This work has a twofold objective: to analyze for the first time the background seismicity of Northern Algeria and its vicinity and to apply a variety of statistical methods to identify its spatiotemporal features (Benali et al., Axioms, 2023).

The earthquake catalog of Northern Algeria and its vicinity includes events from 1950 to 2021 within the region between latitudes 32° and 38° and longitudes -2° and 10°. Based on the investigation of the frequency-magnitude for the overall catalog, the magnitude threshold for completeness is set at 3.7, so that the analyzed dataset includes 1561 earthquakes. The considered area comprises the largest and the most damaging earthquakes ever recorded in the Mediterranean region: the M7.3 earthquake at El Asnam in 1980, the M6.9 earthquake at Boumerdes in 2003, and the M6.7 earthquake at El Asnam in 1954.

The dataset is declustered by applying different methods, namely: the Gardner and Knopoff, Gruenthal, Uhrhammer, Reasenberg, Nearest Neighbour, and Stochastic Declustering methods. Each method identifies a different declustered catalog, that is a different subset of the earthquake catalog that represents the background seismicity, which is usually expected to be a realisation of a homogeneous Poisson process over time. A variety of statistical methods is applied to assess whether the background seismicity identified by each declustering method has the spatiotemporal properties typical of such a Poisson process. The main statistical tools of the analysis are the coefficient of variation, the Allan factor, the Markov-modulated Poisson process (also named switched Poisson process with multiple states), the Morisita index, and the L-function.

Summing up our findings, all declustering methods reduce the time of correlation structures in the background seismicity at small timescales, but temporal correlation still remains at intermediate and higher timescale ranges. In particular, Gruenthal and Stochastic Declustering methods turn out the most effective in removing the time clustering structures from this catalog. The spatial clustering structure is also significantly reduced, but it is not eliminated from the declustered catalogs, due to the natural clustering of seismicity along active fault systems.

How to cite: Varini, E., Benali, A., Jalilian, A., Idrissou, S., and Peresan, A.: Spatiotemporal analysis of the background seismicity in Northern Algeria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5603, https://doi.org/10.5194/egusphere-egu24-5603, 2024.

The Topological Filtering Algorithm, Discrete Perfect Set (DPS) by Agayan et al. (2018), was developed within the framework of discrete mathematical analysis, which involves fuzzy models of discrete analogs of fundamental concepts of classical mathematical analysis. It is designed to select clusters of discrete observations according to a given criterion (classification of discrete observations belonging to one of the clusters) (Gordon, 1981). In this study, we applied the iterative S-DPS modification of the DPS algorithm (Agayan et al., 2022) for the sequential extraction of linear structures from an initial array of point objects. Specifically, we considered the catalogue data from the Baikal Division of the Geophysical Survey, Federal Research Center of the Russian Academy of Sciences, as the initial set of point objects. In each iteration, the densifications identified by S-DPS can be interpreted as discontinuous disturbances of various ranks. With each iteration, weaker yet significant concentrations are discerned. The identification of these discontinuous disturbances as linear structures, and their interpretation were conducted using an expert, non-automated approach.

Derived from expert analysis of a few sequential iterations of the S-DPS algorithm, this facilitated the identification of potential seismogenic structures for two selected territories in the Lake Baikal region. For both territories, the obtained structures were compared with the mapped active faults in the Lake Baikal region (Active Faults of Eurasia Database, Zelenin et al., 2022).

The iterative application of the S-DPS algorithm, combined with expert linear structures analysis, provides a nuanced approach to understanding the complexities of seismogenic features and their potential seismic implications. This methodology offers significant potential for analysing regional seismicity, aiming to discover new or adjust already mapped seismogenic structures.

