims estimated by GMPE. By contrasting the results with those obtained using fixed ground-motion estimates, we demonstrate that neglecting intensity uncertainties can lead to less reliable vulnerability models.
This Bayesian procedure thus offers a significant improvement in modeling accuracy, paving the way for more reliable risk estimates and resilience strategies. The findings underscore the importance of accounting for ground-motion uncertainty when calibrating vulnerability models, which in turn informs the development of seismic design guidelines, enhances damage prediction for future events, and supports informed policymaking for disaster preparedness and resource allocation.
How to cite: Moosapoor, M., Bessason, B., Hrafnkelsson, B., and Darzi, A.: Accounting for Ground Motion Intensity Measures Uncertainty in empirical seismic vulnerability models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19243, https://doi.org/10.5194/egusphere-egu25-19243, 2025.
Multi-hazard disaster risk reduction and mitigation require high-resolution exposure models that grasp the characteristics of assets at the local scale. High-resolution exposure models may allow improving precision/accuracy of risk and damage assessments, especially for hazards which are characterised by high spatial variability or may be influenced by the presence of the assets, such as tsunami or flooding. We propose a methodology for developing a high-resolution population and residential buildings exposure models, to be used for multi-hazard risk reduction purposes at the local scale. This method has been tested and validated for a selected coastal area in the upper Adriatic, exposed to multiple hazards including earthquakes, tsunamis, meteorological events and coastal erosion. For the development of the population exposure model, a high-resolution population density data, collected at global scale, is combined with the national population census data, leveraging both on the accuracy of the national census and on the resolution of the global data. Also, the building census data is complemented with exposure indicators extracted from digital building footprints from the Carta Tecnica Regionale Numerica (CTRN), which is missing in census data, such as average built area, total built area, replacement cost, height and plan regularity. The final exposure layers are assembled at two resolutions: 100 meters and 30 meters, with information also provided at the census unit level. We discuss the development and use of these layers for multi-risk assessment and their potential combination with artificial intelligence.
This research is a contribution to the projects: RETURN Extended Partnership (European Union Next-Generation EU—National Recovery and Resilience Plan—NRRP, Mission 4, Component 2, Investment 1.3—D.D. 1243 2/8/2022, PE0000005); PRIN-PNRR project SMILE: Statistical Machine Learning for Exposure development, funded by the European Union- Next Generation EU, Mission 4 Component 1 (CUP F53D23010780001).
How to cite: Badreldin, H., Scaini, C., M Hassan, H., and Peresan, A.: High-resolution exposure models for coastal cities in Northern Adriatic for multi-risk analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10353, https://doi.org/10.5194/egusphere-egu25-10353, 2025.
Finding a sustainable solution to disaster risk mitigation needs to consider different aspects of the disaster’s impact along with social, economic, and physical characteristics of the region. In this regard, a desirable solution for disaster risk mitigation for a region is the one tailored to the local characteristics. These local characteristics not only help measure the different aspects of a disaster impact but also portray existing pressing issues as priorities. While the former can be modeled using risk and resilience assessment models, the latter can be measured from experts’ points of view. Ultimately, the combination of the expert’s perception on important issues and the output of risk and resilience assessment models can be used to evaluate the optimality of each disaster risk mitigation solution.
In this research, a Multi-Criteria Decision Analysis (MCDA) framework is developed to provide an evaluation of each disaster risk mitigation. The developed framework is designed to be able to run on the action-outcome results from risk and resilience assessment models and the cardinal ranking of the decision criteria, representing decision-makers’ expert opinion on the priorities in mitigating and managing disaster risk. The developed MCDA framework is very practical as it can run on action-outcome results, and these results are accessible from a large variety of risk and resilience assessment models. Furthermore, the developed MCDA framework takes into account the uncertainty in the risk and resilience assessment models. In compatibility with running on minimal available information, the MCDA’s decision model is simplified to one layer with a single layer of the decision criteria.
Additionally, as the number of competing mitigation solutions might increase rapidly in practice, the MCDA framework is developed to handle a huge number of alternatives more efficiently and with relatively limited computational resources. The MCDA framework is developed based on the CAR method of eliciting the preferences among mitigation alternatives. The final results evaluate the competing disaster risk mitigation solution based on available data (as processed by risk and resilience assessment models) and the expert’s opinion on important issues and their preferences on the important aspects of disaster impact. As such, the final results provide an estimation of the expert’s belief on the desirability of each of the disaster risk mitigation solutions.
