In this study, geodynamic processes in the Western Anatolia Region were analyzed using Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite Systems (GNSS) data, incorporating wavelet transformation of InSAR time series based on GNSS observations. InSAR data were processed over a six-year period to produce line of sight (LOS) displacement maps. Ascending and descending track data were merged to derive information on both horizontal and vertical displacements. The InSAR-derived displacements were then compared with GNSS station data from the region to assess variations in the east and up components obtained by both techniques. Significant horizontal and vertical displacements were detected. While the Menemen Plain experienced a subsidence of up to 15 centimeters over a six-year period, the island of Samos experienced a rise of 29 centimeters. The performance of InSAR results was evaluated against GNSS data using Root Mean Square Error (RMSE). The RMSE values significantly decreased after applying corrections to the InSAR processing, indicating improved accuracy. For the DEUG station, the RMSE between InSAR and GNSS time series improved to 1.93% in the east component and 5.29% in the vertical component after wavelet-based noise removal. At the IZMI station, the RMSE was reduced to 2.62% (east) and 6.00% (vertical). Finally, at the CES1 station, the RMSE was reduced to 2.71% for the east component and 5.84% for the vertical component, all after correction.

Mine tailings are increasingly recognized as potential valuable mineral resources. This study applies magnetic susceptibility analysis to three boreholes in flotation tailings from dumps at the Pb-Zn Rudnik Mine, Serbia. The samples were obtained over a small spatial distance (10 cm), allowing for the application of various statistical methods with the obtained dataset. The results indicate that the mutual similarity among boreholes cannot be established with statistical significance; however, certain zones between boreholes exhibit some similarities. These variations are attributed to anthropogenic deposition, where local materials were intermixed and arbitrarily deposited during mining and processing, preventing clear statistical correlations. The study identifies key zones of interest in all three boreholes for further geochemical and mineralogical investigations.

In regions of low seismicity, such as Baranja in northeastern Croatia, seismic hazard assessments rely heavily on the detailed characterization of the few largest known earthquakes. This study focuses on the two strongest historical earthquakes in the area, macroseismic data from Bosnia and Herzegovina, Croatia, Hungary, and Serbia. The number of intensity observations for the earthquake of 1922 was expanded from 106 to 278, whereas the previously macroseismically not analysed event of 1924 is decribed by 14 data points. Using a modified Kövesligethy–Jánosi model that accounts for intensity anisotropy in the epicentral area, we inverted the macroseismic fields to relocate the epicentres and estimate focal depths and magnitudes. Both events were relocated near the village of Zmajevac, within the Bansko Brdo tectonic unit, close to its boundary with the Drava depression. The 1922 epicentre moved 13 km north-northeast of the original location in the which occurred on 24 November 1922 and 12 August 1924. We re-evaluated these events using newly collected Croatian Earthquake Catalogue (CEC), while the 1924 epicentre shifted 22 km westward. Revised moment magnitudes are Mwm 5.3 and Mwm 4.4 for the 1922 and 1924 events, respectively. Estimated focal depths are shallower than previously listed: 11 km and 8 km, compared to the 18 km and 14 km in CEC. These results indicate that significant seismicity in Baranja is confined to the Bansko Brdo unit, with no evidence of strong earthquakes or faults with sufficient seismogenic potential in the Drava Depression or Northern Baranja–Bačka units. This has important implications for regional seismic hazard estimates. Furthermore, we find no instrumental support for the largest catalogued aftershock of the 1922 event and propose its removal. Finally, we interpret the 1924 earthquake as a late aftershock of the 1922 mainshock, suggesting a dependent relationship between the two.