References

Agayan SM, Bogoutdinov SR, Dzeboev, BA, Dzeranov BV, Kamaev DA, Osipov MO DPS Clustering: New Results. Appl. Sci. 2022, 12, 9335. https://doi.org/10.3390/app12189335

Zelenin E, Bachmanov D, Garipova S, Trifonov V, and Kozhurin A: The Active Faults of Eurasia Database (AFEAD): the ontology and design behind the continental-scale dataset, Earth Syst. Sci. Data, 2022, 14, 4489–4503, https://doi.org/10.5194/essd-14-4489-2022.

How to cite: Agaian, A., Nekrasova, A., and Bogoutdinov, S.: Application of topological filtering (DPS) algorithm for identifying linear seismogenic structures in the Lake Baikal region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4914, https://doi.org/10.5194/egusphere-egu24-4914, 2024.

Seismic hazard assessment involves quantitatively estimating ground shaking at site. There are two main approaches for estimating seismic hazard, Deterministic seismic hazard assessment (DSHA) and Probabilistic seismic hazard assessment (PSHA). In the present investigation DSHA is used, it relies on the worst-case scenario earthquake. The state of Kerala (9°N - 13°N and 75°E - 78°E) which lies in the seismic zone III, having a zone factor 0.16, has been considered to estimate seismic hazard in terms of PGA. Based on analyses of seismotectonics of the area, 26 seismogenic sources were identified and used for the PGA estimation. The peak horizontal accelerations (A_h) 0.2344g and peak vertical accelerations (A_v) 0.1395g were computed for the city of Calicut. For other cities, Thiruvananthapuram, Kollam, Palakkad, Kochi, and Thrissur horizontal (A_h) and vertical accelerations (A_v) were also estimated. However, the highest value was found to be at Calicut followed by Palakkad. In case of Palakkad, the values were influenced by cluster of faults located there while, at Calicut was caused by the Fault – 1 (source) which is in the Arabian Sea near the coast of the city. It has been observed that the seismic hazard assessment of Kerala advocate the PGA (A_h) falls in the range of 0.02g to 0.47g and PGA (A_v) varies from 0.01g to 0.33g. These accelerations seem to be more realistic since they are based on consideration of many seismotectonic sources in the region that may rupture causing earthquakes. The ratio of Av/Ah was found to be in the range of 0.70 to 0.38. The prepared contour maps for the region show that larger peak ground accelerations are present in the region where there is a higher density of larger faults and vice versa. The findings highlight the need for further studies and enhanced preparedness measures in Kerala, aiming to mitigate potential seismic risks and ensure the safety of its residents in this seismically active region.

How to cite: Shanker, D. and Rafih AP, M.: Seismic Hazard in terms of Peak Ground Acceleration (PGA) for coast of Calicut, State of Kerala (INDIA), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-437, https://doi.org/10.5194/egusphere-egu24-437, 2024.

Significant earthquake-induced tsunamis in Northern Adriatic are rare and only a few historical events were reported in the literature, with sources mostly located along with central and southern parts of the Adriatic coasts. Recently, a tsunami alert system has been established for the whole Mediterranean area; however, a detailed description of the potential impact of tsunami waves on coastal areas is still missing for several sites. This study aims at modelling the hazard associated with possible tsunamis, generated by offshore earthquakes, with the purpose of contributing to tsunami risk assessment for selected urban areas located along the Northeastern Adriatic coasts. Tsunami modelling is performed by the NAMI DANCE software, which allows accounting for seismic source properties, variable bathymetry, and non-linear effects in wave’s propagation.

Preliminary hazard scenarios at the shoreline are developed for the coastal areas of Northeastern Italy and at selected cities (namely Trieste, Monfalcone, Lignano and Grado). A wide set of potential earthquake-induced tsunamis, located in three distance ranges (namely at Adriatic-wide, regional and local scales), are considered for the modelling; sources are defined according to available literature, which includes catalogues of historical tsunami and existing active faults databases. Accordingly, a preliminary set of tsunami-related parameters and maps are obtained (e.g. arrival times, maximum wave amplitude, synthetic mareograms), relevant towards planning emergency and mitigation actions at the selected sites.