This MCDA framework is developed as part of the Horizon Europe project MEDiate (Multi-hazard and risk-informed system for Enhanced local and regional Disaster risk management). This project is dedicated to creating a decision-support system (DSS) for disaster risk management that not only takes into account the complexities of multiple interacting natural hazards but also tailors the final solution to the characteristics, priorities, and concerns of the local communities and decision-makers. The MEDiate framework is implemented on four different testbeds (Oslo (Norway), Nice (France), Essex (UK), and Múlaþing (Iceland)), each of which has a different multi-hazard pair and different socio-economic characteristics. The deployment of the developed MCDA framework on different natural hazards and socio-economic characteristics shows its flexible practicality.
How to cite: Yeganegi, M. R., Komendantova, N., and Danielson, M.: Measuring the experts’ perception about the suitability of natural disaster risk mitigation solutions using minimal risk assessment information, a Multi-Criteria Decision Analysis approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17936, https://doi.org/10.5194/egusphere-egu25-17936, 2025.
Seismicity is the result of the interaction between tectonic plates in relative motion where the underlying mechanism of earthquake generation in seismic subduction areas is stick-slip. In reality, seismicity is a complex phenomenon as it involves processes that take place from within the Earth. A thorough understanding of seismicity requires theoretical and experimental approaches. The dynamics in subduction zones occur when two tectonic plates, one on top of the other, are in relative motion where the plate below is in motion due to convective processes within the Earth. Due to the roughness of both surfaces, the underlying mechanism that gives rise to seismicity is stick-slip. In this work, an experimental stick-slip model is proposed, which simulates the relative motion of two rough surfaces by the interaction of two blocks covered by sandpaper with a certain degree of roughness. In this experimental model, the interaction between rough surfaces (sandpaper), with a relative motion in opposite directions to each other, produces stick-slip events (synthetic seismicity), which mimic real seismicity. Here we present the first analyses of synthetic seismicity by calculating the Gutenberg-Richter law, temporal correlations and characterization in terms of organization and order from the Fisher-Shannon method for each synthetic catalogue.
How to cite: Ramírez-Rojas, A., Telesca, L., and Flores-Márquez, E. L.: Novel experimental design for the study of seismic processes based on the stick-slip mechanism., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5321, https://doi.org/10.5194/egusphere-egu25-5321, 2025.
Robust Satellite Techniques applied to long-term satellite TIR (Thermal InfraRed) radiances have
been, since more than 25 years, employed to identify those anomalies (in the spatial/temporal
domain) possibly associated to the occurrence of major earthquakes.
The results until now achieved by processing multi-annual (more than 10 years) time series of TIR
satellite images collected in different continents and seismic regimes, showed that more than 67%
of all identified (space-time persistent) anomalies occur in the pre-fixed space-time window around
the occurrence time and location of earthquakes (M≥4), with a false positive rate smaller than 33%.
Moreover, Molchan error diagram analysis gave a clear indication of non-casualty of such a
correlation, in comparison with the random guess function.
After the most comprehensive test performed over Greece, Italy, Turkey and Japan, here, we will
critically discuss the preliminary results achieved over California by applying RST analyses to
long-term series of GOES-17 radiances.
How to cite: Colonna, R., Filizzola, C., Genzano, N., Lisi, M., Mancusi, I., Pietrapertosa, C., and Tramutoli, V.: Recent achievements on the application of Robust Satellite Techniques to the short-term seismic hazard forecast, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8652, https://doi.org/10.5194/egusphere-egu25-8652, 2025.
In recent decades, significant efforts have been devoted to understanding and interpreting the link between ionospheric perturbations and natural or anthropogenic phenomena, such as seismic activity, electrical or geomagnetic storms, and unidentified radio emissions. This is achieved through various methods among which is also the study of electromagnetic (EM) wave propagation in the very low frequency (VLF, 3–30 kHz) and low frequency (LF, 30–300 kHz) bands. These bands enable long-distance communication, navigation, and military applications, including submarine contact, AM broadcasting, lightning detection, and weather systems. Due to their long wavelengths, VLF and LF waves exhibit unique propagation characteristics. VLF waves propagate globally by using Earth-ionosphere waveguides, reflecting off the D and E layers as skywaves, and are influenced by solar and atmospheric conditions. LF waves primarily rely on ground waves for extensive coverage, although they can also utilize ionospheric reflection (skywaves) for longer-distance communication.