Indonesia experiences intense volcanic and tectonic activity due to its strategic location between 6° N–11° S latitude and 95° E–141° E longitude, where several major tectonic plates converge. Physiographically, Mount Pandan is located in the modern Sunda Arc region within the Anticlinorium or Kendeng Zone. Previous research recorded a minor earthquake in 2016, indicating ongoing tectonic activity in the Kendeng Zone, connected to sub-magmatic activity observed in several hot springs in Banyukuning, Jari, and Selogajah. Using gravity data, we identify the relationship between tectonic and magmatic activity through 3D inversion modelling of subsurface structures. This analysis correlates with fault fracture density (FFD) for surfaces with faults or fractures. Identifying the focal mechanism is essential for constructing the fault model of the earthquake source. The movement of Earth’s crust along the Kendeng Fault influences underlying magmatic processes. Evidence of this interaction includes low-density zones and sub-magmatic features, such as the presence of hot springs. Earthquakes around the mountain with magnitudes below 4.0 SR suggest a relationship between the movement of strike-slip faults and oblique reverse faults with magma ascent. 3D inversion modelling reveals four layers: At a depth of 0–0.46 km, with an estimated density range of 1.69–2.69 g/cm³, we interpret the layer as caprock, composed of pyroxene and host rock types. The layer at 0.46–1.14 km, with a density range of 1.31–2.23 g/cm³, is interpreted as a reservoir containing sand and clay rock types. Andesite and volcanic breccia rocks make up the layer at 1.14–1.59 km, with a density range of 2.4–2.8 g/cm³. It is thought to be caprock and intrusion (active fault).We interpret the layer at 1.59–2.43 km, with a density range of 1.43–3.45 g/cm³, as a heat source with basalt rock types and magma content. These findings provide new insights into the subsurface structure and fault dynamics of the Kendeng Zone, contributing to a better understanding of tectono-magmatic interactions in seismically active regions.

This study investigates the spatiotemporal evolution and scaling characteristics of the 2025 seismic swarm in the Santorini–Amorgos region, one of the most seismically and volcanically active zonesin the Aegean arc. Using a high-resolution earthquake catalog including 1,601 events (ML≥ 2.9), we analyze variations in key statistical parameters—including b-value, fractal dimension (Dc), scaling exponent (αb), and the q-parameter—within cumulative time windows. Our findings reveal a marked decrease in b-value and simultaneous increase in Dcduring the swarm’s activation phase, consistent with an inverse energy cascade and progressive rupture. The spatial extent of the seismicswarm (L) contracts significantly before expanding during the decay phase, and vertical migration of the seismic barycenter suggests a fluid-triggered nucleation process. 3D visualizations confirm fault-aligned clustering and upward migration patterns, supporting a hybrid rupture mechanism governed by both tectonic faulting and fluid diffusion. These results provide critical insights into the complex dynamics of swarms in extensional volcanic environments and highlight the relevance of fractal and scaling analysis for short-term seismic hazard assessment.

The silting of dams is a critical issue that requires immediate solutions through a participatory approach involving diverse territorial stakeholders. This approach aims to mitigate sediment inputs and preserve the intrinsic qualities of water resources within dams. In this context, the present study evaluates the siltation rate of the Sidi-Yacoub Dam, located in the Oued El Ardjem watershed, and identifies the primary causes contributing to this phenomenon. The methodology employed combines the analysis of thematic maps with the application of the sedimentation prediction model Previsioni Interimento Serbatoi Artificiali (PISA) to calculate various parameters. The annual average of solid inputs reaching the reservoir is estimated at 376.74 m³/km²/year, corresponding to an overall siltation rate of 0.46 Mm³/year. The preservation of this hydraulic infrastructure is critical to optimizing water resource utilization in the region and supporting its economic and social development. This study underscores the importance of integrating empirical methods, Geographic Information Systems, and bathymetric surveys to achieve a comprehensive understanding of siltation dynamics. The findings indicate that the dam experienced a significant loss of approximately 32.22 Hm³ of capacity between 1985 and 2004, representing an 11.6% reduction in its initial storage volume. These results highlight the urgent need for effective sediment management strategies to ensure the long-term sustainability of water resources in Algeria and other regions confronting similar challenges.