In addition, a fully formalized operational procedure for time-dependent seismic hazard scenarios has been developed during the last two decades, which integrates earthquake forecasting information from pattern recognition analysis (CN algorithm), with the realistic modeling of seismic waves propagation by the neo-deterministic approach (NDSHA). For offshore large earthquakes, this integrated approach can be naturally extended to the definition of time-dependent tsunami scenarios, based on physical models of tsunami waves propagation. We review the outcomes from its applicaton for the recent events that occurred in the Adriatic region (namely: Durres, Albania 2019; Pesaro, Italy 2022), which support the possibility of developing time-dependent tsunamis scenarios, by integrating earthquake forecasts with the physical modelling of tsunami waves propagation.

How to cite: Peresan, A. and Hassan, H. M.: Quantifying Tsunami Hazard for the Northeastern Adriatic Coasts: A Multi-Scenario Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3744, https://doi.org/10.5194/egusphere-egu24-3744, 2024.

Purpose:

Strong earthquakes with greater magnitudes and longer durations can trigger numerous landslides. Several mechanisms have been proposed to explain the triggering process, such as seismic waves providing additional driving force, which increases the shear stress on the sliding plane. Earthquakes may also increase pore pressure, which reduces the effective normal stress on the sliding plane. However, the mechanisms that apply to both wet and dry conditions are not fully understood. Herein, we purposed to study the seismic response and triggering mechanisms through vibration experiments on granular materials.

Methods:

We used a ring shear apparatus to study the mechanical behavior of granular materials under cyclic loading. 0.2-0.4 mm glass spheres (SiO2) were placed in a ring shear box, and constant normal stress and constant shear stress were applied. Then, sinusoidal cyclic shear stress with different numbers of cycles were applied respectively. The frequency of cyclic loading was 1 Hz. We used dynamic triaxial-bender tests to monitor the evolution of the shear modulus of samples under cyclic loading. The confining pressure was 300 kPa and deviatoric stress was 380 kPa, and sinusoidal cyclic dynamic loads with amplitudes of 45 kPa, a frequency of 1 Hz and a cycle number of 200 were applied. All of the experiments were under dry conditions (room humidity).

Results:

The dynamic ring shear experiments show that the co-vibration and post-vibration shear displacements increased with an increase in the number of cycles, and the instability of granular materials can be triggered by a larger number of cycles while the shear stress returned to the initial value after the vibration ended. The stepwise increase curves of co-vibration shear displacement show platform segments and upward segments, the platform segments corresponded to trough segments of cyclic shear stress, and the upward segments to crest segments of cyclic shear stress. The shear displacement value of each upward segment increased with the increase in the number of vibration cycles (Fig. 1). The dynamic triaxial-bender test result shows that the specimen shear modulus decreased with the increase of the number of vibration cycles, while the density decreased slightly and the axial strain increased, roughly follow a logarithmic law during vibration (Fig. 2).

Conclusions:

Our dynamic ring shear experimental observations suggested that it is easier to trigger landslides with longer durations of vibration, and the triggering is closely related to the weakening of shear resistance. The results suggest that the reduction of shear modulus is an important mechanism for triggering failure of earthquake-triggered landslides. We infer that the earthquake-induced decrease in shear modulus was caused by structure changes, particle rearrangement, and slipping at the scale of micro-asperities.

How to cite: Luo, H., Hu, W., and Zhou, L.: Unraveling the Dynamic Weakening Mechanism of Earthquake-Triggered Landslides through Vibration-Induced Frictional Sliding Instability Experiments on Granular Materials, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2516, https://doi.org/10.5194/egusphere-egu24-2516, 2024.