This paper introduces fundamental concepts related to VLF/LF electromagnetic wave emission, propagation, reception, and the perturbing factors that affect them. Additionally, it presents key findings from the European INFREP Receivers Network, which studies seismo-ionospheric anomalies linked to earthquake activity. Established in 2009, the INFREP network monitors VLF/LF signals from transmitters across Europe and neighboring regions. The network currently comprises 10 receivers, built by Elettronika (Italy), and operates at a sampling rate of one sample per minute. The Romanian segment of INFREP includes two receivers, operational since 2009 and 2017, with only brief interruptions, notably during the pandemic when travel restrictions hindered access to the observatories.
The paper discusses the current state of the INFREP network and outlines methods for providing near real-time data access. It highlights advancements in real-time electromagnetic data transmission, archiving, and the use of 2D and 3D online signal visualization and processing techniques. Data access is available through the INFREP headquarters in Graz, Austria (https://infrep.iwf.oeaw.ac.at/data-access/) and the National Institute for Earth Physics in Romania (https://mg.infp.ro/d/ch-aqZXIz/vlf-lf-radio-data?orgId=1&from=now-6M&to=now). The paper also shares findings from the detection of potential ionospheric anomalies in EM signals preceding large earthquakes that occurred between 2012 and 2024. All anomalies are analyzed in correlation with space weather events and extreme meteorological phenomena.
This paper was carried out within Nucleu Program SOL4RISC, supported by MCI, project no PN23360201, and PNRR- DTEClimate Project nr. 760008/31.12.2023, Component Project Reactive, supported by Romania - National Recovery and Resilience Plan
How to cite: Moldovan, I.-A., Toader, V. E., Eichelberger, H. U., Biagi, P. F., Boudjada, M., Anghel, M., Manea, L. M., Mihai, A., and Antonescu, B.: Investigation of VLF/LF electromagnetic wave propagation as recorded by the receivers of the INFREP network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10351, https://doi.org/10.5194/egusphere-egu25-10351, 2025.
Japan is frequently hit by major earthquakes, such as the 2011 off the Pacific coast of Tohoku Earthquake and the 2024 Noto Peninsula Earthquake, which cause enormous human and economic losses. Short-term forecast of earthquakes is effective for mitigating such damage, but this has not been achieved to date. On the other hand, there have been reports of electromagnetic phenomena preceding major earthquakes in various frequency bands, including precursor phenomena in the VLF/LF band (3-300 kHz). In this study, we investigated earthquake-related VLF/LF signals, which has strong electromagnetic emissions due to lightning activity, and it is important to discriminate the VLF/LF signals from those due to lightning activity. In this study, two approaches were attempted: (1) development of a source localization method using VLF/LF broadband interferometry and (2) removal of signals caused by lightning discharges using machine learning.
The first approach is expected to spatially discriminate between VLF/LF signals related to earthquakes (which are located near the epicenter and do not move) and signals related to lightning activity (which move with fronts and thunderclouds). The second is to utilize machine learning technology, which has been rapidly developed in recent years, for detection and removal of lightning discharge signals. For example, Wu et al. at Gifu University have succeeded in classifying lightning discharge waveforms in the thunderstorm activity process with an accuracy of approximately 99% using a machine learning technique called Random Forest. In this study, machine learning is expected to efficiently discriminate and eliminate known lightning discharge signals from a large amount of observation data with high accuracy, and analyze the remaining unknown signals to efficiently investigate the relationship between lightning and earthquakes. In this paper, we will describe the specific methods and results of the above two approaches.
How to cite: Hattori, K., Ota, Y., Yoshino, C., and Imazumi, N.: Construction of a VLF/LF band interferometer using a capacitive circular flat-plane antenna and discrimination and identification of observed VLF/LF band signals by machine learning: Preliminary results, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10447, https://doi.org/10.5194/egusphere-egu25-10447, 2025.