Vehicle mobility across different terrains depends on a multitude of geographical and meteorological variables. Traditional approaches rely on labor-intensive manual field measurements to assess soil conditions for military or humanitarian vehicle passage. In this study, we aimed to explore novel methods for parameterizing the Cone Index, a fundamental metric for assessing passage feasibility, leveraging meteorological data from the global numerical model GFS. Focusing specifically on chernosols, primarily agricultural soils found in flat, open regions conducive to military operations, we utilized machine learning methods to assess how soil conditions affect vehicle mobility. Through rigorous exploratory analysis, we investigated correlations, model performance metrics, and the relative importance of predictors in Cone Index modeling. Our findings highlight the comparative efficacy of different modeling approaches, particularly emphasizing the utility of the Random Forest method. We identified key environmental conditions under which the model reliably predicts the Cone Index. This sets the baseline for spatial modelling in GIS. Despite these insights, our study is constrained by data limitations and the inherent resolution constraints of the GFS model. The obtained solution lays the initial groundwork for implementing the model in a GIS environment to predict the trafficability of chernosols across the broader European region. Future research will aim to expand the dataset, spatial relationships, and employ models with higher resolution for more robust and accurate predictions.

Supercell storms, or super-Cb, are powerful and long-lasting convective systems that can form within statically unstable air mass in favourable dynamic conditions. These severe storm types are characterized by one or more rotation updrafts, usually supported by environmental vertical wind shear. Severity of these systems is usually reflected in heavy precipitation, large hail, hurricane wind gusts and sometimes derecho or tornado events. During July 19th, 2023, super-Cb event hit Croatia, including the capital city, Zagreb. It originated in the western Po River valley, Italy, progressed over Slovenia, Croatia, and finally dissipated over Serbia. Its track crossed about 700 km long path and lasted approximately 9 h, leading to human casualties and heavy property damages. The synoptic and mesoscale conditions leading to the formation of the super-Cb is analysed and discussed. Regular official station data, synoptic charts, upper air soundings, radar, and satellite observations are deployed as well as WRF model simulation performed for this case scenario. This study contributes to better understanding of such phenomena and their destructive potential in the broader region of Croatia and adds to somewhat scarce research field of such weather in this part of the world.

We investigated the human thermal load in Martonvásár (Hungarian lowland, Carpathian region) in anticyclonic weather conditions in the morning, when a) the sky was completely clear and on the other hand, when b) there was fog. A customizable clothing thermal resistance-operative temperature model was used. The body mass index and the basal metabolic flux density values of the person in the simulations were 25 kgm-2 and 40 Wm-2, respectively. During the observations, weather data was provided by the automatic station of the HungaroMet company and it was accessible on the company's website. We had 77 observations in foggy weather, while we had 46 observations under clear sky conditions in the period between 2019–2023. The following main results should be highlighted: 1) clothing thermal resistance (rcl) varied between 0.5-2.5 clo in the case of fog, while in clear-sky cases rcl was between 0.9-3.5 clo. 2) Based on our data analysis, we estimated that the warming effect of the morning fog was around 0.8 clo. 3) We also showed that the effect of inter-personal variability on rcl was significant when the heat deficit was high (rcl ≥ 2.5 clo) and at this time it was comparable with the degree of the warming effect of fog. It should be mentioned that the analysis of typical weather situations from the point of view of human thermal load is a new field of research, since there is little information available on this subject.

This paper proposes a lightweight convolutional neural network model to identify the types of natural earthquake and blasting events quickly and accurately. Since an event is generally recorded by several stations, it is necessary to preprocess and classify the data based on the event beforehand. This ensures that different station waveforms of the same event do not appear in any two of the training sets, validation sets, and test sets. With the three-component waveforms recorded by stations after preprocessing as the input, the network model and hyperparameters are optimized by analyzing the average and variance in the accuracy and loss values of the verification set in the fivefold cross-validation, and the accuracy and loss curves in the training process. Finally, the classification results of all stations that achieve a certain signal-to-noise ratio for each event are taken as the output of this event type based on the principle that the majority prevails over the minority. This study uses 2,190 natural and blasting events recorded by the Hainan Seismic Network before August 2022, which includes 53,067 waveforms, to train and test the effectiveness of the model. Twenty percent of those events are selected randomly as the test set. The results showed that out of 438 randomly selected events, 427 were correctly identified, resulting in an accuracy rate of 97.48%. Specifically, the accuracy rate for seismic events was 95.59%, with a recall rate of 89.04%, while the accuracy rate for blasting events was 97.84%, with a recall rate of 99.18%. In conclusion, the convolutional neural network model proposed in this paper can rapidly and accurately identify natural and blasting types in Hainan.