The potential role of satellite geodesy techniques for seismic hazard assessment, with particular focus on GNSS and InSAR, has been widely investigated in the last decades. These technologies can detect differences in ground velocity of less than one millimeter per year, and could therefore be suitable to highlight the accumulation of tectonic strain. While conventional strain field estimation is performed from a two-dimensional planimetric point of view, a novel approach was introduced by incorporating the independent a-priori tectonic knowledge of the study area to pre-select the directions along which strain accumulation signs should be searched [1, 2]. This method was applied to the earthquakes of Amatrice (2016) and Emilia (2012), analyzing the ground velocity estimated from GNSS station data along two transects of interest. Despite the promising results obtained, the spatial density of GNSS stations was too low to provide a detailed description of the velocity profile along the transects. In this sense, the combination  of GNSS and InSAR techniques could greatly improve these analyses. The recent European Ground Motion Service (EGMS) [3] constitutes an ideal dataset to pursue this objective. In the present work, we evaluated the suitability of EGMS data for seismic hazard assessment. To achieve this, we defined transects covering known high seismic hazard regions of Italy, following the scheme outlined in [2], but greatly improving both the spatial resolution along the transects and their inter-distance, leveraging the high spatial density of InSAR measurement points. We evaluated the velocity profile along the transects using the data provided by the EGMS service, and compared the results obtained with velocities measured from GNSS station data, both projecting GNSS data along the satellite line of sight and retrieving displacements eastward and upward considering SAR acquisitions from  ascending and descending orbits. Through this comparison we assessed whether the accuracy, the revisiting time and the covered temporal window of EGMS data are sufficient to ensure a correct velocity estimation, and took a first step in the direction of a future integration. Preliminary results obtained in the Irpinia region (Italy) suggested a good performance of EGMS data for the detailed description of the velocity profile and an excellent agreement with GNSS station data.

References

[1] Panza, G. F., Peresan, A., Sansò, F., Crespi, M., Mazzoni, A., & Nascetti, A. (2018). How geodesy can contribute to the understanding and prediction of earthquakes. Rendiconti Lincei. Scienze Fisiche e Naturali, 29, 81-93.

[2] Crespi, M., Kossobokov, V., Panza, G. F., & Peresan, A. (2020). Space-time precursory features within ground velocities and seismicity in North-Central Italy. Pure and Applied Geophysics, 177(1), 369-386.

[3] European Ground Motion Service. https://egms.land.copernicus.eu

How to cite: Giaccio, L., Ravanelli, R., Belloni, V., and Crespi, M.: Monitoring seismic hazard with satellite geodesy in Italy: first steps for the integration of GNSS and European Ground Motion Service data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11144, https://doi.org/10.5194/egusphere-egu24-11144, 2024.

Earthquake is one of the most divesting natural events that threaten human life during history. After the earthquake, having information about the damaged or anomaly artificial surface area can be a great help in the relief and reconstruction for disaster managers. It is very important that these measures should be taken immediately after the earthquake because any negligence could be more criminal losses. In this study, we developed a method for near real-time, general and robustly identify anomaly artificial surfaces using 3DTF and ASAI. This method was designed to identify the impervious surface areas using single-temporal imagery without pre-disaster data. The features of the contrast, Gabor, and Con-Gabor features were used to construct 3DTF, which distinguish forest, bare land, shadows with artificial surface. And then it was then used with the K-means classifier to map the artificial surfaces area. Based on the different textures of normal and anomaly artificial surfaces, we constructed the ASAI using entropy and homogeneity, and used the index to detect anomalies in mapped artificial surface areas. The performance of the detecting anomalies method was developed at three different sites in Turkey Earthquake and the mapped results of artificial surface showed that the overall accuracies at sites A-C, and C were > 93%. Using The mapped artificial surface area and ASAI identified anomaly artificial surface. The results showed the overall accuracies were 93.76%, 91.4% and 90.07%. Given the promising and accurate outcomes of this study, further developments remain warranted to determine the robustness of the anomaly artificial surface detecting method in areas with complex artificial surface distribution.

How to cite: Ren, S., Pan, Y., Zhao, C., Gao, Y., Ma, G., Wu, H., Zhu, Y., and Zhang, Z.: A novel artificial surface anomaly index (ASAI) based on post-disaster texture features using single-temporal and high-resolution imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1228, https://doi.org/10.5194/egusphere-egu24-1228, 2024.