The Nearest Neighbour declustering technique is utilized to differentiate dependent events, such as aftershocks and foreshocks from independent events, such as isolated and mainshocks events . The estimated background field could either be stationary or non-stationary over time and may exhibit patterns that depend on both space and time. Any residual deviations from a time-stationary and spatiotemporally-independent Poisson point field could offer insights into regional loading processes and merit further investigation (Zaliapin and Ben-Zion, 2020). We apply the adopted technique on the Southern California region, an area that includes four significant events with magnitudes greater than 7, over the years 1981- 2021 and the catalog's completeness ranges between magnitudes 2 to 3 (Zaliapin and Benzion, 2020). For generating the complete background set, both outdegree and closeness centrality yielded nearly identical mainshock node counts for background detection in our study region, highlighting the robustness of these centrality measures. In a tree network, hierarchy identification might not be straightforward, but utilizing centrality can aid in placing elements accurately. Higher centrality values indicate a simpler structure compared to lower centrality values. Although the traditional highest magnitude method produces results almost similar to those of the centrality measure from network analysis, the network-based approach offers new possibilities for future research in the study of earthquake sequences and their evolution. In a spatially inhomogeneous, temporally homogeneous Poisson process (SITHP), there is a strictly positive probability that two events may occur arbitrarily close to each other and NN method works better for declustering with this condition (Luen and Stark, 2012). In this study, three temporal statistical tests have been conducted: the Conditional Chi square(CC) test, the Brown-Zhao(BZ) test, and the Kolmogorov smirnov (KS) test on the complete background set. It was found that the KS test, which assumes the time series follows a uniform distribution and does not require any adjusting parameters, is more reliable than the other two tests(requires more tuning constants). For almost all magnitude cut-offs, the temporal tests fail the null hypothesis; however, for a magnitude of 3.4, the temporal test is satisfied, but the space time test ( Luen and Stark test) fails the null hypothesis. For the nearest neighbour (NN) method, the null hypothesis is rejected for all magnitude ranges in our study region. Consequently, it can be concluded that NN declustering is not effective for this dataset or the number of data points is low. Notably, the Luen and Stark space time test yielded a value of 0 for most magnitudes, except for magnitudes 4 and 4.2. This suggests two potential scenarios: either the earthquakes are inadequately declustered, leading to some background events being overlooked or there is another possibility that this model is not fit for the Poisson process and suggesting a need for an alternate conditional model.
References:
Luen, B., & Stark, P. B. (2012). Poisson tests of declustered catalogues. Geophysical journal international, 189(1), 691-700. https://doi.org/10.1111/j.1365-246X.2012.05400.x.
Zaliapin, I., & Ben‐Zion, Y. (2020). Earthquake declustering using the nearest‐neighbour approach in space‐time‐magnitude domain. Journal of Geophysical Research: Solid Earth, 125(4), e2018JB017120. https://doi.org/10.1029/2018JB017120.
How to cite: seal, A. and Jana, N.: Statistical Analysis on Background Seismicity of Southern California Region: Application of Nearest Neighbour Declustering and Network Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-693, https://doi.org/10.5194/egusphere-egu25-693, 2025.
Multi-hazard disaster risk analyses in coastal areas requires the integration of data and models concerning hazard, exposure and vulnerability data and models, all developed with high spatial resolution. Indeed, accurate high-resolution models and data are essential for properly assessing the impact of specific hazards that threaten coastal areas, such as tsunamis, floods, landslides, and coastal erosion. Nevertheless, this level of detail remains unachieved for many coastal hazards in various locations. Consequently, critical fine-scale differences in localized risk assessment are overlooked, leading to potential underestimations or overestimations of the actual risk to coastal communities. It is vital to address this gap in order to enhance the accuracy and reliability of risk assessments.
A key step in tsunami hazard and risk assessment involves the development of inundation maps, specifically maps describing inundated areas and related depths. To date, such maps are not yet available at proper resolution for the coastal areas of the Friuli-Venezia-Giulia Region (FVG). Accordingly, this study aims to enhance the characterization of tsunami hazard in the Northern Adriatic by developing detailed inundation maps and possibly addressing the identified research gaps. Leveraging on accurate and high resolution bathymetry and topographic data is crucial for reliable tsunami modelling for the FVG coastal areas. To this purpose, bathymetry and topographic data are refined and are used, along with existing databases of tsunamigenic earthquake sources, for modelling tsunami waves propagation and inundation by means of the NAMI DANCE code (e.g. Yalciner et al. 2014, Mediterranean Sea Oceanography and references therein).