The Central Asia region encompasses a wide variety of climatic areas and geological settings. It is therefore prone to multiple hazards which can affect different parts of the region, including trans-boundary areas. Earthquakes, in particular, are a known threat for the region, and caused substantial damages and financial losses in the past. Here, we develop the first high-resolution regionally-consistent exposure database for Central Asia, encompassing multiple asset types including residential and non-residential buildings and transportation. The dataset was assembled using both global and regional-scale data and country-based information provided by local experts and authorities (e.g. building census, national statistics). This information includes also reconstruction costs, which are usually difficult to retrieve at country level, and images for different building typologies, which could serve as input for the development of image-based classification systems. Country-based data was collected interacting with local experts, and was supported by 5 workshops, one for each country, which fostered data sharing and knowledge transfer. Finally, the residential buildings exposure layer was used as a basis to estimate the projected exposure in 2080 under different Shared Socioeconomic Pathways. The lessons learned while developing the exposure database for Central Asia will be discussed in order to provide insights for developing similar datasets in other areas.

How to cite: Scaini, C., Tamaro, A., and Peresan, A.: Lessons learned developing a regionally-consistent exposure database for Central Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1822, https://doi.org/10.5194/egusphere-egu24-1822, 2024.

Gunung Jerai, or Mount Jerai, is in northern Malaysia, a tropical forest where the intrusion of the granitic batholith and metasedimentary rocks controls the topographical condition. Due to the geological aesthetic and the socio-economic history, Gunung Jerai was gazetted as a National Geopark named Jerai Geopark. The geological disaster at Gunung Jerai on 18th August 2021 significantly impacted approximately 1000 families in the region, driven by a maximum cumulative rainfall of around 200mm per 3 hours. This study aims to determine the geological and geomorphological characteristics of the debris flow event on 18th August 2021, which affects mainly three catchments: Seri Perigi Catchment, Batu Hampar Catchment, and Titi Hayun Catchment by using geospatial technique. Three main remote sensing techniques were utilised to achieve the aim of the study, namely IfSAR, LiDAR, and photogrammetry. Aerial photogrammetry was conducted using fixed-wing UAVs with mounted camera sensors covering the catchment areas to visualise the current terrain condition after the debris flow event, which is utilised for the individual landslide inventory, debris flow path, formation of the natural temporary dam, and deposition of the debris flow materials. LiDAR was also employed separately using multi-rotor UAV to conduct detailed terrestrial mapping of selected major landslides to determine landslide classification such as landslide type, dimensions, activity, distribution, and causal factors. At the regional scale, DEM from IfSAR and field geological mapping is utilised for demarcation and delineation of geomorphological features and to generate morphometric data, including slope curvature, slope gradient, slope aspect, flow direction, and flow accumulation, which is derived from the DEM. This information is crucial for disaster management and mitigation efforts, aiding in better preparedness and response. These abstract aims to explain how each geospatial data is utilised and optimised to characterise the debris flow disaster event.

How to cite: Md.Razli, M. R., Md Aripin, M. F. S., Mohd Hellmy, M. A. A., Hamat, M. F. Q., Mohamad, Z., Jaapar, A. R., Ali, A., and Zakaria, M. S.: Utilizing Various Geospatial Data for Debris Flow Geological Hazard Characterization and Mapping at Jerai Geopark, Malaysia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7234, https://doi.org/10.5194/egusphere-egu24-7234, 2024.