Existing datasets from open access and local data sources are collected and then refined, particularly addressing inaccuracies in lagoon bathymetry. This involves incorporating high-resolution data and considering small-scale coastal features that can significantly impact tsunami inundation. Multiple bathymetry and topography datasets are used to develop high resolution refined data at 25 meters, and 10 meters resolution. The database of co-seismic seafloor displacement for all individual scenarios, developed based upon the DISS-3.3.0 database, is adopted to carry out a reappraisal of tsunami wave amplitude maps (Peresan & Hassan, MEGR 2024 and references therein) and to estimate realistic tsunami inundation maps. Additionally, tsunami sources caused by local earthquakes relevant to the FVG region are investigated, providing local scale maps of wave amplitudes and inundation estimates; this involves using appropriate fault rupture realisations for local tsunami scenarios (ITCS100&101), as specified in the DISS-3.3.0 database.
The outcomes from this study provide the basis for multi-scenario tsunami hazard assessment, contributing to the development of high-resolution and comprehensive tsunami hazard maps for the Northern Adriatic coasts. Moreover, along with high-resolution exposure maps, they contribute improving precision and accuracy of related risk assessment, and hence are an important step in preparedness, response, and prevention efforts in the framework of disaster risk management.
This research is a contribution to the RETURN Extended Partnership (European Union Next-Generation EU—National Recovery and Resilience Plan—NRRP, Mission 4, Component 2, Investment 1.3—D.D. 1243 2/8/2022, PE0000005).
How to cite: Hassan, H. M. and Peresan, A.: High resolution tsunami inundation maps: towards multi-hazard risk analysis., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9938, https://doi.org/10.5194/egusphere-egu25-9938, 2025.
The ultimate objective of our research is to explore the potential of Machine Learning in the dynamic creation of up-to-date exposure layers for buildings. This effort involves integrating remote sensing images, ancillary data such as national census information, and crowdsourced data collected by trained citizens. The crowdsourcing activity builds on a previous successful initiative developed within the CEDAS (building CEnsus for seismic Damage Assessment) project, which engaged high school students from North-East Italy in collecting data on buildings that were either unavailable from conventional exposure data sources or not easily retrievable via remote sensing techniques (Scaini et al., 2022).
To this end, we are developing a complex multimedia information system via web platform designed to collect, process, store, and distribute information to different knowledge users (policymakers, territorial planners, citizens) with targeted visualization strategies. The crowdsourcing initiatives are taking place in selected municipalities of Tuscany and Friuli regions (Italy), exposed to different natural hazards, such as earthquakes, tsunamis and landslides. An online questionnaire has been created to assist the user in building data collection and minimize input errors. Simultaneously, building data, along with their photos, are stored in a structured database for research purposes. For instance, building data and images are used as learning set to train a machine learning algorithm to identify specific features such as roof type, number of floors, and the presence of a basement. These algorithms can then be included in the online questionnaire to facilitate further data collection by automatically suggesting features associated to the buildings. A dedicated visualization tool is being developed on the web platform to showcase the effectiveness of this method in recognition of building features. We will demonstrate the data visualization tools developed on the web platform so far, highlighting the key features of the available exposure databases. The web platform is designed to provide an easy-to-use tool for communicating with various knowledge users, while also enhancing disaster awareness and preparedness, which is attained exploring and collecting data on the built environment.
This study is a contribution to the ongoing PRIN 2022 PNRR project SMILE “Statistical Machine Learning for Exposure development” (code P202247PK9, CUP B53D23029430001) within the European Union-NextGenerationEU program.
How to cite: Artese, M. T., Varini, E., Gagliardi, I., Ciocca, G., Piccoli, F., Rota, C., Del Soldato, M., Bianchini, S., Scaini, C., Peresan, A., and Brondi, P.: A web platform for crowdsourced collection, processing, and visualization of exposure data on buildings, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18225, https://doi.org/10.5194/egusphere-egu25-18225, 2025.