            Since the Chi-Chi earthquake in 1999 and the subsequent Typhoon Morakot in 2009, mass movements such as landslides have become a prominent focus of study. Particularly noteworthy are significant disasters like the Shiaolin Village debris flow and the Yushuishi debris flow in the Southern Cross-Island Highway, which have had a substantial impact on the environment and people's livelihoods. Consequently, the issue of sediment-related disasters has continued to gain attention and expand in recent years. However, estimating the volume of colluvium debris on the slope within the watershed, as well as understanding the transport and deposition of materials in the river channels, poses a challenging issue. This challenge arises from the complexity of geological factors and causes, the prolonged duration of these processes, and the difficulty of implementing engineering solutions. Therefore, effective estimation of the volume, transport, and deposition of sediment, especially in high-risk areas, along with the assessment of potential disaster risks, can be achieved with minimal human resources. This approach can provide effective early warning and reduce the impact of disasters, preventing them before they occur.

           The vigorous development of geospatial information technology has not only yielded positive results in land monitoring but has also gradually extended to other application fields. Hazard monitoring is one of its crucial applications. Geospatial information can be obtained through surveying and mapping technology, and through multi-temporal geospatial data, the production, migration, and accumulation of debris deposits can be quantitatively evaluated in a reasonable time and space within the catchment scale.

         For these purposes, this study focuses on the Laonongshi catchment, which has experienced past disasters and still retains a substantial amount of residual colluvium on its slopes. This study is dedicated in multi-temporal aerial photogrammetry and dataset generation. By combining surface geological investigations with various existing remote sensing images.Detailed DTMs and Orthomosaic images were established, since pre-Typhoon Morakot in 2009, and post events, including 2009, 2013, 2015, and 2018 with 2 meter resolution. The result reveals significant changes in the river channel and numerous reactivation of landslide debris accumulation.

How to cite: Hsu, Y.-W., Xu, J.-Y., and Chang, K.-J.: Assessment of landslide, Sediment Production and Disaster Prevention in in mountainous watersheds in Taiwan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3915, https://doi.org/10.5194/egusphere-egu24-3915, 2024.

Marine debris is a severe environmental problem. It originates from many sources and causes a wide spectrum of environmental, economic, safety, health and cultural impacts. Millions of tonnes enter the oceans every year and tackling the issue is gaining momentum at all levels.

Our project presents an approach to detect and track floating marine debris such as lost fishing nets, debris aggregations or patches based on high-resolution remote sensing time series, machine learning, and ocean current modeling. The goal is to obtain more reliable data regarding quantity, position, material properties and sources of litter as well as to address the lack of understanding of floating debris behavior in the open sea due to the limited monitoring capabilities (Garaba and Dierssen, 2018). In order to achieve this goal, a convolutional neural network was trained with a hand-labeled dataset of objects floating on the sea surface extracted from Planet satellite imagery, to learn the spatial characteristics of these objects. A software pipeline has been developed to automatically retrieve, process and analyze large amounts of satellite images and enable continuous monitoring. As identifying floating objects in the marine environment from space remains a challenging and difficult task and ground truthing is near to impossible, we further apply a mechanism to find matching objects on time series of satellite images using an ocean current model as well as different image processing techniques.

How to cite: Weiß, T. and Bochow, M.: TRACE: An approach to detect, track and monitor large floating marine litter in our seas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12503, https://doi.org/10.5194/egusphere-egu24-12503, 2024.

This research aims to distribute b-value estimation spatially on the Mingachevir water reservoir area, a seismically active zone situated in Azerbaijani part of Kur depression. There are several researches proposing a relation between seismicity and b-value (Babayev G. et. al., 2020, Babayev T. et. al., 2023b, Telesca et. al., 2020). It is shown that, b-value is lower in the high-intensity zones, and higher in the low-intensity zones (Babayev T. et. al., 2023b). b-Value is an important parameter of a linear relation that is called Gutenberg-Richter law. Gutenberg-Richter law represents the earthquakes distribution with respect to the magnitude (Gutenberg and Richter, 1942). The relation below (1) presents Gutenberg-Richter law:

(1) log N = a - bM or N = 10^{a - bM},

where N is the number of earthquakes greater and equal than M, the magnitude of the earthquakes, a and b are the real constants.