Masonry structures are one of the most vulnerable to severe and extensive damage in terms of previous earthquakes. It is significant to quickly evaluate the seismic damage levels of masonry structures, to reduce casualties and economic losses caused by earthquakes. However, traditional methods based on manual judgment or finite element simulations tend to be relatively slower . In this paper, a machine learning-based rapid prediction method was proposed for assessing the seismic damage of masonry structures. By analysis of building data from several cities and combining ground motion with structural characteristics, 11 impact factors were identified as input variables. The LM-BP neural network model was developed by a backpropagation (BP) neural network with strong nonlinear modeling capabilities, and by the Levenberg-Marquardt (LM) algorithm. The accuracy and stability of the model were verified by comparing the predicted values with actual earthquake examples. The results show that the selected seismic damage impact factors can accurately reflect the structural damage level. By comparing methods using parameters on either the structure or ground motion, the predictive accuracy of the proposed method is significantly enhanced. It provides a basis for post-earthquake structural safety assessments and disaster prevention and mitigation work.
How to cite: Zhang, L., Liu, Y., Liu, L., and Zhu, B.: Rapid prediction method of earthquake damage to masonry structures based on machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14734, https://doi.org/10.5194/egusphere-egu25-14734, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 3
EGU25-408 | ECS | Posters virtual | VPS13
Seismic risk assessment using 3D physics-based seismic hazard: A case study for Shimla cityWed, 30 Apr, 14:00–15:45 (CEST) | vP3.1
The seismic risk assessment has gained significant popularity in recent years due to the increasing development of infrastructure and urbanization in seismically active locations across the globe. Earthquakes pose serious issues as natural events because of their unpredictability and the extensive harm they may do to infrastructure, buildings, and people's lives. Ground motion at the time of the earthquake can depend on several local sites and event characteristics, such as the size of the seismic event, the depth of the earthquake focus, the distance from the epicentre, the local geology and soil conditions. However, traditional probabilistic seismic hazards using ergodic ground motion models do not consider these variations, leading to a further less accurate damage or risk assessment. Hence, the present work aims to perform a comprehensive seismic risk assessment by incorporating three-dimensional physics-based numerical modelling, which explicitly incorporates the path and site-specific characteristics that cater for non-ergodicity. Here, physics-based ground motion has been simulated for controlling events corresponding to typical sites present in Shimla city, Himachal Pradesh, India. Furthermore, to assess the associated risk for the region exposure, data of the building inventory of Shimla has been gathered using Google Street View (GSV) images, and for the classification of the building inventory to different building typologies, deep machine learning-based Convolution neural network (CNN) models are trained. The developed CNN model has shown great precision in identifying the building class for the region. After classification, suitable well-known fragility functions are mapped to each class, and subsequent risk is calculated. Finally, the results developed using physics-based hazard are compared with the conventional empirical approach. The study results will provide the respective stakeholders with the technical knowledge for the region's hazard and subsequent risk.
How to cite: Shukla, S. S., Choudhary, R., and Jaya, D.: Seismic risk assessment using 3D physics-based seismic hazard: A case study for Shimla city, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-408, https://doi.org/10.5194/egusphere-egu25-408, 2025.
EGU25-1196 | Posters virtual | VPS13
Ionospheric turbulence modulation by intense seismic activity as a tool of Earthquake risk mitigationWed, 30 Apr, 14:00–15:45 (CEST) | vP3.2
According to the well-known Lithosphere Ionosphere Coupling (LAIC) mechanism, tectonic activity during the earthquake preparation period produces anomalies at the ground level which propagate upwards in the troposphere as Acoustic or Standing gravity waves. Thus observing the frequency content of the ionospheric turbidity in a well extended area, in space and time, around an earthquake event we will observe a decrease of the higher limit of the turbidity frequency band. In this article we review the repeated observational results of TEC turbulent band upper limit (TBUL) on the occasion of strong earthquakes. Regorus earthquake risk estimation can not be extracted from our result since the characteristics of each event is diferent(i.e Magnitude ,epicentral distance of the nearest GPS station ect..). Nevertheless continuous monitoring of the TEC (TBUL) fo and the alarming for further investigation by comparing with the TBUL of distant stations and with the results of seismical monitoring, as well as with the results of other near earth surface precursor methods, if the TBUL tend to around 0.001Hz..
How to cite: Contadakis, M. E.: Ionospheric turbulence modulation by intense seismic activity as a tool of Earthquake risk mitigation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1196, https://doi.org/10.5194/egusphere-egu25-1196, 2025.
Additional speakers
- Dedalo Marchetti, INGV, Italy
- Taner Sengor, Türkiye
- Dimitar Ouzounov, Chapman Univeristy, United States of America