 b-Value is the slope of this linear relation, in terms of mathematics. It is a parameter reflecting the relative size distribution of earthquakes (Babayev T. et. al., 2023a). Several ways were improved to estimate b-value (El-Isa, Eaton, 2014). We applied linear least-square fitting method to logarithmically binned data since it is preferred due to its instructivity (El-Isa, Eaton, 2014; Milojevic, 2010). In order to obtain spatial distribution of b-value the microzonation method of Babayev G. et. al., 2020 was applied. By use of this method, the area was divided to the grids of 10 km * 10 km. The seismic events in the Mingachevir catalog were clustered to each grid and b-value was estimated for each grid with the events. The microzonation of the study area was done on ArcGIS 10.7 (ESRI, 2018) software and the mapping of b-value spatial variation was done on SURFER software. According to the result of the research, we observe that mainly, in the study area the b-value has a low quantity, which is proposed to be related with higher seismic activity (Babayev G. et. al., 2020 and Babayev T. et. al., 2023b). Still, it varies through the study area and in the northern part of Mingachevir water reservoir, that is close to Southern Greater Caucasus and in the south-eastern part of the reservoir, that is close to Yevlakh region b-value is around 1. b-variations are impacted by tectonic stress (El-Isa, 2013). Wiemer and Wyss, 1997; Wiemer et. al., 1998; Zuniga and Wyss, 2001; Gerstenberger et. al., 2001; Wiemer and Wyss, 2002; Schorlemmer, 2004; Schorlemmer et. al., 2005; Wyss et. al., 2004; Wyss and Matsumura, 2006; Wyss and Stefansson, 2006 propose b-decrease with the increase of the tectonic stress. The studied area is a depression zone under compression between Lesser and Greater Caucasus mountains. The Kur depression forms a reverse fault or subduction zone by moving under the Greater Caucasus at a rate of 8 mm/year with the stress applied by Lesser Caucasus (Aktug et. al., 2009, 2013; Reilinger et. al., 2006). Therefore, lower b-values and high number of seismic events allow us to consider the relation among b-value, seismicity and tectonic factors.

How to cite: Babayev, T. and Babayev, G.: Spatial distribution of b-value on the Mingachevir water reservoir area for seismicity analysis applying the microzonation method of Babayev G. et. al., 2020., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1040, https://doi.org/10.5194/egusphere-egu24-1040, 2024.

NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC), a Distributed Active Archive Center (DAAC), is dedicated to archiving remotely-sensed and model observations from multiple disciplines. These datasets, stemming from NASA's Earth-observing satellites, field measurement programs, and collaborations with international partners such as the European Space Agency (ESA) ― including datasets from the TROPospheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor satellite and Radio Occultation data from Sentinel-6, are all publicly accessible. Operating as a multidisciplinary DAAC, GES DISC equips users with the ability to integrate datasets across multiple disciplines while ensuring data quality and provenance.

GES DISC offers comprehensive services, enabling users to map and monitor extreme weather and climate hazards, including tropical cyclones and floods, through high spatial and temporal resolution datasets in near real-time. Giovanni, a visualization and analytical tool designed for users with limited expertise, has been widely utilized in risk and post-disaster assessments, and natural hazards research. Additionally, API services such as Data Rods, offering a long-term time series of climate datasets, aid in monitoring the vulnerability of critical infrastructures to hydro-meteorological conditions.

In response to the increasing data volumes in the Earth System Observatory (ESO) era, GES DISC is currently migrating data and services to the Earthdata Cloud. This cloud migration not only advances hazard and disaster studies, but also empowers users to fully leverage the benefits of the cloud, such as improved accessibility, cost-efficiency, scalability, and collaboration.

How to cite: Kc, B., Meyer, D., Hegde, M., Wei, J., and Khayat, M.: Empowering Hazard and Disaster Informatics: Data Services at NASA GES DISC in the Earth System Observatory Era, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13355, https://doi.org/10.5194/egusphere-egu24-13355, 2024.